5333 results about "Analysis method" patented technology

The invention relates to the technical field of information, in particular to a multi-task data analysis method and device and a storage medium, and the method comprises the steps: constructing a multi-dimensional task state feature vector as the input of a task loss prediction model by collecting a task execution progress, a resource use condition and an abnormal log index in a task operation process; constructing a task dependency graph and a resource conflict graph according to the dependency relationship between the tasks and the resource occupation condition, learning the relevance between the tasks and a resource competition mode through a graph neural network algorithm, and obtaining an influence factor and a propagation path of task loss; the performance of a task loss prediction model is continuously tracked, model parameters are dynamically updated through an incremental learning mechanism, the method adapts to changes of a multi-task environment, prediction accuracy and real-time performance are improved, and a reliable decision basis is provided for task scheduling and resource optimization.

The invention discloses an enterprise global data analysis method based on a knowledge graph and a large language model, and the method comprises the following steps: collecting internal and external multi-source data of an enterprise, cleaning, standardizing and mapping, and constructing a unified data graph; after a user inputs a natural language query, a reasoning target and task description are generated in combination with semantic understanding, two reasoning paths are formed through structural reasoning and semantic reasoning respectively, and the two reasoning paths are fused for interactive verification and optimization. Logic conflicts and data missing are detected in real time in the reasoning process, and supplementary knowledge is automatically called for local reasoning correction. Through the corrected reasoning result, the system carries out semantic alignment and correlation analysis on multi-source data, a unified enterprise data analysis view is dynamically generated, a reasoning path and evolution information are recorded in the reasoning and analysis process, and subsequent traceable query is supported. According to the method, the data integration, intelligent reasoning and decision support capabilities of an enterprise in a complex data environment are improved.

The invention relates to the field of bidding risk assessment, and discloses an AI technology-based intelligent bidding data analysis method and system and a storage medium, and the method comprises the following steps: obtaining bidding data, constructing a multi-dimensional space-time tensor, and extracting low-dimensional features through dynamic tensor decomposition; establishing a dynamic enterprise relationship graph, modeling an enterprise cooperation relationship by using a graph neural network, and capturing a bidding behavior mode between enterprises; a multi-target joint optimization model is constructed, and market trend prediction, enterprise credit assessment and risk early warning are synchronously realized; performing adversarial training by adopting a generative adversarial network, and dynamically adjusting task weights in combination with Pareto optimization to balance multi-task conflicts; and outputting a market prediction report, an enterprise credit portrait and a risk early warning list, and providing policy and regulation change influence analysis and a risk evidence chain. According to the method, bidding and tendering data are deeply mined through the AI technology, the market insight and the abnormal bidding behavior recognition capability are improved, and the method has wide application value.

The invention relates to the technical field of data analysis, and discloses a business data analysis method, device and equipment based on big data and a storage medium, the method comprises the following steps: collecting and preprocessing original log data to obtain standardized log data, and performing adaptive multi-modal feature extraction to obtain comprehensive feature representation; inputting a heterogeneous graph neural network to carry out anomaly detection and multi-level root cause analysis to obtain a hierarchical anomaly event; executing task scheduling and resource allocation of reinforcement learning to obtain a task allocation scheme; performing deep learning enhanced root cause reasoning and knowledge graph construction on the task allocation scheme to obtain a system state diagram and a causal relationship; and generating a multi-dimensional task data analysis report. According to the method, comprehensive analysis, anomaly detection, root cause reasoning and intelligent scheduling of log data are realized. The reliability and performance of the system can be improved, and more intuitive and accurate decision support can be provided for operation and maintenance personnel.

The invention discloses a construction progress control method in project supervision, and the method comprises the steps: calculating the current punctual completion rate and process handover delay time of each subpackage provider according to a real-time progress data set and a pre-established project network diagram, recognizing the progress fluctuation modes of different project stages through a time sequence analysis method, and carrying out the construction progress control according to the progress fluctuation modes. Establishing a correlation model of the sub-packaging quotient and the engineering stage; according to the priority scores, taking minimization of overall construction period delay and resource idleness as objective functions, applying an optimization algorithm, dynamically adjusting a construction plan and a resource allocation scheme of each subpackage provider, and generating an optimized project progress plan which comprises specific work arrangement and resource allocation suggestions of each subpackage provider; a successful coordination strategy in a historical project is analyzed, coordination measures are recommended for project management personnel and a set of communication matrix is generated according to characteristics and progress conditions of a current project, and the coordination measures comprise resource allocation suggestions, process optimization schemes and communication strategy templates.

The invention provides an equipment group collaborative fault prediction analysis method and system for a complex industrial process, and relates to the technical field of fault prediction, and the method comprises the steps: collecting equipment group data, and reconstructing a multi-scale time sequence feature; constructing a quantum walking enhanced dynamic association map; extracting spatial-temporal features and mapping the spatial-temporal features to a Riemannian manifold space; identifying an abnormal mode and generating hidden variable probability distribution; constructing a fault evolution model driven by a non-equilibrium state statistical fluctuation theory; and solving the cooperative field equation set to obtain a control parameter sequence. The method can effectively improve the accuracy and reliability of equipment group collaborative fault prediction and reduce the maintenance cost.

The embodiment of the invention provides a multi-agent-based equipment analysis method and system, equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: firstly, acquiring a user question text about equipment analysis, and performing task splitting and planning on the user question text through a large language model of a supervisor agent to determine a target sub-task and a task agent corresponding to the target sub-task; and calling an application program interface of the task agent through the supervisor agent to execute the target subtask to obtain task feedback, and generating an equipment analysis result by the large language model according to the task feedback and feeding back the equipment analysis result to a user. Various equipment analysis tasks are completed through cooperation of multiple agents, the equipment analysis tasks can be conveniently expanded through the supervisor agent-task agent architecture, and the equipment analysis requirements of a user are flexibly processed.

The invention provides a structured data modeling analysis method based on Transform, and relates to the technical field of data analysis, and the method comprises the steps: carrying out the piecewise linear transformation and adaptive quantum coding of numeric features and category features through a double-flow feature encoder, and carrying out the fusion through a gating mechanism to obtain fusion features; inputting the enhanced training sample into a feature extraction network comprising a hierarchical attention module and a self-calibration module, and extracting global feature representation through a feature affinity graph and a causal attention mechanism; and constructing an information bottleneck feature filter to select a key feature subset, performing prediction and reconstruction by adopting a double-branch network, and analyzing the feature contribution degree by using a structured gradient method. According to the method, heterogeneous features can be effectively processed, the model performance is improved, and meanwhile an interpretable feature importance evaluation mechanism is provided.

The invention discloses a microblog emotion analysis method based on standard dictionaries and semantic rules. The microblog emotion analysis method comprises the following steps: collecting microblog data and manually labeling and marking the emotion value of each microblog; proposing corresponding standard micrblog emotion dictionaries, and establishing an emotion dictionary database; based on the standard emotion dictionaries, adding the semantic rules for assistance, and performing parameter adjustment and optimization on parameters of the semantic rules; based on a real dataset experiment, acquiring the final classification accuracy and precision. The technical scheme provided by the invention is adopted to well analyze the emotion tendency of each microblog user by introducing the standard emotion dictionaries, microblog expression dictionaries and the semantic rules, therefore, higher classification accuracy and precision are achieved.

The invention provides an instrument log recording and intelligent analysis method in a medical instrument software system, and the method comprises the steps: carrying out the preprocessing of operation log data of medical instrument equipment, carrying out the unified conversion of the log data of different sources and different formats, and obtaining the operation log data of the medical instrument equipment after the standardization processing; mining operation state change rules and abnormal modes of the medical equipment in different diagnosis and treatment stages through a time sequence mode according to the extracted log data semantic features in combination with the diagnosis and treatment workflow of the medical equipment; and analyzing the actual process of the equipment participating in the diagnosis and treatment activity from the equipment log data, and comparing and analyzing the actual process with a standard diagnosis and treatment work process to determine the information of the diagnosis and treatment work process, including a key path, a bottleneck link and an abnormal deviation.

The invention relates to the field of production fault analysis, in particular to a fault monitoring analysis method and system for lubricating oil production. The method comprises the following steps: acquiring a multi-dimensional working monitoring parameter and an omnibearing working image; performing abnormal outlier filtering processing on the multi-dimensional working monitoring parameters, and performing time sequence evolution simulation to generate time sequence state evolution data; performing continuous frame optical flow motion analysis on the omnibearing working image, performing dynamic parameter mapping based on time sequence state evolution data, and constructing a dynamic production twinborn model; obtaining a preset production standard demand log; carrying out dynamic process deviation detection based on a preset production standard demand log dynamic production twinborn model, and carrying out production fault analysis so as to obtain stage production fault data; performing potential fault traceability speculation on the staged production fault data to obtain an abnormal fault potential node; and constructing a potential fault mark twinborn model based on the abnormal fault potential nodes. According to the invention, efficient and accurate fault analysis of lubricating oil production is realized.

The invention discloses an intelligent data analysis method and system based on an industry large model, and relates to the technical field of data analysis, and the method comprises the steps: collecting business event data, carrying out the feature vector extraction according to the data type, calculating a zoom dot product attention score matrix through employing a self-attention mechanism, and carrying out the weighted output of a multi-modal fusion vector, constructing an industry knowledge database to determine an embedded vector; and calculating a relevancy value, screening a combination of the multi-modal fusion vector and the knowledge fragment, and forming a gating input vector to carry out fusion vector calculation. According to the method, unified expression and correlation mining of heterogeneous information such as structured data, text information and images can be realized through a multi-modal feature fusion and knowledge fragment retrieval scheme, the attention degree of each modal feature can be adaptively adjusted according to a service scene through a self-attention mechanism and a model, information complementation is realized, and the accuracy of information retrieval is improved. And automatically highlighting the multi-modal dimension most related to the current task.

The invention relates to the technical field of data analysis, in particular to a business abnormal data analysis method based on a financial management system, and the method comprises the following steps: S1, obtaining various business data in an enterprise financial management system in real time; s2, processing the data acquired in the step S1; s3, extracting features for reflecting business exception; s4, establishing an anomaly detection model; s5, comprehensively analyzing the source and influence of the abnormal data to form a multi-dimensional abnormal data analysis report; s6, deeply mining reasons behind the abnormal data, and identifying business processes and operations causing the abnormity; and S7, providing a corresponding decision support suggestion based on an analysis result in S6. According to the invention, through integration of data mining and machine learning technologies, the business abnormity identification, analysis and processing capabilities in an enterprise financial management system are effectively improved, the decision support of an enterprise for coping with a complex business environment is significantly enhanced, and the operation and financial risks are reduced.

The invention relates to the technical field of fault analysis, and particularly provides a monitoring fault analysis method fused with a multi-modal knowledge base, which comprises the following steps: collecting original data of a monitoring fault log, and preprocessing and storing the original data; performing data cleaning and feature extraction on the obtained original data of the monitoring fault log; constructing a searchable knowledge base based on the cleaned data; when the system triggers an alarm, mixed retrieval is executed through a dynamic routing mechanism; aggregating the plurality of retrieval results to generate an executable repair scheme; iteratively optimizing the decision process through manual feedback; and continuously optimizing the knowledge base and the diagnosis model to form a closed loop iteration mechanism. According to the scheme, the accuracy and response efficiency of fault diagnosis are improved.

The invention relates to the technical field of data analysis, can be applied to business scenes of medical health, financial science and technology, culture research and the like, and discloses a knowledge graph-based model analysis method, which comprises the following steps of: acquiring and processing target domain data, extracting and labeling keywords, constructing a knowledge graph comprising domain entities, a relational network and attribute characteristics, and analyzing the knowledge graph. Establishing a professional corpus and generating an analysis task, constructing a multi-dimensional analysis index system, inputting the analysis task to a to-be-analyzed model, obtaining a task output result, performing knowledge verification according to the knowledge graph, evaluating the knowledge verification result based on a multi-dimensional analysis index, and generating a final analysis result. According to the method, by fusing the knowledge graph and the multi-dimensional analysis indexes, the accuracy and comprehensiveness of evaluation are enhanced, the evaluation effect of the model in the aspects of professional knowledge understanding, reasoning ability and standard integrating degree is improved, the evaluation result is more reliable, and the application ability of the large model in the specific field is optimized.

The invention relates to the technical field of business adaptation, in particular to a business adaptation degree intelligent analysis method and system combined with a customer portrait. The method comprises the following steps: firstly, collecting customer data from a plurality of data sources, then processing the collected data, extracting interest labels of customers, constructing a customer-interest label matrix, comprehensively evaluating the purchase ability and loyalty of the customers, updating the portraits of the customers according to the interest preferences of similar customers, and then collecting the business demand data of the customers. The method comprises the following steps: constructing a business demand model, quantifying the demand intensity of a customer, generating a demand score vector, matching a customer portrait with the demand score vector, calculating an adaptation degree score, generating personalized recommendation for the customer, preferentially recommending a product or service with the highest matching degree with the customer demand, and pushing related recommendation through multiple channels. And the satisfaction degree and the conversion rate of customers are improved. According to the invention, the resource utilization efficiency and the customer satisfaction are improved, so that more efficient enterprise operation and customer service are realized.

The invention discloses a metal cutting process parameter optimization analysis method based on machine learning, and particularly relates to the field of machine learning. Comprising multi-dimensional process parameter feature extraction and preprocessing, cutting state intelligent identification based on integrated learning, dynamic process parameter sensitivity analysis and weight calculation, process parameter intelligent optimization under a multi-target constraint condition, and adaptive parameter adjustment and real-time control strategy. According to the method, the interaction relationship between complex nonlinear features and process parameters in the cutting process is comprehensively captured, and accurate and intelligent recognition of different cutting states such as normal cutting, tool abrasion and abnormal flutter is achieved through a three-layer integrated learning architecture; the technical bottlenecks that an existing system lacks real-time self-adaptive adjustment capacity and is low in process optimization efficiency are overcome, pertinence and effectiveness of parameter adjustment are ensured, and the technical current situation that machining quality fluctuates and repeatability is poor due to traditional fixed parameters is changed.

The invention discloses an abnormal behavior detection method based on multi-modal fusion and interpretability analysis, and the method comprises the steps: carrying out the data formatting, and converting unstructured behavior data into structured employee behavior data; thirdly, constructing a daily employee behavior sequence and extracting daily employee behavior characteristics; constructing a daily interaction graph and extracting an embedded representation of each employee node; statistical analysis is introduced to obtain daily employee statistical characteristics; splicing the three features to obtain a daily multi-modal feature fusion vector; and finally, identifying an abnormal vector set through an LOF algorithm, and carrying out importance analysis by adopting an SHAP value analysis method to output an abnormal reason. According to the method, multi-angle and multi-level feature representation is constructed, so that behavior diversity is captured more comprehensively, and perception and representation of complex user behaviors are improved; and meanwhile, fusion detection is carried out based on behavior feature importance analysis, so that abnormal employees can be locked, abnormal behavior reasons can be traced, and the interpretability of an abnormal detection result is enhanced.

The invention belongs to the technical field of stirring control, and particularly relates to an intelligent speed regulation stirring control method and system and computer equipment, and the method comprises the steps: determining a target control value of stirring control according to the requirements of a stirring technology; various sensors are installed on the stirring equipment, it is ensured that key parameters in the stirring process are accurately collected in real time, the data collection frequency is set, and it is ensured that dynamic changes in the stirring process are captured; filtering processing is carried out on the collected sensor data, noise interference is removed, the sensor data is corrected, and the accuracy of the data is ensured; key features in the stirring process of the combustion furnace are extracted through a data analysis method, and a mathematical model of the stirring process is established for prediction and control; a closed-loop control system is established, the stirring speed of the combustion furnace is adjusted according to data monitored in real time, the stability and accuracy of the stirring process are ensured through an intelligent algorithm and real-time parameter monitoring, insufficient stirring or excessive stirring is avoided, and therefore the product quality and the production efficiency are improved.

The invention relates to a power field Text2SQL (Structured Query Language) data analysis method based on LLM (Logical Language Model) and RAG (Random Access Gateway), and solves the defects that a retrieval result contains noise and redundancy and a large model still has a large number of illusion phenomena due to the absence of a vectorization model in a specific field and the singleness of knowledge base types and dimensions in the RAG technology compared with the prior art. The method comprises the following steps: constructing a data set of a Text2SQL data analysis method in the power field; constructing a multi-dimensional text knowledge base of the Text2SQL data analysis method in the power field; fine adjustment of an LLM base of a Text2SQL data analysis method in the electric power field; constructing an RAG retrieval module of the Text2SQL data analysis method in the power field; and constructing an Agent data analysis module of the Text2SQL data analysis method in the power field, and performing data analysis processing. According to the method, related information required by Text2SQL data analysis in the electric power field can be obtained by a large language model through retrieval on the basis of an RAG technology, meanwhile, the privacy and timely updating of the data are ensured by field data stored in a knowledge base form, and the model training cost is also greatly reduced by the LLM-based Text2SQL data analysis method taking a prompt project as a main part; and by setting the mixed retrieval parameters, the retrieval result has relatively high accuracy, and the effect of Text2SQL data analysis is ensured.

The invention discloses an AI server intelligent data analysis method based on edge-cloud collaboration, and relates to the technical field of edge-cloud computing collaboration, edge equipment is deployed at an edge end, original data of each task is collected in real time, the data is preprocessed, task difficulty features are extracted, and a task difficulty feature sequence is formed; and based on the task difficulty feature sequence and a pre-trained AI data analysis model, the task difficulty of the collected task data is evaluated in combination with the current resource condition, and the feasibility of task execution at the edge end is judged. According to the method, equipment is deployed at the edge end and data is preprocessed, so that part of computing tasks are effectively unloaded to the edge end, the computing burden of a cloud AI server is remarkably reduced, the edge end can independently complete simple tasks, complex tasks are uploaded to the cloud according to needs, excessive centralized use of cloud resources is avoided, and the computing efficiency is improved. Therefore, the energy consumption and the operation cost of the cloud server are reduced, and the energy efficiency ratio of the whole system is improved.

The embodiment of the invention discloses a multi-agent collaborative data visualization analysis method and device and a medium, and relates to the technical field of visualization analys.The method comprises the steps that a natural language analysis request input by a user is received, task splitting is conducted on the natural language analysis request through a preset task planning agent, and the task splitting result is obtained; generating corresponding task plan information, obtaining real-time operation data of a preset functional agent cluster, carrying out agent distribution on the plurality of sub-tasks based on the real-time operation data and the task plan information, determining a target functional agent corresponding to each sub-task, and sending the target functional agent to a server; the target function agent comprises any one of a data analysis agent and a visual display agent; and executing the corresponding data analysis subtask through the data analysis agent to obtain a data analysis result, performing agent cooperative verification on the data analysis result, and after the verification is passed, generating visual analysis data corresponding to the data analysis result through the visual display agent.

The invention provides a fusion media user personalized content recommendation and behavior analysis method and system, and relates to the technical field of big data, and the method comprises the steps: obtaining user historical browsing data, constructing a behavior feature vector containing content preference and time preference, employing a dual attention mechanism to process the feature vector, and obtaining a fusion feature vector; and obtaining a user interest prediction result by combining a learnable transformation matrix with a gating loop unit based on the fusion feature vector, establishing a multi-target balance recommendation strategy by combining a real-time hot event propagation trend, and screening and pushing to-be-recommended contents from a content library. According to the method, the timely pushing effect of the hot events can be improved while the individuation of the recommended content is ensured, and the user experience is improved.

The invention relates to the technical field of mine safety production analysis, and discloses a mine safety production data fusion analysis method based on multi-source perception, which comprises the following steps: step 1, carrying out real-time data acquisition on physical parameters related to safety production in a mine environment through various types of sensors arranged in the mine environment, the collected data are stored as independent data streams with timestamps, and the data of different sensors have time inconsistency according to the difference set by the sampling frequency and the clock; and step 2, carrying out time delay compensation processing on the multi-source data with the timestamps acquired in the step 1. An optimization model is established in combination with a variational method to perform time delay compensation and time synchronization processing on sensor data, the problem of time inconsistency caused by different sampling frequencies and clocks of sensors is eliminated, fusion processing of multi-source sensor data on a unified time reference is realized, and an accurate data basis with consistent time sequence is obtained.

The invention provides an intelligent statistical analysis method and system for the implementation progress of a production project, and the method comprises the steps: carrying out the initial classification of all task lists in the production project, and constructing a directed acyclic task graph according to the initially classified task lists to represent the dependency relationship between tasks; performing data acquisition on the execution state of the task according to the initial monitoring granularity of each task, and judging whether the actual progress of each task deviates from the planned progress or not according to the acquired data; behavior data of each project role is collected, then the behavior deviation of each role is calculated based on regularization, the cooperation efficiency among the roles is calculated according to the behavior deviation of each role, and then task allocation and progress management are dynamically adjusted. Re-optimizing the task priority and resource allocation according to a dynamic adjustment result; and feeding back to a project manager after the optimization task is executed, and providing intelligent decision support. The system has higher intelligence and real-time reaction capability.

The invention discloses a power cable fault diagnosis and analysis method and system based on multi-source data, and relates to the technical field of diagnosis and analys.During operation of the system, various sensors are installed in a cable and the surrounding environment of the cable, operation data of the power cable are collected in real time, and after the collected multi-source data are preprocessed, the power cable fault diagnosis and analysis method and system based on the multi-source data are obtained. The method comprises the following steps: calculating key diagnosis parameters by using a machine learning algorithm, obtaining a comprehensive fault diagnosis index CDI, comparing the comprehensive fault diagnosis index CDI with a preset first threshold M and a preset second threshold N, obtaining a fault level and outputting a corresponding processing strategy, and carrying out fault point positioning by using a reflected wave method in combination with multi-source data according to fault characteristics and a cable model. And outputting a specific position of a fault point, accurately positioning the fault point in the cable, integrating analysis results of the modules, and automatically generating a cable fault diagnosis report which comprises a fault type, a fault point position, potential risk analysis and maintenance suggestions.

The invention provides a big-data-combined bus system full-data comprehensive management analysis method and system, and the method comprises the steps: collecting a real-time operation data set uploaded by a plurality of bus terminals in a target region, carrying out the multi-dimensional feature extraction, generating a bus operation feature set, carrying out the abnormality recognition based on a preset abnormality detection model, and obtaining a bus operation feature set; determining an abnormal operation event set; performing time-space association mapping on the bus operation feature set and the historical operation data set, constructing a bus operation knowledge graph, performing dynamic path planning and resource allocation analysis on the graph, generating a target scheduling strategy set, and performing priority adjustment on the abnormal operation event set based on the target scheduling strategy set. Generating a bus scheduling optimization instruction set and issuing the bus scheduling optimization instruction set to a corresponding bus terminal; and updating a weight parameter and a historical operation data set of the multi-task optimization model based on the execution feedback data. According to the method, multi-source real-time data can be integrated, and a scheduling strategy is dynamically optimized, so that the problems of data islands and strategy stiffness are solved.

The invention discloses an index transaction attribution analysis method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining problem information, and decomposing the problem information into an intention, a business index and an index dimension; determining whether the user question needs to be subjected to index attribution or not; if the index attribution needs to be carried out, determining whether the business index is abnormal or not; if the business index is abnormal, determining an attribution diagnosis model according to the intention and the index type, and predicting an attribution result of the business index through the attribution diagnosis model; and determining a target external factor and a target internal factor which influence the attribution result, and obtaining countermeasures of the target internal factor or the target external factor. Through dialogue questioning and introduction of a large language model and an attribution analysis model, the reason of abnormal fluctuation of the index can be quickly positioned, minute-level attribution analysis is realized through a dialogue mode, and the method has wide application scenarios and potential.

The invention provides a BIM (Building Information Modeling) plane cutting analysis method based on Cesium, which comprises the following steps of: obtaining geometrical characteristics, topological relations and semantic attributes of components in a BIM, and calculating importance weights of the components according to a preset evaluation standard; obtaining the viewpoint position and the sight line direction of a user, calculating the visibility of the sub-region, and generating a clipping priority queue by combining the importance weight of the sub-region; dynamically scheduling a cutting task according to the cutting priority queue, and re-evaluating the cutting priority if the user viewpoint distance or the shielding relationship is remarkably changed; obtaining a component list of a to-be-cut sub-region, and calculating an influence weight of a component on a cutting effect according to the distance between the component and the viewpoint and the shielding relationship; and for the simplified and cut component, a progressive grid simplification algorithm is adopted, and the grid resolution is adjusted according to the geometric complexity and cutting precision requirements of the component.

The invention discloses a power distribution area equipment monitoring intelligent analysis method, and relates to the technical field of power system equipment monitoring, and the method comprises the steps: collecting and preprocessing the real-time operation data of power distribution area equipment; performing feature extraction on the real-time operation data by using a deep learning model to obtain an operation state feature vector; performing similarity calculation on the running state feature vector and a normal running state feature vector in a historical database to obtain a preliminary abnormal degree quantitative index; performing equipment state evaluation and classification according to the preliminary abnormal degree quantitative index to obtain an equipment state evaluation result; and taking corresponding measures according to an equipment state evaluation result, automatically generating a fault diagnosis report and pushing the fault diagnosis report to a maintenance terminal. The multi-parameter monitoring and deep learning feature extraction technology adopted by the invention improves the accuracy and comprehensiveness of anomaly detection, and can identify some potential fault features which are difficult to find by a traditional method.