Failure case knowledge management system construction method and device, medium and equipment

Abstract

The embodiment of the invention discloses a failure case knowledge management system construction method and device, a medium and equipment, and relates to the technical field of data management, and the method comprises the steps: carrying out the knowledge conversion of text data, and obtaining a failure keyword; performing embedding construction on the parameter signal data to obtain embedded data; according to the embedded data, performing rule generation by utilizing a large language model to obtain a failure rule; and according to the failure keyword and the failure rule, constructing a failure knowledge network to realize the construction of the knowledge management system. According to the method, the embedding technology is introduced for conversion, non-alignment knowledge-based conversion of multi-modal data of traditional text data and parameter signal data can be realized, and important time sequence information such as the relation and the time sequence of multi-dimensional monitoring parameters is subjected to knowledge-based conversion by three embedding means included in the embedding technology, so that the time sequence information of the multi-dimensional monitoring parameters is greatly improved. While the time sequence characteristics are reserved, the learning ability of the knowledge-based process for the time relationship in the failure process is improved, and the management effect of the knowledge management system of the failure case is improved.

Description

Technical Field

[0001] The present application relates to the field of data management technology, and specifically to a method, device, medium and equipment for constructing a failure case knowledge management system. Background Art

[0002] In the aviation field, various failure cases and fault data are highly correlated. The aviation mechanical structure is complex, and there is information interaction between various module units, which leads to the diversification of fault phenomena and fault types. Due to the close connection between equipment, there is a many-to-many mapping relationship between fault phenomena and fault causes; that is, the same fault cause will lead to multiple fault manifestations, or the same fault phenomenon is caused by different fault causes. However, the existing failure case information management system has a simple structure and cannot achieve efficient association and effective management for such complex relationships. Summary of the invention

[0003] The main purpose of this application is to provide a method, device, medium and equipment for constructing a failure case knowledge management system, aiming to solve the problem of poor management effect of failure case information management in the prior art.

[0004] To achieve the above objectives, the technical solutions adopted in the embodiments of the present application are as follows: In a first aspect, an embodiment of the present application provides a method for constructing a failure case knowledge management system, comprising the following steps: Convert text data into knowledge to obtain invalid keywords; Embedding and constructing parameter signal data to obtain embedded data; wherein the embedding and constructing includes absolute position embedding, temporal causal embedding, and multi-parameter alignment embedding; Based on the embedded data, a large language model is used to generate rules and obtain invalid rules; According to the failure keywords and failure rules, the failure knowledge network is constructed to realize the construction of knowledge management system.

[0005] In a possible implementation manner of the first aspect, embedding and constructing the parameter signal data to obtain embedded data includes: The parameter signal data is embedded and constructed, and the absolute position embedding, temporal causal embedding and multi-parameter alignment embedding of the parameter signal data are calculated respectively, and the calculated multiple embeddings are added to the parameter signal data to obtain the embedded data.

[0006] In a possible implementation of the first aspect, before constructing the invalidation knowledge network according to the invalidation keywords and the invalidation rules to realize the construction of the knowledge management system, the method further includes: Obtain invalid semantic units according to invalid keywords and embedded data; According to the failure keywords and failure rules, the failure knowledge network is constructed to realize the construction of the knowledge management system, including: According to the invalidation keywords and invalidation rules, with the graph network structure as the formal carrier, the invalidation semantic units as the nodes of the graph network, and the invalidation relations as the edges of the graph network, an invalidation knowledge network is constructed to realize the construction of a knowledge management system.

[0007] In a possible implementation of the first aspect, after constructing an invalidation knowledge network according to invalidation keywords and invalidation rules to implement construction of a knowledge management system, the method further includes: Purify the invalidation rules to obtain the target invalidation rules; The target failure rules are mapped back to the failure knowledge network to obtain the target failure knowledge network.

[0008] In a possible implementation of the first aspect, purifying the invalidation rules to obtain target invalidation rules includes: Sampling and extracting the rules in the failure knowledge network to form a rule set; After dividing the rule set into nodes, the rule calculation is performed based on the divided nodes to obtain the target failure rules.

[0009] In a possible implementation of the first aspect, after constructing an invalidation knowledge network according to invalidation keywords and invalidation rules to implement construction of a knowledge management system, the method further includes: Merge rules based on target failure cases to update failure rules; Based on the updated failure rules, the failure knowledge network is updated.

[0010] In a possible implementation of the first aspect, before performing knowledge conversion on the text data to obtain the invalid keywords, the method further includes: Based on historical failure cases, text data and parameter signal data are obtained.

[0011] In a second aspect, an embodiment of the present application provides a device for constructing a failure case knowledge management system, including: The conversion module is used to convert text data into knowledge and obtain invalid keywords; The embedding module is used to embed the parameter signal data to obtain embedded data; wherein the embedding construction includes absolute position embedding, temporal causal embedding and multi-parameter alignment embedding; A generation module is used to generate rules based on the embedded data using a large language model to obtain invalidation rules; Construction module,The construction module is used to construct an invalidation knowledge network according to invalidation keywords and invalidation rules to realize the construction of a knowledge management system.

[0012] In a third aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program. When the computer program is loaded and executed by a processor, the method for constructing a failure case knowledge management system as provided in any one of the first aspects above is implemented.

[0013] In a fourth aspect, an embodiment of the present application provides an electronic device, including a processor and a memory, wherein: The memory is used to store computer programs; The processor is used to load and execute a computer program so that the electronic device executes the failure case knowledge management system construction method provided in any one of the first aspects above.

[0014] Compared with the prior art, the beneficial effects of this application are: The present application proposes a method, device, medium and equipment for constructing a failure case knowledge management system. The method includes: performing knowledge conversion on text data to obtain failure keywords; performing embedding construction on parameter signal data to obtain embedded data; wherein the embedding construction includes absolute position embedding, temporal causal embedding and multi-parameter alignment embedding; according to the embedded data, using a large language model to generate rules to obtain failure rules; according to the failure keywords and failure rules, constructing a failure knowledge network to realize the construction of a knowledge management system. The present application obtains failure keywords and embedded data to generate failure rules to construct a failure knowledge network by performing knowledge conversion on text data and parameter signal data. Since the embedding technology is introduced in the conversion, the non-aligned knowledge conversion of multimodal data of traditional text data and parameter signal data can be realized, and the embedding technology includes absolute position embedding, temporal causal embedding and multi-parameter alignment embedding, and the important time series information such as the relationship and time series of multi-dimensional monitoring parameters is knowledge-based, and the learning ability of the knowledge-based process for the time relationship in the failure process is improved while retaining the time series characteristics, thereby improving the management effect of the knowledge management system of failure cases. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of the structure of an electronic device of a hardware operating environment involved in an embodiment of the present application; Figure 2 A flowchart of a method for constructing a failure case knowledge management system provided in an embodiment of the present application; Figure 3 A flowchart of a method for constructing a failure case knowledge management system provided in an embodiment of the present application in one implementation manner; Figure 4 A schematic diagram of a module of a failure case knowledge management system construction device provided in an embodiment of the present application; Markings in the figure: 101 - processor, 102 - communication bus, 103 - network interface, 104 - user interface, 105 - memory. DETAILED DESCRIPTION

[0016] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0017] The main solutions of the embodiments of the present application are: to convert text data into knowledge to obtain invalid keywords; to embed and construct parameter signal data to obtain embedded data; wherein the embedding construction includes absolute position embedding, temporal causal embedding and multi-parameter alignment embedding; based on the embedded data, a large language model is used to generate rules to obtain invalid rules; based on the invalid keywords and invalid rules, an invalid knowledge network is constructed to realize the construction of a knowledge management system.

[0018] In the aviation field, various failure cases and fault data are highly correlated. The aviation mechanical structure is complex, and there is information interaction between various module units, which leads to the diversification of fault phenomena and fault types. Due to the close connection between equipment, there is a many-to-many mapping relationship between fault phenomena and fault causes. The same fault cause will lead to multiple fault manifestations; the same fault phenomenon may be caused by different fault causes. However, the existing failure case information management system has a simple structure and cannot achieve efficient association and effective management for such complex relationships.

[0019] In addition, after a device fails, its information is mainly recorded in two ways: sensor acquisition parameters and staff records. However, due to the lack of alignment between sensor parameters and records, the existing system can only realize the organization and management of single data, resulting in the inability to meet the comprehensive analysis of equipment failures during the research process of failure information. In particular, the existing failure case management system requires manual input. During this process, the failure recorder cannot rely on subjective means to record data rich in timing parameter information of the failure process, resulting in the formation of islands between failure information of different modes, which has caused great difficulties in the management of failure cases.

[0020] All the above problems indicate that the existing failure case management system is not sufficient to achieve effective management of failure knowledge, which seriously hinders the development of aviation intelligence.

[0021] Therefore, the present application provides a solution, which obtains failure keywords and embedded data through knowledge conversion of text data and parameter signal data to generate failure rules for constructing a failure knowledge network. Since embedding technology is introduced in the conversion, it is possible to achieve non-aligned knowledge conversion of multimodal data of traditional text data and parameter signal data. The embedding technology includes absolute position embedding, time series causal embedding and multi-parameter alignment embedding, which converts important time series information such as the relationship and time series of multi-dimensional monitoring parameters into knowledge, while retaining the time series characteristics, the learning ability of the knowledge process for the time relationship in the failure process is improved, thereby improving the management effect of the knowledge management system of failure cases.

[0022] See attached Figure 1 , attached Figure 1 This is a schematic diagram of the structure of an electronic device of the hardware operating environment involved in the embodiment of the present application. The electronic device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. Among them, the communication bus 102 is used to realize the connection and communication between these components. The user interface 104 may include a display (Display), an input unit such as a keyboard (Keyboard), and the user interface 104 may also include a standard wired interface and a wireless interface. The network interface 103 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 105 may optionally be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (RAM) memory, or it may be a stable non-volatile memory (NVM), such as at least one disk storage. The processor 101 may be a general-purpose processor, including a central processing unit, a network processor, etc., or it may be a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component.

[0023] Those skilled in the art will appreciate that Figure 1 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0024] As attached Figure 1 As shown, the memory 105 as a storage medium may include an operating system, a network communication module, a user interface module, and a failure case knowledge management system construction device.

[0025] In the attached Figure 1 In the electronic device shown, the network interface 103 is mainly used for data communication with the network server; the user interface 104 is mainly used for data interaction with the user; the processor 101 and the memory 105 in the present application can be set in the electronic device, and the electronic device calls the failure case knowledge management system construction device stored in the memory 105 through the processor 101, and executes the failure case knowledge management system construction method provided in the embodiment of the present application.

[0026] See attached Figure 2 Based on the hardware device of the aforementioned embodiment, the embodiment of the present application provides a method for constructing a failure case knowledge management system, comprising the following steps: S10: Perform knowledge conversion on the text data to obtain invalid keywords.

[0027] In the specific implementation process, the existing text data is converted into knowledge, and words or phrases related to the failure topic are automatically extracted from the text. These keywords can summarize the subject and content of the failure record. The failure semantic extraction method mainly includes failure entity extraction, failure relationship extraction, failure time extraction and failure position extraction. The failure keywords are extracted through keyword extraction algorithms, including but not limited to TF-IDF algorithm, TextRank algorithm, LSA algorithm, LDA algorithm, etc., to achieve semantic mapping representation of data. Knowledge conversion is to encode knowledge in a certain form and structure for easy storage, dissemination and sharing. Encoded knowledge can be in the form of text, pictures, audio, video, etc., or it can be structured data and information. Encoded knowledge can be disseminated and shared through digitalization, networking and other technical means, making the acquisition and utilization of knowledge more convenient and efficient.

[0028] S20: Embedding and constructing the parameter signal data to obtain embedded data; wherein the embedding and construction includes absolute position embedding, temporal causal embedding, and multi-parameter alignment embedding.

[0029] In the specific implementation process, embedding and constructing parameter signal data is actually converting it into knowledge. Through rule-oriented time series embedding technology, combined with the technology of logical rules in time series data and knowledge graph, entities, relationships and their information changing over time are embedded and represented, which can capture and utilize the time dependency between entities, thereby providing more in-depth data analysis and more accurate predictions. Among them, absolute position embedding provides the positioning of time series in the overall data collection process, time series causal embedding provides the representation of the correlation between parameters before and after, and multi-parameter alignment embedding is used to provide the expression of correlation between parameters; the absolute position embedding, time series causal embedding, and multi-parameter alignment embedding of the data sample are calculated respectively, and the embedding is added to the data, and finally, a multi-angle information description of the failure degradation data is formed. That is: embedding and constructing the parameter signal data to obtain embedded data, including: The parameter signal data is embedded and constructed, and the absolute position embedding, temporal causal embedding and multi-parameter alignment embedding of the parameter signal data are calculated respectively, and the calculated multiple embeddings are added to the parameter signal data to obtain the embedded data.

[0030] Specifically: Absolute position embedding, used to describe fixed positions in the parameters, adds a fixed vector representation to each position in the sequence to convey position information. These vectors capture the absolute position of each position in the sequence and are relatively independent of other positions.

[0031] Temporal causal embedding is used to capture the temporal and causal relationships in sequence data by encoding the temporal and causal relationships into vector representations and embedding them together with the sequence data.

[0032] Multi-parameter alignment embedding is used to align multiple parameters or features from different data sources into a common embedding space, so that similar features have similar representations in this space. This is because in actual failure environments, the collected data comes from multiple parameters, and different parameters show complex correlations due to the cross-linking relationship of the system. These parameters collaboratively characterize the current state of the system.

[0033] In one embodiment, before converting the text data into knowledge and obtaining invalid keywords, the method further includes: Based on historical failure cases, text data and parameter signal data are obtained.

[0034] In the specific implementation process, the acquisition of text data and parameter signal data can rely on existing failure cases, namely historical failure cases, that is, some failure cases generated in historical work in the aviation field, such as equipment failures.

[0035] S30: Generate rules using a large language model based on the embedded data to obtain invalid rules.

[0036] In the specific implementation process, rule-oriented time series embedding technology is used to realize the knowledge of important information such as the relationship and time series of multi-dimensional monitoring parameters, and large language models are used to realize the automatic generation of rules. Among them, large language models refer to models with a large number of parameters, capable of processing large-scale text data, and with strong language understanding and generation capabilities, including but not limited to GPT-3 models, BERT models, Megatron models, etc.

[0037] S40: Based on the failure keywords and failure rules, a failure knowledge network is constructed to realize the construction of a knowledge management system.

[0038] In the specific implementation process, according to the semantic representation extracted in the above steps, that is, the invalidation keywords, and the generated invalidation rules, the invalidation rules may include rule nodes and invalidation relationships, to construct a knowledge network, using the graph network structure as a formal carrier, invalidation semantic units as nodes of the graph network, and invalidation relationships as edges of the graph network to construct an invalidation knowledge network. The invalidation knowledge network can query the internal nodes of the graph network by retrieving information such as entity names, relationship names, and entity attributes. The invalidation semantic unit is formed through the knowledge process of data, that is: before constructing the invalidation knowledge network to realize the construction of the knowledge management system based on the invalidation keywords and invalidation rules, the method also includes: Obtain invalid semantic units according to invalid keywords and embedded data; According to the failure keywords and failure rules, the failure knowledge network is constructed to realize the construction of the knowledge management system, including: According to the invalidation keywords and invalidation rules, with the graph network structure as the formal carrier, the invalidation semantic units as the nodes of the graph network, and the invalidation relations as the edges of the graph network, an invalidation knowledge network is constructed to realize the construction of a knowledge management system.

[0039] In the specific implementation process, the failure semantic units extracted by the knowledge process serve as the constituent entities of the failure knowledge network. At the same time, the failure semantic units can also form failure rules in the form of a tree structure. The knowledge network construction is based on the network organization of the semantic units formed by the knowledge conversion.

[0040] In this embodiment, through the knowledge conversion of text data and parameter signal data, failure keywords and embedded data are obtained to generate failure rules to construct a failure knowledge network. Since the embedding technology is introduced in the conversion, the non-aligned knowledge conversion of multimodal data of traditional text data and parameter signal data can be realized, and the embedding technology includes absolute position embedding, time series causal embedding and multi-parameter alignment embedding, which converts the relationship and timing of multi-dimensional monitoring parameters into knowledge, and improves the learning ability of the knowledge process for the time relationship in the failure process while retaining the timing characteristics, thereby improving the management effect of the knowledge management system of failure cases.

[0041] In one embodiment, after constructing the failure knowledge network according to the failure keywords and the failure rules to realize the construction of the knowledge management system, the method further includes: Purify the invalidation rules to obtain the target invalidation rules; The target failure rules are mapped back to the failure knowledge network to obtain the target failure knowledge network.

[0042] In the specific implementation process, as shown in the attached Figure 3 As shown in the figure, after obtaining the invalid knowledge network, in order to optimize the network, the rules are purified. After sampling and extracting the rules in the knowledge network to form a rule set, the rule optimization calculation is performed. The nodes are first divided, and then the rule optimization calculation is performed based on the nodes. The evaluation of the nodes is realized by calculating the node attributes to reduce the complexity of the knowledge network. That is, the invalid rules are purified to obtain the target invalid rules, including: Sampling and extracting the rules in the failure knowledge network to form a rule set; After dividing the rule set into nodes, the rule calculation is performed based on the divided nodes to obtain the target failure rules.

[0043] In the specific implementation process, entities are used as rule heads in the knowledge network, and failure case information with rich semantic connotations is extracted based on the associations between entities, so as to formulate the relationship logic between failure semantic units and realize the description of failure relationships. The process is to traverse the failure entity nodes in the network, and form failure rules with each node, the nodes in the local range of the node in the failure knowledge network, and the relationship between nodes. Node division is based on different failure semantic units as nodes and failure relationships as edges. Failure rules with in-degree greater than 1 are defined as large-grained / medium-grained semantic relationships, and those with in-degree equal to 1 are defined as small-grained relationships. By decomposing large-grained rules into small-grained rules, optimization and parallelization of multi-grained rules are achieved.

[0044] Rule calculation is used for optimization, i.e. rule calculation optimization, which can be divided into optimization methods based on rule merging and optimization methods based on rule cost replacement. The optimization method based on rule merging is to merge a large number of repeated calculations to reduce the number of calculations, thereby reducing the overall calculation time and improving efficiency. The optimization method based on rule cost replacement is to replace the rule calculation module with a low cost, thereby achieving optimization and improving calculation efficiency.

[0045] In one embodiment, after constructing the failure knowledge network according to the failure keywords and the failure rules to realize the construction of the knowledge management system, the method further includes: Merge rules based on target failure cases to update failure rules; Based on the updated failure rules, the failure knowledge network is updated.

[0046] In the specific implementation process, as shown in the attached Figure 3 As shown, after the knowledge network is established, in order to realize the update of the knowledge network, rules are merged for the target failure cases. The target failure cases are also the newly added failure cases. According to these cases, knowledge conversion is performed and corresponding failure semantic units are generated and added. Then the corresponding rules are merged to update the failure rules, thereby completing the update of the failure knowledge network.

[0047] See attached Figure 4 Based on the same inventive concept as in the above-mentioned embodiment, the embodiment of the present application further provides a device for constructing a failure case knowledge management system, including: The conversion module is used to convert text data into knowledge and obtain invalid keywords; The embedding module is used to embed the parameter signal data to obtain embedded data; wherein the embedding construction includes absolute position embedding, temporal causal embedding and multi-parameter alignment embedding; A generation module is used to generate rules based on the embedded data using a large language model to obtain invalidation rules; Construction module,The construction module is used to construct an invalidation knowledge network according to invalidation keywords and invalidation rules to realize the construction of a knowledge management system.

[0048] Those skilled in the art should understand that the division of the various modules in the embodiment is merely a division of logical functions, and in actual application, all or part of them can be integrated into one or more actual carriers, and these modules can be implemented entirely in the form of software called by a processing unit, or entirely in the form of hardware, or in the form of a combination of software and hardware. It should be noted that each module in the failure case knowledge management system construction device in this embodiment corresponds one-to-one to each step in the failure case knowledge management system construction method in the aforementioned embodiment. Therefore, the specific implementation method of this embodiment can refer to the implementation method of the aforementioned failure case knowledge management system construction method, which will not be repeated here.

[0049] Based on the same inventive concept as in the aforementioned embodiment, an embodiment of the present application further provides a computer-readable storage medium storing a computer program. When the computer program is loaded and executed by a processor, a method for constructing a failure case knowledge management system as provided in the embodiment of the present application is implemented.

[0050] Based on the same inventive concept as in the above-mentioned embodiment, an embodiment of the present application further provides an electronic device, including a processor and a memory, wherein: The memory is used to store computer programs; The processor is used to load and execute a computer program so that the electronic device executes the failure case knowledge management system construction method provided in the embodiment of the present application.

[0051] In some embodiments, the computer readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or various devices including one or any combination of the above memories. The computer may be various computing devices including intelligent terminals and servers.

[0052] In some embodiments, executable instructions may be in the form of a program, software, software module, script or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0053] As an example, executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file storing other programs or data, such as in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions).

[0054] By way of example, executable instructions may be deployed to be executed on one computing device, or on multiple computing devices located at one site, or on multiple computing devices distributed across multiple sites and interconnected by a communication network.

[0055] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0056] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0057] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a multimedia terminal device (which can be a mobile phone, a computer, a television receiver, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0058] In summary, the present application provides a method, device, medium and equipment for constructing a failure case knowledge management system, the method comprising: performing knowledge conversion on text data to obtain failure keywords; performing embedding construction on parameter signal data to obtain embedded data; wherein the embedding construction includes absolute position embedding, temporal causal embedding and multi-parameter alignment embedding; according to the embedded data, using a large language model to generate rules to obtain failure rules; according to the failure keywords and failure rules, constructing a failure knowledge network to realize the construction of a knowledge management system. The present application obtains failure keywords and embedded data to generate failure rules to construct a failure knowledge network by performing knowledge conversion on text data and parameter signal data. Since the embedding technology is introduced in the conversion, it can realize the non-aligned knowledge conversion of multimodal data of traditional text data and parameter signal data, and the embedding technology includes absolute position embedding, temporal causal embedding and multi-parameter alignment embedding, and the relationship and timing of multi-dimensional monitoring parameters and other important time series information are knowledge-based, while retaining the time series characteristics, the learning ability of the knowledge-based process for the time relationship in the failure process is improved, thereby improving the management effect of the knowledge management system of failure cases.

[0059] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for constructing a failure case knowledge management system, characterized in that: The following steps are involved: Convert text data into knowledge to obtain invalid keywords; Embedding and constructing parameter signal data to obtain embedded data; wherein the embedding and constructing includes absolute position embedding, temporal causal embedding, and multi-parameter alignment embedding; Generate rules using a large language model based on the embedded data to obtain failure rules; According to the failure keywords and the failure rules, a failure knowledge network is constructed to realize the construction of a knowledge management system.

2. The method for constructing a failure case knowledge management system according to claim 1, characterized in that: The embedding and constructing of the parameter signal data to obtain the embedded data includes: The parameter signal data is embedded and constructed, the absolute position embedding, the temporal causal embedding and the multi-parameter alignment embedding of the parameter signal data are calculated respectively, and the calculated multiple embeddings are added to the parameter signal data to obtain embedded data.

3. The method for constructing a failure case knowledge management system according to claim 1, characterized in that: Before constructing the failure knowledge network according to the failure keywords and the failure rules to realize the construction of the knowledge management system, the method further includes: Obtaining invalid semantic units respectively according to the invalid keywords and the embedded data; The constructing of the failure knowledge network according to the failure keywords and the failure rules to realize the construction of the knowledge management system includes: According to the invalidation keywords and the invalidation rules, a graph network structure is used as a formal carrier, the invalidation semantic units are used as nodes of the graph network, and the invalidation relations are used as edges of the graph network, to construct an invalidation knowledge network to realize the construction of a knowledge management system.

4. The method for constructing a failure case knowledge management system according to claim 1, characterized in that: After constructing the failure knowledge network according to the failure keywords and the failure rules to realize the construction of the knowledge management system, the method further includes: Purifying the failure rules to obtain target failure rules; The target failure rules are mapped back to the failure knowledge network to obtain a target failure knowledge network.

5. The method for constructing a failure case knowledge management system according to claim 4, characterized in that: The step of purifying the invalidation rules to obtain target invalidation rules includes: Sampling and extracting rules in the failure knowledge network to form a rule set; After dividing the rule set into nodes, rule calculation is performed based on the divided nodes to obtain target failure rules.

6. The method for constructing a failure case knowledge management system according to claim 1, characterized in that: After constructing the failure knowledge network according to the failure keywords and the failure rules to realize the construction of the knowledge management system, the method further includes: Merging rules according to target failure cases to update the failure rules; Based on the updated failure rules, the failure knowledge network is updated.

7. The method for constructing a failure case knowledge management system according to claim 1, characterized in that: Before converting the text data into knowledge to obtain invalid keywords, the method further includes: The text data and the parameter signal data are obtained according to historical failure cases.

8. A device for constructing a failure case knowledge management system, characterized in that: include: A conversion module, the conversion module is used to perform knowledge conversion on text data to obtain invalid keywords; An embedding module, the embedding module is used to embed the parameter signal data to obtain embedded data; wherein the embedding construction includes absolute position embedding, temporal causal embedding and multi-parameter alignment embedding; A generation module, the generation module is used to generate rules based on the embedded data using a large language model to obtain an invalidation rule; A construction module is used to construct an invalidation knowledge network according to the invalidation keywords and the invalidation rules to realize the construction of a knowledge management system.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is loaded and executed by a processor, the method for constructing a failure case knowledge management system according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: comprising a processor and a memory, wherein: The memory is used to store computer programs; The processor is used to load and execute the computer program so that the electronic device executes the failure case knowledge management system construction method according to any one of claims 1 to 7.

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