963 results about "Diagnostic system" patented technology

Systems, methods, and apparatus are disclosed for omnimodal sensing, data fusion, and autonomous decision-making across physical and digital domains and further integrates a Multimodal Diagnostic System (MDS) and Impairment Recognition and Intervention System (IRIS) with defense architecture or a system architecture that can be compliant with the Modular Open Systems Approach (MOSA) and Sensor Open Systems Architecture (SOSA) to ensure interoperability. The system can utilize real-time multisensory fusion, cryptographic provenance via blockchain, and resilient magnetoelectric communication to support mission-critical decision-making across manned and unmanned platforms in denied or contested environments.

The invention discloses a garlic disease and insect pest dynamic diagnosis system based on temperature and humidity time sequence data, relates to the technical field of agricultural information processing, and solves the problems that in the prior art, a fixed parameter model is prone to sensitivity sudden drop, false alarm sudden increase and systematic deviation under sudden or semi-sudden changes of field environment and management variables. According to the scheme, semantic processing of temperature and humidity and farming events is carried out through an acquisition module, a system coupling module carries out segmented identification on distribution mutation caused by environment and operation, a domain representation module constructs causal threatening features, a prediction calibration module carries out dual-path drift decomposition and rapid correction, and a prediction result is obtained. The sample adding and label collecting module generates anti-fact samples and actively collects labels, and the decision attribution module outputs a structured evidence chain; according to the method, the dynamic adaptive capacity and reliability of the diagnosis system under the conditions of non-stationary distribution and concept drift are remarkably improved.

The invention relates to the technical field of power system monitoring and fault diagnosis, and particularly discloses a fault diagnosis system of an electrical variable measurement insulator detection device. The system comprises a synchronous acquisition module, a multi-physical field feature extraction module, a self-adaptive fault diagnosis engine and a feedback module. By synchronously acquiring voltage harmonic waves, leakage current and temperature data, extracting feature vectors of coupling electric-thermal influence and performing two-stage collaborative diagnosis by using a dynamic threshold value and a mahalanobis distance, high-sensitivity and self-adaptive accurate identification and early warning of an early latent fault of the insulator are realized. According to the system, by constructing a synchronous data acquisition module and a multi-physical field feature extraction module, two key influence factors, namely an operation voltage harmonic component and an environment temperature, are brought into a diagnosis system in a quantifiable feature form for the first time.

The invention belongs to the field of intelligent vehicle-mounted technology, and particularly relates to a multifunctional vehicle-mounted sensing detection system based on multi-source information fusion, which comprises multi-source information acquisition, high-precision space-time synchronization and heterogeneous data preprocessing. A heterogeneous sensing system with feature level deep fusion, vehicle state dynamic performance evaluation and adaptive decision and risk evaluation capabilities is constructed, and a driving decision is dynamically adjusted through fusion perception and vehicle performance. The vehicle state and dynamic performance evaluation module is constructed by deeply integrating vehicle internal state data acquired by a vehicle-mounted diagnosis system into a perception fusion and decision planning process, and the core contradiction of disjunction of a perception result and a vehicle dynamic performance strategy is effectively solved. The system can evaluate key performance parameters such as power, braking, steering and the like of the vehicle and potential faults of the key performance parameters in real time, and the driving strategy and the safety margin are dynamically adjusted in the self-adaptive decision and risk evaluation module according to the actual physical limitation of the vehicle.

The invention relates to the technical field of water conservancy project structure health monitoring and intelligent sensing, in particular to a water conservancy project online monitoring and abnormity diagnosis system based on edge calculation, which comprises a data acquisition module used for acquiring current water level data, current temperature data and actually measured structure response data; the reference reconstruction module is used for generating an ideal state vector; the fault simulation module is used for generating a theoretical damaged simulation state vector; the topology judgment module is used for calculating a first difference value between the actual measurement structure response data and the ideal state vector, calculating a second difference value between the theoretical damaged simulation state vector and the ideal state vector, and calculating the topology similarity based on the first difference value and the second difference value; the risk assessment module is used for generating a structure safety risk index in combination with the fault strength scalar and the topological similarity; according to the method, the problems of data transmission lag and low online calculation efficiency in a traditional centralized processing mode are effectively solved, and the real-time requirement of an engineering site is met.

The invention relates to a pathological section intelligent auxiliary differential diagnosis system based on multi-modal fusion, in particular to the field of clinical pathology, semantic unification of multi-modal data is achieved through meta-task construction and a cross-modal alignment technology, and transferable diagnostic knowledge is extracted by utilizing a meta-learning framework; the method combines a generative model and knowledge constraints to generate high-quality synthetic data, and finally fuses real and synthetic samples through a self-adaptive diagnosis mechanism, thereby remarkably improving the differential diagnosis capability of rare lesions, effectively solving the problem of model generalization in a training data scarcity scene, and improving the accuracy of model identification. And efficient and reliable intelligent auxiliary decision support is provided for clinical pathological diagnosis.

A mask-based diagnostic apparatus is provided for detecting a biomarker contained in exhaled breath of a test subject. An exhaled breath condensate (EBC) collector converts breath vapor received from the lungs and airways of the test subject into a fluid biosample. The EBC collector including a thermal mass, a condensate-forming surface and a fluid conductor disposed on the condensate-forming surface. A fluid transfer system receives the fluid biosample from the EBC collector. A biomarker testing unit receives the fluid biosample from the fluid transfer system and tests the fluid biosample for a target biomarker. A testing system support is provided for supporting the EBC collector, the fluid transfer system and the biomarker testing unit. The testing system support is configured and dimensioned to fit inside a face mask. A face mask is provided forming an exhaled breath vapor containment volume to hold the exhaled breath vapor in proximity to the EBC collector to enable the condensate-forming surface cooled by the thermal mass to coalesce the exhaled breath vapor into the fluid biosample.

The invention specifically relates to a standardized fuse fault monitoring and diagnosis system, and relates to the technical field of power system protection equipment monitoring. A signal processing and impedance spectrum calculation module; an intelligent diagnosis and state evaluation module; and a benchmark management and personalized tracking module. According to the invention, controllable micro test signals are actively injected to obtain frequency domain impedance fingerprints, and accurate capture of microscopic degradation is realized; non-intrusive coupling and signal injection are adopted, the topology of a main loop does not need to be changed, the inherent protection characteristic of the fuse is guaranteed, and power frequency interference can be penetrated; the recognition amount of budding faults such as contact oxidation and melt corrosion is improved in advance, and power failure accidents caused by fault expansion are avoided.

The invention relates to the field of rotating machinery fault diagnosis, and discloses a bearing fault diagnosis method based on a physical perception KAM network, and the method comprises the steps: carrying out the discretization of a bearing vibration signal through a Gabor filter group based on physical prior initialization, and generating modal feature lexical elements with physical frequency band meanings; and inputting the lexical elements into a PC-KAM backbone network, calculating a hidden state vector by using a state space model branch, and dynamically adjusting the position of a primary function node of a Kolmogov-Arnod network branch to realize collaborative dynamic feature extraction. In the training stage, an orthogonal subspace constraint and physical perception low-rank adaptation fine tuning mechanism is introduced. And finally, searching a historical fault case, performing multi-modal fusion on the historical fault case and the deep feature sequence, mapping a fusion representation into a soft prompt, and inputting the soft prompt into a large language model to generate a diagnosis report. According to the method, the problems of poor physical interpretability of characteristics and few-sample diagnosis under variable working conditions are effectively solved, and the generalization and decision-making ability of a diagnosis system are improved.

The application discloses a kind of cognitive impairment early screening and diagnosis system based on multi-modal data, including following module: (1) multi-modal data acquisition module, for obtaining image data, gene data, behavior data and clinical narrative information;The clinical narrative information includes at least two kinds of patient complaint text, interference factor hook item, symptom time axis;(2) causal purification module, for filtering the pseudo-correlation between multi-modal data by clinical narrative anchoring and reverse intervention deduction, including: narrative anchoring unit: based on preset causal conflict rule base, through structured clinical narrative field identification data conflict, trigger freezing or weight reduction operation;Intervention deduction unit: generate the reverse intervention verification package containing low-cost intervention measures and review plan, and update causal rule base based on review data;The application breaks through the statistical correlation limitation of traditional technology, realizes the upgrade of diagnosis and treatment paradigm from "correlation" to "causality".

The invention relates to the technical field of rotating machinery health monitoring and fault diagnosis, and discloses a diversion sealing intelligent diagnosis method and system.The diversion sealing intelligent diagnosis method comprises the steps that an original monitoring data set is collected; performing working condition self-adaptive preprocessing on the original monitoring data set; extracting multi-scale time-frequency-space cooperation features, and performing dimension reduction by using a feature selection method; training by utilizing physical consistency constraint and a small sample learning method to obtain a small sample diagnosis model; inputting the optimized feature vector into a diagnosis model for anomaly detection, failure mode recognition and severity evaluation; a sealing performance degradation model is established, degradation model parameters are estimated, and the remaining service life is predicted; integrating the diagnosis model and the residual life prediction model into an intelligent diagnosis system, and adopting online real-time diagnosis to obtain a diagnosis report; according to the invention, the problems of incomplete monitoring information, difficult early fault detection and lack of life prediction capability in diversion sealing diagnosis are effectively solved.

The invention discloses an intelligent diagnosis system for power utilization abnormal information acquisition, particularly relates to the technical field of power system monitoring, and is used for solving the problems of waveform distortion and broadening caused by transmission line distribution parameters in the long-distance wired transmission process of an existing system. The system collects abnormal characteristic waveform data through a distributed intelligent terminal, selects a characteristic harmonic component in the power frequency voltage of a power line to construct an inherent test signal, extracts the envelope form similarity of a response signal and the test signal through envelope analysis as a first distortion evaluation index, and obtains a second distortion evaluation index after statistical significance judgment. Performing instantaneous frequency stability analysis on the signal with significant distortion to obtain a second distortion evaluation index, and finally generating a corresponding waveform compensation parameter based on double indexes to realize real-time compensation correction; the integrity of the abnormal characteristic waveform in the transmission process is effectively maintained, and the accuracy and reliability of power consumption abnormity diagnosis are remarkably improved.

The invention relates to the technical field of wind power equipment state monitoring, and discloses an AI-based wind power blade health diagnosis system comprising a sensing module used for collecting multi-source monitoring data in a wind power blade operation process, the multi-source monitoring data comprising vibration data, acoustic data, strain data, environmental data and visual image data; the data preprocessing module is in communication connection with the sensing module and is used for performing cleaning, standardization, data fusion and feature extraction on the acquired multi-source monitoring data and outputting a standardized feature data set; and the AI diagnosis model training module is used for constructing a deep learning model and is based on a training set composed of historical health state data and fault data. The method effectively solves the problems of low efficiency, poor accuracy, incapability of early warning and the like in existing wind power blade health diagnosis, provides reliable guarantee for safe and stable operation of a wind turbine generator, and has wide application prospect and practical value.

The invention discloses a high-power and frequency-adjustable gyrotron loading test system for nuclear fusion, which relates to the technical field of nuclear fusion and comprises a 2-megawatt coaxial gyrotron with adjustable frequency of 170 / 240GHz and a loading test platform. The loading test platform comprises a gyrotron part loading system, a strong magnet system, a vacuumizing system, a power supply system, a load system, a cooling system, a monitoring and diagnosing system and a control system. The component loading system is used for precise assembly; the strong magnet system provides an adjustable strong magnetic field; the vacuumizing system maintains ultrahigh vacuum; the power supply system comprises a cathode power supply and a body power supply, and the body power supply is used as a high-voltage amplifier to realize 10kHz rapid power modulation; the cooling system provides independent active cooling; the load system simulates a real load; comprehensive detection of the monitoring and diagnosis system; and the control system realizes coordinated control and rapid protection. According to the invention, a complete solution is provided for development, test, operation and maintenance of the 2-megawatt-level and frequency-adjustable gyrotron.

Systems and methods are provided for automated incident investigation. Anomaly detection is used to identify anomalies in incident data (e.g., alerts, changes, metrics, logs, and / or system health), and the identified anomalies are converted into facts (or textual prompt inputs for a large language model (“LLM”)). A troubleshooting or diagnostic system is run on the anomalies to provide additional facts to identify a root cause of an incident. The facts from the diagnostics, the facts from the anomaly detections are entered into a consolidated explainer that generates a summary of what happened, what is a likely cause, and what to do next to resolve the issue. In examples, anomaly enrichment data including a time correlation result, a weighted list of abnormal transaction patterns, a list of abnormal trace patterns, a list of exception patterns, a difference pattern, and / or region data are input as further facts to enhance the incident investigation process.

The invention relates to the technical field of application computer science and artificial intelligence, and discloses a highway pavement disease rapid identification and diagnosis system based on deep learning, and the system comprises a data collection and preprocessing module which is used for obtaining and processing multi-source time series data; the unified state vector characterization module is used for constructing a unified vector for characterizing the comprehensive state of the pavement; the spatio-temporal evolution and inference module is used for simulating the spatio-temporal evolution process of the pavement state; the model training and optimizing module is used for carrying out optimization training on the evolution model; and the diagnosis and prediction application module is used for generating a final diagnosis report and maintenance decision. According to the method, the development trend of the pavement diseases and the effects under different maintenance scenes are deduced and simulated through the spatio-temporal depth model jointly constrained and optimized by physical and causal knowledge, a complete closed loop of the pavement diseases from static recognition to dynamic diagnosis and decision making is realized, and the scientificity and the intelligent level of road maintenance management are improved.

The invention provides a safety production data detection method and a safety production data detection system, which are applied to the technical field of lock processing, and can more accurately and comprehensively identify and cause potential abnormities in the production process by introducing comparative analysis of multi-dimensional current characteristics and combining standardized processing and micro-cutting strategies, so that the safety production efficiency is improved. The problems of inaccurate diagnosis and easy misjudgment in the prior art are effectively solved, so that the safety production level and efficiency in the precision manufacturing fields such as lock processing are remarkably improved, unnecessary production interruption and resource waste are avoided, and the credibility of an automatic diagnosis system is enhanced.

The invention relates to the technical field of multi-modal ophthalmology image analysis, and particularly discloses a multi-modal ophthalmology image analysis method, model and system based on optimal transmission image diffusion and a medium thereof.The method at least comprises the steps that S100, the distance between fundus color photo and OCT image modals is explicitly calculated through an optimal transmission algorithm, and the distance between fundus color photo and OCT image modals is calculated; carrying out collaborative evolution on the features in a continuous time domain in combination with a graph neural network and a Sheng differential equation model; and step S200, applying self-attention and cross attention in parallel, reserving modal specificity with low-layer fine granularity, and progressively fusing the color photo texture and the OCT depth structure in a high layer to realize cross-layer long-range dependent depth semantic fusion. The method not only promotes the leading-edge development of multi-modal medical image analysis, but also provides important scientific basis and technical support for constructing a high-precision and high-robustness intelligent ophthalmology diagnosis system.

The invention relates to the technical field of medical images, in particular to a medical image intelligent detection and auxiliary diagnosis system based on deep learning, and the system comprises a data access module which is used for obtaining medical image data and clinical text data of a patient; the data fusion module is used for generating a focus feature vector and a text feature vector, and performing cross-modal alignment and fusion to generate a fusion feature vector; the diagnosis analysis module is used for executing focus detection, focus segmentation and focus classification tasks, generating a diagnosis result and generating a diagnosis label based on the diagnosis result; the decision generation module is used for mapping the generated execution result to a preset medical knowledge base and performing deep reasoning to generate an auxiliary diagnosis decision; the decision auditing module is used for performing confidence scoring on the auxiliary diagnosis decisions and selecting the auxiliary diagnosis decision with the highest confidence score as the final auxiliary decision; and the data visualization module is used for carrying out visualization processing on the auxiliary decision and the diagnosis result.

The invention discloses a transformer fault intelligent diagnosis system and method based on a K-Medoid and SMOTE optimization support vector machine, and the system comprises a data collection module which is used for collecting dissolved gas and operation parameters in transformer oil in real time; the data preprocessing module is connected with the data acquisition module and is used for completing data standardization and abnormal value detection; the sample balancing module is connected with the data preprocessing module and used for reducing majority class redundancy through K-Medoid clustering and expanding minority classes through an SMOTE algorithm; the intelligent classification module is connected with the sample balance module and adopts a weighted SVM classifier to train and predict; and the result output and visualization module is connected with the intelligent classification module and is used for displaying the classification result, the confidence coefficient and the key performance indexes. According to the method, the problem of classification performance degradation caused by sample imbalance in the prior art is solved, and the recognition capability of the SVM model on minority class faults and the overall diagnosis precision are improved.

The invention, which relates to the technical field of power cable insulation state detection, discloses a power cable insulation state on-line diagnosis system comprising a multi-parameter acquisition module comprising a multi-type sensor group, a signal conditioning unit and a data transmission unit. The data preprocessing module processes abnormal values and noise, unifies parameter dimensions and synchronizes timestamps; the insulation state diagnosis module is used for constructing a hierarchical index system, calculating scores through combination and empowerment and dividing grades; the fault positioning module transmits a narrow pulse signal, receives a reflected signal and calculates a fault distance; the result output module is used for generating a report, visually presenting the report and pushing the report to multiple terminals; and the system management module is used for dividing authority, recording logs, carrying out double backup data and monitoring the equipment state. The comprehensive performance, real-time performance and accuracy of cable insulation diagnosis are improved, faults are accurately positioned, aging trend prediction and personalized maintenance are supported, data safety is guaranteed, operation and maintenance efficiency is improved, the service life of the cable is prolonged, and power supply reliability is enhanced.

The invention relates to a physical knowledge-guided model interpretability analysis method and diagnosis system, and belongs to the technical field of oil well fault diagnosis, and the method comprises the steps: collecting sensor data in an indicator, carrying out the standardization processing of the data, and obtaining a preprocessed grayscale image, and taking the preprocessed grayscale image as an oil well fault diagnosis offline data set; constructing an oil well working condition intelligent diagnosis model guided by physical knowledge based on improved EffcientNet, and taking a well fault diagnosis offline data set as model input; optimizing parameters of the oil well working condition intelligent diagnosis model by adopting a multi-task cooperative training mechanism based on uncertainty weighting to obtain an optimized oil well working condition intelligent diagnosis model; performing performance quantitative evaluation on the optimized oil well working condition intelligent diagnosis model by adopting a multi-dimensional index; oil well working conditions are monitored and diagnosed in real time, a result is output, and abnormity is responded. According to the invention, accurate and explainable intelligent diagnosis of the real-time working condition of the oil well is realized.

The invention is suitable for the technical field of vehicle diagnosis, and provides a vehicle diagnosis system and method, and the system comprises a processor which is connected with a plurality of electronic control units (ECUs) of a vehicle and a vehicle diagnosis device, and is used for analyzing a diagnosis configuration file in a preset folder under the condition that the vehicle diagnosis device establishes communication connection with the plurality of ECUs, and sending the diagnosis configuration file to the vehicle diagnosis device; and obtaining diagnosis configuration data of the plurality of ECUs. According to diagnosis business process parameters in the diagnosis configuration data, creating a corresponding software channel for each ECU, and packaging all configuration parameters of each software channel in corresponding straight-through structure examples; and managing straight-through structure instances of all software channels through a pointer array and a thread channel mapping table in the global management structure body. And the server is also used for receiving the global channel configuration parameters through the universal thread pool and matching a corresponding working thread for each software channel. According to the system, synchronous diagnosis of a plurality of ECU systems is realized by establishing a parallel diagnosis architecture, and the efficiency of vehicle diagnosis is improved.

The present invention discloses a system and method for healthcare diagnostics using foundation models with uncertainty triage, designed to deliver reliable, explainable, and safety-assured diagnostic outcomes across multimodal clinical data. The invention integrates a foundation model processor pretrained on diverse medical datasets with an uncertainty estimation processor configured to quantify epistemic and aleatoric uncertainties in diagnostic predictions. A triage control unit dynamically classifies cases into high, medium, and low-confidence categories based on computed uncertainty indices, ensuring that only high-confidence cases are automatically finalized, while uncertain or ambiguous cases are routed for clinician review. The system further incorporates a feedback adaptation processor that recalibrates model parameters and uncertainty thresholds based on expert feedback, maintaining alignment with clinical reliability standards over time. Implemented as a hardware-integrated diagnostic device, the invention supports real-time inference, secure data handling, and interpretability visualization through uncertainty heatmaps and attention overlays.

The invention relates to the technical field of traditional Chinese medicine intelligent diagnosis, in particular to a pulse condition feature-based traditional Chinese medicine intelligent diagnosis system, which comprises a pulse signal acquisition module, a depth feature extraction and characterization module, a pulse condition semantic mapping and identification module, a personalized adaptation and robustness enhancement module and a diagnosis report module. According to the invention, comprehensive, accurate and robust analysis of pulse condition information is realized, and the accuracy of traditional Chinese medicine intelligent diagnosis is improved.

The invention discloses an automatic production line fault diagnosis method based on continuous balanced slice sampling, relates to the field of intelligent manufacturing, and provides a scheme for solving the problems of extreme imbalance of samples and the like. Constructing an intelligent fault diagnosis system based on an RF-XGBoost hybrid transfer learning model to carry out fault diagnosis on the automatic production line; constructing a time sequence fault data set through data acquisition and preprocessing; extracting and strengthening a key dynamic evolution mode by using time sequence feature enhancement so as to improve the discrimination of fault features; the problem of sampling deviation is solved through continuous balanced slice sampling; a lightweight RF-XGBoost hybrid transfer learning model is constructed in feature screening and fusion modeling so as to realize fault classification; and cross-production-line distribution self-adaption is carried out through multi-stage transfer learning. The method has the advantages that time sequence integrity and sample balance are kept; strengthening the dynamic description of the fault; high-precision interpretable classification is realized; and cross-line distribution drift is solved. And the practicability and the rapid deployment capability of the industrial field model are remarkably improved.

The invention relates to a multi-agent collaborative diagnosis system and method for ultrasonic multi-modal data, and the method comprises the steps: employing a cloud edge-end hierarchical deployment structure, and setting an end-side micro-agent; the edge collaboration server comprises a plurality of specialized diagnosis agents aiming at different anatomical parts respectively; each specialized diagnosis agent runs as an independent calculation process, cross-process diagnosis information interaction is carried out through a message bus in a publishing and subscribing mode, and the edge collaboration server is further configured with a logic verification unit which executes intermediate quality control and generates an online diagnosis report based on logic mutual identification; the cloud large model platform is in network connection with the edge collaboration server and is deployed with a general medical large model; the problems of misdiagnosis and missed diagnosis caused by single organ isolated diagnosis are effectively solved, and the intelligent level and logic leakproofness of ultrasonic diagnosis are improved.

According to the intelligent alloy quality diagnosis system provided by the invention, a collaborative optimization architecture integrating data acquisition, processing, analysis and service application is constructed, so that multi-source data fusion and intelligent decision-making in the whole alloy smelting process are realized, and quality diagnosis, fault early warning and process optimization can be organically integrated; and an optimization strategy is fed back to a production process control system in real time to form closed-loop control, so that the quality stability of an alloy product, the adaptive capacity of the technological process and the overall optimization level of production benefits are remarkably improved.

The invention relates to the technical field of disease auxiliary diagnosis, in particular to a digital mammary gland artificial intelligence auxiliary diagnosis system based on multi-modal fusion, and the system comprises a multi-modal data collection module which is used for obtaining digital mammary gland image data and rehabilitation scheme data of a patient; the image quality correction module is used for carrying out acquisition quality evaluation and correction on the digital mammary gland image data; the feature modeling and evaluation module is used for performing feature extraction on the corrected digital mammary gland image data and rehabilitation scheme data, and comprises an image-drug action mechanism cooperation unit and an image-rehabilitation bimodal co-learning unit; and the rehabilitation effect prediction module is used for constructing a rehabilitation effect prediction model and outputting a rehabilitation prediction result under the influence of the scheme specificity of the current rehabilitation scheme of the patient. According to the method, the digital mammary gland image and the patient rehabilitation scheme are deeply fused, so that the accuracy and reliability of mammary gland tumor rehabilitation prediction are effectively improved.

The invention discloses an automatic fault detection and diagnosis system and method based on an artificial intelligence optimization operation system and a function library, and belongs to the technical field of intelligent building and industrial equipment operation and maintenance. The system comprises a data acquisition and preprocessing layer, a fault detection layer, a fault diagnosis layer, an intelligent recommendation layer and a visual display layer. The method comprises the following steps: collecting equipment data in real time and preprocessing; performing anomaly detection and performance trend analysis in combination with statistical process control and a machine learning algorithm; performing root cause diagnosis by using a Bayesian reasoning model; classifying faults according to an energy performance type, a controllability type and a thermal comfort correlation type, and evaluating priorities through a multi-dimensional weighted scoring model; and intelligently generating a solution and updating the knowledge base. According to the method, whole-process automation from early warning, diagnosis, classification to recommendation is achieved, the equipment reliability, the energy efficiency and the operation and maintenance intelligent level are improved, and the method is suitable for application scenes such as artificial intelligence middleware and computer visual and auditory software.