Adaptive and updated mine emergency command methods, systems, equipment, and storage media
By constructing an emergency command knowledge graph and conducting emergency drills, emergency command plans are generated and updated, solving the problem of large deviations between existing emergency command plans and actual mine conditions, and improving the scientific nature and effectiveness of emergency response.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHENHUA SHENDONG COAL GRP
- Filing Date
- 2026-01-05
- Publication Date
- 2026-05-26
AI Technical Summary
The existing emergency command plan cannot adapt to the dynamic changes in the risk situation of the mine in a timely manner, resulting in insufficient scientificity and effectiveness of the emergency response.
By acquiring mine status data, an emergency command knowledge graph is constructed to generate emergency command plans. Emergency drills are used to determine the weight of the plans, and the emergency command plans are updated in real time to adapt to dynamic changes in the mine.
It enables adaptive updates to emergency command plans, improving the relevance and timeliness of emergency response and ensuring that plans can be adapted to the actual conditions of the mine in a timely manner.
Smart Images
Figure CN122089537A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of mine emergency management technology, specifically to an adaptive updating method, system, equipment, and storage medium for mine emergency command. Background Technology
[0002] As a traditional energy sector, the safety of the coal industry is fundamental to overall safety. On one hand, establishing scientific production standards can effectively prevent accidents. On the other hand, developing efficient and reliable emergency command plans is crucial for ensuring the safety of personnel and property in the event of an accident.
[0003] In existing technologies, emergency command plans mainly rely on the experience of a few technical experts and relevant standards. However, each mine is unique, with differences in geological conditions, equipment configuration, and personnel distribution, leading to significant deviations between existing emergency command plans and the actual conditions of the mine, resulting in numerous shortcomings. Furthermore, during mining operations, the mine's condition is constantly changing, and risk factors also change accordingly. Existing emergency command plans struggle to be dynamically updated and cannot adapt to new risk situations in a timely manner, thus lacking sufficient scientific rigor and effectiveness in emergency response.
[0004] Therefore, there is an urgent need for a mine emergency command technology that can be adapted to the actual conditions of the mine and can be dynamically updated to address the shortcomings of existing technologies. Summary of the Invention
[0005] To address the aforementioned issues, embodiments of the present invention provide an adaptively updated mine emergency command method, system, equipment, and storage medium, which can generate suitable emergency command plans based on real-time mine status data and achieve dynamic adaptive updates of the plans, thereby improving the scientific nature and effectiveness of emergency command.
[0006] The embodiments of the present invention adopt the following technical solutions: In a first aspect, the present invention provides an adaptive updating method for mine emergency command, comprising: Obtain mine status data and determine multiple risk data based on the mine status data; Acquire industry experience data and construct an emergency command knowledge graph based on the industry experience data; Input multiple risk data into the emergency command knowledge graph to generate multiple emergency command plans corresponding to the multiple risk data; Based on multiple emergency command plans, multiple internal experience data are generated, each corresponding to a different category of risk data. Several emergency command plans were selected for emergency drills, and the weight of each emergency command plan was determined based on the drill results. Real-time acquisition of mine status data; determination of update risk data based on mine status data; and generation of update emergency command plans based on update risk data, internal experience data, and the weights of various emergency command plans.
[0007] Secondly, the present invention also provides an adaptively updated mine emergency command system, comprising: The risk data processing unit is used to acquire mine status data and determine multiple risk data based on the mine status data; The industry data processing unit is used to acquire industry experience data and build an emergency command knowledge graph based on the industry experience data. The scheme generation unit is used to input multiple risk data into the emergency command knowledge graph and generate multiple emergency command schemes corresponding to the multiple risk data. The scheme management unit is used to generate multiple internal experience data corresponding to the categories of multiple risk data based on multiple emergency command schemes. The weight determination unit is used to select several emergency command plans for emergency drills and determine the weight of each emergency command plan based on the drill results. The scheme update unit is used to acquire mine update status data in real time, determine update risk data based on mine update status data, and generate updated emergency command schemes based on update risk data, internal experience data, and the weights of each emergency command scheme.
[0008] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described adaptive updating mine emergency command method.
[0009] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described adaptively updated mine emergency command method.
[0010] The above-described at least one technical solution adopted in the embodiments of the present invention can achieve the following beneficial effects: This invention acquires mine status data and determines multiple risk data based on it; acquires industry experience data corresponding to the mine status data and constructs an emergency command knowledge graph based on it; inputs the multiple risk data into the emergency command knowledge graph to generate multiple emergency command schemes corresponding to the multiple risk data; generates multiple internal experience data corresponding to the categories of the multiple risk data based on the multiple emergency command schemes; selects several emergency command schemes for emergency drills and determines the weight of each emergency command scheme based on the drill results; acquires updated mine status data in real time, determines updated risk data based on the updated mine status data, and generates updated emergency command schemes based on the updated risk data, internal experience data, and the weights of each emergency command scheme.
[0011] Compared with the prior art, the present invention has the following significant advantages: This invention, by acquiring mine status data and combining it with industry experience data to construct an emergency command knowledge graph, can generate emergency command plans adapted to the actual conditions of the mine. This solves the problem of large deviations between existing emergency command plans and actual mine conditions, and improves the pertinence and scientific nature of emergency command.
[0012] This invention extracts internal experience data based on the generated emergency command plan and determines the plan weights through emergency drills. When the mine status is updated, it dynamically generates an updated emergency command plan by combining updated risk data, internal experience data, and plan weights. This achieves adaptive updating of the emergency command plan, ensuring that the plan can adapt to the dynamically changing risk situation in the mine in a timely manner, and improving the timeliness and effectiveness of emergency command. Attached Figure Description
[0013] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings: Figure 1 A flowchart illustrating an adaptively updated mine emergency command method according to an embodiment of the present invention is shown. Figure 2 A schematic diagram of an adaptive updating mine emergency command system according to an embodiment of the present invention is shown; Figure 3 A schematic diagram of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation
[0014] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.
[0015] The purpose of this invention is to provide an adaptive and updated emergency command method for mines, addressing the problems of poor adaptability of existing emergency command schemes to actual mine conditions and the inability to dynamically update them. To achieve the above objective, Figure 1 An adaptive updating mine emergency command method according to an embodiment of the present invention is shown, with reference to Figure 1 As shown, this embodiment includes steps S110 to S160: Step S110: Obtain mine status data and determine multiple risk data based on the mine status data.
[0016] Mine status data includes: mine geological data, mine equipment data, and mine personnel data.
[0017] Mine geological data can be stored and managed based on existing GIS (Geographic Information System) and models can be generated in 2D or 3D form.
[0018] Mining equipment data and mining personnel data are determined based on the actual conditions of the mine. Mining equipment data primarily reflects the installation location and operating mode of the equipment. Mining personnel data primarily reflects the activities of personnel in the mine.
[0019] Risk data includes multiple risk units and multiple incident chains.
[0020] A risk unit includes the risk location and the risk type. Taking a coal mine as an example, the risk location can be any location in the roadway, and the risk type can be water hazard, fire, gas outburst, or high concentration of dust, etc.
[0021] Each risk unit can be represented as an array. For example, Ui = [Pi, Ki], where i represents the index of the risk unit, Pi represents the risk location, and Ki represents the risk type.
[0022] The location and type of risk for a risk unit need to be determined by combining mine geological data, mine equipment data, and mine personnel data.
[0023] First, based on the mine's geological data, identify the risk locations where potential accidents may occur due to the mine's geological conditions.
[0024] Then, based on mining equipment data and mining personnel data, risk locations that may lead to risk accidents due to the influence of equipment and personnel are identified.
[0025] After considering all the risk locations, determine the risk type, that is, what type of risk is likely to occur at the risk location.
[0026] When a risk incident occurs at one risky location, it may trigger other risky incidents at other risky locations as well. For example, in a coal mine, if a fire occurs at one risky location, the spread of the flames may cause fires to occur at other risky locations as well.
[0027] Therefore, different risk units will have certain correlations. To better reflect these correlations, the risk data also includes multiple incident chains. Each incident chain includes at least two risk units, and all risk units in each incident chain have a sequential relationship.
[0028] The incident chain can record the correlation between risk units and also reflect the possible spread and escalation of risk incidents when they occur. When generating emergency command plans in the future, more detailed and comprehensive related emergency command plans can be generated based on the incident chain.
[0029] Therefore, in some optional implementations, step S110 involves acquiring mine status data and determining multiple risk data based on the mine status data, including: acquiring mine geological data, mine equipment data, and mine personnel data; identifying multiple risk units based on the mine geological data, mine equipment data, and mine personnel data; wherein each risk unit includes: risk location and risk type; analyzing the correlation between multiple risk units to construct multiple accident chains; wherein each accident chain includes at least two risk units with a sequential relationship; and treating each risk unit and each accident chain as multiple risk data.
[0030] Step S120: Obtain industry experience data and construct an emergency command knowledge graph based on the industry experience data.
[0031] Industry experience data comprises existing emergency response experiences for risks and accidents within the mining industry, including emergency measures for various risks, appropriate emergency personnel, and necessary emergency equipment. The content of this industry experience data may vary depending on the specific mine.
[0032] Knowledge graphs are an existing technology. They are a modern theory that combines theories and methods from applied mathematics, computer graphics, information visualization, and information science with bibliometric methods such as citation analysis and co-occurrence analysis. By using visualized graphs to vividly display the core structure, development history, cutting-edge fields, and overall knowledge architecture of a discipline, knowledge graphs achieve multidisciplinary integration. Through data mining, information processing, knowledge measurement, and graphical representation, knowledge graphs can reveal complex knowledge domains, showcasing their dynamic development patterns and providing practical and valuable references for disciplinary research.
[0033] This invention constructs an emergency command knowledge graph based on industry experience data, enabling the faster generation of effective emergency command plans. The knowledge graph generation method is a mature existing technology, and when applied to this invention, it can be constructed using the following methods.
[0034] The industry experience data is structured to extract key information such as risk type, risk location, risk spread correlation information, emergency measures, emergency personnel, and emergency equipment.
[0035] Define the entity types and relationship types of the knowledge graph. The entity types include risk unit entities, emergency measure entities, emergency personnel entities, and emergency equipment entities. The relationship types include risk unit-emergency measure relationships, risk unit-emergency personnel relationships, risk unit-emergency equipment relationships, and risk unit-risk unit relationships (used to reflect the association between risk units, i.e., the accident chain relationship).
[0036] The extracted key information is mapped to defined entity and relationship types to form an initial emergency command knowledge graph. To ensure the accuracy of the knowledge graph, redundant data is cleaned and conflicting data is verified on the initial graph, ultimately forming a complete and reliable emergency command knowledge graph.
[0037] Therefore, in some optional implementations, step S120, acquiring industry experience data and constructing an emergency command knowledge graph based on the industry experience data, includes: acquiring industry experience data; performing structured processing on the industry experience data to extract key information; wherein the key information includes at least: risk type, risk location, risk diffusion association information, emergency measures, emergency personnel, and emergency equipment; defining the entity types and relationship types of the knowledge graph; wherein the entity types include: risk unit entity, emergency measure entity, emergency personnel entity, and emergency equipment entity, and the relationship types include: risk unit-emergency measure relationship, risk unit-emergency personnel relationship, risk unit-emergency equipment relationship, and risk unit-risk unit relationship; mapping the key information to the defined entity types and relationship types to form an initial emergency command knowledge graph; and performing redundant data cleaning and conflict data verification on the initial emergency command knowledge graph to form an emergency command knowledge graph.
[0038] Step S130: Input multiple risk data into the emergency command knowledge graph to generate multiple emergency command plans corresponding to the multiple risk data.
[0039] After identifying risk units and incident chains, these units and chains can be input into an emergency command knowledge graph, which can then be used to generate multiple emergency command plans. Each risk unit corresponds to one emergency command plan, and each incident chain corresponds to one emergency command plan.
[0040] An emergency command plan includes emergency measures, emergency personnel, and emergency equipment. Emergency measures include personnel evacuation and accident mitigation; emergency personnel include personnel who need to command and manage risk events; and emergency equipment includes equipment that can be used for personnel evacuation and accident mitigation.
[0041] Specifically, the emergency command plan corresponding to each risk unit is a single-point emergency command plan. A single-point emergency command plan is an on-site response plan, meaning it addresses the specific risk location and risk type on-site.
[0042] Specifically, the emergency command plan corresponding to each accident chain is a related emergency command plan. Related emergency command plans are chain-based response plans, meaning they define clear chain-based coordinated response measures for combinations of multiple risk units.
[0043] Therefore, in some optional implementations, step S130 involves inputting multiple risk data into the emergency command knowledge graph to generate multiple emergency command schemes corresponding to the multiple risk data. This includes: inputting multiple risk units into the emergency command knowledge graph to generate multiple single-point emergency command schemes corresponding to the multiple risk units; inputting multiple accident chains into the emergency command knowledge graph to generate multiple associated emergency command schemes corresponding to the multiple accident chains; and the emergency command schemes include: emergency measures, emergency personnel, and emergency equipment.
[0044] Step S140: Based on multiple emergency command schemes, generate multiple internal experience data corresponding to the categories of multiple risk data.
[0045] During coal mine production, the three-dimensional data of the coal mine constantly changes, which may lead to the creation of new risk units or alterations to existing risk units, particularly changes in the risk types within those units. These changes necessitate adjustments to existing emergency command plans. To improve the efficiency of this adjustment process, this invention designs an adaptive update mechanism.
[0046] After an emergency command plan is generated, the emergency command plan will correspond to the risk unit and the accident chain. That is, when a risk accident occurs in a certain risk unit and triggers the corresponding accident chain, there will be a corresponding single-point emergency command plan and related emergency command plan.
[0047] After generating many emergency command plans, this invention generates internal experience data based on these plans.
[0048] Specifically, for single-point emergency command plans: First, risk units are grouped according to type, resulting in a first grouping result. Each group in the first grouping result contains multiple risk units. Then, the first grouping result is grouped according to location, resulting in a second grouping result. Each group in the second grouping result also contains multiple risk units (the number of risk units in each group in the second grouping result is less than the number of risk units in each group in the first grouping result). Next, single-point emergency command plans are filtered based on the second grouping result, selecting those corresponding to the risk units in each group in the second grouping result, resulting in the single-point emergency command plan grouping result. Finally, the common characteristics of each group of single-point emergency command plans are determined (e.g., how to guide personnel to safety, how to control equipment, and how to handle accidents). After summarizing the common characteristics of each group of single-point emergency command plans, the internal experience data of each single point can be obtained.
[0049] The following example illustrates in detail the process of generating internal experience data at a single point.
[0050] Risk units U1=[P1,K1], U2=[P2,K2], U3=[P3,K3], U4=[P4,K4], U5=[P5,K5], U6=[P6,K6], U7=[P7,K7], U8=[P8,K8], U9=[P9,K9], U10=[P10,K10], each risk unit corresponds to a single-point emergency command scheme Si (i∈[1,10]).
[0051] The risk units are grouped based on type classification to obtain the first grouping results. For example, K1, K3, K5, K7, and K9 belong to the same type classification A, and K2, K4, K6, K8, and K10 belong to the same type classification B. Then the first grouping results are: the first major group U1=[P1, K1], U3=[P3, K3], U5=[P5, K5], U7=[P7, K7], U9=[P9, K9]; the second major group U2=[P2, K2], U4=[P4, K4], U6=[P6, K6], U8=[P8, K8], U10=[P10, K10].
[0052] Based on the location classification, the results of the first grouping are grouped a second time to obtain the second grouping results. For example, if P1, P2, P3, P4, and P5 belong to location classification a, and P6, P7, P8, P9, and P10 belong to location classification b, then the second grouping results are: Group 1 U1=[P1, K1], U3=[P3, K3], U5=[P5, K5]; Group 2 U7=[P7, K7], U9=[P9, K9]; Group 3 U2=[P2, K2], U4=[P4, K4]; Group 4 U6=[P6, K6], U8=[P8, K8], U10=[P10, K10].
[0053] In other words, the first group corresponds to category Aa, the second group corresponds to category Ab, the third group corresponds to category Ba, and the fourth group corresponds to category Bb.
[0054] Based on the second grouping results mentioned above, the single-point emergency command schemes are grouped to obtain the following grouping results: Single-point emergency command scheme group 1 (corresponding to category Aa): S1, S3, S5; Single-point emergency command scheme group 2 (corresponding to category Ab): S7, S9; Single-point emergency command scheme group 3 (corresponding to category Ba): S2, S4; Single-point emergency command scheme group 4 (corresponding to category Bb): S6, S8, S10.
[0055] Extract the common features of each single-point emergency command plan and generate multiple single-point internal experience data corresponding to the categories of multiple risk units. For example, single-point internal experience data C1 extracted from single-point emergency command plan group 1 corresponds to category Aa, single-point internal experience data C2 extracted from single-point emergency command plan group 2 corresponds to category Ab, single-point internal experience data C3 extracted from single-point emergency command plan group 3 corresponds to category Ba, and single-point internal experience data C4 extracted from single-point emergency command plan group 4 corresponds to category Bb.
[0056] Specifically, regarding related emergency command plans: First, accident chains are grouped based on their similarity (e.g., similarity in risk type combinations, similarity in diffusion order, etc.), resulting in accident chain grouping results. Next, related emergency command plans are filtered based on the accident chain grouping results, identifying those corresponding to the accident chains in each group, resulting in related emergency command plan grouping results. Finally, the common characteristics of each group of related emergency command plans are determined (e.g., priority of emergency response, collaborative rescue methods across risk units, etc.). After summarizing the common characteristics of each group of related emergency command plans, the internal experience data for each related plan can be obtained.
[0057] The following example illustrates in detail the process of generating correlated internal experience data.
[0058] An accident chain Hi (i∈[1,10]) is formed, and each accident chain corresponds to an associated emergency command scheme Qi (i∈[1,10]).
[0059] The above incident chains are grouped based on similarity to obtain the incident chain grouping results. For example, H1, H3, H5, H7, and H9 are similar and belong to incident chain class M, while H2, H4, H6, H8, and H10 are similar and belong to incident chain class N. Then the incident chain grouping results are: Group 1: H1, H3, H5, H7, and H9; Group 2: H2, H4, H6, H8, and H10.
[0060] In other words, the first group corresponds to category M, and the second group corresponds to category N.
[0061] Based on the above grouping results, the related emergency command schemes are grouped to obtain the following grouping results: Group 1 (corresponding to category M): Q1, Q3, Q5, Q7, Q9; Group 2 (corresponding to category N): Q2, Q4, Q6, Q8, Q10.
[0062] Extract the common features of each group of related emergency command schemes and form multiple sets of related internal experience data corresponding to the categories of multiple accident chains. For example, the related internal experience data D1 extracted from the first group of related emergency command schemes corresponds to category M, and the related internal experience data D2 extracted from the second group of related emergency command schemes corresponds to category N.
[0063] Therefore, in some optional implementations, step S140, based on multiple emergency command schemes, generates multiple internal experience data corresponding to the categories of multiple risk data, including: grouping multiple risk units based on type classification to obtain a first grouping result; grouping the first grouping result based on location classification to obtain a second grouping result; grouping multiple single-point emergency command schemes based on the second grouping result to obtain single-point emergency command scheme grouping results; analyzing the single-point emergency command schemes included in each group in the single-point emergency command scheme grouping results to obtain common features of the single-point schemes corresponding to each group; and forming multiple single-point internal experience data corresponding to the categories of multiple risk units based on the common features of each single-point scheme. Furthermore, grouping multiple accident chains based on the similarity of multiple accident chains to obtain accident chain grouping results; grouping multiple related emergency command schemes based on the accident chain grouping results to obtain related emergency command scheme grouping results; analyzing the related emergency command schemes included in each group in the related emergency command scheme grouping results to obtain common features of the related schemes corresponding to each group; and forming multiple related internal experience data corresponding to the categories of multiple accident chains based on the common features of each related scheme.
[0064] Step S150: Select several emergency command plans for emergency drills, and determine the weight of each emergency command plan based on the drill results.
[0065] Given that the specific circumstances of each mine are unique, emergency command plans generated based on emergency command knowledge graphs may not be well adapted to the mines. In order to better optimize emergency command plans, it is necessary to select several emergency command plans for emergency drills and determine the weight of each emergency command plan based on the drill results.
[0066] To ensure that the generated emergency command plan is more scientific and effective, it can be verified through emergency drills.
[0067] The following explanation uses a single-point emergency command plan as an example.
[0068] First, assign an initial weight to each individual emergency command plan (for example, all individual emergency command plans should have the same initial weight).
[0069] Then, based on the results of the second grouping, several risk units are selected from each group of the second grouping results, and then the single-point emergency command plan corresponding to the risk unit is determined.
[0070] Next, a drill will be conducted to demonstrate the single-point emergency command plan. After the drill, group experts can evaluate the process based on their experience and historical experience with risk accidents in the coal mining industry, obtaining an evaluation result for the emergency command plan. The evaluation result can be rated as excellent, good, average, or poor.
[0071] Finally, after evaluating the individual emergency command plans, the weights of these plans are adjusted based on the evaluation results. Better evaluation results result in a higher weight for the corresponding plan.
[0072] The associated emergency command plan is similar to the single-point emergency command plan, and will not be described in detail here.
[0073] Considering that emergency drills cannot be conducted on a large scale, otherwise it would affect normal production, the number of emergency drills is limited, which in turn leads to a limited evaluation result for emergency command plans.
[0074] If an emergency command plan receives a "good" rating in an emergency drill, it can be designated as a "good" plan. However, in subsequent emergency drills, if a new emergency command plan from the same "good" plan group is tested and receives an "excellent" rating, the weight of the "good" plan can be reduced.
[0075] In other words, if new and better emergency command plans are discovered as the number of emergency drills increases, the weight of previously overweighted emergency command plans can be reduced. Similarly, if worse emergency command plans are discovered as the number of emergency drills increases, the weight of previously underweighted emergency command plans can be increased. This method also allows for dynamic updating of weights.
[0076] Therefore, in some optional implementations, step S150 involves selecting several emergency command schemes for emergency drills and determining the weight of each emergency command scheme based on the drill results. This includes: initially assigning weights to each emergency command scheme; randomly selecting several emergency command schemes for emergency drills; evaluating the value of each emergency command scheme based on the drill results; and adaptively updating the weights of each emergency command scheme according to their value.
[0077] Step S160: Obtain mine update status data in real time, determine update risk data based on mine update status data, and generate update emergency command plan based on update risk data, internal experience data, and the weights of each emergency command plan.
[0078] During the mining process, real-time monitoring is conducted on changes in mine geological data (such as rock strata movement and hydrological changes), changes in mine equipment data (such as equipment aging and the commissioning of new equipment), and changes in mine personnel data (such as changes in work areas and personnel increases or decreases) to obtain updated mine status data.
[0079] Based on mine update status data, update risk units are identified (such as newly added risk units, changes in existing risk units, etc.), and update accident chains are constructed (such as new accident chains formed by newly added risk units and existing risk units, changes in the diffusion order of existing accident chains, etc.).
[0080] For an updated risk unit, the corresponding target point's internal experience data is matched based on its category (e.g., Aa, Ab, Ba, Bb examples above). For instance, if an updated risk unit U11 belongs to category Aa, then the matched target point's internal experience data is C1. Then, the weights of the existing emergency command schemes for each point belonging to the same category as the updated risk unit are combined (schemes with higher weights have their features referenced more frequently), to generate an updated emergency command scheme for that point.
[0081] In the process of generating and updating single-point emergency command plans—that is, after the formation of updated risk units as coal mining progresses—the adaptive updating of the single-point emergency command plans corresponding to the updated risk units involves not only including the corresponding internal experience data of the single point but also merging all original single-point emergency command plans belonging to the same category as the updated risk unit based on weights. In other words, the characteristics of single-point emergency command plans with higher weights are given priority, followed by the characteristics of single-point emergency command plans with lower weights, and the updated emergency command plan is obtained based on a combination of these characteristics.
[0082] For example, when updating the category of a risk unit, a single-point emergency command plan might include guiding personnel from a risky location to a safe location; this transfer route could be considered a feature. Different single-point emergency command plans may have different features, meaning they might guide personnel to different locations. After assigning weights to single-point emergency command plans based on exercise results, when generating updated single-point emergency command plans, priority should be given to the transfer routes in single-point emergency command plans with higher weights.
[0083] Updating the associated emergency command plan is similar to updating the single-point emergency command plan, so it will not be described in detail here.
[0084] In daily coal mine production, newly generated risk units and accident chains can automatically generate corresponding updated emergency command plans based on internal experience data, thereby achieving adaptive updates of emergency command plans without frequent manual intervention, making it more efficient.
[0085] In addition to adaptive updates based on internal experience data, external experience can also be incorporated. This requires identifying at least one external data source (such as standards and specifications) to guide the design of the updated emergency command plan. When the external data source is updated, the existing emergency command plan is updated based on the external experience gained. However, the updated emergency command plan needs to be manually reviewed to avoid mismatches with the actual conditions of the coal mine. In practical use, existing emergency command plans and external experience can be understood based on a large existing language model, and then the emergency command plan can be updated accordingly.
[0086] Therefore, in some optional implementations, step S160 involves real-time acquisition of mine renewal status data, determination of renewal risk data based on the mine renewal status data, and generation of renewal emergency command plans based on the renewal risk data, internal experience data, and the weights of various emergency command plans. This includes: real-time acquisition of mine renewal geological data, mine renewal equipment data, and mine renewal personnel data; identification of multiple renewal risk units based on the mine renewal geological data, mine renewal equipment data, and mine renewal personnel data; wherein each renewal risk unit includes: renewal risk location and renewal risk type; analysis of the correlation between multiple renewal risk units, and construction of multiple renewal... An incident chain is defined as follows: each updated incident chain includes at least two updated risk units with a sequential relationship; multiple updated risk units are matched with corresponding target single-point internal experience data from multiple single-point internal experience data based on their respective categories; and corresponding updated single-point emergency command plans are generated based on the weights of each target single-point internal experience data and each target single-point emergency command plan; multiple updated incident chains are matched with corresponding target associated internal experience data from multiple associated internal experience data based on their respective categories; and corresponding updated associated emergency command plans are generated based on the weights of each target associated internal experience data and each target associated emergency command plan.
[0087] Figure 2 An adaptive updating mine emergency command system according to an embodiment of the present invention is shown, with reference to Figure 2 As shown, the adaptive updating mine emergency command system 200 includes: Risk data processing unit 210 is used to acquire mine status data and determine multiple risk data based on the mine status data; Industry data processing unit 220 is used to acquire industry experience data and construct an emergency command knowledge graph based on the industry experience data. The scheme generation unit 230 is used to input multiple risk data into the emergency command knowledge graph and generate multiple emergency command schemes corresponding to the multiple risk data. The scheme management unit 240 is used to generate multiple internal experience data corresponding to the categories of multiple risk data based on multiple emergency command schemes; The weight determination unit 250 is used to select several emergency command plans for emergency drills and determine the weight of each emergency command plan based on the drill results. The scheme update unit 260 is used to acquire mine update status data in real time, determine update risk data based on mine update status data, and generate an updated emergency command scheme based on update risk data, internal experience data, and the weights of each emergency command scheme.
[0088] In some optional implementations, in the above system, the risk data processing unit 210 is used to: acquire mine geological data, mine equipment data, and mine personnel data; identify multiple risk units based on the mine geological data, mine equipment data, and mine personnel data; wherein each risk unit includes: risk location and risk type; analyze the correlation between multiple risk units and construct multiple accident chains; wherein each accident chain includes at least two risk units with a sequential relationship; and treat each risk unit and each accident chain as multiple risk data.
[0089] In some optional implementations, in the above system, the industry data processing unit 220 is used to: acquire industry experience data; perform structured processing on the industry experience data to extract key information; wherein the key information includes at least: risk type, risk location, risk diffusion association information, emergency measures, emergency personnel, and emergency equipment; define the entity types and relationship types of the knowledge graph; wherein the entity types include: risk unit entity, emergency measure entity, emergency personnel entity, and emergency equipment entity, and the relationship types include: risk unit-emergency measure relationship, risk unit-emergency personnel relationship, risk unit-emergency equipment relationship, and risk unit-risk unit relationship; map the key information to the defined entity types and relationship types to form an initial emergency command knowledge graph; and perform redundant data cleaning and conflict data verification on the initial emergency command knowledge graph to form an emergency command knowledge graph.
[0090] In some optional implementations, in the above system, the scheme generation unit 230 is used to: input multiple risk units into the emergency command knowledge graph to generate multiple single-point emergency command schemes corresponding to the multiple risk units; input multiple accident chains into the emergency command knowledge graph to generate multiple associated emergency command schemes corresponding to the multiple accident chains; the emergency command scheme includes: emergency measures, emergency personnel and emergency equipment.
[0091] In some optional implementations, in the above system, the scheme management unit 240 is used to: group multiple risk units based on type classification to obtain a first grouping result; group the first grouping result based on location classification to obtain a second grouping result; group multiple single-point emergency command schemes based on the second grouping result to obtain single-point emergency command scheme grouping results; analyze the single-point emergency command schemes included in each group in the single-point emergency command scheme grouping results to obtain common features of the single-point schemes corresponding to each group; and form multiple single-point internal experience data corresponding to the categories to which the multiple risk units belong, based on the common features of each single-point scheme; group multiple accident chains based on the similarity of multiple accident chains to obtain accident chain grouping results; group multiple related emergency command schemes based on the accident chain grouping results to obtain related emergency command scheme grouping results; analyze the related emergency command schemes included in each group in the related emergency command scheme grouping results to obtain common features of the related schemes corresponding to each group; and form multiple related internal experience data corresponding to the categories to which the multiple accident chains belong, based on the common features of each related scheme.
[0092] In some optional implementations, in the above system, the weight determination unit 250 is used to: initially assign weights to each emergency command scheme; randomly select several emergency command schemes for emergency drills; evaluate the value of each emergency command scheme based on the drill results; and adaptively update the weights of each emergency command scheme according to the value of each emergency command scheme.
[0093] In some optional implementations, in the above system, the scheme update unit 260 is used to: acquire mine update geological data, mine update equipment data, and mine update personnel data in real time; identify multiple update risk units based on the mine update geological data, mine update equipment data, and mine update personnel data; wherein each update risk unit includes: update risk location and update risk type; analyze the correlation between multiple update risk units and construct multiple update accident chains; wherein each update accident chain includes at least two update risk units with sequential relationship; match the multiple update risk units with corresponding target single-point internal experience data from multiple single-point internal experience data based on their respective categories, and generate corresponding update single-point emergency command schemes based on the weights of each target single-point internal experience data and each target single-point emergency command scheme; match the multiple update accident chains with corresponding target associated internal experience data from multiple associated internal experience data based on their respective categories, and generate corresponding update associated emergency command schemes based on the weights of each target associated internal experience data and each target associated emergency command scheme.
[0094] It should be noted that the aforementioned adaptively updated mine emergency command system 200 can implement the aforementioned adaptively updated mine emergency command methods one by one, which will not be elaborated further.
[0095] Figure 3 A schematic diagram of the structure of an electronic device according to an embodiment of the present invention is shown. Figure 3 As shown, the electronic device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used for communication with external devices via a network connection. When the computer program is executed by the processor, it implements the functions or steps of an adaptively updated mine emergency command method.
[0096] In one embodiment, the electronic device provided by the present invention includes a memory and a processor. The memory stores a database and a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of the aforementioned adaptively updated mine emergency command method.
[0097] The above is as described in the present invention. Figure 2 The adaptive updating method for the mine emergency command system disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The steps of the method disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.
[0098] In one embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the aforementioned adaptively updated mine emergency command method.
[0099] It should be noted that the functions or steps that the above-mentioned electronic devices or computer-readable storage media can achieve can be referred to the relevant descriptions in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.
[0100] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
[0101] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.
[0102] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.
Claims
1. An adaptive updating method for mine emergency command, characterized in that, include: Obtain mine status data, and determine multiple risk data based on the mine status data; Acquire industry experience data and construct an emergency command knowledge graph based on the industry experience data; The multiple risk data are respectively input into the emergency command knowledge graph to generate multiple emergency command plans corresponding to the multiple risk data; Based on the multiple emergency command schemes, multiple internal experience data are generated that correspond to the categories of the multiple risk data. Several of the aforementioned emergency command schemes were selected for emergency drills, and the weights of each of the aforementioned emergency command schemes were determined based on the drill results. The system acquires mine status update data in real time, determines update risk data based on the mine status update data, and generates an update emergency command plan based on the update risk data, the internal experience data, and the weights of each emergency command plan.
2. The method according to claim 1, characterized in that, The process of acquiring mine status data and determining multiple risk data based on the mine status data includes: Acquire mine geological data, mine equipment data, and mine personnel data; Multiple risk units are identified based on the mine geological data, the mine equipment data, and the mine personnel data; wherein each risk unit includes: risk location and risk type; Analyze the relationships between multiple risk units to construct multiple incident chains; wherein each incident chain includes at least two risk units with a sequential relationship; Each of the aforementioned risk units and each of the aforementioned accident chains are considered as multiple risk data.
3. The method according to claim 2, characterized in that, The acquisition of industry experience data and the construction of an emergency command knowledge graph based on the industry experience data include: Obtain the industry experience data; The industry experience data is structured and key information is extracted; the key information includes at least: risk type, risk location, risk spread correlation information, emergency measures, emergency personnel, and emergency equipment. Define the entity types and relationship types of the knowledge graph; wherein, the entity types include: risk unit entity, emergency measure entity, emergency personnel entity, and emergency equipment entity, and the relationship types include: risk unit-emergency measure relationship, risk unit-emergency personnel relationship, risk unit-emergency equipment relationship, and risk unit-risk unit relationship; The key information is mapped to the defined entity types and relationship types to form an initial emergency command knowledge graph; Redundant data is cleaned and conflicting data is verified on the initial emergency command knowledge graph to form the current emergency command knowledge graph.
4. The method according to claim 2, characterized in that, The step of inputting multiple risk data points into the emergency command knowledge graph to generate multiple emergency command plans corresponding to the multiple risk data points includes: The multiple risk units are input into the emergency command knowledge graph to generate multiple single-point emergency command schemes corresponding to the multiple risk units; The multiple accident chains are input into the emergency command knowledge graph to generate multiple associated emergency command schemes corresponding to the multiple accident chains; The emergency command plan includes: emergency measures, emergency personnel, and emergency equipment.
5. The method according to claim 4, characterized in that, Based on the multiple emergency command schemes, multiple internal experience data corresponding to the categories of the multiple risk data are generated, including: Based on type classification, multiple risk units are first grouped to obtain a first grouping result. Based on location classification, the first grouping result is second grouped to obtain a second grouping result. Based on the second grouping result, multiple single-point emergency command schemes are grouped to obtain single-point emergency command scheme grouping results. The single-point emergency command schemes included in each group in the single-point emergency command scheme grouping results are analyzed to obtain the common features of the single-point schemes corresponding to each group. Based on the common features of each single-point scheme, multiple single-point internal experience data corresponding to the categories to which the multiple risk units belong are formed. Multiple accident chains are grouped based on their similarity to obtain accident chain grouping results. Based on the accident chain grouping results, multiple related emergency command schemes are grouped to obtain related emergency command scheme grouping results. The related emergency command schemes included in each group in the related emergency command scheme grouping results are analyzed to obtain the common features of the related schemes corresponding to each group. Based on the common features of each related scheme, multiple related internal experience data corresponding to the categories to which the multiple accident chains belong are formed.
6. The method according to claim 2, characterized in that, The step of selecting several emergency command schemes for emergency drills and determining the weight of each emergency command scheme based on the drill results includes: Initially assign weights to each of the aforementioned emergency command schemes; Several of the aforementioned emergency command plans were randomly selected for emergency drills. The value of each emergency command plan is evaluated based on the results of the exercise, and the weights of each emergency command plan are adaptively updated according to their value.
7. The method according to claim 6, characterized in that, The process of acquiring real-time mine update status data, determining update risk data based on the mine update status data, and generating an update emergency command plan based on the update risk data, the internal experience data, and the weights of each of the emergency command plans includes: Real-time acquisition of updated geological data, updated equipment data, and updated personnel data for mines; Multiple renewal risk units are identified based on the mine renewal geological data, the mine renewal equipment data, and the mine renewal personnel data; wherein each renewal risk unit includes: renewal risk location and renewal risk type; Analyze the relationships between multiple update risk units to construct multiple update incident chains; wherein each update incident chain includes at least two update risk units with an ordered relationship; The multiple updated risk units are matched with the corresponding target single-point internal experience data from the multiple single-point internal experience data according to their respective categories. The corresponding updated single-point emergency command schemes are generated according to the weights of each target single-point internal experience data and each target single-point emergency command scheme. The multiple updated accident chains are matched with corresponding target-related internal experience data from the multiple associated internal experience data based on their respective categories. The corresponding updated associated emergency command schemes are generated according to the weights of the target-related internal experience data and the target-related emergency command schemes.
8. An adaptively updated mine emergency command system, characterized in that, include: A risk data processing unit is used to acquire mine status data and determine multiple risk data based on the mine status data. An industry data processing unit is used to acquire industry experience data and construct an emergency command knowledge graph based on the industry experience data. The scheme generation unit is used to input multiple risk data into the emergency command knowledge graph respectively, and generate multiple emergency command schemes corresponding to the multiple risk data; The scheme management unit is used to generate multiple internal experience data corresponding to the categories of the multiple risk data based on the multiple emergency command schemes; The weight determination unit is used to select several of the aforementioned emergency command schemes for emergency drills and determine the weight of each of the aforementioned emergency command schemes based on the drill results. The scheme update unit is used to acquire mine update status data in real time, determine update risk data based on the mine update status data, and generate an updated emergency command scheme based on the update risk data, the internal experience data, and the weights of each emergency command scheme.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the adaptively updated mine emergency command method as described in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the adaptively updated mine emergency command method as described in any one of claims 1 to 7.