231 results about "State recognition" patented technology

The invention discloses a transformer state recognition method and device based on multi-scale time-frequency characteristics. The method comprises the steps that voiceprint signals generated when a transformer operates are collected; processing the voiceprint signal, and extracting a multi-scale time-frequency feature to obtain a time-frequency tensor; the time-frequency tensor is input into a pre-constructed multi-scale time-frequency model, and the multi-scale time-frequency model is constructed based on multi-scale convolution, a Transform neural network and a double-branch attention mechanism; and outputting a transformer state identification result through the multi-scale time-frequency model. According to the method, the voiceprint feature expression capability can be remarkably improved by extracting the multi-scale time-frequency features and the multi-scale time-frequency model, so that the transformer state recognition precision and speed are improved, and stable real-time recognition of the transformer state can still be realized in a complex noise environment.

The invention discloses an intelligent diagnosis method for power equipment primitive state recognition and rule fusion, and relates to the technical field of power system dispatching automation. The method is used for substation dispatching master station monitoring picture equipment primitive state recognition and abnormity diagnosis, video and graph model library data acquisition, preprocessing denoising, frame synchronization and ROI extraction. Using the improved YOLOv8-Tiny to extract a multi-modal feature to identify a primitive state; establishing an expert experience rule base, a graph-model association rule base and an anomaly judgment rule base; performing primary diagnosis on the forward chain reasoning fusion rule; performing cross checking on telecommand consistency, telemetering relevance and graph model library integrity; and outputting a result coexistence log. The problems that manual checking is low in efficiency and prone to omission are solved, the reliability of primitive recognition and correlation checking is improved, a D5000 system and a domestic operating system are adapted, complex monitoring scenes are coped, the operation and maintenance burden is relieved, it is guaranteed that monitoring pictures of a dispatching master station are accurate, and safe operation and maintenance of a power grid are facilitated.

The invention relates to the technical field of artificial intelligence and data processing, and discloses an energy storage equipment operation state monitoring method based on Internet of Things acquisition, which comprises the following steps: constructing a vibration data sample; performing state labeling on the vibration data sample to form a labeling database; performing sliding mean filtering and linear interpolation processing on the vibration data sample; carrying out adaptive spectrum noise suppression by adopting a dual-threshold wavelet packet noise reduction function; carrying out frequency band division by adopting a weighted multi-scale spectral entropy feature enhancement method; constructing a deep neural network model for classifying the running state of the energy storage equipment, and training the deep neural network model by using a labeling database; and inputting preprocessed vibration data acquired in real time into the trained deep neural network model, and performing state identification and fault alarm according to a category probability prediction vector output by the model. The running state of the energy storage equipment can be accurately monitored in real time, potential faults can be found in time, and measures can be taken.

The invention provides a gas turbine power plant rotating equipment state monitoring method and system based on voiceprint recognition, and relates to the technical field of power plant equipment monitoring, and the method comprises the steps: collecting an original voiceprint signal through deploying an array type acoustic sensor network; performing signal preprocessing, generating a voiceprint signal map, extracting Mel amplitude spectrum and phase spectrum features, constructing a multi-scale feature set, and generating a multi-dimensional voiceprint feature vector after screening; constructing an adaptive feature fusion network based on an attention mechanism, and fusing the multi-dimensional voiceprint feature vectors to generate voiceprint fusion features; inputting the voiceprint fusion feature into a preset voiceprint classification network, and outputting a real-time state recognition result, a confidence value and a fault prediction trend; according to the method, the early warning level is determined through the preset multi-level early warning mechanism, and the early warning information and the state monitoring report are output, so that high-precision state identification of the gas turbine power plant rotating equipment in a complex noise environment can be realized, and the accuracy and the anti-interference capability of fault diagnosis are remarkably improved.

The invention discloses a ring main unit state online monitoring and intelligent operation and maintenance system based on big data, and relates to the technical field of data monitoring, and the system comprises a ring main unit state data collection module which collects operation parameters in real time based on a sensor array disposed in a ring main unit, and generates a standardized feature vector; the equipment health monitoring module is used for performing state recognition and life prediction on the standardized feature vector based on a deep learning model, and generating an equipment health degree index; the intelligent operation and maintenance decision-making module is used for establishing an intelligent operation and maintenance decision-making model based on the health degree index and a preset operation and maintenance strategy library, and generating a maintenance work order based on the output of the intelligent operation and maintenance decision-making model; and the communication module transmits the monitoring data and receives the control instruction. According to the invention, fault early warning, state identification, life estimation and health degree index generation are carried out by combining the sensor array and edge calculation with deep learning, the operation and maintenance strategy is optimized, and the cost and downtime are reduced.

The invention relates to the technical field of intelligent training and data analysis, in particular to an intelligent model construction method and system for student training data analysis. Comprising the following steps: constructing an SOP time sequence knowledge graph, obtaining a standard operation specification document and expert-level operation demonstration data, and constructing the SOP time sequence knowledge graph; real-time feature extraction: capturing a multi-modal data stream, and generating a real-time behavior feature vector; state recognition and path deduction: inputting the real-time behavior characteristics into an event classifier to recognize a key event, performing path deduction based on deterministic finite state automaton logic in an SOP time sequence knowledge graph, and determining and outputting a current state pointer; deviation analysis and judgment: extracting a compliance mask based on a current state, and injecting the compliance mask into a decoding process of the attention network; and performing feedback and adjustment to generate a corresponding deviation event signal. Through deep analysis, feedback is more targeted, and the accuracy and fineness of training evaluation are remarkably improved.

The invention provides a pump room scene abnormal state recognition system and method based on video monitoring, and relates to the field of monitoring or monitoring activities, in particular to the field of space perception. The system comprises a synchronous trigger device which is used for synchronously triggering the collection of video data and audio data in a pump room in the same monitoring time interval; and the state identification device is used for intelligently identifying a pump room scene abnormity type by adopting an intelligent scene abnormity identification model on the basis of basic contents obtained by analyzing the video data in the pump room and the audio data in the pump room. According to the invention, in order to solve the technical problem that the recognition precision and efficiency of various specific pump room scene abnormal states cannot be considered at the same time, different intelligent scene abnormal recognition models can be designed for different pump room scenes and multi-modal basic contents screened in a targeted manner are introduced; and various specific pump room scene abnormal state identification is completed, so that the technical problem is solved.

The invention discloses a flexible operation and maintenance early warning device based on artificial intelligence. The flexible operation and maintenance early warning device comprises a multi-source data acquisition module, a data preprocessing and fusion module, an AI state recognition module, a risk reasoning and knowledge graph module, a flexible strategy engine module and an early warning output module. The device realizes cleaning, noise reduction, time alignment and feature fusion of multi-source data by collecting equipment operation indexes, log behaviors, security events and environmental parameters. The AI state recognition module is used for recognizing an operation state, a behavior mode and potential abnormity, and the risk reasoning and knowledge graph module completes risk source positioning and reason verification based on an entity relationship and a semantic link. And the flexible strategy engine dynamically generates an early warning strategy according to a reasoning result, and pushes early warning information in a multi-channel manner through an early warning output module. The device can realize high-accuracy, interpretable and self-adaptive operation and maintenance early warning capability, and is suitable for intelligent operation and maintenance management of hospital machine rooms and key business systems.

The invention provides a parking state monitoring system based on RISC-V, and relates to the technical field of automatic control. The parking state monitoring system based on the RISC-V comprises the following modules: a data acquisition module, a signal preprocessing module, a fusion judgment module, a state recognition module, a prediction control module, an energy consumption optimization module, a communication transmission module, a cloud analysis module and a feedback regulation module. Through a multi-source sensing synchronous sampling and time sequence correction method, multiple types of sensing signals are subjected to consistent sampling under a unified time reference, and the space-time matching degree of data is remarkably improved; an adaptive filtering and dynamic noise estimation mechanism is introduced in a signal processing link, so that an input signal keeps a high signal-to-noise ratio and stability in a complex environment; in the feature fusion and state recognition stage, probability weighted fusion and correlation matrix analysis are utilized to realize information multi-layer complementation, and the accuracy and reliability of parking space state recognition are ensured.

The invention belongs to the technical field of state recognition, and particularly relates to a truck wheel state recognition method and system, and the method comprises the following steps: S1, obtaining a to-be-recognized truck wheel image, and extracting a hierarchical feature map of a plurality of resolutions through a backbone network; separating each level of feature map into a low-frequency coefficient matrix and a high-frequency coefficient matrix through discrete cosine transform, performing multi-scale low-frequency feature fusion on the low-frequency coefficient matrixes of different levels to obtain a cross-layer low-frequency association map, and performing operation on the high-frequency coefficient matrixes of the same level to obtain an intra-layer high-frequency structure map; s2, determining a candidate region on the hierarchical feature map, detecting angular points in the candidate region, and calculating a covariance matrix of angular point positions; according to the method, the feature response of a key area is enhanced, the interference of a background and an irrelevant area is inhibited, frequency domain analysis and space geometric structure prior are combined, the quality of final fusion features is improved, and therefore the accuracy of truck wheel state recognition is improved.

The present application relates to the technical field of equipment state monitoring, in particular to a device state monitoring and intelligent early warning method based on edge computing, comprising collecting the nameplate text and control parameters of frequency converters, motors, pump groups and other equipment, splitting fields and judging component relationships, generating component calibration lists; combined with register addresses and other screening binding points, obtaining edge state sequences; verifying start-stop, steering and temperature rise states according to time stamps, obtaining alarm approval lists; combining position and time sequence to merge alarms, establishing early warning trigger lists; writing edge side early warning output tables and hanging state labels, generating state early warning information. The present application screens and binds key measuring points, realizes stable correspondence between data and components; synchronously aligns operating parameters based on time stamps and jointly judges start-stop, steering and temperature rise relationships, improves state recognition consistency; combines spatial position and time interval to group and merge alarms, forms a continuous alarm structure that can be positioned, and enhances early warning integrity.

The application relates to the field of old-age care equipment, and discloses a state recognition navigation recommendation method based on artificial intelligence and an old-age terminal device. The method comprises the following steps: acquiring terrain features, speed features, gait features and physiological features of a user in multiple walking trips when the user uses an intelligent walking stick, and acquiring map information and a walking destination of the user; wherein the terrain features, the speed features and the gait features are detected by a motion detection component of the intelligent walking stick; through a state recognition-navigation recommendation model, a navigation route and a walking guidance suggestion of the user in the next walking trip are determined according to the terrain features, the speed features, the gait features, the physiological features, the map information and the walking destination in the multiple walking trips; and the walking guidance suggestion comprises matters needing attention information when the user walks. The application can identify and detect the physical state of the old people and improve the nursing effect on the old people.

The present application relates to the technical field of intelligent circuit breaker, especially a device running state recognition method based on intelligent circuit breaker and intelligent circuit breaker; when data collected by the intelligent circuit breaker is synchronized, first, a time stamp is generated for the data collected by each sensor; the collected data is encapsulated into a data packet together with the time stamp, and is sent to a data processing unit integrated with the intelligent circuit breaker; then the data processing unit performs data alignment operation on the data packet; wherein the improved spline interpolation method is used for data alignment operation; and a physical model constraint condition is established, a linear equation set is constructed according to the physical model constraint condition, an interpolation coefficient is solved, and a cubic polynomial function is determined; then time-aligned data points are generated according to the cubic polynomial function; by introducing the physical model constraint condition, it is ensured that the interpolation result conforms to the actual physical process, and the reliability of subsequent analysis and state recognition is improved.

This invention belongs to the field of image processing technology and discloses a power equipment status recognition method based on multi-source data fusion. The method includes: acquiring multi-source data of a target transmission line segment and performing time synchronization processing to obtain aligned multi-source data; acquiring line structure parameters and, combined with aligned electrical vector data, analyzing excitation intensity indicators reflecting the intensity of periodic electromagnetic interaction between adjacent lines; determining whether preset suspected risk conditions are met; if so, generating a trigger command and collecting corresponding multi-source data to form an event data packet; extracting motion features from the event data packet to obtain the line's visual motion features; performing correlation analysis based on the corresponding electromagnetic excitation features to obtain physical verification results; and analyzing the risk level of the current line's dynamic swaying based on the physical verification results and visual motion features. This invention effectively warns and prevents the risk of insufficient conductor phase-to-phase distance caused by dynamic power flow attraction.

According to the intelligent diagnosis method and device for the operation state of the air preheater, by constructing a multi-mode state recognition and trend pre-judgment system of the air preheater, three operation states of'normal ', 'slight anomaly' and'serious anomaly 'can be quantitatively distinguished, historical trend visual comparison is supported, and scientific making of a maintenance plan is assisted; a modeling and multi-parameter combined judgment mechanism is adopted, manual experience is effectively replaced, low-probability and high-risk fault symptoms are accurately captured, false alarm and missing alarm are remarkably reduced, and dependence on manual inspection is reduced; each module of the system supports independent modeling and dynamic updating, has good adaptability and expandability, and is suitable for different units, capacities and manufacturer equipment; through early warning of hidden dangers such as blockage, leakage and aging in advance, non-planned furnace shutdown is avoided, meanwhile, the operation and maintenance rhythm is optimized, the operation resistance and energy consumption are reduced, the heat exchange efficiency is improved, and therefore the reliability and economical efficiency of equipment operation are enhanced.

The invention relates to the technical field of electric power intelligent terminal equipment safety state detection, and provides an electric power intelligent terminal equipment safety state detection method and system, equipment and a medium, and the method comprises the steps: carrying out the preprocessing of equipment operation data obtained according to the equipment type of target intelligent terminal equipment, and obtaining to-be-analyzed equipment operation data; according to the to-be-analyzed equipment operation data, performing state identification based on an equipment security state detection model to obtain an equipment security state detection result; state recognition in the equipment security state detection model comprises the steps of performing modal feature extraction on to-be-analyzed equipment operation data, performing intra-modal feature enhancement, inter-modal semantic association and multi-modal adaptive dynamic fusion on multi-modal features in sequence, and performing state recognition on fused features. According to the method, the space-time relevance among the multi-modal data and the distribution difference of the equipment data can be effectively captured, the self-adaptive high-precision fusion of the multi-source data is realized, and the high efficiency and precision of safety state detection and anomaly positioning are effectively improved.

The invention discloses an equipment health state intelligent identification-oriented low-quality data robust learning method. The method comprises the following steps of: obtaining multi-source sensor time sequence data of an electromechanical composite transmission system and preprocessing the multi-source sensor time sequence data; constructing a multi-sensor fusion identification network composed of a feature encoder, a projection head and a classification head; generating neighbor consensus tags and consistency measurement for samples according to the neighbor consistency of the embedded space, dynamically screening high-confidence samples and similar sample pairs, performing characterization pre-training by using a joint target of supervised contrast learning, classified cross entropy and sample pair similarity, and introducing a hybrid enhanced smooth discrimination boundary to obtain a robust encoder; freezing a robust encoder, training a new classification head only by using a high-confidence set, and obtaining a state recognition model; a state recognition model is used for directly reasoning a complete channel sample, a feature channel adaptability calibration module is introduced into a sensor channel data missing scene to dynamically compensate feature distribution offset, and probability distribution of each state category is output.

The application discloses an aircraft state recognition method, device and equipment and a storage medium. The method comprises the following steps: acquiring a to-be-recognized image collected by a target station; acquiring an image recognition scene matched with the to-be-recognized image according to the number of manufacturing areas included in the collection angle of view of the target station and the number of selectable aircraft states in each manufacturing area; and acquiring an image recognition result matched with the to-be-recognized image by using a target recognition model matched with the image recognition scene, wherein the recognition result comprises respective aircraft states in different manufacturing areas. The technical scheme of the embodiment of the application provides an aircraft in-position state recognition method applied to a 5G large aircraft park, and realizes the recognition of the in-position state of the aircraft to improve the accuracy of aircraft state recognition.

The invention relates to the technical field of data processing, in particular to a valve data acquisition and processing intelligent control system based on Internet of Things and edge computing, which comprises a valve state sensing module for acquiring opening and closing state information and continuous opening data of a target valve in real time through a magnetic sensitive switch and an angle encoder device to generate structured original state data; the edge feature extraction module is used for extracting multi-dimensional behavior feature indexes based on valve opening and closing curve change rate analysis, action frequency statistics and response delay evaluation; the intelligent anomaly judgment module takes the multi-dimensional behavior characteristic indexes as input, performs state recognition and classification according to a historical evolution mode, a self-learning empirical rule and a lightweight decision tree model, and outputs a structured diagnosis result; and the multi-channel remote pushing module is used for pushing the structured diagnosis result and the original state data to a remote monitoring platform. According to the invention, the remote intelligent alarm capability, the safety guarantee capability and the adaptive analysis capability of the industrial equipment are improved.

The invention discloses a state recognition method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of data processing.The method comprises the steps that when the wrist of a target user rotates, accumulated rotation angle information is calculated according to angular velocity information of wrist rotation, and calculating maximum continuous growth span information corresponding to the accumulated rotation angle information in the acquisition window according to a dual stack aggregation strategy, and determining rotation state information of the wrist of the target user according to the maximum continuous growth span information and an angle threshold value. According to the method, the calculation complexity of the maximum continuous increase span information is reduced, so that when the wrist rotation state information is identified and determined based on the maximum continuous increase span information, the identification efficiency of the wrist rotation state information can be improved, resource occupation is reduced, and the real-time performance is improved.

This invention describes a digital device for displaying information on a turnout system in the area of ​​the turnout's functional components. Several state identification elements move back and forth within a preset range when the turnout is switched, thus indicating the position of the switch blade. A set of position sensors is arranged on both sides of each state identification element. These sensors detect the proximity to the respective state identification element and determine its position. Each set of position sensors is electrically connected to a state detection unit. The state detection unit acquires the position information output by the position sensors of each state identification element and encodes this position information to obtain digitized turnout system display information.This invention simplifies the acquisition and transmission circuitry for display information by directly acquiring and transmitting the digitized position information of each status identification element. This reduces interference and errors and improves the accuracy of the display information transmission.

The invention belongs to the technical field of equipment monitoring, and particularly relates to a bearing state recognition method and system based on pseudo data, a terminal and a storage medium, and the method comprises the steps: obtaining historical bearing vibration data, carrying out the data processing, and constructing an initial data set; inputting the initial data set into the constructed TimeVQ-VAE model, generating pseudo data of the bearing state, and constructing an enhanced training set based on the pseudo data; training a BiIndRNN (Bidirectional Independent Recurrent Neural Network) model by utilizing the enhanced training set; and adopting a particle swarm optimization (PSO) algorithm to obtain an optimal hyper-parameter combination of the BiIndRNN model. According to the method, the TimeVQ-VAE model is introduced, so that high-quality pseudo data (the similarity index u is greater than or equal to 0.9) highly similar to real data can be generated, model training difficulty caused by scarcity of fault samples and imbalance of the number of various types of samples in an industrial field is effectively relieved, and dependence on a large amount of real labeled data is reduced.

The application discloses a device running state recognition method and device, an imager and a storage medium, and belongs to the technical field of industrial detection. The application realizes automatic recognition of the running state of the measured device based on signals collected in a target scene and reflecting the running state of the measured device. Since manual running state recognition of the measured device is not required, not only manpower cost is saved, but also efficiency is higher, abnormal conditions of the measured device can be found and handled in time, and the reliability and stability of device running are improved. In addition, in addition to outputting the running state recognition result, the scheme also performs visual display based on the generated image. The generated image and the running state recognition result are used to represent the current running state of the measured device from different angles. This visual presentation mode enables an operator to observe and visually check abnormal features such as sound, heat and discharge of the device, and helps the operator to efficiently master the running state of the device.

The application discloses a face abnormal expression recognition method and device, electronic equipment and storage medium, which can be applied to various scenes such as cloud technology, artificial intelligence, intelligent transportation and Internet of Vehicles, and the method comprises the steps of acquiring a face image to be recognized, performing feature extraction on the face image, performing global expression abnormality recognition processing according to the extracted face features to obtain a global expression abnormality recognition result, wherein the global expression abnormality recognition result indicates whether the expression is abnormal, performing state recognition processing on at least one key face area according to the face features to obtain a state recognition result of the at least one key face area, and determining a face abnormal expression recognition result according to the global expression abnormality recognition result and the state recognition result of the at least one key face area. The application can accurately determine the abnormal expression face, and is beneficial to improving the quality of the cover picture.

The application is suitable for the technical field of power equipment state monitoring, and provides a flow blocking valve state identification method, system, device and program product. The method comprises: acquiring a reference image and a to-be-inspected image of a flow blocking valve; identifying first structure key points from the reference image, and identifying second structure key points from the to-be-inspected image; performing geometric correction on the to-be-inspected image based on the first structure key points and the second structure key points, to obtain a corrected image consistent with the reference image in geometric space; calculating a reference angle in the reference image and a current angle in the corrected image respectively, and determining the state of the flow blocking valve according to the angle difference between the current angle and the reference angle. The accuracy and universality of flow blocking valve state identification are improved.

The application relates to the technical field of neural networks, and provides a rail fastener abnormity identification method and system based on deep learning. First, a rail depth map and a corresponding collection position are acquired; then, a fastener detection model is used to locate the position of a fastener in the rail depth map and generate a predicted frame image with the fastener and basic information of the predicted frame image; then, the predicted frame image and the basic information of the predicted frame image are input into a pre-established fastener abnormity state identification model, the fastener in the predicted frame image is classified again by the fastener abnormity state identification model, and the final category of the fastener is obtained. The fastener in the rail depth map is located, the predicted frame image with the fastener is extracted, the final category of the fastener is identified by the fastener abnormity state identification model, the operation complexity is reduced, the detection speed and the identification accuracy are improved, and the detection result can be fed back in time.

The invention relates to the technical field of electric digital data processing, in particular to an entity state recognition method and device based on multi-modal time sequence fusion and a medium. The method comprises the steps of obtaining a description text of a target entity and numerical state index time sequence data of the target entity; encoding the description text of the target entity by using a pre-training language model to generate a text semantic feature vector; splicing the text semantic feature vector with the numerical state index time sequence data to obtain a multi-modal time sequence feature sequence; inputting the multi-modal time sequence feature sequence into a time sequence convolutional network, and performing gating attention fusion on a time sequence fusion feature output by the time sequence convolutional network and the text semantic feature vector to generate a comprehensive state feature vector; and outputting state level classification and state trend prediction results of the target entity based on the comprehensive state feature vector. According to the invention, the accuracy of entity state recognition can be improved.

This invention belongs to the field of physiological state detection technology. It discloses a multimodal biosignal acquisition and state recognition system and method, comprising: acquiring multimodal biosignal data from subjects and performing data cleaning to obtain a raw signal dataset; identifying pseudo-signal segments, outputting abnormal segments and removing them to obtain a valid response dataset; performing signal tracking, outputting signal discrimination results, and performing drift correction based on these results to output a drift-corrected dataset; performing trend completion to obtain a complete feature dataset; performing state judgment reasoning, outputting modal information weights for labeling to obtain a modal label dataset; correcting abnormal labels to obtain a corrected state recognition sequence; constructing a biosignal recognition strategy based on the corrected state recognition sequence and sending it to a preset data receiving end; thereby improving the accuracy and robustness of multimodal biosignal acquisition and state recognition.

A state closed-loop recognition method and system based on force feedback and electroencephalogram signals. It relates to the field of biomedical signal processing and brain-computer interface technology, and specifically relates to a state closed-loop recognition method and system based on force feedback and electroencephalogram signals. The method synchronously acquires original electroencephalogram signal sequences and time-synchronized multi-channel force feedback data sequences, extracts dynamic contact state features and calculates the contact reliability of the corresponding channels, inputs the state recognition basic model after frequency band sensitive double drive feature fusion, obtains the state recognition result and the corresponding confidence, and generates channel contact state adjustment instructions to optimize the input data quality when the confidence is lower than the preset dynamic threshold. Then, the model is parameterized and efficiently online fine-tuned using the newly acquired data to generate a target personalized model. The problem that the brain-computer interface system cannot realize full-link coordinated adaptive response from physical acquisition to intelligent recognition when the signal quality fluctuates is solved.

The invention discloses a bridge expansion joint abnormal state recognition system, and belongs to the technical field of bridge expansion joint abnormal state recognition. Comprising a cross-dimension heterogeneous data acquisition module, a self-adaptive signal purification space-time alignment module, a cross-scale feature mining module, a dynamic space-time coupling analysis module, a digital twin drive anomaly recognition module, an anomaly evolution trend module and a self-adaptive decision feedback calibration module. Quantifying the dynamic coupling relationship of the feature space-time environment; gathering neighbor information through a dynamic adjacency matrix and a graph convolution layer, and capturing mechanical transmission and coupling strength between expansion joint segments; a weighted entropy value is introduced to measure coupling uncertainty, a final coupling matrix is generated, and an abnormal propagation path and a node influence weight are quantified; through a dynamic graph structure modeling real-time coupling relation, an evolution rule of abnormity in space and time dimensions is revealed, and the recognition precision of local damage and structural instability is remarkably improved.