Technical method and platform for aviation maintenance multi-source structured data management

Abstract

The invention provides a technical method and platform for aviation maintenance multi-source structured data governance, and belongs to the field of aviation maintenance management.The method comprises the steps that aviation maintenance metadata is constructed; the method comprises the following steps: defining a static structure of an aviation maintenance data standard by using BDD, describing a dynamic behavior of aviation maintenance data through AC, and generating an association relationship between the aviation maintenance data and the aviation maintenance data standard based on the static structure and the dynamic behavior; establishing a reverse index for the aviation maintenance metadata based on the association relationship; and monitoring the metadata state in real time through the inverted index, and triggering an abnormal warning and generating a maintenance decision suggestion according to a predefined data quality rule. In this way, industry experience is converted into the association rule base capable of being dynamically evolved, the hysteresis of manual static rule maintenance is thoroughly eliminated, and the risk of manual misjudgment is remarkably reduced.

Description

Technical Field

[0001] The present invention belongs to the field of aviation maintenance management, and specifically relates to a technical method and platform for aviation maintenance multi-source structured data governance. Background Art

[0002] In the current aviation maintenance field, there are many types of aviation maintenance data. Specifically, different aviation maintenance services correspond to different aviation data types. Moreover, for the same aviation maintenance service, the corresponding aviation data types also vary according to different aviation maintenance service standards.

[0003] Existing technologies still rely on general-purpose data governance platforms for processing aviation maintenance data. However, these platforms struggle to meet the specialized needs of multi-source structured data in the aviation maintenance sector. This aviation maintenance data comes from diverse sources and contains complex business logic and industry characteristics. However, general-purpose data governance platforms rely on standardized rules and lack in-depth analysis capabilities. This deficiency stems from their broad design and inability to provide customized support for the aviation maintenance sector. This is particularly true given the complex nature of data in the aviation maintenance sector, which leads to frequent changes in demand. Current data governance solutions often rely on static rules and lack dynamic adjustment capabilities. In response to these changing demands, existing systems require manual rule updates, which is time-consuming and error-prone, making it difficult for governance to respond to the business environment in real time. Summary of the Invention

[0004] In order to solve the above problems, the present invention provides a technical method and platform for aviation maintenance multi-source structured data governance.

[0005] In order to achieve the above object, the present invention provides the following technical solutions: A technical method for aviation maintenance multi-source structured data governance, the method comprising: Establish aviation maintenance metadata based on aviation maintenance data standards and multi-source heterogeneous aviation maintenance data; The static structure of aviation maintenance metadata is defined using a SysML block definition diagram (BDD), and the dynamic behavior of the aviation maintenance metadata is defined using a SysML activity diagram (AC). Based on the static structure and dynamic behavior, structured information of the metadata is generated. The static structure is used to represent the data attributes of the aviation maintenance data, the dynamic behavior is used to represent the changing relationship of the aviation maintenance data, and the structured information represents the association between the aviation maintenance data and the aviation maintenance data standard. Based on the association relationship, establishing an inverted index for the aviation maintenance metadata; The inverted index is used to monitor the status of aviation maintenance data in real time, and abnormal warnings of aviation maintenance data standards are triggered according to preset data requirements.

[0006] Optionally, generating structured information of metadata based on the static structure and dynamic behavior includes: The static structure and dynamic behavior are input into the pre-trained Bi-LSTM+Attention model to determine the multi-level association relationship between aviation maintenance data and aviation maintenance data standards at the equipment layer, process layer, and rule layer.

[0007] Optionally, establishing an inverted index for the aviation maintenance data based on the association relationship includes: Performing word segmentation on the text fields in the aviation maintenance data to extract Chinese and English keywords; Bind the keywords to the source document ID, field position, and corresponding levels of association to generate an inverted index table containing semantic context.

[0008] Optionally, triggering abnormal warnings of aviation maintenance data standards and generating maintenance decision suggestions according to predefined data quality rules includes: Monitor the change status of the aviation maintenance data in real time and trigger alarm events based on preset data requirements; Associate alarm events with aviation maintenance data standards, and generate and push processing suggestions based on aviation maintenance data standards.

[0009] A management platform for aviation maintenance multi-source structured data governance, the platform comprising: An acquisition module is used to establish aviation maintenance metadata based on aviation maintenance data standards and multi-source heterogeneous aviation maintenance data; A generation module is configured to define the static structure of aviation maintenance metadata using a SysML block definition diagram (BDD), define the dynamic behavior of the aviation maintenance metadata using a SysML activity diagram (AC), and generate structured information of the metadata based on the static structure and dynamic behavior; the static structure is configured to represent data attributes of the aviation maintenance data, the dynamic behavior is configured to represent a change relationship of the aviation maintenance data, and the structured information represents an association relationship between the aviation maintenance data and an aviation maintenance data standard; A construction module, configured to establish an inverted index for the aviation maintenance metadata based on the association relationship; The management module is used to monitor the status of aviation maintenance data in real time through the inverted index and trigger abnormal warnings of aviation maintenance data standards according to preset data requirements.

[0010] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned technical method for aviation maintenance multi-source structured data governance.

[0011] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned technical method for aviation maintenance multi-source structured data governance is implemented.

[0012] The technical method for aviation maintenance multi-source structured data management provided by the present invention has the following beneficial effects: The present invention adopts SysML dual-mode collaborative modeling technology, uses SysML block definition diagrams (BDDs) to construct the static structure of aviation maintenance metadata, and uses activity diagrams (ACs) to describe dynamic behaviors, forming computable association relationships, realizing the digital precipitation of business logic, and converting industry experience into a dynamically evolving association rule library. When data changes lead to changes in business logic, the system automatically reconstructs the association relationships, completely getting rid of the lag of manual maintenance of static rules; the real-time monitoring engine based on inverted index can capture data anomalies, and based on the associated data standards, it can complete multi-dimensional association analysis in a very short time and generate accurate disposal suggestions. Through this mechanism, the fault misjudgment rate is reduced, and the efficiency of traditional manual rule updates is improved. Ultimately, through predefined data quality rules and real-time data, decision suggestions are automatically generated, significantly reducing the risk of human misjudgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] To more clearly illustrate the embodiments of the present invention and its design, the following briefly introduces the drawings required for this embodiment. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort.

[0014] Figure 1 The present invention is a flowchart of a technical method for aviation maintenance multi-source structured data governance according to an exemplary embodiment of the present invention.

[0015] Figure 2 A block diagram of a management platform for aviation maintenance multi-source structured data governance provided by the present invention according to an exemplary embodiment. DETAILED DESCRIPTION

[0016] In order to enable those skilled in the art to better understand the technical solution of the present invention and to be able to implement it, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention and are not intended to limit the scope of protection of the present invention.

[0017] The technical solutions provided by various embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0018] First, the present invention provides a technical method for aviation maintenance multi-source structured data management, specifically as follows Figure 1 As shown, the following steps are included: S101. Establish aviation maintenance metadata based on aviation maintenance data standards and multi-source heterogeneous aviation maintenance data.

[0019] In this step, an aviation maintenance metadata database can be established. This database is used to store aviation maintenance metadata, aviation maintenance data standards, and multi-source heterogeneous aviation maintenance data. Aviation maintenance metadata represents structured information about the data characteristics and attributes of aviation maintenance data. Aviation maintenance data standards include aviation maintenance industry specifications, aviation maintenance technical documents, and aviation maintenance operation manuals. Aviation maintenance data includes at least one of the following: aviation equipment information, aviation equipment maintenance work order information, or aviation equipment fault record information. A structural framework for aviation maintenance metadata is established based on aviation maintenance data standards and populated based on aviation maintenance data.

[0020] An aviation equipment maintenance work order may include multiple field names, each of which corresponds to one or more attribute values, as shown in Table 1 below.

[0021] Table 1 Example table of field names for aviation equipment maintenance work orders Aviation maintenance industry standards can carry information in the form of standard documents. An example is shown in Table 2 below.

[0022] Table 2 Example of aviation equipment maintenance standards S102. Use a SysML block definition diagram (BDD) to define the static structure of the aviation maintenance metadata, use a SysML activity diagram (AC) to define the dynamic behavior of the aviation maintenance metadata, and generate structured information of the aviation maintenance metadata based on the static structure and dynamic behavior.

[0023] The static structure is used to characterize the data attributes of aviation maintenance data, the dynamic behavior is used to characterize the change relationship of aviation maintenance data, and the structured information characterizes the association relationship between aviation maintenance data and aviation maintenance data standards.

[0024] In this step, the structure and constraint rules of aviation maintenance metadata are defined through SysML, and the association relationship between aviation maintenance data and aviation maintenance data standards is established.

[0025] By defining the structure and constraints of aviation maintenance metadata—specifically, defining the constraints between aviation maintenance data and aviation maintenance data rules—the complexity of aviation maintenance metadata can be reduced, enabling a unified representation of each aviation maintenance operation and data within the aviation maintenance process, thereby improving modeling efficiency. SysML helps standardize the structure of aviation maintenance metadata, ensuring consistency and reusability across maintenance processes.

[0026] For example, we first perform structural modeling, and its pseudo code logic can be as follows: Use SysML Block Definition Diagrams (BDDs) to define metadata structures, for example: Block "Maintenance Work Order" { Attributes: Work Order ID (format: WO-year-serial number), Equipment Name (in both Chinese and English), Fault Code (FC-XXX), Associated Standard (such as AMS-205) Constraint: The work order ID must match the regular expression / WO-\d{4}-\d{3} / } Block "Standard Documentation" { Attributes: Standard number, applicable models, detection method (X-ray inspection, ultrasonic testing) }.

[0027] Secondly, behavioral modeling is performed to describe the dynamic behavior of the maintenance process through activity diagrams (ACs). For example, the activity nodes can be as follows: blade crack detection → X-ray inspection → maintenance measure selection (replacement / repair) → associated standard reference.

[0028] In one embodiment, an association relationship between aviation maintenance data and aviation maintenance data standards may be established through static structure and dynamic behavior.

[0029] Specifically, the structural modeling data and the behavioral modeling data can be input into the Bi-LSTM+Attention model for training to establish an association relationship.

[0030] For example, the static structure and dynamic behavior are input into the pre-trained Bi-LSTM+Attention model to determine the multi-level association relationship between aviation maintenance data and aviation maintenance data standards at the equipment layer, process layer, and rule layer.

[0031] For example, consider device-level relationships. For example, consider the following: ① The standard maintenance manual chapter numbering system established by the International Air Transport Association (e.g., ATA Chapter 21: Air Conditioning Systems); ② Line Replaceable Unit (LRU) attributes for modular components on aircraft that can be directly replaced during line maintenance (e.g., the engine control computer): part number (P / N), serial number (S / N), and installation location; and ③ Shop Replaceable Unit (SRU) attributes for subassemblies that require replacement during shop-level maintenance (e.g., the circuit board inside the LRU): failure mode, maintenance history, and test parameters.

[0032] The following are examples of association relationships: (ATA_21:ATAChapter {name: "Air Conditioning"})-[:CONTAINS]-> (LRU_123:LRU {pn: "65-23456"})-[:COMPOSED_OF]-> (SRU_A01:SRU {pn: "SRU-7890"}) This association shows the following: A node named "ATA_21" with the label "ATAChapter" was created, and the chapter name (name attribute) was "Air Conditioning." A node named "LRU_123" with the label "LRU" (Line Replaceable Unit) and the part number (pn attribute) was created, with the part number "65-23456." A node named "SRU_A01" with the label "SRU" (Shop Replaceable Unit) and the part number (pn attribute) was created, with the part number "SRU-7890." The ATA chapter and LRU are connected via the [:CONTAINS] relationship (indicating that the Air Conditioning chapter contains the LRU component), and the LRU and SRU are connected via the [:COMPOSED_OF] relationship (indicating that the LRU is composed of the SRU).

[0033] Taking process-level relationships as an example, a work order (WO) is the basic unit of maintenance task execution and can include: WO number, aircraft registration number, planned working hours, and actual working hours; a task is a standard operating procedure from the maintenance manual (such as AMM Task 25-61-00). For example, key attributes may include: task type (inspection / replacement / test) and risk level; an operation is the atomic unit of a specific maintenance action (such as torque tightening and wire measurement). Record dimensions may include: tool usage records, operator signature, and timestamp.

[0034] The process-level association relationship can be: establishing a causal chain between operations through time series analysis. An example is as follows:

[0035] (wo:WorkOrder)-[:HAS_TASK]->(t:Task)-[:CONSISTS_OF]->(op1:Operation)-[r:NEXT]->(op2:Operation) WHERE r.time_interval<5min RETURN op1, op2 The process-level relationships show the following: A work order (WorkOrder) contains multiple tasks (Tasks), each task consists of multiple operations (Operations). Operations are connected by the NEXT relationship, indicating the execution order (op1 → op2). The NEXT relationship has a time_interval attribute (indicating the time interval between two operations). Starting from the work order (WorkOrder), the process traverses downwards: Work Order → Task (HAS_TASK relationship), Task → Operation (CONSISTS_OF relationship), Operation → Next Operation (NEXT relationship). The process selects records where the time_interval attribute of the NEXT relationship is less than 5 minutes, and returns all operation nodes that meet the conditions.

[0036] After obtaining the static structure and dynamic behavior of aviation maintenance metadata, the static structure and behavioral logic are input into the pre-trained Bi-LSTM+Attention model to determine the multi-level association relationships between aviation maintenance data and aviation maintenance data standards at the equipment layer, process layer, and rule layer.

[0037] S103: Based on the association relationship, establish an inverted index for the aviation maintenance metadata.

[0038] In this step, we use Elasticsearch to build an inverted index for the aviation maintenance metadata database. By segmenting the aviation maintenance data and mapping terms to documents, the inverted index allows for rapid keyword retrieval, helping users quickly find relevant documents.

[0039] Specifically, the text fields in the aviation maintenance data are segmented to extract Chinese and English keywords; the keywords are bound to the corresponding levels of the source document ID, field position, and association relationship to generate an inverted index table containing semantic context.

[0040] For example, each field value in the above-mentioned aviation equipment maintenance work order information 1 and aviation maintenance industry standard 1 is segmented to generate an aviation maintenance data keyword list, as shown in Table 3. In one implementation, an IK segmenter can be used to process Chinese terms.

[0041] Table 3 Aviation maintenance metadata keyword list An Elasticsearch inverted index is created for aviation maintenance data keywords. Specifically, the keywords are associated with corresponding aviation maintenance data identifiers (IDs) and / or aviation maintenance data standard IDs to record the source and location information of the fields. This is shown in Table 4 below.

[0042] Table 4 A relational table of aviation maintenance metadata S104: Monitor the aviation maintenance data status in real time through the inverted index, and trigger an abnormal warning of the aviation maintenance data standard according to the preset data requirements.

[0043] In this step, the change status of the aviation maintenance data is monitored in real time, and an alarm event is triggered based on the preset data requirements; the alarm event is associated with the aviation maintenance data standard, and processing suggestions are generated and pushed based on the aviation maintenance data standard.

[0044] For example, the retrieval method of the Elasticsearch inverted index is optimized to implement fuzzy queries, further enhancing the intelligence of the aviation maintenance data management solution of this application.

[0045] In one embodiment, rule definition can be used to automatically detect data anomalies and trigger maintenance decisions.

[0046] Specifically, the rules in aviation maintenance data rules can be parsed to construct Drools rules. For example, Clause 5.3 in the AMS-205 standard: Crack detection result judgment: {Crack length ≤ 3mm → Repair allowed; 3mm<Crack length ≤ 5mm → Manual review required; Crack length > 5mm → Blade must be replaced}.

[0047] Convert the rules in the annotation document into a Drools rule file as follows: Rule "AMS-205_Crack Length Exceeding Limit Alarm" dialect "java" salience 10 when $Work Order: Maintenance Work Order( Fault code == "FC-005", Crack length>5, Related Standards contains "AMS-205" ) $standard: standard document (standard number == "AMS-205") then insert(new alarm event( "serious", "Blade cracks exceed the limit and need to be replaced immediately (according to AMS-205 5.3 clause)", $workorder.getworkorderID(), $standard.getstandardNumber() )); $workorder.setMaintenanceMeasures("Replace blades, refer to standard AMS-205"); update($workorder); end In another implementation, users can customize Drools rules. For example, a user interface can prompt users with aviation maintenance data rules. For example, if a user enters "High-frequency fault warnings occurring more than three times within seven days require priority processing," the language model is parsed and converted into Drools syntax as follows. In one implementation, the language model can be, for example, an NLP model, which is not limited in this application.

[0048] rule "User-defined_high-frequency fault warning" when $stats: Failure Statistics( Fault code in ("FC-005", "FC-008"), Statistical period == "7 days", Occurrences >= 3 ) $workorder: Maintenance work order (fault code == $statistics.faultcode) then $workorder.setpriority("high risk"); update($workorder); End.

[0049] Drools rules are then bound to metadata. For example, fields referenced in the rules (such as fault code and crack length) must be consistent with the attributes of the maintenance work order block defined in SysML. After binding, alerts can be triggered based on Drools rules. Real-time monitoring of metadata triggers Drools rules. For example, a crack length greater than 5mm triggers an alert, which can then be followed by a warning resolution, such as blade replacement.

[0050] Finally, based on user feedback or new data standards, the association relationship can be trained and optimized in real time, and the inverted index can be updated synchronously.

[0051] By adopting the above method, the present invention adopts SysML dual-mode collaborative modeling technology, utilizes SysML block definition diagram (BDD) to construct the static structure of aviation maintenance metadata, and describes dynamic behavior through activity diagram (AC) to form computable association relationships, realize the digital precipitation of business logic, and transform industry experience into a dynamically evolving association rule library. When data changes lead to changes in business logic, the system automatically reconstructs the association relationships, completely getting rid of the lag of manual maintenance of static rules; the real-time monitoring engine based on inverted index can capture data anomalies, and based on the associated data standards, it can complete multi-dimensional association analysis in a very short time and generate accurate disposal suggestions. Through this mechanism, the fault misjudgment rate is reduced, and the efficiency of traditional manual rule updates is improved. Ultimately, through predefined data quality rules and real-time data, decision suggestions are automatically generated, significantly reducing the risk of human misjudgment.

[0052] Secondly, the present invention also provides a management platform for aviation maintenance multi-source structured data governance, such as Figure 2 Shown, including: The acquisition module 201 is used to establish aviation maintenance metadata based on aviation maintenance data standards and multi-source heterogeneous aviation maintenance data.

[0053] The generation module 202 is configured to define the static structure of the aviation maintenance metadata using a SysML block definition diagram (BDD), define the dynamic behavior of the aviation maintenance metadata using a SysML activity diagram (AC), and generate structured information of the metadata based on the static structure and dynamic behavior. The static structure is used to represent the data attributes of the aviation maintenance data, the dynamic behavior is used to represent the changing relationship of the aviation maintenance data, and the structured information represents the association between the aviation maintenance data and the aviation maintenance data standard.

[0054] The construction module 203 is configured to establish an inverted index for the aviation maintenance metadata based on the association relationship.

[0055] The management module 204 is used to monitor the status of aviation maintenance data in real time through an inverted index and trigger abnormal warnings of aviation maintenance data standards according to preset data requirements.

[0056] Adopting the above platform, the present invention adopts SysML dual-mode collaborative modeling technology, uses SysML block definition diagrams (BDDs) to construct the static structure of aviation maintenance metadata, and describes dynamic behaviors through activity diagrams (ACs) to form computable association relationships, realize the digital precipitation of business logic, and transform industry experience into a dynamically evolving association rule library. When data changes lead to changes in business logic, the system automatically reconstructs the association relationships, completely getting rid of the lag of manual maintenance of static rules; the real-time monitoring engine based on inverted index can capture data anomalies, and based on the associated data standards, it can complete multi-dimensional association analysis in a very short time and generate accurate disposal suggestions. Through this mechanism, the fault misjudgment rate is reduced, and the efficiency of traditional manual rule updates is improved. Ultimately, through predefined data quality rules and real-time data, decision suggestions are automatically generated, significantly reducing the risk of human misjudgment.

[0057] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 The present invention provides steps for a technical method of aviation maintenance multi-source structured data governance.

[0058] The present invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 The present invention provides steps for a technical method of aviation maintenance multi-source structured data governance.

[0059] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0060] The present invention is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0061] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0062] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0063] It should be noted that the specific embodiments described above can enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although this specification has described the present invention in detail, those skilled in the art should understand that the present invention can still be modified or replaced with equivalents; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are included in the scope of protection of the patent for the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A technical method for aviation maintenance multi-source structured data management, characterized by: The method comprises: Establish aviation maintenance metadata based on aviation maintenance data standards and multi-source heterogeneous aviation maintenance data; The static structure of aviation maintenance metadata is defined using a SysML block definition diagram (BDD), and the dynamic behavior of the aviation maintenance metadata is defined using a SysML activity diagram (AC). Based on the static structure and dynamic behavior, structured information of the aviation maintenance metadata is generated. The static structure is used to represent data attributes of the aviation maintenance data, the dynamic behavior is used to represent the changing relationship of the aviation maintenance data, and the structured information represents the association relationship between the aviation maintenance data and the aviation maintenance data standard. Based on the association relationship, establishing an inverted index for the aviation maintenance metadata; The inverted index is used to monitor the status of aviation maintenance data in real time, and abnormal warnings of aviation maintenance data standards are triggered according to preset data requirements.

2. The technical method for aviation maintenance multi-source structured data management according to claim 1 is characterized in that: Based on the static structure and dynamic behavior, the structured information of metadata is generated including: The static structure and dynamic behavior are input into the pre-trained Bi-LSTM+Attention model to determine the multi-level association relationship between aviation maintenance data and aviation maintenance data standards at the equipment layer, process layer, and rule layer.

3. The technical method for aviation maintenance multi-source structured data management according to claim 2 is characterized in that: Based on the association relationship, establishing an inverted index for the aviation maintenance data includes: Performing word segmentation on the text fields in the aviation maintenance data to extract Chinese and English keywords; Bind the keywords to the source document ID, field position, and corresponding levels of association to generate an inverted index table containing semantic context.

4. The technical method for aviation maintenance multi-source structured data management according to claim 1 is characterized in that: The triggering of abnormal warnings of aviation maintenance data standards and generating maintenance decision suggestions according to predefined data quality rules includes: Monitor the change status of the aviation maintenance data in real time and trigger alarm events based on preset data requirements; Associate alarm events with aviation maintenance data standards, and generate and push processing suggestions based on aviation maintenance data standards.

5. A management platform for aviation maintenance multi-source structured data governance, characterized by: The platform includes: An acquisition module is used to establish aviation maintenance metadata based on aviation maintenance data standards and multi-source heterogeneous aviation maintenance data; A generation module is configured to define the static structure of aviation maintenance metadata using a SysML block definition diagram (BDD), define the dynamic behavior of the aviation maintenance metadata using a SysML activity diagram (AC), and generate structured information of the metadata based on the static structure and dynamic behavior; the static structure is configured to represent data attributes of the aviation maintenance data, the dynamic behavior is configured to represent a change relationship of the aviation maintenance data, and the structured information represents an association relationship between the aviation maintenance data and an aviation maintenance data standard; A construction module, configured to establish an inverted index for the aviation maintenance metadata based on the association relationship; The management module is used to monitor the status of aviation maintenance data in real time through the inverted index and trigger abnormal warnings of aviation maintenance data standards according to preset data requirements.

6. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

7. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 4 when executing the program.

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