Intelligent mine production accident disposal method and system

Abstract

The application relates to a mine production accident intelligent disposal method and system. The method comprises the following steps: collecting and preprocessing mine multi-source production data, extracting event information, characteristic information and production data, updating the event and characteristic information to a production and accident business model in real time, overall scheduling by an intelligent agent arrangement engine, obtaining accident confirmation information, extracting a corresponding business model subgraph snapshot, constructing a holographic semantic scene subgraph in combination with a process node state and a precondition, calling a large language model in an accident chain reasoning intelligent agent, deducing an accident cause and effect chain in combination with a domain knowledge base, generating an instantiated disposal process suitable for the scene by referring to a disposal process template, automatically generating and issuing cross-agent collaborative instructions according to the disposal process, recording the disposal process, forming an evidence chain with a digital signature, generating a standardized accident investigation report in combination with the accident cause and effect chain, the evidence chain and relevant knowledge clauses of responsibility identification. The method can effectively improve the mine accident disposal efficiency.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to an intelligent method and system for handling mining production accidents. Background Technology

[0002] With the rapid development of digital and intelligent technologies in the field of safe production in metal and non-metal mines, the mine accident handling mechanism is gradually evolving towards a closed-loop management system covering the entire process.

[0003] However, traditional mine disposal models lack unified semantic modeling support for all production elements, making it difficult to recreate complete on-site scenarios, and intelligent simulations lack professional business constraints; the application of large language models is prone to reasoning bias due to the lack of semantic boundaries and procedural constraints in the mining field, affecting the accuracy of accident cause analysis; accident disposal still relies on manual offline coordination, resulting in low efficiency of cross-entity collaboration, and the disposal process cannot form traceable and tamper-proof solidified evidence; at the same time, accident investigation reports are mostly compiled manually, making it difficult to effectively link with accident simulations and disposal processes, and lacking standardization and traceability.

[0004] Therefore, how to improve the accuracy of mine accident scene reconstruction, the reliability of accident causal inference, and at the same time improve the efficiency of cross-entity collaborative handling of accidents and the standardization and completeness of handling evidence and accident reports have become urgent technical problems to be solved. Summary of the Invention

[0005] Therefore, it is necessary to provide an intelligent method and system for handling mining production accidents to address the aforementioned technical problems.

[0006] A method for intelligent handling of mine production accidents, the method comprising: The project constructs a production and accident business model, standardized production process templates, accident handling process templates, and a domain knowledge base for mine safety production. The production and accident business model includes entities covering personnel, equipment, environment, and management in mine production, as well as the objective semantic relationships between these entities. Each node in each process template is associated with the corresponding entity in the production and accident business model. Collect multi-source production data from the mine and preprocess it to extract event information, feature information, and production data with business semantics. Then, update the production and accident business model in real time with the event information and feature information. The production data is used to provide the instance status of each node in the production process template. The intelligent agent orchestration engine coordinates and schedules the acquisition of accident confirmation information. Based on the accident confirmation information, it extracts a subgraph snapshot of the production and accident business model at the time of the accident. Based on the subgraph snapshot, the instance status of the accident-related production process nodes, and the fulfillment of preconditions, it obtains a holographic semantic scene subgraph. It calls the LLM inside the accident chain reasoning intelligent agent and generates an accident causal chain based on the domain knowledge base and the holographic semantic scene subgraph model. Based on the accident handling process template and the verified accident causal chain, it generates an instantiated handling process that matches the current accident. The intelligent agent orchestration engine coordinates and schedules the automatic generation of cross-subject collaborative instructions based on the instantiated handling process and issues them for execution. The handling process is recorded to generate an immutable evidence chain with digital signatures and written back to the production and accident business model. The intelligent agent orchestration engine coordinates and schedules the generation of standardized accident investigation reports based on the accident causal chain, the immutable evidence chain, and the knowledge clauses for liability determination in the domain knowledge base.

[0007] A smart system for handling mine production accidents, the system comprising: The model building module is used to construct production and accident business models, standardized production process templates, accident handling process templates, and a domain knowledge base for mine safety production. The production and accident business model includes entities covering personnel, equipment, environment, and management in mine production, as well as the objective semantic relationships between these entities. Each node of each process template is associated with the corresponding entity in the production and accident business model. The data preprocessing module is used to collect multi-source production data from the mine and preprocess it, extract event information, feature information and production data with business semantics, and update the production and accident business model in real time with the event information and feature information; the production data is used to provide the instance status of each node in the production process template. The accident analysis module is used by the intelligent agent orchestration engine to coordinate and schedule the acquisition of accident confirmation information, extract a subgraph snapshot of the production and accident business model at the time of the accident based on the accident confirmation information, obtain a holographic semantic scene subgraph based on the subgraph snapshot, the instance status of the accident-related production process nodes, and the fulfillment of preconditions, call the LLM inside the accident chain reasoning intelligent agent, generate an accident causal chain based on the domain knowledge base and the holographic semantic scene subgraph model, and generate an instantiated handling process matching the current accident based on the accident handling process template and the verified accident causal chain. The collaborative handling module is used to coordinate and schedule the intelligent agent orchestration engine, automatically generate cross-subject collaborative instructions based on the instantiated handling process and issue them for execution, record the handling process to generate an immutable evidence chain with digital signature and write it back to the production and accident business model; The report generation module is used by the intelligent agent orchestration engine to generate a standardized accident investigation report based on the accident causal chain, the immutable evidence chain, and the knowledge clauses for liability determination in the domain knowledge base.

[0008] The aforementioned intelligent method and system for handling mine production accidents, by constructing a production and accident business model covering all elements of mine personnel, equipment, environment, and management, and associating process template nodes with business entities, can establish a unified semantic description system for mine production, overcoming the shortcomings of traditional models where multi-source data is isolated and lacks business semantic support. By semantically parsing multi-source mine production data and updating it to the business model in real time, and relying on the feedback of process node instance status from production data, dynamic synchronization between on-site production status and the digital semantic model can be achieved, providing accurate data support for the complete reconstruction of accident scenarios. By extracting snapshots of the accident business model to construct holographic semantic scene subgraphs, and combining them with a domain knowledge base to deduce the accident causal chain based on a large language model within the intelligent system, professional semantic constraints can be formed on the reasoning of the large language model, effectively reducing reasoning bias and illusion problems, and improving the reliability of accident cause analysis. By automatically generating cross-entity collaborative instructions based on the instantiated handling process, and simultaneously generating an immutable evidence chain with digital signatures, manual handling steps can be reduced, improving the efficiency of cross-entity accident handling and ensuring that the entire handling process is traceable and tamper-proof. The embodiments of the present invention can comprehensively improve the accuracy of mine accident scene reconstruction, the reliability of causal inference, the efficiency of collaborative handling, and the standardization and completeness of accident report generation. Attached Figure Description

[0009] Figure 1 This is a flowchart illustrating an intelligent handling method for mine production accidents in one embodiment; Figure 2 A schematic diagram illustrating an example definition of a mine production and accident business model in one embodiment; Figure 3 This is a schematic diagram illustrating an example of the definition of a production and accident handling process in one embodiment; Figure 4 This is a schematic diagram of the entire risk analysis process in one embodiment; Figure 5 This is a schematic diagram of the entire process of accident handling in one embodiment; Figure 6 This is a schematic diagram of the structure of an intelligent handling system for mining production accidents in one embodiment. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0011] In one embodiment, such as Figure 1 As shown, an intelligent method for handling mine production accidents is provided, including the following steps: Step 102 involves constructing a production and accident business model, standardized production process templates, accident handling process templates, and a domain knowledge base for mine safety production. The production and accident business model includes entities covering personnel, equipment, environment, and management in mine production, as well as the objective semantic relationships between these entities. Each node in each process template is associated with a corresponding entity in the production and accident business model.

[0012] The production and accident business model is a digital model that semantically describes various entities in mine production, including personnel, equipment, environment, and management, as well as the objective semantic relationships between these entities, such as ownership, control, and association. The production process template is a standardized sequence of nodes formed by breaking down routine mine production procedures. The accident handling process template is a standardized sequence of nodes for pre-set emergency response stages for various types of mine accidents. The domain knowledge base is a collection of professional knowledge encompassing mine safety production laws and regulations, job operation procedures, and safety regulations. Each node in both types of process templates is bound to a corresponding entity object in the business model.

[0013] Understandably, this step establishes a unified semantic modeling, process standardization, and knowledge support foundation for intelligent handling of mine accidents. It can define the reasoning boundaries of subsequent large language models to form a semantic sandbox, while also enabling precise association between process nodes and actual business entities, providing a stable foundation for accident scenario reconstruction, causal inference, and process instantiation.

[0014] Step 104: Collect multi-source production data from the mine and preprocess it to extract event information, feature information, and production data with business semantics. Update the production and accident business model in real time with the event information and feature information. The production data is used to provide the instance status of each node in the production process template.

[0015] Multi-source production data in the mine comprises comprehensive production monitoring data collected from various channels, including mine IoT sensors, personnel positioning, equipment PLCs, and manual reporting. Event information consists of business-meaningful events extracted from the raw production data, encompassing normal production operations, operating condition switching, and abnormal state changes. Feature information is extracted from the raw production data and includes key feature data such as equipment mechanistic risks and anomalies (e.g., wire rope tension thresholds, hoist load thresholds) as well as thresholds or outliers in current, voltage, and vibration spectra. The production process node instance status is the actual running progress and execution status of each node in the process template, based on real-time feedback from the raw production data.

[0016] It is understandable that this step completes the cleaning, parsing, and semantic extraction of multi-source production data, synchronizes dynamic business information to the semantic model in real time, and realizes dynamic linkage between the physical production site and the digital semantic model. This provides a real-time and accurate data source for subsequent accident snapshot extraction and process status matching.

[0017] Step 106: The intelligent agent orchestration engine coordinates and schedules the acquisition of accident confirmation information. Based on the accident confirmation information, it extracts a snapshot of the subgraph of the production and accident business model at the time of the accident. Based on the subgraph snapshot, the instance status of the accident-related production process nodes, and the fulfillment of preconditions, it obtains a holographic semantic scene subgraph. It calls the LLM inside the accident chain reasoning intelligent agent and generates an accident causal chain based on the domain knowledge base and the holographic semantic scene subgraph model. Based on the accident handling process template and the verified accident causal chain, it generates an instantiated handling process that matches the current accident.

[0018] Accident confirmation information is structured information representing the accident type, location, time, and initial impact range. The production and accident business model subgraph snapshot is a semantic fragment capturing the state and relationships of all entities at the moment the accident occurs. The holographic semantic scene subgraph is a complete semantic set of the accident scene formed by fusing subgraph snapshots, process node instance states, and the fulfillment of preconditions. The accident chain reasoning agent is a functional unit in the multi-agent framework specifically responsible for accident cause deduction. LLM is a large language model embedded within the agent; the accident causal chain is the chain of causes that organizes the logic of the accident's evolution; and the instantiated handling process is an executable emergency procedure generated based on a standard template and adapted to the actual situation on-site.

[0019] It is worth noting that the accident handling can be initiated by the accident escalation signal output by the risk analysis cluster, or by emergency alarms from on-site personnel or third-party systems. Specifically, the risk analysis cluster outputs an accident escalation signal when it detects an abnormal production event and determines through intelligent analysis that the risk level has been escalated to the accident level.

[0020] This step, understandably, relies on model snapshots to reconstruct a complete holographic scene of the accident, combines domain-specific knowledge to constrain the large language model to complete the causal logic deduction of the accident, and generates executable handling procedures that fit the actual situation on site. This effectively avoids the problem of large language model reasoning becoming detached from the reality of the mine, thus improving the realism of the accident cause analysis and the adaptability of the emergency response process. By assessing the risk level through risk analysis results and outputting accident escalation signals to trigger accident handling procedures, the flexibility of accident response can be improved, enabling automatic connection and response in scenarios where potential hazards escalate.

[0021] Step 108: The intelligent agent orchestration engine coordinates and schedules the process, automatically generates cross-subject collaborative instructions based on the instantiated handling process, issues and executes them, records the handling process, generates an immutable evidence chain with digital signatures, and writes it back to the production and accident business model.

[0022] Cross-entity collaborative instructions are standardized dispatch instructions generated based on the nodes of the response process and adapted to external systems such as relevant regulatory departments, rescue units, and medical institutions. Digital signatures are encrypted identifiers used for data integrity and authenticity verification. An immutable chain of evidence is a set of traceable records that covers the entire process of instruction issuance and system response and is encrypted and solidified.

[0023] It is understandable that this step enables the automated generation and multi-entity distribution of emergency response instructions. At the same time, it preserves the entire process of response interaction through digital signatures and solidifies it into the semantic model. This can significantly reduce the cost of manual interaction, improve the efficiency of cross-entity emergency collaborative response, and ensure that the entire process of accident response is recorded completely, traceable, and cannot be arbitrarily tampered with.

[0024] Step 110: The intelligent agent orchestration engine coordinates and schedules the generation of a standardized accident investigation report based on the accident causal chain, the tamper-proof evidence chain, and the knowledge clauses for liability determination in the domain knowledge base.

[0025] The relevant knowledge clauses for liability determination are legal and safety regulations retrieved from the domain knowledge base and applicable to the division of liability in mine accidents. The standardized accident investigation report is an automated draft generated by a report generation agent that summarizes the causal chain of the accident, the tamper-proof evidence chain, and the knowledge clauses for liability determination. This draft is then manually reviewed and confirmed to form a compliant formal report.

[0026] Understandably, this step relies on an intelligent agent to integrate key elements of the entire process to automatically generate a draft accident report. The draft is then manually reviewed, which can save the workload of manually compiling the report from scratch, improve the efficiency of accident report preparation, and at the same time take into account the standardization of report format, the completeness of content, and the professional rigor of liability determination.

[0027] The aforementioned intelligent method for handling mine production accidents establishes a unified semantic description system for mine production by constructing a production and accident business model covering all elements of mine personnel, equipment, environment, and management, and associating process template nodes with business entities. This overcomes the shortcomings of traditional models where multi-source data is isolated and lacks business semantic support. By semantically parsing multi-source mine production data and updating it to the business model in real time, and relying on the feedback of process node instance status from production data, dynamic synchronization between the on-site production situation and the digital semantic model can be achieved, providing accurate data support for the complete reconstruction of accident scenarios. By extracting snapshots of the accident business model to construct holographic semantic scene subgraphs, and combining this with a domain knowledge base to deduce the accident causal chain using a large language model within the intelligent system, professional semantic constraints can be formed on the large language model's reasoning, effectively reducing reasoning bias and illusion problems, and improving the reliability of accident cause analysis. By automatically generating cross-entity collaborative instructions based on the instantiated handling process, and simultaneously generating an immutable evidence chain with digital signatures, manual handling steps can be reduced, improving the efficiency of cross-entity accident handling and ensuring the entire handling process is traceable and tamper-proof. The embodiments of the present invention can comprehensively improve the accuracy of mine accident scene reconstruction, the reliability of causal inference, the efficiency of collaborative handling, and the standardization and completeness of accident report generation.

[0028] In one embodiment, the steps of constructing a production and accident business model, a standardized production process template, an accident handling process template, and a domain knowledge base for mine safety production include: constructing the production and accident business model in the form of triples, using a <subject, predicate, object> structure to describe the objective semantic relationships between entities; associating each node of the production process template and the accident handling process template with the corresponding entity, execution role, and required resources in the production and accident business model; and storing the mine safety production laws and regulations, job operation procedures, and safety procedures in the domain knowledge base in a vectorized form.

[0029] In this embodiment, as Figure 2 As shown, this diagram illustrates an example of defining a business model for mine production and accidents. Guided by domain ontology modeling methods, this module constructs a semantic network graph of mine production elements by defining a mine-specific namespace (e.g., mine:) and semantic predicates. Figure 2 Each arrow in the text is labeled with a Chinese tag (e.g., monitoring, responsible). These Chinese tags correspond one-to-one with the English semantic predicates described below (e.g., mine:monitors, mine:responsibleFor), without altering the ontology definition and technical essence of the business model. Specific methods include: Entity abstraction: Assign a unique URI to every person, every piece of equipment, and every work site in the mine, and treat it as an RDF resource.

[0030] 1. Metamodel Definition (Relation Definition): The standard RDF triple <subject, predicate, object> is used to describe the objective semantic relationships between entities, such as ownership, manipulation, monitoring, supply, etc.

[0031] 2. Metadata definition (attribute assignment): By using data attributes (such as mine:hasValue, mine:hasQualification), typed information such as state, parameters, and thresholds is attached to the entity, and a mapping is formed with real-time data acquisition, which also forms a data description of the physical object (entity).

[0032] 3. Ontology Reasoning: The model applies RDFS / OWL reasoning rules, including DFS and BFS, to deduce implicit facts from explicit relations (such as the spatial relationship between "sensors monitoring a certain area" and "personnel working in that area"), providing a logical foundation for the subsequent RDF reasoning engine.

[0033] The model comprehensively covers the four elements of mining production: people, machines, environment, and management, forming a multi-dimensional semantic network as shown in the diagram. Typical content includes: 1. Personnel Entities: Such as safety officers, rock drillers, etc., associated with their job positions, qualification certificates, areas of responsibility, etc. As shown in the figure, safety officer D is associated with mining site A through mine:responsibleForr (responsible), and associated with his safety qualification certificate 1 through mine:hasQualification (qualified).

[0034] 2. Equipment Entities: Such as local ventilation fans, rock drilling rigs, sensors, etc., describe their type, technical parameters, and service targets. In the diagram, local ventilation fan C is connected to stope A by mine:providesVentilationTo, and rock drill E is connected to rock drilling rig G by mine:operatess, clearly expressing the equipment's purpose and operational relationship.

[0035] 3. Environmental entities: such as spatial nodes like mining areas, roadways, and intermediate sections, establishing their hierarchical location and spatial topology. In the diagram, mining area A is attributed to intermediate section B via `mine:locatedIn`, forming the semantic basis of the spatial context.

[0036] 4. Management Entities: Management elements such as job procedures, inspection systems, and production plans are also linked to the corresponding personnel, equipment, and areas in the form of entities or annotations, making business rules an integral part of the model.

[0037] 5. Dynamic perception mapping: The model reserves a mechanism for association with IoT data. For example, the top pressure sensor F is connected to the mining area A through mine:monitors. The time series values ​​it collects can be reflected in the state of the sensor entity through attribute updates or event injection mechanisms, realizing the integration of static semantic model and dynamic production reality.

[0038] This module comprehensively describes all possible people, events, objects, environments, and behaviors involved in mine production and accidents from a data perspective. Therefore, it can be seen as using real data to fully describe the various entity roles involved in production activities and accidents. Based on this, through model definition, events that occur in the physical world are completely and realistically mapped into the digital domain.

[0039] The mine production and accident business model provides a domain-restricted semantic sandbox for the analytical reasoning of generative agents. When conducting risk or accident analysis, the system extracts relevant subgraphs from the RDF graph library based on the problem context as input constraints for the LLM. This ensures that all reasoning in the large language model must be based on predefined relationships between people, equipment, and the environment, as well as management requirements. This avoids boundless associations detached from the realities of the mine and significantly improves the accuracy and professionalism of risk identification and causal inference.

[0040] like Figure 3 As shown, a schematic diagram illustrating a production and accident handling process definition is provided. This process allows users to atomize and decompose the production and accident handling processes. Based on the management standards of mining production enterprises, job safety operation regulations, production job manuals, and job operation procedures, the employee behaviors of each production position are broken down into nodes in the event flow. The nodes reference metadata defined in the business model to define the behavior represented by each node.

[0041] Therefore, the definition of production and accident handling procedures provides a standardization of various standards and regulations in actual production.

[0042] Furthermore, the production and accident handling process definition integrates the mine production and accident business model with the actual business process, making every piece of data in the data acquisition module traceable through this integration, and providing it to the subsequent intelligent analysis module to form a complete semantic input and actual data description.

[0043] It should be noted that, due to the complexity and diversity of actual accident situations, standardized accident handling procedures cannot often be directly applied. Therefore, this module defines an accident handling procedure template. Consequently, the intelligent analysis module, based on the production process and actual data of the accident, utilizes the accident handling action planning module to further plan the standard accident handling procedures, creating an executable process instance that is then sent to the process engine.

[0044] Therefore, the process definition module allows users to define basic processes for normal production processes (such as rock drilling-blasting-ventilation-ore extraction) and accident handling processes (such as roof collapse detection-evacuation-reporting-rescue-recovery) based on actual conditions, utilizing the BPMN 2.0 standard and event-driven architecture. Each process node is associated with roles, resources, preconditions, and executable actions. The accident handling process specifically defines "trigger nodes," which automatically trigger the corresponding process based on collected data and feature values. The process engine listens for external events based on Activities, JDBC, etc. It is closely linked to the business model, together forming the "scenario script" of the LLM, defining its analysis temporality and handling logic.

[0045] The data acquisition module of this invention connects to mine IoT sensors (gas concentration, roof pressure, water level, etc.), personnel positioning, equipment PLCs, and personnel-reported data. Data from IoT sensors, personnel positioning, and equipment PLCs is aggregated via the MQTT / OPC UA protocol. Personnel-reported data refers to the results data generated by personnel during their work, reported through the system. The cleaning component performs noise reduction, interpolation, and time alignment on the raw data, and marks outliers. The cleaned data is stored in a data lake constructed using cloud object storage (column-based in Parquet format). Simultaneously, events with extracted features (such as "gas exceeding limits for 30 seconds") are written to data files in the cloud RDF object storage as event attributes of business model entities. The location relationship between model entities and collected data is defined using URIs in RDF. When querying production data, RDF is treated as a query index. This structure speeds up queries, and the object storage method ensures query stability. Therefore, this tiered cold and hot storage significantly reduces overhead while ensuring stable large-scale data queries.

[0046] In one embodiment, the method further includes constructing a multi-agent framework; the multi-agent framework includes an agent orchestration engine and multiple agents; the agents in the multi-agent framework are divided into public support agents, risk analysis agents, and accident handling agents; the public support agents include model query agents and knowledge retrieval agents, and the risk analysis agents and accident handling agents all access the production and accident business models and domain knowledge bases uniformly through the public support agents; the risk analysis agents include risk monitoring agents, event extraction and correlation agents, model query agents, knowledge retrieval agents, risk identification agents, risk assessment and grading agents, and risk warning release agents; the accident handling agents include accident reporting and confirmation agents, accident chain reasoning agents, model query agents, knowledge retrieval agents, contingency plan matching agents, resource scheduling and collaboration agents, rescue progress monitoring agents, evidence chain solidification agents, and report generation agents.

[0047] In this embodiment, the knowledge-enhanced multi-agent analysis framework is the core decision engine of the system, located in the ⑥ Intelligent Analysis and Handling Layer of the overall architecture. It overcomes the context window limitations and procedural decision-making deficiencies of a single LLM in handling complex tasks, forming a traceable and verifiable decision chain through multi-agent orchestration. This framework dynamically injects domain knowledge bases (regulations, safety procedures), RDF business models and process models, and real-time / historical data into each agent according to task stages, enabling the LLM to combine the flexibility of generation capabilities, the professionalism of domain knowledge, and the rigor of business logic when generating risk analyses, accident cause chains, and handling reports.

[0048] The multi-agent architecture for mine production risk analysis and accident handling consists of an agent orchestration engine and multiple agents. The orchestration engine uses Dify to construct the driving conditions between agents, including sequence, branching, and decision conditions. The following agents are defined: Table 1 Definition of Intelligent Agent

[0049] The multi-agent framework achieves unified interaction with the domain model and processes through two types of common supporting agents: 1. Model Query Agent: Encapsulates SPARQL query and RDF inference interfaces for cloud-native RDF graph databases, and is responsible for extracting semantic data such as business model subgraphs, process definition fragments, and historical event chains as needed.

[0050] 2. Knowledge Retrieval Agent: Encapsulates the retrieval interface for vectorized knowledge bases such as laws and regulations, safety procedures, and job operation procedures, and returns structured knowledge clauses.

[0051] All other analytical and analytical agents do not directly access the storage layer. Instead, they are scheduled through the orchestration engine and obtain the necessary semantic context and knowledge constraints with the help of the two common agents mentioned above, thereby ensuring the consistency of data access and the decoupling of the model layer and the agent layer.

[0052] In one embodiment, the method further includes: the intelligent agent orchestration engine coordinates and schedules the acquisition of abnormal event information, schedules the event extraction and association intelligent agents to determine the business entity nodes associated with the abnormal events, extracts the business model subgraph from the production and accident business model with the business entity nodes as the center and according to the preset association range, calls the LLM inside the risk identification intelligent agent, generates preliminary identification conclusions based on the domain knowledge base and the abnormal context subgraph, verifies the key attributes in the preliminary identification conclusions by checking the production and accident business model, performs quantitative evaluation based on the verified preliminary identification conclusions, and issues targeted early warning notifications to the communication terminal.

[0053] In this embodiment, the core objective of risk analysis is to identify potential risks from abnormal signals and issue tiered warnings. The system's production risk analysis is driven by a knowledge-enhanced multi-agent analysis framework. An orchestration engine uniformly schedules risk monitoring agents, event extraction and correlation agents, model query agents, knowledge retrieval agents, risk identification agents, risk assessment and tiering agents, and risk warning dissemination agents, achieving a closed-loop process from abnormal signal capture to tiered warning push notifications. The entire process strictly adheres to a "data + model + knowledge" three-driven constraint mechanism. Each agent communicates via structured task cards, and key reasoning steps undergo semantic verification and knowledge injection through an RDF graph database and a domain knowledge base.

[0054] In one embodiment, the step of extracting a business model subgraph from the production and accident business model centered on a business entity node and according to a preset association range includes: scheduling a model query agent to perform a breadth-first or depth-first traversal based on the business entity node associated with the abnormal event, according to a preset association hop count, to extract all directly and indirectly associated entities, semantic relationships, and dynamic attributes around the business entity node, generating a complete business model subgraph. The step of quantitatively assessing and issuing targeted early warning notifications to terminals based on the verified preliminary identification conclusions includes: scheduling a risk assessment and grading agent to perform risk quantification and grading based on the number of exposed persons, equipment value, and impact range in the production and accident business model; and scheduling a risk early warning issuing agent to generate targeted early warning notifications and push them to the communication terminals of the corresponding personnel based on the risk level and the job responsibility relationships in the production and accident business model.

[0055] In this embodiment, as Figure 4 As shown, a schematic diagram of the entire risk analysis process is provided, which specifically includes: Step 1: Abnormal Signal Capture The risk monitoring agent continuously consumes the real-time event stream injected by the data cleaning layer. Each event in this stream has been linked to an entity in the business model (such as a sensor or a sampling site) via RDF semantics at the time of injection. When a threshold rule is triggered, the risk monitoring agent generates a structured anomaly event card containing the anomaly type, parameters, occurrence area, and occurrence time. This card is then submitted to the orchestration engine to initiate a complete risk analysis task.

[0056] Step 2: Exception Context Extraction After receiving the anomaly event card, the orchestration engine creates a risk analysis instance and first schedules the event extraction and association agent. The core task of this agent is to construct an "anomaly context subgraph" for subsequent reasoning. It translates query rules into SPARQL statements to the RDF graph database through the model query agent, and then performs the query using either DFS or BFS algorithms. For example, it extracts a business model subgraph centered on stope A, containing information on all associated objects within a 2-hop radius, to obtain static semantic relationships and the range of data associated with the event. Simultaneously, the model query agent obtains the current process node (e.g., "drilling in progress") and its preconditions and associated data from the status of the production process instance currently being executed by the target entity. All queried entity relationships, dynamic data, and process status are integrated into an RDF-formatted anomaly context subgraph and returned to the event extraction and association agent. This agent merges the anomaly context subgraph with the original anomaly event, generates an extended analysis card, and submits it back to the orchestration engine. From this point on, all subsequent reasoning is confined to the entities and relationships defined in this subgraph, eliminating the possibility of analysis deviating from the actual conditions of the mine.

[0057] Step 3: Risk Identification (Knowledge Enhancement and Logic Verification) The orchestration engine passes extended analytics cards to the risk identification agent, which is the core of risk assessment. This process involves two parallel, mutually verifying steps: Knowledge Injection Phase: The risk identification agent sends a retrieval request to the knowledge retrieval agent, requesting the acquisition of safety procedures and hazard assessment standards for production operations related to abnormal events (as shown in the figure: "Roof Management" and "Support Acceptance"). The knowledge retrieval agent extracts structured knowledge clauses from a domain knowledge base constructed from laws and regulations, safety procedures, and job operation procedures through vector retrieval and returns them to the risk identification agent.

[0058] The reasoning process under semantic constraints: The LLM within the risk identification intelligent body uses the aforementioned abnormal context subgraph and knowledge clauses as dual inputs, and performs reasoning under strict cue constraints. The cue template explicitly requires that any entities or relationships involved in the risk conclusions generated by the LLM must originate from the provided RDF subgraph, and the judgment criteria cited must come from the injected knowledge clauses.

[0059] Inference Consistency Verification: After LLM generates preliminary identification conclusions (e.g., "Constitutes a major hidden danger of roof collapse, based on the fact that the support status has not been accepted and the roof pressure continues to rise"), the risk identification agent uses the model query agent to query the RDF graph database again to verify whether the attribute truly exists in the business model and whether the value is consistent. After successful verification, the agent generates the final risk identification card, including the risk name, triggering conditions, reference clauses, and confidence level, and submits it to the orchestration engine. This "generation-verification" closed loop fundamentally suppresses the illusion of LLM and ensures the reliability and interpretability of the analysis results.

[0060] Step 4: Risk Assessment and Classification The risk identification card is forwarded to the risk assessment and classification agent, which is responsible for converting qualitative risks into quantitative levels. The agent then queries the model agent again, extracting relevant object attributes from the business model in the RDF graph database. The LLM (Local Level Management) then assesses the likelihood of the risk occurring and the severity of its consequences. The evaluation result is a structured risk assessment card, clearly outputting the risk level and detailed evaluation criteria.

[0061] Step 5: Issuance of Targeted Early Warnings The risk assessment card delivers a risk warning issuing agent. This agent queries the job responsibility relationships in the business model (e.g., safety officer D mine:responsibleFor mining site A) to obtain the specific identity and communication terminal information of the warning recipient. Based on this, it generates targeted warning notifications containing the risk level, affected area, judgment criteria, and recommended measures (e.g., immediate evacuation, cessation of work, and re-inspection of support), which are then pushed to display terminal devices or relevant systems. Upon successful warning issuance, the risk analysis task is completed in a closed loop.

[0062] Through the above five-step process, this system achieves a second-level delay response from underlying sensor anomalies to precise early warnings for frontline personnel. Each decision is based on the dual constraints of real-time business models and professional knowledge, providing mine production safety with intelligent analysis capabilities that combine speed and reliability.

[0063] During the risk analysis process, if the on-site risk continues to deteriorate, exceeds the predetermined safety threshold, or multiple anomalies overlap, the system will trigger a risk escalation mechanism. The risk analysis cluster will output an accident escalation signal and trigger subsequent accident handling procedures. The specific process includes: the risk monitoring agent, event extraction and correlation agent, and orchestration engine in the risk analysis cluster will collaborate based on real-time data streams and preset escalation judgment rules. The escalation judgment rules include, but are not limited to: the risk monitoring agent identifying continuous deterioration of data after a warning through real-time data collection; the event extraction and correlation agent discovering the correlation between multiple abnormal data sources based on RDF business model correlation analysis and determining that multiple abnormal events overlap; sensor data exceeding the risk threshold directly triggering escalation; personnel triggering signal collection, automatically generating an accident escalation signal, and triggering the start of the accident handling cluster.

[0064] When the risk monitoring agent detects one of the above conditions, it does not generate a regular abnormal event card, but instead generates an accident escalation signal card, the structure of which includes: {"signal type": "accident escalation", "associated risk":, "trigger basis":, "location":, "time":}.

[0065] Upon receiving the incident escalation signal card, the orchestration engine immediately performs a task mode switch, freezes the current risk analysis task, and marks its status as "escalated to incident." It then initiates the incident handling multi-agent cluster, initializes the incident handling task context with the information in the incident escalation signal card, schedules the incident reporting and confirmation agent as the first executor of the incident handling process, and passes the incident escalation signal card as its input parameter.

[0066] The system's accident handling is achieved through deep integration of a knowledge-enhanced multi-agent analysis framework with a cloud-native RDF graph database, domain knowledge base, accident handling process templates, and external collaborative systems. From accident confirmation to investigation report generation, the orchestration engine uniformly schedules the accident reporting and confirmation agents, accident chain reasoning agents, model query agents, knowledge retrieval agents, contingency plan matching agents, resource scheduling and collaboration agents, rescue progress monitoring agents, evidence chain solidification agents, and report generation agents, forming a standardized closed loop of "confirmation-reasoning-contingency plan-scheduling-monitoring-reporting." Throughout the process, the business model provides scenario constraints, the accident handling process definition provides action scripts, the knowledge base provides legal support, and the evidence chain solidification agent records data in parallel throughout, ensuring the traceability and compliance of the handling process.

[0067] In one embodiment, the step of obtaining accident confirmation information and extracting a subgraph snapshot of the production and accident business model at the time of the accident based on the accident confirmation information includes: scheduling model query agent, determining the time point of the accident and the associated business entity nodes based on the accident confirmation information; extracting the event sequence and dynamic attributes of the business entity nodes and associated entities within a preset time window before the accident, and generating a subgraph snapshot of the production and accident business model at the time of the accident.

[0068] In one embodiment, the step of invoking the LLM within the accident chain reasoning intelligent body to generate an accident causal chain based on the domain knowledge base and the holographic semantic scene subgraph model includes: scheduling a knowledge retrieval intelligent body to obtain knowledge clauses and an accident tree model related to the accident type from the domain knowledge base; inputting the holographic semantic scene subgraph, accident-related knowledge clauses, and the accident tree model into the large language model within the accident chain reasoning intelligent body, and performing reasoning under the constraints of the holographic semantic scene subgraph and the accident handling process template to generate an accident causal chain; and performing semantic verification by reviewing the production and accident business models based on the key nodes in the accident causal chain.

[0069] In one embodiment, the steps of automatically generating and issuing cross-entity collaborative instructions based on the instantiated handling process, and recording the handling process to generate an immutable evidence chain with digital signatures, include: scheduling resource scheduling and collaborative intelligent agents to automatically generate standardized cross-entity collaborative instructions based on the node requirements of the instantiated handling process, and issuing the cross-entity collaborative instructions to external systems respectively; scheduling evidence chain solidification intelligent agents to record the sending content, timestamps and response information of each external system of the cross-entity collaborative instructions, generate an immutable evidence chain with digital signatures and write it back to the production and accident business model.

[0070] In one embodiment, the step of generating a standardized accident investigation report based on the accident causal chain, the tamper-proof evidence chain, and the knowledge clauses for liability determination in the domain knowledge base includes: scheduling a knowledge retrieval agent to obtain legal and regulatory provisions and safety procedures related to accident liability determination from the domain knowledge base; scheduling a report generation agent to input the accident causal chain, the tamper-proof evidence chain, and the knowledge clauses related to liability determination into the large language model within the report generation agent, and generating a standardized accident investigation report according to the format requirements of the preset report writing specifications; each factual statement in the standardized accident investigation report is accompanied by a source link pointing to the corresponding evidence entity in the production and accident business model.

[0071] In one specific embodiment, such as Figure 5 As shown, a schematic diagram of the entire process of accident handling is provided, specifically including: Step 1: Accident Reporting and Confirmation The incident response can be initiated by an incident escalation signal output by the risk analysis cluster, or by an emergency alarm from on-site personnel or a third-party system. After receiving the initial incident signal, the incident reporting and confirmation agent cross-verifies the authenticity of the incident through multi-source data, eliminates false alarms, generates a structured incident confirmation card, clarifies the incident type, location, time of occurrence, and initial impact range, and submits the card to the orchestration engine to initiate a complete incident response task.

[0072] Step 2: Incident Chain Reasoning The accident chain reasoning agent reconstructs the causal chain of the accident. First, the agent queries the RDF graph database through the model query agent to retrieve the business model subgraph within the time window preceding the incident. It then retrieves the production process instance status and complete event sequence from the production data, including the current production process node and the fulfillment of its preconditions. The model query agent merges these static business models with dynamic process state snapshots to construct a "holographic semantic scene" of the accident moment, returning it as an RDF subgraph. Simultaneously, the accident chain reasoning agent invokes the knowledge retrieval agent to retrieve the accident tree model and relevant safety management clauses related to the roof fall accident from the knowledge base, obtaining structured knowledge fragments. Under the triple constraints of the business model, process state, and knowledge clauses, its internal LLM reconstructs the accident causal chain. Each node in this causal chain is attached with evidence links pointing to the business model entity, process node, and knowledge clause. The reasoning results form an accident cause card, which is submitted to the orchestration engine.

[0073] Step 3: Contingency Plan Matching and Process Planning After the incident cause card is passed to the contingency plan matching agent, this agent is responsible for matching the incident with predefined handling procedures and instantiating them. It queries the RDF graph database for the incident handling procedure template corresponding to the incident type through the model query agent. This template includes a standard sequence of handling nodes, transition conditions between nodes, associated roles, and required resource types. After obtaining the template, the contingency plan matching agent performs replanning and instantiation operations based on the incident snapshot data and the incident cause card: this includes adding or removing handling nodes in the process, binding placeholder resources in the template to specific entities in the business model, and initializing node parameters according to the scope of the incident's impact. This forms an executable handling procedure instance uniquely corresponding to the current incident. The contingency plan matching agent submits the instantiated procedure card to the orchestration engine as the basis for subsequent execution.

[0074] Step 4: Resource Scheduling and Cross-System Collaboration Based on the current node of the process instance, the resource scheduling and collaborative agent executes specific actions. This agent parses the executable actions in the process node definition. Each action includes an API endpoint, message template, and parameter mapping; the required parameters are directly extracted from the business model entity or incident confirmation card. After parsing, the agent sends API call instructions or message synchronization instructions and related data to the collaborative execution system via the MCP interface for external system communication.

[0075] Receipts and response confirmations from external systems are returned to the resource scheduling and collaboration agent, and the scheduling completion card is submitted to the orchestration engine. Simultaneously, all sent instructions, receipt content, and timestamps are sent in real time to the evidence chain solidification agent, which converts them into digitally signed incident data and solidifies them in the storage layer, ensuring that instructions and responses in cross-entity interactions are traceable and tamper-proof throughout the entire chain.

[0076] Step 5: Monitoring and Dynamic Adjustment of Rescue Progress After the rescue operation is initiated, the rescue progress monitoring agent enters a cyclical monitoring state. It queries the model query agent and periodically polls the storage layer for data such as the number of recovered personnel location signals, ambient gas concentrations, and equipment status. This data is continuously injected and updated into the accident data by the underlying data acquisition layer. The monitoring agent compares the actual progress with the expected results of process nodes (e.g., the "evacuation" node requires zero personnel in the area). If a risk or deviation from the plan is detected, an adjustment suggestion card is generated and fed back to the resource scheduling and collaboration agent through the orchestration engine, triggering a rollback of the plan node or new response measures, achieving dynamic closed-loop adjustment of the response strategy. Simultaneously, the status snapshot of each round of monitoring is also captured and recorded by the evidence chain solidification agent.

[0077] Step 6: Generation of the accident investigation report Once all handling process nodes are completed and the rescue operation concludes, the report generation agent generates a standardized accident investigation report. This agent gathers data from multiple sources: the accident chain reasoning agent obtains the cause of the accident, the evidence chain solidification agent obtains the solidified evidence chain for the entire process, and the knowledge retrieval agent obtains relevant legal provisions and similar cases for liability determination. This structured data is injected into LLM prompt templates, strictly following pre-built templates or standards in the knowledge base to generate a draft report covering the accident overview, process, cause, liability determination, handling measures, and rectification suggestions. Each factual statement in the report is accompanied by a source link to the RDF evidence entity and corresponding data identified in the accident data, ensuring the report's authority and verifiability. After the draft report is submitted for manual review and confirmation, it is officially archived, completing the full closed loop of the accident handling process.

[0078] It should be understood that, although Figure 1The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0079] In one embodiment, a smart system for handling mine production accidents is provided, comprising: The model building module is used to build production and accident business models, standardized production process templates, accident handling process templates, and a domain knowledge base for mine safety production. The production and accident business model includes entities covering personnel, equipment, environment, and management in mine production, as well as the objective semantic relationships between entities. Each node of each process template is associated with the corresponding entity in the production and accident business model. The data preprocessing module is used to collect and preprocess multi-source production data from the mine, extract event information, feature information, and production data with business semantics, and update the production and accident business model in real time with event information and feature information; the production data is used to provide the instance status of each node in the production process template. The accident analysis module is used by the intelligent agent orchestration engine to coordinate and schedule the acquisition of accident confirmation information, extract the subgraph snapshot of the production and accident business model at the time of the accident based on the accident confirmation information, obtain the holographic semantic scene subgraph based on the subgraph snapshot, the instance status of the production process nodes related to the accident, and the fulfillment of the preconditions, call the LLM inside the accident chain reasoning intelligent agent, generate the accident causal chain based on the domain knowledge base and the holographic semantic scene subgraph model, and generate the instantiated handling process that matches the current accident based on the accident handling process template and the verified accident causal chain. The collaborative handling module is used by the intelligent agent orchestration engine to coordinate and schedule the automatic generation of cross-subject collaborative instructions based on the instantiated handling process and issue them for execution. It records the handling process, generates an immutable evidence chain with digital signatures, and writes it back to the production and accident business model. The report generation module is used by the intelligent agent orchestration engine to generate standardized accident investigation reports based on the accident causal chain, the tamper-proof evidence chain, and the knowledge clauses for liability determination in the domain knowledge base.

[0080] like Figure 6The diagram illustrates the structure of an intelligent mine production accident handling system. The data acquisition and cleaning module comprises a data perception and acquisition layer (①) and a data cleaning and access layer (②). This layer receives multi-source data from mine IoT sensors, personnel positioning, and equipment PLCs. After protocol conversion and cleaning, the data is stored in cloud object storage, and extracted semantic events and production data are injected into an RDF graph database. The mine production business model definition module and its modeling results are hosted in a cloud-native RDF graph database within the cloud-native storage layer (③). This database stores mine personnel, machine, environment, and management entities and relationships in RDF triples, providing domain semantic constraints for upper-level intelligent analysis. The modeling results of the production process and accident handling process definition module are hosted in a standardized production process and accident handling process database (④). Together with the RDF graph data, these form structured constraints for risk and accident analysis. A domain knowledge base (⑤) stores laws, regulations, job operation procedures, and safety procedures in vector format. The production risk analysis module, accident analysis module, accident handling action planning module, and accident report generation module all run in the intelligent analysis and handling layer (⑥), and are uniformly orchestrated and scheduled by the intelligent agent framework. Among them, the accident analysis module can trigger the accident response action planning module, which, through the external system interface of the cross-entity collaboration module (⑦), pushes the analysis results and response plan to relevant regulatory departments, contracted rescue units, and contracted hospitals according to the event flow definition.

[0081] Specific limitations regarding the intelligent mine accident handling system can be found in the limitations of the intelligent mine accident handling methods described above, and will not be repeated here. Each module in the aforementioned intelligent mine accident handling system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0082] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0083] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for intelligent handling of mine production accidents, characterized in that, The method includes: The project constructs a production and accident business model, standardized production process templates, accident handling process templates, and a domain knowledge base for mine safety production. The production and accident business model includes entities covering personnel, equipment, environment, and management in mine production, as well as the objective semantic relationships between these entities. Each node in each process template is associated with the corresponding entity in the production and accident business model. Collect multi-source production data from the mine and preprocess it to extract event information, feature information, and production data with business semantics. Then, update the production and accident business model in real time with the event information and feature information. The production data is used to provide the instance status of each node in the production process template. The intelligent agent orchestration engine coordinates and schedules the acquisition of accident confirmation information. Based on the accident confirmation information, it extracts a subgraph snapshot of the production and accident business model at the time of the accident. Based on the subgraph snapshot, the instance status of the accident-related production process nodes, and the fulfillment of preconditions, it obtains a holographic semantic scene subgraph. It calls the LLM inside the accident chain reasoning intelligent agent and generates an accident causal chain based on the domain knowledge base and the holographic semantic scene subgraph model. Based on the accident handling process template and the verified accident causal chain, it generates an instantiated handling process that matches the current accident. The intelligent agent orchestration engine coordinates and schedules the automatic generation of cross-subject collaborative instructions based on the instantiated handling process and issues them for execution. The handling process is recorded to generate an immutable evidence chain with digital signatures and written back to the production and accident business model. The intelligent agent orchestration engine coordinates and schedules the generation of standardized accident investigation reports based on the accident causal chain, the immutable evidence chain, and the knowledge clauses for liability determination in the domain knowledge base.

2. The method according to claim 1, characterized in that, The method further includes constructing a multi-agent framework; the multi-agent framework includes the agent orchestration engine and multiple agents; The agents in the multi-agent framework are divided into public support agents, risk analysis agents, and accident handling agents; The public support intelligent agent includes a model query intelligent agent and a knowledge retrieval intelligent agent. The risk analysis intelligent agent and the accident handling intelligent agent both access the production and accident business model and the domain knowledge base through the public support intelligent agent. The risk analysis intelligent agents include risk monitoring intelligent agents, event extraction and correlation intelligent agents, model query intelligent agents, knowledge retrieval intelligent agents, risk identification intelligent agents, risk assessment and classification intelligent agents, and risk warning release intelligent agents; The accident handling intelligent agents include an accident reporting and confirmation intelligent agent, an accident chain reasoning intelligent agent, a model query intelligent agent, a knowledge retrieval intelligent agent, a contingency plan matching intelligent agent, a resource scheduling and collaboration intelligent agent, a rescue progress monitoring intelligent agent, an evidence chain solidification intelligent agent, and a report generation intelligent agent.

3. The method according to claim 1 or 2, characterized in that, The method further includes: The intelligent agent orchestration engine coordinates and schedules the acquisition of abnormal event information. The scheduled event extraction and association intelligent agents determine the business entity nodes associated with the abnormal events. Centered on the business entity nodes, the abnormal context subgraph is extracted from the production and accident business model according to the preset association range. The LLM inside the risk identification intelligent agent is invoked. A preliminary identification conclusion is generated based on the domain knowledge base and the abnormal context subgraph. The key attributes in the preliminary identification conclusion are back-checked in the production and accident business model for verification. Based on the verified preliminary identification conclusion, a quantitative evaluation is performed and a targeted early warning notification is issued to the communication terminal.

4. The method according to claim 3, characterized in that, The step of extracting an anomaly context subgraph from the production and accident business model, centered on the business entity node and according to a preset association range, includes: The scheduling model query agent takes the business entity node associated with the abnormal event as the center, performs breadth-first traversal or depth-first traversal according to the preset association hop number, extracts all directly and indirectly associated entities, semantic relationships and dynamic attributes around the business entity node, and generates a complete business model subgraph.

5. The method according to claim 3, characterized in that, The steps of quantitatively assessing and issuing targeted early warning notifications to communication terminals based on the verified preliminary identification conclusions include: The scheduling risk assessment and classification intelligent agent, combined with the number of exposed persons, equipment value and scope of impact in the production and accident business model, performs risk quantification and classification on the preliminary identification conclusions that have passed the verification; The dispatch risk warning issuing agent generates targeted warning notifications and pushes them to the communication terminals of the corresponding personnel based on the risk level and the job responsibility relationship in the production and accident business model.

6. The method according to claim 1, characterized in that, The steps of obtaining accident confirmation information and extracting a sub-graph snapshot of the production and accident business model at the time of the accident based on the accident confirmation information include: The scheduling model queries the intelligent agent to determine the time point of the accident and the associated business entity node based on the accident confirmation information. Extract the event sequence and dynamic attributes of the business entity nodes and related entities within a preset time window before the accident occurs, and generate a snapshot of the production and accident business model subgraphs containing the time of the accident.

7. The method according to claim 1, characterized in that, The step of invoking the LLM within the incident chain reasoning intelligence body to generate an incident causal chain based on the domain knowledge base and the holographic semantic scene subgraph model includes: The knowledge retrieval agent is scheduled to retrieve knowledge terms and fault tree models related to the accident type from the domain knowledge base; The holographic semantic scene subgraph, accident-related knowledge clauses, and accident tree model are input into the large language model inside the accident chain reasoning agent. Reasoning is performed under the constraints of the holographic semantic scene subgraph and the accident handling process template to generate an accident causal chain. Semantic verification is performed by back-checking the production and accident business model based on the key nodes in the accident causal chain.

8. The method according to claim 1, characterized in that, The steps of automatically generating cross-entity collaborative instructions based on the instantiated processing flow and issuing them for execution, and recording the processing to generate an immutable chain of evidence with a digital signature, include: The scheduling resource scheduling and collaborative intelligent agent automatically generates standardized cross-entity collaborative instructions based on the node requirements of the instantiated processing flow, and distributes the cross-entity collaborative instructions to external systems respectively; The scheduling evidence chain is solidified into an intelligent agent, which records the content of cross-subject collaborative instructions, timestamps, and response information from various external systems, generates an immutable evidence chain with digital signatures, and writes it back to the production and accident business model.

9. The method according to claim 1, characterized in that, The step of generating a standardized accident investigation report based on the accident causal chain, the immutable evidence chain, and the knowledge clauses for liability determination in the domain knowledge base includes: The knowledge retrieval agent is scheduled to retrieve legal and regulatory provisions and safety procedures related to accident liability determination from the domain knowledge base. The dispatch report generating agent inputs the accident causal chain, the tamper-proof evidence chain, and the knowledge clauses related to liability determination into the large language model inside the report generating agent, and generates a standardized accident investigation report according to the format requirements of the preset report writing specifications; each factual statement in the standardized accident investigation report is accompanied by a source link pointing to the corresponding evidence entity in the production and accident business model.

10. An intelligent system for handling mine production accidents, characterized in that, The system includes: The model building module is used to construct production and accident business models, standardized production process templates, accident handling process templates, and a domain knowledge base for mine safety production. The production and accident business model includes entities covering personnel, equipment, environment, and management in mine production, as well as the objective semantic relationships between these entities. Each node of each process template is associated with the corresponding entity in the production and accident business model. The data preprocessing module is used to collect multi-source production data from the mine and preprocess it, extract event information, feature information and production data with business semantics, and update the production and accident business model in real time with the event information and feature information; the production data is used to provide the instance status of each node in the production process template. The accident analysis module is used by the intelligent agent orchestration engine to coordinate and schedule the acquisition of accident confirmation information, extract a subgraph snapshot of the production and accident business model at the time of the accident based on the accident confirmation information, obtain a holographic semantic scene subgraph based on the subgraph snapshot, the instance status of the accident-related production process nodes, and the fulfillment of preconditions, call the LLM inside the accident chain reasoning intelligent agent, generate an accident causal chain based on the domain knowledge base and the holographic semantic scene subgraph model, and generate an instantiated handling process matching the current accident based on the accident handling process template and the verified accident causal chain. The collaborative handling module is used to coordinate and schedule the intelligent agent orchestration engine, automatically generate cross-subject collaborative instructions based on the instantiated handling process and issue them for execution, record the handling process to generate an immutable evidence chain with digital signature and write it back to the production and accident business model; The report generation module is used by the intelligent agent orchestration engine to generate a standardized accident investigation report based on the accident causal chain, the immutable evidence chain, and the knowledge clauses for liability determination in the domain knowledge base.

Images