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CN118278577A - Beidou intelligent emergency rescue command and dispatch system - Google Patents

Beidou intelligent emergency rescue command and dispatch system Download PDF

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Publication number
CN118278577A
CN118278577A CN202410472573.8A CN202410472573A CN118278577A CN 118278577 A CN118278577 A CN 118278577A CN 202410472573 A CN202410472573 A CN 202410472573A CN 118278577 A CN118278577 A CN 118278577A
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China
Prior art keywords
rescue
data
module
command
emergency
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Inventor
李皓瑜
崔玉舸
朱之贞
钟韬
胡建明
杨云淞
朱志磊
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Yunnan Hu Dian Emergency Equipment Manufacturing Co ltd
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Yunnan Hu Dian Emergency Equipment Manufacturing Co ltd
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Priority to CN202410472573.8A priority Critical patent/CN118278577A/en
Publication of CN118278577A publication Critical patent/CN118278577A/en
Pending legal-status Critical Current

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Abstract

The invention discloses a Beidou intelligent emergency rescue command and dispatch system, which accurately predicts disaster development trend by integrating a multi-source data acquisition module and a big data processing and analyzing module and provides scientific basis for decision making; the Beidou satellite navigation system is utilized to ensure that accurate positioning of rescue workers and materials can be realized in any environment, the intelligent decision support module can quickly generate an optimal rescue scheme, the optimal rescue scheme comprises rescue path planning, task allocation and resource scheduling, the decision efficiency and quality are remarkably improved, the accurate matching of the rescue workers and the tasks is realized through the rescue resource database and the intelligent matching and recommending sub-module, the resource utilization rate is improved, the technical problems in the aspects of information acquisition, positioning, decision, resource management, collaborative command and the like in the traditional emergency rescue command scheduling are effectively solved, the emergency response speed and the rescue success rate are remarkably improved, and a powerful support is provided for constructing a modern and intelligent emergency management system.

Description

Beidou intelligent emergency rescue command and dispatch system
Technical Field
The invention belongs to the technical field of emergency rescue, and particularly relates to a Beidou intelligent emergency rescue command and dispatch system.
Background
When dealing with sudden natural disasters, safety accidents and other emergency situations, efficient and accurate emergency rescue command and dispatch is important. The traditional emergency rescue command and dispatch mainly relies on manual decision and communication means, and has the following technical problems:
information acquisition is not timely and comprehensive: in the early stage of disaster occurrence, because communication facilities are damaged and information transmission channels are blocked, the on-site conditions are difficult to quickly and accurately grasp, and timeliness and accuracy of rescue decisions are affected.
The positioning accuracy is limited: rely on traditional GPS positioning system or other ground communication network to fix a position, probably there is signal blind area, can't satisfy the accurate positioning demand of rescue personnel and supplies under the complex disaster environment.
The decision process is complex and time-consuming: the rescue scheme is formulated by manually analyzing a large amount of data, so that resource scheduling is performed, the efficiency is low, the method is easily influenced by human factors, and an optimal decision is difficult to make in a short time.
Relief resource management is rough: the real-time tracking and the fine management of rescue workers, equipment and materials are lacked, and the on-demand, rapid and accurate resource allocation is difficult to realize.
Difficulty in collaborative command: the information communication among the rescue units is unsmooth, the command level is complex, and the response speed is low and the action coordination is poor.
Disclosure of Invention
The invention aims to provide a Beidou intelligent emergency rescue command and dispatch system which fully utilizes the advantages of global coverage, high-precision positioning and short message communication of a Beidou satellite navigation system so as to solve the problems in the background technology.
In order to achieve the above purpose, the invention adopts the following technical scheme: beidou intelligent emergency rescue command and dispatch system includes: the positioning module is in real-time communication with the Beidou satellite navigation system and is used for acquiring geographic position information of accident sites and rescue resources; the data acquisition module is used for receiving and integrating disaster site real-time environment parameters, images and video data acquired from various sensors, monitoring equipment, unmanned aerial vehicles or satellite remote sensing approaches; the big data processing and analyzing module is used for analyzing the acquired data, predicting the disaster development trend and evaluating the rescue difficulty and risk; the rescue resource database is used for storing detailed information of various rescue teams, equipment and materials and real-time states of the rescue teams, the equipment and the materials; the intelligent decision support module is used for generating an optimal rescue scheme based on the data analysis result and a preset emergency plan, and comprises rescue path planning, task allocation and resource scheduling; the interactive command platform is used for providing a visual interface, displaying the situation of a disaster site, the rescue progress and the resource distribution in real time, and supporting the functions of remote command, multiparty collaboration, voice and video communication; the system interface module is used for realizing data exchange and linkage control with emergency management departments, professional rescue institutions and social public service systems at all levels.
Preferably, the method further comprises: the emergency communication enhancement module is integrated with a Beidou short message communication unit and is used for sending positioning information and short text information at disaster sites without public network signal coverage.
Preferably, the data acquisition module comprises a multi-source heterogeneous data fusion submodule, wherein the multi-source heterogeneous data fusion submodule is used for integrating disaster site data from various sensors, multi-spectrum imaging equipment carried by unmanned aerial vehicles, satellite remote sensing images, social media and internet public information and achieving deep fusion and unified management of the multi-source heterogeneous data through data cleaning, format conversion and space-time calibration processing means.
Preferably, the big data processing and analyzing module comprises a deep learning prediction sub-module, wherein the deep learning prediction sub-module is used for training historical disaster data, meteorological data and geographic information data, constructing a disaster development trend prediction model and predicting the spreading range, the intensity change and the indexes of the possibly caused secondary disasters of the disasters.
Preferably, the rescue resource database comprises an intelligent matching and recommending sub-module, and the intelligent matching and recommending sub-module adjusts rescue tasks based on individual characteristic information and disaster types of rescue workers.
Preferably, the system further comprises an emergency material intelligent allocation module, wherein the emergency material intelligent allocation module is used for monitoring the inventory state, the position information and the movement track of the rescue materials, and the demand, the priority, the allocation scheme and the transportation path of the rescue materials are calculated in advance and dynamically adjusted through integration with the big data processing and analyzing module.
Preferably, the interactive command platform comprises a virtual reality and augmented reality auxiliary decision sub-module, the virtual reality and augmented reality auxiliary decision sub-module utilizes a three-dimensional modeling technology to construct a digital twin model of a disaster scene, and the digital twin model is dynamically updated in combination with a real-time data stream driving model to provide an immersive and panoramic disaster scene view for command personnel through VR/AR equipment.
Preferably, the system interface module comprises an edge computing adaptation layer, the edge computing adaptation layer is matched with the distributed edge computing nodes, and partial data processing, analyzing and decision-making functions are sunk to edge equipment close to a disaster site, so that localized preprocessing and low-delay response of data are realized.
Preferably, the system further comprises a blockchain trust and tracing subsystem, wherein the blockchain trust and tracing subsystem utilizes a blockchain technology to construct a decentralised emergency information sharing platform.
Preferably, the system also comprises an artificial intelligence-based fault prediction and self-healing subsystem, wherein the artificial intelligence-based fault prediction and self-healing subsystem can be used for continuously monitoring state parameters of each hardware device, software service and network connection in the system, establishing a fault prediction model by using a machine learning algorithm, identifying abnormal signs before faults occur, and early warning and triggering preventive maintenance operation in advance.
The invention has the technical effects and advantages that: compared with the prior art, the Beidou intelligent emergency rescue command and dispatch system provided by the invention has the following advantages:
According to the invention, through integrating the multi-source data acquisition module and the big data processing and analyzing module, various information on a disaster scene is monitored and integrated in real time, the disaster development trend is accurately predicted, and a scientific basis is provided for decision making; the Beidou satellite navigation system is utilized to ensure that the accurate positioning of rescue workers and materials can be realized in any environment, meanwhile, the reliable communication under extreme conditions is ensured through the emergency communication enhancement module, the intelligent decision support module can quickly generate an optimal rescue scheme based on data analysis results and preset emergency plans, the optimal rescue scheme comprises rescue path planning, task allocation and resource scheduling, the decision efficiency and quality are remarkably improved, the accurate matching of the rescue workers and the tasks and the intelligent allocation and tracking of the emergency materials are realized through the rescue resource database and the intelligent matching and recommending sub-module, the resource utilization rate is improved, the technical problems in the aspects of information acquisition, positioning, decision, resource management, collaborative command and the like in the traditional emergency rescue command scheduling are effectively solved, the emergency response speed and the rescue success rate are remarkably improved, and a powerful support is provided for constructing a modern and intelligent emergency management system.
Drawings
Fig. 1 is a block diagram of the Beidou intelligent emergency rescue command and dispatch system.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. The specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a Beidou intelligent emergency rescue command and dispatch system shown in fig. 1, which comprises the following steps: the system comprises a positioning module, a data acquisition module, a big data processing and analyzing module, a rescue resource database, an intelligent decision support module, an interactive command platform and a system interface module. The system aims to realize accurate positioning based on the Beidou satellite navigation system, monitor and analyze disaster conditions in real time, intelligently formulate and execute rescue strategies, and effectively improve emergency rescue efficiency and effect. Specifically, the following is described.
Specifically, the positioning module is in real-time communication with the Beidou satellite navigation system and is used for acquiring geographic position information of accident sites and rescue resources. Specific embodiments of this key functionality, including hardware options, operating principles, and interactions with other system components, will be described in detail below.
1. Hardware selection
1. Positioning terminal
The positioning terminal is key equipment for acquiring rescue site and rescue resource position information. A positioning chip with high sensitivity and strong anti-interference capability, which supports a Beidou No. three global satellite navigation system, such as u-bloxZED-F9P or QuectelL series, is selected. The chips integrate multi-constellation receivers, can simultaneously receive satellite signals of multiple systems such as Beidou, GPS, GLONASS, galileo and the like, and ensure that positioning service with high precision and high reliability is realized in the global scope.
2. Antenna system
In order to ensure effective reception of the Beidou signals, a high-performance Beidou/GNSS full-band antenna is selected, such as TrimbleBD940 or NovAtelSPAN-IGM-A1. The antenna has the characteristics of wide wave beam, low noise and high gain, and can keep good signal capturing capability in environments such as complex terrains, urban canyons, woodland and the like. The antenna should be equipped with a shield made of anti-interference material to reduce the effects of electromagnetic interference and multipath effects.
3. Data transmission module
In order to realize the return of the real-time position information to the command center, the positioning terminal needs to be provided with a wireless data transmission module. A module supporting Low Power Wide Area Network (LPWAN) technology such as LoRaWAN, NB-IoT or LTE-M is selected, such as SemtechSX1301 or QuectelBC66/BC95. The modules can stably transmit data under the conditions of long distance and low power consumption, and adapt to the possible damage condition of communication infrastructure in emergency rescue scenes.
2. Principle of operation
Positioning:
Satellite signal acquisition: the Beidou positioning terminal receives navigation signals transmitted by Beidou satellites through a built-in GNSS antenna, wherein the navigation signals comprise carrier signals and navigation messages modulated by pseudo random codes (PRNs).
Signal demodulation and processing: the positioning chip demodulates the received satellite signals and extracts navigation messages, wherein the navigation messages comprise satellite ephemeris, clock errors, health states and the like. Meanwhile, the chip calculates the pseudorange (i.e., the satellite-to-receiver range estimate) by comparing the phase difference between the locally generated pseudorandom code and the received PRN code.
Multipath suppression and interference rejection: the algorithm inside the chip recognizes and suppresses the multipath signals, and reduces the influence of the multipath signals on positioning accuracy. Meanwhile, the built-in anti-interference function such as a self-adaptive notch filter, an interference detection and elimination mechanism and the like is utilized to resist potential radio frequency interference.
And (3) positioning and resolving: by receiving pseudo-range measurement values of at least four Beidou satellites, combining satellite position information provided in a navigation circuit, carrying out three-dimensional space positioning calculation by using algorithms such as a least square method and Kalman filtering, and obtaining longitude and latitude, altitude and accurate time information of a positioning terminal.
The data transmission process comprises the following steps:
And (3) packaging position information: the positioning terminal packages the calculated position information, equipment state information and the like into a data packet conforming to the selected wireless communication protocol.
And (3) wireless transmission: the data packets are sent to nearby base stations through a data transmission module (e.g., a LoRaWAN module) or uploaded directly to a cloud server through a cellular network (e.g., NB-IoT/LTE-M).
Data aggregation and processing: the command center server receives and analyzes the data packets from each positioning terminal, stores the data packets in an emergency rescue resource database, and performs visual display through a GIS (geographic information system) platform.
3. Interaction with other system components
The real-time communication between the positioning module and the Beidou satellite navigation system is one of basic functions of the whole emergency rescue command and dispatch system, and the positioning module and other components in the system are in close interaction:
And an emergency communication enhancement module: when the communication environment is bad, the positioning terminal can send short emergency information through the Beidou short message function, and the short emergency information is complementary with an emergency communication enhancement module (such as a satellite phone and an emergency communication vehicle), so that transmission of key information is ensured.
And a big data processing and analyzing module: the positioning information is used as an important data source and is transmitted to the big data processing and analyzing module in real time. The method combines multisource data such as weather, geology and traffic to evaluate disaster situations and predict development trend, and provides support for intelligent decision.
And an intelligent decision support module: the positioning module updates the position information of rescue workers and materials in real time, and is an important basis for the intelligent decision support module to carry out path planning, task allocation and resource scheduling.
And an interactive command platform: the positioning data are visually displayed in a map form on the interactive command platform, and command personnel can monitor the site situation in real time to conduct remote command and dispatch.
In conclusion, the high-precision and real-time positioning of accident scene and rescue resources is realized by selecting the high-performance Beidou positioning terminal, the antenna system and the data transmission module and combining the Beidou satellite navigation system. The realization of the function not only provides an accurate space information basis for emergency rescue command and dispatch, but also remarkably improves the emergency response speed and rescue efficiency through high-efficiency interaction with other components in the system.
Specifically, the data acquisition module is used for receiving and integrating disaster site real-time environmental parameters, images and video data acquired from various sensors, monitoring equipment, unmanned aerial vehicles or satellite remote sensing approaches; further, the data acquisition module comprises a multi-source heterogeneous data fusion submodule, wherein the multi-source heterogeneous data fusion submodule is used for integrating disaster site data from various sensors, multi-spectrum imaging equipment carried by unmanned aerial vehicles, satellite remote sensing images, social media and internet public information and achieving deep fusion and unified management of the multi-source heterogeneous data through data cleaning, format conversion and space-time calibration processing means. Specific embodiments of the module, including hardware options, operating principles, and interactions with other system components are set forth in detail below.
1. Hardware selection
1. Sensor and monitoring device
Environmental monitoring sensor: the sensor with high precision, low power consumption and severe environment resistance is selected, such as a temperature and humidity sensor (DHT 11/22 series), a wind speed and direction sensor (AnemometerAM-485), a barometric pressure sensor (BMP 280), a gas detection sensor (MQ series) and the like, and is used for monitoring environmental parameters of disaster sites in real time.
Video monitoring equipment: high-definition network cameras (such as HikvisionDS-2CD2XXX series) are deployed, infrared night vision, wide dynamic and intelligent analysis functions are supported, and the system is used for all-weather acquisition of field images and video data.
Unmanned aerial vehicle: an industrial unmanned aerial vehicle with long endurance, high load and stable flight performance is selected, for example DJIMatrice RTK series, and equipment such as multispectral cameras (MICASENSEREDEDGE-MX) and thermal imaging cameras (FLIRVueTZ) are carried, so that three-dimensional monitoring in the air is realized.
2. Data acquisition and transmission equipment
Data acquisition unit: data collectors with multiple interfaces, mass storage and remote configuration capability, such as CampbellScientificCR series 300, raspberryPi and the like, are selected for collecting various sensor data and are transmitted to a command center in a wired or wireless mode.
Unmanned aerial vehicle data link: the unmanned aerial vehicle data link system (such as DJIOcuSync3.0) with high bandwidth and low delay is adopted, so that the unmanned aerial vehicle can transmit back high-resolution image and video data in real time.
Satellite remote sensing data receiving station: a dedicated satellite receiving device (such as iDirectX modem, VSAT antenna system) is deployed to receive remote sensing image data from earth observation satellites (such as Sentinel series, landsat series).
3. Data processing and storage hardware
Server cluster: a high-performance server cluster (such as a IntelXeonScalable processor-based server) is built, and a high-speed storage array (such as RAID 5/6) and a high-capacity hard disk are provided for processing and storing massive multi-source heterogeneous data.
GPU acceleration card: the server is configured with a high performance GPU card (e.g., NVIDIATESLAV a) for accelerating parallel computing-intensive tasks such as data cleansing, format conversion, space-time calibration, etc.
2. Principle of operation
Data acquisition and transmission:
And (3) monitoring a sensor: various environmental monitoring sensors continuously collect data such as temperature, humidity, wind speed, air pressure, toxic and harmful gas concentration and the like of a disaster site, and the data are sent to a data collector in the modes such as RS-485, UART, I2C, wi-Fi and the like.
Video monitoring: cameras deployed on site continuously capture images and videos on site and transmit data to a command center server through an IP network (e.g., 4G/5G, wi-Fi, optical fiber).
Unmanned aerial vehicle monitoring: the equipment such as multispectral camera, thermal imaging camera that unmanned aerial vehicle carried gathers aerial image and video on predetermined route, and real-time passback is transmitted to the ground station through unmanned aerial vehicle data link, and the network is uploaded to command center again.
Satellite remote sensing: the satellite receiving equipment receives remote sensing image data sent by the earth observation satellite, decodes the remote sensing image data and stores the remote sensing image data in a local server to wait for further processing.
Data fusion and management:
data cleaning: and (3) performing quality inspection on the collected original data, removing invalid, wrong and repeated data, filling the missing value, and ensuring the integrity and consistency of the data.
Format conversion: various data sources (such as sensor data, video streams and remote sensing images) are converted into a unified data format (such as JSON, CSV, geoTIFF) so as to facilitate subsequent processing and analysis.
Space-time calibration: and carrying out space-time coordinate system conversion and synchronization on the sensor data, the unmanned aerial vehicle monitoring data and the satellite remote sensing data, and ensuring that all the data are fused under the same space-time frame.
Data fusion: and (3) carrying out deep fusion on the multi-source heterogeneous data in multiple dimensions such as space time, attribute and the like by using a data fusion algorithm (such as Kalman filtering, particle filtering and deep learning fusion model) so as to generate comprehensive, accurate and real-time disaster site situation information.
Data storage and management: and storing the fused data in a relational database (such as PostgreSQL/PostGIS) or a NoSQL database (such as MongoDB) according to a predefined data model, so as to realize efficient query, retrieval and update of the data.
3. Interaction with other system components
The data acquisition module and the multi-source heterogeneous data fusion sub-module are used as information sources of the emergency rescue command scheduling system and are closely interacted with other components in the system:
And the Beidou positioning module: the fused disaster site situation information is combined with Beidou positioning data to provide an omnibearing and three-dimensional site view for commanders.
And a big data processing and analyzing module: the fusion data is used as a basis of big data analysis and is used for advanced applications such as disaster development trend prediction, risk assessment, rescue scheme optimization and the like.
And an intelligent decision support module: the real-time updated on-site situation information provides real-time input for intelligent decisions, and supports the rapid generation of an optimal rescue scheme.
And an interactive command platform: the fused data is visually displayed through the GIS platform, so that commanders can intuitively understand the field conditions and conduct remote command and dispatch.
In summary, the data acquisition module and the multi-source heterogeneous data fusion sub-module are constructed by selecting proper sensors, monitoring equipment, unmanned aerial vehicle, satellite remote sensing equipment and data processing hardware, so that efficient acquisition, fusion and unified management of multi-component and heterogeneous data in a disaster scene are realized. The realization of the function provides comprehensive, accurate and real-time information support for emergency rescue command and dispatch, and the scientificity and effectiveness of emergency response are obviously improved.
Specifically, the big data processing and analyzing module is used for analyzing the collected data, predicting the disaster development trend and evaluating the rescue difficulty and risk; further, the big data processing and analyzing module comprises a deep learning prediction sub-module, wherein the deep learning prediction sub-module is used for training historical disaster data, meteorological data and geographic information data, constructing a disaster development trend prediction model and predicting the spreading range, the intensity change and the indexes of the possibly caused secondary disasters of the disasters. Specific embodiments of the module, including hardware selection, operating principles, and interactions with other system components, are detailed below.
1. Hardware selection
1. Big data processing server
The computing server: a server with high computing power and high memory bandwidth (such as DellPowerEdgeR940 and HPEProLiantDL380Gen 10) is selected, and a multi-core high-performance CPU (such as IntelXeonPlatinum series) is carried for executing large-scale data processing and deep learning computing tasks.
The storage server: a high-capacity and high-speed storage server (such as NETAPPFAS series) is configured, and a full flash memory array (such as NVMeSSD) or a hybrid storage architecture (HDD+SSD) is adopted, so that quick reading and writing and persistent storage of data are ensured.
GPU-accelerated computing
GPU server: a GPU acceleration compute server (e.g., NVIDIADGXA) is deployed, integrating multiple high performance GPUs (e.g., a 100) for accelerating the deep learning model training and reasoning process. GPU cluster management software: and the dynamic allocation, task scheduling and monitoring of GPU resources are realized by using GPU cluster management software (such as NVIDIADGXSoftwareStack, apacheHadoopYARN).
3. Distributed computing and storage platform
Distributed computing framework: and a distributed computing framework (such as APACHESPARK, APACHEHADOOP) is adopted to realize parallelization and distributed execution of big data processing tasks. Distributed file system: a high availability, high throughput distributed file system (e.g., HDFS, ceph) is deployed that provides reliable storage and efficient access of large data sets.
2. Principle of operation
Data preparation and preprocessing:
Data integration: and receiving disaster site data acquired in real time and external data sources such as historical disaster data, meteorological data, geographic information data and the like from the data acquisition module. Data cleaning: and performing quality inspection on the integrated data, removing abnormal values, filling missing values, correcting format errors, and ensuring the accuracy and consistency of the data. Characteristic engineering: and extracting characteristic variables related to the disaster development trend, such as disaster index, meteorological parameters, topographical features and the like, and providing input for the deep learning model.
Deep learning prediction submodule: model training: and constructing a disaster development trend prediction model by utilizing historical disaster data, meteorological data and geographic information data and combining a deep learning algorithm (such as LSTM, GRU, transformer, CNN-LSTM and the like). Model training is carried out by using TensorFlow, pyTorch and other deep learning frameworks, and model performance is optimized through cross validation and super parameter adjustment. Model evaluation: and proper evaluation indexes (such as mean square error, average absolute error, correlation coefficient and the like) are adopted to evaluate the performance of the trained model, so that the model is ensured to have higher accuracy on the prediction of the disaster development trend. On-line prediction: and deploying the trained model in a real-time prediction environment, receiving a real-time data stream, and outputting prediction results such as a disaster spreading range, intensity change, secondary disaster possibility and the like.
Risk assessment and decision support: calculating a risk index: based on the prediction results and the site data, indexes of rescue difficulty (such as traffic blocking degree, disaster population scale, infrastructure damage condition and the like) and risk (such as casualties risk, secondary disaster risk, rescue action risk and the like) are calculated. Decision support: combining the risk assessment result with information such as rescue resources, emergency plans and the like, and generating an optimal rescue plan suggestion through an intelligent decision support algorithm (such as a genetic algorithm, fuzzy logic, reinforcement learning and the like) for reference by a commander.
3. Interaction with other system components
The big data processing and analyzing module is used as a brain of the emergency rescue command and dispatch system and is closely cooperated with other components in the system: and a data acquisition module: and receiving and processing the real-time disaster site data, the historical disaster data, the meteorological data, the geographic information data and the like provided by the data acquisition module. And a deep learning prediction sub-module: and carrying out risk assessment and decision support by utilizing the disaster development trend prediction result output by the deep learning prediction sub-module. And an intelligent decision support module: and transmitting the risk assessment result and the decision suggestion to an intelligent decision support module to assist commanders in making rescue schemes. And an interactive command platform: information such as a prediction result, a risk assessment index, a decision suggestion and the like is presented on the interactive command platform in the form of a chart, a map and the like, so that a command staff can intuitively understand and decide.
In summary, the big data processing and analyzing module of the invention combines the deep learning prediction sub-module by selecting the hardware such as the high performance server, the GPU acceleration computing device, the distributed computing and storing platform and the like, thereby realizing the high-efficiency processing, the deep analysis and the accurate prediction of the disaster site data and providing powerful data support and decision basis for the emergency rescue command and dispatch. The realization of the function obviously improves the scientificity, predictability and efficiency of emergency response, and powerfully ensures the smooth progress of rescue work.
Specifically, the rescue resource database is used for storing detailed information of various rescue teams, equipment and materials and real-time states of the rescue teams, the equipment and the materials; further, the rescue resource database comprises an intelligent matching and recommending sub-module, and the intelligent matching and recommending sub-module adjusts rescue tasks based on individual characteristic information and disaster types of rescue workers. The following will describe the working principle of the hardware device selection, database design, intelligent matching and recommendation sub-module in detail.
Hardware device type selection and configuration, server hardware: and a DellPowerEdgeR940 server is selected, and a two-way IntelXeonPlatinum8268 processor (24 cores/48 threads) is mounted, so that powerful data processing capacity is provided. 1TBDDR ECC memories are provided to ensure quick response of data in a large number of concurrent accesses. And 6 blocks of 4TBSAS hard disks configured by RAID5 are adopted to form a 24TB storage space, so that the safety and high throughput of data are ensured. In addition, the server is also provided with a redundant power supply and a hot plug hard disk design, so that the stability and the fault recovery capability of the system are improved.
Network equipment: a CiscoCatalyst-38350-series switch is adopted to support high-speed gigabit or tera Ethernet connection, so that efficient transmission of data among the rescue command center, the data center and the field terminal is ensured. Meanwhile, ciscoASA5506-X firewalls are deployed, system network security is guaranteed, and illegal invasion and data leakage are prevented.
Mobile terminal device: the system is provided with a firm three-proofing tablet personal computer with GPS positioning and data communication functions for rescue workers, such as GetacF G4, which is provided with a IntelCorei processor, runs an Android or Windows operating system, can report data such as position information, task execution progress and the like in real time, and receives task instructions from a command center.
The rescue resource database is designed and mainly comprises the following tables:
Rescue team table: recording basic information (such as team name, affiliated units, responsible persons and the like), professional skills (such as search and rescue, medical treatment, engineering and the like), personnel constitution (including individual characteristic information) and the current task state and the like of each rescue team.
Equipment material table: the information of the model, the number, the storage place, the maintenance state, the use record and the like of various rescue equipment (such as unmanned aerial vehicles, life detectors, breaking tools and the like) and materials (such as tents, foods, medical supplies and the like) is recorded in detail.
Disaster type table: and (3) listing various common disasters (such as earthquakes, floods, fires and the like) and the characteristics, coping strategies and other knowledge bases thereof, and providing reference for the intelligent matching and recommending sub-modules.
Real-time status table: dynamic information such as the positions and the availability of rescue teams and equipment materials is updated in real time, and the dynamic information is realized through data interaction with mobile terminal equipment.
The intelligent matching and recommending sub-module performs task allocation optimization based on a machine learning algorithm (such as decision trees, support vector machines, neural networks and the like) according to input disaster types, disaster area environment information and individual characteristic information of rescue workers to be allocated. The specific flow is as follows:
Data collection and pretreatment: and collecting information such as geographical environment, climate condition, infrastructure damage degree and the like of the disaster place, and individual characteristic information such as professional skills, health conditions, experience grades, current positions and the like of rescue workers to be distributed. The data is cleaned and normalized and ready for subsequent analysis. Rescue requirement analysis: based on the disaster type list, the possible rescue skill combination, equipment material demand and the like required by the disaster are determined, and a preliminary rescue task list is formed. Calculating the resource matching degree: and calculating the matching degree of the individual characteristics of each rescue worker to be allocated and the rescue task requirements. The matching degree considerations include: the degree of agreement between professional skills and required skills of personnel, whether the health condition is adequate for high-intensity rescue work, whether the experience level is adequate for coping with complex situations, and the like. Meanwhile, the gap between the actual stock of equipment materials and the required quantity is calculated, and the resource allocation difficulty is evaluated. Task allocation and recommendation: and (3) applying an optimization algorithm (such as a genetic algorithm, simulated annealing and the like), and generating an optimal task allocation scheme by improving the overall matching degree as much as possible on the premise of meeting task requirements. The recommended results include not only the dispatch sequence of the rescue workers and the task division, but also the allocation paths, the priorities and the like of equipment materials.
Dynamic adjustment and feedback: the progress of rescue actions is monitored in real time, and a task allocation scheme is dynamically adjusted according to on-site feedback information (such as task completion conditions, newly discovered disaster changes and the like). Meanwhile, task execution effect data are collected and analyzed, and intelligent matching and recommending models are optimized continuously.
Specifically, the intelligent decision support module is used for generating an optimal rescue scheme based on the data analysis result and a preset emergency plan, wherein the optimal rescue scheme comprises rescue path planning, task allocation and resource scheduling;
illustratively, the software architecture and core functions of the intelligent decision support module include:
And a data analysis module: based on ApacheHadoop, spark and other big data processing frames, multi-source data such as various sensor data, remote sensing images, weather information and the like are integrated, real-time or batch analysis is carried out, and accurate situation awareness is provided for decision support.
A plan management system: based on OracleDatabase c or other enterprise-level relational databases, various disaster relief plans are stored, including information on disaster type, response level, action guidelines, resource allocation templates, and the like. The plan can be dynamically updated and adjusted according to actual conditions.
An intelligent decision support module: and (3) utilizing programming languages such as Python, java and the like to develop, integrating operation research and artificial intelligent algorithms (such as genetic algorithm, deep reinforcement learning and the like), and combining data analysis results and emergency plans to generate an optimal rescue scheme.
Rescue scheme generation working principle, rescue path planning: data preparation: and acquiring GIS data of the topography, road condition, traffic flow, disaster influence area and the like of the disaster area. Algorithm application: and calculating the shortest or optimal path from the rescue base to each disaster area point on the GIS server by adopting a Dijkstra algorithm, an A search algorithm or a path planning model based on deep learning, and considering factors such as time cost, road blocking risk and the like. And (3) outputting results: and generating a visual rescue path diagram, and marking information such as key nodes, predicted driving time, standby routes and the like for a commander to refer to.
Task allocation:
Individual feature analysis: individual characteristic information such as professional skills, health conditions, experience levels, current positions and the like of rescue workers are collected.
Task demand identification: and identifying the needed rescue skill combination, task priority, personnel number and the like according to the disaster type and disaster area condition.
Matching degree calculation: and calculating the matching degree of each rescue worker and each task by using a machine learning model (such as a support vector machine, a decision tree and the like).
Optimizing distribution: and (3) generating an optimal task allocation scheme considering task requirements and personnel capacity by using optimization methods such as genetic algorithm, simulated annealing and the like.
And (3) resource scheduling:
demand prediction: based on historical data, disaster scale, rescue task characteristics and the like, the number and the types of needed rescue equipment and materials are predicted.
Inventory query: and inquiring a rescue resource database in real time to know the existing stock, distribution position and available state of various equipment and materials.
Scheduling policy: and (3) setting up a resource allocation scheme by using methods such as linear programming, integer programming and the like, and considering factors such as transportation cost, time window, emergency degree and the like.
Performing monitoring: the resource transportation track is tracked through the internet of things technology, so that the situation that the resource transportation track arrives at a designated place according to a plan is ensured, and the abnormal situation is timely adjusted.
Specifically, the interactive command platform is used for providing a visual interface, displaying the situation of a disaster site, the rescue progress and the resource distribution in real time, and supporting the functions of remote command, multiparty collaboration, voice and video communication; further, the interactive command platform comprises a virtual reality and augmented reality auxiliary decision sub-module, the virtual reality and augmented reality auxiliary decision sub-module utilizes a three-dimensional modeling technology to construct a digital twin model of a disaster scene, and the digital twin model is dynamically updated in combination with a real-time data stream driving model to provide an immersive and panoramic disaster scene view for command personnel through VR/AR equipment.
Specifically, the system interface module is used for realizing data exchange and linkage control with emergency management departments, professional rescue institutions and social public service systems at all levels. Further, the system interface module comprises an edge computing adaptation layer, the edge computing adaptation layer is matched with the distributed edge computing nodes, and partial data processing, analyzing and decision-making functions are sunk to edge equipment close to a disaster site, so that localized preprocessing and low-delay response of data are realized.
In some other embodiments, the above Beidou intelligent emergency rescue command and dispatch system further comprises: the emergency communication enhancement module is integrated with a Beidou short message communication unit and is used for sending positioning information and short text information at disaster sites without public network signal coverage.
In the Beidou intelligent emergency rescue command and dispatch system, an emergency communication enhancement module is used as a key component, and aims to solve the communication problem when a public network signal is lost in a disaster scene. The module integrates the Beidou short message communication unit, ensures that rescue teams can send positioning information and short text information in an extreme environment without public network coverage, and maintains effective contact with a command center. Specific embodiments of the emergency communication enhancement module are described in detail below, including functional positioning, hardware composition, software support, and practical application scenarios.
Functional positioning and hardware composition
Functional positioning: the core function of the emergency communication enhancement module is to rely on short message communication service of the Beidou satellite navigation system under the condition of communication interruption of the public network, realize bidirectional positioning information and text information transmission between a rescue site and a command center and ensure continuity and effectiveness of emergency command scheduling.
The hardware comprises the following components: beidou short message communication terminal: the Hua Xin antenna HS-TD01 type Beidou short message terminal has the capabilities of receiving Beidou satellite signals, encoding and decoding short messages, and sending and receiving positioning information and text messages. Terminals are usually designed to be portable or vehicle-mounted and adapted to severe environments. An antenna assembly: including Beidou satellite receiving antennas (such as omni-directional or directional antennas) and GPS/GLONASS/Galileo multimode satellite receiving antennas, ensure that stable satellite signals are received worldwide. And a power management module: the high-efficiency battery or an external power interface is provided, long-time standby and continuous work are supported, and the power requirement in an emergency rescue scene is met.
Software support and function implementation
An embedded operating system: the terminal is internally provided with a customized embedded Linux operating system, provides a stable and low-power-consumption operating environment, and supports efficient running of application programs. Beidou short message communication software: the special short message communication software is developed, and has the following main functions: information encoding and decoding: the coding and decoding of the text information are realized according to the Beidou short message communication protocol, and the reliable transmission of the information on a satellite link is ensured. Positioning service: and receiving and analyzing the positioning data of the Beidou satellite, calculating the accurate geographic position of the terminal, and supporting the reporting and receiving of the position information. User interface: and a concise and visual interaction interface is provided, and a rescuer can rapidly input, view and send information through a touch screen or physical keys. And (3) an integrated interface: the emergency communication enhancement module is in seamless butt joint with other parts of the command scheduling system (such as a command center software platform and a mobile terminal APP) through standard interfaces (such as RS-232, USB, TCP/IP and the like), so that real-time synchronization and sharing of data are realized.
Actual application scene and workflow
And (3) on-site deployment: before entering a public network signal blind area, the rescue team activates the Beidou short message communication terminal to ensure that a stable satellite communication link is established with the command center. Positioning report: the terminal reports the current position information to the command center periodically or automatically according to the need, or immediately sends a positioning report according to the instruction. The command center can monitor the position dynamic of the rescue team in real time and conduct accurate path planning and task adjustment. And (3) information interaction: the rescue workers send short text information through the terminal to report key information such as scene disaster, rescue progress, material demands and the like. The command center can issue instructions such as task instructions, security alarms, resource allocation and the like. The information exchange between the two parties is not limited by the coverage of the public network, so that the timeliness of command and dispatch is ensured. Emergency call: when encountering sudden dangerous situations, rescue workers can trigger the emergency call function of the terminal to immediately send short messages with priority identifiers, and the command center can quickly respond after receiving the short messages and start an emergency rescue plan.
In other embodiments, the Beidou intelligent emergency rescue command and dispatch system further comprises an emergency material intelligent allocation module, wherein the emergency material intelligent allocation module is used for monitoring the inventory state, the position information and the movement track of the rescue materials, and the demand, the priority, the allocation scheme and the transportation path of the rescue materials are calculated and dynamically adjusted in advance through integration with the big data processing and analyzing module.
In some other embodiments, the Beidou intelligent emergency rescue command and dispatch system further comprises a blockchain trust and tracing subsystem, wherein the blockchain trust and tracing subsystem utilizes a blockchain technology to construct a decentralised emergency information sharing platform. The method comprises the following steps:
Technical architecture and core functionality
The technical architecture is as follows: the node construction is jointly participated in by the emergency management department, the rescue organization, the volunteer organization, the material suppliers and the like in a alliance chain mode. The bottom layer selects mature blockchain frameworks such as Ethernet, hyperledgerFabric and the like, and combines core technologies such as intelligent contracts, consensus mechanisms, encryption algorithms and the like to construct a safe and efficient emergency information sharing platform.
Core functions: information chaining: and encrypting key data such as identity authentication information, disaster reports, rescue requests, task allocation, resource scheduling, rescue progress, material use and the like related to rescue actions, and storing the key data in a chain to ensure that the data cannot be tampered and trace is left in the whole process.
Trust construction: through the transparent block chain account book, each participant can review and verify information on the chain in real time, information asymmetry is eliminated, trust relation based on data facts is established, and efficient collaboration across departments and across organizations is promoted.
Tracing and auditing: and by utilizing the time stamp and hash link characteristics of the blockchain, the history circulation process of any piece of information is easily traced, so that post audit, evaluation of rescue effect and summary of experience teaching and training are facilitated.
Data processing flow
And (3) data acquisition: and various data in the emergency rescue process are collected in real time through other modules (such as a rescue resource database, an intelligent decision support module and the like) of the Beidou intelligent emergency rescue command and dispatch system and various front-end devices (such as a mobile terminal, a sensor, an unmanned aerial vehicle and the like).
Data preprocessing: and (3) carrying out preprocessing operations such as cleaning, formatting, desensitizing and the like on the acquired data, ensuring that the data quality meets the uplink requirement, and protecting sensitive information from leakage.
Information chaining: intelligent contract deployment: corresponding intelligent contracts are written and deployed according to different types of emergency information, and rules such as data structures, authority control, business logic and the like are specified. Transaction initiation: the participants package the preprocessed data into blockchain transactions through private key signature and submit the blockchain transactions to the local node. Consensus validation: the local node broadcasts the transaction to the whole network, and each node verifies, sorts and packages according to a consensus mechanism (such as PBFT, raft and the like), finally generates blocks and adds the blocks to a block chain.
Information inquiry and traceability:
On-chain query: parameters such as public keys, transaction hash, time ranges and the like are used by all the participants, information on a chain is queried through a blockchain browser or an API interface, and functions such as complex condition screening, statistical analysis and the like are supported. Tracing and auditing: and (3) tracking the historical change record of the data through recursively analyzing the transaction hash chain to form a complete data life cycle view for auditing, evaluating or dispute mediation.
Application scenario
Emergent material tracing: and recording full life cycle information from production, purchasing, storing, allocating and using to recycling of the rescue materials, ensuring that the materials are clear, and using compliance, and preventing waste and abuse.
The rescue action is transparent: the data of action tracks, task completion conditions, rescue results and the like of rescue teams are disclosed, social supervision is accepted, and the credence of rescue work is improved.
Cross-department collaboration: the government departments, armies, civil organizations and the like share information and coordinate actions on the unified blockchain platform, break information islands and improve emergency response speed and efficiency.
Post-evaluation and insurance claims: and by utilizing the blockchain traceability data, the disaster loss and rescue effect are objectively evaluated, and an accurate basis is provided for policy making and insurance claim settlement.
The block chain technology is utilized to construct a decentralised emergency information sharing platform, so that the trusted storage, transparent sharing and whole-course traceability of rescue information are realized. The subsystem enhances the trust cooperation in the emergency rescue process by enhancing the authenticity and traceability of information, optimizes the resource scheduling efficiency and provides powerful technical support for improving the disaster coping capability.
In other embodiments, the Beidou intelligent emergency rescue command and dispatch system further comprises an artificial intelligence based fault prediction and self-healing subsystem, wherein the artificial intelligence based fault prediction and self-healing subsystem is used for continuously monitoring state parameters of each hardware device, software service and network connection in the system, establishing a fault prediction model by using a machine learning algorithm, and identifying abnormal signs before faults occur, and early warning and triggering preventive maintenance operation in advance.
In summary, the invention integrates the multisource data acquisition module and the big data processing and analyzing module to monitor and integrate various information on disaster sites in real time, accurately predicts disaster development trend and provides scientific basis for decision making; the Beidou satellite navigation system is utilized to ensure that the accurate positioning of rescue workers and materials can be realized in any environment, meanwhile, the reliable communication under extreme conditions is ensured through the emergency communication enhancement module, the intelligent decision support module can quickly generate an optimal rescue scheme based on data analysis results and preset emergency plans, the optimal rescue scheme comprises rescue path planning, task allocation and resource scheduling, the decision efficiency and quality are remarkably improved, the accurate matching of the rescue workers and the tasks and the intelligent allocation and tracking of the emergency materials are realized through the rescue resource database and the intelligent matching and recommending sub-module, the resource utilization rate is improved, the technical problems in the aspects of information acquisition, positioning, decision, resource management, collaborative command and the like in the traditional emergency rescue command scheduling are effectively solved, the emergency response speed and the rescue success rate are remarkably improved, and a powerful support is provided for constructing a modern and intelligent emergency management system.
Finally, it should be noted that: the foregoing description is only illustrative of the preferred embodiments of the present invention, and although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements or changes may be made without departing from the spirit and principles of the present invention.

Claims (10)

1. Beidou intelligent emergency rescue command and dispatch system, which is characterized by comprising:
The positioning module is in real-time communication with the Beidou satellite navigation system and is used for acquiring geographic position information of accident sites and rescue resources;
The data acquisition module is used for receiving and integrating disaster site real-time environment parameters, images and video data acquired from various sensors, monitoring equipment, unmanned aerial vehicles or satellite remote sensing approaches;
the big data processing and analyzing module is used for analyzing the acquired data, predicting the disaster development trend and evaluating the rescue difficulty and risk;
The rescue resource database is used for storing detailed information of various rescue teams, equipment and materials and real-time states of the rescue teams, the equipment and the materials;
the intelligent decision support module is used for generating an optimal rescue scheme based on the data analysis result and a preset emergency plan, and comprises rescue path planning, task allocation and resource scheduling;
The interactive command platform is used for providing a visual interface, displaying the situation of a disaster site, the rescue progress and the resource distribution in real time, and supporting the functions of remote command, multiparty collaboration, voice and video communication;
The system interface module is used for realizing data exchange and linkage control with emergency management departments, professional rescue institutions and social public service systems at all levels.
2. The Beidou intelligent emergency rescue command and dispatch system of claim 1, wherein: further comprises: the emergency communication enhancement module is integrated with a Beidou short message communication unit and is used for sending positioning information and short text information at disaster sites without public network signal coverage.
3. The Beidou intelligent emergency rescue command and dispatch system of claim 1, wherein: the data acquisition module comprises a multi-source heterogeneous data fusion submodule, wherein the multi-source heterogeneous data fusion submodule is used for integrating disaster site data from various sensors, multi-spectrum imaging equipment carried by unmanned aerial vehicles, satellite remote sensing images, social media and internet public information sources, and realizes deep fusion and unified management of the multi-source heterogeneous data through data cleaning, format conversion and space-time calibration processing means.
4. The Beidou intelligent emergency rescue command and dispatch system of claim 1, wherein: the big data processing and analyzing module comprises a deep learning prediction sub-module, wherein the deep learning prediction sub-module is used for training historical disaster data, meteorological data and geographic information data, constructing a disaster development trend prediction model and predicting the spreading range, the intensity change and the indexes of possible secondary disasters of the disasters.
5. The Beidou intelligent emergency rescue command and dispatch system of claim 1, wherein: the rescue resource database comprises an intelligent matching and recommending sub-module, and the intelligent matching and recommending sub-module adjusts rescue tasks based on individual characteristic information and disaster types of rescue workers.
6. The Beidou intelligent emergency rescue command and dispatch system of claim 1, wherein: the system comprises a rescue material processing and analyzing module, an emergency material intelligent allocation module and a control module, wherein the rescue material intelligent allocation module is used for monitoring the inventory state, the position information and the movement track of rescue materials, and the demand, the priority, the allocation scheme and the transportation path of the rescue materials are calculated in advance and dynamically adjusted through integration with the big data processing and analyzing module.
7. The Beidou intelligent emergency rescue command and dispatch system of claim 1, wherein: the interactive command platform comprises a virtual reality and augmented reality auxiliary decision sub-module, the virtual reality and augmented reality auxiliary decision sub-module utilizes a three-dimensional modeling technology to construct a digital twin model of a disaster scene, and the digital twin model is dynamically updated in combination with a real-time data stream driving model to provide an immersive and panoramic disaster scene view for command personnel through VR/AR equipment.
8. The Beidou intelligent emergency rescue command and dispatch system of claim 1, wherein: the system interface module comprises an edge computing adaptation layer, wherein the edge computing adaptation layer is matched with the distributed edge computing nodes, and partial data processing, analyzing and decision-making functions are sunk to edge equipment close to a disaster site, so that localized preprocessing and low-delay response of data are realized.
9. The Beidou intelligent emergency rescue command and dispatch system of claim 1, wherein: the system also comprises a blockchain trust and tracing subsystem, wherein the blockchain trust and tracing subsystem utilizes a blockchain technology to construct a decentralised emergency information sharing platform.
10. The Beidou intelligent emergency rescue command and dispatch system of claim 1, wherein: the system also comprises an artificial intelligence-based fault prediction and self-healing subsystem, wherein the artificial intelligence-based fault prediction and self-healing subsystem is used for continuously monitoring state parameters of each hardware device, software service and network connection in the system, establishing a fault prediction model by using a machine learning algorithm, identifying abnormal signs before faults occur, and early warning and triggering preventive maintenance operation in advance.
CN202410472573.8A 2024-04-19 2024-04-19 Beidou intelligent emergency rescue command and dispatch system Pending CN118278577A (en)

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