Safety production intelligent supervision system based on artificial intelligence

Abstract

The invention relates to the technical field of safety production supervision, in particular to a safety production intelligent supervision system based on artificial intelligence, and the system comprises a safety production database module which integrates a risk hidden danger library, an inspection list library, a regulation standard library and a dynamic case library through employing a multi-source heterogeneous data fusion architecture. The artificial intelligence analysis engine module realizes accident causal chain analysis, high-frequency violation mode mining and dynamic decision generation; the risk and hidden danger management code generation module integrates the risk information to a digital label which can be identified through code scanning, and data security is guaranteed by using a space-time constraint security mechanism; the intelligent law enforcement terminal module integrates AR visualization, intelligent comparison and risk trend prediction functions, supports real-time interaction between a mobile terminal and a background, and assists efficient law enforcement. The intelligent, collaborative and precise levels of safety production supervision are comprehensively improved, the accident rate is effectively reduced, the life and property safety of people and the social stability are guaranteed, and remarkable social benefits and application values are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of safe production supervision, and in particular to an intelligent safe production supervision system based on artificial intelligence. Background Art

[0002] In today's era of accelerated industrialization and rapid technological development, safe production has always been the cornerstone of stable operation and sustainable development of various industries. However, the traditional safe production supervision model faces many severe challenges in practical application and urgently needs the intervention of innovative technologies and solutions. Specific challenges include:

[0003] Data dispersion and information silos: In the past, safety production data was dispersed and stored in different departments, enterprises and systems. Data such as risks, regulations and standards, and law enforcement cases lacked effective integration, resulting in poor information flow and difficulty in coordination among various regulatory links. For example, when formulating policies, government regulatory departments were unable to easily obtain comprehensive risk data from enterprises, and when implementing safety measures, enterprises also found it difficult to quickly refer to the latest regulations and standards, which seriously restricted the efficiency and accuracy of safety production supervision.

[0004] Risk prediction and decision-making lag: Traditional supervision relies on manual experience and judgment, which makes it difficult to conduct real-time and comprehensive analysis of complex and changeable risk factors. Faced with massive production safety data, manual processing is slow and inaccurate, and it is impossible to timely understand the cause and effect relationship of accidents and high-frequency violation patterns, resulting in untimely risk warnings and lack of scientific basis for decision-making. Once an accident occurs, it is often only possible to remedy it afterwards, and it is impossible to effectively prevent it in advance.

[0005] Inefficient law enforcement and supervision: When conducting on-site inspections, law enforcement officers often need to carry a large amount of paper materials and manually record inspection information, which is a cumbersome and error-prone process. At the same time, the lack of real-time data comparison and risk prediction tools makes it difficult to quickly and accurately judge the safety production status of enterprises, and it is difficult to ensure the efficiency and fairness of law enforcement. For example, when inspecting the compliance of enterprise equipment, it is impossible to compare it with standard data on the spot, and verification is required afterwards, which delays the opportunity for rectification.

[0006] The safety capabilities of enterprises vary greatly: the safety management levels of different enterprises vary greatly. Some enterprises lack effective safety management systems and technical means, making it difficult for them to improve their safety capabilities independently. At the same time, it is difficult for enterprises to share safety knowledge and experience, 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 it is difficult for them 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 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;

[0021] 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;

[0022] 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.

[0023] As a preferred solution, 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 .

[0024] As a preferred solution, 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.

[0025] As a preferred solution, 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.

[0026] As a preferred solution, the code generation rule of the risk management code generation module is:

[0027] 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;

[0028] 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.

[0029] As a preferred solution, the risk trend prediction unit of the intelligent law enforcement terminal module adopts an improved spatiotemporal LSTM model, and its gating mechanism formula is: ; ; ; ; ;

[0030] 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 previous 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 status 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 attenuation rate.

[0031] As a preferred solution, it also includes:

[0032] 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;

[0033] 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.

[0034] As a preferred solution, the security protection mechanism of the risk management code generation module includes:

[0035] 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;

[0036] Geographical fence constraints for scanning operations: ,in, Indicates the validity of the code scanning 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.

[0037] It can be seen from the technical solutions provided by the present invention that the present invention provides an artificial intelligence-based safe production intelligent supervision system, which has the following beneficial effects:

[0038] Data integration and utilization upgrade: The production safety database module integrates the risk database, checklist database, regulatory standards database and dynamic case database through a multi-source heterogeneous data fusion architecture, breaking the information island problem of traditional databases; achieving the three-dimensional association of risk-regulation-case, providing comprehensive and accurate data support for precise supervision, and allowing the data value to be fully mined and utilized;

[0039] Intelligent decision-making and risk prevention and control: The artificial intelligence analysis engine module is based on natural language processing, knowledge graphs and deep learning algorithms to achieve accident causal chain analysis, high-frequency violation pattern mining and dynamic decision-making. It can predict risks in advance, generate personalized management and control plans, change passive supervision to active prevention, and effectively reduce the accident rate.

[0040] Efficient and accurate risk control: The risk management code generation module integrates risk point information, control measures and emergency plans into digital tags that can be scanned and identified. It combines unique coding rules, dynamic data association technology and time-space constraint security mechanisms to achieve real-time update, accurate positioning and effective control of risk information, thereby improving the efficiency and accuracy of risk control.

[0041] Intelligent and convenient law enforcement and supervision: The intelligent law enforcement terminal module integrates AR visualization, intelligent comparison and risk trend prediction functions, supports real-time interaction between the mobile terminal and the backend system; provides real-time information support for law enforcement personnel, simplifies the law enforcement process, ensures the fairness and transparency of law enforcement, and greatly improves the efficiency and quality of safety production law enforcement;

[0042] Autonomous improvement of enterprise capabilities: The enterprise adaptive learning module adopts a federated meta-learning algorithm to achieve the collaborative evolution of cross-regional risk models while protecting enterprise data privacy; it helps enterprises improve their safety production capabilities in an individualized manner, share collaborative knowledge, and continuously adapt to the ever-changing safety production environment;

[0043] Effective multi-party collaborative supervision: The multi-subject collaborative platform supports secure data sharing among government regulatory departments, third-party service agencies and enterprises, and uses homomorphic encryption technology to achieve cross-subject data joint computing; it promotes efficient collaboration among all parties, provides support for collaborative decision-making, and forms an all-round, multi-level joint force for safe production supervision. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a schematic diagram of the overall structure of an artificial intelligence-based safe production intelligent supervision system of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 intended to limit the present invention.

[0046] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0047] like Figure 1 As shown, an embodiment of the present invention provides an intelligent production safety supervision system based on artificial intelligence, including a production safety database module, an artificial intelligence analysis engine module, a risk management code generation module and an intelligent law enforcement terminal module.

[0048] In this embodiment, the safety production database module integrates the risk hidden danger library, the checklist library, the regulatory standards library and the dynamic case library through a multi-source heterogeneous data fusion architecture;

[0049] 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.

[0050] 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;

[0051] 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;

[0052] Furthermore, the safety production database module is the core data hub of the entire supervision system, responsible for the aggregation, management and distribution of data, and provides a solid data foundation for the intelligent operation of the system. It integrates various safety production-related data through a multi-source heterogeneous data fusion architecture, breaks down data silos, and realizes efficient circulation and collaborative use of data. Specifically:

[0053] 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;

[0054] 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;

[0055] 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.

[0056] 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.

[0057] 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:

[0058] 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;

[0059] 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;

[0060] 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;

[0061] Specifically, the artificial intelligence analysis engine module, as the core intelligent component of the entire smart production safety supervision system, 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 generation;

[0062] In terms of accident causal chain analysis, it uses a bidirectional gated graph neural network (Bi-GGNN) to build an accident causal analysis model; this model presents various elements such as equipment, personnel, environment and their interrelationships in the production safety system in the form of a graph, and through deep learning of massive historical data and real-time monitoring data, it automatically and accurately identifies the key factors and causal relationships that lead to accidents, and then builds a complete accident causal chain; in order to improve the accuracy and adaptability of the model, an improved weighted cross entropy loss function is used; among them, the weight coefficient Depend on constitute, , The industry risk weight coefficient is set according to the characteristics and risk levels of different industries and is used to adjust the impact of different factors on the loss function;

[0063] TF-IDF By calculating word frequency and inverse document frequency, the importance of key information in the text is highlighted; Focusing on the key parts of the sentence and weighting different information, the model can better capture the key features of the data; finally, a risk warning map is generated based on the accident causal chain, which intuitively displays potential risk points and their correlations, helping regulators to detect and prevent accidents in a timely manner;

[0064] For high-frequency violation pattern mining, an improved weighted Apriori algorithm is used; based on the traditional Apriori algorithm, this algorithm introduces the industry risk level weight of the violation behavior and industry moderating factors , in order to more accurately reflect the risk level and probability of violations in different industries; when calculating the support, the formula Comprehensively consider the occurrence of each violation in different transactions and the corresponding risk weight, so as to accurately identify the combination pattern of high-frequency violations with practical significance; regulatory authorities and enterprises can formulate targeted supervision strategies, optimize internal management processes and reduce the risk of violations accordingly;

[0065] In terms of dynamic decision generation, a dynamic decision generation model is built based on the rule-constrained deep Q network (DQN); this model takes risk characteristics, regulatory standards, and various constraints in actual operations as input, and generates personalized control plans for different risk scenarios through continuous learning and optimization; the reward function To evaluate the effectiveness of decision making, Consider whether the decision is compliant. Measure the impact of decisions on risk reduction, Consider the cost of implementing the decision; , , 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 safe production management;

[0066] Among them, 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 ;

[0067] Among them, 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 ; is the industry adjustment factor;

[0068] Among them, 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.

[0069] In this embodiment, the risk management code generation module integrates risk point information, control measures and emergency plans into digital tags that can be scanned and identified through unique coding rules and dynamic data association technology, and limits the effective range of scanning based on Beidou / GPS dual-mode positioning technology;

[0070] Furthermore, the risk hidden danger management code generation module plays a key role in information integration and safety transmission in the smart production safety supervision system. With the help of advanced technical means, it converts various risk hidden danger related information into convenient and safe management codes, providing an efficient and accurate information carrier for production safety supervision, greatly enhancing the effectiveness of safety management. Specifically:

[0071] 1. Code generation principle:

[0072] Multi-source information fusion basis: The basis of code generation is the organic fusion of multi-source key information; the enterprise ID, as a unique identification of the enterprise, is like the enterprise's exclusive "identity card", which can clearly distinguish the risk information of different enterprises and facilitate the classification and management of risks; the risk type code is compiled based on the characteristics of the risk, the degree of harm and other factors, accurately defining each type of risk, so that staff can quickly identify the nature of the risk; the three-dimensional geographic hash value is a special algorithm calculation of the three-dimensional coordinates (x, y, z) of the risk point, converting the complex geographic location information into a concise hash value, thereby realizing the 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;

[0073] Hash and random number encryption: Use the SHA-3 hash function to process the enterprise ID, risk type code and three-dimensional geographic hash value; the SHA-3 hash function has extremely high security, and the generated hash value is almost never repeated, and it can convert the input information into a hash value of fixed length; then, the generated hash value is XORed with the random number generated by the linear feedback shift register (LSFR) based on the timestamp; the timestamp records the time when the code is generated and is constantly changing. The random number generated by the LSFR further enhances the randomness and unpredictability of the code; through the XOR operation, the optimal hash code finally obtained not only guarantees uniqueness, but also has extremely high security and is difficult to be cracked or forged;

[0074] 2. System architecture and components:

[0075] Code generation core equipment: The module is equipped with a special code generation device, which integrates high-performance computing chips and advanced algorithm programs, and can quickly and accurately process the input enterprise ID, risk type code and three-dimensional geographic hash value; according to the established coding rules, the SHA-3 hash function and LSFR algorithm are called to efficiently generate the optimal hash code; for example, in the safety production management system of a chemical enterprise, the code generation device receives data from the risk monitoring sensor in real time and quickly generates the corresponding management code;

[0076] Information update and version control component: To ensure the timeliness of management code information, the system has an information update and version control component; when regulations change or risk information changes dynamically, this component will update the information according to the version control formula. Processing, where the forgetting factor The original version value Attenuation, while the regulatory changes Incorporate new version values ​​in a certain proportion , thereby ensuring that the information carried by the management code is always consistent with the latest safety production requirements and actual conditions;

[0077] Security protection technology integration component: This component integrates QR code anti-counterfeiting technology based on timestamp encryption and code scanning geo-fence constraint technology. In terms of QR code anti-counterfeiting, the AES-256 encryption algorithm is used to generate a dynamic verification key with the optimal hash code and timestamp modulus result as parameters, effectively preventing the QR code from being forged and abused. For code scanning geo-fence constraints, a 50-meter effective range is defined with the registered location coordinates as the center. Only code scanning operations within this range are considered valid, avoiding the illegal acquisition of risk information in non-designated areas and comprehensively improving data security.

[0078] 3. Risk and hidden danger information management guarantee:

[0079] Information integration and convenient acquisition: The risk management code integrates risk point information, control measures and emergency plans. The staff only needs to scan the management code through a code scanning device to quickly obtain comprehensive risk control information. For example, during daily safety inspections, the inspectors can scan the management code on the equipment with a handheld code scanning device to view the risk points of the equipment, the corresponding control measures and the emergency plan in case of danger in real time, which greatly improves work efficiency.

[0080] Data security and anti-tampering protection: By adopting advanced encryption technology and unique coding rules, the information carried by the management code is ensured to be difficult to be tampered with; at the same time, combined with blockchain evidence storage technology, the update and use records of risk information are stored in an unalterable manner, realizing the full process traceability of risk information, further ensuring the security and credibility of data;

[0081] Accurate positioning and effective control: With the help of three-dimensional geographic hash values ​​and code scanning geographic fence constraints, the risk location can be accurately located and the code scanning operation can be effectively controlled; 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;

[0082] The risk hidden danger management code generation module provides an efficient and safe risk information management method for the smart production safety supervision system through the above principles, architecture and safeguards, which effectively promotes the intelligent and precise development of production safety management.

[0083] Among them, the code generation rules of the risk management code generation module are:

[0084] 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;

[0085] 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.

[0086] 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 terminal and the background system, and displays the device risk history record and real-time monitoring data through augmented reality technology;

[0087] Furthermore, the intelligent law enforcement terminal module is the key support for the front-line law enforcement of the smart production safety supervision system. It integrates a variety of advanced technologies, giving law enforcement personnel powerful information acquisition, analysis and decision-making support capabilities, greatly improving the efficiency, accuracy and intelligence level of production safety law enforcement, and ensuring the scientific, fair and efficient implementation of law enforcement;

[0088] 1. Core technology principle:

[0089] AR visualization technology: Based on augmented reality (AR) technology, virtual information is integrated with real scenes in real time; the camera captures the environmental picture of the law enforcement scene, and uses image recognition and spatial positioning algorithms to intuitively overlay virtual information such as equipment risk history records and real-time monitoring data in the field of vision of law enforcement officers; for example, when law enforcement officers approach a certain production equipment, the AR visualization interface can automatically identify the equipment and present the risk events that have occurred in the past, the values ​​and change trends of various current monitoring indicators, etc., in the form of floating windows or annotations at the corresponding real location of the equipment, so that law enforcement officers can see the equipment status at a glance without having to consult additional information;

[0090] Intelligent comparison technology: Using deep learning algorithms and big data analysis technology, the data collected at the law enforcement site can be quickly compared with the massive standard data and case data in the safety production database. When inspecting the safety production status of enterprises, the intelligent law enforcement terminal can compare and analyze the various data in the on-site inspection list, such as equipment operating parameters, safety protection facilities, etc., with the regulations and standards in the database and industry best practice cases in real time. Once data deviations or non-compliance with standards are found, the system can quickly issue an early warning and provide detailed comparison results and possible problem analysis to help law enforcement personnel accurately determine whether the enterprise has safety hazards and violations.

[0091] Risk trend prediction technology: The improved spatiotemporal LSTM model is used to analyze and predict risk data by fully considering the factors of time and space dimensions. The model captures the laws and trends of risk changes by learning from historical risk data. In the gating mechanism, the input gate , Forget Gate , output gate and cell status and hidden state Calculation of time decay function , can dynamically adjust the model's attention to data at different times, so as to more accurately predict the development trend of future risks; for example, based on the frequency of safety accidents and changes in risk factors of multiple enterprises in a certain area over a period of time, it can predict the safety risk hotspots and high-incidence accident types that may appear in the area in the future, providing a scientific basis for the rational allocation of law enforcement resources;

[0092] 2. System architecture and components:

[0093] Hardware equipment: Smart law enforcement terminals usually adopt portable designs, such as tablet computers or handheld terminal devices, and are rugged, waterproof, dustproof, and have high-resolution displays to adapt to complex law enforcement environments. The devices are equipped with high-performance processors, large-capacity memory and storage devices to ensure that they can quickly run various applications 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 to collect on-site images, locate law enforcement locations, and interact with other smart devices.

[0094] Software system: The software system includes operating systems and various applications. Operating systems are usually customized and developed based on mature mobile operating systems such as Android or iOS to ensure system stability and compatibility. Applications cover multiple functional modules such as law enforcement business management, data collection and analysis, risk warning and prompting, and AR visualization display. For example, the law enforcement business management module is used to record the daily work tasks, law enforcement processes, and case handling of law enforcement personnel. The data collection and analysis module is responsible for collecting on-site data and conducting real-time analysis. The risk warning and prompting module promptly issues risk warnings to law enforcement personnel based on data analysis results. The AR visualization display module presents the analysis results to law enforcement personnel in an intuitive AR format.

[0095] Data transmission and interaction module: This module supports real-time interaction between the mobile terminal and the backend 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 backend safety production database in real time, and the latest regulations, standards, law enforcement tasks, and risk analysis reports issued by the backend system are received at the same time. In addition, it also has data encryption transmission function to ensure the security and integrity of data during transmission;

[0096] 3. Enforcement work guarantee:

[0097] Real-time information support: During the law enforcement process, law enforcement officers can obtain the latest regulatory standards, industry cases and risk warning information at any time through the intelligent law enforcement terminal; when encountering complex law enforcement issues, they can quickly query relevant information, refer to the handling methods of similar cases, and make accurate judgments and decisions; for example, when inspecting a company's production safety facilities, law enforcement officers can query regulatory standards through the terminal to determine whether a certain safety facility of the company meets the requirements, and refer to the penalties for similar violation cases in the past to reasonably deal with the company's violations;

[0098] Efficient law enforcement process: Intelligent law enforcement terminals simplify the law enforcement process and improve work efficiency. Law enforcement officers can directly input inspection data and generate law enforcement documents through the terminal, reducing the manual record and paper document circulation process. At the same time, the intelligent comparison and risk trend prediction functions can quickly identify safety hazards and violations, and take timely measures to deal with them, avoiding the delay and expansion of problems. For example, during a safety inspection, law enforcement officers used the terminal to quickly detect and compare data on the company's equipment. It took only half an hour to complete the inspection work that would have taken several hours in the past. They also accurately pointed out the safety hazards of the company and issued a rectification notice.

[0099] Fairness and transparency of law enforcement: The fairness and transparency of law enforcement are ensured by recording the entire law enforcement process and uploading data in real time. The camera of the law enforcement terminal can automatically record the video of the law enforcement scene, record every operation step of the law enforcement personnel and the communication with the enterprise personnel. These videos and data will be uploaded to the backend system in real time for subsequent query and supervision. At the same time, the law enforcement documents and processing results generated by the law enforcement terminal will also be made public in the system and subject to social supervision, effectively preventing the occurrence of unfair law enforcement and corruption.

[0100] The intelligent law enforcement terminal module provides all-round support for production safety law enforcement through the above core technologies, system architecture and law enforcement guarantee measures, effectively promotes the modernization process of production safety supervision, and plays an important role in protecting people's lives and property safety and social stability.

[0101] Among them, the risk trend prediction unit of the intelligent law enforcement terminal module adopts an improved spatiotemporal LSTM model, and its gating mechanism formula is: ; ; ; ; ;

[0102] 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 previous 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 previous 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 status 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 attenuation rate.

[0103] In this embodiment, the smart production safety supervision system also includes:

[0104] Enterprise Adaptive Learning Module: In the smart production safety supervision system, the enterprise adaptive learning module undertakes the key mission of promoting the independent improvement and coordinated development of enterprises' production safety capabilities. It innovatively uses 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 promote the continuous improvement of the entire production safety management level. Specifically:

[0105] 1. Core technology principle:

[0106] Federated meta-learning basics: Federated meta-learning is an advanced technology that integrates the concepts of federated learning and meta-learning. Federated learning aims to solve the problem of collaborative modeling when the data of multiple participants are dispersed and privacy-sensitive. It protects data privacy by training the model locally on each participant and uploading only the model parameters instead of the original data. 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 companies to train models on their own local data and learn from the excellent experience of other companies at the same time, so as to quickly adapt to different safety production scenarios and changing risk conditions.

[0107] Parameter update mechanism: The module adopts a unique parameter update formula ,in, For the Global model parameters after round of updates; For the The global model parameters of the round, which are model parameters shared by all enterprises in the entire federated learning system; 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 Through this parameter update mechanism, the global model can continuously absorb the excellent experience of various enterprises and achieve self-optimization and adaptive adjustment;

[0108] 2. System architecture and components;

[0109] 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 safety production 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 and works with other components to complete local model training and parameter upload.

[0110] 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 and organizing the exchange and update of model parameters. It receives local model parameters uploaded by each enterprise, calculates new global model parameters according to the parameter update formula, and sends 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.

[0111] 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, thereby protecting the data privacy of the enterprise.

[0112] 3. Improve safety in production:

[0113] Personalized capability enhancement: The enterprise adaptive learning module allows each enterprise to conduct personalized model training locally based on its own safety production 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 safety production strategies. For example, chemical companies can use local learning nodes to train risk warning models suitable for their own production environment based on special risk factors such as high temperature and high pressure in the chemical production process, thereby improving risk prevention capabilities.

[0114] Collaborative knowledge sharing: In the process of federated learning, although enterprises do not directly share original data, they share knowledge and experience through the exchange and integration of model parameters. Each enterprise can learn from the successful experience of other enterprises, avoid repeating mistakes, and jointly improve the level of safety production management. For example, a manufacturing enterprise has a set of mature experience in equipment maintenance management. Through federated learning, other enterprises can learn from this experience and apply it to their own equipment management to improve the reliability and safety of equipment.

[0115] Continuously adapt to changes: With the update of safety production regulations, technological advancement, and the development of corporate business, the safety production environment is constantly changing; the enterprise adaptive learning module can ensure that the enterprise's safety production model and management strategy always remain adaptable to the latest environment through continuous learning and parameter updates; for example, when new safety production regulations are introduced, enterprises can quickly obtain the experience of other enterprises in adapting to new regulations through federated learning, adjust their own management measures, and ensure the compliance of the enterprise's operations;

[0116] Through the above core technologies, system architecture and security measures, the enterprise adaptive learning module provides strong support for improving the enterprise's production safety capabilities, promotes collaboration among enterprises, and jointly builds a safer and more reliable production environment.

[0117] In this embodiment, the smart production safety supervision system also includes:

[0118] 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;

[0119] Furthermore, the multi-agent collaborative platform is a key hub in the smart safety production supervision system to promote in-depth collaboration among all parties and efficient information flow. It breaks the information barriers between government regulatory departments, third-party service agencies and enterprises, and realizes data security sharing and cross-agent joint computing through innovative technical means, which effectively promotes the collaborative and intelligent development of safety production supervision. Specifically:

[0120] 1. Core technology principle:

[0121] 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 computing operations to be performed on ciphertext, and the results are consistent with the results of the same operation on plaintext and then encrypted. In the platform, the data uploaded by each subject is processed by homomorphic encryption, which means that during joint computing, the data always exists in ciphertext form, and the computing process is invisible to the original content of the data. For example, when performing joint risk assessment calculations, the data provided by government regulatory departments, third-party service agencies, and enterprises are each included in the calculation in an encrypted state, and the final assessment results are also encrypted. Only authorized entities with decryption keys can obtain the plaintext results, effectively preventing the risk of data leakage.

[0122] Distributed ledger technology: The introduction of distributed ledger technology ensures data consistency and immutability. A distributed ledger is a decentralized database maintained by multiple nodes, each of which stores a complete copy of the ledger. In a multi-subject collaborative platform, the data operation records of each subject are recorded in a distributed ledger. Any modification of the data needs to be verified by consensus of multiple nodes. Once recorded, it is difficult to be tampered with. This provides a reliable basis for data security traceability and responsibility identification. For example, in the process of law enforcement data sharing and review, the use of distributed ledgers can clearly view the source of the data, modification history and operating subject, ensuring the authenticity and reliability of the data.

[0123] 2. System architecture and components:

[0124] Data sharing interface: The platform has set up special data sharing interfaces for government regulatory departments, third-party service agencies and enterprises. These interfaces follow unified data standards and specifications to ensure that data from different entities can be smoothly connected and interacted. Government regulatory departments can upload the latest laws, policies, law enforcement data, etc. through the interface, and enterprises can share their own production safety data, hidden danger inspection records, etc. Third-party service agencies can provide professional safety assessment reports, technical solutions, etc. Through these interfaces, each entity can easily obtain the required information and realize two-way flow of data.

[0125] Joint computing engine: The joint computing engine is the core component of the platform to realize cross-subject data joint analysis. Based on homomorphic encryption technology, it can perform complex computing operations on encrypted data from different entities, such as risk assessment model calculation and compliance analysis. The joint computing engine is equipped with high-performance computing equipment and optimized algorithms to ensure that various computing tasks can be completed efficiently while ensuring data security, providing accurate data support for safe production decision-making.

[0126] Identity authentication and permission management system: To ensure secure access and operation of platform data, a strict identity authentication and permission management system is set up. When accessing the platform, each subject must pass multiple identity authentications, such as digital certificates, passwords, and biometrics, to ensure the authenticity and legitimacy of the identity. The permission management system allocates corresponding data access rights and operation rights to the subject according to the type of subject and business needs. For example, government regulatory authorities have regulatory query rights to all corporate data, while enterprises can only access and modify their own data. Third-party service agencies obtain corresponding data usage rights based on the service agreement signed with the enterprise to prevent data abuse and illegal access.

[0127] 3. Collaborative work guarantee:

[0128] Secure data sharing: With the help of homomorphic encryption and distributed ledger technology, the multi-subject collaborative platform realizes data sharing in a secure environment; each subject can upload data to the platform without worrying about the risk of data leakage, which promotes the full flow of information; for example, during production safety inspections, government regulatory authorities can obtain equipment testing data and safety assessment reports shared by enterprises and third-party service agencies, fully understand the production safety status of enterprises, and make more scientific regulatory decisions;

[0129] Collaborative decision support: Through cross-subject data joint computing, the platform can provide collaborative decision support for various entities; for example, when formulating regional production safety plans, government regulatory authorities can work with third-party service agencies and enterprises to use the platform's joint computing engine to conduct a comprehensive analysis of the regional production safety data, taking into account factors such as the enterprise's production scale, risk type, and safety technology recommendations provided by third parties, to formulate a more reasonable and effective production safety plan and improve the overall safety level of the region;

[0130] Efficient collaboration mechanism: The multi-agent collaboration platform breaks down the information silos between the various entities and establishes an efficient collaboration mechanism. The various entities can communicate and work together in real time through the platform to jointly deal with various problems in production safety. 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, and jointly formulate rescue plans to improve rescue efficiency and reduce accident losses.

[0131] Through the above-mentioned core technologies, system architecture and security measures, the multi-agent collaborative platform effectively integrates the resources of all parties involved in production safety, improves collaborative supervision and service capabilities, and provides strong support for the smooth development of production safety work.

[0132] In this embodiment, the security protection mechanism of the risk management code generation module includes:

[0133] 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;

[0134] Geographical fence constraints for scanning operations: ,in, Indicates the validity of the code scanning 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.

[0135] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that 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 overlays and displays device risk history records and real-time monitoring data through augmented reality technology.

2. The artificial intelligence-based safe production intelligent supervision system according to claim 1 is characterized by: 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.

3. The artificial intelligence-based safe production intelligent supervision system according to claim 2 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 .

4. The artificial intelligence-based safe production intelligent supervision system according to claim 2 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.

5. The artificial intelligence-based safe production intelligent supervision system according to claim 2 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.

6. 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.

7. 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 previous 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 previous 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 status 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.

8. 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.

9. 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.

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