536 results about "Diagnostic information" patented technology

The invention relates to the technical field of power distribution protection cabinet fault diagnosis, and discloses an intelligent fault diagnosis and rapid isolation method for a power distribution protection cabinet, and the method comprises the steps: synchronously collecting electrical parameters, environment states and equipment motion signals in the cabinet as multi-source monitoring data when the power distribution protection cabinet operates; parameter fluctuation, state change and action time sequence features are extracted after preprocessing, cross-data association analysis is carried out through a multi-dimensional association model to generate an association feature set, a fault diagnosis information set is formed based on the identified abnormal types, fault positions and development trends, an isolation strategy is formulated according to severity levels and influence ranges, and a fault diagnosis result is obtained. And generating a rapid isolation instruction and dynamically adjusting along with fault evolution. According to the method, multi-source data fusion analysis is realized, faults can be accurately diagnosed and rapidly isolated, the operation safety and reliability of the power distribution protection cabinet are improved, and the method is suitable for fault processing of the power distribution protection cabinet in a power system.

The invention relates to an LLM-based power distribution network knowledge graph construction and fault intelligent diagnosis method. The method comprises the steps of constructing a knowledge system, constructing a normal operation state model and an anomaly detection threshold value, performing fault diagnosis through an LSTM-BP cascade network, and performing fault classification. The abnormal parameters and the fault classification result are input into an LLM model, the fault type is judged, corresponding diagnosis information is obtained, and the system generates a scheduling instruction and an emergency decision suggestion according to the diagnosis information; and generating a detailed diagnosis report. According to the system, statistical analysis and deep learning algorithms are adopted for operation parameters such as voltage and current after preprocessing, and automatic detection and fault classification of abnormal parameters are realized in combination with a CNN recognition module; a preset rule base in the knowledge system is combined to accurately determine a fault type, generate a scheduling instruction and an emergency decision suggestion, and assist operation and maintenance personnel in rapid response and dynamic management; finally, a detailed diagnosis report is generated, thereby improving the fault handling efficiency and the guarantee capability of safe and stable operation of a power grid.

The invention discloses a multi-department medical sample collaborative management system and method, and relates to the technical field of medical information, and the system comprises an integrated information interaction platform which is used for achieving the real-time sharing of basic information, clinical diagnosis information and inspection reports of patients; the unique identification module is used for carrying out full-life-cycle tracking on a patient sample through a bar code or a radio frequency tag and dynamically associating the patient sample with patient information; the Internet of Things monitoring module is used for collecting and uploading temperature and humidity parameters of the sample storage environment in real time and triggering abnormal early warning; the automatic storage equipment executes sample classified storage and calling based on a platform instruction, and synchronously updates a sample state with the information system; and the data security module is used for realizing data encryption, dynamic authority management and operation traceability by adopting role-based access control and a block chain technology. According to the technical scheme, the core problems of data islands, low operation efficiency, weak safety and the like in traditional medical management can be systematically solved.

The invention discloses a lingual surface diagnostic information acquisition, diagnosis and analysis method and system, and belongs to the technical field of medical auxiliary diagnos.The method comprises the steps that an acquired lingual surface image is matched with patient information, a symptom associated lingual surface area is determined, the associated lingual surface area is subjected to priority division, and whether the acquired image meets a clear standard or not is judged; the method comprises the steps of constructing an image index evaluation model based on tongue vibration and image texture, performing tongue vibration, texture stability and water vapor fuzzy interference evaluation on an image needing to be processed, constructing a stable frame evaluation model, and importing vibration intensity, texture stability and a water vapor proportion into the stable frame evaluation model to evaluate image area stability. Image registration is carried out on the stable frame set, multi-frame registration and fusion are carried out based on an image stable region, region stability and processing information are recorded, a high-quality image for tongue picture analysis is generated, the image definition of a key diagnosis region is improved, and the accuracy and stability of tongue picture analysis are improved.

The invention relates to a fault judgment system, method and device based on a knowledge graph and a medium, and belongs to the technical field of reliability analysis, and the system comprises a data collection module which is used for obtaining to-be-diagnosed information, the to-be-diagnosed information comprises a device type, a working condition and real-time monitoring data of a target device, the real-time monitoring data comprises operation parameters and state information; the fault diagnosis reasoning module is used for inputting the to-be-diagnosed information into a pre-constructed knowledge graph and performing fault diagnosis on the to-be-diagnosed information by adopting a graph reasoning algorithm to obtain a reasoning result of the target equipment, and the reasoning result comprises a fault type, a fault reason and an influence factor of the target equipment; and the diagnosis result analysis module is used for evaluating the reasoning result to obtain an evaluation result, displaying the reasoning result and the evaluation result based on a visual tool, and generating a fault diagnosis report. According to the method, the accuracy of fault judgment is effectively improved, and misjudgment caused by insufficient experience in a traditional method is reduced.

The embodiment of the invention relates to the technical field of Internet of Vehicles, and particularly provides a network fault diagnosis method and system applied to Internet of Vehicles services, which can capture network state information in various communication modes by acquiring a network state operation log of a target Internet of Vehicles service system from a multi-protocol network communication server, and lay a foundation for accurate diagnosis. By mining the network fault diagnosis embedded representation set, multiple rounds of network fault diagnosis can be presented in a specific representation form, and network fault related characteristics are effectively integrated. Linkage fault diagnosis knowledge characterization is obtained in combination with a diagnosis event distribution quantification label, and the incidence relation among all rounds of diagnosis is fully considered, so that diagnosis information is more comprehensive and accurate. According to the method, the target fault diagnosis analysis algorithm is utilized to obtain the fault judgment confidence coefficient, fault features can be analyzed in a hierarchical and multi-dimensional mode, the accuracy of fault diagnosis is improved, the fault car networking service system can be reliably determined, and the efficiency and precision of network fault diagnosis of the car networking service system are integrally improved.

Techniques include a method, apparatus, system and computer-readable medium to detect, quantify and localize attacks to enhance security for time-synchronized networking. Embodiments include a diagnostic stream producer to produce diagnostic information providing evidence of a timing attack on a node of a time-synchronized network. Embodiments include a diagnostic stream consumer to consume diagnostic information, analyze the diagnostic information, and determine whether a node is under a timing attack. Other embodiments are described and claimed.

The invention discloses a diagnostic information-driven multi-modal medical record similarity retrieval system, which comprises a data acquisition module, a data processing module and a similarity retrieval module, the feature extraction module is used for performing feature extraction on the electronic medical record data; the multi-modal semantic generation module is used for generating multi-modal semantic representation; the diagnosis code processing module is used for constructing a tree structure based on diagnosis codes in medical records, performing low-dimensional representation learning on the diagnosis codes by utilizing a Poincare embedding method, and realizing similarity measurement among diagnosis sets in combination with self-supervised contrast learning; the migration module is used for migrating the similarity knowledge of the diagnosis set to the learning process of the multi-modal semantic representation, and optimizing the multi-modal semantic representation to form medical record similarity representation driven by diagnosis information; and the retrieval module is used for performing similarity matching among the medical records in the shared semantic space and completing a retrieval task of the multi-modal medical records. According to the method, the problems of multi-mode isomerism and annotation data scarcity of the electronic medical record are effectively solved.

The invention relates to the technical field of medical data processing, in particular to a lung cancer risk early warning system based on remote four diagnosis information, which comprises an acquisition module, a generation module, a calculation module, an early warning module and a correction module. According to the method, through multi-modal time sequence fusion and causal map driven closed-loop regulation and control, the more stable, interpretable and adaptive early warning capability for the lung cancer risk is realized, heterogeneous evidences from observation, listening and interrogation are aggregated into a dynamic alarm index according to time and causal paths, instantaneous noise is filtered by using trend consistency and adversarial test, and the early warning effect is improved. Therefore, false alarms caused by short-time fluctuation are remarkably reduced while the sensitivity to real abnormity is kept; the method achieves balance among false alarm reduction, missed diagnosis risk reduction, positive prediction value improvement and user sampling burden optimization, and effectively solves the problem of insufficient early warning accuracy caused by risk identification lag due to dependence on single static medical record information.

The invention discloses a deep learning-based valve opening and closing state recognition and diagnosis method, which comprises the following steps of: acquiring and synchronizing multi-source signals, and generating a standardized multi-mode time sequence sample; extracting modal features by multiple branches and fusing the modal features into a joint feature vector sequence; the combined features are input into a phase change layered decoder, and a layered recognition result is output; constructing a plurality of types of abnormal events in a point process layer modeling phase change stage, and outputting an event modeling result; constructing a condition reversible generation diagnostor, and outputting a consistency checking result; and fusing a result output state and diagnosis information, and executing alarming and filing. Through multi-mode deep learning feature fusion, phase change hierarchical decoding and conditional modeling, accurate recognition of the opening and closing state of the valve, fine division of the phase change stage and intelligent diagnosis of early faults are achieved.

The invention relates to a cloud-edge collaborative medical image intelligent diagnosis method and device, equipment and a medium. The method comprises the following steps: firstly, obtaining original image data, and carrying out calibration processing on the original image data through a preset multi-modal image standardization model to obtain a standard image data set; performing feature extraction and grading processing on the standard image data set at an edge end to obtain a grading feature set, transmitting the grading feature set to a cloud end through a bandwidth allocation strategy, and generating a cloud end receiving feature subset; constructing a feature expression matrix based on the cloud receiving feature subset, using a network topology structure to carry out graph analysis, and extracting a path to generate a diagnosis path set; and finally, feedback diagnosis information is formed based on the diagnosis path set, and a task allocation optimization result is obtained by dynamically adjusting a cloud side task allocation proportion. According to the method, efficient processing and diagnosis optimization of medical image data are realized, efficient collaboration of cloud edge resources is ensured, timeliness and accuracy of medical image diagnosis are effectively improved, and resource scheduling requirements in different scenes are met at the same time.

The invention discloses a non-tracking freestyle three-dimensional ultrasonic image reconstruction method based on deep learning. The method comprises the following steps: obtaining an image data set; constructing an ultrasonic sequence coding module; constructing a wavelet transform convolution module; constructing a mixed attention module; constructing a loss function; constructing a deep learning model by combining an ultrasonic sequence coding module, a wavelet transform convolution module and a mixed attention module, and training the deep learning module by using the training set to obtain a three-dimensional ultrasonic image reconstruction model; scanning a diagnosis and treatment area of a patient by using an image acquisition system and adopting a non-linear scanning track to obtain two-dimensional ultrasonic image sequence data; and inputting the two-dimensional ultrasonic image sequence data as input data into a three-dimensional ultrasonic image reconstruction model for image reconstruction, and outputting a high-quality three-dimensional ultrasonic image. According to the method, the efficiency and the accuracy of three-dimensional reconstruction of the ultrasonic image can be remarkably improved, the adaptability and the robustness of processing the ultrasonic image under different conditions can be improved, and more accurate diagnosis information can be provided for clinic.

The invention provides an intelligent medical multi-round dialogue diagnosis reasoning method and system based on deep learning, and relates to the technical field of deep learning, and the method comprises the steps: constructing time sequence features through employing an attention mechanism for time sequence symptom description, and carrying out bidirectional sequence modeling to generate comprehensive symptom features; using the medical knowledge graph to detect logic contradictions and information loss to generate standardized features; calculating information gain of an inquiry direction based on a deep neural network to generate optimal inquiry content; and iteratively updating according to user feedback until the diagnosis information entropy is lower than a threshold value, and outputting a diagnosis result. According to the invention, the accuracy and efficiency of medical diagnosis are improved.

The invention belongs to the technical field of artificial intelligence, and relates to a distributed photovoltaic system abnormity monitoring and intelligent early warning method. Key parameters of the distributed photovoltaic system are collected at high frequency, noise filtering and signal normalization processing are carried out on data, then multi-domain feature extraction is carried out, anomaly detection is carried out through auto-encoder reconstruction error analysis, convolutional neural network feature classification and a single-class support vector machine, and an anomaly detection result is output. Establishing a multi-dimensional anomaly evaluation model, and synthesizing obtained evaluation results of all dimensions to obtain a final evaluation result; the method comprises the following steps: carrying out hierarchical classification to identify specific anomaly types, carrying out diagnosis of different types of anomalies, identification of typical anomalies and evaluation of fault severity, outputting the specific anomaly types and severity, and providing detailed diagnosis information for early warning decision making. The system state is comprehensively evaluated, corresponding measures are taken according to the grading early warning mechanism and the abnormity severity degree, and complete distributed photovoltaic system abnormity monitoring and intelligent early warning are formed.

The embodiment of the invention discloses an electronic medical record generation method and device, electronic equipment and a storage medium. The method comprises the following steps: in response to an electronic medical record generation instruction for a skin inquiry service, obtaining interaction content between a provider and an acquirer of the skin inquiry service and a deep learning model for realizing electronic medical record generation; acquiring skin data of the skin lesion part inquired by the acquirer; wherein the skin data comprises at least one of a skin image and a skin medical report; and generating an electronic medical record corresponding to the skin inquiry service according to the deep learning model, the interaction content and the skin data. According to the technical scheme provided by the embodiment of the invention, on the basis of the interactive content, the skin data capable of providing main auxiliary diagnosis information for a doctor is combined, so that the deep learning model can be utilized to process the multi-modal information (namely, the interactive content and the skin data) to generate the electronic medical record; therefore, the generation quality of the electronic medical record for the skin inquiry service is improved.

The invention discloses a visual obstetrical image examination processing method, which belongs to the technical field of obstetrical image examination processing, and comprises an image acquisition module, an image processing module, a cyclic influence evaluation module and an output and diagnosis module, the image acquisition module is utilized to acquire and preliminarily process a current obstetrical image, and the current obstetrical image is transmitted to the image processing module; an image processing module is utilized to sequentially calculate and output an image quality evaluation index V, a processing effect enhancement index CZ and a cyclic influence evaluation index CF, and based on the result of the cyclic influence evaluation index CF, the cyclic influence evaluation module is utilized to trigger adjustment of an image processing strategy according to the result of the processing effect enhancement index CZ. According to the method, the image quality and the diagnosis accuracy are improved by comprehensively and quantitatively evaluating the image quality, adjusting the processing method in a personalized manner and introducing a cyclic influence mechanism, so that richer diagnosis information and more accurate decision support are provided.

The invention discloses a multi-modal medical image fusion system based on deep learning, and relates to the technical field of medical images, and the system comprises an image collection module which obtains medical image data from different modalities; the data preprocessing module is used for carrying out denoising, standardization, image enhancement and registration on the acquired image data; the deep learning fusion module is used for carrying out feature extraction and information fusion on the image data of different modalities based on a convolutional neural network; the diagnosis reasoning module is used for evaluating potential disease information in the medical image data through a trained classification and regression model based on the fused image data; and the visualization module is used for visually displaying the processed image data and the diagnosis result and providing an image and a diagnosis report. According to the method, the limitation of a single mode is overcome by fusing the multi-mode images, more comprehensive and accurate diagnosis information is provided, feature extraction and fusion are performed by adopting the convolutional neural network, and the disease recognition precision is improved.

The invention relates to the technical field of hepatitis diagnosis and treatment management, and discloses a block chain-based hepatitis patient full-period management method. The method comprises the following steps: creating a patient identity chain in a block chain network, and storing patient registration data and initial diagnosis information; full-cycle diagnosis and treatment data of a patient are obtained and divided into clinical examination, iconography, medication records and life sign monitoring data. Performing multi-dimensional cleaning on the clinical examination data to generate a standardized data set; inputting the iconography data into a pre-trained hepatitis feature extraction model, and outputting a liver injury feature set; verifying the integrity of the medication record, and generating a non-tampering medication time sequence chain; comparing the life sign monitoring data with preset health reference parameters to generate a sign deviation coefficient set; according to the method, data safety and traceability are guaranteed through the block chain, diagnosis and treatment data quality is improved through multi-dimensional data processing, patient privacy is protected, and reliable support is provided for full-period management of hepatitis patients.

The invention relates to the technical field of heating, ventilation and air conditioning control, in particular to an intelligent laboratory air quality control system and method.The intelligent laboratory air quality control system comprises a data acquisition module, an intelligent decision module, a fault diagnosis module and an execution control module; meanwhile, feedback type multi-target collaborative operation parameters are generated through a plurality of heterogeneous strategy models; the fault diagnosis module analyzes equipment health data through an AI health degree baseline model, and outputs diagnosis information as a dynamic constraint; and the execution control module fuses the compensation control sequence, the cooperative parameters and the diagnosis information, generates a final physical control instruction verified by a safety boundary rule, and drives a heating ventilation air conditioner execution mechanism. According to the invention, through a control strategy integrating prediction, optimization and diagnosis, the stability of the laboratory environment, the operation economy and the long-term operation reliability of the system are significantly improved.

A diagnostic device is configured to capture diagnostic information about a subject (e.g., the subject's ears). The diagnostic device may include a diagnostic extension housing diagnostic elements (e.g., imaging chip, light sources, or lenses) and an ear canal engagement section coupled to the diagnostic extension. The ear canal engagement section may be shaped to position the diagnostic extension superiorly within the ear canal. For example, one side of the ear canal engagement section may be longer than another, which may enable the ear canal engagement section to contact the inferior wall of the ear canal for a longer distance than it contacts the superior wall. This configuration may allow more movement of the diagnostic extension when the device is inserted into the ear, which further improves the alignment of diagnostic elements with a desired area of the ear for diagnoses (e.g., the eardrum).

The embodiment of the invention discloses a medical image AI processing method, a work station, a medium and equipment, and the method comprises the steps: obtaining a to-be-processed MRI image through the work station, and forming a first image set; subsequently, the workstation performs similarity detection on the MRI images in the first image set, and divides them into a plurality of image groups based on structural or feature similarities between the images. The process can effectively identify images with repeated or highly similar contents and construct structured image distribution. Next, the workstation further screens images with significant difference between two adjacent groups, removes redundant information, only retains images with strong representativeness and rich diagnostic information, and forms a processed image set; finally, an image analysis process is performed on the processed image set to obtain a structured MRI image analysis result. Through the similarity grouping and screening mode, the image processing number is effectively reduced, the calculation burden is relieved, and the image analysis efficiency and the workstation response capability are improved.

A Predictive Diagnostic Information Capability-Technology (PreDICT™) system (100) enables users including expert and nonexpert users to provide information regarding a condition of a subject and receive timely and accurate information regarding risk stratification, treatment options and other medical evaluation information. The illustrated system (100) generally includes a user device (102) for use by a user assisting a subject (104), a processing platform (108), and a network (106) for connecting the user device (102) to the processing platform (108). The system (100) may also involve an emergency response network (130) that includes public-safety answering points (PSAPs) (132). The processing platform (108) processes the sensor information and other information from the user device (102), determines risk stratification information as well as medical diagnosis and treatment option information based on machine learning technology, and provides output information to the user device to assist the user in treating the subject (104).

The invention discloses a method and a system for centralized examination and verification of prescriptions based on an integrated medical system, when a doctor makes a prescription, a regional prescription centralized examination and verification platform is utilized to automatically obtain diagnosis information of a patient, and the medication consistency is judged by comparing examination and verification characteristics of the made medicines one by one by a prescription examination pharmacist. Wherein the centralized auditing platform comprises a patient clinical information acquisition module, a centralized auditing module, a knowledge base support module, a prescription generation and record management module, a prescription information query module and an analysis and report module which are connected in sequence. According to the scheme, data sharing of all levels of medical institutions in the medical combination body is gradually achieved, medical resources are utilized to the maximum degree, the existing pharmacist resource value is fully achieved, the reasonable medication level in the medical combination body area is improved, comprehensive butt joint and communication of data of pharmacy, medical service, medical security, medicine supply and the like are achieved, and therefore pharmaceutical prescription information sharing application is promoted; and high-quality further development of new-era pharmaceutical services is promoted.

The embodiment of the invention discloses a diagnosis method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring historical driving data of a target vehicle; acquiring historical diagnosis data of a first vehicle part of the target vehicle; according to the historical diagnosis data and the historical driving data, the reference diagnosis frequency of the first vehicle part in the first time period is determined; the first time period is a preset time period after the current moment; acquiring a target driving route and a target vehicle load of the target vehicle in the first time period; determining a target driving road type and a target driving weather type of the target driving route in the first time period; according to the target vehicle load, the target driving road type and the target driving weather type, the reference diagnosis frequency is adjusted, and the target diagnosis frequency of the first vehicle part is obtained; and diagnosing the first vehicle part according to the target diagnosis frequency to obtain diagnosis information of the first vehicle part. By adopting the embodiment of the invention, the diagnosis efficiency can be improved when the vehicle is diagnosed.

The invention provides a craniocerebral ultrasonic image information processing method, which is executed by a computer or a special processing device and comprises the following steps of: receiving craniocerebral ultrasonic image data; performing image enhancement processing on the received craniocerebral ultrasonic image data; analyzing the data after image enhancement processing by using a deep learning model based on a convolutional neural network so as to automatically detect an area indicating a lesion in the image data and identify information indicating a lesion type; according to the analysis result, analysis result data containing information indicating the position, size, form and the like of the lesion area is generated; according to the analysis result data, generating suggestion data used for assisting in obtaining the diagnosis information; and outputting the analysis result data and the suggestion data. The problems of low diagnosis accuracy, low automation degree, insufficient data processing capability and the like in the prior art are effectively solved, auxiliary diagnosis information is provided for clinical doctors, and the doctors can make diagnosis decisions more quickly and more accurately.

The present invention relates to an AI-based system and method for generating enhanced radiology reports. The system comprises a database for storing multimodal patient data, a natural language processing (NLP) module for extracting clinical information, and a machine learning module for correlating the clinical information with radiology images to identify diagnostic insights. An AI-based report generation module analyzes the images and clinical information to generate a preliminary report, which is refined based on radiologist input. The generated report is then integrated into the patient's electronic health record. The system employs techniques such as multimodal deep learning, active learning, explainable AI, and federated learning to enhance diagnostic accuracy, capture expert feedback, provide transparency, and enable multi-institutional collaboration. The invention aims to improve the accuracy, efficiency, and value of radiology reporting in patient care.

The invention relates to a transformer substation switch state monitoring system based on centralized monitoring, and relates to the technical field of power system automation, and the system comprises a data collection module which is used for integrating multi-source heterogeneous data; the state judgment module outputs the unique real-time comprehensive state of each switch according to the communication state, the alarm condition function and the multi-stage judgment logic of the original remote signaling; the outage diagnosis module is used for classifying events when the switch trips and automatically screening event sequence records (SOE) to position core diagnosis information; the influence domain pre-judgment module is used for determining all potential influenced switch sets through a graph traversal algorithm on the basis of a power grid topological structure when accident tripping occurs; and an interface generation module. According to the invention, through automatic integration and visual linkage of the operation state, the fault reason and the influence range of the switch, the monitoring intuition, the diagnosis accuracy and the emergency disposal decision-making efficiency are significantly improved.

The invention discloses a drug recommendation method based on a beta variational auto-encoder, and relates to the technical field of digital medical treatment, and the method comprises the steps: carrying out the extraction and fusion enhancement of diagnosis information and operation information in treatment record data through the combination of a GRU network and a bidirectional attention mechanism; patients and drugs are regarded as nodes, and drug use records of the patients are regarded as hyperedges, so that a heterogeneous hypergraph is constructed, drug combination information, patient individual differences and a complex association relationship between the patients and the drugs are reserved to the maximum extent, and the model is more accurate when processing new patients and rare illness conditions. A global network structure can be better utilized to find a potential medicine combination mode; a beta variational auto-encoder architecture is adopted to obtain potential features of a patient and a drug, and a drug combination required by the patient is predicted according to the embedded features of the patient and the embedded features of the drug, so that the stability of the model is improved.

The invention provides an electroencephalogram data transmission method combining lossless and lossy compression, belongs to the field of electroencephalogram data transmission and compression processing, and is used for solving the problems of low transmission efficiency of pure lossless compression and easy loss of key diagnosis information of pure lossy compression in related technologies. According to the method, multi-dimensional features of electroencephalogram data are extracted, a key area and a non-key area are divided, lossless compression and lossy compression processing are adopted respectively, the area division proportion is dynamically adjusted in combination with transmission bandwidth, signal features and application scenes, and corresponding data are transmitted through dual protocols. The transmission efficiency can be improved while the clinical diagnosis marker and the core characteristics are reserved, and the scene adaptability and the transmission reliability are achieved at the same time.

The invention provides an AI-driven new energy equipment fault early warning and self-healing method and system, and the method comprises the steps: obtaining multi-source physical data of new energy equipment, carrying out the preprocessing, and extracting a key feature quantity reflecting the health state of the new energy equipment; inputting the key characteristic quantity into a constructed diagnosis model for fault diagnosis to obtain a diagnosis result; if the diagnosis result shows that a fault exists, logic judgment is conducted according to the diagnosis result and a preset intelligent self-healing strategy, and whether self-healing operation is allowed to be executed or not is determined; if the self-healing operation is allowed to be executed, a standardized control instruction is generated and sent to an equipment executing mechanism, and the equipment executing mechanism is driven to complete the self-healing operation; and if the self-healing operation is not allowed to be executed, the diagnosis information, the early warning report and the disposal suggestion are pushed to the operation and maintenance monitoring end, so that accurate prediction, fusion diagnosis and safe self-healing of the fault are realized, and the operation and maintenance efficiency and the equipment reliability are improved.