74 results about "Diagnostic strategy" patented technology

The invention relates to the technical field of power system fault processing, in particular to an improved deep reinforcement learning-based electric energy metering device fault diagnosis method, which comprises the following steps of: acquiring corresponding operation data under various fault types, taking the operation data and an operation state as a sample set, and dividing the sample set into a test sample and a training sample; establishing an interaction strategy based on a classification Markov decision process, and establishing an agent model based on an attention mechanism, a one-dimensional convolutional neural network and a bidirectional conversion gating long-short term memory network; inputting a training sample into the agent model, wherein the agent model performs model training according to the interaction strategy; and inputting a test sample into the trained agent model to complete fault diagnosis of the electric energy metering device. According to the method, the fault diagnosis accuracy can be improved, the optimal diagnosis strategy is autonomously learned, and the network training and parameter updating process is optimized.

The invention relates to a big-small model collaborative diagnosis system based on federated learning and knowledge transfer. Comprising a data processing layer used for acquiring a to-be-diagnosed multi-modal medical examination data set and performing cross-modal feature processing to obtain cross-modal fusion features; the cross-modal fusion feature comprises a cross-modal alignment feature and text embedding; the large-small model collaborative architecture layer is used for performing disease preliminary screening according to the cross-modal fusion features, and calling a corresponding model for disease diagnosis according to a preset diagnosis strategy to obtain a diagnosis result; the encryption federal layer is used for encrypting bidirectional knowledge transmission between the edge end small model and the cloud end large model by using an encryption mechanism; and the bidirectional dynamic knowledge routing layer is used for bidirectionally optimizing the edge-end small model and the cloud-end large model based on a bidirectional dynamic routing mechanism in combination with the diagnosis result. By adopting the system, the diagnosis performance, the real-time response capability and the privacy protection level can be guaranteed, and meanwhile, the dynamic collaboration among model collaboration, knowledge evolution and data compliance is realized.

The invention relates to the technical field of electric valves, in particular to an electric valve abnormal working condition monitoring method and system. Comprising the steps of setting a plurality of monitoring points based on equipment parameters of the electric valve; operation deviation values of all the monitoring points are generated according to a preset state evaluation model, and a first-level diagnosis strategy is set according to all the operation deviation values; generating an abnormal risk value according to the primary diagnosis strategy, and judging whether an early warning instruction is generated or not according to the abnormal risk value; a plurality of monitoring points are established based on the equipment parameters and the historical fault parameters of the electrically operated valve, and the abnormal operation state of each monitoring point is analyzed and pre-warned in time by setting the diagnosis sub-model of each monitoring point, so that the pre-warning efficiency of the abnormal working condition of the electrically operated valve is improved, the fault hidden danger is eliminated in time, and the safe operation of the electrically operated valve is ensured.

The invention discloses a mental stress assessment and intelligent dredging method and system, and belongs to the technical field of digital medical treatment and health informatics. Multi-modal physiological signals are continuously collected through the wearable device, medical diagnosis level pressure state evaluation is carried out based on the personalized physiological baseline, and personalized pressure indexes and levels are generated; secondly, when it is diagnosed that the pressure level exceeds the standard, the system serves as an intelligent decision support system, the environment and schedule information after privacy protection processing is fused, and an optimal grooming action is dynamically selected from a predefined intervention action library and executed; finally, the system serves as a continuous learning system, the intervention efficiency is evaluated in real time according to feedback data of the pressure index after execution, the decision model is updated, and collaborative self-optimization of the diagnosis strategy and the intervention strategy is achieved. According to the method, medical diagnosis, personalized treatment decision and adaptive learning are integrated, and the systematicness, accuracy and intelligent level of mental stress related health problem management are improved.

The invention relates to the technical field of pipeline structure health monitoring, and discloses a trend analysis and early warning method for pipeline expansion monitoring data, which comprises the following steps: acquiring actually measured total strain, temperature and pressure data of a pipeline, and calculating to obtain residual strain; segmenting the residual strain data into response segments according to the change of the working condition; according to the current diagnosis strategy, response morphological characteristics such as hysteretic loop area or creep curvature are extracted from the response fragment; diagnosing the state of the material as one of a healthy elastic state, an initial plastic state, a stable creep state and an accelerated creep state according to the characteristics; adaptively updating a diagnosis strategy based on the current diagnosis result; and when it is judged that state transition to a higher risk level occurs, graded early warning information is generated. By establishing a self-adaptive updating closed loop from a material state diagnosis result to a diagnosis strategy, the problem that early damage is difficult to accurately recognize by adopting a fixed threshold in a traditional method is solved, and the nonlinear degradation process of material performance can be dynamically tracked.

The invention discloses a fault diagnosis method and system for hydraulic element general equipment, and relates to the technical field of hydraulic element fault diagnos.The method comprises the steps that control channels of the general equipment are recognized based on hydraulic elements, transient signals are collected in all the control channels in real time, preset interference constraints are introduced, and fault diagnosis is conducted on the general equipment; generating an interference factor through a high-entropy random disturbance source, performing disturbance analysis on the amplitude, the frequency and the waveform state of the transient signal, and generating an interference identifier; a health state vector is formed in combination with the operation working state, the deviation degree of the health state vector and the health baseline characteristic spectrum is compared, if the deviation degree is smaller than a standard deviation threshold value, a state evaluation strategy is executed in the first diagnosis channel, and a health stability trend is recognized; if the deviation degree is greater than or equal to the standard deviation threshold value, executing a fault diagnosis strategy in the second diagnosis channel, and identifying a fault sign; according to the invention, efficient division of labor of health assessment and fault diagnosis is realized, and the effect of accurately identifying fault signs is achieved.

The invention discloses a multi-mode feature fusion power equipment fault diagnosis model and system, and the model comprises a gating network layer which is used for automatically selecting a mode suitable for participating in fusion according to the features of input data; the DNN and CNN parallel network layer is used for performing feature extraction and classification on different types of input data; the third-level decision-making layer is used for dynamically selecting a diagnosis strategy according to the integrity and the abnormal condition of the input data; the system comprises an acquisition module used for acquiring multi-modal monitoring data of power equipment; the FPGA control module is used for splitting, packaging and caching the collected data; the GPU processing module is used for performing verification, feature extraction and diagnosis on the received data; and the display control module is used for man-machine interaction and data and diagnosis result display. According to the model and the system, efficient, accurate and flexible diagnosis of power equipment faults is realized through multivariate feature fusion and multiple decision-making mechanisms, and the model and the system have important application value.

The invention discloses a regulation and control fault positioning method fusing a diagnosis feature library and power flow verification, and relates to the field of intelligent power grids, and the method comprises the following steps: extracting a power grid regulation and control abnormity related protocol message, carrying out the multi-dimensional protocol analysis, and generating a preliminary fault criterion; performing mode matching on the initial fault criterion and an intelligent diagnosis feature library to generate a mode matching result; constructing a regional power grid power flow distribution model, comparing an expected state with actual power flow distribution, and generating a physical verification result; fusing the preliminary fault criterion, the mode matching result and the physical verification result to obtain a final fault criterion, and outputting a fault positioning result; and performing reliability evaluation on a fault positioning result based on a confidence model, and optimizing a diagnosis strategy through reinforcement learning. According to the method, multi-dimensional diagnosis information and physical closed-loop verification are fused, and an adaptive learning mechanism is introduced, so that more systematic, accurate and reliable positioning of the regulation and control faults of the intelligent power grid is realized.

The application discloses a strategy for extracting fault features of electronic circuit systems, named as SSEST strategy, which is used for perceiving global information and paying attention to notable local information, and mining important local information means realizing expression of intermittent fault features of electronic circuit systems, specifically, first, S transformation is performed on a circuit output time sequence signal to acquire time-frequency domain features, then a squeeze and excitation network attention module is used to distribute channel weights, subsequently, input into a Swin Transformer framework, and pay attention to local information related to faults from global signals, and deep mining is performed on fault features, and two electronic circuits are taken as experimental circuits, the proposed diagnostic strategy realizes rapid and high-precision diagnosis, and shows that the proposed multiple attention mechanism is efficient for feature mining of intermittent faults of electronic circuit systems.

This application provides an AI-based diagnostic fee adjustment method and related apparatus. The method includes: acquiring basic diagnostic data of a target vehicle; performing preliminary AI diagnosis on the basic diagnostic data to obtain a first diagnostic result; the first diagnostic result includes a preliminary diagnostic report; determining a target diagnostic strategy based on the preliminary diagnostic report; the target diagnostic strategy includes any one of the following: AI-only diagnosis, AI-assisted and manual determination, or manual diagnosis only; determining a target diagnostic report based on the target diagnostic strategy; determining the AI ​​contribution based on the target diagnostic report; and determining the target diagnostic fee based on a preset basic fee standard and the AI ​​contribution. By constructing a differentiated human-machine collaboration process based on diagnostic confidence, quantifying the AI ​​contribution, and differentiating fees based on the AI ​​contribution, the accuracy of AI fault diagnosis applications and user engagement are improved.

The invention discloses an intelligent fault diagnosis method and device for a complex aerospace system based on correlation modeling, and relates to the technical field of aerospace system fault diagnosis, and the method comprises the steps: constructing a testability model of the aerospace system; the distinguishing capability of different faults is simplified, and test points are optimized; automatically generating a fault diagnosis strategy based on the optimized testability model; performing simulation evaluation on the fault diagnosis strategy, and counting a fault detection rate and a fault isolation rate; and performing iterative optimization by judging whether the fault detection rate and the fault isolation rate meet preset requirements or not. According to the method, the technical problems of low diagnosis efficiency and insufficient fault detection and isolation accuracy caused by dependence on artificial experience and incomplete static fault tree coverage of complex spaceflight system fault diagnosis in the prior art are solved, intelligentization and precision of complex spaceflight system fault diagnosis are realized, and the fault diagnosis efficiency is improved. And the fault detection rate and the isolation rate are improved.

The application provides application of serum sIL-2R as an ADAM10 inhibitor companion diagnostic marker and a corresponding kit, including two core application scenarios: before treatment, screening of a patient population with high activity of an ADAM10-sIL-2R pathway and most likely to benefit from ADAM10 inhibitor treatment by detecting the serum sIL-2R level of the patient; during and after treatment, evaluating the inhibition effect of the ADAM10 inhibitor on the target pathway and the treatment efficacy by dynamically monitoring the change of the serum sIL-2R level; the liquid companion diagnostic strategy will provide an indispensable supporting tool for the precise application of the ADAM10 inhibitor in pancreatic cancer, and significantly improve the precision and effectiveness of clinical medication.

A digital twin-driven electric servo mechanism health index system construction method belongs to the technical field of electric servo mechanism health management, and is characterized in that a unified functional unit digital twin fault model of an electric servo mechanism covering functional units such as a controller, a power amplifier, a motor, a transmission mechanism, a sensor and the like is constructed; various typical fault modes such as poor contact, power tube open circuit and magnetic steel demagnetization can be simulated, influence rules and characteristic expressions of various faults on dynamic performance of the system are revealed through a step response and sine response simulation system, and accurate twinborn simulation data can be provided for diagnosis strategy design without relying on physical entity fault tests. The complex process of fault influence mechanism analysis and diagnosis basic data acquisition is greatly simplified; the full-period health management requirement of the electric servo mechanism can be met, the universality is high, and the technical application efficiency can be greatly improved.

The invention relates to the field of medical image analysis and computer-aided diagnosis, in particular to a breast cancer detection method based on multi-modal fusion and three-stage prediction, which comprises the following steps: acquiring breast medical image data to be processed and structured clinical data of a patient; processing the to-be-processed breast medical image data and the structured clinical data of the patient by adopting a pre-trained breast cancer detection model based on multi-modal fusion and three-stage prediction to obtain a breast cancer detection result; according to the method, a multivariate diagnosis strategy is adopted to process the visual features and the structured clinical data of the breast medical image, the ResNet-34 network based on adaptive optimization is adopted to evaluate the image shielding degree, and the efficient and light-weight OfficientV2 network is introduced to perform feature extraction, so that the problems of high calculation cost and single diagnosis strategy in the existing breast cancer detection method are solved, and the detection accuracy of the breast cancer is improved. And the method provided by the invention is not easily influenced by tissue shielding.

The invention relates to the technical field of automatic equipment fault diagnosis, in particular to an automatic equipment fault diagnosis method and system based on deep learning. According to the method, the health degree state of the execution mechanism is comprehensively evaluated by taking the life cycle stage and the performance state as the basis and combining with the evaluation model based on deep learning. In consideration of different health degree states of the execution mechanisms, in order to identify potential fault risks as much as possible to ensure smooth proceeding of a production plan, the fault diagnosis strategies for the execution mechanisms with different health degree states are also different, and the execution mechanisms with poor health degree states need to adopt diagnosis strategies with more strict standards; and more monitoring parameters need to be acquired, and the acquisition strategy also needs to be finer, so that potential fault risks can be checked as much as possible and timely maintenance can be carried out. The monitoring data acquired according to the health degree state self-adaptive acquisition strategy is input into the diagnosis model to identify potential faults as much as possible, so that the automation equipment is better managed.

This application relates to the field of computer technology and provides a method and related equipment for generating intelligent agents for diagnosis. The method generates a virtual object containing temporal pathological feature data based on a preset disease evolution template; controls an initial diagnostic agent to interact with the virtual object in multiple rounds to obtain a temporal diagnostic strategy output by the initial diagnostic agent; generates feedback results for the initial diagnostic agent based on the diagnostic strategy and the target diagnostic scheme corresponding to the virtual object; and optimizes the parameters of the initial diagnostic agent based on the feedback results to generate a target diagnostic agent. By constructing a disease evolution template, this application can generate virtual patient data with continuous temporal features at low cost and on a large scale, enabling the diagnostic agent to be exposed to the complete disease progression logic from the latent stage to the critical stage during the training phase, thereby improving the model's generalization ability.

The invention discloses a hand skin health condition detection system and method, and the system comprises a three-dimensional structure and multispectral image collection module which is used for synchronously obtaining the three-dimensional structure contour and multispectral image data of the hand of a user; the multi-modal data fusion and feature extraction module is used for performing spatial registration on the data and extracting features; the skin health state vector generation module is used for fusing the multi-source features into a standardized multi-dimensional health state vector; the method comprises the following steps: firstly, carrying out rapid low-resolution macroscopic pre-scanning, and identifying and positioning a potential risk area; the system dynamically plans a detailed investigation strategy, and only drives the acquisition module to carry out high-resolution multi-mode detailed investigation on the risk area; and carrying out comprehensive diagnosis by fusing macroscopic background and local accurate data. Through deep fusion of multi-modal data, accurate quantitative evaluation of the skin health condition is realized, and meanwhile, an innovative diagnosis strategy remarkably improves the detection efficiency and reduces the system overhead on the premise of ensuring the precision.

The invention provides a gas equipment remote monitoring method and system, and belongs to the field of remote monitoring, and the method comprises the steps: analyzing a single abnormal data signal, building a fault relation mapping table, determining a fault reason, calculating a fault severity index according to the fault repair time length and the fault influence range, and determining the fault severity level. And generating fault severity level information. According to the gas equipment remote monitoring method and system provided by the invention, the problems that the fault pre-judgment accuracy is low due to lack of deep mining of historical data, the fault severity evaluation is lack of a scientific quantitative standard, and an effective decision basis cannot be provided for emergency disposal are solved, the fault classification reference table and the fault relation mapping table are established, and the fault pre-judgment accuracy is improved. Technologies such as a dynamic time warping algorithm and a Bayesian probability model are applied to realize intelligent pre-judgment and accurate positioning of potential faults; diagnosis strategies are respectively made for single and various abnormal data, simple faults can be quickly identified, and the accuracy and efficiency of fault diagnosis are improved.

The application provides a leakage detection method of a fuel evaporation system and an electronic device, and is applied to a fuel evaporation system of a vehicle. The method comprises the following steps: when the parking time of the vehicle exceeds a first preset time length, and the enabled working condition of the fuel evaporation system is normal, the vacuum degree of a first space in which a first electric control valve is in a closed state is obtained; a first diagnosis strategy or a second diagnosis strategy is selected according to the vacuum degree, and leakage detection is performed on the first space and a second space to obtain a detection result, wherein the first diagnosis strategy is a diagnosis strategy corresponding to that the first space has no leakage when the absolute value of the vacuum degree is greater than a first preset threshold. In this way, an air pump does not need to be additionally arranged, so that the hardware cost of the system is reduced. In addition, different diagnosis strategies are beneficial to improving the diagnosis completion rate. Since the first space of the fuel evaporation system rarely leaks, the frequency of using the first diagnosis strategy is usually high, and the detection process of the first diagnosis strategy is less, so that the diagnosis efficiency is improved.

This invention discloses a multimodal state perception and fault diagnosis method based on equipment identification guidance. The method includes: acquiring multimodal information of the power equipment under test to obtain multimodal perception data; performing time synchronization, spatial alignment, and data normalization processing on the multimodal perception data; automatically identifying the power equipment based on visible light imaging information to obtain equipment type information and / or key structural location information; selecting a multimodal fusion and diagnosis strategy matching the corresponding power equipment according to the equipment type information, and jointly analyzing the multimodal perception data; and outputting the state assessment result and / or fault diagnosis result of the power equipment based on the joint analysis result. By using equipment identification results as a priori constraints to guide the multimodal information fusion and diagnosis process, this invention achieves differentiated state perception and fault diagnosis for different types of power equipment, improving detection accuracy and engineering applicability.

The present application provides a method and system for combining on-vehicle diagnosis and remote diagnosis, which relates to the technical field of automotive fault diagnosis. The method includes: converting diagnostic service data into a diagnostic script and deploying it to an on-vehicle diagnostic unit to generate a preliminary diagnostic result based on fault data; sending the preliminary diagnostic result and the fault data to a regional diagnostic center to match corresponding computing resources for coordinated analysis and generate an intermediate diagnostic plan; when the confidence level of the intermediate diagnostic plan is lower than a preset threshold, submitting the preliminary diagnostic result, the fault data, and the intermediate diagnostic plan to a cloud server for in-depth analysis to obtain an optimized diagnostic strategy, and separately sending them to the on-vehicle diagnostic unit and the regional diagnostic center to complete collaborative diagnosis. Implementing this method, by constructing a three-level diagnostic architecture, different architectures complete fault diagnoses of different complexities, relieving the pressure on the remote platform, achieving reasonable allocation of computing resources, and improving the overall diagnostic efficiency.

The present invention relates to the technical field of medical information systems, and more specifically, to an intelligent early liver cancer diagnosis system and method based on multi-phase enhanced CT, comprising: a CT image module, for acquiring multi-phase enhanced CT images of patients; an image super-resolution reconstruction module, connected to the CT image module; a radiomics feature module, connected to the image super-resolution reconstruction module, and a deep feature module, connected to the image super-resolution reconstruction module, for constructing a deep convolutional neural network model; a knowledge distillation module, connected to the deep feature module; an early diagnosis module, connected to the radiomics feature module, the deep feature module, and the knowledge distillation module, for integrating high-throughput radiomics features and deep features; and generating early liver cancer diagnosis results based on an interpretable diagnostic strategy, thereby retaining the prior knowledge of human experts and making full use of the powerful feature learning capabilities of deep learning.

The application discloses a fault diagnosis strategy optimization method considering test uncertainty, and the specific steps are as follows: a simulation model of a measured object is established based on a Modelica language; the relationship between test points and faults is counted based on the simulation model, and a test-fault uncertainty matrix is established; on the basis of the test-fault uncertainty matrix, an error diagnosis cost caused by test uncertainty is introduced, an information entropy algorithm is improved, and a diagnosis strategy is obtained based on the improved information entropy algorithm. The application firstly proposes to establish a test-fault uncertainty matrix based on a simulation model; then, the error diagnosis cost is introduced into the information entropy algorithm, and the accuracy of the diagnosis strategy is improved. Finally, a backtracking step is added to the information entropy algorithm, and the algorithm is prevented from falling into local optimization. The research on the fault diagnosis strategy optimization based on the test uncertainty has important significance for improving the fault diagnosis capability of the measured object. The diagnosis strategy is fed back to the forward design of the system, and a reference is provided for the design of the measured object.

The invention provides a vehicle remote diagnosis method, platform and system based on a gray task, and belongs to the technical field of Internet of Vehicles, and the method comprises the steps: storing a vehicle relation table and a corresponding diagnosis strategy to the remote diagnosis platform; the remote diagnosis platform obtains vehicle information and sends a corresponding diagnosis strategy to the corresponding vehicle end to execute a diagnosis task; and the remote diagnosis platform generates a diagnosis report according to the diagnosis log sent by the vehicle end, and sends the diagnosis report to the vehicle end when an abnormality is found. Through an instant or periodic task triggering mechanism, regular and real-time remote diagnosis can be carried out on the vehicle, so that potential problems can be found in time before a fault occurs or deteriorates, post-remedy is changed into pre-prevention, and the time cost and the maintenance cost of a vehicle owner are effectively saved.

The invention discloses a testability diagnosis strategy generation method and system based on a correlation model, and relates to the technical field of testability engineering.The method comprises the steps that a multi-signal correlation model containing functions, faults and test signals is constructed, a correlation matrix is generated, test points are optimized through an information gain greedy algorithm, a test point optimization set is obtained, and the test point optimization set is obtained; and automatically generating a diagnosis strategy, then performing testability parameter simulation prediction, comparing a predicted value with a distribution index to generate a verification result, and when the verification result is not passed, triggering a closed-loop iterative optimization mechanism to perform optimization adjustment until the predicted value meets the distribution index, and generating a diagnosis optimization strategy. According to the method, the technical problems that index distribution and engineering practice are disjointed and diagnosis scheme design and verification links are separated in complex equipment testability design are solved, and the technical effects that complex equipment testability index distribution fits the practice, a diagnosis scheme and verification form collaboration and can be dynamically optimized, and later reworking is avoided are achieved.

The invention relates to a batch fault abnormity positioning method based on man-machine cooperation and comparative analysis, and the method comprises the following steps: 1, collecting multi-source original fault data, evaluating whether the data meets the feasible conditions of batch fault diagnosis or not, obtaining a final evaluation result, and generating a standardized table mapping suggestion; 2, through structure and integrity verification, semantic check and automatic repair and executable verification, obtaining tabular data which is subjected to structure and semantic verification and potential problem correction and is confirmed to be generated and can be directly executed; and step 3, obtaining historical experience most similar to the current scene through searching a historical scene library and matching, obtaining a recommended diagnosis strategy, performing lightweight trial operation on each candidate strategy, comparing to obtain a most suitable strategy, and further performing a final search process on complete data, discovering a key attribute mode and a complete anomaly positioning report. According to the invention, an end-to-end intelligent diagnosis process from scene identification, data verification to abnormal positioning can be realized.

The invention provides a test equipment abnormal state self-diagnosis report generation and secure transmission system, which adopts a Jenkins-based equipment abnormity automatic diagnosis system, and realizes automatic execution of equipment abnormity diagnosis monitoring and automatic generation and secure transmission of a fault report by utilizing timing triggering of Jenkins. The test efficiency is greatly improved, real-time monitoring of the test equipment is realized, and thus full-automatic periodic execution of diagnosis and monitoring of the test equipment is realized; according to the invention, an abnormal state diagnosis strategy based on principal component analysis and a random forest BP neural network (PCA-RF-BP) is used for improving the diagnosis efficiency of an equipment monitoring system; the method has the advantages of automatically diagnosing and monitoring the abnormity, automatically generating and pushing the fault report, improving the test efficiency and realizing the safety and reliability of information transmission.

The invention discloses a pole-mounted circuit breaker multi-parameter intelligent diagnosis and service life prediction method, and relates to the technical field of pole-mounted circuit breakers, and the method specifically comprises the following operation steps: carrying out the data collection of the multi-parameter operation data of a pole-mounted circuit breaker in real time through employing an edge device; carrying out data transmission on the collected data, and carrying out data preprocessing on the transmitted data; and performing preliminary analysis, abnormal mode identification and fault diagnosis on the processed data by using a preset machine learning model according to a preprocessed data result, and uploading a diagnosis result and key operation data to a cloud server. Data collected by a sensor is subjected to edge calculation in a gateway and then is subjected to data storage and data analysis on a platform, and meanwhile, out-of-limit alarm, fault diagnosis, fault pre-judgment and service life prediction are realized based on a plurality of built functional modules and a diagnosis strategy library, so that powerful technical support is provided for ensuring safe and stable operation of a power system.

The invention relates to the technical field of industrial automation, in particular to a production line anomaly diagnosis method and device, computer equipment and a medium, and the method comprises the steps: obtaining multi-source heterogeneous sensing data of a production line, and carrying out the standardization processing of the multi-source heterogeneous sensing data, and obtaining multi-source isomorphic sensing data; performing feature extraction processing on the multi-source isomorphic sensing data to obtain production line features of a production line; a target diagnosis graph corresponding to the production line features is determined, the target diagnosis graph comprises a directed acyclic graph composed of a plurality of diagnosis nodes and directed edges, each diagnosis node corresponds to a diagnosis strategy, and the directed edges between the diagnosis nodes represent the execution sequence of the diagnosis strategies; the production line features are input to the initial diagnosis node of the target diagnosis graph, diagnosis strategies corresponding to the diagnosis nodes are executed in sequence, and a production line abnormity diagnosis result is output, self-adaptive generation of the diagnosis path can be achieved, and the adaptability to diversified production line scenes is enhanced.