2626 results about "State graph" patented technology

The invention discloses a DDGS three-dimensional storage device based on PLC control and a management method of the DDGS three-dimensional storage device, relates to the technical field of intelligent storage management, and solves the problems that in traditional DDGS feed storage, the storage space utilization rate is low, the storage turning cost is high, risk control is not timely, and a quick response strategy is lacked. According to the method, the dynamic perception metadata warehouse is constructed, and DDGS material characteristic key data is monitored in real time to support anomaly detection; a storage control topology model is generated by using a graph theory modeling and dynamic attribute injection method to map an exception type; periodically collecting equipment data and fusing and updating a storage state; state diagram nodes are enhanced, risk response priorities are calculated, and grading operation is triggered; pLC control logic is dynamically adjusted based on historical data, and real-time monitoring and multi-device linkage control are achieved; the intelligent level of DDGS three-dimensional warehouse management is remarkably improved, the warehouse space utilization rate is increased, and the risk control capability is enhanced.

The invention provides a patient information collection and medical record construction system and method based on multiple rounds of dialogues, and the system comprises an intelligent guide interaction module which receives the natural language input of a patient; the context-aware question and answer engine adopts a dialogue state representation method based on a graph structure to construct entities, relationships and attributes of each round of dialogue into knowledge sub-graphs; the medical record information dynamic builder monitors an updating event of the dialogue state diagram in real time; the abnormal information detection module adopts a mixed conflict detection method combining rules and learning; and the medical knowledge graph support system maps the oral expression of the patient to a standard medical term system in real time through a multi-level semantic matching strategy. According to the invention, the doctor does not need to distract the record in the inquiry process, and can pay more attention to patient observation and clinical thinking. The quality of the medical record first draft automatically generated by the system is high, a doctor only needs to perform a small amount of auditing and supplementing, and the medical record writing time is greatly shortened.

The invention relates to the technical field of intelligent manufacturing and production management, in particular to a self-adaptive production scheduling system based on artificial intelligence, which comprises a data acquisition and reference construction module for analyzing process data to construct a directed acyclic graph representing a non-interference state as a reference map; the theoretical disturbance simulation module is used for converting the interference rule into a graph change instruction, generating a theoretical damaged state graph and obtaining a theoretical difference feature vector; the theoretical difference feature vector comprises, but is not limited to, a vector form obtained after a difference matrix is expanded according to rows or columns in terms of mathematical representation; the real deviation extraction module is used for collecting real-time state data to construct a real-time operation state diagram and calculating a real difference feature vector; a double-domain coupling decision module; an adaptive scheduling execution module; according to the method, the causal relationship is verified by comparing the form of theoretical deduction and actual observation, non-systematic noise is effectively filtered, and accurate response to real faults is realized while the stability of the production rhythm is maintained.

The invention discloses an intelligent question bank retrieval and recommendation system based on an artificial intelligence knowledge graph, and relates to the technical field of artificial intelligence education. Comprising a knowledge graph construction module which is used for processing original education data and constructing a neighborhood knowledge graph comprising a hard preposition relation and a soft incidence relation; the user knowledge state graph construction module is used for constructing a user personal knowledge state graph isomorphic to the domain knowledge graph, and dynamically calculating a mastery index of each knowledge node through a deep knowledge tracking model based on user historical answer data; according to the method, by constructing the domain knowledge graph containing the hard preposition relation and the soft incidence relation, discrete knowledge points are organized into the structured network conforming to the cognitive law, so that the system can understand and follow the internal logic between knowledge, and a learning path which is clear in organization, efficient and coherent is generated.

The invention discloses an industrial ERP dynamic scheduling optimization method based on deep reinforcement learning, and the method comprises the following steps: S1, obtaining to-be-scheduled industrial ERP data, and carrying out the preprocessing of the to-be-scheduled industrial ERP data; s2, constructing a heterogeneous graph structure, and setting node types and edge weight parameters in the graph; s3, performing multi-layer graph feature extraction on the initial scheduling state graph based on a graph attention mechanism to generate a node feature vector; s4, constructing a distributed reinforcement learning environment, and setting an independent strategy sub-network for each type of scheduling sub-tasks; s5, performing joint decision-making on the scheduling action to generate a scheduling scheme as a current round scheduling result; s6, calculating a reward value and feeding back the reward value to the corresponding strategy sub-network; and S7, repeatedly executing the steps S1 to S6, and generating a new scheduling action by using the updated strategy sub-network. According to the method, adaptive optimization of industrial ERP scheduling under multi-source heterogeneous data is realized, and the resource utilization rate and the order delivery efficiency are remarkably improved.

The invention relates to the technical field of cold chain product traceability, and discloses a cold chain product whole-process wireless traceability system based on the Internet of Things, and the system comprises an acquisition module which is used for collecting environment data of a cold chain product; the state matrix construction module is used for constructing an observation state matrix; the feature extraction module is used for extracting observation state matrix features and constructing transportation abnormity and risk grade indexes; the risk evaluation module is used for evaluating whether environment abnormity and state mutation exist or not at present; the data uploading control module is used for sending alarm information to the cloud; a state graph modeling and visualization module; and constructing a state graph based on the alarm information. State estimation is carried out through the state estimation unit, reconstruction compensation can still be carried out on key data when the nodes are in the dormant state, the integrity and continuity of a data chain are guaranteed, a data blind area is effectively filled, the integrity of data detection is improved, and the accuracy of cold chain product traceability is improved.

The invention provides a mountain power station current collection system state online monitoring method and device, and relates to the field of data processing. According to the method, distributed optical fiber data, environmental meteorological data and electrical measurement data are acquired to construct a multi-mode time sequence matrix, an operation state diagram is formed through graph structure representation and graph signal processing, a working condition mode cluster is obtained by combining dynamic clustering, a constraint diagram optimization framework is input, thermal, electrical and mechanical constraints are introduced, and an operation state track is generated. And constructing and fusing a non-stationary degradation path library and environment exposure history to form a dynamic degradation evolution model, and finally outputting in-service reliability and predicting residual life by a risk inference engine to realize online monitoring of the state of the current collection system of the mountain power station. By implementing the technical scheme provided by the invention, the accuracy of online monitoring of the state of the current collection system of the mountain power station is improved.

The invention discloses a research and development test bidirectional synchronization method based on artificial intelligence, and particularly relates to the technical field of artificial intelligence and software testing. Deep semantic modeling of research and development task behaviors is realized by constructing a multi-modal causal intention map, a test coverage blind area is inferred and recognized based on a graph neural network, a completion task is reversely generated, a complete state set is formed in combination with an original task set, and a condition control vector is adopted to drive generation of a structured test case. Collecting behavior and operation logs after the test is executed, detecting a mismatch area through modal alignment, generating a repair suggestion by utilizing a deviation correction model, and automatically writing back a task system; meanwhile, a state graph dynamic updating mechanism and a reverse task scheduling engine are introduced, and linkage feedback of test results on research and development tasks is achieved; the method can improve test coverage precision and research and development cooperation efficiency, and is suitable for agile development and continuous integration scenes.

Methods, systems, and computer readable media for rendering context-aware and interactive artificial intelligence-generated videos in real time. A talking face (“TF”) model may traverse from a first node to a second node of a state graph via an edge based on a TF instruction generated by an interaction model. The TF model may retrieve a transition video associated with the edge and a pre-computed video template associated with the second node from one or more TF databases. The pre-computed video template may include a plurality of masked video frames and a plurality of pre-computed mouth positions for each masked video frame. The TF model may inpaint a pre-computed mouth position into a masked region of each masked video frame to form a video frame stream. The interaction model may generate a video from the transition video and the video frame stream and present the video on a user device.

The invention discloses an API-oriented dynamic fuzz testing and vulnerability detection method, and belongs to the technical field of software security testing. The method comprises the following steps: extracting a dependency relationship, an input parameter, an output response and context state data of API calling, generating an initial API dependency graph, dynamically updating by capturing API state change in real time, forming an API state graph, and executing boundary-oriented variation based on parameter constraint characteristics, so as to obtain an API state graph; generating a variation test parameter, calling an API (Application Program Interface) of the variation test parameter to monitor a process memory behavior and response metadata, and generating a multi-dimensional abnormal signal; and performing mode matching on the abnormal signal and the vulnerability feature knowledge base, outputting a vulnerability type label and generating a path tracing report. According to the method, a context state sensing dynamic graph modeling technology is adopted, and a boundary-oriented intelligent variation strategy and multi-source abnormal behavior collaborative analysis are combined, so that precise vulnerability triggering, intelligent vulnerability judgment and complex scene coverage can be realized.

The invention relates to the technical field of government affair entrepreneurship guidance service intelligent dialogue systems, in particular to a dialogue intention recognition and correction method based on a knowledge graph, and the method comprises the steps: obtaining a voice signal and text information inputted by a user, and carrying out the cross-modal fusion processing to generate a semantic vector; carrying out evolution modeling on the dialogue state by adopting a graph neural network, and outputting a dynamic dialogue state graph containing a time sequence relationship between a user entity and a business object; taking the semantic vector and the focus information as query conditions, executing dynamic subgraph sampling from the government affair knowledge graph, and generating an intention correction strategy vector; and finally, utilizing reinforcement learning to generate personalized guide statements and outputting the guide statements to the user. According to the method, through deep coupling of semantic understanding, state tracking, knowledge reasoning and decision optimization, real-time perception and active completion of dynamic updating of hidden nodes and policies of the government affair process are achieved, and the intention recognition accuracy and systematic adaptive capacity are effectively improved.

1. Name of the product of this design: Environmental monitoring site-focused graphical user interface for electronic equipment. 2. Purpose of this design product: used to display and run programs. 3. The key design point of this design product lies in the graphical user interface on the screen. 4. The picture or photo that best illustrates the key points of the design: main view. 5. Purpose of the graphical user interface: It is used to monitor and analyze environmental monitoring sites. Through the novel focus interface design, it is convenient for users to quickly lock on the target object. Click "Close" in the main view to get the change state diagram 1. Click any map icon in the change state diagram 1 to get the change state diagram 2. Click the "Close" button in the change state diagram 2 and check "Outlet Water Quality" in the legend below to get the change state diagram 3. Click any water quality station in the change state diagram 3 to get the change state diagram 4.

The invention relates to the technical field of cognitive behavior therapy, in particular to a scheme intelligent pushing and task self-adaptive management method based on cognitive behavior therapy, which comprises the following steps: S1, acquiring physiological signals, behavior logs and environment interaction data of a user through a multi-modal data acquisition module; s2, constructing a dynamic psychological assessment model based on a cognitive behavior theory, and fusing multi-source data by adopting a Bayesian network to generate a user cognitive state map; s3, generating a personalized intervention scheme through a reinforcement learning algorithm according to the cognitive state map and a preset CBT intervention rule base; according to the method, through multi-modal data acquisition, dynamic psychological assessment, reinforcement learning scheme generation, task decomposition optimization, difficulty adaptive adjustment and digital twinborn simulation, full-process closed-loop management from data acquisition to intervention optimization is constructed, and efficient, safe and personalized cognitive behavior treatment scheme intelligent pushing and task adaptive management are realized.

The invention discloses a design manuscript code generation and visual error iterative correction method and system based on multi-agent cooperation, and aims to solve the problems of low front-end code generation efficiency, poor visual restoration degree and lack of an automatic correction mechanism in the prior art. The system constructs a four-node state diagram based on LangGraph, automatically initializes a series engineering scaffold, obtains design data and generates a structured protocol, and generates a component code based on the protocol and performs visual verification and iterative correction on four processing nodes. The method comprises the following steps: dynamically generating an engineering scaffold through a large language model agent, converting Figma design data into a structured protocol, and carrying out iterative optimization by utilizing a judger-repairer mode until an error is lower than a threshold value; according to the method, high-fidelity automatic delivery from the design manuscript to the code is realized, and the executable front-end engineering code and the visual reduction degree analysis report are output.

The invention discloses a space chain reasoning-based space intelligent multi-modal space understanding and planning method, relates to the field of space intelligence, solves the problem that the existing space intelligent multi-modal reliability and interpretability are insufficient, and comprises the following steps: S1, obtaining multi-modal space data; s2, constructing a spatial semantic state diagram; s3, task nodes are extracted, the spatial semantic state diagram is traversed, a logical reasoning list is constructed, and a reasoning track is obtained; s4, performing simulation operation on the motion subject model in the space world model based on the reasoning track, recording a simulation position and a simulation operation state, and optimizing the reasoning track to obtain an implementation track; s5, constructing a space cost function, and dynamically optimizing the implementation trajectory according to the space cost function; s6, the reasoning track is corrected; according to the method, the reliability and interpretability of space intelligence are effectively improved in a space chain reasoning mode.

The invention discloses an automatic compliance examination method and system based on graph retrieval enhancement. The method comprises the following steps of: obtaining a billing specification and a historical inquiry report as input documents; the input document is subjected to dual-mode document segmentation based on a large language model, logic blocks and physical blocks are generated, and each logic block is a continuous page unit and is attached with a content abstract generated by the model; constructing a precedent graph, extracting key legal elements in the historical inquiry report through multi-stage recursion, generating semantic network nodes carrying traceability identifiers, and establishing cross-document semantic association; and constructing a state graph, automatically identifying chapters and logic and semantic relationships among the chapters based on a document hierarchical structure, and generating a machine-readable structured index and the like. According to the method, the long text semantic understanding depth, the cross-section consistency and the legal reasoning accuracy are remarkably improved, the manual review cost is reduced, and the method is suitable for a listing compliance review scene under a registration system.

Implementations involve: receiving an unstructured free form natural language input; generating a chat robot based on the unstructured free form natural language input and in response to receiving the unstructured free form natural language input; and causing the chat robot to perform participation in a corresponding session with the additional user. In various implementations, an unstructured free-form natural language input implicitly defines a corresponding dialog state graph (e.g., defines a corresponding dialog state and / or a corresponding dialog state transition) without defining any explicit dialog state and / or explicit dialog state transition. In other implementations, unstructured free-form natural language inputs are assigned to explicit dialog states and / or explicit dialog state transitions. Although, unstructured free-form natural language inputs may be used to trim and / or launch machine learning models that have already been able to be used to conduct generalized sessions. Thus, chat robots may be generated and deployed in a fast and efficient manner.

The invention relates to the technical field of water conservancy project intellectualization, and discloses a water affair system digital twin-driven intelligent early warning and self-adaptive regulation and control platform, which comprises the following steps of: acquiring multi-source data related to operation of a water affair system through Internet of Things equipment, and generating a data set in a uniform format; integrating the multi-source data through a cross-domain data fusion engine to generate a fusion feature set; analyzing a causal relationship among variables in the multi-source data through a time sequence causal inference engine, and generating a dynamic causal graph; a system operation mode is optimized through information interaction between nodes, and distributed cognitive data is generated; generating a state atlas of the water affair system based on the topological network and the dynamic causal graph through a synchronous state atlas visualization engine; and generating a regulation and control instruction according to the multi-target collaborative regulation and control strategy, and updating the data set. According to the method, the abnormal events in the water affair system can be accurately identified through the cross-domain data fusion engine and the time sequence causal inference engine.

The invention relates to the technical field of short message transmission optimization, and discloses an intelligent short message resending system and method based on receipt state recognition. A communication data acquisition module of the system obtains historical short message transmission records in a preset time period of a target communication area and generates a communication state data set; a feature extraction storage module performs receipt feature extraction on the data set to obtain a historical receipt feature set and establish a receipt state change matrix; the topology modeling module constructs a terminal state atlas according to the matrix, and calculates the topology aggregation degree of the historical short message failure events to determine an abnormal characteristic index set; a real-time analysis module extracts real-time receipt features of the current short message transmission data to generate real-time state vectors, and calculates a topology correlation degree with the abnormal feature index set in a terminal state graph to generate real-time risk factors; and the decision generation module generates a short message resending strategy instruction according to the real-time risk factor, the historical receipt feature set and the receipt state change matrix.

The invention discloses an agent evaluation processing method and device, a storage medium and electronic equipment, and the method comprises the steps: determining an agent service object driven based on a transaction processing large model, carrying out the graph structure evaluation benchmark generation processing of the agent service object, obtaining a graph structure task, and carrying out the graph structure evaluation benchmark generation processing of the graph structure task; the graph structured task comprises a directed acyclic graph composed of a plurality of sub-task nodes, controlling the agent service object to call a transaction processing large model to perform transaction task autonomous processing on the graph structured task, in the transaction task autonomous processing process, obtaining sub-task node processing state information of the agent service object, and sending the sub-task node processing state information to the agent service object; and performing node state updating on the graph structured task based on the sub-task node execution state information to obtain a structured task state graph, and performing task processing capability evaluation processing on the agent service object based on the structured task state graph.

The invention discloses a civil engineering construction task automatic scheduling method and system. The method comprises the steps that a heterogeneous graph model representing construction task elements and the incidence relation of the construction task elements is constructed; processing the heterogeneous graph model by using a graph neural network to mine a dependency relationship between processes and extract risk association features related to uncertainty to form a construction state graph; constructing a Bayesian network based on the construction state atlas; probabilistic reasoning is carried out through a Bayesian network, the influence of uncertain factors is quantified, and key information representing risk conduction is fed back to the graph neural network so as to dynamically correct recognition of the graph neural network on a process dependency relationship; the dependency constraint output by the graph neural network and the risk quantification result output by the Bayesian network are fused, and a construction task scheduling scheme is generated; and synchronously updating the heterogeneous graph model, the graph neural network and the Bayesian network according to field construction feedback data to realize closed-loop iterative optimization of a scheduling scheme.

The invention provides an interactive command line tool-oriented intelligent task planning and execution method and system, and the method comprises the steps: S1, generating a corresponding command sequence template for each operation of a target tool through a first large language model, and constructing an executable knowledge base; s2, calculating the semantic similarity between the task description and each command sequence template to obtain a candidate set, and selecting the most relevant template as a retrieval context by using a second large language model; s3, modeling a task execution process as a state diagram search problem, selecting candidate nodes from reachable nodes of a current node based on an algorithm, and selecting an optimal node according to a current state and a retrieval context by utilizing a second large language model; s4, executing an execution action corresponding to the optimal node, and analyzing tool output by using a second large language model to adjust a heuristic value of the corresponding node and execute a corresponding rollback strategy; and S5, repeating the steps S3 to S4 until the reasoning result shows that the task reaches the target node or the number of attempts exceeds a preset threshold.

The invention discloses a dynamic science popularization project management system based on interdisciplinary data fusion. According to the system, a multi-source data warehouse is constructed, astronomy, geography, programming behaviors and evaluation data semantics are aligned into a knowledge graph, and a personalized project type exploration task is generated through semantic path search in combination with a student cognitive state graph; the multi-agent reinforcement learning scheduling engine allocates resources in real time, optimizes task paths, fuses behavior modeling and a fuzzy evaluation model in task execution to generate cognitive feedback and advanced capability evaluation, and realizes automatic task generation, intelligent scheduling and closed-loop evaluation in popular science teaching.

The invention relates to the technical field of psychological health intelligent evaluation and collaborative intervention, and discloses a family-school psychological health collaborative method and system.The method comprises the steps that a multi-dimensional data model is built based on the psychological theory, and family-school double-end data collection is achieved through standardized indexes; constructing a multi-dimensional psychological state diagram structure, and realizing home-school data deep fusion by adopting a time sequence dynamic coding method; a time sequence modeling and multi-level risk assessment method is adopted, and a dynamic mental health risk assessment result and an intelligent early warning signal are output; a cross-platform collaborative intervention management method is adopted, and a personalized intervention strategy and a collaborative task allocation scheme are output; and a multi-dimensional effect evaluation and reinforcement learning optimization method is adopted to realize continuous optimization of an intervention strategy. Through artificial intelligence technologies such as the dynamic graph neural network and deep reinforcement learning, intelligent fusion and cooperative intervention of family-school psychological health data are realized, and the early recognition accuracy and intervention effect of psychological health problems are improved.

The invention discloses a case mediation sentiment analysis and decision support method based on deep learning, and the method comprises the following steps: S1, collecting multi-source data in a mediation process, carrying out the preprocessing, and constructing a multi-modal input feature set; s2, constructing a dual-channel collaborative attention network emotion recognition model; s3, optimizing model structure hyper-parameters by using a wild goat optimization algorithm; s4, reasoning multi-modal features, and outputting a structured emotion vector; s5, constructing a multi-node mediation state map, and depicting an emotional evolution path; s6, generating a mediation behavior scheme based on the multi-node mediation state map and the strategy rule; and S7, collecting feedback data, and updating the model and the strategy library. The invention aims to improve the emotion perception accuracy and the mediation decision intelligent level in the case mediation process through a multi-modal emotion recognition and strategy generation mechanism.

1. The name of the design product: custom window of graphical user interface for customizing display layout of electronic device. 2. The use of the design product: electronic device. 3. The design points of the design product: in graphical user interface. 4. The picture or photo that best indicates the design points: design 1 front view. 5. Design 1 is designated as the basic design. 6. The use of the graphical user interface: enables the user to customize the display layout of data received from one or more inputs connected to the medical console. 7. The human-computer interaction mode of the graphical user interface: interact through the touch screen of the medical console, when the number of screens in the upper right corner of the graphical user interface is selected as "1", the display layout shown in each design front view appears; when the number of screens in the upper right corner of the graphical user interface is selected as "2", the display layout shown in each design change state diagram 1 appears; when the number of screens in the upper right corner of the graphical user interface is selected as "3", the display layout shown in each design change state diagram 2 appears; when the number of screens in the upper right corner of the graphical user interface is selected as "4", the display layout shown in each design change state diagram 3 appears. 8. Other circumstances that need to be explained: the dashed line does not constitute part of the claimed design.

The invention discloses a train intelligent control method and system based on big data analysis, and relates to the technical field of train intelligent control, and the method comprises the steps: obtaining train traffic original data through a traffic sensor network, and constructing a traffic feature matrix; constructing a train track topological structure chart, and constructing an anomaly detection model to detect abnormal nodes; combining an attention mechanism and double-layer time modeling to construct a dynamic graph neural network prediction model, and generating a cellular node prediction state; calculating propagation influence intensity of abnormal nodes based on a dynamic weight propagation formula, generating a dynamic abnormal propagation weight matrix, and calculating an abnormal influence index; and constructing a state space and an action space based on the state diagram, optimizing train scheduling by adopting a DQN of a dual-network architecture, and generating an intelligent scheduling strategy. Full-process optimization from data acquisition, anomaly detection, state prediction, anomaly influence evaluation to intelligent scheduling is realized, and the intelligent level, safety and efficiency of train operation are remarkably improved.

The invention provides an urban road network traffic congestion state prediction method and system based on deep learning, and relates to the technical field of intelligent traffic control, and the method comprises the steps: obtaining congestion state image data of an urban road network, and carrying out the preprocessing to extract structured road congestion information; constructing a road network topological graph based on the road congestion information, and performing structure sensing state coding on the road network topological graph to generate a structure sensing state vector fusing local congestion features and global topological position information; and inputting the structure perception state vector into an improved deep learning prediction model, performing feature deepening and enhancement through a graph neural network which dynamically adjusts a graph structure and aggregates node information, and finally outputting road network congestion state prediction results of a plurality of time scales in the future. According to the method, accurate prediction of the urban road network congestion state can be realized, and reliable technical support is provided for intelligent traffic management, resource scheduling optimization, travel path planning and the like.

The invention relates to the technical field of electric power data, and discloses a multi-source electric power appeal data classification management method, system and device and a medium, and the method comprises the steps: constructing a multilayer graph structure according to an obtained multi-source electric power appeal data set; according to the multi-layer graph structure, a search sample set in which each search sample comprises a color vector, a state vector and a fitness state is constructed, the color vectors are determined by semantic features, and the fitness states are set to reflect the semantic connectivity of a semantic graph, the state fluctuation amplitude of a state graph and the adjacency density of a spatial adjacency graph; an improved peacock optimization algorithm is adopted to conduct iterative updating on the search sample set, the optimal fitness state of each search sample is obtained so as to determine classification labels corresponding to the search samples, and the improved peacock optimization algorithm is set to conduct iterative updating on each search sample according to the disturbance guide vector and the gravitational gradient direction. According to the method provided by the invention, the accuracy and adaptability of power demand sorting are improved.

The invention relates to an intelligent state monitoring method and system for a high-voltage isolation switch operating mechanism, and relates to the technical field of power equipment fault diagnosis, and the method comprises the steps: enabling a collected real-time sequence to update a standard operation response potential energy surface, obtaining a real-time operation response potential energy surface, generating a phase response difference tensor through data projection, and obtaining a real-time operation response potential energy surface; and depicting deviation characteristics of actual and standard operation states. And then, in the constructed graph attention network, on the basis of a real-time sequence, monitoring parameters and video motion characteristics, constructing a multi-modal sequence state graph, and determining a state typing result through fine-grained recognition and interpretable analysis of a fault. And finally, introducing a cyclic fingerprint residual model, performing clustering optimization based on a real-time sequence, determining an abnormal deviation index, and combining the abnormal deviation index with a state typing result to determine a fault diagnosis result. Therefore, the accuracy of intelligent state monitoring of the high-voltage isolation switch operating mechanism can be effectively improved, and the method can be suitable for non-ideal environments, low dominant anomalies and other conditions.