400 results about "Preliminary diagnosis" patented technology

The invention relates to the technical field of data processing, and discloses an intelligent inspection and fault positioning method and system for power distribution equipment. The method comprises the following steps: performing multi-type sensor deployment on key points around power distribution equipment to obtain an intelligent sensor network; performing multi-mode acquisition and multi-path transmission processing on data acquired by the intelligent sensor network to obtain an original monitoring data set; cleaning, standardizing and fusing the original monitoring data set to obtain preprocessed structured data; performing multi-dimensional spatial correlation analysis on the structured data to obtain health state evaluation data of the power distribution equipment; performing dynamic threshold calculation and intelligent diagnosis on the power distribution equipment to obtain early warning information and a preliminary diagnosis result; and performing expert knowledge base matching and multi-criterion decision analysis on the early warning information and the preliminary diagnosis result to obtain a maintenance decision suggestion scheme. According to the invention, the accuracy of intelligent inspection and fault positioning of the power distribution equipment is improved.

The invention relates to the technical field of data processing, and discloses a road infrastructure health monitoring and evaluation method and device, equipment and a medium. The method comprises the following steps: performing multi-mode acquisition and multi-path transmission processing on data acquired by an intelligent sensor network to obtain an original monitoring data set; cleaning, standardizing and fusing the original monitoring data set to obtain preprocessed structured data; performing multi-dimensional spatial correlation analysis on the structured data through a multi-scale time sequence analysis algorithm to obtain health state evaluation data of the road infrastructure; based on the health state evaluation data, performing dynamic threshold calculation and intelligent diagnosis on the road infrastructure to obtain early warning information and a preliminary diagnosis result; and performing expert knowledge base matching and multi-criterion decision analysis on the early warning information and the preliminary diagnosis result to obtain a maintenance decision suggestion and an optimization scheme. The road infrastructure health monitoring and evaluation efficiency and accuracy are improved.

The invention provides a remote fault diagnosis method and system for operation and maintenance of a power grid. The method belongs to the technical field of power system operation and maintenance, and comprises the following steps: collecting operation state data of power grid equipment in real time through a sensor network, and carrying out real-time processing and preliminary analysis on the operation state data through an edge computing node; uploading the preliminary diagnosis result of the edge computing node and the key sensor data to a block chain platform, and storing and verifying the preliminary diagnosis result and the key sensor data through a distributed account book of a block chain; the operation state data of the power grid equipment are collected in real time, and the edge computing nodes are used for preliminary analysis, so that potential fault signals can be quickly found.

The embodiment of the invention relates to the technical field of image processing, in particular to a TP laminating process production process defect diagnosis method and system based on image analysis, and the method comprises the steps: firstly collecting a real-time image sequence corresponding to a to-be-detected laminating assembly continuously transmitted on a TP laminating process production line; the sequence comprises component surface images and edge region images at different fitting stages; secondly, performing defect sensitive feature enhancement processing on the real-time image sequence to obtain a defect sensitive feature set containing surface texture features, edge contour features and regional gray features; then performing feature correlation analysis processing on the defect sensitive feature set through a trained TP fitting defect diagnosis model to generate a defect preliminary diagnosis result; and finally, determining defect types and position distribution information according to the preliminary diagnosis result, and further generating a production process defect diagnosis report containing defect diagnosis contents, thereby realizing accurate diagnosis of the TP laminating process production process defects.

The invention discloses a distribution network cable fault early warning method and system based on multi-terminal synchronous traveling wave detection, and relates to the technical field of cable fault detection. The method comprises the following steps: deploying at least three synchronous detection ends at each branch end point of a cable, and synchronously triggering a sampling rate; establishing a master-slave synchronous network, and performing frequency spectrum correction on the acquired data; constructing a real-time correction model based on the data of the distributed temperature sensor; extracting early fault features, and constructing a three-dimensional feature tensor; carrying out preliminary diagnosis on edge nodes and carrying out feature classification by adopting a transfer learning optimization network; and in combination with the cable load rate and the environment temperature and humidity, an early warning threshold self-adaptive model based on fuzzy PID (Proportion Integration Differentiation) is established. Through multi-terminal cooperative perception and intelligent analysis, the scheme converts a fault processing mode from passive first-aid repair to active defense, improves the manual inspection efficiency of the distribution network cable, and finds early fault hidden dangers in advance.

The invention discloses a power distribution equipment remote diagnosis method based on edge computing, and particularly relates to the technical field of intelligent monitoring of power equipment, and the method comprises the steps: an edge computing node collects the operation state data of the power distribution equipment in real time; performing diagnosis analysis locally at the node to generate a preliminary diagnosis result and key data; uploading the structured data to a cloud according to a preset strategy, and checking the integrity; and the cloud platform performs association analysis on the multi-node data to identify common anomalies, dynamically optimizes a diagnosis algorithm, automatically triggers alarms and work order distribution in a grading manner, and realizes rapid closed-loop processing in combination with the positions and skills of operation and maintenance personnel. Through cooperation of the edge and the cloud, communication bandwidth occupation is reduced, diagnosis accuracy and real-time performance are improved, fault response time is shortened, and the method is suitable for line-level monitoring and operation and maintenance management of the power distribution network.

The invention discloses a defect diagnosis method and system based on multi-modal data cooperative training, and belongs to the technical field of defect diagnosis, and the method specifically comprises the steps: constructing a multi-modal cooperative diagnosis model comprising a feature extraction sub-network and a cross-modal attention module; after multi-modal data is collected and preprocessed, initial features are obtained through the feature extraction sub-network, attention weights are generated through the cross-modal attention module, weighted multi-modal features are obtained, multi-scale fusion features are obtained through the multi-scale feature extraction sub-network, and a preliminary diagnosis result is given through cascade processing. Meanwhile, the data integrity is detected, and a modal missing scene is coped with through cascade collaborative diagnosis; and finally, comparing the two types of diagnosis results with a defect labeling sample to obtain a multi-modal collaborative diagnosis model after training optimization, inputting to-be-diagnosed sample data, outputting a final diagnosis result and updating the defect labeling sample, and realizing efficient and accurate defect diagnosis.

The invention relates to the technical field of equipment operation and maintenance intelligent retrieval, and provides an equipment operation and maintenance data enhanced retrieval method based on a knowledge graph and a large language model, which comprises the following steps: (1) constructing and dynamically maintaining the knowledge graph containing equipment operation and maintenance field entities and relationships; (2) analyzing natural language query of a user, performing multi-hop association retrieval in the knowledge graph, and screening out a related evidence set; (3) constructing a structured cue word based on the evidence set, and driving a large language model to generate a preliminary diagnosis report; and verifying, correcting and formatting the preliminary diagnosis report into a final visual report. According to the method, deep intention understanding and multi-hop association mining of natural language query of a user are realized by constructing a dynamically evolved equipment operation and maintenance knowledge graph, and a reliable evidence chain and a structured cue word constraint mechanism based on the knowledge graph are introduced to ensure that the generated diagnosis report is strictly based on field professional knowledge; the problem that the real semantic intention of user query cannot be deeply understood in traditional retrieval is effectively solved.

The invention provides a small sample tympanic membrane image recognition method based on meta-prompt and knowledge driving, and the method comprises the steps: inputting a small sample training set into an initial multi-mode pre-training model, obtaining a prediction category, comparing the prediction category with a real category, and screening misclassification samples in combination with confidence to construct a meta-task set; and inputting the meta-task set into the primary diagnosis model, and outputting a primary diagnosis report. And then, optimizing the medical description text sample based on the preliminary diagnosis report by utilizing a knowledge refining model, and replacing the original text sample, so as to obtain an updated sample. And finally, iteratively training the initial multi-modal pre-training model by using the updated sample until a termination condition is met. According to the method, a closed-loop optimization system composed of a primary diagnosis model and a knowledge refining model is constructed. Under the condition of small samples, the system dynamically optimizes the visual-semantic understanding ability of the model by using error samples generated by the model, and the accuracy of small sample tympanic membrane image recognition is effectively improved.

The invention provides a medical auxiliary diagnosis method and system based on time sequence and semantic weighting, and belongs to the technical field of medical information processing. Constructing the preprocessed current and historical medical record information of the patient into patient medical record data, and inputting the patient medical record data into a pre-trained medical language model to generate a preliminary diagnosis result; the comprehensive weight of the patient medical record data vector sequence is calculated through a time decay function and the content correlation weight, a medical record fusion vector is obtained through fusion, and the medical record fusion vector and the preliminary diagnosis result are spliced into a query vector; searching candidate fragments in a clinical guide knowledge base based on the query vector, calculating semantic evidence scores and coverage scores based on patient medical record data and tokens of the candidate fragments, obtaining sorting probabilities of the candidate fragments in combination with metadata prior scores of the candidate fragments, and screening out target guide fragments; and the preliminary diagnosis result and the target guide fragment are fused to generate final auxiliary diagnosis and treatment information, so that auxiliary diagnosis and treatment suggestions with traceability, verifiability and authoritative basis are provided.

The invention discloses a distribution transformer monitoring system and method, and relates to the technical field of electric power intelligent monitoring, and the method comprises the steps: obtaining the three-dimensional temperature gradient field distribution and mechanical stress distribution of a distribution transformer based on a standardized multi-source data set, and outputting a comprehensive field matrix through a field coupling analysis method; performing spatial analysis on the comprehensive field matrix by adopting spatial hot spot analysis, identifying and marking temperature and mechanical stress abnormal areas, performing spatial aggregation and noise filtering through an OPTICS density clustering algorithm, and outputting abnormal feature vectors; inputting the abnormal feature vector into a pre-trained intelligent diagnosis model, outputting a comprehensive fault risk score and performing preliminary diagnosis; searching fault data in a historical case library based on the preliminary diagnosis result for comparison verification, and optimizing the preliminary diagnosis result through a machine learning algorithm to generate a final diagnosis result; according to the method, a field coupling analysis method is adopted to establish a heat-force bidirectional action model, and the spatial distribution characteristics of the temperature gradient and the stress tensor are accurately reflected.

A method for performing automated diagnostics, comprising processing first input data relating to a speech or text input signal to generate a representation of the first input data and generating a first output based at least in part on the representation of the input data; determining a preliminary diagnosis output comprising at least one preliminary diagnosis of the problem by processing, using a preliminary diagnosis machine learning model, the first output; determining, at the one or more processors and based at least in part on the preliminary diagnosis output, at least one dialogue system output; outputting, by way of an output of the diagnostics system, the dialogue system output.

The invention discloses a medical intelligent dialogue method based on deep learning, relates to the technical field of intelligent medical treatment, constructs a rare disease knowledge graph, and deduces implicit relations between diseases and symptoms through association relations in the graph even under the condition of insufficient training data. The ability of processing complex cases such as rare diseases is greatly enhanced, a more accurate rare disease list can be provided through a map reasoning mechanism, related diagnosis schemes are reasonably and preferentially displayed to a user, the misjudgment risk is reduced, and multi-source data such as medical record texts, medical images and laboratory detection results are effectively integrated through a multi-mode Transform model, so that the accuracy of diagnosis is improved. According to the method, a unified comprehensive feature representation is generated, the ability of comprehensively analyzing the health condition of the patient from different angles is deeply fused, the diagnosis accuracy is improved, the inquiry sequence and the problem depth can be adjusted according to the specific illness conditions of different patients based on the preliminary diagnosis result, and more accurate and personalized inquiry experience is provided.

The invention discloses a multi-parameter electrical measuring instrument self-diagnosis system based on data analysis, and particularly relates to the technical field of self-diagnosis, the system comprises a parameter acquisition module, a rule preliminary diagnosis module, an algorithm re-diagnosis module and a comprehensive judgment module, the parameter acquisition module acquires electrical parameter data of a multi-parameter electrical measuring instrument and transmits the electrical parameter data to the rule preliminary diagnosis module; the rule preliminary diagnosis module presets a diagnosis rule to diagnose an abnormal fault, the algorithm re-diagnosis module trains a neural network model according to electrical parameter data of the abnormal fault to carry out diagnosis identification, and the comprehensive judgment module synthesizes a diagnosis result based on the diagnosis rule and a neural network algorithm and generates a fault report. And the decision implementation module outputs a self-diagnosis decision according to the fault report and prompts a worker to manage and maintain an abnormal fault, so that the flexibility of the self-diagnosis system is enhanced while the self-diagnosis precision of the multi-parameter electrical measuring instrument is effectively improved, and the problems of manually adjusting rules and extracting features when the environmental working condition changes are avoided.

The embodiment of the invention provides an energy storage system fault processing method and device, equipment, a medium and a product, and relates to the field of photovoltaic power generation, the method solves the problem of low fault processing efficiency in the prior art by constructing a cloud-side-end cooperative fault processing architecture, changes the traditional static threshold judgment logic, and improves the fault processing efficiency. A decision-making mode combining multi-source information fusion and dynamic prediction is adopted, firstly, preliminary diagnosis of real-time data is achieved through edge calculation, the instantaneity of response is ensured, and then historical trend analysis, equipment health prediction and multi-dimensional real-time state evaluation are fused through a cloud platform. According to the method, the dynamic fault evaluation capable of accurately reflecting the actual severity and development trend of the fault is generated, so that the system can be adaptive to equipment aging and environment change, and a grading response strategy accurately matched with the fault grade is triggered, thereby realizing the crossing from passive alarm to active and accurate fault management and control, and improving the fault processing efficiency.

The invention provides a medical agent middleware-based full-specialized collaborative diagnosis and treatment method and system, and relates to the technical field of medical agents, and the method comprises the steps: building a basic medical knowledge graph through reinforcement learning, converting expert diagnosis and treatment experience into diagnosis and treatment probability distribution through a personalized treatment decision model, and forming an enhanced medical knowledge graph; and calculating a matching probability with a disease based on the symptom feature vector of the patient to generate a preliminary diagnosis decision, and selecting and adjusting a treatment scheme through reinforcement learning. According to the invention, general and specialized collaborative diagnosis and treatment can be realized, and medical decision accuracy and diagnosis and treatment efficiency are improved.

The invention provides a construction method and a construction system of a telemedicine expert recommendation model, an expert recommendation method and electronic equipment. The construction method comprises the following steps: acquiring sample information; extracting entities and relationships among the entities from the preliminary diagnosis information of the historical patients and the specialist specialist information of the experts; constructing a knowledge graph according to the extracted entities and the relationship between the entities; performing feature calculation on the sample information, and inputting the calculated disease relevancy and multi-granularity relevancy between multiple groups of historical patients and experts and basic features of the experts corresponding to the historical patients into a deep neural network for training to obtain an expert recommendation model; the method has the beneficial effects that more adaptive experts are recommended for patients, the accuracy and robustness of recommendation results are effectively improved, the efficiency and quality of remote consultation are improved, and the problem of uneven medical resource allocation is solved; the method is suitable for the technical field of telemedicine expert recommendation.

The invention discloses a blood filter early warning method based on multi-parameter coupling and a blood filter, and aims to solve the problems of lagging and high false alarm rate of the existing single parameter threshold alarm. The method comprises the following steps: acquiring time sequence data of at least two monitoring parameters such as transmembrane pressure and venous pressure in real time; constructing a risk state vector based on the sequential data; calculating a risk covariance matrix of the vector in a set time window so as to quantify a collaborative relationship of variation trends among parameters; performing matching degree comparison on the matrix and a pre-stored standard fault feature matrix; and generating and outputting early warning information at least indicating one equipment abnormal mode based on the comparison result. By analyzing a multi-parameter dynamic cooperative relationship instead of a single parameter absolute value, early warning and intelligent preliminary diagnosis of filter blood coagulation, pipeline abnormity and other faults are realized, and the timeliness, accuracy and clinical decision support capability of alarm are remarkably improved.

The invention discloses a wind generating set fault prediction method and system based on edge computing, and the method comprises the steps: collecting the vibration, temperature, rotating speed and other parameters of a wind generating set in real time through a data collection module deployed by edge computing equipment, carrying out the preliminary screening and packaging through a built-in data processing unit, and synchronizing to a constructed digital twinborn model; establishing an edge calculation fault analysis model based on model data, identifying potential fault features, comparing a physical unit operation state with a digital twin model simulation state parameter by parameter, performing preliminary diagnosis in combination with the fault features, evaluating a unit health state according to a fault mechanism model, and outputting a fault prediction result and part information. The system is correspondingly provided with six units including a data acquisition interaction unit, a data processing unit and a model construction updating unit, and all the units work cooperatively, so that accurate prediction and health management of faults of the wind generating set are realized.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an artificial intelligence medical diagnosis system based on multi-dimensional information fusion. Comprising the steps that a self-adaptive diagnosis path planning module judges whether a user request belongs to a preset non-diagnosis and treatment category or not, if yes, a quick response path is activated, and a standardized answer is retrieved and returned to a user; the preliminary diagnosis module generates a candidate disease hypothesis list, verifies the candidate disease hypothesis list and outputs a verified disease hypothesis list; the dynamic knowledge enhancement module generates missing knowledge according to the disease knowledge graph and the query verification disease hypothesis list and supplements the missing knowledge into the medical knowledge graph; a composite confidence evaluation module performs confidence evaluation on each hypothesis disease in the verification disease hypothesis list, and outputs a final disease confidence; the result integration module sorts the final disease confidence in a descending order and integrates the multi-dimensional information of each hypothetical disease to generate a structured differential diagnosis report; the system and the method can assist doctors in realizing high-accuracy, high-reliability and explainable intelligent medical diagnosis.

The invention discloses a power grid fault diagnosis system and method based on big data, belongs to the technical field of fault diagnosis, and solves the technical problems that in the prior art, only a line section where a fault occurs can be diagnosed, the type of the fault cannot be judged, and subsequent fault processing is not facilitated. The method comprises the following steps: constructing a fault anomaly library through historical data, and analyzing real-time data based on the fault anomaly library to obtain a preliminary diagnosis result; technicians can prepare in advance; integrating the real-time data into an index feature sequence, calling a fault diagnosis model, and inputting the index feature sequence into the fault diagnosis model to obtain a fault tag; matching a corresponding fault type based on the fault tag; the accuracy and the reliability of diagnosis can be improved; fault position information is obtained according to the data sensor and fault type analysis, and a technician closest to the fault position is dispatched to process the fault; and the fault can be timely processed, and greater influence caused by untimely processing can be avoided.

The invention provides a multi-sensor electric power intelligent monitoring system, and belongs to the field of electric power monitoring. The system comprises a data processing system, a monitoring center server and a plurality of edge computing nodes deployed on different power devices, and the data processing system comprises a multi-source data acquisition module used for acquiring operation data of the power devices; the fault diagnosis module is used for constructing a distributed knowledge graph according to the basic data and the historical operation data of the power equipment, and storing the constructed knowledge graph in each edge computing node and the monitoring center server in a scattered manner so as to perform preliminary diagnosis on the fault of the power equipment; the early warning module is used for mining a data feature association relationship through a quantum heuristic algorithm and establishing a dynamic early warning mechanism; and the life prediction module is used for predicting the residual life of the power equipment based on the chaos theory and the phase-space reconstruction technology. According to the invention, the problems of low data processing efficiency, inaccurate fault diagnosis, untimely early warning and the like in traditional power monitoring are solved.

The invention discloses a case analysis method and system based on a large language model, and belongs to the technical field of case analysis, and the method comprises the following steps: S1, patient information input, S2, examination item recommendation and determination, S3, preliminary diagnosis generation, S4, case screening and diagnosis and treatment suggestion generation, S5, diagnosis and treatment process monitoring and adjustment, and S6, case report generation and viewing. By means of the powerful knowledge integration and reasoning ability of a large language model, in combination with massive historical case data and professional medical database information, multi-dimensional diagnosis thought reference is provided for doctors, the misdiagnosis and missed diagnosis risks caused by personal experience limitation are reduced, the health indexes of patients can be monitored, and the diagnosis accuracy is improved. Personalized adjustment is performed on the diagnosis and treatment plan according to the disease change and the diagnosis and treatment condition of the patient; the medical database can dynamically expand and correct contents; diagnosis and treatment plans and suggestions can be given according to diagnosis and treatment passes of previously cured patients with similar conditions.

The present invention discloses a pre-consultation system for gastroenterology based on a vector database and a large language model, comprising a user input interface module, a natural language processing module, a vectorization module, a vector database, a similarity judgment module, a precision ranking module, and a user feedback interface module; natural language processing technology is used to better understand patients' complex and ambiguous symptom descriptions, and accurate preliminary diagnosis suggestions are provided; a large language model is used for semantic analysis, and the accuracy and efficiency of the pre-consultation system are improved by improving vectorization representation, similarity calculation, and precision ranking models, thereby optimizing the pre-consultation process for gastroenterology diseases; the present invention also discloses a pre-consultation method for gastroenterology implemented based on the system.

According to some embodiments disclosed in the present document, a battery diagnosis apparatus may comprise: a sensor for measuring battery test data from a battery to be diagnosed; and a processor for dividing the battery test data into a plurality of charging / discharging state datasets on the basis of a plurality of reference values related to a plurality of charging / discharging states, performing preliminary fault diagnosis on each of the plurality of charging / discharging state datasets on the basis of a preliminary diagnosis model, and, on the basis of the result of the preliminary fault diagnosis, making a final determination that the battery to be diagnosed has a fault.

The invention provides a septal tumor accurate diagnosis system based on artificial intelligence, a multi-scale attention fusion module receives multi-modal data, focuses a septal region, dynamically adjusts the weight and outputs a key feature vector, an adversarial self-supervision pre-training module digs tumor morphological features based on the vector, robustness and generalization ability are enhanced, and the accuracy of diagnosis is improved. And the causal intervention diagnosis decision-making module constructs a decision-making tree by using enhanced features to perform preliminary diagnosis and transmits causal information to the meta-learning enhanced diagnosis module, and the meta-learning enhanced diagnosis module adjusts parameters by using a small number of samples for rare subtypes, outputs a final diagnosis result and feeds back the final diagnosis result. According to the system, multi-mode and dynamic attention, cooperative confrontation self-supervision pre-training and causal reasoning are fused, rare tumor diagnosis is optimized through meta-learning, a closed-loop feedback and shared knowledge graph is formed, and the diagnosis precision, interpretability and rare case diagnosis capacity are integrally improved.

The invention discloses a machine fault maintenance method and system based on multi-source data fusion. The method comprises the following steps: S1, collecting a vibration time sequence signal, a temperature time sequence signal, an equipment state parameter signal and a historical maintenance text signal of a machine; s2, generating vibration and temperature feature vector signals, and generating text feature vector signals; s3, constructing a multi-source feature dynamic fusion module, and splicing the vibration and temperature feature vector signal, the equipment state parameter signal and the text feature vector signal; s4, inputting the weighted fusion feature signal into a fault diagnosis model, and outputting a preliminary diagnosis signal containing fault type prediction and corresponding confidence; and S5, constructing a decision arbitration module, and when the uncertainty quantization signal is lower than a preset threshold value, outputting a preliminary diagnosis signal as a final maintenance decision signal. According to the multi-source data fusion-based machine fault maintenance method and system, the problems of low fault prediction accuracy and unreliable maintenance decision of industrial equipment can be solved.

The invention discloses an informatization process processing method for a thoracic pain stroke trauma treatment mode, and the method comprises the following steps: 1, information collection and preprocessing: collecting the physiological sign data of a patient through a mobile terminal, and analyzing the physiological sign data based on a deep learning model, so as to obtain a preliminary diagnosis result of an illness state; classifying and marking the patient according to the preliminary diagnosis result of the illness state to obtain a classification and marking result, and uploading the classification and marking result, the physiological sign data and the geographic position information of the patient to a regional medical collaborative server; step 2, multi-center cooperative distribution: after receiving the classification marking result, the physiological sign data and the geographic position information of the patient, a regional medical cooperative server performs calculation processing based on a preset medical resource scheduling model; and step 3, performing a treatment process: sending the optimal treatment path scheme to a medical information system of a target medical center by the regional medical cooperation server, triggering the target medical center to start an emergency plan, and deploying medical resources.

The invention relates to a smart hospital collaborative diagnosis and treatment management method and system, and the method comprises the steps: receiving the appointment registration information of a patient, building a data mapping relation with a hospital information system based on the appointment registration information, and generating a corresponding treatment identifier; after the patient arrives at the hospital, information of the sign-in patient is collected, a sign-in sequence is established, and according to appointment registration information and diagnosis and treatment service data parameters corresponding to the sign-in patient, a treatment serial number is dynamically distributed and a waiting prompt is pushed; after the doctor receives the diagnosis, generating an examination item list based on the preliminary diagnosis information input by the doctor, obtaining the real-time state and queuing data of equipment required by each examination in the examination item list, constructing an execution time window of examination items, generating an optimal examination sequence, and pushing an examination guide path to the patient terminal; and after the patient finishes seeing a doctor, generating a personalized propaganda and education task list according to the diagnosis and treatment information of each stage, and pushing the personalized propaganda and education task list to the patient terminal. The method has the effect of improving the hospital operation efficiency.

The invention discloses an intelligent power distribution network self-healing control method and system, and relates to the technical field of power grid self-healing control, and the method comprises the steps: carrying out the data collection and processing based on a first method; real-time fault detection and preliminary diagnosis are carried out through the first equipment; and controlling the self-healing operation to execute feedback optimization. According to the intelligent power distribution network self-healing control method provided by the invention, through wired and wireless multi-mode data acquisition, and through real-time fault detection and preliminary diagnosis, the system can accurately position the problem and quickly identify the fault type at the initial stage of fault occurrence, and the fault response time is greatly shortened. According to the method, effective identification of fault types is ensured, the subsequent self-healing strategy of the system is more accurate, the protection and recovery capability of the power grid is enhanced, and better effects are achieved in the aspects of accurate fault positioning and response, safety and resource utilization efficiency.