CN119299484A - A safety and environmental inspection system - Google Patents

Abstract

The present invention discloses a safety and environmental inspection system, which specifically relates to the field of safety and environmental inspection technology, and includes an intelligent data acquisition and transmission module for real-time collection of environmental, equipment and personnel data and secure transmission. The present invention ensures the efficiency and safety of the inspection process by designing full-process intelligent management from data collection, processing, analysis, to inspection management and user feedback. The system can not only monitor and analyze environmental, equipment and personnel data in real time, automatically identify and detect potential risks, but also autonomously perform inspection tasks through intelligent inspection robots, dynamically adjust task priorities, and manage and record abnormal events. In addition, the system provides inspection personnel with a convenient operation interface and real-time data overlay display through a cross-platform user interface and augmented reality technology, and provides comprehensive user support services through 24/7 customer support and online training modules.

Description

A safety and environmental inspection system

Technical Field

The present invention relates to the technical field of safety and environmental inspection, and in particular to a safety and environmental inspection system.

Background Art

The safety and environmental inspection system is an important means to ensure the safety of industrial facilities, public places and corporate environments. It is widely used in petrochemical, electric power, manufacturing, transportation and other industries. Traditional inspection methods usually rely on manual inspections. Inspectors need to check the safety status of equipment and environment according to the predetermined route, record the problems found and deal with them. This method is not only time-consuming and labor-intensive, but also prone to omissions and human errors, making it difficult to grasp the safety dynamics of the inspection area in real time and comprehensively. With the development of the Internet of Things, big data and artificial intelligence technologies, intelligent safety and environmental inspection systems are gradually emerging. By integrating various sensors, data collection and analysis technologies, real-time monitoring and intelligent inspection of the environment and equipment can be achieved, improving inspection efficiency and safety assurance levels.

The existing safety and environmental inspection system has some defects in practical application. First, the data collection and processing capabilities of the traditional inspection system are limited, making it difficult to comprehensively and in real time monitor and analyze environmental, equipment and personnel data, resulting in the inability to identify and handle potential risks in a timely manner. Secondly, the inspection tasks of the existing system usually rely on manual execution, the workload of the inspectors is heavy, the inspection efficiency is low, and there are safety hazards in complex and dangerous environments. In addition, the user interface of the traditional inspection system is cumbersome to operate, lacks intuitive real-time data display and interactive functions, and affects the work efficiency and accuracy of the inspectors. At the same time, the recording and management of inspection data are scattered, making it difficult to form a comprehensive report to provide effective support for decision-making. Therefore, we provide a safety and environmental inspection system.

Summary of the invention

The purpose of the present invention is to solve the shortcomings in the prior art and to propose a safety and environmental inspection system.

In order to achieve the above object, the present invention adopts the following technical solutions:

A safety and environmental inspection system, comprising an intelligent data collection and transmission module for real-time collection of environmental, equipment and personnel data and secure transmission, an advanced data processing and analysis module for data cleaning, standardization, feature extraction, in-depth analysis and modeling, an intelligent inspection management module for inspection route planning, task scheduling and exception handling, and a multi-platform user feedback module for providing a cross-platform user interface and real-time data feedback;

The intelligent data acquisition and transmission module includes a sensor network optimization module for intelligently adjusting sensor layout and acquisition frequency using an adaptive sensor network, a multi-source data fusion module for integrating multi-source data from satellites, drones, mobile devices, etc., and an edge computing module for deploying edge computing devices near sensors for preliminary data processing and filtering;

The advanced data processing and analysis module includes a cloud storage and computing module for utilizing a cloud computing platform for large-scale data processing and storage, an artificial intelligence learning module for applying advanced AI and deep learning techniques to automatically identify complex environmental patterns and potential risks, and a visual report generation module for generating dynamic, interactive inspection reports and environmental assessment charts through advanced data visualization tools;

The intelligent inspection management module includes an automatic inspection machine module for introducing automatic inspection robots that can autonomously navigate and perform inspection tasks, a dynamic task allocation module for intelligently adjusting the priority and allocation of inspection tasks based on real-time data and analysis results, and an abnormal event management module for establishing a comprehensive abnormal event management system;

The multi-platform user feedback module includes a cross-platform user interface module for developing multi-platform user interfaces suitable for PCs, mobile devices, tablets, etc., an augmented reality support module for introducing AR technology to provide patrol personnel with real-time environmental data overlay display and navigation guidance, and a user training and support module for integrating online training modules and 24/7 customer support.

The present invention is further configured as follows: the artificial intelligence learning module includes an emotion-driven safety assessment module for analyzing workplace sound, video and social media data, monitoring the emotional state of the group in real time, and integrating it with environmental data to assess safety risks, a quantum-enhanced sensor fusion module for ensuring the security and integrity of data during transmission using quantum communication technology, a biofeedback optimization module for enabling direct interaction between patrol personnel and the system through brain-computer interface technology, an immersive virtual training module for creating realistic patrol scenes combined with deep learning algorithms using virtual reality technology, a self-repairing data network module for automatically detecting and repairing network faults using AI technology, an intelligent anomaly determination module for dynamically adjusting risk assessment models based on real-time data and historical patterns, and providing accurate anomaly determination and early warning, and a comprehensive report generation module for integrating the results of multimodal data analysis and adaptive risk assessment to generate a comprehensive report;

The intelligent anomaly determination module includes a multimodal analysis branch that integrates and analyzes diverse data from different sensors and data sources to provide comprehensive anomaly detection and situational understanding, and an adaptive assessment branch that dynamically adjusts and optimizes the risk assessment model to provide accurate risk prediction and decision support based on real-time data and historical patterns;

The multimodal analysis branch includes a data fusion preprocessing module for using advanced algorithms to clean, standardize and extract key features from emotion perception data, quantum sensing data and biological monitoring data, a deep learning model training module for developing and training neural network models that combine multiple data types and integrate the output results of multiple models through ensemble learning technology, and a real-time anomaly detection module for establishing a real-time data stream processing framework that can analyze and judge data in real time when it arrives, automatically mark detected abnormal events, and record detailed information for subsequent analysis and processing;

The adaptive assessment branch includes a dynamic risk model construction module for establishing a baseline risk model using historical data and dynamically adjusting risk model parameters according to current environment and situational data; an intelligent decision support module for predicting possible risk development trends by simulating different scenarios and combining expert systems to further enhance the accuracy of risk assessment using expert knowledge and rules; and a feedback and self-learning module for establishing an automatic feedback mechanism, adjusting and optimizing model parameters according to actual detected anomalies and risk events, and introducing a self-learning algorithm, so that the system can learn and improve from each risk assessment and anomaly detection.

The present invention is further configured as follows: the emotion-driven safety assessment module monitors the emotional state of the group in real time by analyzing the sound, video and social media data in the workplace, and transmits the data to the quantum enhanced sensing fusion module; the quantum enhanced sensing fusion module ensures the security and integrity of the data during the transmission process, and provides the transmitted data to the biofeedback optimization module; the biofeedback optimization module collects the biofeedback data of the patrol personnel and transmits the data to the immersive virtual training module; the immersive virtual training module uses virtual reality technology and deep learning algorithms for training, and relies on the self-repairing data network module to ensure the data transmission stability and network reliability of the training environment; the self-repairing data network module ensures the data transmission stability between modules in the system and the automatic detection and repair of network faults, and provides a stable data input source for the intelligent abnormality determination module; the intelligent abnormality determination module transmits the results to the comprehensive report generation module by analyzing and detecting abnormal conditions.

The present invention is further configured as follows: the sensor network optimization module intelligently adjusts the sensor layout and acquisition frequency through an adaptive sensor network, and transmits the optimized data to a multi-source data fusion module; the multi-source data fusion module integrates multi-source data from satellites, drones, mobile devices, etc., and transmits the integrated data to an edge computing module; the edge computing module deploys edge computing devices near the sensors, performs preliminary processing and filtering on the data, and transmits the processed data to a cloud storage and computing module of an advanced data processing and analysis module.

The present invention is further configured as follows: the cloud storage and computing module utilizes the cloud computing platform to perform large-scale data processing and storage, and provides the processed and stored data to the artificial intelligence learning module; the artificial intelligence learning module applies AI and deep learning technology to automatically identify complex environmental patterns and potential risks, and transmits the analysis results to the visualization report generation module.

The present invention is further configured as follows: the automatic inspection machine module introduces an automatic inspection robot to autonomously navigate and execute inspection tasks, and transmits the inspection data and task execution status to the dynamic task allocation module; the dynamic task allocation module intelligently adjusts the priority and allocation of inspection tasks based on real-time data and analysis results, and transmits the updated task information to the abnormal event management module; the abnormal event management module receives and processes the updated task information from the dynamic task allocation module, manages and records abnormal events, and feeds back relevant information to the user training and support module of the multi-platform user feedback module.

The present invention is further configured as follows: the cross-platform user interface module develops user interfaces suitable for multiple platforms such as PCs, mobile devices, and tablet computers, and integrates these interfaces into the augmented reality support module; the augmented reality support module uses AR technology to provide patrol personnel with real-time environmental data overlay display and navigation guidance, and transmits user interaction data and experience information to the user training and support module; the user training and support module integrates an online training module and 24/7 customer support, receives user interaction data transmitted from the augmented reality support module, provides corresponding training and support services, and transmits user feedback information back to relevant modules to optimize system performance.

The beneficial effects of the present invention are:

The present invention ensures the efficiency and safety of the inspection process through intelligent management of the entire process from data collection, processing, analysis to inspection management and user feedback. The system can not only monitor and analyze environmental, equipment and personnel data in real time, automatically identify and detect potential risks, but also autonomously perform inspection tasks through intelligent inspection robots, dynamically adjust task priorities, manage and record abnormal events. In addition, the system provides inspection personnel with a convenient operation interface and real-time data overlay display through a cross-platform user interface and augmented reality technology, and provides comprehensive user support services through 24/7 customer support and online training modules. Finally, the system integrates the results of multimodal data analysis and adaptive risk assessment through a comprehensive report generation module to generate a comprehensive report for decision-making, thereby realizing intelligent inspection management and efficient safety assurance.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a system in the present invention.

FIG. 2 is a partial flow chart of the artificial intelligence learning module in the present invention.

DETAILED DESCRIPTION

The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

Example 1

As shown in FIG1 , a safety and environmental inspection system includes an intelligent data collection and transmission module for real-time collection of environmental, equipment and personnel data and secure transmission, an advanced data processing and analysis module for data cleaning, standardization, feature extraction, in-depth analysis and modeling, an intelligent inspection management module for inspection route planning, task scheduling and exception handling, and a multi-platform user feedback module for providing a cross-platform user interface and real-time data feedback;

The intelligent data acquisition and transmission module includes a sensor network optimization module for intelligently adjusting sensor layout and acquisition frequency using an adaptive sensor network, a multi-source data fusion module for integrating multi-source data from satellites, drones, mobile devices, etc., and an edge computing module for deploying edge computing devices near sensors for preliminary data processing and filtering;

The advanced data processing and analysis module includes a cloud storage and computing module for utilizing a cloud computing platform for large-scale data processing and storage, an artificial intelligence learning module for applying advanced AI and deep learning techniques to automatically identify complex environmental patterns and potential risks, and a visual report generation module for generating dynamic, interactive inspection reports and environmental assessment charts through advanced data visualization tools;

The intelligent inspection management module includes an automatic inspection machine module for introducing automatic inspection robots that can autonomously navigate and perform inspection tasks, a dynamic task allocation module for intelligently adjusting the priority and allocation of inspection tasks based on real-time data and analysis results, and an abnormal event management module for establishing a comprehensive abnormal event management system;

The multi-platform user feedback module includes a cross-platform user interface module for developing multi-platform user interfaces for PCs, mobile devices, tablets, etc., an augmented reality support module for introducing AR technology to provide real-time environmental data overlay display and navigation guidance for inspection personnel, and a user training and support module for integrating online training modules and 24/7 customer support;

The sensor network optimization module intelligently adjusts the sensor layout and acquisition frequency through the adaptive sensor network, and transmits the optimized data to the multi-source data fusion module; the multi-source data fusion module integrates multi-source data from satellites, drones, mobile devices, etc., and transmits the integrated data to the edge computing module; the edge computing module deploys edge computing devices near the sensors, performs preliminary processing and filtering on the data, and transmits the processed data to the cloud storage and computing module of the advanced data processing and analysis module;

The cloud storage and computing module uses the cloud computing platform to process and store large-scale data, and provides the processed and stored data to the artificial intelligence learning module; the artificial intelligence learning module uses AI and deep learning technology to automatically identify complex environmental patterns and potential risks, and transmits the analysis results to the visualization report generation module;

The automatic inspection machine module introduces an automatic inspection robot to autonomously navigate and perform inspection tasks, and transmits inspection data and task execution status to the dynamic task allocation module; the dynamic task allocation module intelligently adjusts the priority and allocation of inspection tasks based on real-time data and analysis results, and transmits the updated task information to the abnormal event management module; the abnormal event management module receives and processes the updated task information from the dynamic task allocation module, manages and records abnormal events, and feeds back relevant information to the user training and support module of the multi-platform user feedback module;

The cross-platform user interface module develops user interfaces suitable for multiple platforms such as PCs, mobile devices, and tablet computers, and integrates these interfaces into the augmented reality support module; the augmented reality support module uses AR technology to provide patrol personnel with real-time environmental data overlay display and navigation guidance, and transmits user interaction data and experience information to the user training and support module; the user training and support module integrates online training modules and 24/7 customer support, receives user interaction data transmitted from the augmented reality support module, provides corresponding training and support services, and transmits user feedback information back to relevant modules to optimize system performance.

In the above embodiment, first, the intelligent data acquisition and transmission module intelligently adjusts the sensor layout and acquisition frequency through the sensor network optimization module, transmits the optimized data to the multi-source data fusion module, integrates the multi-source data and transmits it to the edge computing module for preliminary processing, and then transmits it to the cloud storage and computing module of the advanced data processing and analysis module for large-scale data processing and storage. The processed data is automatically identified by the artificial intelligence learning module using AI and deep learning technology for complex environmental patterns and potential risks, and the analysis results are transmitted to the visual report generation module to generate dynamic, interactive inspection reports and environmental assessment charts. The intelligent inspection management module introduces automatic inspection robots to autonomously navigate and perform inspection tasks, transmits inspection data and task execution status to the dynamic task allocation module, intelligently adjusts the priority and allocation of inspection tasks based on real-time data and analysis results, and transmits the updated task information to the abnormal event management module to manage and record abnormal events, and feedback to the user training and support module of the multi-platform user feedback module. The multi-platform user feedback module develops user interfaces suitable for various devices through the cross-platform user interface module, and integrates it into the augmented reality support module, using AR technology to provide real-time data overlay display and navigation guidance for inspection personnel, and user interaction data and experience information are transmitted to the user training and support module, providing online training and 24/7 customer support, and transmitting user feedback information back to related modules to optimize system performance. Through the collaborative work of these modules, the intrinsically safe environmental inspection system realizes the comprehensive integration of real-time data collection, intelligent analysis and processing, automated inspection management and multi-platform user feedback, ensuring the efficiency and safety of the inspection process.

The data storage in the cloud storage and computing module can utilize cloud storage computing related codes as follows:

Among them, the large-scale data processing and analysis in the cloud storage and computing module can use the cloud computing platform to perform large-scale data processing and analysis, using big data processing frameworks such as MapReduce and Spark. The relevant code is as follows:

Example 2

As shown in FIG1-2, a safety and environmental inspection system, the artificial intelligence learning module includes an emotion-driven safety assessment module for analyzing workplace sound, video and social media data, monitoring group emotional states in real time, and integrating with environmental data to assess safety risks, a quantum-enhanced sensor fusion module for using quantum communication technology to ensure the security and integrity of data during transmission, a biofeedback optimization module for enabling direct interaction between inspectors and the system through brain-computer interface technology, an immersive virtual training module for using virtual reality technology to create realistic inspection scenarios combined with deep learning algorithms, a self-repairing data network module for automatically detecting and repairing network faults using AI technology, an intelligent anomaly determination module for dynamically adjusting risk assessment models based on real-time data and historical patterns to provide accurate anomaly determination and early warning, and a comprehensive report generation module for integrating the results of multimodal data analysis and adaptive risk assessment to generate a comprehensive report;

The intelligent anomaly determination module includes a multimodal analysis branch that integrates and analyzes diverse data from different sensors and data sources to provide comprehensive anomaly detection and situational understanding, and an adaptive assessment branch that dynamically adjusts and optimizes the risk assessment model to provide accurate risk prediction and decision support based on real-time data and historical patterns;

The multimodal analysis branch includes a data fusion preprocessing module for using advanced algorithms to clean, standardize and extract key features from emotion perception data, quantum sensing data and biological monitoring data, a deep learning model training module for developing and training neural network models that combine multiple data types and integrate the output results of multiple models through ensemble learning technology, and a real-time anomaly detection module for establishing a real-time data stream processing framework that can analyze and judge data in real time when it arrives, automatically mark detected abnormal events, and record detailed information for subsequent analysis and processing;

The adaptive assessment branch includes a dynamic risk model building module for using historical data to establish a baseline risk model and dynamically adjust the risk model parameters according to the current environment and situational data, an intelligent decision support module for predicting possible risk development trends by simulating different scenarios and combining expert systems to further enhance the accuracy of risk assessment by using expert knowledge and rules, and a feedback and self-learning module for establishing an automatic feedback mechanism to adjust and optimize model parameters according to the actual detected anomalies and risk events, and introduce a self-learning algorithm to enable the system to learn and improve from each risk assessment and anomaly detection;

The emotion-driven safety assessment module monitors the emotional state of the group in real time by analyzing the sound, video and social media data in the workplace, and transmits the data to the quantum enhanced sensor fusion module; the quantum enhanced sensor fusion module ensures the security and integrity of the data during transmission, and provides the transmitted data to the biofeedback optimization module; the biofeedback optimization module collects the biofeedback data of the patrol personnel and transmits the data to the immersive virtual training module; the immersive virtual training module uses virtual reality technology and deep learning algorithms for training, and relies on the self-repairing data network module to ensure the data transmission stability and network reliability of the training environment; the self-repairing data network module ensures the stability of data transmission between modules in the system and the automatic detection and repair of network faults, providing a stable data input source for the intelligent anomaly determination module; the intelligent anomaly determination module transmits the results to the comprehensive report generation module by analyzing and detecting abnormal situations.

In the above embodiment, first, the emotion-driven safety assessment module monitors the emotional state of the group in real time by analyzing the sound, video and social media data in the workplace, and transmits these data to the quantum enhanced sensor fusion module to ensure the security and integrity of the data during the transmission process. Then, the quantum enhanced sensor fusion module provides the transmitted data to the biofeedback optimization module, which collects the biofeedback data of the inspectors through brain-computer interface technology and transmits it to the immersive virtual training module. The immersive virtual training module uses virtual reality technology and deep learning algorithms to create realistic inspection scenarios and conduct training, while relying on the self-healing data network module to ensure the data transmission stability and network reliability of the training environment.

The self-healing data network module automatically detects and repairs network failures through AI technology, ensuring stable data transmission between modules in the system and providing a stable data input source for the intelligent anomaly determination module. The intelligent anomaly determination module includes a multimodal analysis branch and an adaptive evaluation branch. It integrates and analyzes diverse data from different sensors and data sources to provide comprehensive anomaly detection and situational understanding, and dynamically adjusts the risk assessment model based on real-time data and historical patterns to provide accurate risk prediction and decision support. Finally, the intelligent anomaly determination module transmits the analyzed and detected anomalies and risk assessment results to the comprehensive report generation module, which integrates and generates a comprehensive report for decision-making.

Among them, the data fusion preprocessing module cleans, standardizes and extracts features from multi-source data, and provides the processed data to the deep learning model training module for model training; the deep learning model training module uses the processed data for model training, and applies the trained model to the real-time anomaly detection module.

Among them, the dynamic risk model construction module establishes and adjusts the risk model based on historical data and real-time environmental data, and provides these models to the intelligent decision support module for risk prediction and decision support; the intelligent decision support module provides risk prediction and decision support through scenario simulation and expert system, and feeds back the results to the feedback and self-learning module.

Working principle: When the present invention is in use, first, the data acquisition and transmission module is responsible for collecting data on the environment, equipment and personnel. Specifically, the sensor network optimization module intelligently adjusts the sensor layout and acquisition frequency through the adaptive sensor network, and transmits the optimized data to the multi-source data fusion module. The multi-source data fusion module integrates multi-source data from satellites, drones, mobile devices, etc., and then transmits the integrated data to the edge computing module for preliminary processing and filtering. The edge computing module deploys edge computing devices near the sensors, performs preliminary processing and filtering on the data, and transmits the processed data to the cloud storage and computing module of the advanced data processing and analysis module.

In terms of data processing and analysis, the cloud storage and computing module uses the cloud computing platform for large-scale data processing and storage, and the processed data is provided to the artificial intelligence learning module. The artificial intelligence learning module contains multiple sub-modules. The first is the emotion-driven safety assessment module, which monitors the emotional state of the group in real time by analyzing the sound, video and social media data in the workplace, and then transmits these data to the quantum enhanced sensor fusion module. The quantum enhanced sensor fusion module ensures the security and integrity of the data during transmission, and provides the transmitted data to the biofeedback optimization module. The biofeedback optimization module collects the biofeedback data of the inspectors through brain-computer interface technology and transmits it to the immersive virtual training module. The immersive virtual training module uses virtual reality technology and deep learning algorithms to create realistic inspection scenarios and conduct training, while relying on the self-healing data network module to ensure the data transmission stability and network reliability of the training environment.

The self-healing data network module automatically detects and repairs network failures through AI technology, ensuring stable data transmission between modules in the system and providing a stable data input source for the intelligent anomaly determination module. The intelligent anomaly determination module includes a multimodal analysis branch and an adaptive evaluation branch, which integrates and analyzes diverse data from different sensors and data sources, provides comprehensive anomaly detection and situational understanding, and dynamically adjusts risk assessment models based on real-time data and historical patterns to provide accurate risk prediction and decision support. Finally, the intelligent anomaly determination module transmits the analyzed and detected anomalies and risk assessment results to the comprehensive report generation module, generating dynamic, interactive inspection reports and environmental assessment charts for decision-making.

In terms of inspection management, the system introduces an automatic inspection robot that can autonomously navigate and perform inspection tasks. The automatic inspection machine module transmits inspection data and task execution status to the dynamic task allocation module. The dynamic task allocation module intelligently adjusts the priority and allocation of inspection tasks based on real-time data and analysis results, and transmits the updated task information to the abnormal event management module. The abnormal event management module receives and processes the updated task information from the dynamic task allocation module, manages and records abnormal events, and feeds back relevant information to the user training and support module of the multi-platform user feedback module.

In terms of user feedback and support, the multi-platform user feedback module develops user interfaces for multiple platforms such as PC, mobile devices, and tablets through the cross-platform user interface module, and integrates these interfaces into the augmented reality support module. The augmented reality support module uses AR technology to provide real-time environmental data overlay display and navigation guidance for patrol personnel, and transmits user interaction data and experience information to the user training and support module. The user training and support module integrates the online training module and 24/7 customer support, receives user interaction data, provides corresponding training and support services, and transmits user feedback information back to the relevant modules to optimize system performance.

Specifically, the emotion-driven safety assessment module monitors the emotional state of the group in real time by analyzing the sound, video and social media data in the workplace, and transmits this data to the quantum enhanced sensor fusion module to ensure the security and integrity of the data during transmission. The quantum enhanced sensor fusion module provides the transmitted data to the biofeedback optimization module, which collects the biofeedback data of the inspectors through brain-computer interface technology and transmits it to the immersive virtual training module. The immersive virtual training module uses virtual reality technology and deep learning algorithms to create realistic inspection scenarios and conduct training, while relying on the self-healing data network module to ensure the data transmission stability and network reliability of the training environment.

The self-healing data network module automatically detects and repairs network failures through AI technology, ensuring stable data transmission between modules in the system and providing a stable data input source for the intelligent anomaly determination module. The intelligent anomaly determination module includes a multimodal analysis branch and an adaptive evaluation branch, which integrates and analyzes diverse data from different sensors and data sources, provides comprehensive anomaly detection and situational understanding, and dynamically adjusts the risk assessment model based on real-time data and historical patterns to provide accurate risk prediction and decision support. Finally, the intelligent anomaly determination module transmits the analyzed and detected anomalies and risk assessment results to the comprehensive report generation module to generate a comprehensive report for decision-making.

In terms of data processing and analysis, the data fusion preprocessing module cleans, standardizes and extracts features from multi-source data, and provides the processed data to the deep learning model training module for model training. The deep learning model training module uses the processed data for model training, and applies the trained model to the real-time anomaly detection module. The real-time anomaly detection module analyzes and judges the data immediately when it arrives, automatically marks the detected abnormal events and records detailed information for subsequent analysis and processing.

The dynamic risk model building module in the adaptive assessment branch builds and adjusts risk models based on historical data and real-time environmental data, and provides these models to the intelligent decision support module for risk prediction and decision support. The intelligent decision support module provides risk prediction and decision support through scenario simulation and expert system, and feeds the results back to the feedback and self-learning module. The feedback and self-learning module adjusts and optimizes model parameters based on the actual detected anomalies and risk events, and introduces self-learning algorithms, so that the system can learn and improve from each risk assessment and anomaly detection.

Through the collaborative work of these modules, the safety and environmental inspection system has achieved intelligent management of the entire process from data collection, processing, analysis, to inspection management and user feedback in actual use, ensuring the efficiency and safety of the inspection process. The system can not only monitor and analyze environmental, equipment and personnel data in real time, automatically identify and detect potential risks, but also autonomously perform inspection tasks through intelligent inspection robots, dynamically adjust task priorities, and manage and record abnormal events. In addition, the system provides inspection personnel with a convenient operation interface and real-time data overlay display through a cross-platform user interface and augmented reality technology, and provides comprehensive user support services through 24/7 customer support and online training modules. Finally, the system integrates the results of multimodal data analysis and adaptive risk assessment through a comprehensive report generation module to generate a comprehensive report for decision-making, thereby realizing intelligent inspection management and efficient safety assurance.

The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims (7)

1. A safety and environmental inspection system, characterized by comprising an intelligent data acquisition and transmission module for real-time collection of environmental, equipment and personnel data and secure transmission, an advanced data processing and analysis module for data cleaning, standardization, feature extraction, in-depth analysis and modeling, an intelligent inspection management module for inspection route planning, task scheduling and exception handling, and a multi-platform user feedback module for providing a cross-platform user interface and real-time data feedback; The intelligent data acquisition and transmission module includes a sensor network optimization module for intelligently adjusting sensor layout and acquisition frequency using an adaptive sensor network, a multi-source data fusion module for integrating multi-source data from satellites, drones, mobile devices, etc., and an edge computing module for deploying edge computing devices near sensors for preliminary data processing and filtering; The advanced data processing and analysis module includes a cloud storage and computing module for utilizing a cloud computing platform for large-scale data processing and storage, an artificial intelligence learning module for applying advanced AI and deep learning techniques to automatically identify complex environmental patterns and potential risks, and a visual report generation module for generating dynamic, interactive inspection reports and environmental assessment charts through advanced data visualization tools; The intelligent inspection management module includes an automatic inspection machine module for introducing automatic inspection robots that can autonomously navigate and perform inspection tasks, a dynamic task allocation module for intelligently adjusting the priority and allocation of inspection tasks based on real-time data and analysis results, and an abnormal event management module for establishing a comprehensive abnormal event management system; The multi-platform user feedback module includes a cross-platform user interface module for developing multi-platform user interfaces suitable for PCs, mobile devices, tablets, etc., an augmented reality support module for introducing AR technology to provide patrol personnel with real-time environmental data overlay display and navigation guidance, and a user training and support module for integrating online training modules and 24/7 customer support. 2. A safety and environmental inspection system according to claim 1, characterized in that the artificial intelligence learning module includes an emotion-driven safety assessment module for analyzing the sound, video and social media data in the workplace, monitoring the emotional state of the group in real time, and integrating it with environmental data to assess safety risks, a quantum enhanced sensor fusion module for using quantum communication technology to ensure the security and integrity of data during transmission, a biofeedback optimization module for realizing direct interaction between inspection personnel and the system through brain-computer interface technology, an immersive virtual training module for using virtual reality technology to create realistic inspection scenes combined with deep learning algorithms, a self-repairing data network module for automatically detecting and repairing network faults using AI technology, an intelligent anomaly determination module for dynamically adjusting the risk assessment model based on real-time data and historical patterns to provide accurate anomaly determination and early warning, and a comprehensive report generation module for integrating the results of multimodal data analysis and adaptive risk assessment to generate a comprehensive report; The intelligent anomaly determination module includes a multimodal analysis branch that integrates and analyzes diverse data from different sensors and data sources to provide comprehensive anomaly detection and situational understanding, and an adaptive assessment branch that dynamically adjusts and optimizes the risk assessment model to provide accurate risk prediction and decision support based on real-time data and historical patterns; The multimodal analysis branch includes a data fusion preprocessing module for using advanced algorithms to clean, standardize and extract key features from emotion perception data, quantum sensing data and biological monitoring data, a deep learning model training module for developing and training neural network models that combine multiple data types and integrate the output results of multiple models through ensemble learning technology, and a real-time anomaly detection module for establishing a real-time data stream processing framework that can analyze and judge data in real time when it arrives, automatically mark detected abnormal events, and record detailed information for subsequent analysis and processing; The adaptive assessment branch includes a dynamic risk model construction module for establishing a baseline risk model using historical data and dynamically adjusting risk model parameters according to current environment and situational data; an intelligent decision support module for predicting possible risk development trends by simulating different scenarios and combining expert systems to further enhance the accuracy of risk assessment using expert knowledge and rules; and a feedback and self-learning module for establishing an automatic feedback mechanism, adjusting and optimizing model parameters according to actual detected anomalies and risk events, and introducing a self-learning algorithm, so that the system can learn and improve from each risk assessment and anomaly detection. 3. A safety and environmental inspection system according to claim 2, characterized in that the emotion-driven safety assessment module monitors the emotional state of the group in real time by analyzing the sound, video and social media data in the workplace, and transmits these data to the quantum enhanced sensing fusion module; the quantum enhanced sensing fusion module ensures the security and integrity of the data during the transmission process, and provides the transmitted data to the biofeedback optimization module; the biofeedback optimization module collects the biofeedback data of the inspectors and transmits these data to the immersive virtual training module; the immersive virtual training module uses virtual reality technology and deep learning algorithms for training, and relies on the self-repairing data network module to ensure the data transmission stability and network reliability of the training environment; the self-repairing data network module ensures the data transmission stability between modules in the system and the automatic detection and repair of network faults, providing a stable data input source for the intelligent abnormality determination module; the intelligent abnormality determination module transmits the results to the comprehensive report generation module by analyzing and detecting abnormal conditions. 4. A safety and environmental inspection system according to claim 1, characterized in that the sensor network optimization module intelligently adjusts the sensor layout and acquisition frequency through an adaptive sensor network, and transmits the optimized data to the multi-source data fusion module; the multi-source data fusion module integrates multi-source data from satellites, drones, mobile devices, etc., and transmits the integrated data to the edge computing module; the edge computing module deploys edge computing devices near the sensors, performs preliminary processing and filtering on the data, and transmits the processed data to the cloud storage and computing module of the advanced data processing and analysis module. 5. According to claim 1, a safety and environmental inspection system is characterized in that the cloud storage and computing module uses a cloud computing platform to perform large-scale data processing and storage, and provides the processed and stored data to the artificial intelligence learning module; the artificial intelligence learning module uses AI and deep learning technology to automatically identify complex environmental patterns and potential risks, and transmits the analysis results to the visual report generation module. 6. A safety and environmental inspection system according to claim 1, characterized in that the automatic inspection machine module introduces an automatic inspection robot to autonomously navigate and perform inspection tasks, and transmits the inspection data and task execution status to the dynamic task allocation module; the dynamic task allocation module intelligently adjusts the priority and allocation of inspection tasks based on real-time data and analysis results, and transmits the updated task information to the abnormal event management module; the abnormal event management module receives and processes the updated task information from the dynamic task allocation module, manages and records abnormal events, and feeds back relevant information to the user training and support module of the multi-platform user feedback module. 7. A safety and environmental inspection system according to claim 1, characterized in that the cross-platform user interface module develops user interfaces suitable for multiple platforms such as PC, mobile devices, and tablet computers, and integrates these interfaces into the augmented reality support module; the augmented reality support module uses AR technology to provide real-time environmental data overlay display and navigation guidance for inspection personnel, and transmits user interaction data and experience information to the user training and support module; the user training and support module integrates online training modules and 24/7 customer support, receives user interaction data transmitted from the augmented reality support module, provides corresponding training and support services, and transmits user feedback information back to relevant modules to optimize system performance.