An intelligent supervision system for work safety based on artificial intelligence

Abstract

The present invention relates to the technical field of work safety supervision, and in particular to a smart work safety supervision system based on artificial intelligence, including a work safety database module that integrates a risk and hidden danger library, an inspection checklist library, a regulations and standards library, and a dynamic case library using a multi-source heterogeneous data fusion architecture; an artificial intelligence analysis engine module that realizes accident causal chain analysis, high-frequency violation pattern mining, and dynamic decision generation; a risk and hidden danger management code generation module that integrates risk information into a scannable digital label and uses a spatio-temporal constraint security mechanism to ensure data security; an intelligent law enforcement terminal module that integrates AR visualization, intelligent comparison, and risk trend prediction functions, supports real-time interaction between the mobile end and the background, and helps with efficient law enforcement. The present invention comprehensively improves the intelligent, collaborative, and precise levels of work safety supervision, effectively reduces the accident incidence rate, safeguards people's lives and property safety and social stability, and has significant social benefits and application value.

Description

Technical Field

[0001] The present invention relates to the technical field of work safety supervision, and specifically to an intelligent work safety supervision system based on artificial intelligence. Background Art

[0002] In the current era of accelerating industrialization and rapid technological development, work safety has always been the cornerstone for the stable operation and sustainable development of various industries. However, the traditional work safety supervision mode faces many severe challenges in practical applications and urgently needs the intervention of innovative technologies and solutions. The specific challenges include:

[0003] Data dispersion and information silos: Past work safety data is scattered and stored in different departments, enterprises, and systems. Data such as risk hazards, regulations and standards, and law enforcement cases lack effective integration, resulting in poor information circulation and difficulty in coordinating various supervision links. For example, when formulating policies, government supervision departments cannot conveniently obtain comprehensive risk hazard data of enterprises, and when enterprises implement safety measures, it is also difficult to quickly refer to the latest regulations and standards, seriously restricting the efficiency and accuracy of work safety supervision.

[0004] Risk prediction and decision-making lag: Traditional supervision relies on manual experience judgment and is difficult to conduct real-time and comprehensive analysis of complex and changeable risk factors. Facing a large amount of work safety data, manual processing is slow and inaccurate, unable to timely identify the causal relationship of accidents and high-frequency violation patterns, resulting in untimely risk warnings and decision-making lacking scientific basis. Once an accident occurs, it is often only possible to take remedial measures afterwards and it is impossible to effectively prevent it beforehand.

[0005] Low law enforcement supervision efficiency: When law enforcement officers conduct on-site inspections, they often need to carry a large amount of paper materials and manually record inspection information. The process is cumbersome and error-prone. At the same time, there is a lack of real-time data comparison and risk prediction tools, making it difficult to quickly and accurately judge the work safety status of enterprises, and it is difficult to guarantee the law enforcement efficiency and fairness. For example, when inspecting the compliance of enterprise equipment, it is impossible to compare with standard data on the spot and needs to be verified afterwards, delaying the rectification time.

[0006] Uneven enterprise safety capabilities: There are large differences in the work safety management levels of different enterprises. Some enterprises lack effective safety management systems and technical means and are difficult to independently improve their safety capabilities. At the same time, it is difficult to share safety knowledge and experience among enterprises, and it is impossible to form a good situation of coordinated development. Some small and medium-sized enterprises are relatively weak in safety management but difficult to learn from the advanced experience of large enterprises.

[0007] There are many difficulties in multi-party collaboration: there is a lack of efficient collaboration mechanism between government regulatory departments, third-party service agencies and enterprises, and there are security risks and technical barriers in data sharing; in production safety supervision, information communication among all parties is not smooth, making it difficult to form a joint supervision force; for example, in the emergency response to major safety accidents, due to untimely information sharing, rescue resources are not allocated rationally, affecting the rescue effect.

[0008] Therefore, in response to the above problems, an intelligent production safety supervision system based on artificial intelligence is proposed. Summary of the invention

[0009] The purpose of the present invention is to provide an intelligent production safety supervision system based on artificial intelligence to solve the problems raised in the above-mentioned background technology.

[0010] To achieve the above object, the present invention provides the following technical solutions:

[0011] An intelligent production safety supervision system based on artificial intelligence, including:

[0012] Safety production database module: integrates risk hazard database, checklist database, regulatory standards database and dynamic case database through multi-source heterogeneous data fusion architecture;

[0013] The risk database stores risk data of equipment and sites in the industry and trade. The risk data includes the three-dimensional mapping relationship between risk point coordinates, accident types and prevention and control measures.

[0014] The inspection list library includes general inspection items and industry-specific inspection items, and adopts a multi-level classification label system to achieve scenario-based intelligent matching;

[0015] The regulatory standards database integrates regulatory documents at four levels, from national to local, links national standards with industry standards, and establishes a dynamic weight association model between regulatory clauses and risk types;

[0016] Artificial intelligence analysis engine module: Based on natural language processing technology, knowledge graph and deep learning algorithm, it realizes accident cause and effect chain analysis, high-frequency violation pattern mining and dynamic decision-making generation functions;

[0017] Risk management code generation module: Through unique coding rules and dynamic data association technology, risk point information, control measures and emergency plans are integrated into digital tags that can be scanned and identified, and the effective range of scanning is limited based on Beidou / GPS dual-mode positioning technology;

[0018] Smart law enforcement terminal module: integrates AR visualization, intelligent comparison and risk trend prediction functions, supports real-time interaction between mobile terminals and backend systems, and overlays and displays device risk history records and real-time monitoring data through augmented reality technology.

[0019] The AI ​​analysis engine modules include:

[0020] Accident Causality Analysis Model: An improved weighted cross-entropy loss function is adopted to construct an accident causal chain based on the Bidirectional Gated Graph Neural Network (Bi-GGNN) to generate a risk warning graph.

[0021] Violation Pattern Mining Algorithm: An improved weighted Apriori algorithm is adopted to support the identification of high-frequency violation behavior combination patterns and the calculation of the industry risk feature matrix.

[0022] Dynamic Decision Generation Model: Based on the rule-constrained Deep Q-Network (DQN), it combines risk features and regulatory standards to generate personalized control solutions.

[0023] As an optimal solution, the formula for the weighted cross-entropy loss function of the accident causality analysis model is:

[0024] , where represents the weighted cross-entropy loss function; is the weight coefficient, and ; , are the industry risk weight coefficients; TF-IDF represents the term frequency-inverse document frequency; Attention is the sentence-level attention weight; is the number of samples; is the true label value; is the predicted label value; is the L2 regularization parameter; is the model parameter vector; represents the square of the L2 norm of

[0025] As an optimal solution, the support calculation formula of the violation pattern mining algorithm is:

[0026] ,

[0027] where represents the weighted support; is the violation behavior item set; represents the th transaction in the database ; is the transaction database; is the industry risk level weight of the violation behavior ; is the violation behavior item; is the indicator function, when the condition in the parentheses is true , otherwise ; is the industry adjustment factor.

[0028] As a preferred solution, the reward function of the dynamic decision-making generation model is defined as:

[0029] , where represents the reward function; represents the current state; represents the action taken; represents the action 's regulatory compliance score; is the risk reduction rate; is the implementation cost; , , are the dynamic weight coefficients.

[0030] As a preferred solution, the encoding generation rule of the risk hidden danger management code generation module is:

[0031] Generate a unique code using the enterprise ID, risk type code, and three-dimensional geographical hash value:

[0032] , where represents the optimal hash code; is the hash function; is the enterprise identification code; represents the string concatenation operation; is the risk type code; is the three-dimensional geographical hash value; is the three-dimensional geographical coordinate; represents the exclusive OR operation; is the random number generated by the linear feedback shift register; is the timestamp;

[0033] When the information is updated, it is dynamically adjusted through the version control formula:

[0034] , where is the updated version value; is the version value before the update; is the forgetting factor; is the time interval; is the regulatory change amount.

[0035] As a preferred solution, the risk trend prediction unit of the intelligent law enforcement terminal module adopts an improved spatio-temporal LSTM model, and its gating mechanism formula is:

[0036] ;

[0037] ;

[0038] ;

[0039] ;

[0040] ;

[0041] Among them, is the input gate; is the activation function; is the input to the weight matrix of the input gate; is the input at time is the hidden state at the previous time to the weight matrix of the input gate; is the hidden state at time is the cell state at the previous time to the weight matrix of the input gate; is the cell state at time represents the Hadamard product; is the bias vector of the input gate; is the forget gate; is the input to the weight matrix of the forget gate; is the hidden state at the previous time to the weight matrix of the forget gate; is the cell state at the previous time to the weight matrix of the forget gate; is the bias vector of the forget gate; is the cell state at time ; tanh is the activation function; is the input to the weight matrix of the cell state; is the hidden state at the previous time to the weight matrix of the cell state; is the bias vector of the cell state; is the output gate; is the input to the weight matrix of the output gate; is the hidden state at the previous time to the weight matrix of the output gate; is the cell state at time ; to the weight matrix of the output gate; is the bias vector of the output gate; is Hidden state at a moment; is a time decay function, is the initial weight, is the decay rate.

[0042] As a preferred solution, it further includes:

[0043] Enterprise adaptive learning module: Adopting the federated meta - learning algorithm, its parameter update formula is:

[0044] , where is the global model parameter after the th round of update; is the global model parameter in the th round; is the learning rate; is the number of enterprises; represents the th enterprise; is the th enterprise's local loss function at the th round with respect to the parameter gradient; is the th enterprise's local loss function; is the federated learning balance coefficient; The KL - divergence term is used to control the model difference; is the th enterprise's local model distribution at the th round; is the global model distribution at the th round;

[0045] Multi - agent collaborative platform: Supports data - secure sharing among government regulatory departments, third - party service agencies and enterprises, and adopts homomorphic encryption technology to achieve cross - agent data joint calculation.

[0046] As a preferred solution, the security protection mechanism of the risk and hidden danger management code generation module includes:

[0047] Two - dimensional code anti - counterfeiting technology based on timestamp encryption to generate a dynamic verification key:

[0048] , where represents the dynamic verification key; is the Advanced Encryption Standard; represents the optimal hash encoding; is the timestamp; represents the modulo operation;

[0049] Geofence constraint conditions for the QR - code scanning operation:

[0050] , where represents the validity identifier for the code scanning operation, indicating validity, indicating invalidity; is the code scanning position coordinate; is the registration position coordinate; represents calculating the distance between two points.

[0051] It can be seen from the technical solution provided by the present invention above that a safety production intelligent supervision system based on artificial intelligence provided by the present invention has the following beneficial effects:

[0052] Upgraded data integration and utilization: The safety production database module integrates the risk and hidden danger library, inspection checklist library, regulation and standard library, and dynamic case library through a multi-source heterogeneous data fusion architecture, breaking the information island problem of traditional databases; realizing three-dimensional association of risks - regulations - cases, providing comprehensive and accurate data support for precise supervision, and enabling the full excavation and utilization of data value;

[0053] Intelligent decision-making and risk prevention and control: The artificial intelligence analysis engine module realizes accident causal chain analysis, high-frequency violation pattern mining, and dynamic decision generation based on natural language processing, knowledge graph, and deep learning algorithms; it can predict risks in advance, generate personalized control plans, change passive supervision to active prevention, and effectively reduce the accident incidence rate;

[0054] Efficient and accurate risk control: The risk and hidden danger management code generation module integrates risk point information, control measures, and emergency plans into a digitally scannable label, and combines a unique coding rule, dynamic data association technology, and a spatio-temporal constraint security mechanism to realize real-time update, precise positioning, and effective control of risk information, improving the efficiency and accuracy of risk control;

[0055] Intelligent and convenient law enforcement supervision: The intelligent law enforcement terminal module integrates AR visualization, intelligent comparison, and risk trend prediction functions, supporting real-time interaction between the mobile end and the background system; providing real-time information support for law enforcement personnel, simplifying the law enforcement process, ensuring the fairness and transparency of law enforcement, and greatly improving the efficiency and quality of safety production law enforcement;

[0056] Independent improvement of enterprise capabilities: The enterprise adaptive learning module adopts the federated meta-learning algorithm to realize the collaborative evolution of cross-regional risk models while protecting the privacy of enterprise data; helping enterprises to individually improve their safety production capabilities, sharing collaborative knowledge, and continuously adapting to the changing safety production environment;

[0057] Multi-party collaborative supervision is powerful: The multi-agent collaborative platform supports data security sharing among government supervision departments, third-party service agencies, and enterprises, and uses homomorphic encryption technology to achieve cross-agent data joint computing; it promotes efficient collaboration among all parties, provides support for collaborative decision-making, and forms an all-round and multi-level joint force for work safety supervision. Brief Description of the Drawings

[0058] Figure 1 This is a schematic diagram of the overall structure of an intelligent work safety supervision system based on artificial intelligence according to the present invention. Detailed Embodiment

[0059] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0060] In order to better understand the above technical solutions, the above technical solutions will be described in detail below in conjunction with the specification drawings and specific embodiments.

[0061] As Figure 1 shown, an embodiment of the present invention provides an intelligent work safety supervision system based on artificial intelligence, including a work safety database module, an artificial intelligence analysis engine module, a risk and hidden danger management code generation module, and an intelligent law enforcement terminal module.

[0062] In this embodiment, the work safety database module integrates a risk and hidden danger library, an inspection checklist library, a regulation and standard library, and a dynamic case library through a multi-source heterogeneous data fusion architecture;

[0063] The risk and hidden danger library stores risk data of equipment and places in the industrial and trade industries. The risk data includes the three-dimensional mapping relationship of risk point coordinates, accident types, and prevention and control measures;

[0064] The inspection checklist library includes general inspection items and industry-specific inspection items, and uses a multi-level classification label system to achieve scenario-based intelligent matching;

[0065] The regulation and standard library integrates national to local four-level regulation documents, associates national standards with industry standards, and establishes a dynamic weight association model between regulation clauses and risk types;

[0066] Furthermore, the work safety database module is the core data hub of the entire supervision system, undertaking the tasks of data aggregation, management, and distribution, and providing a solid data foundation for the intelligent operation of the system; through the multi-source heterogeneous data fusion architecture, it integrates various work safety-related data, breaks data islands, and realizes the efficient circulation and collaborative utilization of data; specifically:

[0067] Risk database: Focusing on the industry and trade sector, it comprehensively stores risk data for 192 major risks and 627 types of equipment / locations; the risk point coordinates locate the source of risk with precise geographic information, the accident types record in detail all possible accident situations, and the three-dimensional mapping relationship of prevention and control measures builds a close connection between risks, accidents and response strategies. Once a risk warning is triggered, the corresponding prevention and control plan can be quickly matched to effectively reduce the risk of accidents;

[0068] Inspection list library: It covers 104 general inspection items and 492 industry-specific inspection items. It uses a multi-level classification label system to intelligently select suitable inspection items according to the characteristics of different industries and inspection scenarios, ensuring that safety inspections are comprehensive, efficient and targeted, and accurately covering all kinds of potential risk points;

[0069] Regulatory standards library: It integrates 508 regulatory documents at four levels from national to local levels, and is deeply associated with 1,042 national standards and 1,328 industry standards. By establishing a dynamic weight association model between regulatory clauses and risk types, it can adjust the relationship between the two in real time according to risk trends and regulatory updates, so that risk management and control always meet the latest regulatory requirements and ensure legal and compliant production safety.

[0070] Dynamic case library: contains 5,024 law enforcement and penalty cases and 1,385 accident investigation reports, and uses case feature vector extraction technology to build a similar case recommendation model; in actual work, when faced with new safety issues or law enforcement scenarios, the system can quickly retrieve similar cases, provide references for decision-making, and help law enforcement personnel make scientific judgments. At the same time, it draws lessons from accident cases and continuously optimizes risk prevention and control measures.

[0071] In this embodiment, the artificial intelligence analysis engine module realizes the accident causal chain analysis, high-frequency violation pattern mining and dynamic decision-making generation functions based on natural language processing technology, knowledge graph and deep learning algorithm; the artificial intelligence analysis engine module includes:

[0072] Accident causal analysis model: Using the improved weighted cross entropy loss function, the accident causal chain is constructed based on the bidirectional gated graph neural network Bi-GGNN to generate a risk warning map;

[0073] Violation pattern mining algorithm: adopts the improved weighted Apriori algorithm to support the identification of high-frequency violation behavior combination patterns and the calculation of industry risk feature matrix;

[0074] Dynamic decision generation model: Based on the rule-constrained deep Q network DQN, it combines risk characteristics and regulatory standards to generate personalized control solutions;

[0075] Specifically, as the core intelligent component of the entire intelligent safety supervision system for production, the artificial intelligence analysis engine module relies on natural language processing technology, knowledge graphs, and deep learning algorithms, and shoulders the key tasks of accident causal chain analysis, high-frequency violation pattern mining, and dynamic decision-making generation;

[0076] In terms of accident causal chain analysis, it constructs an accident causal analysis model with the help of a bidirectional gated graph neural network (Bi-GGNN); this model presents various elements such as equipment, personnel, and environment in the production safety system and their interrelationships in the form of a graph. By deeply learning a large amount of historical data and real-time monitoring data, it automatically and accurately identifies the key factors and causal relationships leading to accidents, and then constructs a complete accident causal chain; to improve the accuracy and adaptability of the model, an improved weighted cross-entropy loss function is adopted; among them, the weight coefficient is composed of and , is the industry risk weight coefficient, which is set according to the characteristics and risk levels of different industries and is used to adjust the influence of different factors on the loss function;

[0077] TF-IDF highlights the importance of key information in the text by calculating the term frequency and inverse document frequency; Focusing on the key parts of the sentence, weighting different information, enabling the model to better capture the key features of the data; finally, a risk warning graph is generated based on the accident causal chain, intuitively showing potential risk points and their associated relationships, helping supervisors detect and prevent accidents in a timely manner;

[0078] For high-frequency violation pattern mining, an improved weighted Apriori algorithm is used; on the basis of the traditional Apriori algorithm, this algorithm introduces the industry risk level weight of violation behaviors and the industry adjustment factor to more accurately reflect the risk degree and occurrence probability of violation behaviors in different industries; when calculating the support degree, the formula comprehensively considers the occurrence of each violation behavior in different transactions and the corresponding risk weights, so as to accurately identify meaningful high-frequency violation behavior combination patterns; regulatory departments and enterprises can accordingly formulate targeted supervision strategies and optimize internal management processes respectively to reduce violation risks;

[0079] In terms of dynamic decision-making generation, a dynamic decision-making generation model is constructed based on the rule-constrained deep Q-network (DQN); this model takes risk characteristics, regulatory standards, and various constraints in actual operations as inputs, and through continuous learning and optimization, generates personalized control plans for different risk scenarios; the reward function is used to evaluate the decision-making effect, considering whether the decision is compliant, Measure the role of decision-making in reducing risks, consider the implementation cost of decision-making; 、 、 As a dynamic weight coefficient, it is adjusted according to different risk scenarios and regulatory objectives to achieve the best decision-making effect and provide scientific and reasonable decision-making suggestions for safety production management;

[0080] Among them, the weighted cross-entropy loss function formula of the accident causal analysis model is:

[0081] , where, represents the weighted cross-entropy loss function; is the weight coefficient, and ; 、 is the industry risk weight coefficient; TF-IDF represents the term frequency-inverse document frequency; Attention is the sentence-level attention weight; is the number of samples; is the true label value; is the predicted label value; is the L2 regularization parameter; is the model parameter vector; represents the square of the L2 norm of;

[0082] Among them, the support degree calculation formula of the violation pattern mining algorithm is:

[0083] , where, represents the weighted support degree; is the violation behavior item set; represents the database in the th transaction; is the transaction database; is the industry risk level weight of the violation behavior ; is the violation behavior item; is the indicator function, when the condition in the parentheses is true , otherwise ; is the industry adjustment factor;

[0084] Among them, the reward function of the dynamic decision-making generation model is defined as:

[0085] , where, represents the reward function; Indicates the current status; Indicates the action taken; Indicates the action Regulatory compliance score of; Is the risk reduction rate; Is the implementation cost; 、 、 Are dynamic weight coefficients.

[0086] In this embodiment, the risk and hidden danger management code generation module integrates risk point information, control measures and emergency plans into a scannable digital label through a unique coding rule and dynamic data association technology, and limits the scannable effective range based on the Beidou / GPS dual-mode positioning technology;

[0087] Furthermore, the risk and hidden danger management code generation module plays a key role in information integration and security transmission in the intelligent supervision system for work safety; it uses advanced technical means to transform various risk and hidden danger-related information into convenient and secure management codes, providing an efficient and accurate information carrier for work safety supervision, and greatly enhancing the effectiveness of safety management; specifically:

[0088] 1. Coding generation principle:

[0089] Multi-source information fusion basis: The basis for coding generation is the organic fusion of multi-source key information; the enterprise ID, as the unique identifier of the enterprise, is like the exclusive "ID card" of the enterprise, which can clearly distinguish the risk information of different enterprises and facilitate the classification management of risks; the risk type code is compiled according to factors such as the characteristics and harm degree of the risk, accurately defining each type of risk, enabling staff to quickly identify the nature of the risk; the three-dimensional geographical hash value is obtained by performing a special algorithm operation on the three-dimensional coordinates (x, y, z) of the risk point, transforming complex geographical location information into a concise hash value, thereby achieving precise locking of the risk location; the combination of these three types of information lays a solid foundation for generating unique and highly recognizable codes;

[0090] Hash and random number encryption: Use the SHA-3 hash function to process the enterprise ID, risk type code and three-dimensional geographical hash value; the SHA-3 hash function has extremely high security, and the generated hash values are almost never repeated, and can transform the input information into hash values of a fixed length; subsequently, perform an exclusive OR operation on the generated hash value and the random number generated by the linear feedback shift register (LSFR) according to the timestamp; the timestamp records the time when the code is generated and is constantly changing, and the random number generated by the LSFR further enhances the randomness and unpredictability of the code; through the exclusive OR operation, the finally obtained optimal hash code not only ensures uniqueness but also has extremely high security and is difficult to be cracked or forged;

[0091] 2. System Architecture and Components:

[0092] Encoding Generation Core Device: A dedicated encoding generation device is equipped within the module. It integrates a high-performance computing chip and advanced algorithm programs, and can quickly and accurately process the input enterprise ID, risk type code, and three-dimensional geographical hash value; according to the established encoding rules, it calls the SHA-3 hash function and the LSFR algorithm to efficiently generate the optimal hash code; for example, in the safety production management system of chemical enterprises, the encoding generation device receives data from risk monitoring sensors in real time and quickly generates the corresponding management code encoding;

[0093] Information Update and Version Control Component: To ensure the timeliness of management code information, the system is equipped with an information update and version control component; when regulations change or risk information undergoes dynamic changes, this component processes according to the version control formula where the forgetting factor attenuates the original version value while integrating the regulation change amount into the new version value in a certain proportion, thus ensuring that the information carried by the management code always conforms to the latest safety production requirements and actual situations;

[0094] Security Protection Technology Integration Component: This component integrates the two-dimensional code anti-counterfeiting technology based on timestamp encryption and the scanning geographical fence constraint technology; in terms of two-dimensional code anti-counterfeiting, using the AES-256 encryption algorithm, a dynamic verification key is generated with the optimal hash code and the modulo result of the timestamp as parameters, effectively preventing the two-dimensional code from being forged and misused; for the scanning geographical fence constraint, a 50-meter effective range is delimited with the registered location coordinates as the center, and only the scanning operations within this range are determined to be valid, avoiding the illegal acquisition of risk information in non-designated areas and comprehensively enhancing the security of data;

[0095] 3. Risk and Hidden Danger Information Management Guarantee:

[0096] Information Integration and Convenient Access: The risk and hidden danger management code integrates risk point information, control measures, and emergency plans; staff can quickly obtain comprehensive risk control information by simply scanning the management code with a scanning device; for example, during daily safety inspections, inspection personnel can scan the management code on the device with a handheld scanning device to view the risk points of the device, corresponding control measures, and emergency plans in case of danger in real time, greatly improving work efficiency;

[0097] Data Security and Tamper - proof Guarantee: By adopting advanced encryption technologies and unique coding rules, it ensures that the information carried by the management code is difficult to be tampered with. At the same time, combined with blockchain evidence - storing technology, it stores the update and usage records of risk information in an un - tamperable manner, realizes the full - process traceability of risk information, and further guarantees the security and credibility of data;

[0098] Precise Positioning and Effective Control: With the help of three - dimensional geographical hash values and scanned - code geographical fence constraints, it can accurately locate the risk position and effectively control the scanned - code operation. Only authorized personnel near the risk point can scan the code to obtain information, which not only ensures the accuracy of information acquisition but also prevents the leakage of risk information, providing strong support for the precise control of risk hazards;

[0099] Based on the above principles, architectures and guarantee measures, the risk hazard management code generation module provides an efficient and secure risk information management method for the intelligent supervision system of work safety, strongly promoting the intelligent and precise development of work safety management;

[0100] Among them, the coding generation rule of the risk hazard management code generation module is as follows:

[0101] Generate a unique code using the enterprise ID, risk type code, and three - dimensional geographical hash value:

[0102] , where, represents the optimal hash code; is the hash function; is the enterprise identification code; represents the string concatenation operation; is the risk type code; is the three - dimensional geographical hash value; is the three - dimensional geographical coordinate; represents the exclusive - OR operation; is a random number generated by a linear feedback shift register; is the timestamp;

[0103] Dynamically adjust through the version control formula when information is updated:

[0104] , where, is the updated version value; is the version value before update; is the forgetting factor; is the time interval; is the regulatory change amount.

[0105] In this embodiment, the intelligent law enforcement terminal module integrates AR visualization, intelligent comparison, and risk trend prediction functions, supports real-time interaction between the mobile end and the background system, and superimposes and displays the device risk history record and real-time monitoring data through augmented reality technology;

[0106] Furthermore, the intelligent law enforcement terminal module is a key support for the safety production intelligent supervision system in the front-line law enforcement link. It integrates a variety of advanced technologies, endows law enforcement officers with powerful information acquisition, analysis, and decision-making support capabilities, greatly improves the efficiency, accuracy, and intelligence level of safety production law enforcement, and ensures the scientific, fair, and efficient conduct of law enforcement work;

[0107] 1. Core technical principles:

[0108] AR visualization technology: Based on augmented reality (AR) technology, it fuses virtual information with the real scene in real time; captures the environmental images of the law enforcement site through the camera, and uses image recognition and spatial positioning algorithms to superimpose and display virtual information such as device risk history records and real-time monitoring data in an intuitive manner in the field of vision of law enforcement officers; for example, when a law enforcement officer approaches a certain production device, the AR visualization interface can automatically identify the device and present information such as the risk events that have occurred to the device in the past, the values and change trends of current monitoring indicators, etc. in the form of floating windows or annotations at the corresponding real position of the device, enabling law enforcement officers to have a clear understanding of the device status without additional reference to materials;

[0109] Intelligent comparison technology: Utilizes deep learning algorithms and big data analysis technology to quickly compare the data collected at the law enforcement site with the massive standard data and case data in the safety production database; when inspecting the safety production status of an enterprise, the intelligent law enforcement terminal can compare and analyze the data in the on-site inspection list in real time, such as device operation parameters and the configuration of safety protection facilities, with the regulatory standards and industry best practice cases in the database; once data deviation or non-compliance is found, the system can quickly issue a warning and provide detailed comparison results and analysis of possible problems to help law enforcement officers accurately judge whether the enterprise has potential safety hazards and violations;

[0110] Risk trend prediction technology: Adopts an improved spatio-temporal LSTM model, fully considering the factors of time and space dimensions to analyze and predict risk data; this model captures the laws and trends of risk changes through learning historical risk data; in the gating mechanism, the input gate the forget gate the output gate as well as the cell state and the hidden state and the calculation of the time decay function , it can dynamically adjust the model's attention to data at different times, thus more accurately predicting the development trend of future risks; for example, based on data such as the frequency of safety accidents and changes in risk factors of multiple enterprises in a certain area over a period of time, predicting the possible hot spots of safety risks and high-incidence accident types in that area in the future, providing a scientific basis for the reasonable allocation of law enforcement resources;

[0111] 2. System Architecture and Components:

[0112] Hardware Devices: Smart law enforcement terminals usually adopt a portable design, such as tablet computers or handheld terminal devices, with features such as being rugged, waterproof, dustproof, and having a high-resolution display to adapt to complex law enforcement environments; the devices are built-in with high-performance processors, large-capacity memory, and storage devices to ensure the ability to quickly run various application programs and process large amounts of data; at the same time, they are equipped with a variety of sensors such as high-definition cameras, GPS positioning modules, and NFC modules for collecting on-site images, positioning the law enforcement location, and data interaction with other smart devices;

[0113] Software System: The software system includes an operating system and various application programs; the operating system is usually customized and developed based on mature mobile operating systems such as Android or iOS to ensure the stability and compatibility of the system; the application programs cover multiple functional modules such as law enforcement business management, data collection and analysis, risk warning and prompt, and AR visual display; for example, the law enforcement business management module is used to record the daily work tasks, law enforcement processes, and case handling situations of law enforcement personnel; the data collection and analysis module is responsible for collecting on-site data and performing real-time analysis; the risk warning and prompt module issues risk warnings to law enforcement personnel in a timely manner according to the data analysis results; the AR visual display module presents the analysis results to law enforcement personnel in an intuitive AR form;

[0114] Data Transmission and Interaction Module: This module supports real-time interaction between the mobile end and the background system. Through wireless communication technologies such as 4G / 5G networks or Wi-Fi, the data collected by the law enforcement terminal is uploaded to the background safety production database in real time, and at the same time, it receives information such as the latest regulations and standards, law enforcement tasks, and risk analysis reports issued by the background system; in addition, it also has the function of encrypted data transmission to ensure the security and integrity of data during transmission;

[0115] 3. Law Enforcement Work Guarantee:

[0116] Real-time information support: During law enforcement, law enforcement officers can obtain the latest regulatory standards, industry cases, and risk warning information at any time through intelligent law enforcement terminals; when encountering complex law enforcement issues, they can quickly query relevant materials, refer to the handling methods of similar cases, and make accurate judgments and decisions; for example, when inspecting the safety production facilities of an enterprise, law enforcement officers query regulatory standards through the terminal to determine whether a certain safety facility of the enterprise meets the requirements, and refer to the penalty situations of previous similar violation cases to reasonably handle the enterprise's violations.

[0117] Efficient law enforcement process: Intelligent law enforcement terminals simplify the law enforcement process and improve work efficiency; law enforcement officers can directly enter inspection data and generate law enforcement documents through the terminal, reducing the links of manual recording and paper document circulation; at the same time, using intelligent comparison and risk trend prediction functions, potential safety hazards and violations can be quickly discovered, and measures can be taken in a timely manner to avoid the delay and expansion of problems; for example, during a safety inspection, law enforcement officers used the terminal to quickly detect and compare data of the enterprise's equipment, completing an inspection that previously took several hours in only half an hour, accurately pointing out the potential safety hazards of the enterprise and issuing a rectification notice.

[0118] Fairness and transparency of law enforcement: By recording the entire law enforcement process and uploading data in real time, the fairness and transparency of law enforcement work are ensured; the camera of the law enforcement terminal can automatically record the video of the law enforcement scene, recording every operation step of law enforcement officers and their communication with enterprise personnel; these videos and data will be uploaded to the background system in real time for subsequent query and supervision; at the same time, the law enforcement documents and handling results generated by the law enforcement terminal will also be made public in the system to accept social supervision, effectively preventing law enforcement injustice and corruption.

[0119] Through the above core technologies, system architectures, and law enforcement guarantee measures, the intelligent law enforcement terminal module provides comprehensive support for safety production law enforcement work, strongly promoting the modernization process of safety production supervision work and playing an important role in ensuring people's lives, property safety, and social stability.

[0120] Among them, the risk trend prediction unit of the intelligent law enforcement terminal module adopts an improved spatio-temporal LSTM model, and its gating mechanism formula is:

[0121] ;

[0122] ;

[0123] ;

[0124] ;

[0125] ;

[0126] Among them, is the input gate; is the activation function; is the input to the weight matrix of the input gate; is the input at time is the hidden state at the previous time to the weight matrix of the input gate; is the hidden state at time is the cell state at the previous time to the weight matrix of the input gate; is the cell state at time represents the Hadamard product; is the bias vector of the input gate; is the forget gate; is the input to the weight matrix of the forget gate; is the hidden state at the previous time to the weight matrix of the forget gate; is the cell state at the previous time to the weight matrix of the forget gate; is the bias vector of the forget gate; is the cell state at time ; tanh is the activation function; is the input to the weight matrix of the cell state; is the hidden state at the previous time to the weight matrix of the cell state; is the bias vector of the cell state; is the output gate; is the input to the weight matrix of the output gate; is the hidden state at the previous time to the weight matrix of the output gate; is the cell state at time ; to the weight matrix of the output gate; is the bias vector of the output gate; is the hidden state at time ; is the time decay function, is the initial weight, is the decay rate.

[0127] In this embodiment, the intelligent safety production supervision system further includes:

[0128] Enterprise Adaptive Learning Module: In the intelligent safety production supervision system, the Enterprise Adaptive Learning Module undertakes the key mission of promoting the independent improvement and collaborative development of enterprises' safety production capabilities; it innovatively applies the federated meta-learning algorithm to achieve the sharing and integration of knowledge and experience among different enterprises while ensuring the privacy and security of enterprise data, and promotes the continuous progress of the entire safety production management level; specifically:

[0129] 1. Core Technical Principle:

[0130] Foundation of Federated Meta-Learning: Federated meta-learning is an advanced technology that combines the concepts of federated learning and meta-learning; Federated learning aims to solve the problem of collaborative modeling in the case of decentralized and privacy-sensitive data among multiple participants. By training models locally at each participant and only uploading model parameters instead of raw data, it protects data privacy; while meta-learning focuses on learning how to learn and can quickly adapt to new tasks and new environments; the Enterprise Adaptive Learning Module combines the two, allowing different enterprises to train models on their local data while learning the excellent experiences of other enterprises, and achieving rapid adaptation to different safety production scenarios and changing risk situations;

[0131] Parameter Update Mechanism: The module adopts a unique parameter update formula , where is the global model parameter after the th round of update; is the global model parameter of the th round, which is the model parameter shared by all enterprises in the entire federated learning system; is the learning rate; is the number of enterprises; represents the th enterprise; is the gradient of the local loss function of the th enterprise with respect to the parameter at the th round; is the local loss function of the th enterprise; is the federated learning balance coefficient; the KL divergence term is used to control the model difference; is the local model distribution of the th enterprise at the th round; is the global model distribution of the th round; through this parameter update mechanism, the global model can continuously absorb the excellent experiences of each enterprise and achieve self-optimization and adaptive adjustment;

[0132] 2. System Architecture and Components;

[0133] Enterprise Local Learning Node: Each participating enterprise has a local learning node, which consists of a local data storage device, a computing device, and a learning algorithm module; the local data storage device is used to store the enterprise's internal work safety data, such as equipment operation data, accident records, employee training data, etc.; the computing device is responsible for running the learning algorithm on the local data and calculating the gradient of the model parameters according to the local loss function; the learning algorithm module implements the local part of the federated meta-learning algorithm, works in coordination with other components to complete local model training and parameter uploading;

[0134] Federated Learning Coordination Server: As the core hub of the entire federated learning system, the federated learning coordination server is responsible for managing the information of participating enterprises, organizing the exchange and update of model parameters; it receives the local model parameters uploaded by each enterprise, calculates the new global model parameters according to the parameter update formula, and distributes the updated global model parameters to each enterprise; at the same time, the coordination server is also responsible for monitoring the process of federated learning to ensure the security and stability of data transmission and the smooth progress of model training;

[0135] Secure Communication Component: To ensure the security of data during transmission, the system is equipped with a secure communication component; this component uses technologies such as homomorphic encryption and differential privacy to encrypt the transmitted model parameters and other information to prevent data from being stolen or tampered with; even if the data is intercepted during transmission, the attacker cannot obtain the content of the original data, thus protecting the data privacy of enterprises;

[0136] 3. Guarantee for Work Safety Improvement:

[0137] Improvement of Personalized Capabilities: The enterprise adaptive learning module allows each enterprise to perform personalized model training locally according to its own work safety data and actual needs; through continuous learning and optimization, enterprises can better identify and respond to the unique risks they face and formulate more accurate work safety strategies; for example, chemical enterprises can use local learning nodes to train a risk warning model suitable for their own production environment based on special risk factors such as high temperature and high pressure in the chemical production process, improving the risk prevention ability;

[0138] Collaborative Knowledge Sharing: During the process of federated learning, although enterprises do not directly share the original data, through the exchange and integration of model parameters, knowledge and experience are shared; each enterprise can learn from the successful experiences of other enterprises, avoid making the same mistakes, and jointly improve the level of work safety management; for example, a manufacturing enterprise has a set of mature experiences in equipment maintenance management. Through federated learning, other enterprises can learn this experience and apply it to their own equipment management to improve the reliability and safety of equipment;

[0139] Continuous adaptation to changes: As safety production regulations are updated, technology advances, and the business of enterprises develops, the safety production environment is constantly changing; the enterprise self-adaptive learning module can, through continuous learning and parameter updates, keep the enterprise's safety production model and management strategies always adaptable to the latest environment; for example, when new safety production regulations are introduced, the enterprise can quickly obtain the experience of other enterprises in adapting to the new regulations through federated learning, adjust its own management measures, and ensure the compliance operation of the enterprise;

[0140] The enterprise self-adaptive learning module provides strong support for the improvement of the enterprise's safety production capacity through the above core technologies, system architectures, and guarantee measures, promotes collaborative cooperation among enterprises, and jointly constructs a safer and more reliable production environment.

[0141] In this embodiment, the intelligent safety supervision system for production also includes:

[0142] Multi-agent collaborative platform: It supports the secure sharing of data among government supervision departments, third-party service agencies, and enterprises, and uses homomorphic encryption technology to achieve cross-agent data joint computing;

[0143] Furthermore, the multi-agent collaborative platform is a key hub in the intelligent safety supervision system for production that promotes in-depth cooperation among all parties and efficient information circulation. It breaks the information barriers among government supervision departments, third-party service agencies, and enterprises, realizes secure data sharing and cross-agent joint computing through innovative technical means, and strongly promotes the collaborative and intelligent development of safety production supervision work; specifically:

[0144] 1. Core technical principle:

[0145] Homomorphic encryption technology: The multi-agent collaborative platform uses homomorphic encryption technology to ensure the security of data during sharing and joint computing; homomorphic encryption allows specific computational operations to be performed on ciphertext, and the result is the same as that obtained by performing the same operation on the plaintext and then encrypting it; in the platform, the data uploaded by each agent is encrypted using homomorphic encryption, which means that during joint computing, the data always exists in ciphertext form, and the original content of the data is invisible during the computing process; for example, during the joint computing of risk assessment, the data provided by government supervision departments, third-party service agencies, and enterprises participates in the computing in encrypted form, and the final assessment result is also encrypted. Only the authorized agent with the decryption key can obtain the plaintext result, effectively preventing the risk of data leakage;

[0146] Distributed Ledger Technology: Introducing distributed ledger technology ensures data consistency and immutability. A distributed ledger is a decentralized database jointly maintained by multiple nodes, and each node stores a complete copy of the ledger. In a multi-agent collaboration platform, the data operation records of each agent are recorded on the distributed ledger. Any modification to the data requires consensus verification by multiple nodes. Once recorded, it is difficult to be tampered with. This provides a reliable basis for data security traceability and liability identification. For example, in the process of law enforcement data sharing and review, the distributed ledger can be used to clearly view the data source, modification history, and operating entity, ensuring the authenticity and reliability of the data.

[0147] 2. System Architecture and Components:

[0148] Data Sharing Interface: The platform has set up dedicated data sharing interfaces for government regulatory departments, third-party service providers, and enterprises respectively. These interfaces follow unified data standards and specifications to ensure the smooth docking and interaction of data among different agents. Government regulatory departments can upload the latest regulations, policies, law enforcement data, etc. through the interface. Enterprises can share their own work safety data, hidden danger investigation records, etc. Third-party service providers can provide professional safety assessment reports, technical solutions, etc. Through these interfaces, each agent can conveniently obtain the required information and achieve two-way data flow.

[0149] Joint Computing Engine: The joint computing engine is the core component of the platform to achieve cross-agent data joint analysis. Based on homomorphic encryption technology, it can perform complex computing operations on encrypted data from different agents, such as risk assessment model calculation, compliance analysis, etc. The joint computing engine is equipped with high-performance computing devices and optimized algorithms to ensure the efficient completion of various computing tasks while ensuring data security, providing accurate data support for work safety decision-making.

[0150] Identity Authentication and Permission Management System: To ensure the secure access and operation of platform data, a strict identity authentication and permission management system is set up. When each agent accesses the platform, it needs to pass multiple identity verifications, such as digital certificates, passwords, biometric identification, etc., to ensure the authenticity and legality of the identity. The permission management system assigns corresponding data access permissions and operation permissions according to the type of agent and business needs. For example, government regulatory departments have the right to supervise and query all enterprise data, while enterprises can only access and modify their own data. Third-party service providers obtain corresponding data usage permissions according to the service agreements signed with enterprises to prevent data abuse and illegal access.

[0151] 3. Collaboration Work Guarantee:

[0152] Data Security Sharing: With the help of homomorphic encryption and distributed ledger technology, the multi-agent collaborative platform realizes the sharing of data in a secure environment; each agent does not need to worry about the risk of data leakage and can safely upload data to the platform, promoting the full circulation of information; for example, in safety production inspections, government regulatory departments can obtain equipment inspection data, safety assessment reports, etc. shared by enterprises and third-party service agencies, comprehensively understand the safety production status of enterprises, and make more scientific regulatory decisions;

[0153] Collaborative Decision Support: Through cross-agent data joint calculation, the platform can provide collaborative decision support for each agent; for example, when formulating a regional safety production plan, government regulatory departments can cooperate with third-party service agencies and enterprises to comprehensively analyze the safety production data in the region using the platform's joint calculation engine, considering factors such as the production scale of enterprises, risk types, and safety technology suggestions provided by third parties, and formulate a more reasonable and effective safety production plan to improve the overall safety level of the region;

[0154] Efficient Collaboration Mechanism: The multi-agent collaborative platform breaks the information silos between agents and establishes an efficient collaboration mechanism; each agent can communicate and work together in real time through the platform to jointly address various issues in safety production; for example, when dealing with sudden safety accidents, government regulatory departments, third-party emergency rescue service agencies, and enterprises can quickly share accident information, rescue resource information, etc. on the platform, jointly formulate rescue plans, improve rescue efficiency, and reduce accident losses;

[0155] Through the above core technologies, system architectures, and guarantee measures, the multi-agent collaborative platform effectively integrates the resources of all parties in safety production, improves the collaborative supervision and service capabilities, and provides strong support for the smooth development of safety production work.

[0156] In this embodiment, the security protection mechanism of the risk and hidden danger management code generation module includes:

[0157] Two-dimensional code anti-counterfeiting technology based on timestamp encryption to generate a dynamic verification key:

[0158] , where, represents the dynamic verification key; is the Advanced Encryption Standard; represents the optimal hash encoding; is the timestamp; represents the modulo operation;

[0159] Geofence constraint conditions for the QR code scanning operation:

[0160] , where, represents the validity identification of the QR code scanning operation, represents valid, Indicates invalid; Is the coordinate of the code scanning position; Is the coordinate of the registration position; Indicates calculating the distance between two points.

[0161] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent production safety supervision system based on artificial intelligence, characterized by: include: Safety production database module: integrates risk hazard database, checklist database, regulatory standards database and dynamic case database through multi-source heterogeneous data fusion architecture; The risk potential database stores risk data of equipment and places in the industry and trade, and the risk data includes a three-dimensional mapping relationship between risk point coordinates, accident types and prevention and control measures; The inspection list library contains general inspection items and industry-specific inspection items, and adopts a multi-level classification label system to achieve scenario-based intelligent matching; The regulatory standards database integrates regulatory documents at four levels, from national to local, links national standards with industry standards, and establishes a dynamic weight association model between regulatory clauses and risk types; Artificial intelligence analysis engine module: Based on natural language processing technology, knowledge graph and deep learning algorithm, it realizes accident cause and effect chain analysis, high-frequency violation pattern mining and dynamic decision-making generation functions; Risk management code generation module: Through unique coding rules and dynamic data association technology, risk point information, control measures and emergency plans are integrated into digital tags that can be scanned and identified, and the effective range of scanning is limited based on Beidou / GPS dual-mode positioning technology; Smart law enforcement terminal module: Integrates AR visualization, intelligent comparison and risk trend prediction functions, supports real-time interaction between mobile terminals and backend systems, and displays device risk history records and real-time monitoring data through augmented reality technology; The artificial intelligence analysis engine module includes: Accident causal analysis model: Using the improved weighted cross entropy loss function, the accident causal chain is constructed based on the bidirectional gated graph neural network Bi-GGNN to generate a risk warning map; Violation pattern mining algorithm: adopts the improved weighted Apriori algorithm to support the identification of high-frequency violation behavior combination patterns and the calculation of industry risk feature matrix; Dynamic decision generation model: Based on the rule-constrained deep Q network DQN, it combines risk characteristics and regulatory standards to generate personalized management and control solutions.

2. The artificial intelligence-based safe production intelligent supervision system according to claim 1 is characterized by: The weighted cross entropy loss function formula of the accident causal analysis model is: ,in, represents the weighted cross entropy loss function; is the weight coefficient, and ; , is the industry risk weight coefficient; TF-IDF Represents word frequency inverse document frequency; Attention is the sentence-level attention weight; is the sample size; is the true label value; is the predicted label value; is the L2 regularization parameter; is the model parameter vector; express The square of the L2 norm of .

3. The artificial intelligence-based safe production intelligent supervision system according to claim 1 is characterized by: The support calculation formula of the violation pattern mining algorithm is: ,in, represents weighted support; is the violation item set; Represents a database The affairs; It is a transactional database; For violations The industry risk level weights; For violation items; It is an indicator function. When the condition in the brackets is true ,otherwise ; It is the industry adjustment factor.

4. The artificial intelligence-based safe production intelligent supervision system according to claim 1 is characterized by: The reward function of the dynamic decision generation model is defined as: ,in, represents the reward function; Indicates the current state; Indicates the action taken; Indicates action Regulatory compliance score; is the risk reduction rate; For implementation costs; , , is the dynamic weight coefficient.

5. The artificial intelligence-based safe production intelligent supervision system according to claim 1 is characterized by: The code generation rule of the risk hidden danger management code generation module is: A unique code is generated using the enterprise ID, risk type code and 3D geo-hash value: ,in, represents the optimal hash code; is a hash function; is the enterprise identification code; Represents a string concatenation operation; Code the risk type; is a three-dimensional geohash value; is the three-dimensional geographic coordinate; Represents the exclusive OR operation; Random numbers generated for linear feedback shift registers; is the timestamp; When the information is updated, it is dynamically adjusted through the version control formula: ,in, is the updated version value; is the version value before the update; For the forgetting factor; is the time interval; The amount of regulatory change.

6. The artificial intelligence-based safe production intelligent supervision system according to claim 1 is characterized by: The risk trend prediction unit of the intelligent law enforcement terminal module adopts an improved spatiotemporal LSTM model, and its gating mechanism formula is: ； ； ； ； ； in, is the input gate; is the activation function; For input The weight matrix to the input gate; for Input of time; Hide the state for the last moment The weight matrix to the input gate; for The hidden state of the moment; The cell state at the last moment The weight matrix to the input gate; for The cell state at a given moment; represents the Hadamard product; is the bias vector of the input gate; For the Gate of Oblivion; For input To the weight matrix of the forget gate; Hide the state for the last moment To the weight matrix of the forget gate; The cell state at the last moment To the weight matrix of the forget gate; is the bias vector of the forget gate; for The cell state at the moment; tanh is the activation function; For input to the weight matrix of the cell state; Hide the state for the last moment to the weight matrix of the cell state; is the bias vector of the cell state; is the output gate; For input The weight matrix to the output gate; Hide the state for the last moment The weight matrix to the output gate; for Cell state at each moment The weight matrix to the output gate; is the bias vector of the output gate; for The hidden state of the moment; is the time decay function, is the initial weight, is the decay rate.

7. The artificial intelligence-based safe production intelligent supervision system according to claim 1 is characterized by: Also includes: Enterprise adaptive learning module: adopts the federated meta-learning algorithm, and its parameter update formula is: ,in, For the Global model parameters after round of updates; For the Global model parameters of the wheel; is the learning rate; is the number of enterprises; Indicates Enterprises; For the The company in Round-time local loss function About parameters The gradient of For the The local loss function of each enterprise; is the federated learning balance coefficient; the KL divergence term is used to control model differences; For the The company in Local model distribution of wheels; For the Global model distribution of wheels; Multi-agent collaborative platform: supports secure data sharing among government regulatory agencies, third-party service agencies and enterprises, and uses homomorphic encryption technology to achieve cross-agent data joint computing.

8. The artificial intelligence-based safe production intelligent supervision system according to claim 1 is characterized by: The security protection mechanism of the risk hidden danger management code generation module includes: QR code anti-counterfeiting technology based on timestamp encryption generates dynamic verification keys: ,in, Represents a dynamic authentication key; is Advanced Encryption Standard; represents the optimal hash code; is the timestamp; Represents modulo operation; Geographical fence constraints for scanning operations: ,in, Indicates the validity of the scan code operation. Indicates that it is effective. Indicates invalidity; The coordinates of the scanned location; is the registration location coordinates; Calculates the distance between two points.

Images