5342 results about "Digital data" patented technology

The invention discloses a multi-source remote sensing image semantic segmentation method based on Transform, Mama and a diffusion model, belongs to the technical field of electric digital data processing, graphic data reading and image data processing, and is used for extracting image information of a remote sensing image. Comprising the following steps: acquiring remote sensing images, constructing an image information extraction network based on the remote sensing images, and respectively inputting the remote sensing images in a training set, a verification set and a test set into the constructed remote sensing image information extraction network based on Transform, Mama and diffusion models. According to the method, the RGB features and the depth features are subjected to Transform feature extraction, and a cross Mama method is provided to perform cross enhancement on the RGB features and the depth features; according to the method, a row and column pooling Transform module is used for replacing a traditional Transform module, and detail texture information and large-scale structure information of a feature map in the vertical direction and the horizontal direction are extracted at the same time; according to the invention, a cross Mama attention feature fusion module is used to replace a traditional Mama module, and the modeling capability for complex scenes and diversified targets is enhanced.

The invention provides an environment monitoring data anomaly detection method, a medium and a system, and belongs to the technical field of electrical digital data process.The method comprises the steps that firstly, an environment monitoring data original matrix is constructed, time sequence segmentation is conducted, and a rule matrix and an anomaly matrix are obtained through matrix decomposition; calculating a historical mean value, a historical standard deviation, a spatial feature and a time feature of the environmental parameters, designing an anomaly detection optimization equation set based on the features, including an environmental parameter weight equation, an anomaly threshold equation and a comprehensive anomaly degree equation, and calculating an anomaly detection result by calculating an environmental parameter weight vector, a dynamic anomaly threshold matrix and a comprehensive anomaly degree index. Finally, dynamic optimization of anomaly detection is achieved through the gain vector and the feedback vector, anomaly detection of real-time environment monitoring data is completed, and the technical problem that in the prior art, an environment monitoring data anomaly detection method is difficult to give consideration to dynamic relevance of multi-dimensional environment parameters and the accuracy of anomaly detection at the same time is solved.

An intelligent search agent that can accept search request input in various ways to initiate a search of databases, websites, documents, PDF files, photos, and other digital data. Search requests can be initiated manually, through automated scheduling, and other ways and can be initiated in multiple ways including voice input, text input, form filling, large language model (LLM) processes, AI agents, selecting from dashboard menus, computer instructions and other methods. Applications of machine learning (ML) and natural language processing (NLP) and others are used to enhance the search agent's capabilities while minimizing user effort requirements. The Search Agent works in conjunction with a knowledge base function to dynamically form and manage one or more knowledge bases using search results and to automatically integrate search results into workflow processes.

The invention relates to the technical field of electric digital data processing, in particular to an agricultural planting optimization system and method based on big data analysis. The method comprises the following steps: acquiring agricultural planting environment parameter data, and performing preprocessing and standardization to obtain a standardized environment feature vector; acquiring image, sound and smell data of crops; performing deep feature extraction on the image, sound and smell data to generate a multi-modal feature representation matrix; and performing multi-modal data fusion based on the standardized environment feature vector and the multi-modal feature representation matrix to obtain a comprehensive feature space. According to the invention, through multi-modal feature fusion and digital twinborn modeling, the precision and real-time performance of crop growth risk and pest and disease prediction are improved, and optimal regulation and control and efficient resource utilization of precision agriculture are realized.

The invention discloses an intelligent server management method and system in an edge computing environment, and relates to the field of electrical digital data process.The method comprises the steps that a dynamic load buffer database is established; calculating a load stability coefficient of each server node; when the load stability coefficient of the server node is smaller than a first preset threshold value, marking the server node as a fluctuation node; calculating a node cooperation efficiency value; determining a target server candidate set, and calculating to obtain a task allocation weight; distributing tasks on the fluctuation nodes to server nodes in the target server candidate set; when the continuous preset duration of the real-time load value of the fluctuation node is lower than a third preset threshold value, calculating a processing efficiency improvement proportion; for the task of which the processing efficiency improvement proportion is greater than a fourth preset threshold value, keeping continuous processing at the current server node; and for the tasks of which the processing efficiency improvement proportion is smaller than or equal to a fourth preset threshold value, returning to the original fluctuation node. The method and the device are used for reducing communication overhead and service processing delay between nodes.

The invention belongs to the technical field of electrical digital data processing and resource allocation, and provides an agent lightweight deployment method and a computing power elastic allocation method, lightweight deployment realizes one-time development of multi-platform deployment by constructing a plug-in container packaging tool and a multi-architecture compiling engine; static analysis is used for stripping redundant dependence of the model, and the model is dynamically cut in combination with edge resources; developing an intelligent resource description language, integrating a control group and a filtering technology to realize container-level resource monitoring, constructing a 12-dimensional dynamic state space fusing a node state, task characteristics and a network environment through computing power elastic distribution, and introducing a time delay, cost and reliability three-dimensional weighted reward model to quantify distribution earnings; an edge cloud cooperative training framework and an edge execution strategy are designed, experience is collected, a cloud end trains a Q network through federal learning, an optimization decision is played back in combination with priority experience, the agent deployment efficiency and the resource utilization rate are improved, and data privacy is guaranteed.

The invention relates to a multi-modal intelligent robot system and an interaction method, and belongs to the technical field of robot digital data processing, and the method comprises the following steps: S1, multi-modal input collection; s2, carrying out multi-modal input preprocessing; s3, performing multi-modal information fusion, inputting the information into a pre-trained multi-modal feature fusion model, and performing feature fusion on each modal feature through a quantitative feature data fusion mechanism to obtain a user interaction intention vector and a user emotional state vector; s4, interaction response generation: matching a preset interaction response strategy library based on the user interaction intention vector to obtain basic response content, and performing emotion adaptation adjustment on the basic response content in combination with the user emotion state vector to generate multi-modal response content including text information, voice information and action information; s5, interactive feedback is executed; the method has the beneficial effects that the robot is fused with multi-modal information through the basic response content and the user emotional state vector, and interaction is dynamically optimized.

The invention provides a multi-source information fused virtualized network fault rapid positioning method, and relates to the technical field of electric digital data processing, and the method comprises the steps: classifying and sorting historical fault events, constructing a historical dynamic evaluation model, correcting a calibration index, capturing equipment performance changes and abnormities, and enhancing the fault recognition capability. Similar faults are accurately classified and preferentially processed according to severity; in the real-time monitoring stage, real-time multi-source and abnormal data are combined, a real-time dynamic evaluation model and a correction calibration index are calculated, a ratio is applied to calculate a fine adjustment strategy, the real-time performance and accuracy of fault positioning are remarkably improved, and network dynamic changes are quickly responded. And carrying out difference analysis on the adjusted real-time model and the historical model, calibrating a preliminary fault positioning result, and improving the positioning precision and reliability.

The invention relates to the related technical field of digital data processing, in particular to a personalized federated learning method and system for a heterogeneous multi-source industrial internet, and the method comprises the steps: connecting a client, evaluating a load, time delay and modal similarity to generate a dynamic association table, deploying a hierarchical encryption protocol, and constructing a priority queue; a cache mechanism is set to coordinate distributed iterative optimization, so that the technical problem that network oscillation and computing resource waste are aggravated due to overhigh load of part of nodes caused by frequent access and exit of equipment and data volume difference in the industrial internet and repeated migration of clients and nodes is caused is solved, cross-equipment shared knowledge base vectors are extracted, and the computing efficiency is improved. The technical effects of reducing the influence of model isomerism on aggregation, dynamically scheduling high-frequency parameter local aggregation and low-frequency parameter cloud synchronization, optimizing the association weight of a client and a fog node in real time, realizing privacy protection and efficient personalized federated learning, and ensuring the privacy and security of user data in the training process are achieved.

The invention discloses an equipment state sensing system based on peak shifting and danger situation sensing, which relates to the technical field of electric digital data processing and comprises a data acquisition unit, a model training and optimizing unit, a situation sensing unit, a peak shifting regulation and control unit and a system interface and user interaction unit. According to the method, the device state data feature similarity is dynamically calculated by using the Gaussian kernel function, the similarity matrix is adjusted in combination with the gradient update item, dynamic weighting is performed in feature extraction, and redundant features are reduced by adopting the adaptive discarding probability, so that the feature extraction efficiency of the model in a high-dimensional feature space is improved; the problem that the dimension reduction effect is affected by too many redundant features of the equipment state data is solved. Equipment state features are extracted layer by layer through a deep forest algorithm of a multi-layer random forest structure, a decision tree is optimized in combination with a dynamic adjustment weight mechanism and a reinforcement learning strategy, and the classification capability of the model for equipment state changes and abnormal states is enhanced.

An anomaly detection system may receive uniformly spaced digital data from a plurality of sensors connected to the monitored system and physical model parameters specific to components of the monitored system. An orthogonal function processor generates an initial set of orthogonal kernel basis functions to process the digital data to detect signature anomalies. An adaptive analysis is performed based on the number of feature components and the width of each feature component. When the number of feature components is greater than the basis functions, additional basis functions are used for reprocessing. When the feature width is less than the basis function, the resolution of the basis function is increased and reprocessing of digital data is performed. Based on the adaptive analysis of each feature component, signature anomalies are identified and displayed.

The invention provides a heart multi-mode digital twinning simulation method and system, electronic equipment and a medium, and belongs to the technical field of digital twinning. The method comprises the following steps: S1, collecting multi-modal data of a heart; s2, preprocessing the acquired multi-modal data to obtain heart digital data; s3, based on the heart digital data, constructing a multi-modal digital twinborn model of the heart and optimizing the multi-modal digital twinborn model; s4, performing real-time simulation on the electrophysiological activity of the heart by using the optimized multi-modal digital twin model and using a Runge-Kutta method in combination with a PSO (Particle Swarm Optimization) algorithm; s5, based on the optimized multi-mode digital twinborn model and the simulation result, simulation and planning of virtual cardiac surgical treatment are carried out; and S6, dynamically evaluating the multi-modal digital twin model, and monitoring the state change of the heart in real time. According to the heart multi-mode digital twinborn simulation method and system, the electronic equipment and the medium, efficient simulation and real-time monitoring of the heart state can be achieved.

The invention relates to the technical field of electrical digital data processing, in particular to an anomaly detection and processing method and system based on deep learning. The method comprises the following steps: firstly, performing feature extraction and fusion on a log text sequence, a log timestamp sequence and a running state index sequence set to obtain an initial feature vector sequence; then, constructing a hierarchical graph neural network to perform feature updating, propagation and fusion on the initial feature vector sequence to obtain a multi-level fusion feature vector sequence, and calculating a standardized mode vector sequence in combination with a log text sequence and a running state index sequence set; obtaining a self-adaptive abnormal threshold value according to the standardized mode vector sequence and detecting candidate abnormal logs; and finally, verifying the candidate abnormal logs, determining verified abnormal logs, classifying the verified abnormal logs, generating a processing suggestion text according to a classification result, and carrying out early warning prompt. The false detection rate can be reduced, and the log anomaly detection accuracy is improved.

The invention discloses an operation and maintenance mode analysis model generation method and system, a medium and a program product, and relates to the field of electric digital data processing. Establishing an equipment basic life model based on the equipment fault time data and the non-fault working time data; calculating an environment correction coefficient according to the equipment operation environment parameter data and a preset environment influence factor, and combining the environment correction coefficient with the basic life model to obtain a corrected life prediction model; calculating the correlation degree of each equipment node based on the equipment topological structure, and constructing an equipment importance evaluation model; calculating equipment maintenance cost, replacement cost and shutdown loss according to a prediction result of the corrected life prediction model to obtain a cost loss model; and carrying out weighted fusion on an output result of the equipment importance evaluation model and a calculation result of the cost loss model to generate an operation and maintenance mode analysis model. The scientificity and accuracy of operation and maintenance mode analysis are improved.

The invention provides a self-adaptive computing power scheduling system for large model training, and relates to the field of electric digital data processing, the self-adaptive computing power scheduling system comprises a computing power management module, a task analysis module, a self-adaptive scheduling module and a data interaction module, the computing power management module is used for managing and monitoring overall computing power resources, and the task analysis module is used for analyzing the overall computing power resources; the task analysis module is used for analyzing requirements and characteristics of training tasks, the self-adaptive scheduling module performs computing power scheduling according to real-time resource conditions and task requirements, and the data interaction module is used for managing data interaction among different computing power nodes; according to the system, the training efficiency can be effectively improved by assigning the training tasks to different nodes and adjusting the allocated resources in the task execution process.

The invention discloses an information management method and system for a smart ward, a medium and a program product, and relates to the field of electrical digital data processing.The method comprises the steps that physiological index data and behavior characteristic data of a target patient are collected, and a characteristic data sequence is constructed; analyzing each piece of data in the feature data sequence, and determining a current characterization behavior and a behavior change sequence of the target patient; obtaining individual information of a patient, and inputting the individual information of the patient, the current characterization behavior and the behavior change sequence into a behavior evaluation model to obtain a risk characterization behavior and a behavior risk score; determining an early warning level and an early warning response time limit based on the behavior risk score; when the early warning level is higher than a preset early warning threshold value, generating a task execution list according to the early warning response time limit and the risk characterization behavior, and sending the task execution list to the medical care terminal; and updating the behavior risk score according to an early warning response result returned by the medical terminal. By implementing the application, patient state monitoring of the smart ward can be optimized, and potential safety hazards can be avoided to a greater extent.

The invention relates to the technical field of electric digital data processing, in particular to a traditional Chinese medicine data management system and method for data retrieval. The method comprises the following steps: acquiring morphology data of a traditional Chinese medicinal material sample; performing grade evaluation based on the traditional Chinese medicinal material morphology data to generate traditional Chinese medicinal material grade label data; acquiring a to-be-detected traditional Chinese medicinal material image; preprocessing the to-be-detected traditional Chinese medicinal material image, and performing entity recognition and relation extraction to obtain traditional Chinese medicinal material semantic annotation data; and fusing the traditional Chinese medicinal material grade label data and the traditional Chinese medicinal material semantic annotation data to obtain traditional Chinese medicinal material semantic annotation enhanced data. According to the method, the information of the traditional Chinese medicinal materials is expressed through structured processing and semantic enhancement, the knowledge graph containing dominant and implicit semantic relationships is constructed, and the retrieval depth, flexibility and intelligent application of knowledge of the traditional Chinese medicinal materials are improved.

This invention pertains to an intelligent electronic device designed for sophisticated monitoring and analysis of electrical power distribution. The device integrates at least one sensor for detecting electrical parameters from a distribution system to a load, coupled with an analog-to-digital converter to transform sensed analog signals into digital data. A processing module, linked to the converter, employs customized moving average filters to calculate the frequency of electrical power distribution, enhancing measurement accuracy by adjusting for transient fluctuations. It retrieves frequency threshold settings from memory to define operational bounds, logging frequency data around identified deviation events when measurements surpass these thresholds. This system facilitates precise frequency analysis and robust event logging, providing a comprehensive solution for maintaining electrical power distribution within defined standards.

The invention relates to the technical field of electrical digital data processing, and provides a task scheduling optimization method and system based on collaborative analysis. Initializing task information of the target task, and disassembling the target task into a task set composed of a plurality of sub-tasks; performing priority ranking on the sub-tasks according to the emergency degrees, the important parameters and the resource requirements of the sub-tasks; obtaining current execution resources through a resource manager to generate an execution unit set, and generating a collaborative strategy in combination with the priority ranking of the subtasks; and according to the priority ranking and the collaborative strategy, calculating a target value of each task scheduling mode in the intelligent scheduling algorithm to select the scheduling mode, and finally arranging the sub-tasks to the corresponding execution units. Through fine task disassembly, priority ranking, real-time resource monitoring and an intelligent scheduling algorithm, the accuracy and efficiency of task execution are improved, and the flexibility and expandability of the system are enhanced.

A cellular communication network based non-fungible token creation system with integrated creator biometric authentication comprises an electronic device of the user; a passive biometric user identification technology coupled to the electronic device and configured for identifying the individual with the passive biometric user identification technology by passively obtaining biometric characteristics that are unique to each human; and a one click non-fungible token forming component which is configured for identifying digital data files on an electronic device with a single user click; wherein the digital data files include the biometric characteristics of the creator, and at least one of audio, video, data and image files, and wherein the system is configured for creating authentication data comprising the digital data files and for recording on a blockchain the authentication data forming the non-fungible token; and systems for managing and transferring ownership of the non-fungible token.

The invention discloses a method and a system for realizing automatic code generation by using multiple AI Agents, and relates to the field of electric digital data processing, a test driving agent receives a task transmitted by a previous node, and generates a task number, a task description, test data, a unit test script and a code statement; the code generation agent generates or updates codes according to the received task description and code declaration; the test driving agent returns an integrated test script and test data according to the task number; the continuous integration agent performs integration verification and regression test and feeds back information; the test driving agent returns a function test script, a performance test script and test data according to the task number; and the continuous deployment agent carries out continuous delivery and deployment and feeds back information. By means of mutual conversation and cooperative work of multiple AI Agents and in combination with a feedback mechanism, the problem that a single large model cannot complete complex software development tasks is solved, full-process operation automation can be achieved, and the code generation quality and efficiency can be improved.

The invention relates to the technical field of electronic digital data processing, provides a financial multi-source data processing system and method, and aims to flexibly schedule tasks, dynamically adjust the acquisition frequency, ensure timely and accurate data acquisition and save resources in the aspect of data acquisition according to financial market rules, realize multi-module collaborative operation in the aspect of data processing and improve the data processing efficiency. The data quality is effectively improved from preliminary cleaning to standardized processing, a rule engine can update rules in real time and adapt to market and supervision changes, a semantic mapping module accurately processes financial terms and improves data understanding and integration, and an intelligent strategy adjustment module optimizes a processing strategy, reasonably allocates resources and improves processing efficiency. And the real-time monitoring and feedback module is convenient for management personnel to control the system state and timely adjust the strategy, an efficient and intelligent data processing scheme is integrally provided for the financial industry, and financial decision making and business development are assisted.

The invention discloses an uninsulated superconducting coil electromagnetic characteristic prediction method, system and device and a medium, and relates to the field of electrical digital data processing. Constructing a loss function based on the data fitting loss and physical constraint loss including a Kirchhoff's current law loss item, a T-A equation loss item and an E-J power law loss item; training, testing and verifying the network model by adopting a sample data set, balancing each loss item in a loss function by adopting an adaptive weight distribution strategy in the training process, and minimizing the loss function through an Adam optimization algorithm to obtain an electromagnetic characteristic prediction model; and inputting the obtained current working condition parameters into the electromagnetic characteristic prediction model to obtain an electromagnetic characteristic prediction result. According to the method, the problems of low calculation efficiency, poor physical consistency and generalization ability and the like in the existing method are solved, and a feasible tool is provided for rapid design, optimization and operation of the uninsulated superconducting coil.

The invention provides a time sequence fusion and health state prediction method and system for multi-dimensional physiological data, and relates to the technical field of electrical digital data processing.The method includes the steps that high-precision sensing equipment and a parameter calibration model are adopted to calibrate data, time synchronization and error compensation are achieved through a weighted fusion algorithm, and the accuracy of physiological parameters is improved; meanwhile, a fatigue index and a pressure index are corrected in real time by using a closed-loop feedback mechanism, and a comprehensive state index is generated based on an evaluation result for further analysis and prediction; the change trend of the comprehensive state index is predicted through a machine learning technology, and a targeted dynamic rehabilitation scheme including training intensity optimization, diet adjustment, rest cycle planning and the like is provided in combination with physiological data of the user; and a timing feedback mechanism is established, so that the user can obtain health state change and adjustment suggestions in real time, thereby improving scientificity and timeliness of a rehabilitation scheme, remarkably optimizing the rehabilitation effect and efficiency, and realizing intelligent health management at the same time.

The invention discloses a road protection multi-scene coupling simulation method and system based on digital twinning, and relates to the field of electric digital data processing, and the method comprises the steps: collecting dynamic response data (strain, acceleration and displacement) of a traffic protection facility through multiple sensors, and building a scene feature library containing vehicle collision, wind load and seismic waves; and projecting response data to a unified time axis, identifying a multi-scene overlapping interval, and determining an action sequence and duration. Primary and secondary scenes are judged according to scene action parameters, and mechanical correction values and standard response parameters are extracted. And establishing a digital twinborn model for coupling simulation to obtain a stress distribution condition, evaluating a structural damage level and generating a safety performance report. By implementing the method, the deviation between the mechanical property simulation result of the protection facility and the actual working condition can be reduced.

The invention belongs to the technical field of electric digital data processing, and discloses a dynamic remote sensing monitoring method and system based on land utilization. The method comprises the steps of obtaining a multi-source heterogeneous time sequence data set, and generating standardized spatio-temporal data through preprocessing; extracting space-time correlation features based on the dynamic graph convolutional network, and generating dynamic mode data through multi-dimensional feature fusion; performing classification decision and state transition analysis on the dynamic mode data, identifying a state change area and generating a dynamic analysis report; constructing an interactive visualization engine to map a space-time thermodynamic diagram and a trend graph; and in combination with the incremental learning optimization model, outputting a long-term evolution prediction result. Through the dynamic graph convolutional network and the adaptive cross-modal alignment technology, the problems that multi-source data space-time correlation modeling efficiency is low and the noise suppression capability is insufficient are solved, the dynamic mode recognition precision is remarkably improved, real-time interaction analysis and long-term prediction are supported, and the method is suitable for accurate decision making of industrial monitoring, traffic planning and other scenes.

The invention provides an industrial data online migration method, medium and system of a productivity middle platform, and belongs to the technical field of electrical digital data processing.The method comprises the steps that firstly, historical access records of an industrial data source are collected, and a deep neural network model comprising a multi-head attention layer, a time sequence feature extraction layer and a residual connection layer is constructed; and the prediction of the data change rate is realized. And based on a prediction result, dynamically calculating a data change rate demarcation point by utilizing an optimization equation set, and dividing a data table into different change frequency categories. And then constructing a data dependency relationship matrix, and determining a migration sequence by adopting an improved topological sorting algorithm. Batch migration, incremental migration and real-time synchronization mechanisms are adopted for different types of data, and model updating is triggered through real-time monitoring, so that dynamic optimization of a migration strategy is ensured. Finally, data consistency verification is carried out, migration accuracy is guaranteed, and the technical problem that the online migration efficiency of industrial data is low due to real-time dynamic change of the data change rate is solved.

The invention provides a robot control instruction analysis method and system fusing continuous instructions, and relates to the technical field of electric digital data processing, and the method comprises the steps: carrying out the quantitative evaluation of the instruction execution quality and resource consumption according to prediction data, and generating a quality score and a consumption estimation; constructing a multi-objective optimization framework based on the quality score and the consumption estimation, and determining a candidate path set; switching cost and execution time are calculated according to the candidate path set, and optimized path schemes meeting constraint conditions are screened; the instruction execution priority and the resource allocation proportion are adjusted in combination with the real-time environment data, and a scheduling strategy is generated. According to the method, environment changes and task quality are monitored in real time, instruction priorities and resource allocation are dynamically adjusted, when execution efficiency is lower than a threshold value, online updating of a prediction model is triggered, model parameters are finely adjusted to adapt to new conditions, and through continuous optimization and monitoring, the execution efficiency and task quality of an instruction sequence can be improved, and the execution efficiency is improved. And intelligent instruction scheduling and resource allocation are realized.

The invention discloses a solid state disk data processing method and device, a storage medium and a program product, and relates to the field of electrical digital data processing.The method comprises the steps that a network data transmission request is obtained, a network bandwidth state corresponding to the network data transmission request is detected, and bandwidth state parameters are generated according to the network bandwidth state; calculating a data transmission time consumption predicted value, and determining a buffer capacity threshold according to the data transmission time consumption predicted value; adjusting the capacity of the data buffer area according to the buffer area capacity threshold, and adjusting the data buffer area; monitoring the data occupancy rate of the adjusted data buffer area, and when the data occupancy rate is lower than a preset occupancy rate threshold value, calculating an idle time window of the target solid state disk according to the network bandwidth state; determining a target power consumption mode of the target solid state disk according to the duration of the idle time window; and controlling the target solid state disk to enter the target power consumption mode in the idle time window. By implementing the application, the energy consumption of the solid state disk can be reduced.

The invention discloses a fault diagnosis method and device for a server, and relates to the technical field of electric digital data processing, and the method comprises the steps: collecting the actual operation data of a target server, obtaining the configuration information and historical fault data, and detecting whether the target server breaks down or not; when a fault occurs, inputting the configuration information, the actual operation data and the historical fault data into a pre-constructed fault path generation model so as to output a corresponding diagnosis path; and determining a diagnosis rule, a diagnosis label and a fault type when the target server is diagnosed according to the diagnosis path to generate a current fault diagnosis result, thereby solving the technical problems that the flow is solidified and single, the dynamic adjustment cannot be realized, the flexibility and pertinence are lacked, the diagnosis efficiency is low and the diagnosis precision is poor, and achieving the purposes of shortening the diagnosis time and improving the diagnosis efficiency. The server energy consumption and maintenance cost are reduced, the fault positioning accuracy is improved, the fault solving time is shortened, and the technical effect of automatic fault diagnosis is achieved.