CN119784121A - A digital production operation management method for data quality assessment and analysis - Google Patents

Abstract

The invention discloses a digital production operation management method for data quality assessment and analysis, which comprises the steps of 1, constructing a multi-level distributed data acquisition system, 2, designing a multi-mode data fusion frame, combining a graph neural network and a time sequence data processing technology, realizing data fusion and multi-dimensional analysis, 3, constructing an intelligent scheduling engine, realizing a dynamic self-adaptive scheduling strategy to cope with real-time changes in a production environment, 4, designing a decentralised self-organizing production network, realizing flexible adjustment and optimization of a production process, 5, realizing collaborative optimization of product design and equipment maintenance through topology optimization, multi-objective optimization and a machine learning algorithm, and 6, constructing a virtual production network, and realizing real-time monitoring, dynamic prediction and optimization decision support of the production system. The enterprise can break the limitation of the traditional production mode, improve the production efficiency, flexibly cope with the market demand change and optimize the resource allocation.

Description

Digital production operation management method for data quality evaluation and analysis

Technical Field

The invention relates to the technical field of resource management, in particular to a digital production operation management method for data quality evaluation and analysis.

Background

With the advent of industry 4.0, digital technology and intelligent management gradually become the core driving force for modern production operation management. Traditional production methods cannot meet increasingly complex market demands and production environments, and enterprises face ever-increasing production efficiency requirements and higher customization demands. In this case, how to improve the data utilization efficiency in the production process, and how to realize the flexibility and efficiency of the production process through accurate analysis and management have become the key of the enterprises to dominate in the global competition. The quality, accuracy and real-time performance of the data play a vital role in production management, and how to extract valuable information from mass data and perform scientific evaluation and analysis becomes a difficult problem for improving productivity and enterprise competitiveness.

In the conventional production management mode, the production plan often depends on manual experience and historical data, and cannot rapidly adapt to rapid changes of market demands and fluctuation of external environments. In addition, the production system often operates in an isolated mode, and coordination and optimization among various links are insufficient, so that the production efficiency is low, the resource waste is serious, and flexible and changeable production requirements cannot be met. As the manufacturing industry changes to custom, small lot, flexible production, traditional manual intervention, quantitative production scheduling approaches have been difficult to cope with complex and rapidly changing production scenarios.

Therefore, enterprises are urgent to optimize each link in the production process by means of modern technical means, particularly data analysis and intelligent scheduling technologies, and improve the accuracy and flexibility of resource allocation. By the advanced data quality evaluation and analysis method, a more reliable basis can be provided for production decisions, and each decision in the production process is ensured to be based on real-time and accurate data support. With the maturation of technologies such as big data, cloud computing and artificial intelligence, intelligent management of production data becomes more feasible, and especially when a plurality of systems and complex processes are involved in a production link, the combination of intelligent scheduling and data analysis can remarkably improve production efficiency and reduce operation cost. In this context, it becomes particularly important to create a production network capable of self-organizing, automatically adapting to production demand changes by means of algorithms and intelligent scheduling.

Disclosure of Invention

In order to solve the problems, the invention provides a digital production operation management method for data quality evaluation and analysis.

In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:

a digital production operation management method for data quality assessment and analysis comprises the following steps:

step 1, constructing a multi-level distributed data acquisition system, acquiring multi-dimensional production data through a sensor, a camera and embedded equipment, and carrying out real-time transmission and preliminary processing by combining an edge computing technology to ensure high-quality input of the data;

Step 2, designing a multi-mode data fusion framework, combining a graph neural network and a time sequence data processing technology, realizing fusion and multidimensional analysis of sensor data, video images and equipment log heterogeneous data, and mining potential association of the data;

Step 3, based on the micro-service architecture and the event-driven mechanism, constructing an intelligent scheduling engine, and combining an optimization algorithm and a reinforcement learning technology, realizing a dynamic self-adaptive scheduling strategy so as to cope with real-time change in the production environment;

Step 4, designing a decentralised self-organizing production network by adopting a distributed autonomous system and a multi-agent technology, and realizing flexible adjustment and optimization of the production process through dynamic task allocation, resource coordination and evolution mechanisms;

Step 5, combining a generating type design technology and a predictive maintenance model, realizing the collaborative optimization of product design and equipment maintenance through topology optimization, multi-objective optimization and a machine learning algorithm, and improving the reliability and efficiency of a production system;

And 6, constructing a virtual production network, combining a digital twin technology, realizing real-time monitoring, dynamic prediction and optimization decision support of a production system, and guiding adjustment and optimization of a physical production system through a closed-loop feedback mechanism.

Further, the step 1 comprises the following steps:

The method comprises the steps that through a multi-level distributed data acquisition system, a sensor, a camera and embedded equipment are utilized to acquire multi-dimensional data of a production site, a sensor layer acquires equipment operation state and environment change data, a video monitoring layer acquires quality detection visual data, and the embedded data acquisition layer transmits equipment operation data in real time;

Carrying out noise filtering, formatting and standardized preprocessing operation on the original data on the edge node, and identifying and correcting abnormal values and missing values by utilizing a self-supervision learning algorithm, so as to reduce transmission load and improve data instantaneity;

And dynamically scoring the acquired data through a data quality evaluation and automatic correction mechanism, and automatically repairing the data.

Further, the step 2 comprises the following steps:

Constructing a multi-mode data fusion framework, abstracting equipment, production lines and material elements in the production process into nodes of a graph by adopting a graph neural network as a core model, and carrying out information propagation and fusion on each data node through graph convolution operation;

aiming at the fusion problem of time sequence data and non-time sequence data in a production environment, a space-time attention mechanism is introduced, the time dependency of the time sequence data is captured through a long-period memory network or a gating circulation unit, and the non-time sequence data extracts characteristics through a convolutional neural network;

Heterogeneous data are processed by adopting a multi-view joint modeling strategy, a plurality of data view angles are defined according to different stages of a production process, view angle data are fused in a hidden space through a depth generation model, common characteristics are extracted, and the prediction capability of the model is enhanced;

Multidimensional analysis and mining are carried out on the fused data, association rules in the production process are found by utilizing a frequent item set mining algorithm, trend prediction and anomaly detection are carried out through a deep learning model, and a real-time alarm mechanism is set.

Further, the step 3 comprises the following steps:

the scheduling engine is designed by adopting a micro-service architecture, a complex production scheduling task is split into a plurality of independent service modules, each micro-service exchanges data through a message queue, the flexibility and expandability of the system are ensured, and meanwhile, an event-driven mechanism is introduced, so that the dynamic adjustment of a scheduling strategy can be triggered in real time by equipment state change, material arrival and order change events in the production process;

Constructing a scheduling decision system based on a rule engine and a machine learning model, and combining a business rule and a scheduling policy through the rule engine to realize rule-driven scheduling decision;

Constructing a multi-objective optimization model by adopting a mixed integer linear programming and heuristic optimization algorithm;

And designing a dynamic self-adaptive scheduling strategy, and combining reinforcement learning and model predictive control to enable a scheduling system to continuously optimize the scheduling strategy according to real-time feedback.

Further, the step 4 comprises the following steps:

By adopting the design of distributed autonomous nodes, equipment, a production line and a warehouse unit in a production network are regarded as autonomous nodes, and each node has independent decision making and information processing capabilities;

Introducing a multi-agent system model, taking nodes in a production network as intelligent agents, and realizing cooperation and coordination through local perception and information sharing;

Designing a dynamic task allocation and resource coordination mechanism, and realizing dynamic optimization allocation of tasks and resources through a task allocation and self-adaptive scheduling algorithm based on a market mechanism;

The self-adaptive evolution mechanism of the self-organizing network is constructed, the production network can carry out self-adjustment and evolution according to long-term change and emergency through the evolution game theory and the feedback mechanism, nodes participate in games through optimizing own strategies, nash equilibrium is finally achieved, and stability and optimization of the system are achieved.

Further, the step 5 comprises the following steps:

introducing a generating type design technology, and automatically generating a high-efficiency and high-manufacturability product design scheme by using a topological optimization system, a multi-objective optimization system and an artificial intelligence aided design system;

Establishing a predictive maintenance model, carrying out real-time monitoring and fault prediction on equipment states based on the Internet of things technology and big data analysis by combining a machine learning algorithm, and identifying potential faults in advance by analyzing historical fault records and real-time operation data of equipment, so that equipment downtime is reduced;

In the design stage, maintainability requirements of equipment are embedded into product design, maintainability and reliability of key components are optimized, and meanwhile, the information is fed back into a generating design system through collecting equipment operation data and maintenance history in real time, so that closed-loop optimization is realized.

Further, the step 6 comprises the following steps:

constructing a virtual production network corresponding to an actual production system through physical simulation, system modeling and digital twin technology;

The method comprises the steps of establishing a data fusion and real-time monitoring system, collecting equipment state and production data information in a physical production system in real time through the technology of the Internet of things, and fusing the equipment state and the production data information with a virtual production network;

and predicting equipment faults and demand fluctuation in the production process and optimizing production scheduling and resource allocation by a machine learning algorithm and an optimizing algorithm.

Compared with the prior art, the invention has the following technical progress:

In the traditional production mode, the production decision often depends on manual experience and historical data, and the mode has the problems of strong subjectivity, information lag and the like. The digital management method for data quality evaluation and analysis can ensure that high-quality and accurate information is extracted from mass production data.

Modern manufacturing is faced with rapidly changing market demands and production environments that require the ability of the production system to respond quickly. By introducing an intelligent scheduling algorithm and a self-organizing production network, the invention can adapt to the fluctuation of the demand in real time in the production process, and the automatic and intelligent adjustment capability enables the production system to adapt to the external change rapidly, thereby avoiding the waste of production resources or the idle equipment caused by market fluctuation and further enhancing the flexibility of the production system.

The digital production operation management method can accurately evaluate the running condition of equipment, the production bottleneck and the efficiency of resource allocation through real-time monitoring and analysis of production data. The intelligent scheduling and optimizing algorithm can dynamically adjust the production schedule and equipment arrangement, eliminate the bottleneck in the production process and maximize the utilization rate of equipment and resources. Not only improves the production efficiency, but also effectively reduces the downtime, stock backlog and production cost. Under the traditional mode, each link on the production line may have an uncoordinated condition, and the cooperative work of all links can achieve the best effect through intelligent scheduling and optimization.

In the traditional production process, the decision often depends on manual judgment and is easily influenced by experience, subjective bias or working pressure of operators. In the digital management method, by introducing an automatic intelligent scheduling system, human intervention and misoperation can be reduced, and each link in the production process can be ensured to make an optimal response according to real-time data. The production network can accurately predict potential problems and perform early warning under the support of the digital twin model, so that faults or production accidents of the system are avoided, and the operation risk in the production process is effectively reduced.

With the increasing demand for customized production, traditional production modes are difficult to efficiently cope with small-batch and multi-variety orders. The digital production operation management method combines data analysis and intelligent scheduling, so that not only can large-scale conventional production be optimized, but also the production plan and resource allocation can be flexibly adjusted according to different production requirements, and customized production scheduling can be realized. For example, based on analysis of the virtual production network, the production system can quickly respond to personalized orders, adjust the production line configuration, and realize efficient customized production.

By combining intelligent scheduling and an algorithm model, the production system can not only adjust according to real-time data, but also form self-organizing and automatic adaptation capacity in the whole production process. The production network can dynamically adjust the operation mode and the resource allocation mode of the production network according to the information such as real-time data flow, equipment state, production progress and the like, so that the production activity can be ensured to be operated efficiently and stably under any environment. The self-adaptive capacity not only improves the production efficiency, but also greatly reduces the requirement of human intervention, so that the production flow is more efficient and intelligent.

Finally, this approach allows enterprises to cope with rapid changes in the market with lower costs, higher efficiency and more flexible production through intelligent management, big data analysis and automated scheduling. Enterprises can rapidly adjust the production strategy and meet the personalized demands of clients while ensuring the product quality and the production efficiency, and the market response speed is improved, so that the enterprises occupy favorable positions in highly competitive markets.

Therefore, the enterprise can break the limitation of the traditional production mode, improve the production efficiency, flexibly cope with the market demand change and optimize the resource allocation. Through the combination of intelligent scheduling and self-organizing capability, the production network can realize automatic adjustment and self-adaptive optimization, so that the resource utilization rate is improved to the maximum extent, the operation cost is reduced, and the high-efficiency and stable operation is ensured in a dynamic production environment, thereby enhancing the overall competitiveness of enterprises.

Drawings

The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.

In the drawings:

FIG. 1 is a flow chart of the present invention.

Detailed Description

The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.

As shown in fig. 1, the invention discloses a digital production operation management method for data quality evaluation and analysis, which comprises the following steps:

specifically, the specific implementation process of the step 1 includes:

1.1 construction of a Multi-level distributed data acquisition System

The method aims at ensuring that required production data are accurately collected in real time on different layers and links of a production network to form multidimensional input of the data.

The realization process comprises the steps of deploying various data acquisition devices including sensors, cameras, embedded devices and the like, connecting and integrating the data acquisition devices based on an Internet of things platform to form a real-time data stream, wherein the specific steps are as follows:

And the sensor layer is used for installing sensors (such as temperature, pressure, vibration, humidity, load and the like) on key equipment (such as a mechanical arm, a conveyor belt and a robot) on a production site and collecting the running state of the equipment and environmental change data in the process in real time.

And the video monitoring layer is used for acquiring visual data, especially images and videos of quality detection links, on the production line by utilizing an industrial camera to form data related to the appearance and the processing precision of the product.

And the equipment embedded data acquisition layer is used for embedding intelligent hardware (such as an embedded computer, an intelligent controller and the like) into production equipment, acquiring operation data of the equipment through an embedded sensor and connecting the equipment with a data processing center in real time through a wireless network.

Through the multi-level acquisition system, the diversity and the comprehensiveness of data sources are ensured, and enough information is provided for subsequent processing.

1.2 Real-time Transmission of data streams and edge computation preliminary processing

The method aims to ensure that data acquired from field devices can be quickly and seamlessly transmitted to a data processing system, and preliminary processing is performed through an edge computing technology, so that delay is reduced and response speed is improved.

The realization process comprises the following steps of adopting an edge computing node and a data stream processing platform, reducing the transmission cost of a large amount of data through near-end processing, and providing real-time feedback:

And selecting and optimizing a data transmission protocol, namely transmitting data acquired by a sensor and video equipment to an edge computing node in a real-time flow mode through a wireless sensor network or an industrial Ethernet, and adopting an efficient transmission protocol (such as MQTT and CoAP) to ensure the stability and timeliness of the data flow.

And the edge computing node can apply basic data cleaning technology (such as mean interpolation and abnormal value identification) and perform preliminary statistical analysis aiming at specific data types (such as temperature and pressure) so as to reduce unnecessary transmission burden and ensure real-time performance.

The addition of the edge calculation layer greatly improves the processing efficiency of the real-time data and reduces the delay and bandwidth pressure caused by large-scale data transmission.

1.3 Data quality assessment and automatic correction

The method aims at ensuring that the quality of the collected data meets the requirement of production management, eliminating the data with unqualified quality, and automatically correcting missing or abnormal data, thereby providing accurate input for subsequent data analysis and decision.

The realization process comprises the steps of adopting a data quality evaluation algorithm, dynamically evaluating the data quality by combining a machine learning model and a self-supervision learning model, and repairing the data by a self-adaption algorithm, wherein the specific steps are as follows:

Data quality scoring system a comprehensive 'data quality scoring system' is designed based on a data quality assessment framework (such as ISO8000 standard or data quality scoring model) to evaluate the collected data in real time. The system scores the data by combining the dimensions of accuracy, completeness, consistency, timeliness and the like of the data, and if the score is lower than a certain threshold value, the data is regarded as low-quality data.

Anomaly detection and correction of self-supervised learning, namely automatically identifying anomaly values and missing values in sensor data by using anomaly detection algorithms (such as self-encoders, variational self-encoders and the like) based on self-supervised learning, and filling and repairing by intelligent algorithms (such as K nearest neighbor, regression models and the like). For example, for a missing temperature sensor data, the system automatically presumes missing values based on historical data and other states of the device, ensuring data integrity.

And the data quality feedback mechanism is used for automatically triggering the feedback mechanism once the system identifies the data quality problem, and feeding the abnormal data back to the edge computing node or field technician for timely equipment calibration or replacement so as to prevent the abnormal data from further influencing the production process.

The step ensures that the quality of the acquired data is high, and the requirements of subsequent data analysis and decision making can be met, thereby providing a reliable basis for intelligent scheduling and production optimization.

1.4 Data integration and data warehouse construction

The method aims at integrating and archiving the preprocessed and corrected high-quality data and providing historical data support for subsequent analysis and decision making.

The realization process comprises the following steps of using a data integration platform and a distributed database system to uniformly integrate and store the multi-mode data acquired by each layer in the efficient database system, wherein the specific steps are as follows:

And a multi-source data integration platform adopts modern data integration technology (such as ETL (Extract-Transform-Load), data virtualization and the like) to uniformly process data from different sources (sensors, equipment, video monitoring and the like). The batch importing and real-time synchronizing of the data are performed by using a streaming technology (such as APACHE KAFKA or APACHE FLINK), so that the data can be updated in real time and accurately reflected in a data warehouse.

The distributed data warehouse is constructed by deploying a distributed database (such as Hadoop, APACHE HIVE, click House and the like) on a cloud platform or locally, so as to provide efficient query and processing capacity for data storage. Through data partitioning, indexing and compression technology, the storage efficiency and access speed of data are optimized, and through multi-dimensional analysis model (OLAP) design, convenient support is provided for subsequent data analysis.

The data integration and the construction of the data warehouse are used for centralizing and structuring the data, and providing data basis for subsequent multidimensional analysis, predictive maintenance and intelligent scheduling.

1.5 Continuous monitoring and data quality tracking

The method aims to ensure continuous monitoring of the data acquisition process, discover and correct any problem in the data acquisition in time, and ensure that the data quality is always at an acceptable level.

The realization process comprises the following steps of adopting a real-time monitoring system and a data quality tracking technology to establish a data quality dynamic tracking and alarming mechanism:

And the data monitoring platform is used for constructing a real-time monitoring system based on a large data platform (such as APACHE SPARK, prometheus and the like) and monitoring abnormal conditions in the data acquisition, processing and transmission processes in real time, such as data transmission delay, packet loss, quality degradation and the like.

And the data quality tracking and feedback mechanism is used for automatically recording the quality change of each data acquisition link by establishing a data quality tracking system and tracking the source, change and correction record of each piece of data by adopting a data blood system technology so as to quickly trace the source problem and make timely intervention. Through continuous monitoring and data quality tracking, the data collected in the production process is ensured to meet the requirements in each link, potential problems are found in time and adjusted, and the follow-up decision is ensured to be based on high-quality data.

Through the integration and innovation of the technical means, the whole process of data acquisition and preprocessing can be efficiently and accurately carried out, a solid foundation is laid for subsequent data analysis, intelligent scheduling and production optimization, the acquired multi-level and multi-mode data are subjected to strict quality evaluation and correction, the integrity, accuracy and timeliness of the data are ensured, efficient storage and management are realized through a data integration platform and a data warehouse, and finally powerful data support is provided for intelligent scheduling and self-optimization of a digital production network.

Specifically, the specific implementation process of the step 2 includes:

2.1 Multi-modal data fusion framework design

The purpose is to design a fusion framework that can handle data from different sources (e.g., sensor data, device logs, video images, external environmental information, etc.), and that allows different forms of data to complement each other while maintaining their unique characteristics, forming a unified data representation.

The realization process comprises the steps of using a graph neural network as a core model of data fusion, modeling multi-source data through a graph structure, and carrying out information propagation and fusion on each data node by utilizing graph convolution operation, wherein the specific steps are as follows:

The production network diagram is constructed by abstracting each element (such as equipment, production line, materials, environment and the like) in the production process into nodes of the diagram, modeling the relationship (such as material flow, equipment interaction, environmental influence and the like) between the nodes as edges, and forming a 'diagram model' of the production process. The nodes may include data sources of different modalities, such as sensor data, video surveillance data, and the like.

The graph convolution network propagates and merges node information, namely modeling the relation between nodes through a graph convolution layer and integrating characteristic information from different data sources. For example, the data such as temperature, humidity and the like collected from the sensor through the graph neural network are combined with the appearance information in the video monitoring data to perform multi-mode fusion. The representation of each node is dynamically updated based on the state of its neighborhood nodes, thereby obtaining a node feature that comprehensively considers various information.

The multi-modal graph neural network is expanded, namely, for complex multi-modal data, the multi-modal graph neural network can be further introduced, the model not only carries out information transmission among nodes on a graph structure, but also independently encodes the data of different modes, and then the data of different modes are combined. In this way, different modalities (e.g., sensor data and image data) can be efficiently interacted with and information transferred through the shared graph structure.

2.2 Fusion of time sequential data and non-time sequential data

The goal is that in a production environment, many data are time-sequential (e.g., equipment sensor data, production process monitoring data, etc.), while some data are non-time-sequential (e.g., product quality inspection images, environmental information, etc.), how to effectively fuse these time-sequential and non-time-sequential data is a critical issue.

The implementation process comprises the following steps of adopting a space-time attention mechanism to carry out intelligent selection and weighted fusion of data:

And the time sequence data processing is to adopt a long-term memory network or a gating circulation unit to capture long-term dependency relationship in a time sequence for time sequence data such as temperature, pressure and the like of a sensor, and the models can learn the change trend and the periodic fluctuation of the data in the time dimension.

Non-time sequence data processing, namely extracting deep features of non-time sequence data such as images, equipment state information and the like through a convolutional neural network. Features of the image data may be extracted through a pre-trained ResNet or EFFICIENTNET network, and the device state data classified and processed through a conventional machine learning model (e.g., support Vector Machine (SVM)).

And a space-time attention mechanism, wherein the space-time attention mechanism is introduced, and the characteristics of the time sequence data and the non-time sequence data are combined in a weighting way. For time series data, the spatio-temporal attention mechanism focuses on the important time period by dynamically adjusting the attention weights, while for non-time series data, the attention layer of the spatial dimension determines which features are most critical to the current production state. The mechanism can flexibly adjust the contribution degree of different data sources and realize accurate fusion.

2.3 Joint modeling of heterogeneous data

The method aims to effectively process heterogeneous data from different production environments, equipment types and sensor types, ensure that different data dimensions can jointly act, and finally form a unified knowledge representation.

The realization process comprises the steps of modeling heterogeneous data from different perspectives and integrating the heterogeneous data in a model by adopting a multi-perspective joint modeling strategy, wherein the specific steps are as follows:

Viewing angle definition and data preprocessing, namely defining a plurality of data viewing angles (such as a viewing angle of production equipment, a viewing angle of a production line, a viewing angle of quality control and the like) according to different stages of a production process, and respectively carrying out feature extraction and normalization processing on the data of each viewing angle. For example, the data in the production facility perspective may include temperature, vibration, load, etc., while the data in the quality control perspective may include product images, quality scores, etc.

The multi-view fusion method is to design a multi-view joint learning framework based on a depth generation model (such as a variational self-encoder (VAE), a generation countermeasure network (GAN), and the like) and fuse data of all views in a hidden space. By this method, the commonality features of different types of data can be integrated in the potential space of the model, thereby enhancing the predictive capability of the model.

For example, using a variational self-encoder to map data at different perspectives to a shared potential space:

Wherein, Is an implicit variable which is used for the control of the system,Is input data (including data from different perspectives),Representing hidden variablesIs a priori distribution of (a), indicating that without any observation, hidden variableIs provided for the distribution of (a),Representing a variational posterior distribution, expressed in given input dataUnder the condition of (1) hidden variablesIs provided for the distribution of (a),Is the mean and variance extracted from each view,Then it is the parameters of the potential space that the generative model learns. Through optimizing the potential space, the joint modeling and fusion of the heterogeneous data can be realized.

2.4 Multidimensional analysis and mining of fused data

The method aims at carrying out deep analysis on the fused data, mining potential association relations and modes among the data, and providing insight for production decisions.

The implementation process comprises the following steps of adopting a multidimensional data analysis and deep learning model, carrying out complex mode recognition, trend prediction, anomaly detection and other tasks after processing the fusion data, and specifically comprising the following steps:

association rule mining-mining of association rules in the fused data based on frequent item set mining algorithms (e.g., apriori or FP-Growth) helps to discover potential association factors in the production process (e.g., increased temperature may lead to increased risk of equipment failure).

Deep learning predictive models for trend prediction and pattern recognition in data may be modeled using deep neural networks, convolutional neural networks, or recurrent neural networks. The models can extract complex nonlinear relations among data when processing high-dimensional data, so that accurate prediction is provided for production scheduling, quality control and the like.

Abnormality detection and automatic alarm, namely, based on a fusion result of multi-mode data, adopting a self-supervision learning or clustering-based abnormality detection method (such as DBSCAN and Isolation Forest) to identify abnormal modes in the production process, and setting a real-time automatic alarm mechanism to ensure that the abnormal modes can respond quickly when problems occur in the production process.

Through the steps, the integration problem of multi-source heterogeneous data is effectively solved by data fusion and multi-mode analysis, deep association between the data can be fully mined and converted into valuable information by an innovative space-time attention mechanism and multi-view joint modeling method, and the realization of intelligent decision is further promoted. By the method, data of different modes can form a more comprehensive and accurate data model while maintaining respective characteristics, and powerful data support is provided for optimization and production scheduling of the production process.

Specifically, the specific implementation process of the step 3 includes:

3.1 Intelligent scheduling Engine architecture design

The engine architecture is designed to process various data inputs in the production environment in real time and dynamically adjust the production plan and the scheduling strategy according to multiple factors such as production requirements, equipment states, order priorities and the like.

The implementation process comprises the following steps of adopting a dispatching engine framework based on a micro-service architecture and an event driving mechanism, and optimizing the dispatching process by combining a rule engine and a machine learning model:

The micro-service architecture design is that the scheduling engine adopts the micro-service architecture to split the complex production scheduling task into a plurality of independent services, thereby being convenient for flexible expansion and maintenance. Each microservice module is responsible for different tasks such as production demand prediction, equipment status monitoring, order management, etc., running independently and exchanging data through a message queue (e.g., kafka).

Event-driven mechanism, in which each node (such as equipment state change, material arrival, order change, etc.) triggers related events in the production process by event-based driving mechanism, and the scheduling engine dynamically adjusts scheduling strategies according to the types of the events. For example, when a device fails, the system may trigger a "device failure" event, and the scheduling engine automatically adjusts the priority of the production task.

And combining a rule engine (such as Drools) with the decision logic to combine the business rules with the scheduling policy so as to realize rule-driven scheduling decision. Specifically, a series of scheduling rules such as "priority emergency orders", "reduce workload when equipment load exceeds 80%", etc. may be set, and an appropriate production schedule is automatically selected based on real-time data.

The architecture design of the micro-service and event driving enables the intelligent scheduling engine to quickly respond to external changes, has higher expandability and flexibility, and meets the requirements of dynamic production environments.

3.2 Scheduling decision model construction based on optimization algorithm

The method aims to provide an optimal scheduling decision for the production process under the multi-objective condition by adopting an optimization algorithm so as to improve the production efficiency, reduce the cost, reduce the production period and ensure the product quality.

The implementation process comprises the steps of adopting Mixed Integer Linear Programming (MILP) and heuristic optimization algorithms (such as genetic algorithm and simulated annealing algorithm) to solve the complex production scheduling problem, and particularly when facing a plurality of optimization targets and constraint conditions, the specific steps are as follows:

Multi-objective optimization model in production scheduling, multiple optimization objectives, such as minimizing production cost, maximizing resource utilization, minimizing delivery period, etc., are generally considered, and the present embodiment designs the multi-objective optimization model as follows:

Wherein, As vectors of the objective function, represent a plurality of objectives that need to be optimized simultaneously,,,...,For different objective functions, X is a scheduling decision variable (e.g., task allocation, device scheduling, etc.),,,...,For the weight of each target, the optimal scheduling scheme after comprehensively considering all targets is solved by the optimization problem.

Constraint conditions are introduced that production scheduling problems are not only concerned with optimizing the objective function, but also various constraint conditions such as production capacity, material supply, equipment availability and the like are required to be considered. Constraints can be expressed by linear or nonlinear equations:

Wherein, Representing constraints, such as material supply constraints, equipment capacity constraints, etc., the system needs to meet these constraints simultaneously and perform optimal scheduling based on these constraints.

The heuristic algorithm is applied, and for large-scale and complex production scheduling problems, the traditional optimization method may have overlarge calculated amount, so that genetic algorithm, particle swarm optimization or simulated annealing and other heuristic algorithms can be adopted, and the algorithms can be used for quickly searching under a multi-constraint and multi-objective environment to find a scheduling scheme close to the optimal.

The basic idea of the genetic algorithm is to simulate natural selection and genetics principles, construct an initial population, then continuously generate a new generation population through operations such as selection, crossing, mutation and the like, and finally obtain a better production scheduling scheme.

The particle swarm optimization algorithm simulates the foraging process of the bird swarm, continuously adjusts the position through the movement of particles in the search space, and finally converges to the global optimal solution.

By introducing the optimization algorithms, complex constraint conditions in various production scheduling can be processed, and the high efficiency and the executable performance of scheduling results are ensured.

3.3 Dynamic adaptive scheduling policy

The intelligent scheduling system can automatically adjust the scheduling scheme according to the production environment (such as equipment faults, production bottlenecks, order burst demands and the like) which changes in real time, so that the flexibility and the adaptability of the production network are improved.

The implementation process comprises the steps of constructing an intelligent decision system by combining reinforcement learning with a self-adaptive scheduling algorithm, wherein the system can continuously optimize a scheduling strategy according to real-time feedback, and the specific steps are as follows:

Reinforcement learning model design, namely designing a reinforcement learning-based scheduling optimization framework, wherein a scheduling engine is used as an agent, and tasks of the scheduling engine are to select optimal actions (i.e. scheduling decisions) according to environmental states (such as production progress, equipment states, order priorities and the like). The agent continuously obtains rewarding signals (such as production efficiency, delivery period, quality control and the like) through interaction with the environment, and optimizes strategies according to the feedback.

The core goal of reinforcement learning is to maximize the jackpot, and the model can be expressed as:

Wherein, Is in a state ofExecute action downwardsIs added to the value of (a),For the purpose of instant rewards,As a discount factor, the number of times the discount is calculated,In order to perform the new state to which the action is transferred,Selecting for the next action.

Adaptive scheduling mechanisms-scheduling mechanisms based on Model Predictive Control (MPC) are employed in conjunction with environmental changes (e.g., equipment failures, production demand fluctuations, etc.). The scheduling strategy is adjusted to cope with sudden changes by acquiring production data in real time and predicting future production states. For example, when the system predicts that the equipment may fail, the scheduling engine may automatically adjust task scheduling to schedule tasks preferentially to the production line with better equipment status.

3.4 Real-time feedback and optimization of scheduling results

The method aims at dynamically evaluating the scheduling effect and further optimizing the scheduling decision through a feedback mechanism of real-time monitoring and scheduling results.

The implementation process comprises the following steps of adopting a closed-loop control mechanism and data-driven optimization adjustment to enable a scheduling engine to continuously optimize a scheduling scheme according to feedback in the production process:

And the real-time feedback system is used for continuously tracking the scheduling result through a real-time data monitoring system (such as a SCADA system) and inputting actual production conditions (such as equipment running state, material supply conditions, production progress and the like) into the scheduling engine through a feedback mechanism to dynamically adjust.

Self-learning and model updating, namely self-adjusting the scheduling strategy through a machine learning algorithm (such as online learning and incremental learning). With the continuous accumulation of production data, the scheduling engine can gradually improve the decision model and improve the scheduling precision.

By constructing a highly dynamic and intelligent scheduling engine and combining an advanced optimization algorithm and a reinforcement learning mechanism, the production scheduling system can respond to production demand changes in real time and make optimal decisions. Meanwhile, the system has self-learning and adaptive adjustment capability, can continuously optimize the scheduling result when facing complex and dynamic production environments, and provides powerful support for efficient operation of the production network.

Specifically, the specific implementation process of the step 4 includes:

4.1 self-organizing structural design of production network

The method aims at designing a production network architecture which is decentralised, flexible and capable of self-adjusting according to actual demands, and optimizing the whole target through local interaction among nodes.

The realization process comprises adopting a technical framework of a distributed autonomous system and a multi-agent system to simulate the cooperation and self-adaptive adjustment between each node (such as equipment, a production line, a warehouse and the like) in a production network, and comprises the following specific steps:

And designing distributed autonomous nodes, namely designing each production unit (such as equipment, procedures, warehouses and the like) as autonomous nodes in a production network, wherein each node has independent decision making capability and information processing capability. Through local data sensing and decision making capabilities, each node can independently sense environmental changes and adjust its behavior based on local information.

And the multi-agent system model is characterized in that each node in the production network is used as an intelligent agent, and each agent cooperates and shares information with other agents through local perception and information exchange. Each agent can autonomously determine a production scheduling strategy according to information such as production tasks, equipment states, material flows and the like, and coordinate with other agents. For example, when a device fails, its affiliated agent automatically reports the failure status to the system, and other agents can adjust the priority of production tasks or reallocate resources based on this information.

4.2 Dynamic task Allocation and scheduling in an ad hoc network

The purpose is to design a self-organizing loom system capable of dynamically carrying out task allocation and production scheduling, so that a production network can self-adjust the scheduling of production tasks according to real-time factors such as demand fluctuation, production bottlenecks, equipment states and the like.

The implementation process comprises the following steps of adopting a mode of combining a task allocation method (such as an auction mechanism) based on a market mechanism with an adaptive scheduling algorithm (such as an adaptive genetic algorithm) to realize dynamic optimization allocation of tasks and resources:

The market mechanism of task allocation is that the market mechanism simulates the bidding and allocation process of the task, the production task is regarded as auction commodity, each autonomous node (such as equipment and work center) in the production network is used as a bidder, and bidding is performed according to the resource state (such as load, production capacity, scheduling and the like) of the self. Tasks will be assigned according to price (i.e., task processing power, response time, etc.). The task allocation model can be expressed as:

Wherein, Is a nodeIs used for the bidding price of (1),As a result of the load condition of the node,For the production capacity of the node,The task processing time of the node is predicted, and the task is automatically distributed to the most suitable node through the mechanism, so that the optimal allocation of the resource is realized.

And the self-adaptive scheduling algorithm is used for adjusting the task allocation scheme according to real-time data (such as equipment faults, production bottlenecks and the like) by combining a self-adaptive genetic algorithm and a particle swarm optimization algorithm. The genetic algorithm explores the task allocation space through selection, crossing and mutation operations, and particle swarm optimization carries out fine adjustment on a scheduling scheme through global search.

4.3 Resource coordination and optimization in an ad hoc network

The method aims to enable a production network to realize optimal coordination and distribution of resources, eliminate bottlenecks in the production process and improve the overall production efficiency through self-organized weaving and cooperative strategies.

The implementation process comprises the following steps of adopting a collaborative filtering algorithm and a collaborative optimization model to realize resource coordination and optimization:

collaborative filtering and resource matching, namely predicting the demand and supply of resources based on historical production data, resource use conditions and other information by using a collaborative filtering algorithm through each node in the production network, and performing resource matching. For example, in the production process, when the workload of a certain production line is too large, available resources (such as idle equipment, available working procedures and the like) can be found through a collaborative filtering algorithm, so that task redistribution is performed, and the bottleneck is relieved.

The basic idea of the collaborative filtering algorithm is to predict future resource demands based on similar history conditions by calculating the similarity between production nodes, and the basic formula is as follows:

Wherein, To predict nodesResource demand pair node of (a)I.e. the resource usage,Is a nodeAnd nodeThe degree of similarity between the two,Is a nodeAt the resourcePractical requirements are made.

The collaborative optimization algorithm is applied, namely, a collaborative optimization algorithm based on a global optimal solution is designed for eliminating the bottleneck in production, so that all nodes in a production network can achieve global optimal resource allocation through information sharing and collaborative decision under the condition of limited resources. For example, node a may not be able to complete a task alone, but by cooperating with node B, C, an efficient completion of the overall task may be achieved, which may be achieved by a constraint optimization algorithm:

Subordinate to

Wherein, Is the cost of the resources and,Is allocated to nodeIs used for the amount of resources of (a),Is a matrix of resource allocation and,Is the total demand of resources, and through the collaborative optimization strategy, all nodes in the production network can share the resources and coordinate scheduling so as to maximize the production benefit of the whole system.

4.4 Self-adaptive evolution mechanism of self-organizing production network

The purpose is that the production network can self-adjust and evolve through self-organizing mechanism when facing long-term change and emergency, so as to adapt to the dynamic change of external environment.

The implementation process comprises the following specific steps of adopting an evolution game theory and an adaptive feedback mechanism to realize the evolution and adaptive adjustment of the self-organizing production network:

And constructing an evolution game model, namely taking each node in the production network as a game participant, and improving the utility of each node by optimizing own strategies (such as resource allocation, production scheduling and the like). The game strategy between the nodes evolves along with time, and the Nash equilibrium point is finally found by repeating the game adjustment strategy, so that the stability and the optimization of the system are realized, and the game model can be expressed as follows:

Wherein, Is a nodeIn the strategyThe utility function of the lower part of the system,Is a nodeIs used in the field of the present invention,Is a nodeAnd nodeThe cost of the collaboration between them,Is a collaborative decision.

And the self-adaptive feedback and evolution mechanism combines a real-time feedback mechanism and an evolution game theory, and the production network can carry out self-adjustment and evolution according to information such as actual production requirements, resource use conditions, cooperation results among nodes and the like. For example, when the production efficiency of a certain node is low, the game strategy of the node can be adjusted, and the benefit of the node is improved by adding resource allocation or optimizing production scheduling, so that the evolution of the whole system is finally promoted.

By designing a decentralised, self-adaptive and self-organizing production network model and combining a distributed autonomous system, a multi-agent system and an evolutionary game theory, the production network can automatically adjust resource allocation, task arrangement and production strategies according to the change of a real-time environment, so that the production efficiency is improved, the cost is reduced, the resource waste is reduced, and the overall adaptability and the flexibility of the system are enhanced.

Specifically, the specific implementation process of step 5 includes:

5.1 introduction and optimization of the generative design

The method aims to automatically generate the optimized product design scheme through an intelligent algorithm by utilizing a generation type design technology, so that the design cost is reduced, the design efficiency is improved, and the customization requirement in the production process is met.

The implementation process comprises the steps of introducing a topology optimization algorithm, a multi-objective optimization algorithm and an artificial intelligence design auxiliary system, fully considering product structure, material characteristics, manufacturing process and production constraint conditions in the production type design process, and ensuring that the generated design scheme has high-efficiency manufacturability and operability in actual production, wherein the specific steps are as follows:

Topology optimization and generation type design, namely automatically generating a structural design meeting performance requirements by using a topology optimization algorithm through an initial framework and constraint conditions of a given product design. The algorithm will find the optimal way to distribute the material in the design space to achieve a specific physical property objective (e.g., stiffness, strength, weight, etc.). For example, by optimizing the structural design, unnecessary material use is reduced, the efficiency and the light weight degree of the structure are improved, and the mathematical expression of the topology optimization problem is as follows:

Wherein, Is the displacement vector of the structure,Is a matrix of stiffness of the material,For design space, the goal is to minimize the energy consumption of the structure by optimizing the material distribution of the structure while meeting the constraints of strength and rigidity.

And (3) adopting a multi-objective optimization algorithm to comprehensively consider different design objectives (such as cost, weight, functionality and the like) and constraint conditions (such as manufacturing difficulty, material selection and the like) to generate a plurality of optimal design schemes. And a genetic algorithm or heuristic algorithms such as particle swarm optimization are utilized, and a global optimal solution can be found in an iterative mode, so that a design team is helped to explore and select the most suitable product design scheme.

Artificial intelligence aided design, combining deep learning and generating an countermeasure network (GANs), automatically generating a product design scheme, and especially when facing complex and changeable design requirements, the AI can rapidly bring forward an innovative design. By training the generative model, the system can automatically generate a viable design based on historical data and design requirements. For example, in product customization design, AI models are utilized to learn different design parameters, providing a real-time, personalized design solution.

5.2 Establishment and implementation of predictive maintenance model

The method aims to identify potential faults of equipment in advance through a predictive maintenance technology, reduce equipment downtime and improve production stability, thereby enhancing the overall efficiency of a production network.

The implementation process comprises the following steps of realizing real-time monitoring and fault prediction of equipment states based on the Internet of things technology and big data analysis by combining a machine learning algorithm (such as a random forest, a support vector machine, a long-short-term memory network LSTM and the like), wherein the specific steps are as follows:

And (3) equipment data acquisition and sensor network construction, namely acquiring data of the running state of equipment in real time by deploying intelligent sensors (such as temperature sensors, vibration sensors, pressure sensors and the like). The data are transmitted to the cloud for centralized processing through the Internet of things platform. The sensor data acquisition system may include real-time monitoring of the operating state of the device, load changes during operation, temperature fluctuations, etc., to provide the necessary input data for predictive maintenance.

Feature extraction and data preprocessing, namely performing feature extraction and preprocessing on the collected sensor data, selecting key features (such as vibration frequency, temperature change rate, working period and the like), and performing denoising, standardization and the like on the data so as to improve the accuracy of a prediction model. Common data processing techniques include wavelet transformation, principal component analysis, etc., which help extract key features that affect the health of the device.

And (3) establishing a fault prediction model, namely establishing the equipment fault prediction model by using a machine learning algorithm and combining the historical fault record and the current operation data of the equipment. For example, LSTM (long short term memory network) is employed to process time series data, predicting the probability of failure of a device over a period of time in the future. The model can identify potential failure modes in advance through learning a large amount of historical data and predict the residual service life (RUL) of equipment, and the basic prediction model can be expressed as:

Wherein, As an input feature of time series data (such as sensor data),For the LSTM model obtained by training, the predicted value is the remaining service life (RUL) of the device.

And the real-time fault diagnosis and early warning system establishes a real-time fault diagnosis and early warning system according to the equipment health data output by the predictive maintenance model, and when the equipment is predicted to have fault risks, the system automatically gives out warnings and provides maintenance suggestions (such as immediate shutdown, load adjustment and the like). In addition, by combining equipment maintenance history and production plans, intelligent maintenance scheduling can be realized, so that the maintenance cost and the downtime of the equipment are optimized.

5.3 Collaborative optimization of generative design and predictive maintenance

The method aims to combine the generated design with predictive maintenance and optimize the equipment design and maintenance strategy in the production network so as to improve the availability, reliability and production efficiency of the equipment.

The implementation process comprises the steps of forming a cooperatively optimized closed-loop system by combining product structure optimization in the generated design with equipment state monitoring in predictive maintenance, wherein the steps are as follows:

The design stage is synchronized with the maintenance requirement, and in the process of the generated design, the predictive maintenance requirement of the equipment is embedded into the product design by considering the maintainability and maintainability of the equipment. For example, components that are easy to maintain and replace are designed in the design stage to reduce downtime in the event of equipment failure, taking into account the frequency of failure of critical components of the equipment.

And (3) the design feedback based on the health data is realized by collecting the operation data and the maintenance history data of the equipment in real time, and feeding the data back to the generating design system for closed-loop optimization. The design system automatically adjusts the design scheme according to the actual running condition and maintenance history of the equipment, and optimizes the long-term stability and maintenance cost of the product. For example, when the failure rate of a certain type of equipment is high, the design system can analyze the reasons through a machine learning algorithm, and automatically modify the design of the component in the subsequent design, so that the reliability and maintenance efficiency of the component are improved.

The self-adaptive maintenance strategy and design iteration are combined to form the collaborative optimization of the design and predictive maintenance, so that the design of the existing product can be optimized, and the design of the future product can be fed back in real time to form the self-adaptive design and maintenance strategy. For example, by monitoring the state of the device in real time, the device operation data is obtained, and the design system can adjust the product design according to the information, so as to realize dynamic optimization.

By means of deep fusion of the generated design and the predictive maintenance technology, the innovation and the efficiency of the product design can be improved, and the running reliability of equipment and the stability of a production system can be improved. The design of the generation type enables the optimal configuration of the product from the structure to the material selection, and the predictive maintenance ensures the healthy and efficient operation of the production equipment in a data driving mode. The cooperative optimization of the two can greatly enhance the self-adaptive capacity and continuous operation capacity of the production network, and promote the whole production system to advance towards the intelligent and efficient direction.

Specifically, the detailed implementation process of step 6 includes:

6.1 construction and integration of virtual production networks

The method aims at comprehensively simulating the behavior and state of a physical production system by creating a virtual production network model, further providing support for production decision, production planning, resource allocation and optimization, and realizing global monitoring and scheduling of the system.

The realization process comprises the steps of adopting physical simulation, system modeling and digital twin technology, and digitizing information of production equipment, production lines, process flows, resource allocation and the like, thereby constructing a virtual production network model corresponding to actual production. The method comprises the following specific steps:

System modeling and simulation, namely simulating the behaviors of production lines, equipment operation, resource consumption, process flow and the like by constructing a mathematical model of a production system by using system dynamics and Discrete Event Simulation (DES) technology. These models should include factors such as production units, equipment performance, production plans, and demand fluctuations to ensure that the virtual production network can truly reflect the dynamic changes in actual production.

And (3) a system dynamics model, wherein the model simulates the dynamic behavior of the production process by establishing a feedback loop among factors such as inventory, productivity, demand fluctuation and the like. The mathematical expression typically comprises a plurality of differential equations representing the varying relationships of the various parts of the production system at different points in time. For example, the dynamic equation for inventory variation can be expressed as:

Wherein, Time of presentationThe amount of stock at the moment in time,In order to achieve a production rate of the product,Is the demand rate.

Discrete event simulation, namely, through a simulation model, different events (such as machine faults, production task completion, resource arrival and the like) in the production process can be simulated on a time axis. The method is suitable for simulating complex and discrete production processes, and can help judge production bottlenecks, evaluate resource allocation strategies and optimize production plans.

And (3) constructing a digital twin model by combining the physical production system with the virtual system, so that the virtual model can reflect the state of the physical production system in real time. The digital twinning technique utilizes sensors, ioT devices, and a real-time data acquisition system to ensure synchronicity of the virtual production network with the actual production system, and can predict potential bottlenecks or problems by simulating and analyzing different production scenarios.

6.2 Data fusion and real-time monitoring system

The method aims to realize real-time monitoring, diagnosis and optimization of the production process by collecting data of a physical production system in real time and fusing the data with a virtual production network, so that the production system can make quick and accurate reactions under different conditions.

The realization process comprises the following steps of adopting the technology of the Internet of things and big data analysis, combining the fusion of real-time data flow and historical data, and constructing an omnibearing and multidimensional production monitoring platform, wherein the specific steps are as follows:

and the data acquisition and real-time monitoring platform is used for acquiring various data (such as equipment temperature, load, voltage, vibration, production quantity and the like) in the production process in real time by arranging sensors, cameras and Internet of things equipment on production equipment, production lines and resource nodes. The data are transmitted to a data center for centralized processing and analysis in real time through a transmission network. Based on the real-time data flow, the state of the production line can be monitored in real time and reflected to the virtual production network in real time, so that the synchronization of the virtual network and an actual production system is ensured.

Data fusion and preprocessing, namely, in the process of data transmission, the problems of data quality difference, inconsistent formats, noise interference and the like from different sources can exist. The data from different sensors are preprocessed and fused by applying a data fusion algorithm (such as Kalman filtering, particle filtering and the like) and a multi-sensor data fusion technology, so that uncertainty and noise of the data are eliminated, a standardized data model is formed, and accuracy and consistency of the data are ensured.

And the Kalman filtering is used for estimating and optimizing the time sequence data, effectively filtering noise and predicting future data trend. 6.3 prediction and optimization decision support

The method aims to realize dynamic prediction, optimal scheduling and resource allocation of the production network by utilizing the fusion result of the virtual production network and real-time data and through a data analysis and machine learning model, and improve the overall production efficiency and flexibility.

The realization process comprises the steps of adopting machine learning (such as reinforcement learning and regression analysis) and optimization algorithm (such as genetic algorithm and particle swarm optimization) to dynamically predict and optimize the virtual production network, wherein the specific steps are as follows:

And (3) predicting the production state and analyzing the trend, namely training historical production data by using a machine learning algorithm, predicting future production states, equipment faults, demand fluctuation and the like, and providing decision basis. Through regression analysis, a model of the relationship between demand and production rate can be established to predict future fluctuations in demand. Optimizing production scheduling and resource allocation, namely optimizing scheduling and allocation of production resources according to data in a virtual production network by utilizing an optimization algorithm such as genetic algorithm or particle swarm optimization. By simulating the effects of different scheduling strategies, the optimal resource allocation scheme is selected, so that the production bottleneck is reduced, the equipment utilization rate is improved, and the production cost is reduced to the greatest extent.

Particle swarm optimization algorithm, namely searching an optimal solution by simulating the foraging process of the bird swarm, wherein the formula of the optimization process is as follows:

Wherein, Is the firstThe particles are at the firstThe speed of the steps is such that,For the best position of the particle history,As a global optimum position for the device,,In order for the acceleration constant to be high,,In the form of a random number,For inertial weights, the search range and convergence speed are determined, typically decreasing with time,Is the position of the particle representing the firstThe particles are at the firstThe current position of the generation, the position of the particles is the current production scheduling scheme, and the aim is to find the optimal solution.

6.4 Real-time application of digital twinning and feedback mechanism

The method aims to realize real-time production system optimization and feedback through seamless butt joint of digital twin and physical production networks, and ensure that dynamic adjustment and optimization strategies in the production process can be immediately applied and verified.

The implementation process comprises the following steps of establishing a closed-loop feedback mechanism, enabling data flow between a virtual production network and an actual production system to be transmitted in a two-way mode, enabling a virtual model to reflect the state of the actual system, and guiding adjustment of a physical system according to a real-time optimization result, wherein the method comprises the following specific steps:

And (3) real-time synchronization and feedback control, namely ensuring that the optimal decision of the virtual production network can be fed back to the physical production system through real-time monitoring and data transmission. If the virtual production network finds out a production bottleneck or a potential problem, the control system immediately sends out an instruction to adjust the production plan, the resource allocation or the equipment scheduling.

And (3) real-time optimization application and adjustment, namely implementing real-time adjustment based on the optimization result of the virtual production network. For example, when the virtual network predicts that a device will be subject to failure, the system may schedule the backup device in advance and adjust the production plan to avoid physical production system stalling or inefficiency.

By constructing a virtual production network and applying a digital twin technology, the production system can be simulated and optimized in real time, and dynamic and flexible production decision and adjustment can be realized. The combination of the virtual production network and the digital twin enables each link in the production process to be tested and optimized in the virtual environment, thereby improving the production efficiency to the greatest extent, reducing the downtime, lowering the cost and enhancing the adaptability and the stability of the production system.

It should be noted that the above-mentioned embodiments are merely preferred embodiments of the present invention, and the present invention is not limited thereto, but may be modified or substituted for some of the technical features thereof by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims (7)

1. A digital production operation management method for data quality assessment and analysis, comprising:

step 1, constructing a multi-level distributed data acquisition system, acquiring multi-dimensional production data through a sensor, a camera and embedded equipment, and carrying out real-time transmission and preliminary processing by combining an edge computing technology to ensure high-quality input of the data;

Step 2, designing a multi-mode data fusion framework, combining a graph neural network and a time sequence data processing technology, realizing fusion and multidimensional analysis of sensor data, video images and equipment log heterogeneous data, and mining potential association of the data;

Step 3, based on the micro-service architecture and the event-driven mechanism, constructing an intelligent scheduling engine, and combining an optimization algorithm and a reinforcement learning technology, realizing a dynamic self-adaptive scheduling strategy so as to cope with real-time change in the production environment;

Step 4, designing a decentralised self-organizing production network by adopting a distributed autonomous system and a multi-agent technology, and realizing flexible adjustment and optimization of the production process through dynamic task allocation, resource coordination and evolution mechanisms;

Step 5, combining a generating type design technology and a predictive maintenance model, realizing the collaborative optimization of product design and equipment maintenance through topology optimization, multi-objective optimization and a machine learning algorithm, and improving the reliability and efficiency of a production system;

And 6, constructing a virtual production network, combining a digital twin technology, realizing real-time monitoring, dynamic prediction and optimization decision support of a production system, and guiding adjustment and optimization of a physical production system through a closed-loop feedback mechanism.

2. The method for managing digital production operations for data quality assessment and analysis according to claim 1, wherein the step 1 comprises:

The method comprises the steps that through a multi-level distributed data acquisition system, a sensor, a camera and embedded equipment are utilized to acquire multi-dimensional data of a production site, a sensor layer acquires equipment operation state and environment change data, a video monitoring layer acquires quality detection visual data, and the embedded data acquisition layer transmits equipment operation data in real time;

Carrying out noise filtering, formatting and standardized preprocessing operation on the original data on the edge node, and identifying and correcting abnormal values and missing values by utilizing a self-supervision learning algorithm, so as to reduce transmission load and improve data instantaneity;

And dynamically scoring the acquired data through a data quality evaluation and automatic correction mechanism, and automatically repairing the data.

3. The method for managing digital production operations for data quality assessment and analysis according to claim 2, wherein said step2 comprises:

Constructing a multi-mode data fusion framework, abstracting equipment, production lines and material elements in the production process into nodes of a graph by adopting a graph neural network as a core model, and carrying out information propagation and fusion on each data node through graph convolution operation;

aiming at the fusion problem of time sequence data and non-time sequence data in a production environment, a space-time attention mechanism is introduced, the time dependency of the time sequence data is captured through a long-period memory network or a gating circulation unit, and the non-time sequence data extracts characteristics through a convolutional neural network;

Heterogeneous data are processed by adopting a multi-view joint modeling strategy, a plurality of data view angles are defined according to different stages of a production process, view angle data are fused in a hidden space through a depth generation model, common characteristics are extracted, and the prediction capability of the model is enhanced;

Multidimensional analysis and mining are carried out on the fused data, association rules in the production process are found by utilizing a frequent item set mining algorithm, trend prediction and anomaly detection are carried out through a deep learning model, and a real-time alarm mechanism is set.

4. A digital production operation management method for data quality assessment and analysis according to claim 3, wherein said step 3 comprises:

the scheduling engine is designed by adopting a micro-service architecture, a complex production scheduling task is split into a plurality of independent service modules, each micro-service exchanges data through a message queue, the flexibility and expandability of the system are ensured, and meanwhile, an event-driven mechanism is introduced, so that the dynamic adjustment of a scheduling strategy can be triggered in real time by equipment state change, material arrival and order change events in the production process;

Constructing a scheduling decision system based on a rule engine and a machine learning model, and combining a business rule and a scheduling policy through the rule engine to realize rule-driven scheduling decision;

Constructing a multi-objective optimization model by adopting a mixed integer linear programming and heuristic optimization algorithm;

And designing a dynamic self-adaptive scheduling strategy, and combining reinforcement learning and model predictive control to enable a scheduling system to continuously optimize the scheduling strategy according to real-time feedback.

5. The method for managing digital production operations for data quality assessment and analysis according to claim 4, wherein said step 4 comprises:

By adopting the design of distributed autonomous nodes, equipment, a production line and a warehouse unit in a production network are regarded as autonomous nodes, and each node has independent decision making and information processing capabilities;

Introducing a multi-agent system model, taking nodes in a production network as intelligent agents, and realizing cooperation and coordination through local perception and information sharing;

Designing a dynamic task allocation and resource coordination mechanism, and realizing dynamic optimization allocation of tasks and resources through a task allocation and self-adaptive scheduling algorithm based on a market mechanism;

The self-adaptive evolution mechanism of the self-organizing network is constructed, the production network can carry out self-adjustment and evolution according to long-term change and emergency through the evolution game theory and the feedback mechanism, nodes participate in games through optimizing own strategies, nash equilibrium is finally achieved, and stability and optimization of the system are achieved.

6. The method for managing digital production operations for data quality assessment and analysis according to claim 5, wherein said step 5 comprises:

introducing a generating type design technology, and automatically generating a high-efficiency and high-manufacturability product design scheme by using a topological optimization system, a multi-objective optimization system and an artificial intelligence aided design system;

Establishing a predictive maintenance model, carrying out real-time monitoring and fault prediction on equipment states based on the Internet of things technology and big data analysis by combining a machine learning algorithm, and identifying potential faults in advance by analyzing historical fault records and real-time operation data of equipment, so that equipment downtime is reduced;

In the design stage, maintainability requirements of equipment are embedded into product design, maintainability and reliability of key components are optimized, and meanwhile, the information is fed back into a generating design system through collecting equipment operation data and maintenance history in real time, so that closed-loop optimization is realized.

7. The method for managing digital production operations for data quality assessment and analysis according to claim 6, wherein said step 6 comprises:

constructing a virtual production network corresponding to an actual production system through physical simulation, system modeling and digital twin technology;

The method comprises the steps of establishing a data fusion and real-time monitoring system, collecting equipment state and production data information in a physical production system in real time through the technology of the Internet of things, and fusing the equipment state and the production data information with a virtual production network;

and predicting equipment faults and demand fluctuation in the production process and optimizing production scheduling and resource allocation by a machine learning algorithm and an optimizing algorithm.