1356 results about "Abnormality detection" patented technology

The invention discloses a power distribution room anomaly detection system based on cloud side-end cooperation, and belongs to the technical field of intelligent power grids. In order to solve the problems of high network bandwidth pressure, insufficient edge computing capability, low anomaly detection accuracy, difficulty in multi-source data fusion and the like caused by the adoption of an end-cloud direct connection architecture in an existing power distribution room monitoring system, the system comprises: a data acquisition layer configured with various heterogeneous sensors to acquire operating parameters and environmental data in real time; the edge storage and calculation layer carries out local real-time processing, anomaly detection, model training and visual display, an anomaly detection module of the edge storage and calculation layer carries out research and judgment on real-time data to generate early warning information, and a prediction and detection linkage module monitors an anomaly probability trend and adjusts a sampling frequency; the edge gateway realizes protocol conversion and data forwarding; and the cloud decision-making layer aggregates multi-edge node data, optimizes a global model through federal learning, and issues and updates a local model. The system is used for improving the accuracy, real-time performance and reliability of anomaly detection of the power distribution room, reducing the operation and maintenance cost and realizing intelligent operation and maintenance.

The invention discloses a method for detecting abnormal metering performance of an intelligent electric energy meter, which belongs to the technical field of electric energy metering equipment and comprises the following steps of: 1, establishing a reference response curved surface of parasitic parameters of a voltage sampling resistor and a current transformer in the electric energy meter relative to temperature and humidity; step 2, during the operation period of the electric energy meter, acquiring the real-time measurement value of the parasitic parameter in situ; and step 3, matching the real-time measurement value with the reference response curved surface. According to the method, an active matching model library can be constructed by fusing an underlying physical model of a component and a mapping rule set for a complex environment with sudden change of plateau outdoor temperature and humidity, the internal mechanism that parasitic parameters change along with temperature and humidity is explained from the physical essence level, and the limitation of a pure data statistical method is made up; the dynamic error judgment threshold value can adapt to environmental stress changes in real time, and the accuracy of metering performance anomaly detection and the complex environment adaptability are effectively improved.

The invention relates to the technical field of data processing, and provides a communication bus anomaly detection method and device, electronic equipment and a readable storage medium. The method comprises the steps of collecting multi-dimensional communication parameters of at least one communication bus in a vehicle, wherein the multi-dimensional communication parameters comprise protocol layer parameters and physical layer electrical parameters; the multi-dimensional communication parameters are input into a health degree scoring model, the health degree score of the communication bus is obtained, and the health degree scoring model is obtained through training according to historical communication parameter data; determining a state level of the communication bus according to a comparison result of the health degree score and a preset threshold value; when the state level is abnormal, performing matching with a predefined abnormal mode feature library based on the multi-dimensional communication parameters to obtain an initial abnormal type; and performing consistency verification according to the initial exception type and the state verification data from different data sources to obtain a target exception type of the communication bus.

The invention discloses a method, a system and equipment for predicting the stability of integrated circuit test equipment and a medium, and belongs to the technical field of integrated circuit test. The method comprises the following steps: firstly, carrying out preprocessing and feature selection on historical data in an FT test stage, and adopting a Gaussian mixture model (GMM) to cluster and identify different operation condition clusters of a test machine; then, establishing a health state GMM reference for each working condition cluster, calculating a KL divergence value of a normal sample and the reference, and setting a dynamic anomaly detection threshold by 99.73% quantile of the KL divergence value; and finally, in real-time monitoring, calculating a KL divergence value of real-time data and a corresponding working condition cluster benchmark, and comparing the KL divergence value with a dynamic threshold value to realize accurate anomaly marking. The method effectively solves the problem of abnormal detection of the test data of the integrated circuit under complex and changeable working conditions, and improves the monitoring accuracy and working condition adaptability.

The invention belongs to the technical field of stator slot wedge detection, and discloses a generator stator slot wedge online detection method and system based on acousto-optic monitoring fusion. The method comprises the following steps: acquiring laser ranging data, acoustic monitoring data and generator working condition parameter data of online looseness detection of a generator stator slot wedge; preprocessing the laser ranging data, the acoustic monitoring data and the generator working condition parameter data to obtain laser ranging features, acoustic features and working condition parameters; and inputting the optical ranging features, the acoustic features and the working condition parameters into a pre-trained auto-encoder for anomaly detection, and obtaining an online looseness detection result of the generator stator slot wedge. By fusing the optical ranging technology, the optical fiber acoustic monitoring technology and the unsupervised deep learning algorithm, the important technical breakthrough in the field of generator stator slot wedge looseness detection is realized, and online stator slot wedge looseness detection can be carried out.

The invention relates to the technical field, and discloses a power distribution network multi-time scale fault scene deduction method comprising the following steps: obtaining meteorological and power grid operation data, establishing a time sequence fault tree model, and analyzing the trigger probability of multi-line disconnection and rainstorm short circuit faults in a target time period; extracting cross-level fault propagation features, analyzing a dynamic coupling relationship among multi-line disconnection, transformer substation flooding and cascading trip, forming a fault feature mode set, and identifying a single fault and a cascading fault in combination with time sequence analysis; historical fault data are analyzed, time sequence features and topological features of single and cascading faults are extracted, if a single fault propagation path is in a single level, a support vector machine is used for being combined with the features to judge fault types, and a classification result is output; and performing anomaly detection and confidence evaluation on a fault classification result, outputting a fault evolution path and risk evaluation, and analyzing the contribution degree of cascading trip to a large-area power failure risk in combination with historical blackout data to obtain power failure risk probability distribution.

The invention discloses a distribution line abnormity monitoring and early warning method and system, and relates to the technical field of distribution line intelligent monitoring. The method comprises the following steps: acquiring operation data acquired by multiple types of sensors, and performing time alignment, normalization and fusion processing to form a comprehensive data set; inputting the comprehensive data set into a generative denoising model based on noise and abnormal signal distribution separability learning, and outputting a clean data sequence through noise suppression and abnormal feature fidelity joint optimization; identifying an abnormal evolution trend with a nonlinear amplification characteristic by using a Lyapunov index and a Hurst index, and generating a risk assessment result; and constructing and dynamically adjusting a self-adaptive early warning threshold set according to a risk assessment result, and outputting abnormal early warning information when a risk index exceeds a threshold. The method realizes parallel noise suppression and abnormal feature fidelity, has dynamic identification and self-learning capabilities, and significantly improves the accuracy of distribution line anomaly detection and the stability of an early warning system.

The invention discloses a hydraulic power plant equipment full life cycle health management system and method based on the Internet of Things, and relates to the technical field of hydraulic power plant equipment management, and the method comprises the steps: collecting operation parameters through a sensor network disposed on various types of equipment of a hydraulic power plant, and forming a data sequence after time synchronization and analog-to-digital conversion; the data sequence is subjected to noise reduction processing at the edge computing node, whether an abnormal state exists or not is judged on the basis of the data analyzed and processed by the lightweight anomaly detection model, the data is graded and marked according to a judgment result, and only statistical characteristics of abnormal data or non-abnormal data marked as high priorities are uploaded to a cloud data center; and the cloud data center identifies the type of the equipment component according to the uploaded data, calls the corresponding physical degradation evolution model, dynamically calculates the degradation degree of each component in combination with the real-time operation state, determines the weight relationship of each degradation dimension according to the service stage of the equipment, and generates a comprehensive health index.

The invention relates to the field of production line state monitoring, and particularly provides a production line state monitoring and MES integration notification method and system, and the method comprises the steps: collecting multi-modal data, and constructing a unified state context object; performing space-time alignment and dynamic weight fusion based on a process stage on the multi-source asynchronous data to obtain a feature vector; reasoning a semantic current state of the production line through a time sequence mode recognition model; generating an enhanced data set based on the state context and the current state of the production line, and outputting a structured state event through anomaly detection and root cause analysis; outputting an optimal response strategy through a predefined decision rule in combination with MES service information; and packaging the strategy into an operation instruction which can be identified by the MES and issuing the operation instruction, and driving the MES to execute work order creation, billboard updating or message notification. According to the method, semantic diagnosis, global decision and automatic response of the production line state are realized, so that the production transparency, the decision accuracy and the operation and maintenance automation level are improved.

The invention discloses a hardware equipment anomaly detection and fault prediction method and system, and relates to the technical field of server room operation and maintenance, and the system comprises a data collection module which is used for collecting CPU utilization rate, memory utilization rate, fan rotation speed and mainboard temperature indexes in real time from baseboard management controller (BMC) equipment; the graph causal modeling module is used for modeling the correlation and causal relationship among multiple equipment indexes to form a causal graph structure; the time sequence prediction module is used for predicting the equipment operation state in a period of time in the future through graph convolution and a causal attention mechanism; the anomaly detection module is used for generating an anomaly score and identifying a potential fault based on the deviation between a predicted value and a real value; and the alarm module is used for feeding an abnormal detection result back to the operation and maintenance system to realize early warning and decision support. The abnormal detection accuracy is remarkably improved, faults are predicted in advance, the interpretability of the causal relationship between equipment is provided, and the operation and maintenance risk of a machine room is reduced.

The invention relates to the technical field of sewage treatment, and discloses a chemical industry park emergency sewage treatment device abnormal discharge tracking method, which comprises the following steps: data synchronization: synchronously obtaining multi-source water quality time sequence data and hydraulic operation data of multiple monitoring points of a park pipe network; anomaly detection: establishing a time-varying adaptive baseline model based on the time sequence data, and calculating a residual error of the time-varying adaptive baseline model to detect a water quality anomaly event; response core construction: constructing a time-varying transport response core from a potential source point to a monitoring point through a dynamic model and data assimilation technology based on pipe network topology and real-time hydraulic data; and traceability inversion: inputting the residual error and the time-varying transport response kernel into an inverse problem solving model in which physical constraint and sparse prior are introduced. The abnormal emission tracking method for the emergency sewage treatment device in the chemical industry park aims at solving the problem that it is difficult to achieve accurate and reliable source tracing of abnormal emission events under the condition that the hydraulic working condition of a pipe network of the chemical industry park changes dynamically.

The invention discloses an intelligent tooling for paving a photovoltaic module and a control method thereof, the method is suitable for a robot end effector, and the method comprises the following steps: collecting three-dimensional space point cloud data of the photovoltaic module to generate pose information of the photovoltaic module, and controlling the end effector to carry out adaptive pose adjustment according to the pose information; the tooling is controlled to press down, and after it is judged that fitting is in place by monitoring the compression stroke of the suction cup, a vacuum adsorption instruction is started; the adsorption state is judged based on vacuum pressure change curve parallel monitoring and a dynamic balance algorithm, and a carrying permission signal is generated after a preset adsorption success condition is met; continuously carrying out anomaly detection in the carrying process, and if an anomaly is found, triggering a fault processing strategy; and releasing the negative pressure to place the photovoltaic module after the target position is reached, and rechecking the placement pose. Intelligent and self-adaptive picking and placing of the photovoltaic module by the tooling are realized, and the reliability, the safety and the automation level in the carrying process are improved.

The invention discloses an anomaly detection method and system for transaction flow data, and relates to the technical field of big data analysis. The method comprises the following steps: based on transaction flow data, acquiring operation behavior data of a target user in a transaction process and interaction behavior data after the transaction is completed, and sorting according to timestamps to form a user operation behavior sequence and a user interaction behavior sequence; inputting the sequence into a pre-trained abnormal risk prediction model, and outputting an initial risk coefficient, wherein the model integrates an individual behavior baseline and an adaptive weight module; acquiring a group behavior baseline of the similar user group, and correcting the initial risk coefficient in combination with a matching result of the sequence and the group baseline to obtain a final risk coefficient; and performing classification abnormity early warning operation on the final risk coefficient based on a preset risk threshold. According to the method, through whole-process behavior sequence analysis, individual and group baseline dual calibration and dynamic weight adjustment, the accuracy and timeliness of transaction flow data anomaly detection are effectively improved.

The invention relates to a transient electromagnetic and seismic wave multi-mode joint inversion imaging method based on physical information Transform, and belongs to the crossing field of geophysical exploration and artificial intelligence. Comprising the following steps: carrying out anomaly detection, interpolation, filtering and normalization preprocessing on transient electromagnetic and seismic wave original data; extracting features representing electrical property, elasticity and cross physical significance; serializing the spatial data through a gridding and alternating fusion strategy, and constructing an enhanced code fusing absolute and relative positions and physical attributes; a physically constrained encoder-decoder architecture is designed to carry out multi-scale forward modeling-inversion; and quantizing the uncertainty of an inversion result by adopting a Bayesian Monte Carlo method, and generating a confidence map. According to the method, through physical rule driving and multi-modal depth complementary fusion, the fine recognition capability and interpretation reliability of hidden disaster-causing geologic bodies such as underground goaf and collapse columns are effectively improved while the physical consistency of data is kept.

The invention discloses an anomaly detection method and device, equipment and a medium, and relates to the technical field of Internet. The method comprises the following steps: acquiring account association information, wherein the account association information comprises account interaction information and login equipment; generating an account association map according to the account association information; classifying atlas nodes in the account association atlas to obtain at least one node set; obtaining at least one abnormal account, and screening at least one target set where each abnormal account is located in each node set; and for each target set, performing security detection on the target set according to the relationship of each atlas node in the target set in the account association atlas and the account association information. According to the embodiment of the invention, the account security can be improved.

The invention provides a machine tool machining shaft anomaly detection method and system based on double-order excitation, and relates to the technical field of numerical control machine tools. The method comprises the steps that a key machining shaft of a numerical control machine tool is determined; constructing a shaft transmission anomaly detection model; a double-order reverse test sequence is injected into the key machining shaft through the controller, and a double-order pulse response sequence corresponding to the double-order reverse test sequence is recorded through the encoder; a pulse arrival time sequence is analyzed, and reverse response delay, static lag section length and speed transient characteristics are calculated; and inputting into a shaft transmission anomaly detection model for analysis, and outputting a reverse gap detection value of the key processing shaft. The technical problem that in the prior art, due to the fact that tiny anomalies of the numerical control machine tool are difficult to accurately detect, the machining anomaly detection precision is insufficient is solved, and the machining anomaly detection accuracy of the numerical control machine tool is improved through the double-order reverse test sequence and the shaft transmission anomaly detection model.

The invention discloses a paper product counting method based on machine vision, and relates to the technical field of image processing and industrial automation. The method is used for solving the problem of low technical accuracy of paper products in complex industrial environments. The method comprises the following steps of: quantitatively recording inherent interference characteristics of a counting area by constructing an interference baseline library capable of being dynamically updated; performing real-time anomaly detection and positioning, acquiring images through a multispectral camera, comparing baseline features, and generating a difference thermodynamic diagram to position a paper candidate area; performing multi-modal counting, adopting track coding counting logic for a dynamic transmission scene, and adopting thickness gradient counting logic for a static stacking scene; counting results are subjected to cross check, dynamic and static counting results are compared, and accuracy is ensured through confidence evaluation; and the accuracy and the reliability of a paper product technology are obviously improved.

The invention provides a gas sensor compliance adjustment detection method and system based on a double rule engine, and relates to the technical field of sensor detection.The method comprises the steps that backtracking segmentation is conducted, and M pieces of time sequence concentration data are obtained; carrying out monomer-level adjustment compliance verification, and outputting an adjustment anchor point stamp sequence; performing cross-data-source space-time alignment segmentation to obtain a plurality of space-time associated data blocks; constructing a cross check relation topology; executing space-level adjustment compliance dynamic verification, and outputting a plurality of space compliance factors; performing adjustment compliance conflict judgment, and outputting a plurality of dynamic risk entropies; performing risk attenuation weighting, and outputting a real-time health degree index; and mapping and outputting a real-time gas sensing risk level. The technical problems that in the prior art, due to the fact that the source of gas concentration fluctuation cannot be accurately recognized by depending on a general anomaly detection algorithm or a manual checking means, the illegal behavior that a sensor is not adjusted according to regulations cannot be accurately recognized, and the safety and reliability of gas monitoring are affected are solved.

The invention relates to the technical field of video detection, in particular to an anomaly detection method and system fusing large and small models and cooperating with cross-time domain perception. Comprising the steps of configuring a small model, a multi-modal large model and a cross-time domain parameter based on an electric power scene; acquiring a monitoring video stream, calculating a dynamic frame extraction frequency based on a scene change coefficient of continuous video frames of the monitoring video stream, extracting a frame sequence and recording a timestamp of each frame; inputting a frame sequence into the small model to obtain a candidate target, removing redundancy through non-maximum suppression, screening an effective target in combination with a single-frame confidence coefficient and an inter-frame time domain continuous overlapping rate, marking an alarm image and generating a preliminary analysis result; a time-domain-crossing video frame, power scene sensor data and environment data are collected according to a dynamic time window by taking an alarm image timestamp as a center, a visual time-domain feature and a power scene sensor time sequence feature are extracted, and a time-domain-crossing fusion feature is obtained through fusion. The problem of misjudgment and missed judgment caused by single-frame dependence in power scene anomaly detection is solved.

The invention relates to the technical field of industrial internet, in particular to a control system and method for stirrer production. Comprising an edge data preprocessing unit, a flexible production instruction distribution unit and a working condition diagnosis and protection control unit. By constructing an integrated control parameter mapping model, an industrial operation signal is converted into a collaborative sensing digital primitive; the production task is compiled into an intelligent production execution process cluster based on a resource scheduling algorithm, and equipment is driven to execute differentiated production; and performing situation assessment on the digital primitives by using the anomaly detection model, and generating a regulation and control instruction to execute process logic recombination and safety protection. According to the invention, the problems of low production information integration level and tight data in each link of a production control system are solved, and flexible scheduling and active security defense in the production process are realized.

The invention discloses an intelligent grabbing system anomaly detection and processing method and device based on 3D vision, and belongs to the technical field of industrial robot 3D vision guide grabbing. The method comprises the steps of collecting 3D point cloud data of stacked flexible plates; carrying out point cloud preprocessing and template matching, and determining a plate pose and a grabbing point; generating a cushion block detection area and a deformation detection area based on the grabbed point pose; counting the number of point clouds in the detection area, comparing the number with a threshold value, and judging whether cushion block residues or plate excessive deformation exists or not; if yes, triggering processing, and if not, outputting a grabbing instruction. The device comprises a truss manipulator, an end effector, a 3D visual sensor, a visual processing unit and a robot control unit. According to the method, the detection area is dynamically generated, point cloud threshold judgment is fused, self-adaptive detection and processing of excessive deformation of the cushion block and the plate are achieved, the robustness, safety and automation level of a grabbing system are remarkably improved, and the method is suitable for automatic unstacking operation of stacked flexible plates.

The invention discloses a power supply cell state diagnosis method and device, a storage medium and a program product, and belongs to the technical field of battery safety management. The method comprises the following steps: acquiring internal stress, temperature, terminal voltage and charge and discharge current values of a battery unit; based on the parameters, constructing a thermal runaway symptom index through a statistical correlation algorithm; inputting the historical state parameters into a long-short term memory (LSTM) network model, and predicting a future terminal voltage value; calculating the residual error between the predicted terminal voltage and the actual terminal voltage, and carrying out anomaly detection on the residual error; and performing comprehensive state diagnosis by combining the thermal runaway symptom index and the anomaly detection result. Internal stress parameters are introduced, LSTM prediction and OC-SVM detection are combined, early degradation symptoms can be captured from mechanical and electrochemical double angles, and the accuracy and advance of thermal runaway early warning are improved.

The invention provides an HTTPS protocol flow detection method, a program product, an electronic device and a storage medium, and is applied to the technical field of network security, the HTTPS protocol flow detection method is applied to a security gateway which does not decrypt the HTTPS protocol flow, and the method comprises the following steps: obtaining a session record corresponding to the HTTPS protocol flow, the session record comprises time sequence feature metadata, packet length feature metadata and TLS handshake metadata; feature extraction is carried out on the session record to obtain corresponding multi-dimensional behavior features, and the multi-dimensional behavior features comprise a traffic statistical feature, a TLS fingerprint feature and a time sequence behavior feature; and performing anomaly detection on the multi-dimensional behavior features to obtain a corresponding detection result. According to the scheme, an SSL decryption function does not need to be started, the problem that traditional feature code detection fails in an encryption scene is solved, and meanwhile, the performance loss and privacy disclosure risks caused by decryption are avoided.

The invention relates to the technical field of battery management, and discloses a battery fault diagnosis method and system for a CCS module, and the method comprises the steps: obtaining a multi-dimensional data sequence of the voltage, current and temperature of a battery pack, carrying out the abnormality detection, and obtaining a potential abnormal time period and a multi-dimensional data subset; performing clustering analysis on the multi-dimensional data subset to obtain an abnormal feature clustering center, and performing dynamic association analysis to obtain an association mode vector set; calculating the similarity of each single data and a fault propagation path, and matching the propagation path with a preset mode to obtain a fault propagation score; fusing the clustering center and the propagation score, positioning a fault monomer through a support vector machine regression model, and determining a fault monomer identifier; and carrying out segmented clustering and logic judgment on the data corresponding to the identifier, and determining a fault type. According to the invention, accurate positioning and type determination of fault monomers can be realized.

The invention relates to a welding seam abnormity identification method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring ultrasonic scanning data of a target welding seam; wherein the ultrasonic scanning data comprises an ultrasonic scanning result and corresponding time data; according to the time data, carrying out time-frequency conversion on the ultrasonic scanning data to obtain ultrasonic time-frequency data; inputting the ultrasonic time-frequency data into a preset anomaly detection model, and outputting to obtain an anomaly detection result; wherein the anomaly detection model is used for extracting target features according to input time-frequency data, and outputting a recognition result of the anomaly of the target welding seam based on the target features. By adopting the method, the abnormal welding seam can be identified more accurately, effective mapping from abstract time-frequency data to specific defect characteristics is realized, and a reliable basis is provided for welding seam quality evaluation.

The invention provides a log data-oriented system fault mining method, which belongs to the technical field of information, and comprises the following steps: collecting original log data of a system, and preprocessing original system logs, including analysis, key information extraction, vectorization and generation of a structured log event sequence; processing the log event sequence by adopting a two-stage model fusing anomaly detection and mode clustering so as to distinguish accidental anomaly and a real fault mode; and for each candidate fault mode cluster, analyzing a time sequence causal relationship between internal events, and positioning a root cause event according to the causal flow score of the node. The method has the advantages that dependence on artificial experience is reduced through full-process automatic modeling and analysis; according to the method, log analysis, feature extraction, anomaly detection and root cause positioning are all automatically completed by adopting a preset algorithm process, and the detection effect is maintained through self-adjustment of the model, so that the flexibility and sustainability of fault mining are improved.

The embodiment of the invention provides an anomaly detection method and device, electronic equipment, a storage medium and a computer program product, and is at least applied to the field of artificial intelligence, and the method comprises the steps: carrying out the standardization processing of an index data sequence of a to-be-detected object in a preset time window, and obtaining a standardized index sequence; performing data reconstruction on the standardized index sequence to obtain reconstructed index data; determining a reconstruction error of the to-be-detected object in a preset time window based on the index data sequence and the reconstruction index data; and performing anomaly detection on the to-be-detected object based on the reconstruction error. According to the invention, the detection efficiency of the anomaly detection process can be improved, and the detection quality of the anomaly detection process is ensured.

The invention provides a new energy automobile battery pack temperature abnormity real-time monitoring and fault diagnosis method, and relates to the technical field of new energy automobile battery management, and the method comprises the steps: building a temperature gradient field and heat flow conduction path topological relation through obtaining the temperature and current load values of a battery unit, and carrying out the temperature field reconstruction; a theoretical temperature response value is calculated based on a current load, correction is carried out in combination with a space thermal coupling strength distribution matrix to obtain a temperature reference value, a dynamic characteristic quantity is extracted to determine an abnormal evolution mode by calculating a deviation degree between a measured value and the reference value and a time evolution trajectory thereof, and finally a fault source position is positioned. According to the invention, the battery abnormity detection accuracy and the fault diagnosis efficiency can be improved.

The invention relates to an energy storage system health state online evaluation method and system based on digital twinning. The method comprises the following steps: acquiring real-time electrical data, real-time temperature data, a periodic event log and electrochemical fingerprint data of an edge end energy storage system, and preprocessing to obtain state time sequence data; extracting multi-source time sequence features from the state time sequence data according to a preset sliding time window, and performing time sequence coding to obtain a time sequence feature vector and a corresponding feature time window matrix; performing anomaly detection on the state time sequence data by adopting double criteria based on the feature time window matrix, and repairing abnormal and / or missing time sequence segments to obtain repaired time sequence data; and based on an edge end mixed digital twinborn model, performing state-parameter estimation according to the repaired time sequence data and time sequence feature vectors to obtain instantaneous state of charge estimation, local health state indexes and thermal field prediction. By adopting the method, high-precision and low-delay dynamic evaluation of the multi-dimensional health state of the energy storage system can be realized.

The invention provides a transformer abnormity monitoring method and system based on artificial intelligence, relates to the technical field of transformer state monitoring, and solves the technical problems of poor scene adaptability, multi-source data isolation and weak early warning in the prior art. The method comprises the following steps: acquiring real-time data and historical data of a transformer; preprocessing the real-time data through a preprocessing algorithm to obtain standard data; constructing an AI anomaly monitoring model based on historical data; wherein the AI anomaly monitoring model comprises a dynamic baseline module, an anomaly detection module and an anomaly classification module; and inputting the standard data into the AI anomaly monitoring model to generate an anomaly alarm. The method is used in the transformer abnormity monitoring process.