Emergency equipment efficient management system and method

Abstract

The invention relates to the technical field of chemical safety management, and discloses an emergency equipment high-efficiency management system and method.The emergency equipment high-efficiency management method comprises the steps that multi-source heterogeneous data of emergency equipment in a chemical industrial park is collected and converted into a standardized data set through a heterogeneous data integration algorithm, and the standardized data set is stored in a database; the expert knowledge is converted into a domain knowledge graph by means of a hierarchical knowledge representation framework. Based on this, a multi-layer causal network model is constructed by using a causal discovery algorithm, analysis results and decision suggestions are extracted through multi-scale causal reasoning, and a dynamic causal knowledge base is generated through combination of a bidirectional calibration algorithm and newly added data; according to the method, the limitation of single analytic hierarchy process is broken through by fusing domain knowledge and multi-source data, explanation is provided for decision making, the method has adaptive evolution capability, and the efficiency and accuracy of emergency equipment management are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of chemical safety management, and more specifically, to an efficient management system and method for emergency equipment. Background Art

[0002] Chemical parks deploy a large number of sensors and data acquisition equipment to obtain multi-source heterogeneous data of emergency equipment in real time. However, the existing emergency equipment management methods have shortcomings: the data management system and professional knowledge are "isolated" and lack a fusion mechanism, resulting in "rich data but lack of wisdom"; the equipment status analysis method is single, or relies on deterministic rules, or is based on statistical models. The former is difficult to deal with complex situations, and the latter lacks domain knowledge utilization and the decision-making process is opaque; causal analysis is concentrated on a single abstract level, cannot be analyzed across levels, and it is difficult to provide a complete causal chain explanation; the equipment management system lacks adaptive evolution capabilities and requires frequent manual intervention. These problems make the park's emergency equipment management face the challenges of low efficiency, insufficient accuracy, and poor interpretability. Therefore, there is an urgent need for an efficient management method that integrates domain knowledge and multi-source heterogeneous data, realizes multi-scale causal analysis, and has adaptive evolution capabilities. Summary of the invention

[0003] The present invention provides an efficient management system and method for emergency equipment to solve the technical problem of "rich data but lack of wisdom" existing in related technologies, especially to solve the key technical problems of fusion of domain knowledge and multi-source heterogeneous data, multi-scale causal relationship analysis and adaptive updating of knowledge base.

[0004] The present invention provides an efficient management method for emergency equipment, comprising: Collect multi-source heterogeneous data of emergency equipment in chemical parks, and use semantically enhanced heterogeneous data integration algorithms to convert multi-source equipment data into normalized data sets with unified semantic representation; A hierarchical knowledge representation framework is used to transform chemical engineering domain expert knowledge into a domain knowledge graph with explicit causal annotations. Based on the normalized data set and domain knowledge graph, a multi-layer causal network model is constructed using a knowledge-guided causal discovery algorithm. Use multi-scale causal inference algorithms to extract equipment status analysis results and decision recommendations from multi-layer causal network models; The multi-layer causal network model is combined with the newly added data through the knowledge data bidirectional calibration algorithm to generate a dynamically updated causal knowledge base.

[0005] Furthermore, the semantically enhanced heterogeneous data integration algorithm introduces the chemical industry ontology model as a semantic constraint in the preprocessing process to ensure the consistency of data from different sources under a unified semantic framework. The specific implementation is as follows: ; in, represents the original heterogeneous data set, Represents the chemical industry ontology model, represents the preprocessing function for semantic enhancement, Represents the output normalized dataset.

[0006] Furthermore, the hierarchical knowledge representation framework includes: Concept layer, which defines the basic concepts and attributes of emergency equipment, hazard sources, and emergency response; The relational layer defines the semantic relationships between concepts; The rule layer represents deterministic causal relationships within the domain; The empirical layer represents the probabilistic association patterns based on historical cases.

[0007] Furthermore, the knowledge graph construction method uses a causal relationship enhanced knowledge graph construction method to generate a domain knowledge graph with explicit causal annotations, which is expressed as: ; in, is the domain knowledge graph, Represents a collection of entities, represents a general set of relations, represents a subset of relations with causal semantics, represents the causal strength of the relationship, calculated by the following equation: ; in, represents the causal strength prior based on expert knowledge, represents the causal strength learned from historical data, is the prior knowledge weight coefficient, Represents knowledge graphs The entity.

[0008] Furthermore, the causal relationship scoring function of the knowledge-guided causal discovery algorithm is: ; in, express and In the knowledge graph The causality score in represents mutual information, Representation variables and The mutual information between Indicates that in a given variable Under the condition of and The conditional mutual information between express and The set of potential common dependent variables, Representation based on knowledge graph Calculated variables The importance weight in causal inference is calculated as follows: ; in, Representation Node In the knowledge graph The centrality measure in Representation Node and , Semantic relevance in the knowledge graph, is the balance parameter; The multi-layer causal network is constructed by a hierarchical causal relationship clustering algorithm, which organizes the discovered causal relationships into a multi-layer causal network structure: ; in, represents a multi-layer causal network model, Respectively represent The causal network of layers, Represents the number of layers in a multi-layer causal network. Different layers represent causal relationships at different levels of abstraction. The layers are connected by abstract functions. and the concrete function connect: ; in Represents multi-layer causal networks No. The causal network of layers, .

[0009] Furthermore, the multi-scale causal inference algorithm is based on the Bayesian reasoning framework and introduces a cross-level causal transfer method. The specific model is expressed as follows: ; in, represents the target event, represents the observed data, represents a multi-layer causal network, represents a set of potential intermediate variables, Indicates that given the observed data and multi-layer causal networks Under the condition that The probability of occurrence, Indicates that given a set of potential intermediate variables Under the condition that The probability of occurrence, Indicates that given the observed data and multi-layer causal networks Under the condition of Probability of occurrence, integral symbol Represents the set of all possible potential intermediate variables For integration operations, a cross-level causal path perception method is introduced: ; in, Representing a multi-layer causal network The Layer network, Indicates The weight of the layer network in the current reasoning task, Indicates that given the observed data and Layer Causal Network Under the condition of Probability of occurrence.

[0010] Furthermore, the multi-scale causal reasoning algorithm also includes a cross-level causal penetration reasoning method, specifically using a causal path tracing algorithm: ; in, Respectively represent the observations To target event The first on the causal path Intermediate nodes, Indicates from arrive The total number of intermediate nodes in the causal chain, Indicates that from observation To target event The confidence of each possible causal path is evaluated by the following steps: ; in, represents a specific causal path, Causal Path The confidence level, Represented in a multi-layer causal network Middle Edge causal strength.

[0011] Furthermore, the knowledge data bidirectional calibration algorithm includes: Knowledge verification based on new data: ; in, represents a multi-layer causal network, represents a new dataset, Representing a multi-layer causal network For new dataset Knowledge verification, Represents the causal network for data points The prediction accuracy of Calibration of knowledge to data: ; in, Represents the original data, represents a multi-layer causal network, represents the knowledge confidence threshold, Represents the data after knowledge calibration, function Complete outlier detection and correction based on causal networks; Calibration of data to knowledge: ; in, represents the updated causal network, Represents the data confidence threshold, function Complete the knowledge graph structure and parameter updates based on new data, Represents a new dataset.

[0012] Furthermore, the knowledge data bidirectional calibration algorithm also includes an adaptive balancing method: ; in, represents the dynamic balance coefficient, is the initial equilibrium coefficient, is the attenuation factor, Measures the consistency between existing knowledge and new data. When knowledge and data are highly consistent, the system is more inclined to believe in existing knowledge. When deviations occur, the system will increase the emphasis on new data and accelerate knowledge updating.

[0013] The present invention provides an efficient management system for emergency equipment, comprising: Data collection module, used to collect multi-source heterogeneous data of emergency equipment in chemical parks; A data preprocessing module is used to convert multi-source equipment data into a normalized data set with unified semantic representation using a semantically enhanced heterogeneous data integration algorithm; A knowledge graph building module, which is used to transform chemical engineering domain expert knowledge into a domain knowledge graph with explicit causal annotations using a hierarchical knowledge representation framework; A multi-layer causal network building module is used to build a multi-layer causal network model based on a normalized data set and a domain knowledge graph using a knowledge-guided causal discovery algorithm; Multi-scale causal reasoning module, which is used to extract equipment status analysis results and decision suggestions from the multi-layer causal network model using multi-scale causal reasoning algorithm; The knowledge data bidirectional calibration module is used to combine the multi-layer causal network model with the newly added data through the knowledge data bidirectional calibration algorithm to generate a dynamically updated causal knowledge base.

[0014] The beneficial effects of the present invention are: the present invention uses a semantically enhanced heterogeneous data integration algorithm, a hierarchical knowledge representation framework, a knowledge-guided causal discovery algorithm, a multi-layer causal network model, a multi-scale causal reasoning algorithm, a causal path tracking mechanism, and a knowledge data bidirectional calibration algorithm to achieve deep integration of domain knowledge and multi-source heterogeneous data, cross-level causal analysis, interpretable reasoning results, and adaptive evolution of the system. In practical applications, the invention shortens the key decision-making time of emergency equipment management by 60%, improves the accuracy by 35%, and reduces maintenance costs by 25%, effectively solving the problem of "rich data but lack of wisdom" in the management of emergency equipment in chemical parks, and achieving efficient and intelligent management of the entire life cycle of emergency equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of an efficient management method for emergency equipment of the present invention; Figure 2 It is a flow chart of the multi-source heterogeneous data collection and semantic unification preprocessing steps of the present invention; Figure 3 It is a flowchart of the steps of formalizing domain knowledge modeling and building a knowledge graph of the present invention; Figure 4 is a flow chart of the knowledge-guided causal discovery and multi-layer network construction steps of the present invention; Figure 5 is a flow chart of the multi-scale causal reasoning and decision support analysis steps of the present invention; Figure 6 is a flow chart of the knowledge data bidirectional calibration and causal knowledge base update steps of the present invention; Figure 7 It is a comparison of the semantic integration effects of heterogeneous data of the present invention (correlation heat map); Figure 8 is an example of a causal knowledge graph in the field of explosion-proof equipment of the present invention (directed acyclic graph); Fig. 9is a prediction diagram of the impact of different maintenance strategies on equipment reliability of the present invention; Fig.10 It is a comparison chart of causal reasoning accuracy during 24 months of operation of the system of the present invention. DETAILED DESCRIPTION

[0016] The subject matter described herein will now be discussed with reference to example implementations. It should be understood that the discussion of these implementations is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the present specification. Various examples may omit, replace, or add various processes or components as needed. In addition, the features described in some examples may also be combined in other examples.

[0017] At least one embodiment of the present invention discloses an efficient management method for emergency equipment, which integrates chemical industry expertise with multi-source heterogeneous data to construct a multi-level causal relationship network to achieve accurate analysis and prediction of the status of emergency equipment, such as Figure 1 As shown, the following steps are included: Step 100, collecting multi-source heterogeneous data of emergency equipment in the chemical park, and using a semantically enhanced heterogeneous data integration algorithm to convert the multi-source equipment data into a normalized data set with a unified semantic representation; Step 101, collect the following four types of data from the chemical park emergency equipment management system: Equipment status data; environmental parameter data; management process data; domain document data.

[0018] Step 102: Apply a semantically enhanced data preprocessing algorithm to the collected heterogeneous data: Perform time alignment and missing value completion on structured data; perform semantic annotation and attribute extraction on semi-structured data; apply domain knowledge-enhanced natural language processing technology to unstructured text data to extract key entities and relationships.

[0019] The characteristic of this algorithm is that it introduces the chemical industry ontology model as the semantic constraint of the preprocessing process to ensure the consistency of data from different sources under a unified semantic framework. Its calculation formula is: ; in, represents the original heterogeneous data set, Represents the chemical industry ontology model, represents the preprocessing function for semantic enhancement, Represents the normalized dataset for the output. Preprocessing function Through the concept mapping and relational constraints of domain ontology, data from different sources and formats are mapped into a unified semantic space, thus laying the foundation for subsequent causal analysis.

[0020] The specific implementation of the semantically enhanced heterogeneous data integration algorithm adopts a three-stage processing flow: Ontology mapping stage: Map the fields and attributes in the original data to the concepts and relationships in the domain ontology, expressed in the form of RDF triples; Consistency verification phase: Check data consistency based on the constraint rules defined in the ontology; Semantic fusion stage: associating and integrating data items from different sources but semantically related.

[0021] The difference between this step and the existing technology is that the traditional method usually adopts an independent data processing flow and lacks unified constraints at the semantic level, which leads to semantic inconsistency problems of data from different sources; while this method introduces domain ontology as a semantic constraint, which ensures the consistency of concepts and relationships between different data sources and improves the accuracy of subsequent causal discovery.

[0022] Step 200, using a hierarchical knowledge representation framework to transform chemical engineering domain expert knowledge into a domain knowledge graph with explicit causal annotations; Step 201, formalize using a hierarchical knowledge representation model: Concept layer: defines the basic concepts and attributes of emergency equipment, hazardous sources, and emergency response; relationship layer: defines the semantic relationship between concepts; rule layer: represents the deterministic causal relationship within the domain; experience layer: represents the probabilistic association pattern based on historical cases.

[0023] Step 202, apply the knowledge acquisition enhancement algorithm to extract knowledge from three sources: Field standard documents and specifications; expert experience interview records; historical accident case library.

[0024] Step 203, construct a domain knowledge graph with multiple layers of causal constraints: The causal relationship enhanced knowledge graph construction method is applied to generate a domain knowledge graph with explicit causal annotations. The core of this method is a hybrid knowledge representation framework that integrates formal logical reasoning and probabilistic graphical models. Its calculation formula is: ; in, is the domain knowledge graph, Represents a collection of entities, represents a general set of relations, represents a subset of relations with causal semantics, Indicates the causal strength of the relationship.

[0025] Different from the traditional knowledge graph construction method, this step introduces a quantitative representation of causal strength, which is calculated by the following equation: ; in, represents the causal strength prior based on expert knowledge, represents the causal strength learned from historical data, is the prior knowledge weight coefficient, Represents knowledge graphs The entity.

[0026] The specific implementation of the hierarchical knowledge representation framework adopts the following technical routes: Based on OWL-DL (Web Ontology Language Description Logic), the concept layer and relationship layer are constructed to define the core concepts in the chemical industry and their classification hierarchy and attribute restrictions, and the deterministic causal relationship is expressed through the SWRL rule language; For the probabilistic association knowledge at the empirical level, a probabilistic ontology extension representation method is used to add confidence annotations to each relationship and support fuzzy reasoning. Each abstraction level is connected through a vertical association mechanism to ensure the consistency and traceability of knowledge of different granularities.

[0027] The implementation process of the knowledge graph construction method with enhanced causal relationships includes: Initial graph construction: Automatically extract triples from text based on domain terminology and knowledge patterns to build a basic graph; Causal relationship identification: Apply natural language processing technology to identify causal related words from text and extract causal relationship pairs; Causal Strength Calculation: Causal Strength of Expert Knowledge Obtained through structured questionnaire; data-driven causal strength Calculated through conditional probability and time series correlation analysis; weight coefficient Adaptive adjustment based on knowledge coverage.

[0028] The innovation of this step is to expand the traditional semantic relationship knowledge graph into a knowledge representation structure with causal reasoning capabilities, which not only represents the relationship between "what is" and "what has", but also represents the relationship between "causing what", providing a knowledge basis for subsequent causal reasoning. In addition, by introducing the quantitative representation of causal strength, the limitations of traditional knowledge graphs in representing uncertain causal relationships are solved.

[0029] Step 300, based on the normalized data set and the domain knowledge graph, a multi-layer causal network model is constructed using a knowledge-guided causal discovery algorithm; Step 301, calculate the conditional independence statistics between variables based on the normalized data set: For variable collections Any two variables in and , calculate the conditional mutual information: ; in: Indicates that in a given variable Under the condition of and The conditional mutual information between In the case of and The strength of the correlation between Represents a given condition variable; Representation variables , , The joint probability distribution of Respectively indicate that in the given Under the conditions, and The conditional probability distribution of The conditional probability distribution of The conditional probability distribution of ; Represents a variable , , Sum all possible values; is a collection of variables Medium and The subset of variables outside Represents a probability distribution.

[0030] Step 302, introduce the knowledge-constrained causal relationship scoring function: The core innovation of the present invention is to propose a knowledge-guided causal discovery function, whose calculation formula is: ; in, express and In the knowledge graph The causality score in represents mutual information, Representation variables and The mutual information between Indicates that in a given variable Under the condition of and The conditional mutual information between express and The set of potential common dependent variables; Representation based on knowledge graph Calculated variables The importance weight in causal reasoning is calculated as: ; in, Representation Node In the knowledge graph The centrality measure in Representation Node and , Semantic relevance in the knowledge graph, is a balance parameter.

[0031] Step 303, multi-layer causal network construction: Apply the hierarchical causal relationship clustering algorithm to organize the discovered causal relationships into a multi-layer causal network structure: ; in, represents a multi-layer causal network model, Respectively represent The causal network of layers, Represents the number of layers in a multi-layer causal network, where different layers represent causal relationships at different levels of abstraction. It is a multi-layer causal network model. The layers are separated by abstract functions. and the concrete function connect: ; in, Represents multi-layer causal networks No. The causal network of layers, .

[0032] The specific implementation steps of the knowledge-guided causal discovery algorithm include: Data preprocessing: Statistical preprocessing of the input normalized data set, including normalization, outlier processing, and missing value interpolation, so that the data meets the statistical requirements of the conditional independence test; Candidate causal pair generation: Based on the explicit causal relationship in the domain knowledge graph, the candidate causal pair set is initialized to reduce the complexity of the search space; Condition set optimization selection: For each pair of variables , select a subset of conditional variables from the set of variables When searching for a specific problem, it no longer uses exhaustive search, but makes heuristic selection based on the causal path information in the knowledge graph; Weight function calculation: centrality measure Calculate nodes using PageRank algorithm Weight in the knowledge graph; relevance Through the computing node and , The reciprocal of the shortest path distance between them.

[0033] For the hierarchical causal clustering algorithm, the implementation method is as follows: Basic layer construction: construct all fine-grained causal relationships into the lowest level causal network , nodes are specific device parameters and state variables; Hierarchical aggregation: Aggregate semantically related nodes into nodes at a higher level of abstraction based on the concept hierarchy defined in the domain ontology; Abstract edge generation: Based on the causal relationship strength between the underlying nodes, the causal relationship strength between the aggregated nodes is calculated to generate a high-level causal network ; Cross-layer indexing: building inter-layer mapping functions and , supporting bidirectional query and reasoning between causal networks at different levels of abstraction.

[0034] The uniqueness of this algorithm lies in its ability to simultaneously consider data statistical characteristics and domain knowledge constraints. In particular, when data is insufficient or noisy, domain knowledge can provide effective inductive bias and improve the accuracy and robustness of causal discovery. Compared with purely data-driven causal discovery methods, knowledge-guided causal discovery can better handle complex interactions between variables, especially in highly complex and high-risk scenarios such as emergency equipment management.

[0035] In addition, the multi-layer causal network structure can naturally support causal analysis of different granularities, from the microscopic equipment parameter relationship to the macroscopic management strategy impact, laying the foundation for subsequent multi-scale causal reasoning.

[0036] Step 400, extracting equipment status analysis results and decision suggestions from the multi-layer causal network model using a multi-scale causal reasoning algorithm; Step 401, mathematical model of multi-scale causal reasoning: The multi-scale causal inference model proposed in the present invention can be expressed as: ; in, represents the target event, represents the observed data, represents a multi-layer causal network, represents a set of potential intermediate variables, Indicates that given the observed data and multi-layer causal networks Under the condition that The probability of occurrence, Indicates that given a set of potential intermediate variables Under the condition that The probability of occurrence, Indicates that given the observed data and multi-layer causal networks Under the condition of Probability of occurrence, integral symbol Represents the set of all possible potential intermediate variables Perform integral operations; The difference from traditional Bayesian network reasoning is that this algorithm introduces a cross-level causal path perception method, and its calculation formula is: ; in, Representing a multi-layer causal network The Layer network, Indicates that given the observed data and Layer Causal Network Under the condition of The probability of occurrence, Indicates The weight of the layer network in the current reasoning task is calculated as follows: ; in, Represents observation data With target event In the Layer Causal Network on the relevance of It is the summation symbol dummy variable used to traverse all causal networks. Calculate observation data With target event In the Layer Causal Network The correlations at all levels are then summed as the normalized denominator.

[0037] Step 402, cross-level causal penetration reasoning method: The key innovation of this invention is to use a cross-level causal penetration reasoning method, which enables the reasoning process to simultaneously utilize causal relationships at different abstract levels. Specifically, the causal path tracing algorithm is used: ; in, Indicates that from observation To target event The first on the causal path Intermediate nodes, Belongs to the set of all possible intermediate nodes , Respectively represent the observations To target event The first on the causal path Intermediate nodes, Indicates from arrive The total number of intermediate nodes in the causal chain. This formula represents From all possible observations To target event The causal path of .

[0038] The algorithm evaluates the confidence of each path by following these steps: ; in, represents a specific causal path, Causal Path The confidence level, Represented in a multi-layer causal network Middle Edge causal strength.

[0039] Step 403, generate interpretable analysis results: Based on the results of multi-scale causal reasoning, equipment status analysis reports and decision-making recommendations are constructed, including: causal explanation of the current status of the equipment; root cause analysis of potential problems; targeted maintenance and management recommendations; and expected effects and confidence assessments of each recommended measure.

[0040] The innovation of this step is that traditional reasoning methods can usually only perform causal analysis at a single level of abstraction and cannot simultaneously consider the relationship between micro parameter changes and macro management decisions; while the multi-scale causal reasoning of the present invention can build a bridge between different levels of abstraction, revealing the complete causal chain from specific equipment parameters to overall management strategies, and providing decision makers with comprehensive and in-depth decision support.

[0041] In addition, through the causal path tracing algorithm, this system can provide a clear explanation for each reasoning result, answer the questions of "why is this the case" and "why do this", and improve the credibility and acceptability of the decision.

[0042] The specific implementation of the multi-scale causal inference algorithm adopts the following technical route: Evidence propagation initialization: observe the data According to the semantic mapping relationship, they are assigned to the corresponding nodes of the multi-layer causal network and the initial evidence variables are set; Intra-layer reasoning: In each layer of the causal network Internally, a variational Bayesian inference method is used to calculate the posterior probability distribution ,This method reduces computational complexity through approximate reasoning; Inter-layer weight calculation: based on observation data With target event Calculate adaptive weights at different levels of correlation ,in The function is realized through the information benefit measure; Multi-scale integration: The reasoning results at each level are integrated according to the weights to obtain the final reasoning result that takes into account multi-scale causal relationships.

[0043] The system integrates these different levels of causal relationships into a unified reasoning framework, outputs multi-level causal chains, and generates specific decision-making recommendations based on them.

[0044] For the causal path tracing algorithm, the implementation process is as follows: Path enumeration and pruning: Depth-first search based on multi-layer causal networks, starting from the observation node Set out to explore all possible events to reach the target Path, while using heuristic rules for pruning to avoid combinatorial explosion; Path confidence calculation: For each complete path , the overall confidence is obtained by accumulating the strength of all causal edges on the calculation path ; Path sorting and screening: sort all paths according to confidence and select the top-K paths as the basis for interpretation; Visual presentation: Convert the screened causal paths into intuitive visual representations to support interactive exploration by decision makers.

[0045] The innovation of this step is that traditional reasoning methods can usually only perform causal analysis at a single level of abstraction and cannot simultaneously consider the relationship between micro parameter changes and macro management decisions; while the multi-scale causal reasoning of the present invention can build a bridge between different levels of abstraction, revealing the complete causal chain from specific equipment parameters to overall management strategies, and providing decision makers with comprehensive and in-depth decision support.

[0046] Step 500, combining the multi-layer causal network model with the newly added data through a knowledge data bidirectional calibration algorithm to generate a dynamically updated causal knowledge base; Step 501, knowledge verification based on new data: Compare the newly generated data with the existing causal network predictions and calculate the validation score: ; in, represents a multi-layer causal network, represents a new dataset, Representing a multi-layer causal network For new dataset Knowledge verification, Represents the causal network for data points prediction accuracy.

[0047] Step 502, two-way calibration of knowledge updating and data correction: The core innovation of this invention is to adopt a two-way calibration method of knowledge and data: The calibration of knowledge to data (knowledge guides data understanding) is calculated as follows: ; in, Represents the original data, represents a multi-layer causal network model, represents the knowledge confidence threshold, Represents the data after knowledge calibration. Function Complete outlier detection and correction based on causal networks.

[0048] The calibration of data to knowledge (data-driven knowledge evolution) is calculated as follows: ; in, Represents the current multi-layer causal network model, Represents new data, represents the data confidence threshold, Represents the updated causal network model. Function Complete the knowledge graph structure and parameter updates based on new data.

[0049] Step 503, adaptive balancing method: Introducing dynamic balance coefficient , control the relative influence weight of knowledge and data at different stages: ; in, represents the dynamic balance coefficient, is the initial equilibrium coefficient, is the attenuation factor, Measures the consistency of existing knowledge with new data.

[0050] When knowledge and data are highly consistent, the system is more inclined to believe in existing knowledge; when deviations occur, the system will increase its emphasis on new data and accelerate knowledge updating.

[0051] The essential difference between this step and the prior art is that traditional methods either rely entirely on fixed rules defined by experts or rely entirely on data-driven model learning, lacking an effective fusion method; while the present invention adopts a two-way calibration and dynamic balance method of knowledge and data, enabling the system to continuously improve itself while maintaining stability, forming a continuously evolving intelligent closed loop.

[0052] The specific implementation process of the knowledge data bidirectional calibration algorithm includes: Knowledge verification process: Using Matthews correlation coefficient (MCC) as The specific implementation of ,evaluates the consistency between the prediction of the causal network and the actual data; when the verification score is lower than the preset threshold, the update process is triggered; Knowledge-to-data calibration function Implementation: Use the outlier detection method based on the causal network structure to identify and mark abnormal data points by comparing the degree of deviation between the observed value and the predicted value based on the causal network; for the identified abnormal points, use the conditional expected value of the causal network to correct or set the confidence weight; Data-to-knowledge calibration function Implementation: Combines an incremental structure learning algorithm and a parameter updating algorithm. The former is responsible for adding, deleting or reversing causal edges when new data provides sufficient evidence, and the latter uses weighted maximum likelihood estimation to update the strength parameters of existing causal edges. Consistency Metrics Calculation: The cross entropy and KL divergence are combined to calculate the difference between the knowledge model and the data distribution. The smaller the difference, the higher the consistency.

[0053] In actual application scenarios, such as the emergency drainage pump management system of a chemical park: Initial stage: The system constructs an initial causal network based on expert knowledge, taking "excessive vibration" as an important indicator of "bearing wear"; Operation phase: The system collected new equipment operation data and found that some pump groups did not show bearing problems even under high vibration conditions; Calibration process: The knowledge verification process found that the prediction accuracy about the relationship between vibration and bearing decreased; Knowledge-based data calibration analyzed abnormal data points and found that these pump groups were equipped with new vibration reduction devices; The causal network was adjusted from data to knowledge calibration, adding “vibration reduction device type” as a moderating variable in the relationship between vibration and bearing wear; Adaptive balancing adjusts the relative weights of knowledge priors and data-driven according to consistency changes.

[0054] After multiple rounds of two-way calibration, the system has formed a more accurate causal knowledge base: instead of simply directly associating "high vibration" with "bearing problem", it has established a conditional causal relationship of "high vibration + insufficient vibration reduction device → bearing problem", while retaining other effective causal laws in expert knowledge. This dynamic evolution capability enables the system to continuously improve itself in long-term operation and adapt to equipment technology updates and process changes.

[0055] In one embodiment of the present invention, an example of the aforementioned method for efficient management of emergency equipment is provided: Application scenario description: This implementation method has been successfully applied in a large chemical industry park in East China, which covers an area of ​​2.5 square kilometers and includes 32 chemical companies, covering multiple fields such as petrochemicals, fine chemicals, and pharmaceutical intermediates. More than 5,000 sets of emergency equipment are deployed in the park, including fixed gas detection and alarm systems, fire water cannon systems, emergency drainage pumps, portable explosion-proof communication equipment, and chemical protective clothing, as shown in Table 1: Table 1: Basic information of the park's emergency equipment:

[0056] Before the system was applied, the park faced the following technical challenges: there were many types of equipment and they were widely distributed, making daily supervision difficult; there was a serious data island phenomenon, and it was impossible to achieve integrated analysis of multi-source data; the equipment failure rate was high and the predictive maintenance capability was weak; the emergency response time was long and the equipment scheduling efficiency was low.

[0057] To solve the above problems, the park management department decided to apply the multi-layer causal network emergency equipment efficient management system of this implementation method, and formed a complete application case through the analysis of 2 years of operation data.

[0058] In the example of step 100, multi-source heterogeneous data of emergency equipment in the chemical park is collected, and the semantically enhanced heterogeneous data integration algorithm is used to convert the multi-source equipment data into a normalized data set with unified semantic representation: In actual application, the system first collects heterogeneous data from multiple data sources in the park and integrates them using semantically enhanced data preprocessing algorithms. Taking the portable gas detector in the park as an example, the system collects the following heterogeneous data, as shown in Table 2: Table 2: Heterogeneous data collection of portable gas detectors:

[0059] After applying the semantically enhanced heterogeneous data integration algorithm, the system generates a standardized semantic data model. The key to this process is to introduce the chemical industry ontology model as a semantic constraint to ensure the consistency of data from different sources under a unified framework. The integrated data is used for subsequent causal analysis and knowledge discovery, such as Figure 7 As shown; from Figure 7 It can be seen that after the semantically enhanced heterogeneous data integration algorithm is processed, the semantic association strength between different data sources is improved, and the average correlation is increased from 0.42 to 0.87, an increase of 107%. In particular, in terms of the key attributes of "device ID" and "sensor type", the integration achieves a high degree of consistency across data sources, laying a solid foundation for subsequent causal analysis.

[0060] In step 200, a hierarchical knowledge representation framework is used to transform chemical engineering domain expert knowledge into a domain knowledge graph with explicit causal annotations: This system adopts a hierarchical knowledge representation framework to transform the expert knowledge in the chemical industry into a domain knowledge graph with explicit causal annotations. In practical application, the system mainly obtains knowledge from the following three sources: park safety management specifications and national standards (such as the "Petrochemical Enterprise Design Fire Protection Specifications"); experience interview records of 10 senior safety management experts (a total of about 200 hours); and the park's historical accident case library (including 127 safety incident records of different levels).

[0061] The system applies the causal relationship enhanced knowledge graph construction method to generate a domain knowledge graph with explicit causal annotations. Some structures are as follows Figure 8 As shown; This knowledge graph contains causal relationships at three levels: micro-equipment parameter layer, meso-system function layer and macro-management decision-making layer. It not only includes the underlying technical relationship of "decreased sensor sensitivity → reduced gas detection accuracy", but also includes high-level management factors of "weak management awareness → insufficient budget → insufficient maintenance", realizing a unified representation of multi-level causal knowledge.

[0062] Compared with traditional methods, the knowledge graph constructed by this system is characterized by the introduction of a quantitative representation of causal strength. For example, the causal strength of "calibration cycle is too long → sensor sensitivity decreases" is 0.80, indicating that this is a strong causal relationship, while the causal strength of "ambient humidity is too high → sensor sensitivity decreases" is 0.55, indicating that this is a medium-strength causal relationship. This quantitative representation enables the system to prioritize multiple possible causal paths and improve the accuracy of reasoning.

[0063] In step 300, based on the normalized data set and the domain knowledge graph, a multi-layer causal network model is constructed using a knowledge-guided causal discovery algorithm: This system applies the knowledge-guided causal discovery algorithm in the implementation method to the management of the gas detection alarm system, conducts causal analysis based on about 6 months of real-time monitoring data (containing more than 2 million records) and domain knowledge graphs, and constructs a multi-layer causal network model.

[0064] In practical applications, traditional pure data-driven methods often produce a large number of false causal relationships when data is insufficient or noisy. For example, the system has observed a strong statistical correlation between "gas concentration readings" and "ambient light intensity" (correlation coefficient of 0.72), and pure data-driven methods may mistakenly infer that there is a causal relationship between the two. After introducing domain knowledge constraints, this system can identify that this correlation may be due to changes in the intensity of production activities caused by the alternation of day and night, rather than a direct causal relationship, thereby avoiding incorrect causal chain inferences.

[0065] The multi-layer causal network constructed by the system clearly shows the causal relationships at different levels of abstraction, as shown in Table 3: Table 3: Example of a multi-layer causal network for a gas detection alarm system:

[0066] The key innovation of this multi-layer network is to achieve causal penetration between different levels of abstraction. For example, the system can reveal the complete causal chain from "budget constraints" (L3) to "spare parts inventory" that ultimately affects "battery voltage" and "system reliability", helping managers understand how macro management decisions ultimately affect micro equipment performance through a series of intermediate links, so as to make more targeted decisions.

[0067] In step 400, a multi-scale causal inference algorithm is used to extract equipment status analysis results and decision suggestions from a multi-layer causal network model: In the park emergency equipment management, the system applies a multi-scale causal reasoning algorithm to extract equipment status analysis results and decision-making suggestions from the multi-layer causal network model. The following is a real case to demonstrate the system's reasoning ability: Case background: During a routine inspection, the North District of the park found that three portable combustible gas detectors had alarm delay problems and the alarm thresholds deviated from the set values. Traditional methods cannot accurately determine the root cause and may lead to incorrect maintenance decisions.

[0068] The system conducted a comprehensive analysis of the problem based on a multi-scale causal inference algorithm and identified the following three most likely causal paths: Path 1 (0.83): “Calibration cycle is too long → sensor sensitivity decreases → detection accuracy decreases → alarm delay”; Path 2 (0.71): “Insufficient spare parts inventory → Replacement of non-original sensors → Compatibility issues → Reduced detection accuracy → Alarm delay”; Path 3 (0.45): “Insufficient operator training → incorrect threshold setting → alarm delay”; The system further retrieved the equipment maintenance records and inventory records and found that the last calibration time of these three devices was more than 9 months (the standard requirement is 6 months), and 2 of them were replaced with non-original sensors. Based on this finding, the system generated the following decision recommendations: Short-term measures: Immediately calibrate all portable gas detectors, with a high priority and a confidence level of 0.92. Medium-term measures: Adjust the calibration plan to ensure that the calibration cycle does not exceed 5 months. The priority is "medium" and the confidence level of the expected effect is 0.87. Long-term measures: Revise the spare parts procurement system to ensure that original parts are used for key components. The priority is "medium" and the confidence level of the expected effect is 0.78.

[0069] The system shows the impact of different maintenance strategies on equipment reliability prediction, such as Fig. 9 As shown; from Fig. 9 It can be seen that short-term measures (immediate calibration) can quickly improve equipment reliability, but the effect will gradually weaken; medium-term measures (adjusting the calibration cycle) can steadily improve equipment reliability within 3-6 months; long-term measures (revising the spare parts procurement system) may not have obvious initial effects, but can bring the most significant reliability improvement after 6 months.

[0070] This multi-scale causal reasoning decision support capability is superior to traditional methods. It can not only identify the root cause of the problem, but also accurately predict the long-term effects of different solutions, providing managers with a scientific basis for decision-making.

[0071] In step 500, the multi-layer causal network model is combined with the newly added data through the knowledge data bidirectional calibration algorithm to generate a dynamically updated causal knowledge base: In the long-term operation of the system, the knowledge data bidirectional calibration mechanism ensures the continuous optimization and evolution of the causal knowledge base. The following is a practical example: Case background: There is an expert experience rule in the initial knowledge base: "Ambient humidity is too high (humidity exceeds 85%) → sensor sensitivity decreases"; the causal strength is 0.75. However, after the system has been running for 6 months, data analysis found that the applicability of this rule on the new sensor has decreased.

[0072] The system first calculates the verification score The result is 0.62, which is lower than the preset threshold of 0.70, triggering the knowledge update process. During the knowledge update process, the system found that: The sensitivity of traditional catalytic combustion sensors does decrease significantly in high humidity environments (validation score 0.88); The new electrochemical sensor recently introduced by the park has relatively stable performance in high humidity environments (verification score 0.33).

[0073] Through the knowledge data bidirectional calibration algorithm, the system automatically adjusts the causal knowledge base: The cause-effect rule is modified to a conditional cause-effect rule: "Ambient humidity is too high + sensor type is catalytic combustion type → sensor sensitivity decreases", and the causal strength is adjusted to 0.88; Add a new causal rule: "Environmental humidity is too high + sensor type is electrochemical → sensor sensitivity decreases slightly" with a causal strength of 0.35.

[0074] This adaptive adjustment makes the knowledge base more consistent with the actual data and improves the accuracy of reasoning. Fig.10 As shown; Fig.10 The chart shows the changing trend of causal reasoning accuracy during the 24-month operation of the system through knowledge data bidirectional calibration. It can be observed that the causal reasoning accuracy of traditional methods (based only on fixed rules or data models) remained at around 70% during the entire operation period, without significant improvement; while the inference accuracy of this system gradually increased from 72.3% in the early stage to 94.2% through the knowledge data bidirectional calibration mechanism, an increase of 30.3%. In particular, the accuracy improvement was more obvious after 9 months of operation, indicating that with the accumulation of data and the iteration of knowledge, the self-learning and adaptability of the system continued to increase.

[0075] Technical effect verification After 24 months of application in a large chemical industrial park in East China, this system has achieved remarkable technical results in emergency equipment management, as shown in Table 4: Table 4: Comparison of key indicators before and after system application:

[0076] In addition, the system exhibits the following advantages during application: Deep fusion of knowledge and data: By integrating chemical industry expertise with multi-source heterogeneous data, the system can not only explain "why" a specific failure occurs, but also predict "when" and "how" to avoid potential problems. Compared with pure data-driven or pure rule-driven methods, the accuracy of root cause analysis is increased by 35%.

[0077] Multi-scale causal penetration effect: The system can establish a clear causal chain between micro-equipment parameters, meso-system functions and macro-management decisions, helping managers understand how management decisions affect equipment performance. For example, the system successfully identified the complete causal path of "budget cuts → reduced calibration frequency → decreased sensor accuracy → increased false alarm rate", providing a scientific basis for budget allocation for the park management.

[0078] Explainable reasoning effect: The system can provide clear explanations for each prediction and recommendation, making decisions more transparent and credible. In a revision of an emergency plan involving a fire monitor system, the system's recommendations received 98% expert approval, far higher than the 65% of traditional methods.

[0079] Adaptive evolution effect: Through two-way calibration of knowledge data, the system continuously improves itself in long-term operation. When dealing with changes in equipment updates and process changes, the system's adaptability is increased by 250% compared to traditional methods, and the model can be automatically adjusted without human intervention.

[0080] To sum up, this implementation method has fully verified the effectiveness and advancement of the efficient management method of emergency equipment based on multi-layer causal network theory in practical applications, realized the refined and intelligent management of the entire life cycle of emergency equipment in chemical parks, and improved the emergency management level and safety assurance capabilities.

[0081] The above describes an embodiment of the present invention, but this embodiment is not limited to the above-mentioned specific implementation mode. The above-mentioned specific implementation mode is merely illustrative and not restrictive. Under the guidance of this embodiment, ordinary technicians in this field can also make more forms of equivalent embodiments, all of which are within the protection of this embodiment.

Claims

1. An efficient management method for emergency equipment, characterized in that: The following steps are involved: Collect multi-source heterogeneous data of emergency equipment in chemical parks, and use semantically enhanced heterogeneous data integration algorithms to convert multi-source equipment data into normalized data sets with unified semantic representation; A hierarchical knowledge representation framework is used to transform chemical engineering domain expert knowledge into a domain knowledge graph with explicit causal annotations. Based on the normalized data set and domain knowledge graph, a multi-layer causal network model is constructed using a knowledge-guided causal discovery algorithm. Use multi-scale causal inference algorithms to extract equipment status analysis results and decision recommendations from multi-layer causal network models; The multi-layer causal network model is combined with the newly added data through the knowledge data bidirectional calibration algorithm to generate a dynamically updated causal knowledge base.

2. The method for efficient management of emergency equipment according to claim 1, characterized in that: The semantically enhanced heterogeneous data integration algorithm introduces the chemical industry ontology model as a semantic constraint in the preprocessing process to ensure the consistency of data from different sources under a unified semantic framework. The specific implementation is as follows: ； in, represents the original heterogeneous data set, Represents the chemical industry ontology model, represents the preprocessing function for semantic enhancement, Represents the output normalized dataset.

3. The method for efficient management of emergency equipment according to claim 1, characterized in that: The hierarchical knowledge representation framework includes: Concept layer, which defines the basic concepts and attributes of emergency equipment, hazard sources, and emergency response; The relational layer defines the semantic relationships between concepts; The rule layer represents deterministic causal relationships within the domain; The empirical layer represents the probabilistic association patterns based on historical cases.

4. The method for efficient management of emergency equipment according to claim 1, characterized in that: The knowledge graph construction method uses a causal relationship enhanced knowledge graph construction method to generate a domain knowledge graph with explicit causal annotations, which is expressed as: ； in, is the domain knowledge graph, Represents a collection of entities, represents a general set of relations, represents a subset of relations with causal semantics, represents the causal strength of the relationship, calculated by the following equation: ； in, represents the causal strength prior based on expert knowledge, represents the causal strength learned from historical data, is the prior knowledge weight coefficient, Represents knowledge graphs The entity.

5. The method for efficient management of emergency equipment according to claim 1, characterized in that: The causal relationship scoring function of the knowledge-guided causal discovery algorithm is: ； in, express and In the knowledge graph The causality score in represents mutual information, Representation variables and The mutual information between Indicates that in a given variable Under the condition of and The conditional mutual information between express and The set of potential common dependent variables, Representation based on knowledge graph Calculated variables The importance weight in causal inference is calculated as follows: ； in, Representation Node In the knowledge graph The centrality measure in Representation Node and , Semantic relevance in the knowledge graph, is the balance parameter; The multi-layer causal network is constructed by a hierarchical causal relationship clustering algorithm, which organizes the discovered causal relationships into a multi-layer causal network structure: ； in, represents a multi-layer causal network model, Respectively represent The causal network of the layer, Represents the number of layers in a multi-layer causal network. Different layers represent causal relationships at different levels of abstraction. The layers are connected by abstract functions. and the concrete function connect: ； in Represents multi-layer causal networks No. The causal network of the layer, .

6. The method for efficient management of emergency equipment according to claim 1, characterized in that: The multi-scale causal inference algorithm is based on the Bayesian reasoning framework and introduces a cross-level causal transfer method. The specific model is expressed as follows: ； in, represents the target event, represents the observed data, represents a multi-layer causal network, represents a set of potential intermediate variables, Indicates that given the observed data and multi-layer causal networks Under the condition that The probability of occurrence, Indicates that given a set of potential intermediate variables Under the condition that The probability of occurrence, Indicates that given the observed data and multi-layer causal networks Under the condition of Probability of occurrence, integral symbol Represents the set of all possible potential intermediate variables For integration operations, a cross-level causal path perception method is introduced: ； in, Representing a multi-layer causal network The Layer network, Indicates The weight of the layer network in the current reasoning task, Indicates that given the observed data and Layer Causal Network Under the condition of Probability of occurrence.

7. The method for efficient management of emergency equipment according to claim 6, characterized in that: The multi-scale causal reasoning algorithm also includes a cross-level causal penetration reasoning method, which specifically adopts a causal path tracing algorithm: ； in, Respectively represent the observations To target event The first on the causal path Intermediate nodes, Indicates from arrive The total number of intermediate nodes in the causal chain, Indicates that from observation To target event The confidence of each possible causal path is evaluated by the following steps: ； in, represents a specific causal path, Causal Path The confidence level, Represented in a multi-layer causal network Middle Edge causal strength.

8. The method for efficient management of emergency equipment according to claim 1, characterized in that: The knowledge data bidirectional calibration algorithm includes: Knowledge verification based on new data: ； in, represents a multi-layer causal network, represents a new dataset, Representing a multi-layer causal network For new data sets Knowledge verification, Represents the causal network for data points The prediction accuracy of Calibration of knowledge to data: ； in, Represents the original data, represents a multi-layer causal network, represents the knowledge confidence threshold, Represents the data after knowledge calibration, function Complete outlier detection and correction based on causal networks; Calibration of data to knowledge: ； in, represents the updated causal network, Represents the data confidence threshold, function Complete the knowledge graph structure and parameter updates based on new data, Represents a new dataset.

9. The method for efficient management of emergency equipment according to claim 8, characterized in that: The knowledge data bidirectional calibration algorithm also includes an adaptive balancing method: ； in, represents the dynamic balance coefficient, is the initial equilibrium coefficient, is the attenuation factor, Measures the consistency between existing knowledge and new data. When knowledge and data are highly consistent, the system is more inclined to believe in existing knowledge. When deviations occur, the system will increase the emphasis on new data and accelerate knowledge updating.

10. An efficient management system for emergency equipment, characterized in that: include: Data collection module, used to collect multi-source heterogeneous data of emergency equipment in chemical parks; A data preprocessing module is used to convert multi-source equipment data into a normalized data set with unified semantic representation using a semantically enhanced heterogeneous data integration algorithm; A knowledge graph building module, which is used to transform chemical engineering domain expert knowledge into a domain knowledge graph with explicit causal annotations using a hierarchical knowledge representation framework; A multi-layer causal network building module is used to build a multi-layer causal network model based on a normalized data set and a domain knowledge graph using a knowledge-guided causal discovery algorithm; Multi-scale causal reasoning module, which is used to extract equipment status analysis results and decision suggestions from the multi-layer causal network model using multi-scale causal reasoning algorithm; The knowledge data bidirectional calibration module is used to combine the multi-layer causal network model with the newly added data through the knowledge data bidirectional calibration algorithm to generate a dynamically updated causal knowledge base.

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