226 results about "Remote diagnosis" patented technology

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 verifiable medical data query method based on a DAG block chain architecture, and belongs to the technical field of block chains and medical information management. According to the method, medical events such as diagnosis and treatment records and image reports are modeled into DAG nodes, an on-chain topology with time sequence and causal association is constructed by encrypting a hash pointer, dynamic indexing and multi-keyword verifiable query are achieved in combination with an off-chain authentication data structure, a verification object containing a matching node set and a verification path is received, and the verification object is subjected to dynamic indexing and multi-keyword verifiable query. The authenticity, integrity and timeliness verification of the result is independently completed based on the authentication root stored on the chain without depending on a third party. According to the method, through an on-chain and off-chain collaborative storage and incremental authentication updating mechanism, on the premise of ensuring privacy and tamper-proofing of medical data, query efficiency, verification performance and expandability under high concurrency are remarkably improved, communication and resource expenses are lower, and the method is suitable for large-scale popularization and application. The method is suitable for application scenes such as cross-institution medical data sharing, remote diagnosis and treatment and compliance auditing.

The invention belongs to the technical field of image processing, and discloses a remote digital image analysis cooperation system. The virtual diagnosis module is used for collecting a continuous pathological section sequence and carrying out space registration to obtain a registration section; the model building module is used for performing three-dimensional voxel reconstruction on the registration slices to obtain a three-dimensional voxel reconstruction model, and performing semantic enhancement to obtain a three-dimensional pathological voxel model; the collaborative interaction module is used for generating virtual avatars of G experts based on the VR technology, labeling the three-dimensional pathological voxel model and obtaining space anchor point labeling data of the experts; the graph driving module is used for constructing a structured semantic tree according to the space anchor point annotation data, detecting annotation conflicts and obtaining an annotation conflict detection result; the conflict resolution module is used for performing conflict resolution on the marking conflict detection result to obtain a consensus suggestion; the collaborative efficiency and conclusion reliability of remote diagnosis are improved, and a systematic solution is provided for precise diagnosis of complex pathological cases.

The invention discloses a fault remote diagnosis and digital process maintenance method for water treatment equipment, and relates to the field of water treatment equipment operation and maintenance. The method comprises the steps of unifying time and semantics, generating a quality marking time sequence, and performing working condition benchmark and tracing; according to self-adaptive identification mechanism parameters, constructing a digital twinning and trusted domain, and comparing to obtain a residual error and anomaly in real time; forming root cause evidence and sorting under topology and energy and material closed constraints; generating scripts and rollback, gray scale issuing, sentry monitoring and transaction records through steady / transient simulation and constraint checking; and convergence, energy consumption, chemical dosing, water quality evaluation, precipitation versioning template recharging and closed-loop updating are carried out. Through unified working condition data and digital twinning mechanism-data fusion diagnosis, simulation-driven gray reversible execution and knowledge template recharging under quantitative evaluation, a traceable, reusable and migratable full-link remote diagnosis and process maintenance closed loop is constructed.

The invention belongs to the technical field of battery health detection, and particularly relates to a remote diagnosis and maintenance guiding system for a vehicle battery pack box. The remote diagnosis and maintenance guidance system comprises a data acquisition edge processing module, a multi-mode communication and cache transmission module, a composite fault diagnosis module, a sensor self-calibration and environment compensation module, a cloud big data analysis and model iteration module, a remote maintenance guidance and AR visualization module and a system monitoring module. According to the invention, through multi-mode communication and edge caching, data is ensured not to be lost in an extreme environment; a composite fault is identified through a fusion diagnosis algorithm, and maintenance direction deviation is avoided; according to the invention, a sensor self-calibration and compensation mechanism is provided, false alarms caused by low temperature / high humidity are inhibited, and the accuracy of battery data monitoring is improved.

The invention provides a TBOX-based vehicle fault diagnosis system, which is characterized in that a TBOX is installed in a vehicle and is used for acquiring vehicle data and judging whether the vehicle has a fault based on the vehicle data, and when the vehicle has the fault, the TBOX enters a diagnosis and maintenance mode, and the diagnosis and maintenance mode comprises a field diagnosis mode and a remote diagnosis mode; the diagnosis end is used for establishing a data communication channel with the TBOX in a diagnosis maintenance mode; and the TBOX is also used for transmitting the vehicle fault data to the diagnosis end through the data communication channel, receiving a vehicle configuration instruction issued by the diagnosis end, and issuing the vehicle configuration instruction to a target ECU unit of the vehicle to realize vehicle fault diagnosis full-link diagnosis operation. According to the scheme, a traditional fault diagnosis instrument is omitted, hardware cost of vehicle service diagnosis is reduced, usability and convenience of vehicle diagnosis are provided, and transmission safety and reliability between the TBOX and a platform or a terminal are guaranteed through a self-developed data transmission protocol.

The invention relates to the technical field of artificial intelligence medical diagnosis, and discloses a remote disease diagnosis system based on artificial intelligence driving. The system comprises five functional modules, a real-time medical data acquisition module acquires multi-modal physiological data of a patient from a remote terminal in real time, and the multi-modal physiological data comprises vital sign time sequence data, medical image data and symptom description text data; the cross-modal correlation analysis module carries out feature fusion on the data, extracts a correlation mode between modals and generates an abnormal feature data set with a weight mark; establishing a dynamic diagnosis baseline by the dynamic diagnosis baseline model, and adapting to individual physiological feature changes by updating parameters; the abnormal behavior detection module is used for comparing the real-time data with a baseline, identifying abnormal indexes and generating a marking result; the deep pathogenesis tracing module analyzes a potential disease mode and traces a pathogenesis path in combination with a patient's historical medical record. The system can optimize the remote diagnosis effect and assist clinical diagnosis and treatment.

The invention relates to the technical field of industrial robot fault diagnosis, and discloses a fault early warning and remote diagnosis method for an industrial robot electrical control system, and the method comprises the steps: obtaining a mechanical load parameter through a potential load model, an inertia load model and a basic dynamics model, obtaining an electrical state parameter through an electrical efficiency model, and carrying out the fault early warning and remote diagnosis. A correction factor is generated through a correction model in combination with the environment temperature, a dynamic current threshold value is calculated through a threshold value model based on the correction factor and a basic current threshold value, and finally health scores and fault early warning information are output through a diagnosis model in combination with the vibration acceleration and servo ring following errors. According to the method, mechanical, electrical and environmental multi-source parameters are fused, dynamic threshold adjustment and multi-dimensional health assessment are realized, normal working conditions and abnormal states are effectively distinguished, the fault early warning accuracy is remarkably improved, the false alarm rate is reduced, and a reliable remote diagnosis scheme is provided for the industrial robot.

The invention relates to the technical field of sensing data processing, and discloses a spark machine remote monitoring and fault diagnosis system and method, and the system comprises a data collection unit which is used for collecting the multi-source operation data of a spark machine, and carrying out the preprocessing and feature extraction of the multi-source operation data; the multi-source operation data at least comprise electrical parameters, motion parameters, state parameters and vibration signals; the remote diagnosis platform is deployed at a cloud end and is in communication connection with the data acquisition unit; the user terminal is used for receiving and displaying the diagnosis result and the maintenance suggestion; the remote diagnosis platform comprises a rapid screening layer, a feature classification layer and a prediction layer; the rapid screening layer firstly judges input data, if the fault cannot be determined, the feature classification layer calculates an input feature vector, and if the fault still cannot be determined, the prediction layer is triggered to carry out deep analysis; through a three-layer progressive architecture, the rapid insight of the operation state of the spark machine and the accurate diagnosis of the fault are realized.

The invention discloses a transfer device for ultrasound contrast of an abdominal injury patient, and relates to the field of medical apparatuses and instruments. Comprising an examination bed, a longitudinal sliding adjusting mechanism, an ultrasonic examination mechanical arm, an image acquisition adjusting support, an image acquisition positioning mechanism, an ultrasonic probe pressing force acquisition mechanism, an ultrasonic probe clamping mechanism, an ultrasonic probe, a controller and an upper computer, the ultrasonic inspection mechanical arm is fixed to the second longitudinal sliding adjusting mechanism, the image collecting adjusting support is fixed to the upper portion of the ultrasonic inspection mechanical arm, the ultrasonic probe pressing force collecting mechanism is fixed to the front end of the ultrasonic inspection mechanical arm, and the ultrasonic probe clamping mechanism is arranged in the ultrasonic probe pressing force collecting mechanism in a sliding mode. The device can improve the efficiency of remote ultrasonic inspection and the standardization of the inspection; the device detects the pressing force applied to the abdomen by the ultrasonic probe through the pressure sensor.

The invention provides an intelligent charging pile management system and method with a remote diagnosis function, and relates to the technical field of power control, and the method comprises the steps: carrying out the one-dimensional variational mode decomposition and multi-dimensional variational mode decomposition of load data and operation state signals of a charging pile, and obtaining a stationary component, a noise component and a state sub-mode signal set; determining load identification characteristics according to the stationary component, the external environment data and the noise component, and performing joint analysis processing on the state sub-mode signal set to obtain fault diagnosis characteristics of the charging pile; carrying out feature fusion on the load identification features and the fault diagnosis features to obtain a modal fusion matrix, and then carrying out joint diagnosis analysis on the modal fusion matrix to obtain a load prediction curve and a fault diagnosis result of the charging pile; according to the method and the device, the charging piles can be subjected to collaborative analysis on the basis of load prediction and fault diagnosis, so that the accuracy of remote scheduling control is improved.

The invention relates to the technical field of power dispatching, and discloses a power supply system dispatching method and a power supply system thereof, and the power supply system comprises a monitoring module, a dispatching module, a protection module, a data prediction module, an energy storage module, a remote control and communication module, an artificial intelligence and adaptive dispatching module, a power quality monitoring module and an intelligent load management module. The power quality monitoring module collects harmonic wave and voltage fluctuation data in real time, gives an alarm immediately when the harmonic wave and voltage fluctuation data exceed the standard, and pushes governance suggestions; and the scheduling module is matched to adjust the non-linear load operation time, so that quality problems are reduced from the source. Scene features are identified through deep learning, and an emergency strategy is automatically generated; and the scheduling module receives feedback in real time and corrects the strategy. The protection module accurately locates a fault point through multi-sensor cross validation and automatically cuts off a fault loop; the remote control module pushes fault information to the operation and maintenance terminal in real time, remote diagnosis is supported, and the on-site inspection frequency is reduced.

The invention relates to the technical field of underground pump fault early warning, in particular to a side cloud collaborative diagnosis early warning system for a tower type pumping unit cloud platform. The system comprises an edge end module and a cloud platform module, wherein the edge end module comprises a data acquisition unit used for acquiring operation data of the tower type pumping unit and a local diagnosis unit used for determining a fault moment; the cloud platform unit comprises a remote diagnosis and decision-making unit used for obtaining a fault response value according to the correlation degree of the same type of operation data and the importance degree of the difference of each type of operation data in an analysis period before and after the fault moment; determining a response characteristic sequence based on the fault response value of the same type of operation data at the fault moment; and training a neural network by using the neural network to obtain an oil pumping unit fault diagnosis model, and further performing diagnosis and early warning on the tower type oil pumping unit. According to the invention, the accuracy of diagnosis and early warning of the fault of the oil pumping unit is effectively improved.

The invention provides an intelligent inspection and remote diagnosis expert method and system for a fire pump station, and relates to the technical field of intelligent inspection, and the method comprises the steps: mapping the real-time operation data and historical maintenance records of the fire pump station to a domain knowledge graph, constructing a fault diagnosis reasoning chain, analyzing an abnormal state propagation path, generating maintenance suggestions, and retrieving historical cases. And evaluating scheme feasibility, constructing an optimal maintenance execution sequence and tracking a maintenance effect. The method can achieve the precise recognition of the abnormal state of the fire pump station, improves the fault diagnosis efficiency, optimizes the maintenance resource configuration, reduces the equipment fault rate, and guarantees the safe and reliable operation of the pump station.

The invention relates to the technical field of electric fireplace fault diagnosis, in particular to an electric fireplace fault remote diagnosis method, which comprises the following steps of: acquiring an air outlet temperature sequential sequence of an electric fireplace in a stable temperature rise period; matching the air outlet temperature sequential sequence with a temperature change judgment condition corresponding to a temperature sensor fault, obtaining a current associated characteristic value of the electric fireplace in a stable temperature rise time period after matching fails, and counting the number of the current associated characteristic values falling into a corresponding associated characteristic value reference range; the initial heating element fault type of the electric fireplace is screened and determined, when the initial heating element fault type is not unique, the integrating degree of the current correlation characteristic value and each initial heating element fault type is calculated, and the fault type of the electric fireplace is selected based on the integrating degree, so that clear and complete fault basis is provided for remote maintenance personnel, and fault hidden dangers are found in time; the stable and safe operation of the electric fireplace is guaranteed, and the user experience is improved.

The invention discloses a mine electrical method integrated exploration system based on matrix electrodes and a use method, and belongs to the technical field of mine water disaster prevention and control, the system comprises a multi-core cable, matrix electrodes, an on-site host and the like, the host integrates an intelligent data analysis module, a data visualization module and the like, and the electrodes are arranged in a double-row matrix mode. When in use, exploration is completed through the steps of system deployment and self-inspection, parameter configuration, multi-frequency data acquisition, real-time analysis and anomaly recognition and remote collaboration and decision support. The system has the beneficial effects that an intelligent algorithm is built in the field host to realize data real-time preprocessing and low-resistance abnormity identification, so that the processing speed is improved, and minute-level response of the water disaster risk is realized; the data visualization module converts the data into a three-dimensional model, and the geological condition is visually presented; and the automatic control module automatically adjusts pole distribution parameters, remote fault diagnosis is realized by combining a remote monitoring function, and the reliability and the safety of the system are improved.

The invention provides a big data processing method and system based on smart medical treatment, and relates to the technical field of smart medical treatment, and the method comprises the following steps: collecting health data of a patient through a medical device and an information system, and carrying out the data preprocessing of the health data of the patient through distributed data storage, and obtaining the preprocessed health data of the patient; analyzing the preprocessed health data of the patient by using a deep learning model to obtain a health analysis result of the patient; and performing disease prediction and risk assessment based on the health analysis result of the patient. The intelligent medical big data processing system has the beneficial effects that the safety, the real-time performance, the accuracy, the low power consumption, the fault tolerance and the like of the intelligent medical big data processing system are remarkably improved, so that the whole system can more efficiently support disease risk prediction, clinical early warning and remote diagnosis and treatment; and the intelligent and refined development of intelligent medical treatment is greatly promoted.

The invention belongs to the technical field of operation monitoring of ultrasonic cleaning machines, and particularly discloses and provides an ultrasonic cleaning machine remote monitoring and diagnosis method based on the Internet of Things, and the method comprises the steps: matching a cleaning mode according to the smudginess information of an object to be cleaned, and the total duration, the cleaning standard and the cleaning operation index of each cleaning stage in the corresponding mode; when cleaning is executed, bubble parameters, water body parameters and cleaned object surface dirt state parameters at the current stage are collected in real time; performing collaborative analysis on the bubble and water body parameters, starting dirt state verification, and if an abnormal signal is triggered or a cleaning standard is not reached, marking an abnormal stage and identifying an abnormal index and degree; if the abnormal stage is not completed or is not the final stage, dynamically adjusting and recording the cleaning strategy based on the abnormal index and degree; after all the stages are completed, a cleaning efficiency diagnosis report is generated according to the positions and degrees of the abnormal stages and then uploaded to the cloud. Therefore, precise remote diagnosis and processing of the cleaning machine are realized.

The invention relates to an IMEI code-based heat pump equipment fault detection and reporting method, system, equipment and medium, and the method comprises the steps: verifying the validity of an Internet-of-Things code, determining whether the read Internet-of-Things code is invalid, if the Internet-of-Things code is invalid, introducing an IMEI code as a standby identity label of heat pump equipment, constructing a two-factor redundancy authentication mechanism, and carrying out the verification of the two-factor redundancy authentication mechanism. The problem of communication interruption caused by reading failure of the internet-of-things code is effectively solved, and it is ensured that the device can still complete identity authentication and continuously report fault information in an abnormal environment; the hardware state is continuously monitored in the communication connection process, a communication link is analyzed, and accurate recognition of faults is achieved; according to the method, the communication success rate and the operation and maintenance efficiency in a complex environment are effectively improved, and complete tracing and remote diagnosis of fault information are realized.

The invention discloses a fundus disease multi-label detection method based on an OfficientNet and a space attention mechanism, and belongs to the crossing field of medical image processing and artificial intelligence. According to the method, a convolutional neural network with an OfficientNet-B4 as a backbone is constructed, and a space attention module is embedded, so that concurrent detection of various common fundus diseases in a fundus image is realized. For the problems of high resolution, uneven image quality, unbalanced categories, label scarcity and the like of fundus images, a data preprocessing strategy including CLAHE brightness enhancement and monocular label reconstruction is provided; according to the method, a Focal Loss loss function and a Cosine Annealing with Warm Restarts learning rate scheduler are introduced into model training, so that the learning ability of the model for a few categories and the overall convergence efficiency are improved. And the constructed model supports multi-label independent prediction. Experimental results show that the method is superior to an existing method in evaluation indexes such as accuracy, precision and recall rate, has good robustness and popularization value, and is suitable for various application scenes such as primary ophthalmology screening, remote diagnosis and auxiliary clinical decision making.

This application provides an intelligent charging pile management system and method with remote diagnostic capabilities, relating to the field of power control technology. It involves performing one-dimensional variational mode decomposition and multi-dimensional variational mode decomposition on the load data and operating status signals of the charging pile, respectively, to obtain stationary components, noise components, and a set of state sub-mode signals. Based on the stationary components, external environment data, and noise components, load identification characteristics are determined. The state sub-mode signal set is then jointly analyzed to obtain fault diagnosis characteristics of the charging pile. The load identification characteristics and fault diagnosis characteristics are fused to obtain a mode fusion matrix. This matrix is ​​then subjected to joint diagnostic analysis to obtain the load prediction curve and fault diagnosis results of the charging pile. Based on the load prediction curve and fault diagnosis results, the charging pile is remotely dispatched. This application can perform collaborative analysis of the charging pile based on load prediction and fault diagnosis to improve the accuracy of remote dispatch control.

Embodiments of the present application disclose a connection method, device and equipment for remote diagnosis order and a storage medium. The method is applied to a diagnosis device, the diagnosis device comprises a first diagnosis module and a second diagnosis module, and the first diagnosis module is connected with a target vehicle through a target joint. The method comprises the following steps: obtaining a vehicle identification code of the target vehicle through the first diagnosis module after the target joint is powered on; obtaining the number of order tasks based on the vehicle identification code; determining a target order task based on the number of order tasks; setting the connection state of the target order task as a first connection state, the first connection state being used for making the target order task in a selectable state; displaying the target order task to the second diagnosis module based on the first connection state; obtaining a target touch parameter of the second diagnosis module, the target touch parameter being used for selecting the target order task; and establishing a connection between the first diagnosis module and the second diagnosis module based on the target touch parameter, so as to execute the target order task.

This disclosure provides a remote diagnosis method, system, platform, and storage medium for an air conditioning system on a vehicle, which relates to the field of engineering machinery. The method includes: obtaining setting information, operating data, and environmental data of the air conditioning system via an on-vehicle terminal; and determining whether the air conditioning system is abnormal based on one or more of the setting information, the operating data, and the environmental data, in a case where a return air temperature does not meet a preset condition within a preset operating time length.

The invention provides an integrated TBOX remote diagnosis data optimization transmission method and system, and relates to the technical field of Internet of Vehicles. Comprising the steps that a cloud side sends a diagnosis instruction to a TBOX in an encrypted communication mode, and the TBOX conducts physical layer transparent transmission on the diagnosis instruction and transmits the diagnosis instruction to a vehicle-mounted MCU through a UART interface in a transparent mode; the vehicle-mounted MCU identifies a diagnosis instruction from the cloud based on an integrated UDS / J1939 protocol analysis engine, triggers and executes a diagnosis task, obtains a diagnosis result and carries out structured packaging; the vehicle-mounted MCU transmits the diagnosis result subjected to structured packaging back to the TBOX through the UART interface, and the TBOX finally transmits the diagnosis result subjected to structured packaging back to the cloud end in an encryption communication mode. According to the invention, the end-to-end transmission delay of the diagnosis instruction and the vehicle-mounted network protocol is reduced, the diagnosis response efficiency is improved, and high-reliability and low-delay remote diagnosis technical support is provided for the health management of the whole life cycle of the vehicle.

The application provides a kind of intelligent zero point positioning quick change system, mechanical manufacturing system and its working method, intelligent zero point positioning quick change system includes quick change support plate, transfer device, waiting device, quick change device and zero point positioning base plate, its characteristics are: it further includes positioning state air tightness detection module, clamping force detection module and remote diagnosis device, positioning state air tightness detection module judges whether zero point positioning state is qualified by pressure sensor;Clamping force detection module judges whether zero point positioning state is qualified by tension sensor;Alarm for unqualified zero point positioning state.The zero point positioning state automatic detection device is wirelessly communicated with the remote diagnosis device, for simultaneously real-time remote transmission of detection and recording results, and remote diagnosis, early warning and reasonable maintenance scheme reporting.The automation and intelligent level is high, and its zero point positioning state automatic detection device ensures the positioning reliability of zero point positioning device through detection and early warning, and realizes remote control.

The invention relates to the field of data processing, in particular to a remote diagnosis method, device and equipment and a storage medium, and aims to realize accurate remote diagnosis of vehicle faults. The method comprises the steps of generating at least one corresponding diagnosis instruction according to a remote diagnosis request under the condition that the remote diagnosis request is generated for a target part of a vehicle end; the diagnosis instruction is stored in a cache; serially issuing at least one diagnosis instruction from the cache to the vehicle end, and executing the issuing of the next diagnosis instruction in the serial issuing process under the condition that the received diagnosis response information corresponding to each diagnosis instruction is final state response information; and performing remote diagnosis on the target part according to the diagnosis response information reported by the vehicle end.

An ultrasound remote diagnosis system according to an embodiment of the present disclosure includes a main body including a main panel, a touch panel, and a control panel, and a remote device in communication with the main body, wherein the remote device is configured to: independently receive, among display data which is real-time image information of the main panel, first control data which is real-time image information of the touch panel, and second control data which is a virtual control panel corresponding to the control panel, at least each information corresponding to the display data and the first control data, from the main body; and the main body is configured to receive a marker information for measurement input to at least one of the display part, the first controller, and the second controller of the remote device and display on the main panel.

The application provides a portable magnetic resonance imaging-based brain disease remote diagnosis system, and belongs to the cross field of medical imaging and remote medical treatment.The system comprises a portable magnetic resonance imaging device, a remote data transmission and cooperation network and a remote diagnosis and AI auxiliary workstation; wherein the portable magnetic resonance imaging device is arranged in a primary medical institution, is used for performing standardized scanning on a patient's brain disease under a low-field magnetic field, and acquires brain image data of the patient; the remote data transmission and cooperation network is used for connecting the device end and the diagnosis end, and is used for transmitting the brain image data to the remote diagnosis and AI auxiliary workstation; and the remote diagnosis and AI auxiliary workstation is used for performing brain disease analysis based on the brain image data and generating a structured report, and the report is returned to the primary medical institution.The application combines remote medical treatment and on-site magnetic resonance scanning, optimizes medical resources, and improves diagnosis effect.

The invention discloses an intelligent monitoring and operation and maintenance management system for agricultural mechanical equipment, and relates to the technical field of equipment management, and the system comprises a data collection module which is used for carrying out the data collection, and obtaining a target equipment parameter; the hidden fault module is used for obtaining hidden equipment abnormity according to the equipment performance change parameters; the anomaly identification module is used for generating a core parameter change curve to obtain equipment anomaly parameters of the agricultural mechanical equipment; the reason reasoning module is used for constructing a fault knowledge graph to obtain troubleshooting and maintenance suggestions; the work order distribution module is used for generating a work order and distributing the work order to the screened target maintenance personnel; the active prevention module is used for generating preventive inspection work orders according to historical maintenance records and distributing the preventive inspection work orders in advance; and the remote diagnosis module is used for performing remote diagnosis and debugging, recording maintenance guidance and issuing the maintenance guidance to local maintenance personnel. The application has the effects of improving the adaptability of data acquisition, the intelligence of fault diagnosis and the initiative and collaboration of operation and maintenance management.

The invention relates to the technical field of multi-source data processing, in particular to an intelligent power distribution cabinet remote diagnosis and maintenance system based on multi-source information fusion. According to the system, the stability index is determined by evaluating the change stability condition of data of data source parameters in a local interval; comparing the stability of each data source parameter time sequence local area, and obtaining a stable confidence time period through the stability condition after the local area is expanded; a relevance index is obtained by analyzing the stability approximation condition and the value change consistency condition of every two data sources; and analyzing the fault evaluation index according to the deviation change of the subsequent relevance to obtain a fault evaluation result. According to the method, the historical stable time period is determined through multi-source information data fusion analysis, and the fault condition is evaluated in combination with the correlation trend change among the multi-source data, so that the system can find the fault problem of the intelligent power distribution cabinet more timely and accurately, timely and reliable maintenance is guaranteed, and the power distribution cabinet can operate safely and reliably.