Safety production supervision and management system and management method thereof

Abstract

The invention belongs to the technical field of industrial production management, and provides a safety production supervision and management system and a management method thereof, and the system comprises a data collection module, an intelligent analysis module, an early warning response module, an emergency command module, a feedback optimization module, and a block chain recording module. The data acquisition module, the intelligent analysis module, the early warning response module, the emergency command module, the feedback optimization module and the block chain recording module are electrically connected in sequence; according to the invention, by integrating the data acquisition module, the intelligent analysis module, the early warning response module, the emergency command module, the feedback optimization module and the block chain recording module, real-time monitoring, deep analysis, intelligent early warning, efficient emergency command, continuous optimization and data transparency and traceability of production field safety parameters are realized, and the efficiency and accuracy of safety production management are greatly improved.

Description

Technical Field

[0001] The invention belongs to the technical field of industrial production management, in particular to a production safety supervision management system and a management method thereof. Background Art

[0002] At present, with the acceleration of industrialization and the expansion of production scale, production safety has become the focus of attention of enterprises and society. The traditional production safety management method relies on manual inspection and post-processing, which is difficult to effectively prevent accidents, and the information transmission efficiency is low, making it difficult to achieve rapid response. With the development of technologies such as the Internet of Things, big data, and artificial intelligence, it has become possible to build an intelligent and real-time production safety supervision and management system.

[0003] However, most existing technical supervision and management systems focus on single-aspect monitoring or response, lacking systematic solutions that fully integrate data analysis, intelligent early warning, emergency command and continuous improvement capabilities.

[0004] To this end, those skilled in the art have proposed a production safety supervision management system and a management method thereof to solve the problems raised by the background technology. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a production safety supervision management system and a management method thereof, so as to solve the problems that the supervision management systems in the prior art mostly focus on single-aspect monitoring or response, lack of systematic solutions that comprehensively integrate data analysis, intelligent early warning, emergency command and continuous improvement capabilities.

[0006] The production safety supervision and management system and its management methods include:

[0007] Data acquisition module, used to collect safety parameters, equipment status, personnel behavior and environmental information of the production site in real time;

[0008] Intelligent analysis module, which integrates machine learning algorithms to conduct in-depth analysis of collected data and identify safety hazards and abnormal behavior patterns;

[0009] The early warning response module automatically triggers early warning signals based on the results of the intelligent analysis module and sends instant notifications or instructions to relevant personnel, including but not limited to SMS, email, APP push and on-site alarms;

[0010] The emergency command module automatically or manually activates the emergency plan in an emergency, coordinates resource allocation, and guides on-site personnel to evacuate safely or take emergency measures;

[0011] Feedback optimization module, which collects feedback on early warning response and emergency handling effects to optimize the accuracy of intelligent analysis models and the effectiveness of emergency plans;

[0012] The blockchain recording module is used to record all security incidents, warnings, response measures and effect evaluations in an unalterable manner to ensure data transparency and traceability.

[0013] Preferably, a remote monitoring platform is also included, which enables the management to remotely view the safety status of the production site, receive real-time alarms, and issue management instructions.

[0014] Preferably, a standardization algorithm is introduced in the data acquisition module to convert data of different magnitudes to the same magnitude, thereby improving the stability and accuracy of the algorithm model.

[0015] Preferably, in the data acquisition module, a weighted average method is introduced to fuse information obtained from multiple sensors or data sources to improve the accuracy and reliability of the data.

[0016] Preferably, the intelligent analysis module further includes a sentiment analysis submodule for analyzing the emotional state of people in the surveillance video and identifying possible fatigue, tension or improper operating behaviors. The sentiment analysis submodule introduces a sentiment analysis algorithm for analyzing the emotional state of people in the surveillance video.

[0017] Preferably, the formula integrated with the machine learning algorithm is as follows:

[0018]

[0019] Among them, α i is the Lagrange multiplier, y i is the sample label, K(x i ,x) is the kernel function used to calculate the similarity between samples, and b is the bias term.

[0020] Preferably, in the feedback optimization module, in order to optimize the accuracy of the intelligent analysis model and the effectiveness of the emergency plan, a gradient descent method is introduced.

[0021] Preferably, the production safety supervision and management method, using the above-mentioned production safety supervision and management system, comprises the following steps:

[0022] S1. System initialization, configuration of data collection points, warning thresholds and emergency plans;

[0023] S2, collect production site data in real time and identify safety risks through intelligent analysis modules;

[0024] S3. Automatically trigger the corresponding level of early warning response according to the risk level;

[0025] S4. Implement emergency command, activate emergency plans when necessary, and record the entire process;

[0026] S5. Regularly analyze early warning response and emergency processing data, and optimize system parameters and management processes;

[0027] S6. Use the blockchain record module to ensure the integrity and credibility of all security-related data.

[0028] A processor is configured to execute the above-mentioned production safety supervision management system.

[0029] A computer-readable storage medium stores a computer program, which implements the above-mentioned production safety supervision management system when executed by a processor.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. The present invention provides a comprehensive production safety supervision and management system and its management method by integrating data analysis, intelligent early warning, emergency command and continuous improvement capabilities, solving the problem that most supervision and management systems in the prior art focus on single-aspect monitoring or response.

[0032] 2. The present invention collects safety parameters, equipment status, personnel behavior and environmental information of the production site in real time through the data acquisition module, and combines it with the intelligent analysis module for in-depth analysis, which can timely discover safety hazards and abnormal behavior patterns, and improve the real-time and accuracy of safety production management.

[0033] 3. The early warning response module of the present invention automatically triggers the early warning signal according to the results of the intelligent analysis module, and sends instant notifications or instructions to relevant personnel, including but not limited to text messages, emails, APP push and on-site alarms, thereby realizing an intelligent early warning and response mechanism.

[0034] 4. In an emergency, the emergency command module of the present invention can automatically or manually start the emergency plan, coordinate resource allocation, and guide on-site personnel to evacuate safely or take emergency measures, thereby improving the efficiency and effectiveness of emergency handling.

[0035] 5. The feedback optimization module of the present invention collects feedback on the effects of early warning response and emergency treatment, which is used to optimize the accuracy of the intelligent analysis model and the effectiveness of the emergency plan, forming a closed-loop management of continuous improvement.

[0036] 6. The blockchain recording module of the present invention is used to record all security events, warnings, response measures and effect evaluations in an unalterable manner, ensuring the transparency and traceability of the data and providing strong data support for safe production management.

[0037] 7. Through the remote monitoring platform, the management of the present invention can remotely view the safety status of the production site, receive real-time alarms, and issue management instructions, thus realizing the convenience of remote monitoring and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a framework diagram of the production safety supervision and management system of the present invention;

[0039] Figure 2 It is a flow chart of the production safety supervision management method of the present invention. DETAILED DESCRIPTION

[0040] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0041] Embodiment: The present invention provides a production safety supervision and management system, such as Figure 1 As shown, it includes a data acquisition module, an intelligent analysis module, an early warning response module, an emergency command module, a feedback optimization module and a blockchain recording module, and the data acquisition module, the intelligent analysis module, the early warning response module, the emergency command module, the feedback optimization module and the blockchain recording module are electrically connected in sequence:

[0042] Data acquisition module, used to collect safety parameters, equipment status, personnel behavior and environmental information of the production site in real time;

[0043] Intelligent analysis module, which integrates machine learning algorithms to conduct in-depth analysis of collected data and identify safety hazards and abnormal behavior patterns;

[0044] The early warning response module automatically triggers early warning signals based on the results of the intelligent analysis module and sends instant notifications or instructions to relevant personnel, including but not limited to SMS, email, APP push and on-site alarms;

[0045] The emergency command module automatically or manually activates the emergency plan in an emergency, coordinates resource allocation, and guides on-site personnel to evacuate safely or take emergency measures;

[0046] Feedback optimization module, which collects feedback on early warning response and emergency handling effects to optimize the accuracy of intelligent analysis models and the effectiveness of emergency plans;

[0047] The blockchain recording module is used to record all security incidents, warnings, response measures and effect evaluations in an unalterable manner to ensure data transparency and traceability.

[0048] From the above, we can see that by integrating data collection, intelligent analysis, early warning response, emergency command, feedback optimization and blockchain recording modules, real-time monitoring, in-depth analysis, intelligent early warning, efficient emergency command, continuous optimization and transparent and traceable data of production site safety parameters are achieved, greatly improving the efficiency and accuracy of production safety management.

[0049] Furthermore, it also includes a remote monitoring platform, which enables management to remotely view the safety status of the production site, receive real-time alarms, and issue management instructions.

[0050] From the above, we can see that by adding a remote monitoring platform, the management is given the ability to remotely view the safety status of the production site, receive alarm information in real time and issue management instructions, which significantly improves the flexibility and response speed of production safety management, helps the management make decisions in a timely manner and effectively respond to potential safety risks.

[0051] Furthermore, in the data acquisition module, a standardization algorithm is introduced to convert data of different magnitudes to the same magnitude to improve the stability and accuracy of the algorithm model. The formula of the standardization algorithm is as follows:

[0052]

[0053] in, x is the original data, μ is the mean, σ is the standard deviation.

[0054] From the above, we can see that by introducing standardized algorithms, data of different magnitudes can be successfully converted to the same magnitude. This measure significantly enhances the stability and accuracy of the algorithm model, provides a more reliable data basis for subsequent intelligent analysis and early warning response, and thus optimizes the performance of the entire production safety supervision management system.

[0055] Furthermore, in the data acquisition module, a weighted average method is introduced to fuse the information obtained from multiple sensors or data sources to improve the accuracy and reliability of the data. The formula of the weighted average method is as follows:

[0056]

[0057] Among them, w i is the weight of the ith data source, Data i It is the data provided by the i-th data source.

[0058] From the above, we can see that by introducing the weighted average method, the information from multiple sensors or data sources can be effectively integrated. This method significantly improves the accuracy and reliability of the data, provides more comprehensive and accurate data input for the intelligent analysis module, and thus enhances the overall efficiency and decision-making support capabilities of the safety production supervision management system.

[0059] Furthermore, the formula integrated with the machine learning algorithm is as follows:

[0060]

[0061] Among them, αi is the Lagrange multiplier, y i is the sample label, K(x i ,x) is the kernel function, which is used to calculate the similarity between samples. b is the bias term.

[0062] From the above, we can see that by integrating machine learning algorithms with specific formulas, we can deeply mine data features and accurately identify safety hazards and abnormal behavior patterns; the algorithm uses key elements such as Lagrange multipliers, sample labels, kernel functions and bias terms to effectively improve the accuracy and efficiency of the analysis, provide strong support for early warning response and emergency command, and further enhance the intelligence level of the safety production supervision and management system.

[0063] Furthermore, the intelligent analysis module further includes a sentiment analysis submodule for analyzing the emotional state of personnel in the surveillance video and identifying possible fatigue, tension or improper operation behavior. The sentiment analysis submodule introduces a sentiment analysis algorithm for analyzing the emotional state of personnel in the surveillance video. The formula of the sentiment analysis algorithm is as follows:

[0064]

[0065] Among them, w i is the i-th word in the video or text, score(w i ) is the sentiment score of the word in the sentiment dictionary, and H′ is the overall sentiment score.

[0066] From the above, we can see that by adding a sentiment analysis sub-module and introducing a specific sentiment analysis algorithm, we have successfully achieved accurate analysis of the emotional state of people in surveillance videos; the algorithm can identify potential safety hazards such as fatigue, tension or improper operating behavior of personnel, and provide more detailed and comprehensive data support for early warning response; it enhances the intelligence and humanization level of the safety production supervision and management system, and helps enterprises to promptly discover and correct improper behavior in personnel operations, thereby effectively preventing the occurrence of safety accidents.

[0067] Furthermore, in the feedback optimization module, in order to optimize the accuracy of the intelligent analysis model and the effectiveness of the emergency plan, the gradient descent method is introduced. The formula of the gradient descent method is as follows:

[0068]

[0069] Among them, θ t is the model parameter of the tth iteration, α is the learning rate, is the loss function J(θ t ) about the model parameter θ t gradient.

[0070] From the above, we can see that by introducing the gradient descent method, the accuracy of the intelligent analysis model and the effectiveness of the emergency plan are continuously optimized; this method adjusts the model parameters iteratively to minimize the loss function, thereby significantly improving the prediction accuracy of the model and the practical effect of the emergency plan; it not only enhances the adaptability and robustness of the production safety supervision and management system, but also helps enterprises to flexibly adjust management strategies according to actual conditions and achieve more efficient and accurate production safety management.

[0071] The production safety supervision and management method, using the production safety supervision and management system, comprises the following steps:

[0072] S1. System initialization, configuration of data collection points, warning thresholds and emergency plans;

[0073] S2, collect production site data in real time and identify safety risks through intelligent analysis modules;

[0074] S3. Automatically trigger the corresponding level of early warning response according to the risk level;

[0075] S4. Implement emergency command, activate emergency plans when necessary, and record the entire process;

[0076] S5. Regularly analyze early warning response and emergency processing data, and optimize system parameters and management processes;

[0077] S6. Use the blockchain record module to ensure the integrity and credibility of all security-related data.

[0078] Working principle: The data acquisition module collects safety parameters and other information from the production site in real time. The intelligent analysis module uses machine learning algorithms to conduct in-depth analysis of the data to identify safety hazards. The early warning response module automatically triggers early warnings and notifies relevant personnel based on the analysis results. The emergency command module initiates emergency plans and guides emergency measures in emergency situations. The feedback optimization module collects feedback to optimize models and plans. The blockchain recording module ensures data transparency and traceability. At the same time, the remote monitoring platform enables management to remotely monitor and manage.

[0079] The embodiment of the present application provides an electronic device, which is applicable to the above-mentioned production safety supervision and management system, including:

[0080] Memory, used to protect computer programs and data;

[0081] Processor, used to run system programs.

[0082] An embodiment of the present application provides a computer storage medium, which is applicable to the above-mentioned production safety supervision management system, and performs hierarchical confidentiality management on the above-mentioned system and data in accordance with confidentiality management requirements.

[0083] Those skilled in the art will appreciate that the embodiments of the present application may be provided as a system or a computer program product. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0084] The present application is described with reference to the flowcharts and / or block diagrams of the devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0085] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0086] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0087] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0088] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0089] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0090] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, commodity or device including the elements.

[0091] The embodiments of the present invention are provided for the purpose of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. Production safety supervision and management system, characterized by: include: Data acquisition module, used to collect safety parameters, equipment status, personnel behavior and environmental information of the production site in real time; Intelligent analysis module, which integrates machine learning algorithms to conduct in-depth analysis of collected data and identify safety hazards and abnormal behavior patterns; The early warning response module automatically triggers early warning signals based on the results of the intelligent analysis module and sends instant notifications or instructions to relevant personnel, including but not limited to SMS, email, APP push and on-site alarms; The emergency command module automatically or manually activates the emergency plan in an emergency, coordinates resource allocation, and guides on-site personnel to evacuate safely or take emergency measures; Feedback optimization module, which collects feedback on early warning response and emergency handling effects to optimize the accuracy of intelligent analysis models and the effectiveness of emergency plans; The blockchain recording module is used to record all security incidents, warnings, response measures and effect evaluations in an unalterable manner to ensure data transparency and traceability.

2. The production safety supervision and management system as claimed in claim 1, characterized in that: It also includes a remote monitoring platform that enables management to remotely view the safety status of the production site, receive real-time alarms, and issue management instructions.

3. The production safety supervision and management system as claimed in claim 1, characterized in that: In the data acquisition module, a standardization algorithm is introduced to convert data of different magnitudes to the same magnitude.

4. The production safety supervision and management system as claimed in claim 1, characterized in that: In the data acquisition module, the weighted average method is introduced to fuse the information obtained from multiple sensors or data sources.

5. The production safety supervision and management system as claimed in claim 1, characterized in that: The formula integrated with the machine learning algorithm is as follows: Among them, α i is the Lagrange multiplier, y i is the sample label, K(x i ,x) is the kernel function used to calculate the similarity between samples, and b is the bias term.

6. The production safety supervision and management system as claimed in claim 1, characterized in that: The intelligent analysis module further includes an emotion analysis submodule for analyzing the emotional state of people in the surveillance video and identifying possible fatigue, tension or improper operating behavior.

7. The production safety supervision and management system as claimed in claim 1, characterized in that: In the feedback optimization module, the gradient descent method is introduced to optimize the accuracy of the intelligent analysis model and the effectiveness of the emergency plan.

8. A method for production safety supervision and management, characterized by: Using the production safety supervision and management system described in any one of claims 1 to 7, comprising the following steps: S1. System initialization, configuration of data collection points, warning thresholds and emergency plans; S2, collect production site data in real time and identify safety risks through intelligent analysis modules; S3. Automatically trigger the corresponding level of early warning response according to the risk level; S4. Implement emergency command, activate emergency plans when necessary, and record the entire process; S5. Regularly analyze early warning response and emergency processing data, and optimize system parameters and management processes; S6. Use the blockchain record module to ensure the integrity and credibility of all security-related data.

9. A processor, characterized in that: The system is configured to execute a production safety supervision management system according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, a production safety supervision management system according to any one of claims 1 to 7 is implemented.

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