3038 results about "Abnormality detection" patented technology

The invention relates to the technical field of artificial intelligence, and discloses an anomaly detection emergency processing system and method based on artificial intelligence, and the system comprises a data collection module; a data preprocessing module; an anomaly detection module; an emergency decision module; an emergency execution module; a real-time monitoring and state feedback module; a multi-mode communication and coordination module; a man-machine interaction and visualization module; and a knowledge updating and model iteration module. The method is reasonable in design, the accuracy and timeliness of anomaly detection are remarkably improved through a multi-source heterogeneous data fusion and dynamic threshold adjustment technology, and the model robustness is enhanced in combination with incremental learning and an adversarial training mechanism; the intelligent decision-making module realizes multi-objective optimization processing by relying on a knowledge graph and a digital twinborn pre-judgment risk; redundant fault-tolerant execution and distributed consistency guarantee ensure high reliability of the system, and a man-machine cooperation mechanism considers both automation efficiency and manual intervention accuracy.

The invention discloses a distributed slope monitoring system based on edge cloud collaborative intelligent adaptive decision. The distributed slope monitoring system comprises a plurality of intelligent sensing unit ISU nodes deployed at key positions of a slope and a data processing and intelligent analysis unit, each intelligent sensing unit ISU node is used for transmitting data to an edge gateway in an ad hoc network wireless mode or directly uploading the data to a cloud platform and carrying out slope monitoring based on a local adaptive monitoring strategy; the data processing and intelligent analysis unit comprises an edge intelligent module, a cloud gateway and an edge gateway; the edge gateway serves as a middle layer and is used for protocol conversion, data aggregation, temporary storage and preliminary analysis; the cloud gateway is used for providing calculation and storage resources, training a more complex AI model based on historical and real-time data, and performing pattern recognition, prediction analysis and anomaly detection tasks; the edge intelligent module comprises a plurality of edge computing nodes and is internally provided with a lightweight AI reasoning unit, and the edge intelligent module is arranged on the edge side and used for implementing edge intelligent processing.

The invention relates to the technical field of biomedical engineering, in particular to a multi-mode brain anomaly detection method and system based on machine learning. The method comprises the following steps: acquiring brain medical image data of different modalities, and realizing spatial registration and alignment through a multi-modal registration algorithm based on mutual information; a multi-branch feature extraction model including a convolutional neural network, a converter and a state space model is utilized to perform feature embedding on the original image of each modal; performing frequency decoupling on the features of each mode through adaptive approximate wavelet transform, and decomposing the features into high-frequency detail information and low-frequency global information; a frequency band fusion strategy based on an attention mechanism is implemented on high and low frequency features of different modal images, and fused frequency sub-band features are input into a space-frequency Mama module. Through the adaptive frequency domain decomposition and cross-modal fusion mechanism, the multi-modal brain image information is effectively integrated, and the accuracy and robustness of brain anomaly detection are remarkably improved.

The invention relates to the technical field of motor anomaly detection, in particular to a motor data anomaly detection method based on the Internet of Things. The method comprises the following steps: acquiring motor operation data, evaluating motor operation demand overload and load response conditions, and detecting bearing kinetic energy chain coupling recession and motor structure connection coordination decline trends, so as to determine dynamic magnetic field unbalance conditions in motor operation; based on the dynamic magnetic field unbalance condition, estimating the electromagnetic interference risk and the frequency oscillation growth trend of the motor, monitoring the error accumulation condition, and detecting the abnormal state of the motor; performing optimal configuration processing on motor operation parameters in combination with an abnormal detection result; according to the invention, the motor operation is more stable and efficient through the motor data anomaly detection.

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 smart factory management system and method based on MES, and relates to the technical field of smart manufacturing, and the method comprises the steps: calculating a health degree score based on equipment historical maintenance records, and generating a time-space correlation multi-dimensional analysis data set containing an equipment topological structure and health degree parameters in combination with a digital twin network; performing spatial-temporal feature coupling and topological weight dynamic adjustment on the spatial-temporal correlation multi-dimensional analysis data set through a dynamic topological analysis algorithm, and generating a high-risk equipment list and an anomaly control instruction set; based on the high-risk equipment list, constructing a standardized multi-dimensional abnormal feature vector, and generating an aging weighted danger level signal through an entropy weight method; according to the method, space-time alignment of equipment operation state data and physical topology data is realized through a multi-rate Kalman filtering algorithm and an iterative nearest point algorithm, a high-fidelity digital twin network is constructed in combination with a dynamic graph convolutional network, and accurate anomaly detection and health degree evaluation are supported.

The invention relates to the technical field of quality detection, in particular to a product quality control method and system based on machine vision, and the method comprises the following steps: obtaining product surface image data, calculating the gray gradient value of each pixel, extracting the gray gradient change rate, recording the gradient amplitude and direction information, and generating product surface gradient data. According to the method, through pixel-level gray scale gradient calculation, the product local feature expression ability is improved, multi-scale gradient change trend analysis is combined, the accurate recognition ability of a product defect area is improved, through texture direction angle calculation and vector field construction, the direction change anomaly detection reliability is enhanced, and the direction change anomaly detection accuracy is improved based on the combination of a direction deviation accumulated value and an abrupt change threshold value. Effective identification of a structure sudden change area is ensured, adjustment is carried out for curvature continuity abnormal points, defect boundary fitting precision is optimized, defect area internal gradient distribution and boundary feature comparative analysis are carried out, accurate classification of defect types is realized, and stability and adaptability of automatic product quality detection are ensured.

The invention discloses an unmanned aerial vehicle cruising method and system based on a deep learning artificial intelligence image recognition algorithm. According to the method, an unmanned aerial vehicle carrying an improved YOLOv7-SwinT target recognition model collects real-time image data of an inspection area, and the model fuses a single-stage target detection architecture of YOLOv7 and a visual feature extraction network of Swin Transform. Progressive target detection is realized by adopting a three-level recognition architecture, wherein the progressive target detection comprises primary anomaly detection based on lightweight CNN, intermediate accurate positioning in combination with an attention mechanism and advanced target classification of multi-sensor data fusion. And the system combines the electric quantity of the unmanned aerial vehicle, the environmental condition and the task priority according to the identification result, generates a dynamic inspection path through an adaptive path planning algorithm, and realizes multi-vehicle collaborative operation by using an intelligent task allocation algorithm. In the inspection process, sensor data are processed in real time through edge computing equipment, and charging scheduling is optimized by adopting an intelligent energy management system. The target recognition precision and the cruising efficiency of the unmanned aerial vehicle in a complex environment are remarkably improved, and the method is suitable for application scenes such as electric power inspection and security monitoring.

The invention relates to the technical field of image analysis, in particular to a calcium carbonate master batch wind shear forming detection system based on machine vision. A calcium carbonate master batch wind shear forming detection system based on machine vision comprises an edge pixel collection module, a boundary trend recognition module, a distortion track repair module, an internal texture analysis module and a forming anomaly detection module. According to the method, the gray difference and gradient angle of continuous pixels are calculated, an edge distribution diagram is constructed, boundary detail expression is enhanced, the pixel arrangement direction and gray change analysis are combined, continuous edge line segments are extracted, the boundary recognition precision is improved, a closed contour is repaired according to line segment connection offset, and structural integrity is ensured; the method comprises the following steps: calculating a gray change rate in an internal region block, drawing a texture fluctuation map, accurately marking a sudden change region, realizing abnormal particle identification, fusing pixel-level gray features and region structure analysis, and improving the precision and integrity of form perception and defect identification.

The invention discloses a semiconductor packaging test optimization method and system, and relates to the field of packaging testing, and the method comprises the steps: collecting and preprocessing a multi-dimensional abnormal signal, generating a standard data set, carrying out the feature extraction of data in the standard data set, obtaining a standard feature vector, and inputting the standard feature vector into a constructed abnormality detection model, the method comprises the steps of obtaining an abnormal signal report, distributing a test item for a chip through the abnormal signal report and a predefined mapping rule, generating a structure map and an electrical map, inputting the structure map and the electrical map into a multi-mode Transform model, outputting a fusion map, calculating a test weight according to the fusion map, and generating a test weight map and a region priority list. According to the invention, the testing efficiency, the micro defect detection precision and the boundary failure prediction of advanced packaging are improved.

The invention relates to the technical field of electric energy meter metering, and discloses an electric energy meter metering anomaly analysis method and system, and the method comprises the steps: collecting data, such as voltage waveforms, to generate a metering feature vector set, and constructing an anomaly detection rule base; setting a scene parameter type set, and establishing an abnormal association judgment model; dynamically correcting the threshold value by combining the model, and generating an optimized error threshold value set and an abnormal triggering condition set; and updating a metering analysis strategy to generate an abnormal judgment scheme, and correcting data verification sequential logic. The system comprises a data acquisition module, an abnormal rule base construction module, a scene parameter configuration module, a correlation model training module, a dynamic threshold optimization module, a strategy updating module and a time sequence correction module. Through multi-dimensional data modeling, scene-based threshold configuration and dynamic time sequence calibration, the accuracy and adaptability of electric energy meter measurement anomaly detection are improved, and the method is suitable for measurement anomaly analysis of diversified power consumption scenes in a smart power grid.

The invention discloses an air conditioner supply and demand collaborative optimization scheduling method, which relates to the technical field of air conditioner supply and demand scheduling, and comprises a sensing prediction module, an acquisition comparison module, a deviation correction driving module, a control adjustment module, a cold source control module, a strategy adjustment module and an anomaly detection module. When the abnormality detection module detects that the air conditioner terminal breaks down, the control adjusting module enhances the power of the peripheral equipment to fill the cooling capacity gap, if the water chilling unit breaks down, the cold source control module starts the standby unit to switch the cooling capacity output path, and when the cooling capacity is insufficient, the control adjusting module is in linkage to reduce the air volume of the low-priority area to preferentially guarantee cooling of the core area; meanwhile, a strategy adjusting module automatically matches a predefined strategy chain according to the fault type, for example, global average temperature control is switched when temperature control fails, a load balancing strategy is triggered to be distributed to a standby unit when equipment is overloaded, and the strategy is gradually rolled back to the optimal strategy based on historical data after the fault is repaired; and strategy conflicts and equipment fluctuations are avoided through progressive improvement of cold energy supply.

The invention discloses a security monitoring video intelligent analysis method based on multi-algorithm cooperation and unified architecture, and relates to the technical field of intelligent video monitoring, and the method comprises the steps: collecting a security monitoring video, carrying out the preprocessing, carrying out the spatial-temporal feature extraction and fusion through a CNN-LSTM spatial-temporal fusion engine, and outputting a fusion feature map; performing target detection, behavior recognition and anomaly detection on the fused feature map to obtain a multi-algorithm analysis result; a cross-module fusion mechanism based on an attention mechanism is utilized to perform weighted integration processing on the multi-algorithm analysis result to obtain a fusion event representation vector; performing event type identification and risk level evaluation on the fusion event representation vector to obtain an event classification result and an event risk level; according to the invention, through the CNN-LSTM space-time fusion engine, the front-end perception capability of abnormal behaviors in a complex scene is improved, and the event detection accuracy and the anti-interference capability are improved.

The invention relates to the technical field of mining mechanical equipment, and particularly discloses a mining mechanical equipment anomaly detection system and method. Current signals, voltage signals and vibration signals in the operation process of mining mechanical equipment are obtained; the method comprises the following steps: extracting an equipment operation current-voltage correlation feature vector and a mining mechanical equipment time frequency correlation feature vector, fusing to obtain a mining mechanical equipment anomaly classification feature vector, and obtaining a classification result through a classifier to indicate whether the mining mechanical equipment is abnormal or not, thereby realizing anomaly detection of the mining mechanical equipment.

The invention relates to the technical field of user power utilization chain analysis, and discloses a special transformer user power utilization abnormal chain construction method fusing deep learning, which comprises the following steps: collecting time sequence power utilization data and power grid topological data of special transformer users, taking each special transformer user as a node, constructing a graph structure comprising a physical connection edge and a behavior association edge, and constructing a graph structure comprising a physical connection edge and a behavior association edge; and inputting the constructed graph structure into a space-time graph neural network model, introducing a power grid physical constraint condition into an optimization target, taking a power grid operation rule as a constraint embedding model, and outputting node-level, edge-level and sub-graph-level multi-level anomaly detection results. Constructing a heterogeneous graph containing user nodes, anomaly type nodes and time slice nodes through the multi-level anomaly detection result, performing path search in the heterogeneous graph through a predefined association mode template, generating a candidate anomaly chain, performing causal strength verification on the candidate anomaly chain by adopting a time sequence causal relationship verification model, and obtaining a multi-level anomaly detection result of the user nodes, the anomaly type nodes and the time slice nodes. And intelligent analysis of the user electricity consumption abnormity chain is realized.

The invention provides an anomaly detection method and system based on motor operation vibration sound, and relates to the technical field of motors, and the method comprises the steps: obtaining an original motor signal, determining the amplitude of the original motor signal, and marking an abnormal signal therein; determining spectrum distribution characteristics of vibration signals in the abnormal signals, performing multi-scale decomposition on the vibration signals to obtain a plurality of sub-band signals, and integrating the sub-band signals to obtain a multi-band combined signal; separating the multi-band combined signal into a plurality of intrinsic mode components by adopting a variational mode decomposition algorithm, and integrating the intrinsic mode components to obtain a reconstructed signal; performing time domain analysis and frequency domain analysis on the reconstructed signal to obtain frequency domain distribution characteristics; and inputting the frequency domain distribution characteristics into a trained motor anomaly detection model to obtain an anomaly detection result. According to the invention, the multi-dimensional abnormal features in the motor operation process can be effectively extracted, and the accuracy and reliability of abnormal detection are improved.

The invention discloses a data ownership verification and authorization control method based on zero knowledge proof. The method comprises the following steps: constructing a traceable Merk tree structure on a block chain or a distributed account book; a data owner calculates data fingerprints and packages the data fingerprints into declaration nodes to be inserted into the traceable Merk tree; storing the ownership zero knowledge proof and the traceable Merk tree root hash into a block chain together; the verification party executes the zero-knowledge verifier contract on the chain to complete ownership verification without reading original data or identity plaintext; access control is realized after on-chain verification of the service party; triggering local heavy hash to update the traceable Merk tree root hash, and broadcasting a revocation event on a chain; and confirming legality of evolution and authorization change of all nodes. According to the method, the historical traceability and structural consistency of the data ownership and authorization relationship are ensured, and efficient traceability and anomaly detection of the node evolution chain are also realized.

The invention discloses a detection system for operation abnormity of an electric drive axle motor of a new energy commercial vehicle, and belongs to the technical field of motor abnormity detection. The system comprises a multi-source sensing module, a dynamic coupling analysis module, a harmonic distortion traceability module, a dynamic stability analysis module, an anomaly fusion decision module, a self-adaptive threshold generation module and a fault tree reasoning module. Multi-dimensional signals such as current, vibration, temperature and a rotor position angle are synchronously acquired through the multi-source sensing module, and multi-physics field collaborative analysis is performed through the dynamic coupling analysis module and the harmonic distortion traceability module, so that the fault diagnosis accuracy of operation abnormity of the electric drive axle motor of the new energy commercial vehicle is improved; the problem that in the prior art, detection only depends on a single physical quantity, and misjudgment or missing detection is easily caused by interference is solved.

The invention discloses a fruit and vegetable juice production line equipment fault tracing method, which comprises the following steps of: acquiring parameters such as temperature, pressure, vibration, rotating speed and motor current in real time through a multi-channel sensor, and establishing a working condition characteristic database by combining filtering, normalization, statistics and frequency domain characteristic extraction; based on a support vector regression algorithm, a nonlinear mapping model of working condition features and anomaly detection thresholds is constructed, and dynamic threshold adaptive output and real-time anomaly judgment for different working conditions are realized; according to a detection result, a fault signal is automatically triggered, a model is continuously incremented and trained, the adaptability to new working conditions is improved, the accuracy, intelligence and stability of equipment anomaly detection are effectively improved, misinformation and missing information can be reduced, and the automatic operation and maintenance level of a production line is enhanced.

The invention relates to the technical field of machine learning, and provides a building equipment abnormity identification method and system based on machine learning, which are used for realizing accurate detection and accurate early warning of building equipment abnormity. The method comprises the following steps: acquiring a continuous operation data set of target building equipment, wherein the continuous operation data set comprises multiple segments of equipment state recording units with timestamp marks; performing time-frequency domain feature extraction processing on the continuous operation data set to obtain a time-frequency domain feature set of the equipment state recording unit; calling a pre-constructed hybrid machine learning model to perform anomaly detection processing on the time-frequency domain feature set, and generating an anomaly recognition result of the equipment state recording unit; determining the anomaly type of the target building equipment and the distribution feature information of the anomaly type in the time dimension according to the anomaly recognition result; and generating a target early warning instruction containing a time positioning identifier based on the exception type and the time distribution feature information, and sending the equipment early warning instruction to a target equipment management terminal.

The invention discloses a new energy equipment intelligent operation and maintenance system and method based on digital twinning, and relates to the technical field of new energy equipment operation and maintenance management. The system comprises a data acquisition module, a digital twin construction module, a model adaptive module, an intelligent analysis module, a decision optimization module and a knowledge closed-loop module. The data acquisition module realizes multi-source heterogeneous data fusion and standardization; the digital twin construction module generates geometric, physical and behavior models and is linked with real-time data; the model adaptive module dynamically calibrates the key parameters; the intelligent analysis module completes anomaly detection, fault diagnosis and life prediction; the decision optimization module formulates a maintenance scheduling and operation strategy and realizes a control closed loop; and the knowledge closed-loop module constructs a structured fault graph through text analysis to continuously optimize the model. The method can be widely applied to wind power, photovoltaic, energy storage and other scenes, and efficient and intelligent operation and maintenance of new energy equipment are achieved.

The invention discloses an intelligent automobile interpretable abnormity diagnosis method and system, and relates to the technical field of intelligent traffic. The method comprises the steps of collecting multi-dimensional sensor data based on an intelligent automobile test platform, and constructing a directed causal graph and a causal adjacency matrix which are used for describing a causal relationship between the sensor data; designing a causal constrained graph attention mechanism based on the causal adjacency matrix, and constructing a causal constraint enhanced graph attention anomaly diagnosis model; and based on the directed causal graph and the graph attention anomaly diagnosis model, constructing a hierarchical anomaly diagnosis strategy integrating a feature reconstruction error, a variable causal relationship and a graph attention network weight, positioning an anomaly root cause and identifying a propagation path of the anomaly in the sensor network. According to the invention, the problems of false correlation and lack of exception explanation ability of graph attention network learning in the prior art can be overcome, and reliable exception detection and root cause diagnosis of intelligent automobile multi-sensor data are realized.

The invention provides a tumor electric field treatment system, an electrode slice temperature detection method and an electrode slice anomaly detection method, the system comprises an alternating current signal generator, an electrode slice and a controller, the electrode slice comprises a plurality of electrode units and a plurality of temperature detection units, the plurality of temperature detection units are divided into a plurality of row groups and a plurality of column groups, and the plurality of electrode units are connected with the controller. The grounding ends of the temperature detection units in each row group are jointly connected to the same grounding wire, and the signal ends of the temperature detection units in each column group are connected to the same dual-purpose signal wire after being in short circuit with the corresponding electrode units; when the dual-purpose signal line is connected to the temperature sampling point, the grounding wires are sequentially conducted, so that temperature detection signals detected by the temperature detection units are sampled based on the temperature sampling point; when the dual-purpose signal line is connected to the alternating power line, alternating electric signals are transmitted to the electrode units. Therefore, the temperature of the electrode plate can be detected by using fewer conductive traces, and whether the electrode plate is abnormal or not can be judged through temperature detection.

The invention provides an anomaly detection method and device for image data and a storage medium, and relates to the technical field of image processing, and the method comprises the steps: carrying out the feature extraction of the image data, fusing the spectrum and spatial features to obtain a joint feature matrix, and inputting an anomaly detection model; the model adopts an alternating direction multiplier algorithm to solve a low-rank sparse decomposition problem, and an objective function comprises a data fidelity item, a regularization item and a waveband weight item; the regularization item comprises a low-rank constraint and a sparse constraint, and the wave band weight item acts on the low-rank constraint in a weighting form; updating a background low-rank tensor, an abnormal sparse tensor, a Lagrange multiplier, a sparse constraint weight, a wave band weight item and penalty parameters of an algorithm by adopting an iteration mode in a solving process; and repeating iteration until a preset termination condition is reached, calculating an abnormal score graph pixel by pixel based on the abnormal sparse tensor, and comparing to determine an abnormal target. The problem that an abnormal target is difficult to accurately recognize in a complex scene can be solved, and detection precision and efficiency are improved.

The invention relates to the technical field of mutual inductor tests, and provides a mutual inductor test abnormal data automatic filtering analysis method and system, and the method comprises the steps: obtaining magnetic field data, electric field data and thermal field data, respectively carrying out the feature extraction and weighted fusion, and obtaining a multi-physical field fusion feature vector; constructing an electromagnetic induction chain type propagation graph, calculating the weight of an edge in the graph, constructing a magnetic flux conservation constraint graph convolutional neural network, learning propagation and evolution modes of transformer test abnormity, and obtaining a coupling abnormity feature vector; the coupling anomaly feature vectors are classified, normal data, single physical field abnormal data and multi-physical field coupling abnormal data are recognized, and corresponding data are judged as abnormal data and filtered; and carrying out physical mechanism analysis on the filtered abnormal data to obtain an abnormal detection report. According to the invention, high-precision automatic identification, filtering analysis and physical mechanism traceability diagnosis of transformer test abnormal data are realized.

The invention belongs to the technical field of integrated circuits, and particularly relates to a speed-adjustable fan driving and anomaly detection circuit with high reliability and low cost. Comprising a current expansion driving module for providing a voltage FCC stabilized by an LDO for a fan and performing current expansion on an output current of the LDO to drive the fan; the logic control module is connected with the current expansion driving module and is used for adjusting a control signal ADJlt; n: 0gt; the value of the voltage FCC output by the LDO is changed; the open circuit and locked-rotor detection module is connected with the logic control module and the VIN port of the LDO; and the short circuit detection module is connected with the logic control module and the output end of the LDO, outputs a signal to a state flag bit FTO, and judges whether the fan is in a normal working state, an open circuit state, a locked-rotor state or a short circuit state or not according to the waveform and the level of the state flag bit FTO. The speed-adjustable fan driving circuit is integrated, and the function of detecting various abnormal states of the fan is achieved.

The invention relates to the technical field of photovoltaic power generation fault identification, in particular to an equipment anomaly detection method and a photovoltaic power generation equipment detection system.The equipment anomaly detection method comprises the steps that convolution processing is carried out on the power offset ratio of each node in a path through a graph convolution neural network, and adjacent conduction is carried out on difference values in combination with the upstream and downstream connection sequence of the nodes; a structure with a continuous power increase trend in a path has a cross-node continuity expression capability, a multi-channel label classification structure is constructed by taking nodes as granularity in kick synchronization judgment through a random forest, and synchronous high-variable unit screening is completed through high-frequency channel category combination and variation amplitude sorting. According to the method, sudden change synchronism judgment breaks through a traditional three-channel single-variable difference threshold mode, and fault separability, response granularity and type description definition in a complex scene are improved through progressive increase aggregation of an offset trend between serial number structures, superposition of a recovery period and offset rate statistics of a reference shielding curve.

The invention discloses a battery change cabinet battery abnormity detection method and system based on a thermal image, and relates to the related field of battery safety monitoring, and the method comprises the steps: collecting a surface thermal image sequence in a battery compartment through an infrared thermal imager arranged in a battery change cabinet, and enabling the surface thermal image sequence to have a time sequence and a charging stage label; extracting temperature field features of a space-time dimension from the thermal image sequence, and identifying an abnormal state and an abnormal region of the battery based on the temperature field features; and acquiring real-time battery management system (BMS) data of the battery, performing correlation verification on the abnormal state or the abnormal area and the BMS data, and determining an abnormal level and positioning a fault battery unit according to a correlation verification result. According to the invention, the technical problems of inaccurate and untimely detection and inaccurate fault positioning in the existing battery abnormality detection of the battery changing cabinet are solved, and the technical effects of improving the accuracy, timeliness and positioning accuracy of battery abnormality detection are achieved.

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 belongs to the technical field of integrated circuits, and particularly relates to a speed regulation driving and anomaly detection circuit of a cooling fan. Comprising a current expansion driving module for providing a voltage FCC stabilized by an LDO for a fan and performing current expansion on an output current of the LDO to drive the fan; the logic control module is connected with the current expansion driving module and is used for adjusting a control signal ADJlt; n: 0gt; the value of the voltage FCC output by the LDO is changed, so that the speed of the fan is regulated; and the sampling detection module is connected with the logic control module and the VIN port of the LDO, samples the current of the fan through a sampling resistor, converts the current into a voltage V0, compares the voltage V0 with a reference voltage Vref, and outputs a voltage signal to the level shift circuit. The speed-adjustable fan driving circuit is integrated, detection of various abnormal states of the fan is achieved, and the speed-adjustable fan driving circuit has the outstanding advantages of being simple in PCB layout and low in cost.