Intelligent detection and maintenance method for aviation equipment based on LangGraph

Abstract

The invention discloses an aviation equipment intelligent detection and maintenance method based on LangGraph, and the method comprises the steps: S1), carrying out the demand analysis and system design based on LangGraph according to the test demands of aviation equipment in combination with the characteristics of the aviation industry; s2) collecting various data of the aviation equipment through a sensor, a database and an image video, and performing data feature analysis to extract useful information and modes; s3) analyzing the collected data by applying an intelligent algorithm, and predicting potential faults; the method comprises the following steps of (S1) analyzing an analysis result and a suggestion, (S2) analyzing the analysis result and the suggestion, (S4) presenting and feeding back the analysis result and the suggestion through a user interface, (S5) forming a visual workflow by using LangGraph, and (S6) making each process of test and maintenance into an independent intelligent agent by using the LangGraph in combination with RAG and Agent, and dialogue user requirements by using LLM. According to the method, digitization and intellectualization of the whole system are realized, and the efficiency and the accuracy of detection and maintenance are improved.

Description

Technical Field

[0001] The present invention relates to an aviation equipment detection and maintenance method, and in particular to an aviation equipment intelligent detection and maintenance method based on LangGraph. Background Art

[0002] At present, the inspection and maintenance of aviation equipment mainly rely on manual experience and traditional means, and there are problems such as information islands, low efficiency, and inconsistent data. With the increase in the complexity and number of aviation equipment, traditional methods are difficult to complete the inspection and maintenance of equipment in a timely and efficient manner, affecting the safety and reliability of aviation equipment. With the rapid progress in the design, research and development and manufacturing of aviation products, and the continuous introduction of new aviation materials and manufacturing processes, non-destructive testing technology faces new challenges and opportunities. In particular, in terms of new materials, new processes, new defect characterization and evaluation technologies, as well as non-destructive testing technology in the intelligent manufacturing process, there is a significant demand for development.

[0003] At this stage, the progress of large models and hardware computing power has brought artificial intelligence into a new stage. The combination of the aviation industry and artificial intelligence technology has also become a direction that needs to be studied and implemented.

[0004] New technologies such as artificial intelligence, big data, and cloud computing will play an increasingly important role in the intelligent development of aviation equipment testing and assurance. These technologies play a key role in self-calibration, self-compensation, self-detection, self-diagnosis, self-monitoring, self-repair, self-learning, self-adaptation, and self-decision-making.

[0005] Therefore, it is necessary to try to build a new artificial intelligence architecture that combines the aviation industry's own knowledge base with large model technology to create an intelligent system of human-computer interaction, allowing aviation inspection and maintenance to enter the intelligent digital stage. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide an intelligent detection and maintenance method for aviation equipment based on LangGraph, which can realize the digitization and intelligence of the entire system and improve the efficiency and accuracy of detection and maintenance.

[0007] The technical solution adopted by the present invention to solve the above technical problems is to provide an intelligent detection and maintenance method for aviation equipment based on LangGraph, including the following steps: S1) Based on the test requirements of aviation equipment and the characteristics of the aviation industry, demand analysis and system design are performed based on LangGraph; S2) Various data of aviation equipment are collected through sensors, databases and images and videos, and data feature analysis is performed to extract useful information and patterns; S3) Intelligent algorithms are applied to analyze the collected data to predict potential failures; S4) The analysis results and suggestions are presented and fed back through the user interface; S5) LangGraph is used to digitize complex detection and maintenance processes to form a visual workflow; S6) LangGraph is used to combine RAG and Agent to make each test and maintenance process into a separate intelligent entity; based on the framework of LangGraph, LLM is used to communicate user needs.

[0008] Furthermore, the step S1 provides test points and a test environment based on historical data and standard documents.

[0009] Furthermore, step S2 includes: using airborne sensors and screen image recognition to obtain raw data of aviation equipment, wherein the raw data includes flight parameters, environmental parameters and equipment performance parameters; then performing missing value analysis and outlier processing on the raw data, and then performing feature extraction of images and texts.

[0010] Furthermore, step S2 includes: using a deep network as a base model to obtain the Embedding features of the image and the annotation content; designing a dual-stream architecture to encode the image and text separately, and then realizing feature interaction through a cross-attention module, and calculating the similarity using the cosine distance formula: ; in It is the Embedding feature of "demand content". It is the Embedding feature of "annotation content"; The relationship between user needs and images is retrieved based on the matching content, so as to identify the test equipment and generate test cases.

[0011] Furthermore, the step S3 includes: using LSTM to predict the time series value, and then scoring the prediction error through the statistical method Z-Score to identify abnormal fluctuations in the system load and prevent possible service interruptions.

[0012] Furthermore, step S4 includes: linking analysis requirements with chart types, presenting complex data analysis results through charts, trend lines and heat maps, and providing an interactive feedback system to provide real-time feedback based on user input.

[0013] Furthermore, the step S5 includes: defining nodes and connections between nodes in LangGraph, wherein the nodes are processing units in the workflow, and each node is a function or a Langchain executable object for executing preset detection or maintenance tasks.

[0014] Furthermore, step S5 uses edges to define connections between nodes, indicating how data and control flows are transferred between nodes, and the edges are conditional branches or fixed transitions, which are used to determine which node to execute next based on the current state; the LangGraph in step S5 allows dynamic determination of which node to execute next based on conditions, and supports loop structures, allowing certain nodes or subgraphs to be repeatedly executed until preset conditions are met.

[0015] Furthermore, the step S6 includes: after analyzing the user's needs, generating a specific task, and then calling a specific Agent according to the task; in each Agent, using RAG technology to call the local knowledge base as a knowledge graph to provide users with known professional inspection and maintenance services.

[0016] Compared with the prior art, the present invention has the following beneficial effects: the LangGraph-based intelligent detection and maintenance method for aviation equipment provided by the present invention establishes an intelligent workflow by utilizing LangGraph, thereby realizing the digitization and intelligence of the entire system and improving the efficiency and accuracy of detection and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Flowchart of the simplest LangGraph component used in the present invention; Figure 2 It is a schematic diagram of the system architecture of the present invention; Figure 3 This is a flowchart of intelligent detection and maintenance of aviation equipment based on LangGraph of the present invention; Figure 4 The present invention adopts Agent to establish a demand analysis schematic diagram; Figure 5 The present invention automatically generates a schematic diagram for testing requirements of aviation equipment; Figure 6 This is a schematic diagram of the present invention using a deep model for fine-tuning to perform target detection; Figure 7This is a schematic diagram of the data collection and integration system built using Agent in the present invention; Figure 8 This is a schematic diagram of the agent analysis requirements and intelligent interaction used in the present invention; Fig. 9 This is a schematic diagram of using LSTM to predict time series values ​​in the present invention; Fig.10 The present invention utilizes LangGraph in combination with RAG and Agent technology to provide users with a known professional inspection and maintenance service flow chart. DETAILED DESCRIPTION

[0018] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0019] The purpose of the present invention is to provide an intelligent detection and maintenance system and method for aviation equipment based on LangGraph, which can realize the digitization and intelligence of the entire system by establishing an intelligent workflow using LangGraph, thereby improving the efficiency and accuracy of detection and maintenance. Figure 1 It is the simplest component of LangGraph. Figure 1 As can be seen from the figure, LangGraph includes a whole process from the beginning to the end; the chatbot in it is a robot that can interact with people in natural language. This robot calls the Large Language Model (LLM) and external tools to complete specific tasks. Based on this feature, in the detection and maintenance of aviation equipment, the present invention establishes a system based on artificial intelligence interaction, starting from demand analysis to the completion of intelligent maintenance, and the whole process is intelligent and digitalized.

[0020] The whole system architecture of the present invention is as follows: it includes a data acquisition module, a LangGraph workflow module, an intelligent analysis module and a user interaction module. The data acquisition module is responsible for collecting real-time operation data and historical maintenance data of aviation equipment; the LangGraph workflow module is used to establish and manage the inspection and maintenance process; the intelligent analysis module uses machine learning and big data analysis technology to evaluate and predict the equipment status; the user interaction module provides a friendly interface for users to view and operate, such as Figure 2 shown.

[0021] Please continue to see Figure 3 The LangGraph-based intelligent detection and maintenance method for aviation equipment provided by the present invention comprises the following steps: S1. Requirements analysis and system design: Based on the test requirements of aviation equipment and the characteristics of the aviation industry, an intelligent process based on the nature of LangGraph is established. According to the test requirements, a requirements analysis agent is designed, and based on historical data and standard documents, test points and test environments are provided; a requirements analysis agent is established such as Figure 4 shown.

[0022] Design an automated demand generation system for the test requirements of aviation equipment. For example, refine the test equipment, test links, test preparation conditions, etc. according to the test requirements, and integrate these factors into the test process. Combine the test technology and test process to form a test project.

[0023] In this process, the present invention uses a large natural language model to identify the user's test requirements, that is, to upgrade the test requirements from the previous fixed format requirements to natural language, extract various features required for the test project from human natural language, and then automatically generate the test project. The entire user requirements and test project are finally stored in the test requirements management library to form an automated interactive requirements management system. The specific process is as follows Figure 5 shown.

[0024] S2. Data collection and integration: Collect various data of the equipment through sensors, databases, images and videos. Use onboard sensors and screen image recognition to obtain raw data, including flight parameters, environmental parameters, equipment performance parameters, etc. Integrate data from different sensors and image recognition to obtain more comprehensive and accurate equipment status information.

[0025] For image recognition, the present invention uses a deep model for fine-tuning to detect objects and uses OCR technology for text recognition. In particular, in the fine-tuning part, the present invention combines the needs of natural language types and corresponding pictures to fine-tune and train its own multimodal model. The main process is as follows: Figure 6 shown.

[0026] The image model fine-tuning adopts the few-shot fine-tuning method, using a deep network as the base model to obtain the embedding features of the image and the annotation content. A two-stream architecture is designed to encode the image and text separately, and then the feature interaction is realized through the cross-attention module (such as Transformer). This architecture is used to train a feature extraction model that takes both images and text into consideration.

[0027] The similarity is calculated using the cosine distance formula: in It is the Embedding feature of "demand content". It is the Embedding feature of "annotated content". The relationship between user needs and images is retrieved based on the matching content, so as to identify the test equipment, generate test cases, and form a test report after the entire test process.

[0028] Data fusion technology can improve the accuracy and reliability of data processing and reduce false positives and negatives. The collected data is analyzed for quality, including missing value analysis and outlier analysis, to ensure the accuracy of the data.

[0029] Build a data system Agent such as Figure 7 As shown: S3. Intelligent analysis: Apply intelligent algorithms to analyze the collected data and predict potential failures. Use machine learning algorithms, such as time series analysis, anomaly detection algorithms, and deep learning techniques (CNN, RNN), to conduct in-depth analysis of the collected data. These algorithms can effectively identify abnormal fluctuations in system load, prevent possible service interruptions, and show great potential in fault prediction. By continuously monitoring and analyzing the operating data of the equipment, the intelligent prediction function can reveal the health trend of the equipment in advance, helping operation and maintenance personnel to promptly discover and solve potential problems that may affect equipment performance, such as Figure 8 shown.

[0030] For outlier detection of time series data, traditional data mining technology uses island forest, clustering, and autoregression methods. With the emergence of deep learning methods, the present invention combines deep learning and data mining methods, taking into account the advantages of both. The present invention uses LSTM to predict time series values, and then scores the prediction error using the statistical method Z-Score. The process is as follows: Fig. 9 shown.

[0031] The Analysis Agent of the present invention is a data analysis agent that performs intelligent analysis and statistics on structured data. With reference to the data statistical analysis function in Excel software, a data analysis agent is developed, and after inputting data, the Analysis Agent is called to perform analysis.

[0032] S4. Results presentation and feedback: Feedback analysis results and suggestions to maintenance personnel through the user interface to guide actual operations. Design an intuitive user interface to ensure that maintenance personnel can easily understand and operate. The interface should include charts, graphs, and indicators to intuitively display analysis results and maintenance suggestions. Present complex data analysis results through visualization methods such as charts, trend lines, and heat maps, so that maintenance personnel can quickly identify problems and anomalies. Implement an interactive feedback system that allows maintenance personnel to communicate with the system, ask questions, or obtain more information. The system can provide real-time feedback based on user input.

[0033] In the process of generating a chart interface, the project of the present invention designs a chart generation agent, that is, according to the data type and requirements, the chart generation function is called to generate different charts. First, the data requirements are classified, and different chart generation functions are called according to different requirements, such as generating a line chart for trend analysis requirements and generating a scatter plot for distribution analysis requirements. The analysis requirements are linked with the chart type, and different chart generation functions are called according to different requirements.

[0034] S5. Workflow establishment: Use LangGraph to digitize complex inspection and maintenance processes to form a visual workflow. Define nodes in LangGraph. Nodes are processing units in the workflow. They can be a function or a Langchain executable object, such as a tool. Each node can perform specific inspection or maintenance tasks, such as calling LLM to get answers to questions, or calling search engines to get results. Define edges. Edges define the connections between nodes, indicating how data and control flows are passed between nodes. Edges can be conditional branches or fixed transitions, which are used to determine which node to execute next based on the current state. LangGraph allows dynamic determination of which node to execute next based on conditions, supports loop structures, and allows certain nodes or subgraphs to be executed repeatedly until specific conditions are met.

[0035] S6. Using LangGraph combined with RAG and Agent technology, each test and maintenance process is made into a separate intelligent agent. Based on the framework of LangGraph, LLM technology is used to communicate with user needs. After analyzing user needs, specific tasks are generated, and then specific agents are called according to the tasks. In each agent, the present invention uses RAG technology to call the local knowledge base as a knowledge graph to provide users with known professional test and maintenance services; this process is as follows Fig.10 shown.

[0036] The present invention uses LangGraph technology to transform the traditional manual inspection and maintenance process into an automated workflow, thereby improving the efficiency and accuracy of the process. The automated process reduces human errors and ensures the consistency of operations. Using the multi-agent system built with LangGraph, the present invention can simulate the collaboration of human teams, and multiple agents can collaborate to complete tasks, improving the ability to handle complex tasks. The present invention can also be used as an education and training tool to help new maintenance personnel learn and master inspection and maintenance skills through simulation and actual operation. The specific advantages are as follows: 1. Technical framework innovation: This invention adopts the LangGraph technical framework, which is a new AI agent framework for building complex multi-agent applications. LangGraph allows developers to accurately define the processing of LLM applications, including nodes and edges, through the state graph, thereby achieving more refined control capabilities. This framework supports LLM call "loops" and more refined control of Agent processes, making up for the shortcomings of traditional LangChain in Agent development, especially complex multi-agent system customization.

[0037] 2. Digital workflow: The workflow built with LangGraph realizes the digitalization and intelligence of the aviation equipment inspection and maintenance process. This workflow not only improves the efficiency and accuracy of inspection and maintenance, but also provides timely suggestions for maintenance by predicting the operating status of the equipment in real time, significantly reducing downtime, extending the service life of equipment, and reducing the company's operating costs.

[0038] 3. Solve industry pain points: The present invention establishes a complete set of maintenance plan project optimization analysis systems and standards, so that aircraft operators can effectively use the execution of maintenance tasks and operating failures to reflect the execution effect of maintenance plan projects, thereby optimizing maintenance plans and gradually establishing personalized maintenance plans for aircraft operators.

[0039] 4. Improve safety and reliability: By completing the inspection and maintenance of equipment in a timely and efficient manner, the present invention improves the safety and reliability of aviation equipment. This improvement is achieved through a predictive maintenance strategy, which relies on big data technology and data analysis tools, and uses visual analysis software to explore data, summarize rules and combine them with interpretable data models to build monitoring models and predictive maintenance strategies.

[0040] 5. Multi-agent collaboration: Support multi-agent systems, which means that multiple agents can collaborate to complete tasks, simulating the collaboration of human teams. The method in the present invention is applicable to game systems that require a dynamic number of game characters to collaborate to complete tasks, and can be used as a multi-agent artificial intelligence system in the game.

[0041] 6. Human-computer interaction: The present invention supports human-computer interaction, allowing human users to intervene in the process of graph execution, improving the flexibility and adaptability of the system. The intuitiveness of this human-computer interaction and the optimization of user experience are key factors in mobile operating systems such as the Android system, and are also an important innovation of the present invention in the field of aviation equipment inspection and maintenance.

[0042] 7. Data collection and organization: The present invention involves the collection, organization and analysis of aviation equipment inspection and maintenance data, including the integration of fault and event information sources and monitoring and maintenance data sources. This data-driven approach is the basis for predictive maintenance. It collects and analyzes aircraft operation data and maintenance data to achieve real-time monitoring of aircraft systems and provide predictions and maintenance recommendations.

[0043] 8. Predictive maintenance: Through a data-driven approach, the present invention implements predictive maintenance to improve the safety and efficiency of aircraft. This predictive maintenance strategy uses data analysis and machine learning algorithms to identify possible equipment failures in advance and provide timely suggestions for maintenance.

[0044] Although the present invention has been disclosed as above with preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art may make some modifications and improvements without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be based on the definition of the claims.

Claims

1. An intelligent detection and maintenance method for aviation equipment based on LangGraph, characterized in that: The steps include: S1) Based on the testing requirements of aviation equipment and the characteristics of the aviation industry, we conduct demand analysis and system design based on LangGraph; S2) Collect various data from aviation equipment through sensors, databases, images and videos, and perform data feature analysis to extract useful information and patterns; S3) Apply intelligent algorithms to analyze the collected data and predict potential failures; S4) presenting and providing feedback on analysis results and suggestions through the user interface; S5) Use LangGraph to digitize complex inspection and maintenance processes to form a visual workflow; S6) Use LangGraph to combine RAG and Agent to make each test and maintenance process into a separate intelligent agent; based on the LangGraph framework, use LLM to communicate with user needs.

2. The LangGraph-based intelligent detection and maintenance method for aviation equipment according to claim 1, characterized in that: The step S1 provides test points and a test environment according to historical data and standard documents.

3. The LangGraph-based intelligent detection and maintenance method for aviation equipment according to claim 1, characterized in that: The step S2 includes: using airborne sensors and screen image recognition to obtain the original data of aviation equipment, the original data including flight parameters, environmental parameters and equipment performance parameters; then performing missing value analysis and outlier processing on the original data, and then performing feature extraction of images and texts.

4. The LangGraph-based intelligent detection and maintenance method for aviation equipment according to claim 3, characterized in that: The step S2 includes: using a deep network as a base model to obtain the Embedding features of the image and the annotation content; designing a dual-stream architecture to encode the image and text separately, and then realizing feature interaction through a cross-attention module, and calculating the similarity using the cosine distance formula: ； in It is the Embedding feature of "demand content". It is the Embedding feature of "annotation content"; The relationship between user needs and images is retrieved based on the matching content, so as to identify the test equipment and generate test cases.

5. The intelligent detection and maintenance method for aviation equipment based on LangGraph as claimed in claim 1, characterized in that: The step S3 includes: using LSTM to predict the time series value, and then scoring the prediction error through the statistical method Z-Score to identify abnormal fluctuations in the system load and prevent possible service interruptions.

6. The LangGraph-based intelligent detection and maintenance method for aviation equipment according to claim 1, characterized in that: The step S4 includes: linking the analysis requirements with the chart types, presenting the complex data analysis results through charts, trend lines and heat maps, and providing an interactive feedback system to provide real-time feedback based on user input.

7. The LangGraph-based intelligent detection and maintenance method for aviation equipment according to claim 1, characterized in that: The step S5 includes: defining nodes and connections between nodes in LangGraph, wherein the nodes are processing units in the workflow, and each node is a function or a Langchain executable object for executing a preset detection or maintenance task.

8. The LangGraph-based intelligent detection and maintenance method for aviation equipment according to claim 7, characterized in that: The step S5 uses edges to define connections between nodes, indicating how data and control flows are transferred between nodes, and the edges are conditional branches or fixed transitions used to determine which node to execute next based on the current state; The LangGraph in step S5 allows dynamic determination of which node to execute next based on conditions, and supports a loop structure, allowing certain nodes or subgraphs to be repeatedly executed until a preset condition is met.

9. The LangGraph-based intelligent detection and maintenance method for aviation equipment according to claim 1, characterized in that: The step S6 includes: after analyzing the user's needs, generating a specific task, and then calling a specific Agent according to the task; in each Agent, using RAG technology to call the local knowledge base as a knowledge graph to provide users with known professional inspection and maintenance services.

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