Security management knowledge graph construction method

Abstract

The invention provides a security management knowledge graph construction method. The method comprises the steps of obtaining target data required for constructing a security management knowledge graph from a preset database; the preset database comprises a coal preparation standard database, an accident case database and / or an operation instruction database; the target data is data related to coal dressing; identifying a target entity in the target data by adopting a preset large model, and generating a corresponding triple; determining relevance between the target entities based on the triple by adopting a deep learning model; and constructing a security management knowledge graph according to the target entities and the relevance among the target entities. According to the security management knowledge graph construction method, the security data is managed by constructing the knowledge graph, so that the query efficiency can be improved, and the security problem processing efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a method for constructing a security management knowledge graph. Background Art

[0002] With the continuous development of the coal industry, the amount of data generated in the coal preparation process is becoming increasingly large. This data is of great significance for improving coal preparation efficiency and ensuring production safety. However, traditional data management and analysis methods make it difficult to effectively integrate and utilize this information.

[0003] At present, traditional safety manuals, safety training and other materials on the market are all stored in databases or binary files as standard data. The knowledge obtained when consulting the data is relatively scattered, and the consulting efficiency is low, which affects the efficiency of handling safety issues.

[0004] Therefore, the current data management has the problem of low access efficiency, which affects the efficiency of handling security issues. Summary of the invention

[0005] The present application provides a method for constructing a security management knowledge graph to solve the problem that current data management has low access efficiency, which affects the efficiency of handling security issues.

[0006] The first aspect of the present application provides a method for constructing a security management knowledge graph, comprising:

[0007] Acquire target data required for constructing a safety management knowledge graph from a preset database; the preset database includes a coal preparation standard database, an accident case database and / or an operating procedure database; the target data is data related to coal preparation;

[0008] Using a preset large model to identify the target entity in the target data and generate a corresponding triple;

[0009] Determine the association between target entities based on the triples using a deep learning model;

[0010] Construct a security management knowledge graph based on the target entities and the associations between them.

[0011] Furthermore, in the above method, the step of obtaining target data required for constructing a security management knowledge graph from a preset database includes:

[0012] Acquire corresponding initial data from the coal preparation standard database; the initial data includes equipment information, operating procedure data, and emergency treatment measures data of the coal preparation industry;

[0013] Remove noise and process missing values ​​from the initial data to generate corresponding intermediate data;

[0014] In response to a user's instruction to approve the review of the intermediate data, the corresponding approved intermediate data is determined as target data.

[0015] Furthermore, in the above method, the step of using a preset large model to identify the target entity in the target data and generating a corresponding triplet includes:

[0016] Performing standardization processing on the target data to generate standard data corresponding to the target data;

[0017] The preset coal preparation terminology categories, preset prompt words, and standard data are input into a preset large model, the target entities in the standard data are extracted using named entity recognition, and the triples are generated using natural language processing; the preset coal preparation terminology categories include coal types, coal preparation processes, and pollutant emissions.

[0018] Furthermore, in the above method, the adopting of a deep learning model to determine the association between target entities based on the triples includes:

[0019] The triples are input into a deep learning model, and word vector technology is used to identify similarities between different target entities to determine the association.

[0020] Furthermore, the method as described above, after constructing the security management knowledge graph according to the target entities and the associations between the target entities, further includes:

[0021] In response to the user's knowledge retrieval operation on the security management knowledge graph, retrieve and determine whether the retrieved corresponding data is abnormal;

[0022] If it is determined that the corresponding data is abnormal, an early warning prompt will be given to the user.

[0023] Furthermore, the method as described above, before acquiring the target data required for constructing the security management knowledge graph from the preset database, further comprises:

[0024] Acquire basic knowledge of coal preparation standards from a knowledge collection platform; the basic knowledge is manually collected and input into the knowledge collection platform;

[0025] The basic knowledge is augmented by using a large model to generate the required coal preparation standard data;

[0026] The coal preparation standard data is stored in the preset database.

[0027] The second aspect of the present application provides a security management knowledge graph construction device, including:

[0028] An acquisition module is used to acquire target data required for constructing a safety management knowledge graph from a preset database; the preset database includes a coal preparation standard database, an accident case database and / or an operating procedure database; the target data is data related to coal preparation;

[0029] An identification module, used to identify the target entity in the target data using a preset large model and generate a corresponding triple;

[0030] A determination module, configured to determine the association between target entities based on the triples using a deep learning model;

[0031] The construction module is used to construct a security management knowledge graph based on target entities and the associations between target entities.

[0032] Furthermore, in the above device, the acquisition module is specifically used for:

[0033] Acquire corresponding initial data from the coal preparation standard database; the initial data includes equipment information, operating procedure data, and emergency treatment measures data of the coal preparation industry; perform noise removal and missing value processing on the initial data to generate corresponding intermediate data; and in response to the user's review and approval instruction for the intermediate data, determine the corresponding passed intermediate data as the target data.

[0034] Furthermore, in the above device, the identification module is specifically used for:

[0035] The target data is standardized to generate standard data corresponding to the target data; the preset coal preparation terminology categories, preset prompt words, and standard data are input into a preset large model to extract the target entities in the standard data using named entity recognition, and the triples are generated using natural language processing; the preset coal preparation terminology categories include coal types, coal preparation processes, and pollutant emissions.

[0036] Further, in the above device, the determining module is specifically used for:

[0037] The triples are input into a deep learning model, and word vector technology is used to identify similarities between different target entities to determine the association.

[0038] Furthermore, the device as described above further comprises:

[0039] The early warning module is used to respond to the user's knowledge retrieval operation on the security management knowledge graph, retrieve and determine whether the corresponding data retrieved is abnormal; if it is determined that the corresponding data is abnormal, an early warning prompt is given to the user.

[0040] Furthermore, the device as described above further comprises:

[0041] The knowledge collection module is used to obtain basic knowledge of coal preparation standards from a knowledge collection platform; the basic knowledge is manually collected and input into the knowledge collection platform; the basic knowledge is augmented by a large model to generate the required coal preparation standard data; and the coal preparation standard data is stored in a preset database.

[0042] A third aspect of the present application provides an electronic device, including: a memory and a processor;

[0043] The memory stores computer-executable instructions;

[0044] The processor executes the computer-executable instructions stored in the memory to implement the security management knowledge graph construction method as described in any one of the first aspects.

[0045] The fourth aspect of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the security management knowledge graph construction method described in any one of the first aspects.

[0046] The fifth aspect of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the security management knowledge graph construction method described in any one of the first aspects.

[0047] The present application provides a method for constructing a safety management knowledge graph, which includes: obtaining target data required for constructing a safety management knowledge graph from a preset database; the preset database includes a coal preparation standard database, an accident case database and / or an operating procedure database; the target data is data related to coal preparation; using a preset large model to identify the target entity in the target data and generate corresponding triples; using a deep learning model to determine the correlation between target entities based on the triples; and constructing a safety management knowledge graph based on the target entity and the correlation between each target entity. The method for constructing a safety management knowledge graph of the present application obtains the target data required for constructing a safety management knowledge graph from a preset database, uses a preset large model to identify the target entity in the target data, and generates corresponding triples. At the same time, a deep learning model is used to determine the correlation between target entities based on the triples, so that a safety management knowledge graph can be constructed based on the target entity and the correlation between each target entity. Managing safety data by constructing a knowledge graph can improve query efficiency and improve the efficiency of handling safety issues. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0049] Figure 1 A scenario diagram for implementing the method for constructing a security management knowledge graph according to an embodiment of the present application;

[0050] Figure 2 Schematic diagram of the process of constructing the security management knowledge graph provided for this application Figure 1 ;

[0051] Figure 3 Schematic diagram of the process of constructing the security management knowledge graph provided for this application Figure 2 ;

[0052] Figure 4 A schematic diagram of the overall process of the security management knowledge graph construction method provided for this application;

[0053] Figure 5 A schematic diagram of the structure of the security management knowledge graph construction device provided in this application;

[0054] Figure 6 A schematic diagram of the structure of the electronic device provided in this application.

[0055] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0056] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0057] It should be noted that the method for constructing a security management knowledge graph disclosed in the present invention can be used in the field of artificial intelligence technology. It can also be used in any field other than the field of artificial intelligence technology. The application field of the method for constructing a security management knowledge graph disclosed in the present invention is not limited.

[0058] The technical solution of the present application is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0059] In order to clearly understand the technical solution of this application, the concept of this solution is first introduced in detail. Traditional data management methods are difficult to cope with the complex data environment of coal preparation plants, and there are problems such as data islands and information asymmetry.

[0060] With the continuous development of the coal industry, the amount of data generated in the coal preparation process is becoming increasingly large. This data is of great significance for improving coal preparation efficiency and ensuring production safety. However, traditional data management and analysis methods make it difficult to effectively integrate and utilize this information.

[0061] At present, traditional safety manuals, safety training and other materials on the market are all stored in databases or binary files as standard data. The knowledge obtained when consulting the data is relatively scattered, and the consulting efficiency is low, which affects the efficiency of handling safety issues.

[0062] Therefore, the current data management has the problem of low access efficiency, which affects the efficiency of handling safety issues. Therefore, in response to the problems in the prior art, the inventors found in their research that in order to solve the problem, the coal preparation safety knowledge data can be integrated through the knowledge graph to improve the efficiency of information access. At the same time, through the knowledge graph, users can quickly locate safety issues, thereby improving the efficiency of handling safety issues. Among them, the knowledge graph is a technology for representing and organizing information, which aims to describe entities and their relationships in the form of nodes and edges.

[0063] Specifically, the target data required for constructing the safety management knowledge graph is obtained from the preset database. The preset database includes a coal preparation standard database, an accident case database and / or an operating procedure database. The target data is data related to coal preparation. The preset large model is used to identify the target entities in the target data and generate corresponding triples. The deep learning model is used to determine the correlation between the target entities based on the triples. The safety management knowledge graph is constructed based on the target entities and the correlation between the target entities.

[0064] The method for constructing a security management knowledge graph of the present application obtains the target data required for constructing the security management knowledge graph from a preset database, uses a preset large model to identify the target entities in the target data, and generates corresponding triples. At the same time, a deep learning model is used to determine the correlation between target entities based on triples, so that a security management knowledge graph can be constructed based on the target entities and the correlation between each target entity. Managing security data by constructing a knowledge graph can improve query efficiency and improve the efficiency of handling security issues.

[0065] Based on the above creative findings, the inventor proposed the technical solution of the present application.

[0066] The following is an introduction to the application scenarios of the security management knowledge graph construction method provided in the embodiment of the present application. Figure 1 As shown, 1 is a first electronic device, and 2 is a second electronic device. The first electronic device 1 can be a computer, a tablet computer, etc. The second electronic device 2 can be various database servers, etc. The database server can be a coal standard database, an accident case database, or an operating procedure database, etc.

[0067] For example, when it is necessary to construct a safety management knowledge graph, the first electronic device 1 obtains the target data required for constructing the safety management knowledge graph from the second electronic device 2, wherein the target data is data related to coal preparation. That is, ① the second electronic device 2 sends the target data to the first electronic device 1, and the first electronic device 1 performs the following processing:

[0068] ② Use the preset large model to identify the target entities in the target data and generate corresponding triples.

[0069] ③ Use deep learning models to determine the association between target entities based on triples.

[0070] ④ Construct a security management knowledge graph based on the target entities and the correlation between them.

[0071] After constructing the safety management knowledge graph, the safety management knowledge graph can be further reviewed and verified through expert review to improve the accuracy of the safety management knowledge graph.

[0072] The embodiments of the present application are introduced below in conjunction with the drawings in the specification.

[0073] Figure 2 Schematic diagram of the process of constructing the security management knowledge graph provided for this application Figure 1 ,like Figure 2 As shown, in this embodiment, the execution subject of the embodiment of the present application is a security management knowledge graph construction device, which can be integrated into an electronic device, such as a tablet computer, a computer, etc. The security management knowledge graph construction method provided in this embodiment includes the following steps:

[0074] S101, obtaining target data required for constructing a safety management knowledge graph from a preset database. The preset database includes a coal preparation standard database, an accident case database and / or an operating procedure database. The target data is data related to coal preparation.

[0075] In this embodiment, the preset database may be a combination of a coal standard database, an accident case database, and an operating procedure database, or any one of them. The coal standard database is used to store standard data on coal preparation, such as coal types, coal preparation processes, etc., the accident case database is used to store case data related to safety occurrences, and the operating procedure database is used to store safety-related operating procedures.

[0076] S102, using a preset large model to identify target entities in the target data and generate corresponding triples.

[0077] In this embodiment, the preset large model can adopt a language large model, and the language large model can identify the target entity in the target data through natural language processing (NLP) technology and named entity recognition (NER) technology, and generate corresponding triples. Named entity recognition technology is a technical task in natural language processing (NLP), which aims to identify and classify named entities from text. Named entities usually include words or phrases of specific categories such as names of people, places, names of organizations, time expressions, and quantity expressions. In this embodiment, the named entities can be entities such as coal types, coal preparation processes, and safety specifications.

[0078] In this embodiment, a triple is a basic structure for representing knowledge. A triple consists of three parts: subject, predicate, and object. This structure is usually used to describe entities and their relationships, similar to a simple sentence in natural language. Generating a triple corresponding to the target data can provide a basis for subsequently determining the relationship between entities.

[0079] S103, using a deep learning model to determine the association between target entities based on triples.

[0080] In this embodiment, the deep learning model can adopt models such as BERT (Bidirectional Encoder Representations from Transformers) and open source versions of GPT (Generative Pre-trained Transformer), so as to improve the effect of determining the correlation between target entities.

[0081] S104, constructing a security management knowledge graph based on target entities and the associations between target entities.

[0082] In this embodiment, a basic framework schema can be created based on the target entities and the associations between the target entities. According to the schema, tools such as Excel can be used to standardize the cleaned data, such as the correspondence between rows and columns, and mapping them to corresponding entity types.

[0083] Based on the above knowledge representation, a security management knowledge graph is constructed. The graph represents entities through nodes and the relationship between entities through edges. The extracted data is then displayed in the form of tables and Excel. The data with high credibility is selected by using the identification ability of large models or manual review methods, and then stored in a graph database such as Neo4j to store and manage the knowledge graph, thus forming a knowledge network containing rich semantic information.

[0084] Optionally, the latest standards and safety management information can be obtained from the coal preparation standard database on a regular basis to automatically update the knowledge graph to ensure the real-time and accuracy of the graph. At the same time, a maintenance mechanism is established to correct erroneous information in the graph and improve the graph structure.

[0085] A method for constructing a safety management knowledge graph provided in an embodiment of the present application includes: obtaining target data required for constructing a safety management knowledge graph from a preset database. The preset database includes a coal preparation standard database, an accident case database and / or an operating procedure database. The target data is data related to coal preparation. A preset large model is used to identify target entities in the target data, and corresponding triples are generated. A deep learning model is used to determine the correlation between target entities based on triples. A safety management knowledge graph is constructed based on the target entity and the correlation between each target entity.

[0086] The method for constructing a security management knowledge graph of the present application obtains the target data required for constructing the security management knowledge graph from a preset database, uses a preset large model to identify the target entities in the target data, and generates corresponding triples. At the same time, a deep learning model is used to determine the correlation between target entities based on triples, so that a security management knowledge graph can be constructed based on the target entities and the correlation between each target entity. Managing security data by constructing a knowledge graph can improve query efficiency and improve the efficiency of handling security issues.

[0087] Figure 3 Schematic diagram of the process of constructing the security management knowledge graph provided for this application Figure 2 ,like Figure 3 As shown, the security management knowledge graph construction method provided in this embodiment is further refined on the basis of the security management knowledge graph construction method provided in the previous embodiment of this application. The security management knowledge graph construction method provided in this embodiment includes the following steps.

[0088] S201, obtaining corresponding initial data from the coal preparation standard database. The initial data includes equipment information, operating procedure data, and emergency treatment measure data of the coal preparation industry.

[0089] In this embodiment, the operation procedure data includes data such as equipment operation and coal preparation process, and the equipment information of the coal preparation industry includes various equipment information of the coal preparation industry, such as screening equipment, analyzers, etc. The emergency treatment measure data includes the emergency treatment measure data corresponding to safety problems, such as how to operate the equipment, how to perform self-rescue, etc.

[0090] Optionally, in this embodiment, before S201, a knowledge expansion and collection process may also be included, which is specifically as follows:

[0091] The basic knowledge of coal preparation standards is obtained from the knowledge collection platform. The basic knowledge is manually collected and input into the knowledge collection platform.

[0092] A large model is used to augment the basic knowledge and generate the required coal preparation standard data.

[0093] The coal preparation standard data is stored in the preset database.

[0094] In this embodiment, the human being may be an expert in the field of coal preparation, and inputs the collected knowledge in the field into the knowledge collection platform.

[0095] In this embodiment, a large model is used to augment the basic knowledge, which can be to augment synonyms or antonyms, thereby expanding the capacity of the basic knowledge and making the final generated coal preparation standard data cover a wider range, providing a better data capacity foundation for the subsequent generation of knowledge graphs.

[0096] For professional terms, they can be vectorized in the form of documents or slices and stored in a vector database, and then the large model can be trained to understand and perform synonym self-augmentation.

[0097] When augmenting, the augmented data can also be further reviewed and screened through expert review and verification to retain the data available in the coal preparation field.

[0098] S202, performing noise removal and missing value processing on the initial data to generate corresponding intermediate data.

[0099] In this embodiment, noise removal and missing value processing of the initial data can also increase the verification process, such as using the identification ability of the large model and experts to review the knowledge to ensure the reliability of the intermediate data, thereby improving the accuracy of the knowledge graph.

[0100] S203, in response to the user's instruction to approve the review of the intermediate data, determining the corresponding approved intermediate data as target data.

[0101] In this embodiment, the reliability of the intermediate data can be improved through manual methods, such as further review of the intermediate data by experts, thereby improving the accuracy of the knowledge graph.

[0102] S204, using a preset large model to identify the target entity in the target data and generate a corresponding triple.

[0103] Optionally, in this embodiment, S204 may be specifically as follows:

[0104] The target data is standardized to generate standard data corresponding to the target data.

[0105] The preset coal preparation term categories, preset prompt words, and standard data are input into the preset large model to extract the target entities in the standard data using named entity recognition, and generate triples using natural language processing. The preset coal preparation term categories include coal types, coal preparation processes, and pollutant emissions.

[0106] In this embodiment, a) coal types include: anthracite, bituminous coal, and lignite. Description: The characteristics and application fields of different coal types, and their influence on equipment and processes in the coal preparation process.

[0107] b) Coal preparation processes include: heavy medium separation, flotation, and screening. Note: Process description of each process, applicable conditions, and its impact on the selection of coal types and product quality.

[0108] c) Pollutant emissions include: sulfur dioxide, nitrogen oxides, and dust. Note: Emission standards, testing methods, and potential impacts on the environment.

[0109] In this embodiment, examples of preset prompt words are as follows:

[0110] Extract all eligible entity pairs and corresponding relationship types from a given document. Your generated content must meet all the following conditions.

[0111] 1. The entity type only contains {entity_list}.

[0112] {entity_detail}

[0113] 2. The relation type only contains {relation_list}.

[0114] 3. The entity types and relationship types corresponding to the entity pairs can only have the following sets of construction patterns:

[0115] {triplet_detail}

[0116] 4. Entities must be explicitly mentioned in the document, and only two entities with a certain relationship can be considered as entity pairs.

[0117] 5. Output format: a list consisting of json format data. Be careful not to output duplicate content and output a complete list.

[0118] [ {"head entity": "", "head entity type": "", "relationship type": "", "tail entity": "", "tail entity type": ""}, {"head entity": "", "head entity type": "", "relationship type": "", "tail entity":"", "tail entity type": ""}]

[0119] S205, inputting the triples into the deep learning model, and using word vector technology to identify the similarities between different target entities to determine the relevance.

[0120] In this embodiment, since the technical terms in the coal preparation field are relatively professional, for synonyms or ambiguous words in the coal preparation field, word vector technology such as Word2Vec or GloVe can be used to identify the similarities between different target entities and establish a mapping relationship, thereby determining the correlation between similar target entities.

[0121] For non-similar target entities that are logically related or have specific associations in the coal preparation field, the associations between entities can be directly determined through a pre-trained deep learning model.

[0122] S206, constructing a security management knowledge graph based on the target entities and the associations between the target entities.

[0123] Optionally, in this embodiment, after S206, an early warning function may be added when the knowledge graph is actually used, as follows:

[0124] In response to the user's knowledge retrieval operation on the security management knowledge graph, retrieve and determine whether the corresponding data retrieved is abnormal.

[0125] If it is determined that the corresponding data is abnormal, an early warning prompt will be given to the user.

[0126] In this embodiment, the data in the knowledge graph can be updated regularly. At the same time, for the corresponding data retrieved by the user, it can be determined whether it is a normal value or an abnormal value. Therefore, if the data is abnormal, an early warning prompt can be given to the user to avoid security-related impacts.

[0127] Optionally, in this embodiment, the large model and the large screen display system at the user's site can be used to monitor in real time whether the specific data displayed on the large screen is normal or abnormal. The backend server actively calls the graph data for query detection and gives possible reasons and solutions. The user can also actively ask for abnormal reasons and solutions in the large model system.

[0128] In order to further illustrate the security management knowledge graph construction method of this application, the following will be combined with Figure 4 Describe the overall construction process.

[0129] like Figure 4 As shown, the security management knowledge graph construction method of this embodiment mainly includes six processes:

[0130] 1. Data collection: Collect relevant data from the coal preparation standard database, including equipment information, operating procedures, safety standards and other professional books. Establish a knowledge collection platform and set up a knowledge expert system to use the knowledge collection platform to collect basic knowledge. With the help of a large model, the basic data is summarized and expanded to obtain high-quality data that meets the field.

[0131] 2. Data cleaning: remove noise and irrelevant information, process missing values, and ensure data quality. This part requires the identification ability of large models and the review of knowledge by experts to ensure the reliability of subsequent data processing.

[0132] 3. Entity recognition: manually or with the help of large models to identify key entities in the data, such as names of people, places, organizations, etc.

[0133] 4. Extract triples: Generate triples through a large model based on preset coal preparation terminology categories, preset prompt words, and standard data.

[0134] 5. Relationship extraction: Analyze the associations between entities and extract the relationships between them.

[0135] 6. Graph construction: Integrate the above information into a graph, using nodes to represent entities and edges to represent the relationships between entities.

[0136] like Figure 4 As shown, the data collection and data cleaning steps can be implemented manually or by equipment automation to ensure the reliability and standardization of the data, which is the basis for building a high-quality and highly reliable map. Data related to safety management is collected from the coal preparation standard database, including but not limited to operating procedures, risk assessment standards, emergency response measures, etc. The collected data is cleaned, formatted and standardized to ensure the consistency and integrity of the data. This step can be implemented manually using third-party tools.

[0137] During the entity recognition step, technicians can start to intervene and standardize the acquired data or information, such as document format conversion and document content acquisition, which all need to be operated through programs. Only after the data is acquired can the entity be recognized, such as abstractly extracting the corresponding nouns from the text data. This process can use natural language processing (NLP) technology to extract key elements in the data.

[0138] In the process of associating entities, the following methods can also be adopted: 1. Reinforcement learning: Introduce reinforcement learning technology to optimize the model's decision-making in the association and extraction process. 2. Expert knowledge feedback: Establish an expert review mechanism to regularly review and correct the extracted entities and relationships to improve accuracy. 3. Dynamic update mechanism: Build a mechanism to introduce new terms and technological developments in a timely manner to maintain the timeliness of the knowledge graph.

[0139] In addition, graph databases (such as Neo4j) can be used to store and manage knowledge graphs, build data layers and model layers, and define entities, relationships, and attributes.

[0140] The safety management knowledge graph construction method of this embodiment, by introducing natural language processing technology, realizes the automatic understanding and knowledge extraction of standard texts and operating procedures, thereby reducing the workload of manual processing. At the same time, it solves the problem that safety production knowledge is difficult to apply and retrieve. In addition, safety management knowledge can also be classified, the essence can be extracted, and the correlation relationship of knowledge can be found. This can more efficiently solve safety production problems, solve standardization problems in coal preparation plant data management, and improve data management efficiency.

[0141] In implementation, the method of this embodiment automatically generates a safety management knowledge graph by capturing data from the coal preparation standard database, accident case database, and operating procedure documents, combined with natural language processing technology. Users can query safety specifications and risk assessment results related to a specific operation or equipment through the system interface. The system will generate safety management suggestions based on the information in the graph and issue an early warning when potential risks are detected. Through this system, managers can monitor and manage safety risks in the coal preparation process more efficiently to ensure the continued stability and safety of production.

[0142] Figure 5 A schematic diagram of the structure of the security management knowledge graph construction device provided in this application, such as Figure 5 As shown, in this embodiment, the security management knowledge graph construction device 300 can be set in an electronic device, and the security management knowledge graph construction device 300 includes:

[0143] The acquisition module 301 is used to acquire target data required for constructing the safety management knowledge graph from a preset database. The preset database includes a coal preparation standard database, an accident case database and / or an operating procedure database. The target data is data related to coal preparation.

[0144] The identification module 302 is used to identify the target entity in the target data using a preset large model and generate a corresponding triple.

[0145] The determination module 303 is used to determine the association between target entities based on triples using a deep learning model.

[0146] The construction module 304 is used to construct a security management knowledge graph based on the target entities and the associations between the target entities.

[0147] The security management knowledge graph construction device provided in this embodiment can be executed Figure 2 The technical solution of the method embodiment shown in the figure has the same implementation principle and technical effect as Figure 2 The method embodiments shown are similar and will not be described in detail here.

[0148] The security management knowledge graph construction device provided in this application is based on the security management knowledge graph construction device provided in the previous embodiment, and the security management knowledge graph construction device is further refined. The security management knowledge graph construction device 300 includes:

[0149] Optionally, in this embodiment, the acquisition module 301 is specifically used for:

[0150] The corresponding initial data is obtained from the coal preparation standard database. The initial data includes equipment information, operating procedure data, and emergency treatment measures data of the coal preparation industry. The initial data is subjected to noise removal and missing value processing to generate the corresponding intermediate data. In response to the user's review and approval instruction for the intermediate data, the corresponding passed intermediate data is determined as the target data.

[0151] Optionally, in this embodiment, the identification module 302 is specifically used for:

[0152] Standardize the target data and generate standard data corresponding to the target data. Input the preset coal preparation term categories, preset prompt words, and standard data into the preset large model, extract the target entities in the standard data by using named entity recognition, and generate triples by using natural language processing. The preset coal preparation term categories include coal type, coal preparation process, and pollutant emissions.

[0153] Optionally, in this embodiment, the determination module 303 is specifically used to:

[0154] The triplets are fed into a deep learning model, and word embedding techniques are used to identify similarities between different target entities to determine relevance.

[0155] Optionally, in this embodiment, the security management knowledge graph construction device 300 further includes:

[0156] The early warning module is used to respond to the user's knowledge search operation on the security management knowledge graph, retrieve and determine whether the corresponding data retrieved is abnormal. If it is determined that the corresponding data is abnormal, an early warning prompt is given to the user.

[0157] Optionally, in this embodiment, the security management knowledge graph construction device 300 further includes:

[0158] The knowledge collection module is used to obtain the basic knowledge of coal preparation standards from the knowledge collection platform. The basic knowledge is manually collected and input into the knowledge collection platform. The basic knowledge is augmented by a large model to generate the required coal preparation standard data. The coal preparation standard data is stored in a preset database.

[0159] The security management knowledge graph construction device provided in this embodiment can be executed Figure 2-Figure 4 The technical solution of the method embodiment shown in the figure has the same implementation principle and technical effect as Figure 2-Figure 4 The method embodiments shown are similar and will not be described in detail here.

[0160] According to an embodiment of the present application, the present application also provides an electronic device, a computer-readable storage medium and a computer program product.

[0161] like Figure 6 As shown, Figure 6 is a schematic diagram of the structure of the electronic device provided by the present application. The electronic device is intended to be electronic devices of various forms, such as a tablet computer, a computer, etc. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0162] like Figure 6 As shown, the electronic device includes: a processor 401 and a memory 402. The various components are connected to each other using different buses and can be installed on a common mainboard or in other ways as required. The processor can process instructions executed in the electronic device.

[0163] The memory 402 is a non-transient computer-readable storage medium provided in the present application. The memory stores instructions executable by at least one processor to enable at least one processor to execute the security management knowledge graph construction method provided in the present application. The non-transient computer-readable storage medium of the present application stores computer instructions, which are used to enable a computer to execute the security management knowledge graph construction method provided in the present application.

[0164] The memory 402 is a non-transient computer-readable storage medium that can be used to store non-transient software programs, non-transient computer executable programs and modules, such as the program instructions / modules corresponding to the security management knowledge graph construction method in the embodiment of the present application (for example, the attached Figure 5 The processor 401 executes various functional applications and data processing of the electronic device by running the non-transient software programs, instructions and modules stored in the memory 402, that is, the security management knowledge graph construction method in the above method embodiment is implemented.

[0165] In the above embodiments, it should be understood that the processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the invention can be directly implemented as a hardware processor, or can be implemented by a combination of hardware and software modules in the processor.

[0166] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (NVM), such as at least one disk storage.

[0167] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

[0168] At the same time, this embodiment also provides a computer product. When the instructions in the computer product are executed by the processor of an electronic device, the electronic device can execute the security management knowledge graph construction method of the above embodiment.

[0169] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0170] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0171] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0172] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0173] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0174] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0175] Those skilled in the art will appreciate that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed. The aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various media that can store program codes.

[0176] Those skilled in the art will readily come up with other implementations of the embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modifications, uses or adaptations of the embodiments of the present application, which follow the general principles of the embodiments of the present application and include common knowledge or customary technical means in the art that are not disclosed in the embodiments of the present application.

[0177] It should be understood that the embodiments of the present application are not limited to the precise structures described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the embodiments of the present application is limited only by the appended claims.

Claims

1. A method for constructing a safety management knowledge graph, characterized in that: include: Obtain the target data required for building a security management knowledge graph from the preset database; The preset database includes a coal preparation standard database, an accident case database and / or an operating procedure database; the target data is data related to coal preparation; Using a preset large model to identify the target entity in the target data and generate a corresponding triple; Determine the association between target entities based on the triples using a deep learning model; Construct a security management knowledge graph based on the target entities and the associations between them.

2. The method according to claim 1, characterized in that The step of obtaining target data required for constructing a security management knowledge graph from a preset database includes: Acquire corresponding initial data from the coal preparation standard database; the initial data includes equipment information, operating procedure data, and emergency treatment measures data of the coal preparation industry; Remove noise and process missing values ​​from the initial data to generate corresponding intermediate data; In response to a user's instruction to approve the review of the intermediate data, the corresponding approved intermediate data is determined as target data.

3. The method according to claim 1, characterized in that The using of a preset large model to identify the target entity in the target data and generating a corresponding triplet includes: Performing standardization processing on the target data to generate standard data corresponding to the target data; The preset coal preparation terminology categories, preset prompt words, and standard data are input into a preset large model, the target entities in the standard data are extracted using named entity recognition, and the triples are generated using natural language processing; the preset coal preparation terminology categories include coal types, coal preparation processes, and pollutant emissions.

4. The method according to claim 1, characterized in that The adopting of a deep learning model to determine the association between target entities based on the triples includes: The triples are input into a deep learning model, and word vector technology is used to identify similarities between different target entities to determine the association.

5. The method according to any one of claims 1 to 4, characterized in that: After the security management knowledge graph is constructed according to the target entities and the associations between the target entities, the following further includes: In response to the user's knowledge retrieval operation on the security management knowledge graph, retrieve and determine whether the retrieved corresponding data is abnormal; If it is determined that the corresponding data is abnormal, an early warning prompt will be given to the user.

6. The method according to any one of claims 1 to 4, characterized in that: Before obtaining the target data required for building the security management knowledge graph from the preset database, the method further includes: Acquire basic knowledge of coal preparation standards from a knowledge collection platform; the basic knowledge is manually collected and input into the knowledge collection platform; The basic knowledge is augmented by using a large model to generate the required coal preparation standard data; The coal preparation standard data is stored in the preset database.

7. A security management knowledge graph construction device, characterized in that: include: An acquisition module is used to obtain target data required for building a security management knowledge graph from a preset database; The preset database includes a coal preparation standard database, an accident case database and / or an operating procedure database; the target data is data related to coal preparation; An identification module, used to identify the target entity in the target data using a preset large model and generate a corresponding triple; A determination module, configured to determine the association between target entities based on the triples using a deep learning model; The construction module is used to construct a security management knowledge graph based on target entities and the associations between target entities.

8. An electronic device, characterized in that: include: Memory and processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the security management knowledge graph construction method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the security management knowledge graph construction method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, it implements the security management knowledge graph construction method as described in any one of claims 1 to 6.

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