Intelligent safety management method for gas operating enterprise

Abstract

The invention discloses an intelligent safety management method for a gas operating enterprise. According to the invention, through deep fusion of Internet of Things perception and intelligent decision technologies, the full-period management capability of safety risk prevention and control is significantly improved. On the aspect of beforehand prevention, the global sensing network and the dynamic risk assessment system form double defense lines, hidden danger signals such as pipe network pressure abnormity and equipment aging are captured in real time, the risk evolution trend is pre-judged in combination with meteorological environment changes, and accurate deployment of protection resources is guided. The autonomous inspection robot cluster breaks through the space-time limitation of manual inspection, continuously obtains high-precision detection data under complex terrains and severe weather, and ensures that hidden danger recognition is free of dead angles. The user side safety enabling system converts professional protection knowledge into visual interaction guidance, so that residents can check hidden dangers of gas appliances by themselves, and an enterprise-user linkage basic protection net is constructed.

Description

Technical Field

[0001] The present invention belongs to the technical field of gas safety management, and specifically relates to an intelligent safety management method for a gas business enterprise. Background Art

[0002] Gas operation safety management involves rigorous safety oversight and management of the entire process of gas supply companies, from gas procurement, storage, transmission and distribution to end-user consumption, to ensure the safe operation of gas facilities and prevent accidents. This includes establishing and improving safety management systems, developing emergency response plans, conducting regular safety inspections, strengthening employee safety training, and promoting knowledge about safe gas use. Through technical means and management measures, such as installing leak alarms, using explosion-proof equipment, and maintaining pipeline integrity, the risks of gas leaks, fires, and explosions are reduced, safeguarding the safety of people and property and ensuring social stability. Gas operation safety management is a crucial component of public safety and plays a vital role in improving urban safety.

[0003] However, existing gas safety management technologies generally have multi-dimensional defects: reliance on manual inspections leads to temporal and spatial blind spots in hazard identification, static risk assessment models are difficult to integrate real-time environmental variables, and paper emergency plans lack dynamic deduction capabilities, resulting in decision-making lags, leading to response delays and decision-making deviations in risk prevention and control. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent safety management method for gas business enterprises in order to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: an intelligent safety management method for a gas business enterprise, the method comprising the following steps:

[0006] S1: Deploy an intelligent sensor network, use LPWAN technology to achieve real-time data collection of pipelines, storage tanks, and user terminals, and establish a 3D visualization digital twin platform;

[0007] S2: Using a spatiotemporal big data analysis engine, integrating 32-dimensional characteristic parameters such as meteorological data, gas load, and equipment life, a dynamic risk heat map is generated, achieving a risk location accuracy of 98.7%;

[0008] S3: Configure a multimodal inspection robot (pipeline crawling / drone / wheeled), equipped with a laser methane telemeter and thermal imaging module, and build an autonomous inspection path planning system based on the ant colony optimization algorithm;

[0009] S4: Develop a leak prediction model based on federated learning, achieve a 50ms leak response through edge computing nodes, and link sound and light alarms, automatic shut-off valves, and ventilation systems to form a three-dimensional closed-loop disposal system.

[0010] S5: Push the AI safety manager APP to end users, integrate AR gas appliance self-inspection, gas usage pattern analysis and virtual safety officer functions, and establish a two-way risk feedback mechanism between users and enterprises.

[0011] S6: Build a multimodal emergency decision-making engine, transform historical accident cases, emergency plans, and expert experience into a semantic network, combine it with real-time situation to generate dynamic disposal plans, and support VR emergency drills and simulations.

[0012] S7: Establish a blockchain archive for the entire life cycle of the equipment, automatically trigger maintenance work orders through smart contracts, and apply vibration and noise spectrum analysis technology to achieve early warning of faults.

[0013] S8: Develop wearable AR smart glasses to display operating procedures, risk warnings and expert remote guidance in real time, and monitor operational compliance through eye tracking technology.

[0014] S9: Introduce information entropy theory to build a security situation assessment model, generate a dynamic improvement roadmap from the three dimensions of organization, technology, and culture, and achieve quantitative improvement in security management efficiency.

[0015] In a preferred embodiment, in step S1, high-precision pressure sensors, flow meters, and combustible gas detectors are deployed at key pipeline nodes. Low-power wide area network technology is used for continuous data transmission, ensuring instant updates of the pipeline network's operating status. To address hidden risks, such as aging pipelines or underground valve wells, embedded miniature fiber-optic vibration sensors can capture subtle pipeline deformations or abnormal vibrations caused by third-party construction. All sensor data is synchronized to a three-dimensional digital twin platform, forming a three-dimensional monitoring network from gas storage stations to end users, providing an accurate, real-time data foundation for subsequent risk analysis.

[0016] In a preferred embodiment, step S2 analyzes the impact of meteorological changes on pipeline pressure using a spatiotemporal convolutional neural network to identify high-risk pipeline sections during typhoons or rainstorms. For critical equipment such as pressure regulating equipment and valve blocks, the remaining life is calculated by combining material corrosion rates with operating hours, generating a visual risk heat map. This heat map allows operators to intuitively understand regional risk levels. For example, in cold weather, pipeline sections prone to frost cracking can be automatically marked to guide the prioritization of preventive maintenance work.

[0017] In a preferred embodiment, in step S3, a wheeled robot equipped with a laser methane detection module penetrates the valve well to scan gas concentrations. A drone equipped with a high-resolution infrared camera performs comprehensive thermal imaging inspections of the overhead pipeline. The robot swarm dynamically optimizes its inspection route using a biomimetic path planning algorithm, avoiding obstacles and hazardous areas based on real-time environmental data. In harsh conditions such as heavy rain or extreme cold, the robots automatically switch to enhanced protection mode to ensure continuous monitoring of critical pipeline sections.

[0018] In a preferred embodiment, in step S4, edge nodes deploy a lightweight leak prediction model to identify abnormal signals by analyzing pressure fluctuations and gas concentration changes. Upon detecting a potential leak, the system immediately activates forced ventilation via the intelligent manhole cover and simultaneously sends a targeted alarm message to nearby residents. For complex scenarios such as commercial complexes, multi-sensor data fusion technology is employed to eliminate false alarms and ensure accurate triggering of emergency commands. During emergency response, the system tracks the leak's spread in real time and dynamically adjusts control strategies.

[0019] In a preferred embodiment, in step S5, the AR gas appliance self-diagnosis module uses the mobile phone camera to identify the cooker model and automatically compares it to the safety standards database, alerting users of potential hazards such as hose aging and missing flameout protection. The system analyzes gas usage patterns based on user habits and issues warnings for abnormally long periods of time when the valve is left open or during unusual hours. For elderly users, a voice interaction function simplifies the operation process and integrates with the community grid management system to provide proactive safety services.

[0020] In a preferred embodiment, the step S6 specifically includes the following steps:

[0021] S6-1 Multi-source heterogeneous data fusion:

[0022] Constructing a historical accident database: Collecting gas accident cases from the past 10 years and using natural language processing technology to extract 32 types of structured features, including accident type, cause chain, and disposal measures;

[0023] Digital emergency plan: Convert the three-level response mechanism and 156 standard processes into computable logical units, and establish a mapping matrix between the plan, resources, and responsible persons;

[0024] Extracting expert experience: Analyzing over 1,000 hours of expert consultation videos and extracting implicit decision rules using graph convolutional networks;

[0025] S6-2 Dynamic Knowledge Graph Construction:

[0026] Define the gas emergency ontology: divide the semantic framework into 4 categories and 58 subcategories: accidents, equipment, environment, and disposal measures;

[0027] Generate semantic network: Use the TransR algorithm to transform cases, plans, and experiences into entity-relationship-attribute triples, and build a Neo4j graph database containing 27,000 nodes;

[0028] S6-3 real-time situational awareness fusion,

[0029] Access to multimodal data: Integrate SCADA system real-time pressure and flow data, inspection robot video stream, and weather warning information;

[0030] Spatial situation mapping: Based on the GIS engine, a Gaussian smoke plume diffusion model and a three-dimensional digital twin are superimposed to generate a crisis heat map with spatiotemporal labels;

[0031] S6-4 Dynamic disposal plan generation:

[0032] Run the GNN-Q reinforcement learning inference engine: Integrate the Q-learning algorithm into the graph neural network to search for the optimal processing path of the knowledge graph;

[0033] Perform Monte Carlo multi-strategy simulations: generate three scenarios: conservative, balanced, and aggressive, and evaluate the confidence interval of 92% to 97% success rate for each scenario;

[0034] Output intelligent operation instructions: Generate time-stamped instruction sets based on resource data such as repair team location and valve inventory, automatically triggering the opening of intelligent manhole covers and the demarcation of drone no-fly zones;

[0035] S6-5 VR emergency simulation system:

[0036] Constructing a 3D physical scene: Using Unreal Engine to develop a virtual accident environment with pipe rupture flame simulation and smoke diffusion particle effects;

[0037] Enable multi-person collaborative drills: Commanders use VR headsets to conduct three-dimensional situation analysis and assessment, and voice commands directly drive the virtual repair team's actions;

[0038] Generate intelligent review reports: compare the exercise decision path with the recommended solution of the knowledge graph, and automatically update the decision weight of the case library;

[0039] S6-6 closed-loop optimization mechanism:

[0040] Dynamically adjust node confidence: Optimize knowledge graph node parameters based on Bayesian network analysis of actual combat and drill results;

[0041] Triggering the knowledge evolution process: Automatically initiate expert consultation requests when new accident patterns are detected, continuously expanding the coverage of the semantic network;

[0042] The GNN is combined with the Q-learning algorithm, and the calculation formula of the GNN-Q reinforcement learning decision function in the dynamic disposal plan generation is:

[0043]

[0044] Where θ represents the weight matrix of the graph neural network, which encodes the node relationships of the knowledge graph through a 200-dimensional hidden space

[0045] h_v^{(k)} represents the node embedding vector after k=5 graph convolution iterations, integrating the semantic associations within the surrounding 3 hops.

[0046] The output dimension is: 128-dimensional decision feature vector, including device status (40 dimensions), environmental factors (32 dimensions), and resource constraints (56 dimensions)

[0047] λ = 0.78 represents the case similarity weight coefficient (determined by grid search optimization)

[0048] Simcase represents the matching calculation function based on the Dynamic Time Warping (DTW) algorithm

[0049] Ci represents the triple feature of the i-th similar case in the historical case database (accident mode × disposal measures × result feedback)

[0050] γ=0.93 represents the future benefit discount factor, reflecting the time sensitivity of emergency response

[0051] E[·] represents the expected value of 100 deduction paths generated by Monte Carlo Tree Search (MCTS).

[0052] In a preferred embodiment, in step S7, each device's factory information, maintenance records, test reports, and other data are encrypted and uploaded to the blockchain, creating an unalterable electronic history. Smart contracts automatically analyze device operating parameters, triggering scheduled maintenance work orders or spare parts replacement reminders. For core equipment like compressors and storage tanks, voiceprint recognition technology is used to analyze operating noise characteristics, capturing early signs of bearing wear or seal failure, providing a basis for preventive maintenance decisions.

[0053] In a preferred embodiment, in step S8, AR smart glasses overlay a 3D diagram of the equipment structure in the maintenance personnel's field of view, providing step-by-step guidance on procedures such as flange removal and seal replacement. An eye-tracking module monitors operational compliance, triggering voice reminders when the operator's gaze strays from a critical step or protective equipment is missing. Remote experts can provide guidance on complex troubleshooting through first-person video sharing, ensuring standardized execution of high-risk operations.

[0054] In a preferred embodiment, in step S9, the model calculates entropy based on three dimensions: organizational response efficiency, completeness of technical measures, and depth of safety culture penetration, to identify weaknesses in the management system. For example, for issues related to multi-departmental collaboration, entropy analysis can reveal information gaps in process connectivity and guide the optimization of emergency command responsibilities. The assessment results generate customized improvement plans, driving the transition of safety management from reactive response to systematic prevention.

[0055] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0056] 1. In the present invention, the full-cycle management capability of safety risk prevention and control is significantly improved through the deep integration of IoT perception and intelligent decision-making technology. At the pre-emptive prevention level, the global perception network and the dynamic risk assessment system form a double line of defense, capturing hidden danger signals such as abnormal pipe network pressure and equipment aging in real time, and predicting the risk evolution trend in combination with meteorological and environmental changes to guide the precise deployment of protection resources. The autonomous inspection robot cluster breaks through the time and space limitations of manual inspections, continuously obtains high-precision detection data in complex terrain and severe weather, and ensures that there are no blind spots in the identification of hidden dangers. The user-side safety empowerment system transforms professional protection knowledge into visual interactive guidance, enabling residents to independently check for hidden dangers in gas appliances and build a grassroots protection network that links enterprises and users.

[0057] 2. In the present invention, the emergency decision-making knowledge graph system realizes the intelligent transition from historical experience to real-time handling. When an emergency occurs, the system quickly generates a three-dimensional situation map through multi-source data fusion, dynamically matches historical handling cases with the expert experience database, and simultaneously deduces the implementation effects of various response plans to provide a reliable basis for command decision-making. The VR deduction platform converts text plans into interactive three-dimensional scenes, and effectively improves the on-the-spot handling capabilities of commanders by simulating multiple variables in real accidents. The closed-loop optimization mechanism ensures that the experience of each emergency response can be precipitated as a new node in the knowledge graph, promoting the continuous evolution of the safety management system. The equipment health management blockchain and the employee intelligent safety assistant strengthen the safety foundation from the two dimensions of hardware maintenance and personnel skills, forming a virtuous cycle of collaborative optimization of technology, equipment, and personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, 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 intended to limit the present invention.

[0060] Example:

[0061] Reference Figure 1 ,

[0062] An intelligent safety management method for a gas business enterprise, the method comprising the following steps:

[0063] S1: Deploy an intelligent sensor network, use LPWAN technology to achieve real-time data collection of pipelines, storage tanks, and user terminals, and establish a 3D visualization digital twin platform;

[0064] S2: Using a spatiotemporal big data analysis engine, integrating 32-dimensional characteristic parameters such as meteorological data, gas load, and equipment life, a dynamic risk heat map is generated, achieving a risk location accuracy of 98.7%;

[0065] S3: Configure a multimodal inspection robot (pipeline crawling / drone / wheeled), equipped with a laser methane telemeter and thermal imaging module, and build an autonomous inspection path planning system based on the ant colony optimization algorithm;

[0066] S4: Develop a leak prediction model based on federated learning, achieve a 50ms leak response through edge computing nodes, and link sound and light alarms, automatic shut-off valves, and ventilation systems to form a three-dimensional closed-loop disposal system.

[0067] S5: Push the AI safety manager APP to end users, integrate AR gas appliance self-inspection, gas usage pattern analysis and virtual safety officer functions, and establish a two-way risk feedback mechanism between users and enterprises.

[0068] S6: Build a multimodal emergency decision-making engine, transform historical accident cases, emergency plans, and expert experience into a semantic network, combine it with real-time situation to generate dynamic disposal plans, and support VR emergency drills and simulations.

[0069] S7: Establish a blockchain archive for the entire life cycle of the equipment, automatically trigger maintenance work orders through smart contracts, and apply vibration and noise spectrum analysis technology to achieve early warning of faults.

[0070] S8: Develop wearable AR smart glasses to display operating procedures, risk warnings and expert remote guidance in real time, and monitor operational compliance through eye tracking technology.

[0071] S9: Introduce information entropy theory to build a security situation assessment model, generate a dynamic improvement roadmap from the three dimensions of organization, technology, and culture, and achieve quantitative improvement in security management efficiency.

[0072] In step S1, high-precision pressure sensors, flow meters, and combustible gas detectors are deployed at key pipeline nodes. Low-power wide area network technology is used for continuous data transmission, ensuring instant updates of the pipeline network's operating status. To address hidden risks, such as aging pipelines or underground valve wells, micro-fiber optic vibration sensors are embedded to detect subtle pipeline deformations or abnormal vibrations caused by third-party construction. All sensor data is synchronized to a three-dimensional digital twin platform, forming a three-dimensional monitoring network from gas storage stations to end users, providing an accurate real-time data foundation for subsequent risk analysis.

[0073] In step S2, a spatiotemporal convolutional neural network analyzes the impact of meteorological changes on pipeline pressure, identifying high-risk pipeline sections during typhoons or heavy rainstorms. For critical equipment such as pressure regulating equipment and valve blocks, the remaining life is calculated by combining material corrosion rates with operating hours, generating a visual risk heat map. This heat map allows operators to intuitively understand regional risk levels. For example, in cold weather, pipeline sections prone to frost cracking are automatically marked to guide the prioritization of preventive maintenance work.

[0074] In step S3, a wheeled robot equipped with a laser methane detection module penetrates the valve well to scan gas concentrations. A drone equipped with a high-resolution infrared camera performs comprehensive thermal imaging inspections of the overhead pipeline. The robot swarm dynamically optimizes its inspection route using a biomimetic path planning algorithm, avoiding obstacles and hazardous areas based on real-time environmental data. In harsh conditions such as heavy rain or extreme cold, the robots automatically switch to enhanced protection mode to ensure continuous monitoring of critical pipeline sections.

[0075] In step S4, the edge node deploys a lightweight leak prediction model, which identifies abnormal signals by analyzing pressure fluctuations and changes in gas concentration. Upon detecting a potential leak, the system immediately activates forced ventilation via the smart manhole cover and simultaneously sends a targeted alarm message to nearby residents. For complex scenarios like commercial complexes, multi-sensor data fusion technology is used to eliminate false alarms and ensure accurate triggering of emergency commands. During emergency response, the system tracks the spread of leaks in real time and dynamically adjusts control strategies.

[0076] In step S5, the AR gas appliance self-check module uses the phone's camera to identify the stove model and automatically compares it to a safety standards database, alerting users of potential hazards such as hose aging and missing flameout protection. The system analyzes gas usage patterns based on user habits and issues warnings for unusually long periods of time when the valve is left open or during unusual hours. For elderly users, a voice interaction function simplifies the operation process and integrates with the community grid management system to provide proactive safety services.

[0077] Step S6 specifically includes the following steps:

[0078] S6-1 Multi-source heterogeneous data fusion:

[0079] Constructing a historical accident database: Collecting gas accident cases from the past 10 years and using natural language processing technology to extract 32 types of structured features, including accident type, cause chain, and disposal measures;

[0080] Digital emergency plan: Convert the three-level response mechanism and 156 standard processes into computable logical units, and establish a mapping matrix between the plan, resources, and responsible persons;

[0081] Extracting expert experience: Analyzing over 1,000 hours of expert consultation videos and extracting implicit decision rules using graph convolutional networks;

[0082] S6-2 Dynamic Knowledge Graph Construction:

[0083] Define the gas emergency ontology: divide the semantic framework into 4 categories and 58 subcategories: accidents, equipment, environment, and disposal measures;

[0084] Generate semantic network: Use the TransR algorithm to transform cases, plans, and experiences into entity-relationship-attribute triples, and build a Neo4j graph database containing 27,000 nodes;

[0085] S6-3 real-time situational awareness fusion,

[0086] Access to multimodal data: Integrate SCADA system real-time pressure and flow data, inspection robot video stream, and weather warning information;

[0087] Spatial situation mapping: Based on the GIS engine, a Gaussian smoke plume diffusion model and a three-dimensional digital twin are superimposed to generate a crisis heat map with spatiotemporal labels;

[0088] S6-4 Dynamic disposal plan generation:

[0089] Run the GNN-Q reinforcement learning inference engine: Integrate the Q-learning algorithm into the graph neural network to search for the optimal processing path of the knowledge graph;

[0090] Perform Monte Carlo multi-strategy simulations: generate three scenarios: conservative, balanced, and aggressive, and evaluate the confidence interval of 92% to 97% success rate for each scenario;

[0091] Output intelligent operation instructions: Generate time-stamped instruction sets based on resource data such as repair team location and valve inventory, automatically triggering the opening of intelligent manhole covers and the demarcation of drone no-fly zones;

[0092] S6-5 VR emergency simulation system:

[0093] Constructing a 3D physical scene: Using Unreal Engine to develop a virtual accident environment with pipe rupture flame simulation and smoke diffusion particle effects;

[0094] Enable multi-person collaborative drills: Commanders use VR headsets to conduct three-dimensional situation analysis and assessment, and voice commands directly drive the virtual repair team's actions;

[0095] Generate intelligent review reports: compare the exercise decision path with the recommended solution of the knowledge graph, and automatically update the decision weight of the case library;

[0096] S6-6 closed-loop optimization mechanism:

[0097] Dynamically adjust node confidence: Optimize knowledge graph node parameters based on Bayesian network analysis of actual combat and drill results;

[0098] Triggering the knowledge evolution process: Automatically initiate expert consultation requests when new accident patterns are detected, continuously expanding the coverage of the semantic network;

[0099] The GNN is combined with the Q-learning algorithm, and the calculation formula of the GNN-Q reinforcement learning decision function in the dynamic disposal plan generation is:

[0100]

[0101] Where θ represents the weight matrix of the graph neural network, which encodes the node relationships of the knowledge graph through a 200-dimensional hidden space

[0102] h_v^{(k)} represents the node embedding vector after k=5 graph convolution iterations, integrating the semantic associations within the surrounding 3 hops.

[0103] The output dimension is: 128-dimensional decision feature vector, including device status (40 dimensions), environmental factors (32 dimensions), and resource constraints (56 dimensions)

[0104] λ = 0.78 represents the case similarity weight coefficient (determined by grid search optimization)

[0105] Simcase represents the matching calculation function based on the Dynamic Time Warping (DTW) algorithm

[0106] Ci represents the triple feature of the i-th similar case in the historical case database (accident mode × disposal measures × result feedback)

[0107] γ=0.93 represents the future benefit discount factor, reflecting the time sensitivity of emergency response

[0108] E[·] represents the expected value of 100 deduction paths generated by Monte Carlo Tree Search (MCTS).

[0109] In step S7, each device's factory information, maintenance records, and test reports, among other data, are encrypted and uploaded to the blockchain, creating an unalterable electronic record. Smart contracts automatically analyze device operating parameters, triggering scheduled maintenance work orders or spare parts replacement reminders. For core equipment like compressors and storage tanks, voiceprint recognition technology is used to analyze operating noise characteristics, detecting early signs of bearing wear or seal failure, providing a basis for preventive maintenance decisions.

[0110] In step S8, AR smart glasses overlay a 3D diagram of the equipment's structure onto the maintenance worker's field of view, providing step-by-step guidance on procedures such as flange removal and seal replacement. An eye-tracking module monitors operational compliance, triggering voice alerts if the operator's gaze strays from a critical step or if protective equipment is missing. Remote experts can provide guidance on complex troubleshooting through first-person video sharing, ensuring standardized execution of high-risk operations.

[0111] In step S9, the model calculates entropy based on three dimensions: organizational response efficiency, completeness of technical measures, and depth of safety culture penetration, identifying weaknesses in the management system. For example, when addressing issues with multi-departmental collaboration, entropy analysis can reveal information gaps in process connectivity and guide the optimization of emergency command responsibilities. The assessment results generate customized improvement plans, driving the transition of safety management from reactive response to systematic prevention.

[0112] From the above we can know:

[0113] In this invention, through the deep integration of IoT perception and intelligent decision-making technology, the full-cycle management capability of safety risk prevention and control has been significantly improved. At the pre-emptive prevention level, the global perception network and the dynamic risk assessment system form a double line of defense, capturing hidden danger signals such as abnormal pipe network pressure and equipment aging in real time, and predicting the risk evolution trend in combination with meteorological and environmental changes to guide the precise deployment of protection resources. The autonomous inspection robot cluster breaks through the time and space limitations of manual inspections, continuously obtains high-precision detection data in complex terrain and severe weather, and ensures that there are no blind spots in the identification of hidden dangers. The user-side safety empowerment system transforms professional protection knowledge into visual interactive guidance, allowing residents to independently check for hidden dangers in gas appliances and build a grassroots protection network that links enterprises and users.

[0114] In the present invention, the emergency decision-making knowledge graph system realizes the intelligent transition from historical experience to real-time handling. When an emergency occurs, the system quickly generates a three-dimensional situation map through multi-source data fusion, dynamically matches historical handling cases and expert experience databases, and simultaneously deduces the implementation effects of various response plans, providing a reliable basis for command decision-making. The VR deduction platform converts text plans into interactive three-dimensional scenes, and effectively improves the on-the-spot handling capabilities of commanders by simulating multiple variables in real accidents. The closed-loop optimization mechanism ensures that the experience of each emergency response can be precipitated as a new node in the knowledge graph, promoting the continuous evolution of the safety management system. The equipment health management blockchain and the employee intelligent safety assistant strengthen the safety foundation from the two dimensions of hardware maintenance and personnel skills, forming a virtuous cycle of collaborative optimization of technology, equipment, and personnel.

[0115] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also includes elements that are inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0116] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An intelligent safety management method for a gas business enterprise, characterized by: The method comprises the following steps: S1: Deploy an intelligent sensor network, use LPWAN technology to achieve real-time data collection of pipelines, storage tanks, and user terminals, and establish a 3D visualization digital twin platform; S2: Using a spatiotemporal big data analysis engine, integrating 32-dimensional characteristic parameters such as meteorological data, gas load, and equipment life, a dynamic risk heat map is generated, achieving a risk location accuracy of 98.7%; S3: Configure a multimodal inspection robot equipped with a laser methane telemeter and a thermal imaging module, and build an autonomous inspection path planning system based on an ant colony optimization algorithm; S4: Develop a leak prediction model based on federated learning, achieve a 50ms leak response through edge computing nodes, and link sound and light alarms, automatic shut-off valves, and ventilation systems to form a three-dimensional closed-loop disposal system. S5: Push the AI safety steward app to end users, integrating AR gas appliance self-checking, gas usage pattern analysis, and virtual safety officer functions to establish a two-way risk feedback mechanism between users and enterprises; S6: Build a multimodal emergency decision-making engine to transform historical accident cases, emergency plans, and expert experience into a semantic network, generate dynamic disposal plans based on real-time situation, and support VR emergency drills and simulations; S7: Establish a blockchain archive for the entire life cycle of the equipment, automatically trigger maintenance work orders through smart contracts, and apply vibration and noise spectrum analysis technology to achieve early warning of faults; S8: Develop wearable AR smart glasses that display operating procedures, risk warnings, and expert remote guidance in real time, and monitor operational compliance through eye tracking technology; S9: Introduce information entropy theory to build a security situation assessment model, generate a dynamic improvement roadmap from the three dimensions of organization, technology, and culture, and achieve quantitative improvement in security management efficiency.

2. The intelligent safety management method for a gas business enterprise according to claim 1, characterized in that: In step S1, high-precision pressure sensors, flow meters, and combustible gas detectors are deployed at key nodes of the pipeline, and low-power wide area network technology is used to achieve continuous data transmission, ensuring that the operation status of the pipeline network is updated in seconds.

3. The intelligent safety management method for a gas business enterprise according to claim 1, characterized in that: In step S2, the impact of meteorological changes on pipeline network pressure is analyzed based on the spatiotemporal convolutional neural network to identify high-risk pipeline sections during typhoons or rainstorms. For key facilities such as pressure regulating equipment and valve groups, the remaining life is calculated by combining the material corrosion rate and operating time to generate a visual risk heat map.

4. The intelligent safety management method for a gas business enterprise according to claim 1, characterized in that: In step S3, the wheeled robot is equipped with a laser methane detection module to scan the gas concentration deep inside the valve well; the drone is equipped with a high-resolution infrared camera to perform all-round thermal imaging inspection of the overhead pipeline; the robot cluster dynamically optimizes the inspection route through a bionic path planning algorithm, avoiding obstacles or dangerous areas based on real-time environmental data; in harsh conditions such as heavy rain and extreme cold, the robot automatically switches to enhanced protection mode to ensure the continuous monitoring capability of key pipeline sections.

5. The intelligent safety management method for a gas business enterprise according to claim 1, characterized in that: In step S4, the edge node deploys a lightweight leakage prediction model to identify abnormal signals by analyzing pressure fluctuations and gas concentration changes. When a potential leak is detected, the system immediately activates the smart manhole cover to start forced ventilation and simultaneously sends a directional alarm message to surrounding residents. For complex scenarios such as commercial complexes, multi-sensor data fusion technology is used to eliminate false alarm interference and ensure the accurate triggering of emergency instructions. During the emergency response process, the system tracks the leakage diffusion trend in real time and dynamically adjusts the control strategy.

6. The intelligent safety management method for a gas business enterprise according to claim 1, characterized in that: In step S5, the AR gas appliance self-check module identifies the stove model through the mobile phone camera, automatically compares it with the safety standard database, and prompts hidden dangers such as hose aging and lack of flameout protection; the system analyzes gas usage patterns based on user living habits, and issues early warnings for abnormally long periods of time without closing the valve or for gas usage during abnormal periods; for the elderly user group, a voice interaction function is set to simplify the operating process, and it is connected to the community grid management system to realize proactive safety services.

7. The intelligent safety management method for a gas business enterprise according to claim 1, characterized in that: The step S6 specifically includes the following steps: S6-1 Multi-source heterogeneous data fusion: Constructing a historical accident database: Collecting gas accident cases from the past 10 years and using natural language processing technology to extract 32 types of structured features, including accident type, cause chain, and disposal measures; Digital emergency plan: Convert the three-level response mechanism and 156 standard processes into computable logical units, and establish a mapping matrix between the plan, resources, and responsible persons; Extracting expert experience: Analyzing over 1,000 hours of expert consultation videos and extracting implicit decision rules using graph convolutional networks; S6-2 Dynamic Knowledge Graph Construction: Define the gas emergency ontology: divide the semantic framework into 4 categories and 58 subcategories: accidents, equipment, environment, and disposal measures; Generate semantic network: Use the TransR algorithm to transform cases, plans, and experiences into entity-relationship-attribute triples, and build a Neo4j graph database containing 27,000 nodes; S6-3 real-time situational awareness fusion, Access to multimodal data: Integrate SCADA system real-time pressure and flow data, inspection robot video stream, and weather warning information; Spatial situation mapping: Based on the GIS engine, a Gaussian smoke plume diffusion model and a three-dimensional digital twin are superimposed to generate a crisis heat map with spatiotemporal labels; S6-4 Dynamic disposal plan generation: Run the GNN-Q reinforcement learning inference engine: Integrate the Q-learning algorithm into the graph neural network to search for the optimal processing path of the knowledge graph; Perform Monte Carlo multi-strategy simulations: generate three scenarios: conservative, balanced, and aggressive, and evaluate the confidence interval of 92% to 97% success rate for each scenario; Output intelligent operation instructions: Generate time-stamped instruction sets based on resource data such as repair team location and valve inventory, automatically triggering the opening of intelligent manhole covers and the demarcation of drone no-fly zones; S6-5 VR emergency simulation system: Constructing a 3D physical scene: Using Unreal Engine to develop a virtual accident environment with pipe rupture flame simulation and smoke diffusion particle effects; Enable multi-person collaborative drills: Commanders use VR headsets to conduct three-dimensional situation analysis and assessment, and voice commands directly drive the virtual repair team's actions; Generate intelligent review reports: compare the exercise decision path with the recommended solution of the knowledge graph, and automatically update the decision weight of the case library; S6-6 closed-loop optimization mechanism: Dynamically adjust node confidence: Optimize knowledge graph node parameters based on Bayesian network analysis of actual combat and drill results; Triggering the knowledge evolution process: Automatically initiate expert consultation requests when new accident patterns are detected, continuously expanding the coverage of the semantic network; The GNN is combined with the Q-learning algorithm, and the calculation formula of the GNN-Q reinforcement learning decision function in the dynamic disposal plan generation is: Where θ represents the weight matrix of the graph neural network, which encodes the node relationships of the knowledge graph through a 200-dimensional hidden space h_v^{(k)} represents the node embedding vector after k=5 graph convolution iterations, integrating the semantic associations within the surrounding 3 hops. The output dimension is: 128-dimensional decision feature vector, including device status (40 dimensions), environmental factors (32 dimensions), and resource constraints (56 dimensions) λ=0.78 represents the case similarity weight coefficient Simcase represents the matching calculation function based on the Dynamic Time Warping (DTW) algorithm Ci represents the triple feature of the i-th similar case in the historical case database γ=0.93 represents the future benefit discount factor, reflecting the time sensitivity of emergency response E[·] represents the expected value of 100 deduction paths generated by Monte Carlo Tree Search (MCTS).

8. The intelligent safety management method for a gas business enterprise according to claim 1, characterized in that: In step S7, the factory information, maintenance records, test reports and other data of each device are encrypted and uploaded to the chain to form an unalterable electronic resume; the smart contract automatically analyzes the equipment operating parameters and triggers regular maintenance work orders or spare parts replacement reminders; for core equipment such as compressors and storage tanks, voiceprint recognition technology is used to analyze the operating noise characteristics, capture early signals of bearing wear or seal failure, and provide a decision-making basis for preventive maintenance.

9. The intelligent safety management method for a gas business enterprise according to claim 1, characterized in that: In step S8, AR smart glasses overlay a three-dimensional diagram of the equipment in the maintenance personnel's field of view, step by step guiding the maintenance personnel through the operation process such as flange removal and sealing ring replacement; the eye tracking module monitors the standardization of the operation and triggers a voice reminder when the operator's line of sight deviates from the key steps or protective equipment is missing; remote experts can guide complex fault handling through the first-person video sharing function to ensure the standardized execution of high-risk operations.

10. The intelligent safety management method for a gas business enterprise according to claim 1, characterized in that: In step S9, the model calculates entropy values from three dimensions: organizational structure response efficiency, technical measures coverage completeness, and safety culture penetration depth, to identify weak links in the management system.

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