Helicopter fault knowledge graph self-learning construction and optimization method and related device

Abstract

This invention discloses a self-learning construction and optimization method and related apparatus for helicopter fault knowledge graphs, belonging to the field of fault diagnosis technology. The method includes: acquiring and preprocessing multi-source fault data to obtain standardized data; identifying entities and relationships from the standardized data to construct an initial candidate graph; calculating the fault association complexity of candidate entity pairs, dynamically allocating graph attention weights, and forming an optimized graph after updating and scoring via a graph neural network; performing missing knowledge mining and erroneous association correction on the optimized graph through a self-learning completion mechanism; and iteratively updating in real time through an active dynamic update mechanism to output the optimized graph. This invention enables the graph propagation strength to dynamically adjust with the fault association complexity, improving the accuracy and extraction efficiency of complex fault causal relationship identification. It possesses dynamic adaptation and self-learning capabilities, achieving automated construction and continuous evolution of the knowledge graph without manual intervention, ensuring the completeness and accuracy of the fault knowledge system.

Description

Technical Field

[0001] This invention relates to the field of helicopter fault diagnosis technology, and in particular to a self-learning construction and optimization method and related apparatus for helicopter fault knowledge graph. Background Technology

[0002] The failure mechanisms of critical components in helicopter systems, such as engines, gear reducers, and drive shafts, are characterized by their diverse and complex interrelationships. Current technologies for constructing failure mechanism knowledge graphs typically involve first extracting entities and relationships from multi-source heterogeneous data, such as experimental data, maintenance records, and academic literature, using machine learning or rule templates; secondly, performing cross-source knowledge fusion based on semantic similarity calculations or ontology mapping methods to form an initial knowledge graph; and finally, completing and maintaining the graph through manual review or static rules.

[0003] Existing solutions have the following problems when processing multi-source heterogeneous fault data: (1) Low knowledge extraction efficiency and difficulty in integrating cross-data source and domain knowledge. When dealing with multi-source heterogeneous fault data, the existing solutions mostly use static preset data cleaning rules and knowledge extraction models, which are difficult to adapt to the significant differences in representation granularity and record format of different data sources. At the same time, for the complex relationships such as multi-hop causality and cross-component coupling in the fault mechanism, the existing static models cannot dynamically adjust the extraction strategy, resulting in low knowledge extraction efficiency. Furthermore, semantic conflicts are easily generated when integrating cross-source and cross-domain knowledge, resulting in insufficient integration accuracy.

[0004] (2) The constructed fault knowledge graph is prone to incomplete information. The initially constructed fault knowledge graph generally suffers from missing relationships, missing mechanism chains, and insufficient edge fault knowledge. Existing technologies mainly rely on manual review or rules based on fixed logic to complete the graph. They cannot actively mine the hidden fault mechanism chains from existing knowledge and lack the self-learning ability to continuously optimize with the accumulation of fault knowledge, which makes it difficult to guarantee the completeness and accuracy of the knowledge graph.

[0005] (3) The lack of dynamic updating and maintenance mechanisms makes it impossible to capture the latest results of the fault knowledge system for key helicopter components in real time. The updating of the constructed fault knowledge graph usually relies on manual periodic summarization or passive import, which makes it impossible to capture the latest results of the fault knowledge system for key helicopter components in real time and makes it difficult to meet the actual needs of fault diagnosis of key components of helicopter systems. Summary of the Invention

[0006] The purpose of this invention is to overcome the problems of the prior art and provide a self-learning construction and optimization method and related apparatus for helicopter fault knowledge graph.

[0007] The objective of this invention is achieved through the following technical solution: a self-learning construction and optimization method for helicopter fault knowledge graphs, comprising the following steps: Acquire multi-source fault data related to the target critical components and preprocess it, converting the multi-source fault data into standardized data in a unified format; Entities are identified from standardized data, and candidate triples are generated based on syntactic dependencies, trigger word patterns, temporal sequence relationships, and source co-occurrence relationships. Using identified entities as nodes and candidate triples as edges, an initial fault relationship candidate graph is constructed. The head and tail entities in each candidate triple constitute candidate entity pairs. The fault association complexity between candidate entity pairs in the initial fault relationship candidate graph is calculated. Graph attention weights are dynamically allocated based on the fault association complexity, and message passing updates are performed using a graph neural network based on these weights. Candidate triples are scored, and candidate triples whose scores meet the threshold are written into the initial knowledge graph, forming a fault knowledge graph to be optimized. The fault knowledge graph to be optimized is improved by a self-learning completion mechanism to mine missing knowledge and correct erroneous associations. The corrected fault knowledge graph is iteratively updated in real time through an active dynamic update mechanism, and an optimized fault knowledge graph is output.

[0008] In one embodiment, preprocessing of multi-source fault data includes: For time-series data in multi-source fault data, a sliding window segmentation is adopted, and the statistical features within each window are calculated to form a quality assessment vector. The statistical features include at least one of the following: mean, standard deviation, peak-to-peak value, kurtosis, skewness, and missing rate. For abnormal sample data in multi-source fault data, a combined screening method of statistical discrimination and operating condition constraints is adopted. When the sample data simultaneously meets the statistical anomaly conditions and the physical operating constraint violation conditions, it is judged as abnormal noise data. Preferably, the statistical anomaly conditions are judged by the anomaly discrimination threshold, and the anomaly discrimination threshold is automatically adjusted according to the noise level of the current batch of data. For text data in multi-source fault data, terminology normalization, unit conversion, time alignment, entity normalization, and template completion are performed to convert the original text data into an intermediate standard format.

[0009] In one embodiment, the method further includes cross-source semantic alignment of candidate triples: Construct a fault feature anchor point library, where anchor points include component location, feature mode, operating conditions, and typical faults; Calculate the matching score between candidate entities or candidate relations and anchor templates, where candidate relations are the relation types in candidate triples; Using a domain-adaptive semantic mapping model, the general semantic space is mapped to the helicopter fault domain semantic space to obtain domain semantic vectors. Based on the domain semantic vectors, the domain semantic similarity between candidate triples and predefined standard entities or standard relations in the anchor point library is calculated. The matching score and domain semantic similarity are combined, and the standard entity or standard relationship with the highest score that exceeds the threshold is used as the normalization result.

[0010] In one embodiment, after forming the fault knowledge graph to be optimized, the method further includes: Construct a formal knowledge graph of failure mechanisms, including an entity layer, a relation layer, and an evidence layer; Establish a degradation chain, which includes at least two stages: normal, slight wear, moderate wear, localized peeling, functional degradation, and failure. Based on the operating conditions and the stage transition scoring function, the degradation stage transition probability is defined, and the degradation chain and transition probability are updated to the fault knowledge graph to be optimized.

[0011] In one embodiment, the self-learning completion mechanism includes: The graph completion task is modeled as a sequence decision problem based on reinforcement learning; The environment state is defined as the entity node at the current inference time, the path already traversed, and the local graph context. The action is defined as selecting the next relation and the next hop entity from the current node. The policy network is defined based on the environment state and the action. The probability distribution of each action is output by the policy network. A value function is defined based on the reward obtained after the state transition. The advantage function is estimated based on the value function. The policy network parameters are updated through policy gradient based on the advantage function and the probability distribution output by the policy network. The missing knowledge is mined from the fault knowledge graph to be optimized using the policy network with updated parameters.

[0012] In one embodiment, the self-learning completion mechanism further includes: The graph completeness improvement rate can be determined by the ratio of the number of newly added correct entities or relations to the total number of entities or relations in the graph before completion, or by weighting the connectivity of key entities and the closure of degenerate chains to obtain the graph completeness improvement rate. The association accuracy bonus is calculated based on the ratio of the number of newly verified relationships to the total number of newly added relationships. The total reward is obtained by weighting the completeness improvement rate, the association accuracy rate, and the incorrect association penalty. Based on the total reward, the strategy network, rule candidate pool, and statistical prior model are updated synchronously to complete the missing knowledge mining and error association correction of the fault knowledge graph to be optimized.

[0013] In one embodiment, the proactive dynamic update mechanism includes: Retrieval tasks are generated based on areas of weak knowledge, hot components, and recent abnormal cases, forming a query set; Retrieve a set of candidate documents from external data sources and reorder them based on relevance, source authority, and recentity. The top-ranked documents are used as the generation context, input into the knowledge generation model to generate candidate knowledge entries, and then written into the fault knowledge graph.

[0014] In one embodiment, before writing candidate knowledge entries to the fault knowledge graph, the method further includes: The candidate entries are compared with the core mechanism chain, key parameter range, and component hierarchy of the existing graph, and a consistency score is calculated. The authenticity score is calculated based on whether there is a case number, source organization, original measurement point, maintenance record, or cross-verification from multiple sources. The overall credibility is calculated based on the quantity, diversity of types, temporal continuity, and reliability of sources of evidence. When the weighted sum of consistency score, authenticity score, and overall credibility exceeds a preset threshold, the candidate knowledge item is written into the knowledge graph.

[0015] It should be further noted that the technical features corresponding to the above examples can be combined or replaced to form new technical solutions.

[0016] The present invention also includes a computer program product comprising a computer program that, when executed by a processor, implements the steps of the self-learning construction and optimization method for a helicopter fault knowledge graph formed by any or a combination of the above examples.

[0017] The present invention also includes a storage medium storing computer instructions that, when executed, perform the steps of the self-learning construction and optimization method for a helicopter fault knowledge graph formed by any or more of the above examples.

[0018] The present invention also includes a terminal comprising a memory and a processor, wherein the memory stores computer instructions executable on the processor, and the processor executes the steps of the self-learning construction and optimization method for the helicopter fault knowledge graph formed by any or more of the above examples when executing the computer instructions.

[0019] Compared with the prior art, the beneficial effects of the present invention are: 1. By calculating the fault association complexity between candidate entity pairs and dynamically allocating graph attention weights based on the complexity, the graph propagation strength can be dynamically adjusted according to the fault association complexity, making the model's expressive power adapt to the difficulty of the relationship. This enhances the representation capability for complex fault links such as multi-hop causality and cross-component coupling, while avoiding overly smooth relationships and misconnections caused by simple fixed neighborhood propagation. This effectively improves the identification accuracy and extraction efficiency of complex fault causal relationships in key helicopter components, possessing dynamic adaptability and significantly improving the accuracy of complex fault causal relationship identification. Through a self-learning completion mechanism, it can proactively mine hidden fault mechanism chains and autonomously correct erroneous associations without relying on manual intervention, possessing self-learning capabilities and ensuring information completeness and accuracy. Through an active dynamic update mechanism, it can capture the latest fault knowledge in real time and iteratively update the graph. Simultaneously, by combining data preprocessing, self-learning completion, and active update mechanisms, it can achieve automated construction and continuous evolution from multi-source heterogeneous data to an optimized knowledge graph. It can proactively mine missing knowledge and iteratively update the graph in real time without manual intervention, ensuring the completeness, accuracy, and timeliness of the fault knowledge system.

[0020] 2. By using a two-layer fusion mechanism of fault feature anchor matching and domain adaptive semantic mapping, it is possible to achieve semantic alignment of cross-source and cross-domain knowledge while preserving the interpretability of fault physical constraints. This effectively reduces duplicate nodes caused by synonyms and relationship pollution caused by erroneous merging, and improves the consistency and accuracy of multi-source fault knowledge fusion.

[0021] 3. By constructing a formal fault mechanism knowledge graph containing entity, relation, and evidence layers, and establishing performance degradation chains and stage transition probabilities, static fault relationships and dynamic degradation evolution laws can be organically integrated. This enables the knowledge graph to not only support static retrieval of fault mechanisms, but also to describe the complete degradation process of key components from normal to failure, providing structured knowledge support for fault prediction and management.

[0022] 4. By transforming the graph completion task into a reinforcement learning sequence decision problem and utilizing policy networks to autonomously explore high-value reasoning paths, the completion process can be transformed from relying on manual rules and manual edge completion to the system autonomously mining hidden fault mechanism chains, effectively improving the ability to discover complex fault relationships and multi-hop causal chains.

[0023] 5. By constructing a dual reward function consisting of the graph completeness improvement rate and the association accuracy rate, and simultaneously updating the policy network, rule candidate pool, and statistical prior model, the completion task is transformed into a constrained multi-objective optimization problem. This avoids policy bias caused by a single reward objective, which would introduce a large amount of erroneous knowledge. It can achieve synergistic optimization between the quantity and quality control of the expanded graph, ensuring that the system is always constrained by the existing reliable knowledge boundary when exploring unknown relationships, thereby improving the robustness of the completion results.

[0024] 6. By proactively generating retrieval tasks around weak knowledge areas, hot components, and recent abnormal cases, and by using retrieval enhancement generation technology to capture the latest fault knowledge from external data sources in real time, the map can continuously absorb new external knowledge, no longer relying on manual periodic summarization and batch updates, thus improving the timeliness and evolution capability of the map.

[0025] 7. By performing triple credibility verification—consistency check, authenticity verification, and evidence sufficiency assessment—before candidate knowledge is added to the database, the introduction of pseudo-knowledge, conflicting knowledge, and low-quality information during dynamic updates is effectively avoided, ensuring the accuracy, stability, and engineering usability of the knowledge graph after long-term iteration. Attached Figure Description

[0026] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. The accompanying drawings are provided to provide a further understanding of the present application and constitute a part of the present application. The same reference numerals are used in these drawings to denote the same or similar parts. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application.

[0027] Figure 1 This is a flowchart of a method provided in an embodiment of the present invention. Detailed Implementation

[0028] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0030] In one embodiment, such as Figure 1 As shown, a self-learning construction and optimization method for helicopter fault knowledge graphs is proposed, which includes the following steps: S1: Acquire multi-source fault data related to the target critical components and preprocess it, converting the multi-source fault data into standardized data in a unified format.

[0031] In step S1, the multi-source fault data includes at least two of the following: time-series data, text data, semi-structured data, and structured data. Time-series data includes vibration, temperature, pressure, speed, and current data collected by test benches or airborne sensors; text data includes text information from maintenance work cards, troubleshooting records, health assessment reports, and fault case libraries; semi-structured data includes data from academic literature, standard manuals, maintenance procedures, and log forms; and structured data includes data extracted from image recognition results, inspection reports, and tabular files. This embodiment acquires various types of fault-related raw multi-source fault data by constructing a multi-source data acquisition interface. Preprocessing the multi-source fault data involves denoising, field alignment, dimensional unification, and preliminary semantic standardization, providing a unified input for subsequent knowledge extraction. This allows for the acquisition of diverse and complex data features adapted to the fault mechanisms of key helicopter components, enabling adaptive noise removal from various types of fault data (experiments, maintenance, and literature).

[0032] S2: Identify entities from standardized data and generate candidate triples (head entity, relation type, tail entity) based on syntactic dependencies, trigger word patterns, temporal sequence relationships, and source co-occurrence relationships. Using the identified entities as nodes and the candidate triples as edges, construct an initial fault relation candidate graph. The head and tail entities in the candidate triples constitute candidate entity pairs. Calculate the fault association complexity between candidate entity pairs in the initial fault relation candidate graph. Dynamically allocate graph attention weights based on the fault association complexity. Perform message passing updates of the graph neural network based on the graph attention weights. Score the candidate triples and write the candidate triples whose scores meet the score threshold into the initial knowledge graph to form the fault knowledge graph to be optimized.

[0033] In step S2, entity types include component entities, fault mode entities, fault symptom entities, operating condition entities, damage form entities, maintenance measure entities, time entities, and evidence source entities. After preprocessing, this step extracts entities and relationships from the standardized data, constructs an initial fault relationship candidate graph, and dynamically adjusts the relationships and knowledge extraction algorithms based on graph neural networks. By dynamically adapting network weights to the fault association complexity, it accurately identifies multi-level causal relationships such as component-fault, fault-mechanism, and mechanism-damage, significantly improving knowledge fusion accuracy, greatly reducing manual costs, and enhancing the efficiency and basic quality of graph construction.

[0034] S3: Through a self-learning completion mechanism, missing knowledge is mined and error associations are corrected in the fault knowledge graph to be optimized.

[0035] In step S3, a self-learning inference engine based on reinforcement learning loads the fault knowledge graph to be optimized and the reinforcement learning optimization model, transforms the graph completion task into a sequence decision problem, iteratively optimizes the inference rules and model, realizes missing knowledge mining and error association correction, and outputs an optimized fault mechanism knowledge graph with high completeness and high precision.

[0036] S4: The corrected fault knowledge graph is iteratively updated in real time through an active dynamic update mechanism, and the optimized fault knowledge graph is output.

[0037] In step S4, a real-time fault knowledge monitoring engine is built based on retrieval enhancement generation technology to capture the latest knowledge information. Valid information is filtered through a credibility verification mechanism that verifies knowledge consistency and case authenticity. The engine automatically generates standardized knowledge entries to update the graph, realizing proactive dynamic iteration of the graph.

[0038] Furthermore, based on the optimized fault knowledge graph, when the engine, reducer, or drive shaft exhibits abnormal behavior such as excessive vibration or abnormal temperature, the feature parameters in the real-time monitoring data can be matched with the fault phenomenon nodes in the fault knowledge graph. The multi-hop causal chain in the fault knowledge graph can be used to quickly locate the root cause of the fault and the current stage of the damage evolution.

[0039] In one embodiment, preprocessing of multi-source fault data includes: For numerical time series data, a sliding window segmentation method is used, where the segmentation is defined as follows: Sample sequences within a window for: ; in, Represent data labels. Calculate statistical features within each window to form a quality assessment vector. : ; in, The mean, Standard deviation, Peak-to-peak value For ravine, For skewness, This refers to the missing rate or outlier rate.

[0040] For outlier samples, a combined statistical discrimination and operating condition constraint screening method is used for preprocessing. If the sample data... Simultaneously satisfy: ; And the physical operating constraints of the corresponding component under this operating condition are not met. If the data is abnormal noise, then it is considered abnormal noise data. Indicates the current operating condition label. This is the anomaly detection threshold. Preferably, the anomaly detection threshold for data cleaning is automatically adjusted based on the noise level of the current batch of data, and the adaptive threshold update formula is defined as: ; in, This represents the average noise rate of the current batch. For the target noise rate, To update the step size, For batch indexing.

[0041] For text data, terminology normalization, unit conversion, time alignment, entity normalization, and template completion are performed sequentially to convert the original text into "components". —Working Conditions —Symptoms of a malfunction — Failure Mode —timestamp —Source Identifier intermediate standard format , denoted as: .

[0042] This embodiment uses an adaptive intelligent cleaning model guided by fault characteristics to remove noise, and then uses data format adjustment and conversion technology to unify the format of multi-source heterogeneous data, realizing fully automated and standardized processing of multi-source heterogeneous fault data. The output standardized dataset serves as the basis for subsequent knowledge extraction.

[0043] In one embodiment, step S2 extracts entities and relationships from the standardized data to construct an initial candidate graph of fault relationships, including: Entities are jointly created using rule templates and domain language models. Identify, let the extracted entity set be... for: ; in, This is the entity index. For any text fragment u, the probability that it belongs to entity type y. for: ; in, Represents the probability distribution function; For text encoding vectors, For weight parameters, Bias parameters.

[0044] Then, based on syntactic dependencies, trigger word patterns, temporal sequence relationships, and source co-occurrence relationships, a set of candidate edges for relations is generated. : ; in, For the first One entity; For the first One entity; The relationship type includes at least one of the following: “inducing”, “representing”, “acting on”, “causing”, “accompanying”, “exacerbating”, “repaired”, and “occurring at”.

[0045] To generate candidate heterogeneous graphs for subsequent graph neural network processing, an initial candidate graph of fault relationships is established. : ; in, It is a set of entity attributes, including the time of occurrence, operating condition range, number of pieces of evidence, and credibility of the source.

[0046] In one embodiment, to address the issues of multi-hop causality, cross-component coupling, and high ambiguity in fault relationships of critical helicopter components, this invention does not employ a fixed propagation depth or fixed weights. Instead, it first calculates the fault association complexity of candidate relationships and then dynamically allocates graph propagation weights. The specific implementation is as follows: For candidate entity pairs Candidate entity pairs are quantified based on at least two dimensions, including local neighborhood density, number of multi-hop paths, relation ambiguity, strength of time dependency, and number of evidence sources. The fault association complexity is obtained by weighted summation of these dimensions. ; in, For nodes With nodes The complexity of fault correlation between them; Represents the local neighborhood density; Indicates the number of multi-hop paths; Indicates the degree of ambiguity in the relationship; Indicates the strength of time dependence; Indicates the quantity of sources of evidence or the degree of cross-verification; All are weighting coefficients.

[0047] Generate attention weights based on fault association complexity. : ; in, Represent vectors for different nodes. For scoring functions, For nodes With nodes The complexity of fault correlation between them Represents a node The neighborhood set, This represents a neighborhood node in the neighborhood set.

[0048] Perform message passing update again: ; in, For nodes exist Layer representation vector, For activation function, For nodes With nodes exist Fault association complexity of the layer, for Layer weight parameters, for Layer bias parameters; For nodes exist The layer's representation vector.

[0049] Scoring of candidate triples: ; in, The score for the triplet. For relationships between entities, For nodes Represents the transpose of a vector. For relation type The corresponding parameter matrix. If , If the score threshold is set, then the relationship is written into the initial knowledge graph.

[0050] In one embodiment, to address the issues of synonyms, inconsistent granularity of representation, and mixed use of hierarchical and superordinate concepts for the same fault knowledge from different sources, cross-source semantic alignment processing is performed on the extraction results, i.e., candidate triples: (1) Construct a fault feature anchor point library. Anchor points are formed by a combination of component location, feature pattern, operating condition, and typical fault. Let the nth anchor point template be... for: ; in For component location, For feature patterns, For operating condition constraints, For corresponding fault or damage labels.

[0051] (2) For any candidate entity or relation description 𝑧, calculate the matching score between the candidate entity or relation and the anchor template. : ; in, It can be a cosine similarity or edit distance normalized score. Component positions in candidate entities or relations For candidate entities or relations, feature patterns These are the working condition constraints in candidate entities or relationships. , , All are weighting coefficients.

[0052] (3) Using the domain adaptive semantic mapping model, the general semantic space is mapped to the helicopter fault domain semantic space to obtain the domain semantic vector, and the domain semantic similarity between the candidate triples and the predefined standard entities or standard relations in the anchor point library is calculated based on the domain semantic vector.

[0053] Mapped vector Represented as: ; in The original semantic vector, To adapt weight parameters to the domain, Adapt bias parameters to the domain.

[0054] (4) Fusion Anchor Matching Score Domain semantic similarity The standard entity or standard relation with the highest score that exceeds the threshold is used as the normalization result.

[0055] The expression for calculating the fusion of anchor point matching score and domain semantic similarity is as follows: ; in, To achieve a score through fusion, These are the weighting coefficients; Domain semantic vectors for standard entities or relations.

[0056] In this embodiment, anchor point matching provides physical symptom constraints, and domain semantic mapping provides flexible adaptation capabilities. The combination of the two can simultaneously ensure interpretability and generalization ability.

[0057] In one embodiment, after forming the fault knowledge graph to be optimized, the method further includes: (1) Construct a formal fault mechanism knowledge graph, including an entity layer, a relation layer and an evidence layer.

[0058] The entity layer includes at least one of the following: component, failure mode, symptom, damage, maintenance action, operating condition, and source of evidence; the relationship layer includes at least one of the following: subordinate, inducement, characterization, accompaniment, temporal evolution, maintenance target, and evidence support; and the evidence layer includes at least one of the following: document fragment, monitoring data fragment, maintenance record, case number, and credibility score.

[0059] Formal Fault Mechanism Knowledge Graph Recorded as: ; in, For a collection of entities, For a set of relations, For a set of triples, It is a set of evidence and attributes.

[0060] (2) Establish a degradation chain : ; in, This is an index of the degradation chain stages, which includes at least two stages: normal, minor wear, moderate wear, localized peeling, functional degradation, and failure.

[0061] (3) Define the degradation stage transition probability based on the working conditions and the stage transition scoring function, and update the degradation chain and transition probability to the fault knowledge graph to be optimized in order to describe the evolution law of the performance degradation process of key components.

[0062] Degeneration stage transition probability Represented as:

[0063] in, , which is the index of the current stage of the degenerate chain; For operating conditions, For the stage transition scoring function, This serves as an index for the next stage.

[0064] In one embodiment, to address the issues of missing relationships, missing mechanism chains, and insufficient edge fault knowledge in the initial atlas, a self-learning inference engine based on reinforcement learning is designed. The self-learning completion mechanism includes: (1) The graph completion task is modeled as a sequence decision problem based on reinforcement learning.

[0065] (2) Define the environmental state Define actions for the entity node at the current inference moment, the path already traversed, and the local graph context. To select the next relationship and next-hop entity from the current node, and define the policy network based on the environment state and actions: ; The policy network outputs in the current state The following actions are permissible probability distribution .

[0066] (2) The probability distribution of each action is output by the policy network. The value function is defined based on the reward obtained after the state transition. The advantage function is estimated based on the value function. The policy network parameters are updated by the policy gradient based on the advantage function and the probability distribution output by the policy network, so as to train the policy network to have the ability to autonomously infer the missing fault mechanism path. The missing knowledge is mined by the policy network with updated parameters to continuously complete and optimize the fault knowledge graph.

[0067] Specifically, a reward is obtained after the state transition. A positive reward is given when a path successfully fills in missing knowledge and aligns with existing mechanisms; a negative reward is given when erroneous loops, conflicting relationships, or low-reliability extensions occur. Value Function for: ; The expected value of the trajectory is the average of the policy distribution over all possible trajectories. As a discount factor, Indicates time step Beginning, then The immediate reward received after each time step. Policy parameters are updated via policy gradient: ; in, The gradient of the policy objective function, For parameters Find the gradient. This is for estimating the advantage function.

[0068] In one embodiment, to avoid increasing pseudo-knowledge by only pursuing the amount of map expansion, or making the completion too conservative by only pursuing local accuracy, a dual reward function is constructed regarding the map completeness improvement rate and the association accuracy rate, including the following steps: (1) Determine the graph completeness improvement rate based on the ratio of the number of newly added correct entities or relations to the total number of entities or relations in the graph before completion. : ; in, To complete the total number of correct entities or relations in the final graph, To complete the total number of entities or relations in the previous graph, To add the correct number of entities or relationships.

[0069] Alternatively, a weighted calculation can be performed on the connectivity of key entities and the closure of degenerate chains to obtain the map completeness improvement rate.

[0070] (2) Calculate the association accuracy bonus based on the ratio of the number of newly verified relations to the total number of newly added relations: ; in, To verify the number of newly added relations that have passed the verification, This represents the total number of newly added relationships.

[0071] (3) The completeness improvement rate, association accuracy rate and erroneous association penalty are weighted to obtain the total reward. :

[0072] in, For error-related penalty items, This is the adjustment coefficient.

[0073] (4) After each round of training, the policy network, rule candidate pool and statistical prior model are updated synchronously according to the total reward to complete the missing knowledge mining and error association correction of the fault knowledge graph to be optimized, so as to balance the quantity and quality of knowledge growth in the completion process, realize the autonomous correction of error association, and finally complete the missing knowledge mining and error association correction of the fault knowledge graph to be optimized.

[0074] In one embodiment, to address the problem of the fault knowledge graph relying on regular manual updates and lacking timeliness, a proactive update process is constructed, encompassing real-time retrieval, intelligent generation, and verification before storage. Specifically, this proactive dynamic update mechanism includes: (1) Generate retrieval tasks based on weak knowledge areas, hot components, and recent abnormal cases to form a query set. : ; in, To query the total number.

[0075] (2) Retrieve candidate document sets from external data sources The data are then reordered based on relevance, source authority, and time relevance.

[0076] ; in, Indicates a search query; For the first 1 candidate document.

[0077] (3) Sort the first K documents As the generation context, input knowledge generation model Generate candidate knowledge entries Candidate knowledge entries are then written into the fault knowledge graph. (Generate candidate knowledge entries) The calculation expression is: .

[0078] Among them, candidate knowledge items It should include at least a standardized fault description, candidate entities, candidate relationships, applicable operating conditions, and a summary of evidence.

[0079] In one embodiment, before writing candidate knowledge entries to the fault knowledge graph, the method further includes: (1) Connect candidate entries with the core mechanism chain of the existing map. The consistency score is calculated by comparing the range of key parameters and the hierarchical relationship of components. : ; in, This represents the similarity function.

[0080] (2) Calculate the authenticity score based on whether there is a case number, source organization, original measurement point, maintenance record, or cross-verification from multiple sources. : ; in, For the first Weight of class evidence, , indicating the first Does such evidence exist?

[0081] (3) Calculate the overall credibility based on the quantity, type diversity, temporal continuity and source reliability of the evidence. :

[0082] in, To score for sufficiency of evidence, This is the consistency weighting coefficient. This is the authenticity weighting coefficient. This is the sufficiency weighting coefficient.

[0083] (4) When the weighted sum of consistency score, authenticity score and overall credibility exceeds a preset threshold, the candidate knowledge item will be automatically supplemented with standard fields and written into the knowledge graph: ; Otherwise, write it to the pending review area.

[0084] Combining the above embodiments yields a preferred embodiment of the present invention, in which the solution includes the following steps: S10: Acquire multi-source fault data related to the target key components, perform noise reduction, field alignment, unit unification and preliminary semantic normalization on the multi-source fault data, and convert it into standardized data in a unified format.

[0085] S20: Identify entities and relationships from standardized data and construct an initial candidate graph of fault relationships.

[0086] Cross-source semantic alignment is performed on candidate triples in the initial fault relationship candidate graph to construct a fault feature anchor point library. The matching score between candidate entities or candidate relations and anchor point templates is calculated. Using a domain adaptive semantic mapping model, the general semantic space is mapped to the helicopter fault domain semantic space to obtain domain semantic vectors. Based on the domain semantic vectors, the domain semantic similarity between candidate triples and predefined standard entities or standard relations in the anchor point library is calculated. The matching score and domain semantic similarity are fused, and the standard entity or standard relation with the highest score and exceeding the threshold is taken as the normalization result.

[0087] The fault association complexity between candidate entity pairs in the initial fault relationship candidate graph is calculated. The graph attention weights are dynamically allocated according to the fault association complexity. The message passing update of the graph neural network is performed based on the graph attention weights. The candidate triples are scored. The candidate triples whose scores meet the score threshold are written into the initial knowledge graph to form the fault knowledge graph to be optimized.

[0088] A formal fault mechanism knowledge graph is constructed, including an entity layer, a relation layer, and an evidence layer; a degradation chain is established, which includes at least two stages from normal, slight wear, moderate wear, local spalling, functional degradation, and failure; the degradation stage transition probability is defined based on operating conditions and a stage transition scoring function, and the degradation chain and transition probability are integrated into the fault knowledge graph to be optimized to describe the evolution law of the performance degradation process of key components.

[0089] S30: A self-learning completion mechanism is used to mine missing knowledge and correct erroneous associations in the fault knowledge graph to be optimized, including: The graph completion task is modeled as a sequence decision problem based on reinforcement learning. The environment state and action are defined, and a policy network is defined based on the environment state and action. The probability distribution of each action is output by the policy network. The value function is defined based on the reward obtained after the state transition. The advantage function is estimated based on the value function. The policy network parameters are updated through policy gradient based on the advantage function and the probability distribution output by the policy network. The missing knowledge is mined from the fault knowledge graph to be optimized using the policy network with updated parameters. The total reward is obtained by weighting the completeness improvement rate, association accuracy rate and erroneous association penalty. The policy network, rule candidate pool and statistical prior model are updated synchronously according to the total reward to complete the missing knowledge mining and erroneous association correction of the fault knowledge graph to be optimized.

[0090] S40: Through a self-learning completion mechanism, missing knowledge is mined and erroneous associations are corrected in the fault knowledge graph to be optimized, and the optimized fault knowledge graph is output, including: A query set is formed by generating retrieval tasks around weak knowledge areas, hot components and recent abnormal cases. A candidate document set is recalled from external data sources and reordered according to relevance, source authority and time relevance. The top-ranked documents are used as the generation context and input into the knowledge generation model to generate candidate knowledge entries. The system performs consistency checks, authenticity verifications, and evidence sufficiency assessments on candidate knowledge entries. Candidate knowledge entries that pass multiple checks are automatically supplemented with standard fields and written into the knowledge graph, enabling proactive dynamic iteration of the graph.

[0091] The method of this invention adopts a full-process self-learning closed-loop design, which includes an adaptive data preprocessing step S10, a dynamic knowledge extraction and fusion step S20, a reinforcement learning-driven self-learning completion step S30, and a retrieval-enhanced proactive update step S40. The modules work together to realize the automated, high-precision construction and dynamic iteration of the fault mechanism knowledge graph.

[0092] Specifically, by employing a fault-adaptive data cleaning model and a dynamic weight-adjustment knowledge extraction algorithm, fully automated and standardized processing of diverse and heterogeneous fault data is achieved. This accurately identifies complex causal relationships in faults, significantly improves knowledge fusion accuracy, drastically reduces manual costs, and enhances the efficiency and fundamental quality of knowledge graph construction. Through a reinforcement learning-driven self-learning completion mechanism, the system proactively mines missing knowledge and autonomously corrects erroneous associations. It possesses self-learning characteristics that continuously optimize with the accumulation of fault knowledge, effectively improving the completeness and accuracy of the knowledge graph and completely resolving the problems of incomplete information and the need for manual review and correction in existing knowledge graphs. By constructing a proactive dynamic update closed loop, the system overcomes the timeliness bottleneck of existing static knowledge graphs. Through retrieval-enhanced generation technology and a credibility verification mechanism, real-time capture and uninterrupted updates of fault knowledge are achieved. This ensures that the knowledge system always matches the latest research results and field fault cases, providing dynamic and accurate knowledge support for helicopter fault diagnosis, effectively reducing the rate of missed and false fault diagnoses, and improving the level of diagnostic intelligence.

[0093] The self-learning closed-loop design method of this invention has strong adaptability, can adapt to the differences in fault characteristics of different key components of helicopters, does not require targeted adjustment of the core algorithm, has strong scalability, and can be directly transferred to other fault knowledge graph construction scenarios of aviation power machinery.

[0094] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the self-learning construction and optimization method for a helicopter fault knowledge graph formed by any or a combination of the above examples. The processor may be a single-core or multi-core central processing unit or a specific integrated circuit, or one or more integrated circuits configured to implement this invention.

[0095] The present invention also provides a storage medium having the same inventive concept as the self-learning construction and optimization method for helicopter fault knowledge graph formed by any or more of the above examples, wherein computer instructions are stored thereon, and the computer instructions, when executed, perform the steps of the self-learning construction and optimization method for helicopter fault knowledge graph formed by any or more of the above examples.

[0096] Based on this understanding, the technical solution of this embodiment, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0097] This invention also provides a terminal that shares the same inventive concept as any or a combination of examples corresponding to the aforementioned self-learning construction and optimization method for helicopter fault knowledge graphs. The terminal includes a memory and a processor. The memory stores computer instructions executable on the processor. When the processor executes the computer instructions, it performs the steps of the aforementioned self-learning construction and optimization method for helicopter fault knowledge graphs. The processor may be a single-core or multi-core central processing unit or a specific integrated circuit, or one or more integrated circuits configured to implement this invention.

[0098] In one example, the terminal, i.e., the electronic device, is represented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit (processor) mentioned above, at least one storage unit mentioned above, and a bus connecting different system components (including storage units and processing units).

[0099] The storage unit stores program code that can be executed by the processing unit, causing the processing unit to perform the steps described in the "Exemplary Methods" section above, based on various exemplary embodiments of the present invention. For example, the processing unit can execute the aforementioned self-learning construction and optimization method for a helicopter fault knowledge graph.

[0100] The storage unit may include readable media in the form of volatile storage units, such as random access memory (RAM) and / or cache storage units, and may further include read-only memory (ROM).

[0101] The storage unit may also include a program / utility having a set (at least one) of program modules, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0102] A bus can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus that uses any of the various bus structures.

[0103] The electronic device can also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication can be performed via input / output (I / O) interfaces. Furthermore, the electronic device can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. The network adapter communicates with other modules of the electronic device via a bus. It should be understood that other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0104] Through the above description, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to this exemplary embodiment can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the method of the exemplary embodiment of this application.

[0105] The above detailed embodiments are a description of the present invention. It should not be considered that the specific embodiments of the present invention are limited to these descriptions. For those skilled in the art, several simple deductions and substitutions can be made without departing from the concept of the present invention, and all of these should be considered to fall within the protection scope of the present invention.

Claims

1. A self-learning construction and optimization method for helicopter fault knowledge graphs, characterized in that, Includes the following steps: Acquire multi-source fault data related to the target critical components and preprocess it, converting the multi-source fault data into standardized data in a unified format; Entities are identified from standardized data, and candidate triples are generated based on syntactic dependencies, trigger word patterns, temporal sequence relationships, and source co-occurrence relationships. The identified entities are used as nodes, and the candidate triples are used as edges to construct an initial fault relationship candidate graph. The head and tail entities in the candidate triples constitute candidate entity pairs. The fault association complexity between candidate entity pairs in the initial fault relationship candidate graph is calculated. The graph attention weights are dynamically allocated according to the fault association complexity. The message passing update of the graph neural network is performed based on the graph attention weights. The candidate triples are scored. The candidate triples whose scores meet the score threshold are written into the initial knowledge graph to form the fault knowledge graph to be optimized. The fault knowledge graph to be optimized is improved by a self-learning completion mechanism to mine missing knowledge and correct erroneous associations. The corrected fault knowledge graph is iteratively updated in real time through an active dynamic update mechanism, and an optimized fault knowledge graph is output.

2. The self-learning construction and optimization method for helicopter fault knowledge graphs according to claim 1, characterized in that, The method also includes cross-source semantic alignment processing for candidate triples: Construct a fault feature anchor point library, where anchor points include component location, feature mode, operating conditions, and typical faults; Calculate the matching score between candidate entities or candidate relations and anchor templates, where candidate relations are the relation types in candidate triples; Using a domain-adaptive semantic mapping model, the general semantic space is mapped to the helicopter fault domain semantic space to obtain domain semantic vectors. Based on the domain semantic vectors, the domain semantic similarity between candidate triples and predefined standard entities or standard relations in the anchor point library is calculated. The matching score and domain semantic similarity are combined, and the standard entity or standard relationship with the highest score that exceeds the threshold is used as the normalization result.

3. The self-learning construction and optimization method for helicopter fault knowledge graphs according to claim 1, characterized in that, After forming the fault knowledge graph to be optimized, the process also includes: Construct a formal knowledge graph of failure mechanisms, including an entity layer, a relation layer, and an evidence layer; Establish a degradation chain, which includes at least two stages: normal, slight wear, moderate wear, localized peeling, functional degradation, and failure. Based on the operating conditions and the stage transition scoring function, the degradation stage transition probability is defined, and the degradation chain and transition probability are updated to the fault knowledge graph to be optimized.

4. The self-learning construction and optimization method for helicopter fault knowledge graphs according to claim 1, characterized in that, The self-learning completion mechanism includes: The graph completion task is modeled as a sequence decision problem based on reinforcement learning; The environment state is defined as the entity node at the current inference time, the path already traversed, and the local graph context. The action is defined as selecting the next relation and the next hop entity from the current node. The policy network is defined based on the environment state and the action. The probability distribution of each action is output by the policy network. A value function is defined based on the reward obtained after the state transition. The advantage function is estimated based on the value function. The policy network parameters are updated through policy gradient based on the advantage function and the probability distribution output by the policy network. The missing knowledge is mined from the fault knowledge graph to be optimized using the policy network with updated parameters.

5. The self-learning construction and optimization method for helicopter fault knowledge graphs according to claim 4, characterized in that, The self-learning completion mechanism also includes: The graph completeness improvement rate can be determined by the ratio of the number of newly added correct entities or relations to the total number of entities or relations in the graph before completion, or by weighting the connectivity of key entities and the closure of degenerate chains to obtain the graph completeness improvement rate. The association accuracy bonus is calculated based on the ratio of the number of newly verified relationships to the total number of newly added relationships. The total reward is obtained by weighting the completeness improvement rate, the association accuracy rate, and the incorrect association penalty. Based on the total reward, the strategy network, rule candidate pool, and statistical prior model are updated synchronously to complete the missing knowledge mining and error association correction of the fault knowledge graph to be optimized.

6. The self-learning construction and optimization method for helicopter fault knowledge graphs according to claim 1, characterized in that, The proactive dynamic update mechanism includes: Retrieval tasks are generated based on areas of weak knowledge, hot components, and recent abnormal cases, forming a query set; Retrieve a set of candidate documents from external data sources and reorder them based on relevance, source authority, and recentity. The top-ranked documents are used as the generation context, input into the knowledge generation model to generate candidate knowledge entries, and then written into the fault knowledge graph.

7. The self-learning construction and optimization method for helicopter fault knowledge graphs according to claim 6, characterized in that, Before writing candidate knowledge entries into the fault knowledge graph, the process also includes: The candidate entries are compared with the core mechanism chain, key parameter range, and component hierarchy of the existing graph, and a consistency score is calculated. The authenticity score is calculated based on whether there is a case number, source organization, original measurement point, maintenance record, or cross-verification from multiple sources. The overall credibility is calculated based on the quantity, diversity of types, temporal continuity, and reliability of sources of evidence. When the weighted sum of consistency score, authenticity score, and overall credibility exceeds a preset threshold, the candidate knowledge item is written into the knowledge graph.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the self-learning construction and optimization method for helicopter fault knowledge graphs as described in any one of claims 1-7.

9. A storage medium storing computer instructions thereon, characterized in that, When the computer instructions are executed, they perform the steps of the self-learning construction and optimization method for helicopter fault knowledge graphs as described in any one of claims 1-7.

10. A terminal comprising a memory and a processor, wherein the memory stores computer instructions executable on the processor, characterized in that, When the processor executes the computer instructions, it performs the steps of the self-learning construction and optimization method for helicopter fault knowledge graphs as described in any one of claims 1-7.

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