Special equipment inspection management system and method based on artificial intelligence and big data

Abstract

The invention discloses a special equipment inspection management system and method based on artificial intelligence and big data, and relates to the technical field of special equipment inspection. The method comprises the following steps: accessing multi-modal test data, establishing a unified data ontology model after data preprocessing, and outputting standardized JSON record parameters; based on JSON recording parameters, constructing a four-dimensional constraint embedded heterogeneous hypergraph, and introducing a time decay factor and a working condition grouping mechanism to obtain a Gaussian kernel weight; the technical key points are as follows: the scheme realizes accurate identification and clear explanation of the composite logic error in the special equipment inspection report, not only can automatically discover relevant hidden risks such as numerical value compliance but trend abnormity, standard reference failure or conclusion and data contradiction and the like, but also can actively send targeted inquiry to an inspection party, so that the accuracy of the inspection is improved. The supplementary technical description is guided; the operation of the whole scheme can significantly reduce the common false alarm dispute of the traditional management method, and enables the inspection party to turn from passive error correction to active cooperation.

Description

Technical Field

[0001] This invention relates to the field of special equipment inspection technology, specifically to a special equipment inspection management system and method based on artificial intelligence and big data. Background Technology

[0002] Current special equipment inspection technology mainly relies on manual review and rule engine-based auxiliary systems. Inspection data is collected through business systems, including basic equipment information, inspection parameters, and PDF / Word report documents. The review process is usually divided into several independent steps: using keyword matching or regular expressions to check whether the terminology conforms to the standards; judging the compliance of numerical values ​​based on fixed thresholds; verifying the integrity of the report structure through template comparison; some systems also introduce OCR to recognize signatures or seals and perform simple comparisons with registration information before entering the inspection report. Due to the large number of special equipment, data entry errors or filling errors are inevitable. Since special equipment is related to the bottom line of people's livelihood and safety, the management of inspection reports needs to be given extra attention.

[0003] However, current special equipment inspection report review mainly relies on manual labor or systems based on simple rule engines. These methods often treat various inspections in isolation: terminology verification relies on keyword matching, numerical verification uses fixed thresholds (e.g., wall thickness ≥ 6mm), structural integrity relies on template comparison, and the validity of relevant rule references is often overlooked. This isolated approach leads to a large number of complex errors going undetected. For example, a pressure vessel's measured wall thickness is 8.2mm, exceeding the minimum limit and seemingly compliant. However, considering its material Q345R, design pressure 2.5MPa, and service life, calculations according to GB / T 150 show a significant error between the theoretically required wall thickness and 8.2mm. This indicates that it actually does not meet the strength requirements, yet it is judged as compliant by the traditional system. Another example is a report conclusion stating that the equipment is basically qualified, which, while not violating literal rules, does not comply with TSG (Total Safety and Security). Z7001-2021 stipulates that there must be a mandatory requirement to clearly determine whether something is compliant or non-compliant. More commonly, there are both abnormal data trends and optimistic conclusions. Traditional management solutions lack mechanisms to link historical data and cannot question the reasons, resulting in underreporting. The root cause of these problems is that traditional technologies lack the ability to semantically align multi-source data, joint reasoning mechanisms for cross-dimensional constraints, and interpretable feedback channels for abnormal results. This leads to superficial audits that are unable to address the intertwined logical contradictions and hidden risks in real-world scenarios. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A special equipment inspection management method based on artificial intelligence and big data, the steps of which are as follows:

[0006] After receiving multimodal test data and preprocessing the data, a unified data ontology model is established, standardized JSON record parameters are output, and the data is stored in a vector database.

[0007] Based on JSON record parameters, a heterogeneous hypergraph with four-dimensional constraint embedding is constructed. A time decay factor and working condition grouping mechanism are introduced to derive Gaussian kernel weights. A dynamic graph attention network with multi-constraint fusion is deployed to perform differentiated message passing on multiple types of hyperedges contained in the heterogeneous hypergraph. The fusion process is completed through attention fusion gating, and anomaly judgment and early warning actions are triggered. A multi-granularity evidence chain is generated for any output early warning.

[0008] A domain-specific large language model with multi-task fine-tuning is constructed. Several historical qualified inspection reports are obtained for multi-objective instruction fine-tuning to train the objective function. A multi-dimensional collaborative verification mechanism is executed to obtain inspection results, and multi-granularity correction suggestions and confidence scores are generated based on the inspection results.

[0009] Furthermore, the multimodal test data includes at least three forms of raw data: structured, semi-structured, and unstructured. Among them, the structured data includes fields such as basic equipment information and test parameters; the unstructured data includes attachments such as test reports, scanned copies of remarks, photos of equipment nameplates, images of safety valve seals, and scanned copies of signature pages; and the semi-structured data is the strength calculation table embedded in the test report.

[0010] Furthermore, data preprocessing for multimodal testing data includes at least: noise filtering, error correction, and missing data completion; the operation of the unified data ontology model is as follows: based on the established standards, integrating the established safety technical specifications and industry terminology library, defining a standard attribute set, and outputting JSON record parameters through mapping;

[0011] The standard attribute set must include at least the standard attribute name and the allowed value range.

[0012] Furthermore, a heterogeneous hypergraph with four-dimensional constraint embedding is constructed to encode several classes of prior knowledge as nodes and hyperedges in the graph structure to form a reasoning space; wherein, the node definition includes at least: device instance node d, JSON record parameter node p. j Standard Clause Node r k and historical state node h t The definition of a hyperedge includes at least: rule hyperedge e_rule, statistical hyperedge e_stat, physical hyperedge e_phys, and semantic hyperedge e_sem; where a hyperedge connects multiple nodes.

[0013] Furthermore, the operation process of the time decay factor and the operating condition grouping mechanism is as follows:

[0014] Based on the j-th JSON record parameter v of historical device k k Combined with the same working conditions group G j Inspection time t of historical device k k Get the weighted average value μ of the parameters of the j-th JSON record of the same type of device at the current time t. j (t); synchronously acquire the weighted standard deviation σ under the current operating condition. j (t); the weighted average μ of the parameters of the j-th JSON record of similar devices. j (t) and the weighted standard deviation σ under the current operating conditions j (t) to generate Gaussian kernel weights w ij .

[0015] Furthermore, the operational process of the multi-constraint fusion dynamic graph attention network is as follows:

[0016] In the differentiated message passing of multiple types of hyperedges contained in a heterogeneous hypergraph, message generation is completed according to the hyperedge type;

[0017] Among them, the rule hyperedge: if the hard constraint of the rule is violated, a strong negative signal vector [-1, 0, ..., 0] ∈ ℝ is generated. d ;ℝ d Let d represent a d-dimensional real vector space, where d is the embedding dimension of the dynamic graph attention network MC-GAT; Statistical hyperedge: message vector = w ij ×h pj , where h pj For parameter node embedding, w ij Scaling factor; Physical hyperedge: Obtain the strength formula residual δ_1: δ_1=|P_calc−P_design|, message=exp(-δ_1 / τ)×h_material; where P_calc is the theoretical maximum bearing capacity, P_design is the nominal design pressure of the equipment, τ represents the temperature scale parameter, and h_material represents the embedding vector of the material parameter node; Semantic hyperedge: message= sim(LLM(p j ), LLM(conclusion) × h_text; where LLM is a domain-target large language model encoder fine-tuned by multiple tasks, and conclusion is the natural language conclusion paragraph in the report; h_text∈ℝ d The original embedding of the conclusion text is reflected, and sim is the cosine similarity.

[0018] Furthermore, the operational basis for the multi-constraint fusion dynamic graph attention network also includes:

[0019] Configure attention fusion gating to handle device instance node d and derive its aggregated message m_d; where the attention weight α applied to the hyperedge e is...e It is generated by a learnable gating network; it triggers anomaly detection and early warning actions, and completes the process based on the aggregated message m_d and generates a multi-granularity evidence chain for any output early warning; the application process of the anomaly detection and early warning actions is as follows: the final device embedding hm_d is obtained based on the aggregated message m_d, and the anomaly score anomaly_score is calculated; when the anomaly score anomaly_score exceeds the preset tolerance threshold and there is no rule-based over-edge triggering, an early warning signal is triggered and a multi-granularity evidence chain is generated.

[0020] Furthermore, the training objective function is based on:

[0021] The obtained standard cross-entropy loss L_ce, data-text consistency loss L_consistency, and structural integrity loss L_structureze are weighted and calculated to generate the total loss function L. Among them, the data-text consistency loss L_consistency uses comparative learning to bring the embedding distance between the conclusions and data in the qualified inspection report closer and push away the distance of abnormal combinations. The implementation process of structural integrity loss L_structureze is as follows: for each inspection report, it is marked whether it contains a set chapter, and the probability p_section of the existence of the set chapter is output, with the loss being the binary cross-entropy.

[0022] Furthermore, the basis for obtaining inspection results through the multi-dimensional collaborative verification mechanism is as follows:

[0023] The operation of the multi-dimensional collaborative verification mechanism includes at least four sub-tasks: terminology and rule verification, data and text verification, signature and qualification verification, and document structure verification. The second sub-task, data and text verification, involves identifying the parameter dimension that contributes most to anomalies from the final device embedding `hm_d` through gradient backpropagation. The identification result and its historical sequence are then used as context and input into a multi-task fine-tuned domain target large language model to generate natural language queries. For each type of check result, multi-granularity correction suggestions and confidence scores are generated. The multi-granularity correction suggestions are natural language instructions containing rule-based justifications. The confidence score is calculated based on the model's internal attention weights and consistency scores.

[0024] The special equipment inspection and management system based on artificial intelligence and big data is applied to the special equipment inspection and management method based on artificial intelligence and big data. The system includes: a multimodal data storage module, which receives multimodal inspection data, establishes a unified data ontology model after data preprocessing, outputs standardized JSON record parameters, and stores them in a vector database;

[0025] The heterogeneous processing early warning module constructs a heterogeneous hypergraph with four-dimensional constraint embedding based on JSON record parameters, introduces a time decay factor and working condition grouping mechanism to derive Gaussian kernel weights, and deploys a dynamic graph attention network with multi-constraint fusion to perform differentiated message transmission on the various types of hyperedges contained in the heterogeneous hypergraph. It completes the fusion processing through attention fusion gating and triggers anomaly judgment early warning actions, generating a multi-granularity evidence chain for any output early warning.

[0026] The fine-tuning verification management module constructs a domain-specific large language model for multi-task fine-tuning, obtains several historical qualified inspection reports for multi-objective instruction fine-tuning to train the objective function, and executes a multi-dimensional collaborative verification mechanism to obtain inspection results. Based on the inspection results, it generates multi-granularity correction suggestions and confidence scores.

[0027] The present invention provides a special equipment inspection management system and method based on artificial intelligence and big data, which has the following beneficial effects: (1) This solution aims to solve the complex logical errors caused by the failure of multi-dimensional constraint coupling in the special equipment inspection report. By embedding a heterogeneous hypergraph with four-dimensional constraints and a graph attention network with dynamic fusion of multiple constraints, it realizes the deep identification of complex logical errors in the special equipment inspection report. It not only solves the coupling anomaly that conventional solutions cannot handle, but also improves the efficiency of human-machine collaboration through an interpretable evidence chain, taking into account both the improvement of safety and usability.

[0028] (2) This solution not only completes the traditional text compliance task by executing a multi-dimensional collaborative verification mechanism, but also achieves terminology standardization, prevents word errors, avoids black box error exceptions, verifies identity to prevent qualification fraud, and standardizes structure to ensure that the format is not missing. Among them, the joint application of data and text verification in the second item greatly improves the usability and human-machine trust of the overall verification management system.

[0029] (3) The application of this solution can maintain the original technical effect on the one hand, namely, the compliance rate of terminology and the detection rate of structural defects can be effectively improved. On the other hand, it adds the ability to clarify abnormalities through human-machine collaboration. Most of the situations identified by triggering abnormal judgment and warning actions can be effectively explained through questioning, so that the management method designed in this solution can reduce the overall false alarm rate and improve availability through multimodal collaboration.

[0030] In summary, by standardizing multimodal inspection data, constructing a dynamic graph reasoning engine that integrates rules, statistics, physical, and semantic constraints, and deploying a domain-specific target language model that is deeply integrated with it and fine-tuned through multiple tasks, this solution achieves accurate identification and clarification of complex logical errors in special equipment inspection reports. It can not only automatically detect hidden risks such as numerical compliance with abnormal trends, invalid standard references, or contradictions between conclusions and data, but also proactively issue targeted inquiries to the inspection party, guiding them to supplement technical explanations. The overall solution significantly reduces false alarm disputes common in traditional management methods, enabling the inspection party to shift from passive error correction to proactive collaboration. Actual operation shows that while the overall report compliance rate has significantly improved, the inspection party's willingness to adopt the output suggestions has also increased markedly. The audit blind spots previously caused by data silos and rigid rules have been effectively covered, thus truly achieving a simultaneous improvement in safety control and work efficiency. Attached Figure Description

[0031] Figure 1 This is a simplified flowchart illustrating the overall steps of the management method in this invention. Detailed Implementation

[0032] The technical solutions of the embodiments 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0033] Example 1:

[0034] Please see Figure 1 This embodiment provides a special equipment inspection management method based on artificial intelligence and big data. This method is used to automatically identify and correct problems such as input errors, logical contradictions or non-compliance with standards in inspection reports and recorded data in the special equipment inspection integrated management system, thereby improving report compliance and reducing safety liability risks.

[0035] Specifically, the steps of this method are explained as follows:

[0036] S1. Access multimodal test data, establish a unified data ontology model after data preprocessing, output standardized JSON record parameters, and store them in a vector database;

[0037] Multimodal test data is acquired synchronously from existing test systems without loss and with low latency; multimodal test data includes at least three forms of raw data: structured, semi-structured, and unstructured.

[0038] For structured data, data such as equipment basic information and inspection parameters are retrieved in real time from the existing inspection system's business database via database middleware, such as CDC or RESTful API. Equipment basic information includes at least: equipment number, manufacturing unit, and usage registration certificate number. Inspection parameters include at least design pressure, test pressure, wall thickness measurement, and medium temperature. For unstructured data, a file monitoring service is used to automatically capture uploaded PDF / Word inspection reports, scanned copies of handwritten notes, photos of equipment nameplates, images of safety valve seals, scanned copies of signature pages, and other attachments. Semi-structured data is the strength calculation table embedded in a Word report. It should be noted that the use of CDC and file monitoring dual channels ensures transactional consistency of structured data while also allowing flexible uploading of unstructured documents, avoiding business interruptions due to system modifications.

[0039] The following is an explanation of the data preprocessing for multimodal testing data:

[0040] Data preprocessing for multimodal test data includes noise filtering, error correction, and missing data completion. Among them, data preprocessing is divided into two processing paths: numerical and textual. (1) Numerical field cleaning and imputation: For missing or abnormal values, such as empty wall thickness measurement values, the sliding window mean imputation method is used, based on the following:

[0041] ;

[0042] In the formula, x_t(filled) represents the repair value of the missing parameter of the current special equipment at time t, k represents the half width of the sliding window, which can be set to 2 according to the actual situation, that is, taking 5 historical valid values ​​for 2 years before and after, x iThe historical valid parameter values ​​of special equipment of the same type and working condition at time i are represented, for example, pressure vessels of Q345R material and design pressure of 2.5 MPa; the summation range i=tk to t+k ensures the use of local time context rather than global average; the corresponding logical explanation is: this method is better than global mean interpolation because it considers the aging trend of equipment, such as the wall thickness decreasing year by year, avoiding the repair of old equipment to the level of new equipment; (2) Text field cleaning and key information extraction: for the text after OCR recognition, such as handwritten conclusions or PDF report text, the BERT-BiLSTM-CRF joint model is used to perform named entity recognition (NER); among them, BERT encoder: generates context-aware word vectors; BiLSTM layer: captures long-distance dependencies, for example: the association between 2.8 MPa and safety valve in the safety valve setting pressure of 2.8 MPa; CRF decoding layer: ensures the compliance of the label sequence, for example: the unit must be followed by the value; the BERT-BiLSTM-CRF joint model outputs standardized triples: (entity type, entity value, confidence).

[0043] The purpose of establishing a unified data ontology model is to solve the problem of synonyms, such as the correspondence between work pressure and operational pressure, and to construct a domain ontology to achieve semantic normalization. The operation of the unified data ontology model is based on the following:

[0044] Based on or centered on established standards, and integrating the TSG series of safety technical specifications and industry terminology databases, a set of standard attributes is defined, including standard attribute names and permissible value ranges. The established standard can be selected as: "GB / T 33215-2016 Special Equipment Inspection and Testing Data Recording and Reporting Format". Standard attribute names include, for example, operating pressure, design temperature, and material grade. Permissible value ranges include cases such as MaterialGrade∈{Q235B, Q345R, S30408, ...}.

[0045] The mapping rules are shown in the following example:

[0046] Original field name Mapping target Work pressure OperatingPressure Operating pressure OperatingPressure Operating pressure OperatingPressure Design temperature DesignTemperature

[0047] The mapping is achieved through fuzzy matching and a rule base for manual review, supporting spelling variants with a Levenshtein distance ≤ 2;

[0048] Output format: All fields are converted to JSON-LD format, i.e., the standardized JSON record parameters mentioned above, which facilitates subsequent graph construction. An example is: { "@id": "R2025-0876", "type": "PressureVessel", "OperatingPressure": { "value": 2.5, "unit": "MPa"}, "MaterialGrade": "Q345R", "WallThickness": { "value": 8.2, "unit": "mm"}}; It should be noted that this standardized JSON is the only trusted input source for the subsequent S2 knowledge graph construction and also the structured context for LLM in the subsequent S3.

[0049] Specifically, S1 in this solution ensures that the accuracy of the input data exceeds 98%, as demonstrated by testing with tens of thousands of samples, providing a high-quality foundation for subsequent S2 / S3. Simultaneously, through standard ontology and JSON-LD output, it supports the creation of graph nodes in S2 and the injection of structured context in S3, forming a seamless data flow. In summary, S1 not only completes traditional ETL functions but also becomes a reliable foundation for the entire intelligent verification system through domain ontology-driven semantic standardization and multimodal joint cleaning.

[0050] S2. Based on JSON record parameters, construct a heterogeneous hypergraph with four-dimensional constraint embedding, introduce time decay factor and working condition grouping mechanism to dynamically derive Gaussian kernel weights; and deploy a dynamic graph attention network with multi-constraint fusion to perform differentiated message transmission on multiple types of hyperedges contained in the heterogeneous hypergraph. After completing the fusion processing through attention fusion gating, trigger anomaly judgment and early warning actions, and generate multi-granularity evidence chains for any output early warning.

[0051] Constructing a heterogeneous hypergraph with four-dimensional constraint embedding is used to encode several classes of prior knowledge as nodes and hyperedges in a graph structure to form a unified reasoning space.

[0052] The node definition includes: device instance node d and JSON record parameter node p. j Standard Clause Node r k and historical state node h t Specifically, the device instance node d represents a specific special-purpose device, including attributes such as the device's unique identifier ID, device type, and manufacturing date; the JSON record parameter node p... j This indicates a parameter in a JSON record, such as the set pressure of a safety valve or the wear amount of a brake; standard clause node r kThis represents a corresponding existing standard clause, such as Clause X of TSG 11-2020, which contains relevant information such as the standard number, effective / repeal date, and constraint expressions; historical state node h t This represents the inspection record of the same device in year t, used for modeling time series evolution.

[0053] The definition of a hyperedge includes: rule hyperedge e_rule, statistical hyperedge e_stat, physical hyperedge e_phys, and semantic hyperedge e_sem; a hyperedge connects multiple nodes and expresses complex constraint relationships; the relationship between each hyperedge definition and each node can be understood by referring to Table 1 below;

[0054] Table 1. Correspondence between hyperedges and connected nodes:

[0055] Super Edge Type Connecting nodes Source of constraints Encoding method Rule superedge e_rule <![CDATA[(p a ,p b ,r k )]]> Hard constraints of rules <![CDATA[Boolean predicate, i.e., p a ≤ c × p b , + standard version validity flag]]> Statistical analysis of superedges e_stat <![CDATA[(p j ,{p j (i)}(i=1、2、...、N))]]> Historical distribution of similar equipment <![CDATA[Gaussian kernel weight w ij , as described later]]> Physics hyperedge e_phys (p_pressure, p_wall thickness, p_material) GB / T 150 Strength Calculation Formula Residuals of functional relationships between parameters Semantic hyperedge e_sem <![CDATA[(p j ,conclusion_text)]]> Text-Data Consistency Cosine similarity of LLM embedding vectors

[0056] Among them, regarding p in the connection node a p b p j The overall term represents a specific technical parameter in the inspection of special equipment. For example, it can represent the set pressure of a safety valve, the design pressure, and a placeholder for the parameter of the j-th JSON record. The dimensionless coefficient mentioned in the Boolean predicate, i.e., the constant multiple, is used to define two parameters, namely p. a p b The legal proportional relationship between them; p j (i)} (i=1, 2, ..., N) represents the historical parameter set of similar equipment. It represents the set of parameters of the j-th JSON record of N equipment of the same type and working condition as the current equipment in the historical qualified report database. It can be used for statistical modeling. i represents the i-th historical sample. p_pressure, p_wall thickness, and p_material represent the pressure parameter node, wall thickness parameter node, and material parameter node, respectively. Among them, the pressure parameter node specifically refers to the load-related parameters such as the design pressure, test pressure, or working pressure of the equipment, and the unit is usually MPa. The wall thickness parameter node refers to the measured minimum wall thickness of the pressure-bearing components such as the cylinder and head of the equipment, and the unit is usually mm. The material parameter node refers to the material grade of the pressure-bearing components of the equipment, such as the setting of Q345R, which indicates the allowable stress value of the material. conclusion_text is the conclusion text, which refers to the natural language conclusion paragraph written by the inspector in the inspection report, such as the equipment meets the XX requirements and can continue to be used.

[0057] It should be noted that the basis or source of the strength calculation formula in GB / T 150 is the core standard for the design, manufacture and inspection of steel pressure vessels in GB / T 150 "Pressure Vessels". It contains the calculation formula for the minimum required wall thickness of pressure-bearing components under internal pressure, which will not be explained in detail here.

[0058] The introduction of the time decay factor and operating condition grouping mechanism is to overcome the inability of static statistical distribution to adapt to equipment aging and process evolution, and the operation of the time decay factor and operating condition grouping mechanism is based on the following:

[0059] S2.1, Based on the j-th JSON record parameter v of historical device k k Combined with the same working conditions group G j Inspection time t of historical device k k Dynamically obtain the weighted average value μ of the j-th JSON record parameter of similar devices at the current time t. j (t);

[0060] Among them, dynamically obtaining μ j The basis for (t) is as follows:

[0061] ;

[0062] In the formula, G represents the homogeneous working condition group. j This refers to a set of qualified devices of the same type, medium, and service life as the current device, such as those aged 5 to 10 years. λ represents the time decay coefficient, ranging from 0 to 1. In this embodiment, it can be set to 0.2 to give higher weight to recent data. It should be noted that the above formula calculates the typical value of the j-th JSON record parameter of the same type of device at the current time t using exponential time decay weighting. The weight is exp(-λ(t−t)). k This approach allows recent test data to contribute more, while the impact of long-term data gradually diminishes. By controlling the time decay coefficient, sampling is only performed from homogeneous groups of operating conditions, ensuring that the statistical benchmark is highly comparable to the current equipment. This effectively addresses long-term drift caused by equipment aging and process improvements, ensuring that the mean reflects the true typical level under current operating conditions, rather than a static historical average.

[0063] S2.2, Synchronously obtain the weighted standard deviation σ under the current operating condition. j (t);

[0064] Among them, the weighted standard deviation σ under the current operating conditions is calculated. j The basis for (t) is as follows:

[0065] ;

[0066] In the formula, |G j | represents the number of devices in the homogeneous operating condition group; it should be noted that this formula applies to dynamic μ. j Based on (t), calculate the parameters of similar devices around this μ. jThe weighted dispersion of (t) is calculated by weighting the squared historical deviations over time to highlight recent fluctuations. The denominator uses the size of a homogeneous set of operating conditions, rather than the traditional weighted sum, to maintain consistency in the dimensions of the standard deviation and avoid variance distortion due to sample sparsity. Therefore, the resulting weighted standard deviation σ under the current operating conditions is... j (t) Dynamically characterizes the fluctuation range of parameters under the current working conditions, providing an adaptive threshold basis for subsequent anomaly detection.

[0067] S2.3, The weighted average value μ of the j-th JSON record parameter based on similar devices. j (t) and the weighted standard deviation σ under the current operating conditions j (t) to generate Gaussian kernel weights w ij ;

[0068] Among them, the Gaussian kernel weight w is calculated. ij The basis is as follows:

[0069] ;

[0070] In the formula, the Gaussian kernel weight w ij The JSON record parameter v of the current device i Compatibility weights with dynamic distributions, whose values ​​range from (0, 1], when v i =μ j (t), w ij =1; when the deviation exceeds 2σ j (t), w ij If the value is less than 0.135, it is considered abnormal. It should be noted that the overall design of this time decay factor and the working condition grouping mechanism makes the statistical constraints evolve adaptively with time and working conditions, avoiding the masking of local anomalies by the global static distribution.

[0071] The operational basis for the dynamic graph attention network with multi-constraint fusion is as follows:

[0072] The first step involves differentiated message passing for various types of hyperedges within a heterogeneous hypergraph, with message generation based on hyperedge type. For regular hyperedges: if the hard constraints of the rule are violated, a strong negative signal vector [-1, 0, ..., 0] ∈ ℝ is generated. d ;ℝ dLet d represent a d-dimensional real vector space, where d is the embedding dimension of the dynamic graph attention network MC-GAT. For hard constraints, in this embodiment, it can be specifically selected as: the safety valve set pressure is greater than the product of the equipment design pressure and the set multiple, which indicates a violation of the hard constraint. This design encodes the hard constraint as a clear and non-negligible negative signal. The first dimension is set to -1 to be significantly identified by the attention mechanism in subsequent aggregation, avoiding dilution by other soft constraints. This vector does not rely on learning, ensuring the highest priority of the rule. Once triggered, it is forcibly marked as high risk regardless of statistical or semantic signals, ensuring compliance.

[0073] Statistical hyperedge: message vector = w ij ×h pj , where h pj The parameter nodes are embedded, specifically learned through an embedding layer; the w corresponding to statistical typicality is then used. ij As a scaling factor, it acts on h corresponding to the parameter semantic representation. pj If the parameters are highly typical, i.e., w ij If ≈1, then the complete semantics are conveyed; otherwise, w is not. ij →0 suppresses its impact and achieves data-driven soft constraints; it can effectively identify hidden risks that are not in violation but are extremely atypical, such as situations where the wall thickness of some new equipment is much lower than the average of similar products.

[0074] Physical Hyperedge: Obtaining the strength formula residual δ_1: δ_1=|P_calc−P_design|, message=exp(-δ_1 / τ)×h_material; where P_calc is the theoretical maximum bearing capacity derived from the strength formula in GB / T 150, in MPa; P_design represents the nominal design pressure of the equipment, and the mentioned strength formula residual δ_1 reflects whether the measured parameters can support the design load; τ represents the temperature scale parameter, ranging from 0 to 1, which can be set to 0.1 in this embodiment to control the exponential decay rate; h_material represents the embedding vector of the material parameter node, and h_material∈ℝ d It should be noted that the larger the residual δ_1 of the strength formula, the closer the device is to structural failure; the exponential function corresponding to the message maps it to the interval (0, 1). When δ=0, it indicates a complete match, and the weight is 1; when δ=0.3MPa, the weight is ≈0.05, which is close to suppression; combined with material embedding, the message carries the joint semantics of material and strength; it is different from the conventional threshold judgment and realizes a more in-depth security verification.

[0075] Semantic Hyperedge: Message = sim(LLM(p j), LLM(conclusion) × h_text; where sim is the cosine similarity, LLM is the domain-specific large language model encoder mentioned in S3 for fine-tuning, which converts text or structured parameters into semantic vectors; conclusion is the natural language conclusion paragraph in the report; h_text∈ℝ d The original embedding of the conclusion text is reflected; if the data implies risk, such as the wall thickness approaching the lower limit, but the conclusion is good, the LLM embedding direction will be significantly different, the sim value will be low, and the message will be weakened; otherwise, it will be enhanced, thereby achieving data and text semantic alignment supervision, which can effectively identify contradictory situations where the data is abnormal but the conclusion is optimistic, and improve the credibility of the report.

[0076] In this embodiment, the four types of hyperedges are designed to be fused in a unified graph space through attention gating, enabling the system to simultaneously possess rule rigidity, data sensitivity, physical authenticity, and semantic consistency, resulting in significant collaborative reasoning capabilities, thereby improving the detection rate of composite anomalies and the trust between humans and machines.

[0077] The second step is to configure attention fusion gating to respond to the device instance node d and obtain its aggregated message m_d;

[0078] The basis for deriving the aggregate message m_d is as follows:

[0079] ;

[0080] In the formula, E(d) represents the set of all hyperedges connected to the device instance node d, which in this embodiment corresponds to four categories: rule-based, statistical, physical, and semantic; α e Let α represent the attention weight of the hyperedge e, the relative importance of this constraint in the current context, and let α range from 0 to 1; MSG(e)∈ℝ d This reflects the message vector generated by the hyperedge e, which, as mentioned earlier, is generated differentially according to type; unlike simple averaging or fixed weights, α e Dynamically generated by a learnable network, the model can adaptively adjust the credibility of each constraint according to the device status;

[0081] Attention weight α of hyperedge e e Generated by a learnable gating network, based on the following:

[0082] ;

[0083] The above equation uses Softmax normalization, where q∈ℝ d The device query vector is generated by a linear transformation of the initial device embedding h_d involved in the subsequent third step, representing the focus of attention of the current device; in this embodiment, k e ∈ℝ 4, representing the hyperedge type encoding vector, uses one-hot encoding to identify the hyperedge category, for example: regular hyperedge corresponds to [1, 0, 0, 0], statistical hyperedge corresponds to [0, 1, 0, 0], physical hyperedge corresponds to [0, 0, 1, 0], and semantic hyperedge corresponds to [0, 0, 0, 1]; W e ∈ℝ 4×d The type-specific projection matrix learns independent feature maps for each type of hyperedge, making constraints of different types comparable in a unified space; e' represents the dummy variable that iterates through all hyperedges in E(d); it should be noted that this design interacts with device state and constraint type. For ease of understanding, a subsequent example will illustrate this: for a new device, q and the rule / physical class W e k e The inner product is larger, therefore α e Higher; for older devices, q is more matched with statistical / semantic classes, so the weight of historical trends and text consistency increases, thereby achieving scenario-adaptive constraint trust allocation.

[0084] The third step is to trigger an anomaly detection and early warning action, which is completed based on the aggregated message m_d and generates a multi-granularity evidence chain for any output early warning. The application process of the anomaly detection and early warning action is as follows:

[0085] The final device embedding hm_d is derived from the aggregated message m_d: hm_d = MLP([h_d; m_d]); where the initial device embedding h_d is generated by the embedding layer from device attributes, such as type, manufacturing year, etc.; [h_d; m_d] ∈ ℝ 2d The vector concatenation operation combines the original features with the aggregated message as input. MLP stands for Multilayer Perceptron, which in this embodiment typically contains two fully connected layers with ReLU activation for nonlinear fusion. The original features are preserved to prevent information loss, while graph reasoning results are injected. The MLP learns how to combine prior knowledge with dynamic evidence to form a comprehensive judgment. The final device embedding `hm_d` is called to calculate the anomaly score `anomaly_score`: `anomaly_score = 1 - cos(hm_d, c_class)`, where `cos` represents the cosine similarity, and `c_class ∈ ℝ`. dThe cluster centers representing the embedded clusters of qualified devices of the same type are obtained by pre-calculating each type of device using K-Means on the hm_d of historical qualified reports. Qualified devices should cluster near c_class. If the hm_d direction deviates significantly, for example due to parameter coupling mismatch or abnormal trends, the cosine similarity decreases and the anomaly_score increases. When the anomaly score (anomaly_score) exceeds the preset tolerance threshold and no rule-based over-edge triggering occurs, an early warning signal is triggered, and a multi-granularity evidence chain is automatically generated. In this embodiment, a three-layer evidence chain is embodied: the first layer is the rule layer, which points out the currently valid standards and clauses that are violated; the second layer is the data layer, which quantifies the degree of parameter deviation; and the third layer is the semantic layer, which reveals the contradiction between the textual conclusion and the implicit information in the data. Rule-based over-edge triggering indicates the existence of a violation, while the above indicates the absence of a violation.

[0086] This step aims to address complex logical errors in special equipment inspection reports caused by the failure of multidimensional constraint coupling. Traditional methods rely on isolated rules or statistical thresholds, which are difficult to identify hidden risks such as numerical compliance but abnormal combinations, incorrect standard citations, and contradictions between trend changes and textual conclusions. This solution achieves deep identification of complex logical errors in special equipment inspection reports by using a heterogeneous hypergraph with four-dimensional constraint embedding and a graph attention network with dynamic fusion of multiple constraints. It not only solves the coupling anomalies that conventional solutions cannot handle, but also improves human-machine collaboration efficiency through interpretable evidence chains, achieving a dual improvement in security and usability.

[0087] S3. Construct a domain-target large language model for multi-task fine-tuning, obtain several historical qualified inspection reports for multi-target instruction fine-tuning to train the objective function; and execute a multi-dimensional collaborative verification mechanism to obtain inspection results, and generate multi-granularity correction suggestions and confidence scores based on the inspection results.

[0088] In this embodiment, the domain-specific large language model with multi-task fine-tuning is constructed as a Domain-Specific LLM. Based on the open-source LLaMA-3-8B, it uses a massive amount of historical qualified inspection reports for multi-target instruction fine-tuning. Here, "massive amount" can refer to 100,000 reports. The training objective function is based on:

[0089] ;

[0090] In the formula, L is the total loss function, Q_1, Q_2, and Q_3 are the corresponding hyperparameter weights, all ranging from 0 to 1, with the values ​​of Q_1, Q_2, and Q_3 decreasing sequentially. The specific values ​​can be set according to the actual situation. L_ce represents the standard cross-entropy loss, used to supervise the model to generate standardized terms and sentences. L_consistency reflects the data and text consistency loss, ensuring semantic alignment between the conclusion paragraph and the data table. L_structureze represents the structural integrity loss, used to supervise the existence of the defined chapters.

[0091] Among them, L_consistency narrows the embedding distance between the conclusions in the qualified inspection report and the data through contrastive learning, and widens the distance between anomalous combinations. Therefore, the basis for the data-text consistency loss L_consistency is as follows:

[0092] ;

[0093] In the formula, e_concl∈ℝ d Let e_data represent the embedding vector of the conclusion segment extracted by the LLM encoder. d , is the data semantic vector obtained by projecting the final device embedding hm_d of the device instance node d in the S2 graph; e_data(f) represents the negative sample data vector, which comes from other devices or perturbation data; sim is the cosine similarity function, wt is the temperature coefficient, with a value range of 0 to 0.1. In this embodiment, the specific value can be set to 0.05 to control the distribution sharpness, f=1, 2, ..., F, where F is the total number of negative samples; this design enables LLM to perceive the underlying data state when generating conclusions; for example, when an abnormal wall thickness trend is detected in S2, that is, hm_d deviates from the qualified cluster, LLM will have difficulty generating a conclusion of no abnormal wear, otherwise the consistency loss will increase dramatically; the implementation process of structural integrity loss L_structureze is expressed as: for each inspection report, mark whether it contains a set section, which in this embodiment refers to a mandatory section, such as inspection basis, non-conformity handling opinions, etc.; the model outputs the probability p_section of the existence of the set section, and the loss is the binary cross-entropy, defined as:

[0094] L_structureze=-[ylog( p_section)+(1-y)log(1- p_section)];

[0095] In the formula, y∈{0,1} represents the true label of whether the chapter exists.

[0096] Description of the process of obtaining inspection results by executing a multi-dimensional collaborative verification mechanism: The operation of this mechanism includes at least 4 sub-tasks, the first two of which work synergistically with the technical solution recorded in S2. The sub-tasks are: terminology and rule verification, data and text verification, signature and qualification verification, and document structure verification.

[0097] Specifically, regarding the first item, terminology and rule verification, the method is as follows: load the terminology database constructed from a preset standard document, use a Trie tree to create an index, and use a sliding window to match the inspection report to identify non-standard expressions. The preset standard document can be a relevant standard document such as the "Special Equipment Safety Technical Specifications," and the terminology database contains over 10,000 standard terms. The sliding window can be set to 2 to 6 characters. For example, input text: "Performed pressure resistance test"; matching result: non-standard terminology corresponding to "pressure resistance test"; standard corresponding item: "pressure resistance test, according to GB / TXX"; output: marker position, suggested replacement words, and rule basis. Therefore, the purpose of the first item is to avoid technical ambiguity caused by inappropriate terminology.

[0098] The second item, data and text verification, is the core of this embodiment. It can directly receive the output of S2 to achieve cross-modal anomaly interpretation. Through gradient backpropagation or attention weight analysis, the parameter dimension that contributes most to the anomaly is identified in the final device embedding hm_d obtained from S2. This parameter dimension and its historical sequence are used as context and input into the multi-task fine-tuned domain target large language model (LLM) constructed in S3. The LLM generates natural language questions. For example, if the parameter dimension that contributes most to the anomaly is the brake wear trend, and its corresponding historical sequence is 0.3→0.5→0.7 mm, the generated data shows that the brake wear rate has slowed down abnormally, with a trend of 0.3→0.5→0.7 mm in the past three years and 0.8 mm in the present case. Please explain whether the measurement method or component should be changed? The reason for setting up this operation is that traditional systems only report anomalies but cannot explain why they are abnormal. Through data and text verification, the implicit reasoning results of S2 are transformed into understandable and operable human-computer dialogue, thereby significantly improving the acceptance rate of inspectors.

[0099] The third step, signature and qualification verification, involves using the signature or seal image extracted by OCR and a pre-trained Siamese network for identity comparison. The network structure uses ResNet-34 as the backbone, shares weights, and outputs a 512-dimensional feature vector. The distance formula is based on:

[0100] ;

[0101] In the formula, ds represents the distance, and the smaller the value, the higher the similarity. f() is the feature extraction function of the Siamese network, which maps the image to an embedding vector. x_input represents the signature / stamp image in the current inspection report, and x_template represents the standard template image in the registered inspector database. If ds does not exceed the verification threshold, the signature / stamp is considered valid, and the inspector's qualification verification is passed. The verification threshold is obtained by optimizing the ROC curve on the verification set to balance the false rejection rate and the risk of impersonation. In this embodiment, the specific value can be set to 0.8.

[0102] The document structure verification method for the fourth item is as follows: combining regular expressions and keyword matching, verify whether the inspection report contains the required chapters as specified by the rules. If any are missing, the structure is marked as incomplete. The required chapters are the mandatory chapters mentioned above, such as: inspection basis, main problems and handling opinions, inspection conclusions, and the next inspection date.

[0103] By implementing a multi-dimensional collaborative verification mechanism, not only are traditional text compliance tasks completed, but terminology standardization is also achieved through deep coupling with the S2 recording scheme. This prevents errors in word choice, avoids the abnormality of black-box error reporting, makes the identity verifiable to prevent qualification fraud, and standardizes the structure to ensure that the format is not missing. Among these, the joint application of data and text verification in the second item greatly improves the usability and human-machine trust of the overall verification management system.

[0104] The following explains how to generate multi-granularity correction suggestions and confidence scores:

[0105] For each type of inspection result, multi-granularity correction suggestions and confidence scores are generated. The multi-granularity correction suggestions are natural language instructions containing rule-based justifications. The confidence score is calculated based on the model's internal attention weights and consistency score. The confidence score (Con) is calculated using the following formula:

[0106] Con=σ(m1×attention_score+m2×(1−anomaly_score)+b)×100%;

[0107] In the formula, attention_score represents the attention weight of LLM to the question keywords when generating suggestions, reflecting the degree of model focus. anomaly_score, as explained in S2, is the anomaly score; the higher the value, the more suspicious the data. Therefore, (1-anomaly_score) is used to represent the data credibility. m1, m2, and b are all parameters learned through training, balancing text certainty and data consistency, and all have values ​​greater than 0. For example, when LLM pays high attention to basically qualified data and S2 does not report any data anomalies, the confidence level Con is close to 100%. This design allows the confidence level to reflect both the certainty of the language model and the rationality of the underlying data, avoiding high-confidence recommendations based on erroneous data, thereby improving the credibility and adoption rate of the suggestions. It should be noted that the confidence level Con here is also the confidence score.

[0108] The application of this solution maintains the original technical effect, namely, the compliance rate of terminology and the detection rate of structural defects are effectively improved. On the other hand, it adds the ability to clarify anomalies through human-machine collaboration. Most of the situations identified by S2 can be effectively explained through S3 inquiry. Therefore, the management method designed in this solution can reduce the overall false alarm rate and improve availability through multimodal collaboration.

[0109] When implementing the system in conjunction with S2, we will still take the previous abnormal elevator wear as an example:

[0110] S2 outputs anomaly score of 0.41, with a high contribution parameter being the brake wear trend, and no rule violations. S3 receives the final device embedding hm_d and parameter list. S3 performs a terminology check: no issues; a structural check: complete; and a consistency check: triggers challenge generation, i.e., the core solution of the second item is running. S3 output data shows that the brake wear rate has slowed abnormally, from 0.3 to 0.5 to 0.7 mm in the past three years, to 0.8 mm this time. Please explain whether the measurement method or component has been changed? The confidence level is 92%. The inspector replied: due to the replacement of the laser thickness gauge and adjustment of the measurement position. This explanation is fed back to the graph in S2, updating the device attributes, and can continue to optimize the next inference. Further details are not provided here.

[0111] By standardizing multimodal inspection data, constructing a dynamic graph reasoning engine that integrates rules, statistics, physical and semantic constraints, and deploying a domain-specific target language model that is deeply integrated with it and fine-tuned through multiple tasks, this solution achieves accurate identification and clarification of complex logical errors in special equipment inspection reports. It can not only automatically detect hidden risks such as numerical compliance with abnormal trends, invalid standard references, or contradictions between conclusions and data, but also proactively issue targeted inquiries to the inspection party, guiding them to supplement technical explanations. The overall solution significantly reduces misreporting disputes common in traditional management methods, enabling the inspection party to shift from passive error correction to proactive collaboration. Actual operation shows that while the overall report compliance rate has significantly improved, the inspection party's willingness to adopt the output suggestions has also increased markedly. The audit blind spots previously caused by data silos and rigid rules have been effectively covered, thus truly achieving a simultaneous improvement in safety control and work efficiency.

[0112] Example 2:

[0113] Based on Example 1, this example also provides a special equipment inspection management system based on artificial intelligence and big data, applied to the method mentioned in Example 1. The system includes: a multimodal data storage module, which accesses multimodal inspection data, establishes a unified data ontology model after data preprocessing, outputs standardized JSON record parameters, and stores them in a vector database; a heterogeneous processing early warning module, which constructs a heterogeneous hypergraph with four-dimensional constraint embedding based on JSON record parameters, introduces a time decay factor and a working condition grouping mechanism to derive Gaussian kernel weights; and deploys a dynamic graph attention network with multi-constraint fusion to perform differentiated message transmission on multiple types of hyperedges contained in the heterogeneous hypergraph, completes fusion processing through attention fusion gating, and triggers anomaly judgment early warning actions, generating multi-granularity evidence chains for any output early warning; and a fine-tuning verification management module, which constructs a domain target large language model for multi-task fine-tuning, obtains several historical qualified inspection reports for multi-target instruction fine-tuning to train the objective function; and executes a multi-dimensional collaborative verification mechanism to obtain inspection results, and generates multi-granularity correction suggestions and confidence scores based on the inspection results.

[0114] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0115] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0116] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A special equipment inspection management method based on artificial intelligence and big data, characterized in that, The steps of this method are as follows: After receiving multimodal test data and preprocessing the data, a unified data ontology model is established, standardized JSON record parameters are output, and the data is stored in a vector database. Based on JSON record parameters, a heterogeneous hypergraph with four-dimensional constraint embedding is constructed. A time decay factor and a working condition grouping mechanism are introduced to derive Gaussian kernel weights. It also deploys a dynamic graph attention network with multi-constraint fusion to perform differentiated message passing on multiple types of hyperedges contained in heterogeneous hypergraphs, completes fusion processing through attention fusion gating, and triggers anomaly judgment and early warning actions, generating multi-granularity evidence chains for any output early warning; Construct a domain-target large language model with multi-task fine-tuning, obtain several historical qualified inspection reports for multi-target instruction fine-tuning, and train the objective function; It also executes a multi-dimensional collaborative verification mechanism to obtain inspection results, and generates multi-granularity correction suggestions and confidence scores based on the inspection results.

2. The special equipment inspection management method based on artificial intelligence and big data according to claim 1, characterized in that, Multimodal test data includes at least three types of raw data: structured, semi-structured, and unstructured. Structured data includes fields such as basic equipment information and test parameters. Unstructured data includes attachments such as test reports, scanned copies of remarks, photos of equipment nameplates, images of safety valve seals, and scanned copies of signature pages. Semi-structured data consists of strength calculation tables embedded in the test report.

3. The special equipment inspection management method based on artificial intelligence and big data according to claim 1, characterized in that, Data preprocessing for multimodal test data includes at least: noise filtering, error correction and missing data completion; the operation of the unified data ontology model is as follows: based on the established standards, integrating the established safety technical specifications and industry terminology library, defining standard attribute sets, and outputting JSON record parameters through mapping; The standard attribute set must include at least the standard attribute name and the allowed value range.

4. The special equipment inspection management method based on artificial intelligence and big data according to claim 1, characterized in that, A heterogeneous hypergraph with four-dimensional constraint embedding is constructed to encode several classes of prior knowledge as nodes and hyperedges in the graph structure to form an inference space; wherein, the node definition includes at least: device instance node d and JSON record parameter node p. j Standard Clause Node r k and historical state node h t The definition of a hyperedge includes at least: rule hyperedge e_rule, statistical hyperedge e_stat, physical hyperedge e_phys, and semantic hyperedge e_sem; where a hyperedge connects multiple nodes.

5. The special equipment inspection management method based on artificial intelligence and big data according to claim 1, characterized in that, The operation process of the time decay factor and operating condition grouping mechanism is as follows: Based on the j-th JSON record parameter v of historical device k k Combined with the same working conditions group G j Inspection time t of historical device k k Get the weighted average value μ of the parameters of the j-th JSON record of the same type of device at the current time t. j (t); synchronously acquire the weighted standard deviation σ under the current operating condition. j (t); the weighted average μ of the parameters of the j-th JSON record of similar devices. j (t) and the weighted standard deviation σ under the current operating conditions j (t) to generate Gaussian kernel weights w ij .

6. The special equipment inspection management method based on artificial intelligence and big data according to claim 4, characterized in that, The operation process of the dynamic graph attention network with multi-constraint fusion is as follows: In the differentiated message passing of multiple types of hyperedges contained in a heterogeneous hypergraph, message generation is completed according to the hyperedge type; Among them, the rule hyperedge: if the hard constraint of the rule is violated, a strong negative signal vector [-1, 0, ..., 0] ∈ ℝ is generated. d ;ℝ d Let d be a d-dimensional real vector space, where d is the embedding dimension of the dynamic graph attention network MC-GAT; Statistical hyperedge: message vector = w ij ×h pj , where h pj For parameter node embedding, w ij Scaling factor; Physical hyperedge: Obtain the strength formula residual δ_1: δ_1=|P_calc−P_design|, message=exp(-δ_1 / τ)×h_material; where P_calc is the theoretical maximum bearing capacity, P_design is the nominal design pressure of the equipment, τ represents the temperature scale parameter, and h_material represents the embedding vector of the material parameter node; Semantic hyperedge: message= sim(LLM(p j ), LLM(conclusion) × h_text; where LLM is a domain-target large language model encoder fine-tuned by multiple tasks, and conclusion is the natural language conclusion paragraph in the report; h_text∈ℝ d The original embedding of the conclusion text is reflected, and sim is the cosine similarity.

7. The special equipment inspection management method based on artificial intelligence and big data according to claim 6, characterized in that, The operational basis for dynamic graph attention networks with multi-constraint fusion also includes: Configure attention fusion gating to handle device instance node d and derive its aggregated message m_d; where the attention weight α applied to the hyperedge e is... e It is generated by a learnable gating network; it triggers anomaly detection and early warning actions, and completes the process based on the aggregated message m_d and generates a multi-granularity evidence chain for any output early warning; the application process of the anomaly detection and early warning actions is as follows: the final device embedding hm_d is obtained based on the aggregated message m_d, and the anomaly score anomaly_score is calculated; when the anomaly score anomaly_score exceeds the preset tolerance threshold and there is no rule-based over-edge triggering, an early warning signal is triggered and a multi-granularity evidence chain is generated.

8. The special equipment inspection management method based on artificial intelligence and big data according to claim 1, characterized in that, The training objective function is based on: The obtained standard cross-entropy loss L_ce, data-text consistency loss L_consistency, and structural integrity loss L_structureze are weighted and calculated to generate the total loss function L. Among them, the data-text consistency loss L_consistency uses comparative learning to bring the embedding distance between the conclusions and data in the qualified inspection report closer and push away the distance of abnormal combinations. The implementation process of structural integrity loss L_structureze is as follows: for each inspection report, it is marked whether it contains a set chapter, and the probability p_section of the existence of the set chapter is output, with the loss being the binary cross-entropy.

9. The special equipment inspection management method based on artificial intelligence and big data according to claim 7, characterized in that, The basis for obtaining inspection results by implementing a multi-dimensional collaborative verification mechanism is as follows: The operation of the multi-dimensional collaborative verification mechanism includes at least four sub-tasks: terminology and rule verification, data and text verification, signature and qualification verification, and document structure verification. The second sub-task, data and text verification, involves identifying the parameter dimension that contributes most to anomalies from the final device embedding hm_d through gradient backpropagation. The identification result and its historical sequence are then used as context and input into a multi-task fine-tuned domain target large language model to generate natural language queries. For each type of check result, multi-granularity correction suggestions and confidence scores are generated. The multi-granularity correction suggestions are natural language instructions containing rule-based justifications. The confidence score is calculated based on the model's internal attention weights and consistency scores.

10. A special equipment inspection management system based on artificial intelligence and big data, applied to any one of the special equipment inspection management methods based on artificial intelligence and big data as described in claims 1-9, characterized in that, The system includes: The multimodal data storage module receives multimodal test data, establishes a unified data ontology model after data preprocessing, outputs standardized JSON record parameters, and stores them in a vector database. The heterogeneous processing early warning module constructs a heterogeneous hypergraph with four-dimensional constraint embedding based on JSON record parameters, introduces a time decay factor and working condition grouping mechanism to derive Gaussian kernel weights, and deploys a dynamic graph attention network with multi-constraint fusion to perform differentiated message transmission on the various types of hyperedges contained in the heterogeneous hypergraph. It completes the fusion processing through attention fusion gating and triggers anomaly judgment early warning actions, generating a multi-granularity evidence chain for any output early warning. The fine-tuning verification management module constructs a domain-specific large language model for multi-task fine-tuning, obtains several historical qualified inspection reports for multi-objective instruction fine-tuning to train the objective function, and executes a multi-dimensional collaborative verification mechanism to obtain inspection results. Based on the inspection results, it generates multi-granularity correction suggestions and confidence scores.

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