29253 results about "Electrical equipment" patented technology

The invention discloses an electrical equipment fault diagnosis and prediction analysis system, which relates to the field of intelligent operation and maintenance of a power system and comprises an acquisition and preprocessing module, an extraction fusion module, a fault diagnosis modeling module, a prediction evaluation module and an update feedback module. According to the invention, through fusion of structured sensing data and unstructured image data, multi-modal depth feature joint representation is realized, and the accuracy and robustness of fault identification are significantly improved; a fusion time sequence prediction model is introduced, and a health degree scoring system is combined, so that accurate prediction of key parameter trends and quantitative estimation of the residual life of equipment are realized; a transfer learning and incremental learning mechanism is adopted, when a new fault or small sample data appears, model parameters can be quickly updated, and efficient adaptation to a new scene is achieved; a data alignment mechanism with time-space synchronization and an auto-encoder anomaly detection algorithm are constructed, and the multi-source heterogeneous data processing capacity and the real-time fault early warning capacity are remarkably improved.

The invention discloses an industrial and commercial park electrical load power prediction method, and relates to the technical field of electrical load prediction, and the method comprises the steps: obtaining park electrical equipment power data, and obtaining a standardized time sequence power matrix through normalization preprocessing; performing wavelet denoising and empirical mode decomposition on the power matrix, and separating a power trend component from a fluctuation component; constructing a trend prediction sub-model by using a long-short term memory network, and constructing a fluctuation prediction sub-model by using a seasonal ARIMA model; carrying out weighted combination on the two sub-models through an integrated learning method, and constructing a comprehensive prediction model; and finally, based on the historical prediction error, setting an adaptive threshold value for constraint processing, and outputting a final prediction result. According to the method, power data features are adaptively separated through a multi-level decomposition strategy, a dual-model collaborative prediction mechanism is adopted, environmental factors are introduced to improve prediction precision, a self-adaptive correction mechanism is designed to ensure result reliability, and accuracy and stability of park power load prediction are remarkably improved.

The invention relates to the technical field of electrical equipment fault diagnosis, in particular to an intelligent fault diagnosis method and system for electrical equipment, and the method comprises the steps: constructing a multi-dimensional tensor model, uniformly fusing the equipment state information, electrical distance weighted connection and phase dynamic coupling relation, and extracting an abnormal propagation mode through high-order singular value decomposition; designing a space-time-frequency coupling interference stripping mechanism, and combining structure guide disturbance deconstruction, multi-scale dictionary learning and sparse low-rank decomposition to accurately separate transmissible and non-transmissible interferences; reconstructing a fault trajectory based on a generative adversarial mechanism, coupling a graph structure dynamic encoder, a topology consistency discriminator and a time controllable generator, and restoring a real propagation path; and finally, tensor semantic compression, a three-view graph neural network and fault label back projection interpretation are integrated through a multi-source semantic fusion mechanism. According to the method, cross-space-time and cross-structure fault diagnosis and traceability are realized, and the accuracy and interpretability are improved.

The invention discloses an existing line state comprehensive analysis method based on a three-dimensional mobile scanning system. The method comprises the steps that fine scanning is conducted on a steel rail based on a high-precision three-dimensional mobile laser scanning system, and whole-line data of an existing line are rapidly collected; collecting field data; local point cloud data of the steel rail and a point cloud result of a full-section large scene are obtained based on preprocessing and fusion registration calculation of original point cloud data, inertial navigation data, GNSS data and encoder mileage data; establishing a whole line mileage calibration ledger based on the point cloud result; performing multi-term detection based on the whole line mileage calibration ledger and the scanning data, and outputting an existing line comprehensive detection graph and comprehensively evaluating the steel rail state in a segmented manner; the multiple detection items comprise linear design adjustment detection, track geometric state detection, fastener detection, track surface disease detection, profile abrasion detection, limit detection, industrial and electrical equipment detection and existing line linear fine design detection. The invention further discloses a corresponding system, electronic equipment and a computer readable storage medium.

The present invention relates to the technical field of electrical equipment monitoring, and in particular to an electrical equipment state monitoring and dynamic test evaluation system, comprising a data acquisition module, a data processing module, a feature extraction module, a dynamic test evaluation module and a state evaluation and prediction module; wherein: the data acquisition module is used for real-time acquisition of raw data during the operation of the electrical equipment; the data processing module is used for processing the raw data collected by the data acquisition module to generate a standardized data set; the feature extraction module is used for extracting relevant feature parameters reflecting fatigue damage accumulation from the raw data; the dynamic test evaluation module is used for simulating the fatigue damage evolution process of the electrical equipment under complex working conditions; the present invention, by real-time monitoring of fatigue damage accumulation of the electrical equipment, combines dynamic test evaluation with finite element simulation technology, accurately predicts the remaining life of the equipment and the time of occurrence of a fault, and improves the accuracy and early warning capability of the equipment health state evaluation.

The invention discloses an electrical equipment fault monitoring and positioning system based on data analysis, and relates to the technical field of electrical equipment monitoring. The problem that in the prior art, the comprehensiveness of data processing, the intelligence of fault detection, the accuracy of fault positioning and the like are insufficient is solved. According to the method, the sensor acquisition frequency is dynamically adjusted according to the equipment operation state, the effectiveness and pertinence of data are ensured, the accuracy and reliability of fault feature extraction are improved by combining time domain and frequency domain analysis with network model training, the known fault type is quickly and accurately judged by calculating the similarity and performing partition positioning, and the fault diagnosis accuracy is improved. Meanwhile, unknown fault features are analyzed and predicted, determined unknown fault information is updated to a database, fault types, equipment topological structures and structural information are combined, fault propagation paths and influence ranges are simulated, fault position ranges are determined, evaluation optimization is carried out, accurate information is provided for fault maintenance, and the fault maintenance efficiency is improved. And the equipment maintenance cost and the downtime are reduced.

The invention discloses a GIS partial discharge intelligent diagnosis system and method based on one-dimensional ultrahigh frequency signal analysis, and relates to the technical field of power electrical equipment intelligent monitoring, and the system comprises a signal collection and preprocessing module which is used for collecting ultrahigh frequency signals of GIS equipment and obtaining preprocessed signal data through a dynamic threshold algorithm; the discharge initial judgment module is used for performing multi-dimensional sequential judgment to eliminate interference discharge data so as to obtain effective discharge signal data; the feature extraction module is used for performing time domain kurtosis and pulse width analysis, frequency domain energy distribution analysis and time-frequency domain wavelet entropy calculation based on the multi-dimensional features of GIS partial discharge, and generating an optimized feature subset; and the type identification module is used for identifying the partial discharge type by using the integrated learning model to obtain a diagnosis result. According to the invention, the problem of unstable recognition accuracy caused by insufficient signal preprocessing, single feature representation and single classification algorithm in the prior art is solved.

The invention discloses an electrical equipment operation state monitoring method, system and terminal, full life cycle health management of equipment is realized through multi-dimensional perception and intelligent analysis, a composite sensor network can be constructed from the level of the method, and high-frequency current, ultrahigh frequency, fiber grating temperature vibration, multi-parameter gas and acoustic sensors are integrated. Electromagnetic characteristics, mechanical states, environmental parameters and voiceprint characteristics are covered; the adaptive signal processing technology performs classification and noise reduction on multi-source data, and the three-dimensional digital twin model realizes time-space fusion of a temperature field, a vibration field, an electric field and a sound field; a lightweight space-time convolutional network is deployed to fuse a time domain waveform, a spectrogram and spatial distribution characteristics for diagnosis, and a hidden semi-Markov model and a particle filter algorithm are combined to dynamically predict the residual service life of equipment. The system architecture comprises a self-organizing sensor network with edge computing capability, a time-sensitive industrial communication network and a containerized analysis engine, and supports mixed reality visual interaction.

The invention discloses an electric equipment energy-saving control method and system based on the Internet of Things, and relates to the technical field of smart power grid and Internet of Things application, and the method comprises the steps: S1, obtaining the operation data and historical use records of user electric equipment, extracting the habit diversity and personalized preference analysis characteristics of a user, obtaining an equipment use preference model, and carrying out the operation of the user electric equipment; s2, extracting an operation rule of each device from the preference model, judging a priority adjustment parameter of the device, and obtaining a dynamic priority sequence, and S3, calculating an available power threshold value under current power resource limitation through real-time power grid load monitoring data, and determining a distribution power value required by important device guarantee; according to the electric equipment energy-saving control method and system based on the Internet of Things, the preference model and the priority sequence can be updated in time, and optimal distribution of electric power resources is achieved. The method can effectively balance the power demand of the user and the load of the power grid, improves the utilization efficiency of power resources, and provides a new solution for the management of the intelligent power grid.

The invention discloses an electrical equipment life prediction system and method based on deep learning, and belongs to the field of electrical equipment life prediction, and the method comprises the steps: constructing a data set, and carrying out the preprocessing; performing convolution operation on the time sequence data in the data set, extracting local features to obtain a convolutional neural network, capturing a long and short term dependency relationship of the time sequence data in the data set by using LSTM to obtain a recurrent neural network, and fusing output features of the convolutional neural network and the recurrent neural network to obtain comprehensive feature representation; constructing a deep learning model based on the fusion of a convolutional neural network and a recurrent neural network, adding an attention mechanism, adopting a mean square error as a loss function, optimizing model parameters, carrying out model training by utilizing a training data set, evaluating model performance by adopting a verification set, and adjusting hyper-parameters; and inputting real-time data into the trained model, predicting the residual life of the electrical equipment, and designing an online learning mechanism. According to the invention, the accuracy and real-time performance of prediction are improved.

The invention relates to the technical field of electrical safety monitoring, in particular to an intelligent detection method and system for indoor electrical potential safety hazards, and the method comprises the following steps: constructing a synchronous data set based on electrical equipment and environment data, extracting a power current sequence, analyzing the change rate, screening abrupt change signals, and combining with environment recognition risk fragments; and extracting a fluctuation interval to judge critical offset, positioning an electrical sequence offset distance, and counting risk frequency to mark a high-incidence area to generate an alarm node list. According to the method, high-precision synchronization of power consumption behaviors and environmental conditions is realized by collecting the electric participation environment data of the electric connection points and unifying time labels, the anomaly identification accuracy is improved, the power and the current sequence change rate are extracted, the line offset is dynamically pre-warned, and the hidden anomaly in the stable stage is revealed through track mapping; a high-incidence area is positioned through frequency statistics and space mapping, closed-loop monitoring from multi-dimensional data fusion to risk area marking is achieved, and the intelligence and foresight of electrical hidden danger recognition are remarkably improved.

The invention relates to the technical field of electrical equipment defect detection, in particular to an electrical equipment defect detection system and method based on deep learning, which are characterized in that a three-dimensional model of a power grid region is constructed, and based on historical defect data, a neural network model is adopted to mark an importance score of an inspection object in the three-dimensional model; quantitative evaluation of the equipment fault risk level is completed, and the matching degree of inspection resources and defect risk distribution is improved. A power grid three-dimensional model and importance scores are combined, a reinforcement learning model is utilized to construct an unmanned aerial vehicle inspection route planning strategy, the inspection route comprehensively considers power equipment defect risks and space factors in the planning stage, task allocation is optimized, and the problem that a static route cannot adapt to equipment changes is solved. In the inspection execution process, the unmanned aerial vehicle is dispatched according to the planning strategy, and the image flow is synchronously acquired for defect detection, so that the linkage of the inspection action and the detection process is realized, and the response efficiency and the detection quality of potential defects are improved.

The invention relates to the technical field of electrical equipment monitoring, in particular to a power distribution equipment state intelligent diagnosis system and method, and the system comprises a current fluctuation detection module, a voltage stability evaluation module, a heat effect diffusion analysis module, a fault point positioning module and an equipment state evaluation module. According to the invention, through extracting current change characteristics of key nodes and judging continuity of fluctuation differences, rapid identification of current anomalies can be realized, a voltage distortion region and a distribution range thereof can be positioned in combination with phase deviation and symmetry change of voltage waveforms, and the current anomalies can be identified based on a temperature gradient path and jump point screening. A hot spot diffusion trend and a thermal disturbance boundary can be described, morphological analysis and coordinate labeling are performed on the basis of multiple fault areas, multi-point concurrent positioning is realized, association between thermal disturbance and connection defects is identified in combination with temperature difference and current path change characteristics, fault diffusion trends and state disturbance blocks can be accurately presented, and the accuracy of fault diagnosis is improved. And the power distribution state identification and intervention efficiency is improved.

The invention relates to the technical field of electrical equipment analysis, and particularly discloses an electrical equipment operation state monitoring system and method, and the method comprises the steps: the system is deployed at an electrical equipment site, integrates a low-power-consumption processor and an FPGA acceleration module, and is used for collecting multi-source data in real time, and executing lightweight model calculation and local early warning; and storing full data and operating a global analysis model, and communicating with the edge computing node to realize data collaborative analysis. According to the electrical equipment operation state monitoring system and method provided by the embodiment of the invention, through edge computing lightweight deployment, data preprocessing and local early warning are completed on the equipment site, and high efficiency of monitoring response and low-power-consumption hardware adaptation are realized; by means of an electromagnetic interference dynamic evaluation module, an acquisition strategy is intelligently adjusted, noise is suppressed, the reliability of multi-source data is guaranteed, the efficiency, precision and intelligent capability of electrical equipment state monitoring are remarkably improved, and technical support is provided for reliable operation of power system equipment.

The invention discloses an intelligent house household electric appliance remote control system, which relates to the technical field of intelligent homes, and comprises an instruction receiving module, an instruction analysis module, a priority judgment module, a decision processing module and a state verification and feedback module: the instruction receiving module is used for receiving control instructions from a plurality of users and an automatic program; by introducing authority priority management, multi-dimensional judgment and intelligent scheduling, the system effectively solves instruction conflicts, and stability and reliability are improved. In family and security scenes, high-priority instructions respond in time, low-priority instructions are delayed reasonably, and the correct equipment state is ensured. The transparent feedback mechanism improves the trust of the user, and optimizes the operation efficiency and experience at the same time.

The invention provides a detection method and system for an insulation detection function. Wherein in the operation state of the to-be-detected electrical equipment, the temperature change of the surface of the to-be-detected electrical equipment is monitored in real time through a non-contact sensor, and a dynamic thermogram is formed; according to a temperature fluctuation mode of a preset area in the dynamic thermogram, in combination with an environment temperature compensation algorithm, identifying a potential insulation defect area, and generating a preliminary insulation defect report; for the insulation defect area marked in the preliminary insulation defect report, simulating electromagnetic field distribution conditions of the insulation defect area under different working conditions by adopting an electromagnetic field analysis technology, evaluating the aging degree and damage risk of the insulation material, and obtaining an evaluation result; and integrating the preliminary insulation defect report and the evaluation result, calculating the probability that the insulation defect area develops into an actual fault through a fault prediction model, and outputting a final insulation performance detection report. According to the technical scheme provided by the invention, the operation efficiency and safety of the equipment are improved.

The invention relates to the technical field of electrical equipment fault diagnosis, and discloses an electrical equipment multi-modal fault diagnosis method based on dynamic self-adaption, and the method comprises the steps: obtaining an original multi-modal signal flow containing vibration, temperature and current signals, inputting the original multi-modal signal flow into a dynamic self-adaption diagnosis network, obtaining a fault feature matching result set, and determining each modal result. The network is trained by historical fault data, and the data comprises time domain, frequency domain and fusion feature parameters extracted from continuous multi-mode signals, and corresponding fault type labels and confidence coefficients. The network comprises a cross-modal feature fusion module, a spatial-temporal feature coding module and the like, and when the confidence coefficient of at least two modal results exceeds a dynamic threshold value, three-level early warning is triggered. The method improves the comprehensiveness, accuracy and real-time performance of diagnosis, and is suitable for fault diagnosis of electrical equipment.

The invention relates to the technical field of data management, in particular to an electrical equipment fault prediction system and method based on data fusion, and the system intelligently adjusts a data collection strategy by accurately capturing electromagnetic signal characteristic parameters of an electrical equipment operation environment and scientifically evaluating an electromagnetic interference index. The accuracy and efficiency of data acquisition are effectively improved, meanwhile, by monitoring the operation state parameters of the electrical equipment, the characteristics of the electrical equipment can be deeply extracted, the data acquisition compliance index is calculated, a powerful basis is provided for data preprocessing, finally, the multi-dimensional operation state parameters of the electrical equipment are highly fused, operation degradation factors can be accurately identified, and the accuracy and efficiency of data acquisition are improved. According to the invention, the method achieves the precise prediction of the fault level of the electrical equipment, gives out an early warning in time, remarkably improves the accuracy and timeliness of fault prediction, effectively reduces the operation and maintenance cost, enhances the stability and safety of a power system, and provides a powerful technical guarantee for the intelligent management and maintenance of the electrical equipment.

The invention discloses a system for testing an electromagnetic shielding coupler of a testing transformer based on intelligent sensing, and relates to the technical field of electrical equipment testing, and the system comprises a flexible distributed intelligent sensing array which is composed of a plurality of high-density miniature electromagnetic sensors and a temperature-vibration composite sensing unit, a non-uniform topological structure is embedded into a seam and an insulating interface area on the surface of the electromagnetic shielding coupler. According to the test system of the test transformer electromagnetic shielding coupler based on intelligent sensing, through dynamic electromagnetic field reconstruction of the flexible distributed intelligent sensing array and multi-band interference coupling modeling, the limitation of a traditional static test is broken through; and real-time capture and multi-dimensional decoupling of shielding effectiveness attenuation characteristics in a complex alternating electromagnetic environment are realized. Compared with a traditional method, the method has the advantages that the shielding effectiveness dynamic quantization error is reduced, and the engineering guidance value of test data is improved.

The invention relates to an electric energy metering box, and relates to the technical field of metering boxes, the electric energy metering box comprises a box body and a box door hinged to the box body through a hinge shaft, and the side surface of the box body is provided with a plurality of heat dissipation ports; a plurality of supporting plates are fixedly arranged on the inner wall of the box body, a mounting plate for mounting related electrical equipment is jointly and fixedly arranged on the end parts, far away from the box body, of the plurality of supporting plates, and a circulation gap is preset between the mounting plate and the inner wall of the box body; according to the electric energy metering box, due to the cooperative arrangement of the circulation gap and the heat dissipation opening, in the application process of the electric energy metering box, air in the box body can have enough circulation space through the arrangement of the circulation gap, and exchange of internal and external air can be carried out in time through the heat dissipation opening, so that the heat dissipation performance of the electric energy metering box is ensured; and potential safety hazards caused by insufficient heat dissipation are reduced.

The invention relates to the technical field of fire early warning, in particular to an electrical fire early warning method, device and equipment based on leakage current temperature detection. The method comprises the following steps: acquiring information data of electrical equipment; performing electrical equipment power line position positioning on the electrical equipment information data to generate electrical equipment power line position information data; performing leakage current sensor data acquisition on the electrical equipment power line position information data to obtain standard electrical equipment leakage current monitoring data; and performing maximum fluctuation curve segment confirmation on the leakage current monitoring data of the standard electrical equipment to obtain a maximum fluctuation curve of the leakage current of the electrical equipment. According to the invention, by introducing leakage current fluctuation mode identification, accurate power line positioning, intelligent anomaly detection and heat map analysis, the accuracy, response speed and positioning capability of the electrical fire early warning system are improved.

The invention discloses an electrical fire real-time monitoring system based on a wireless sensor network, and relates to the technical field of electrical fire monitoring. The cloud monitoring platform is in communication connection with an electrical data acquisition module, an electrical fire abnormity analysis module, an abnormity early warning module, a node management configuration module and a visual monitoring module, and all the modules are in electric signal connection; the electrical data acquisition module is used for acquiring monitoring data of electrical nodes by using a deployed wireless sensor and transmitting the monitoring data to the cloud monitoring platform through a wireless communication technology, and the monitoring data comprises current, voltage, temperature, humidity and leakage current. Multi-parameter fusion monitoring is carried out through the wireless sensors deployed at the key nodes of the electrical equipment, compared with a traditional wired sensor, the wireless sensors do not need wiring, installation is flexible, early signals of an electrical fire can be captured more comprehensively, and the accuracy of fire early warning is remarkably improved.

The invention relates to a multi-sensor fault feature fusion diagnosis method for electrical equipment, which comprises the following steps: synchronously acquiring operation data of the electrical equipment through a vibration sensor, a temperature sensor, a current sensor and an ultrasonic sensor, dynamically adjusting the sampling frequency according to the physical characteristics of each sensor, and the sampling rate of the temperature signal is not lower than 1Hz. Through a multi-source sensor data synchronous acquisition and time sequence alignment technology and a signal alignment method combining a dynamic time warping (DTW) algorithm and Hilbert-Huang transformation, the problem of time asynchronization of heterogeneous sensor data such as vibration and temperature is solved, so that the time alignment precision of multi-source data is improved, the feature extraction accuracy is improved, and the accuracy of feature extraction is improved. Through hierarchical feature extraction and graph convolutional network fusion, a feature incidence matrix based on mutual information is constructed, deep correlation between vibration signal TKEO features and cross-modal features such as current harmonics is mined by using GCN, the feature dimension is reduced, and the fault feature separability index is improved.

The invention relates to the technical field of fire safety, in particular to an intelligent fire-fighting electrical fire monitoring method, device and equipment and a medium, and the method comprises the steps: obtaining electrical parameters; according to the electrical parameters, a fire hazard identification model is established by using an LSTM algorithm and a CNN algorithm, the operation state of the electrical equipment is predicted according to the fire hazard identification model, an operation state trend prediction result is obtained, and a potential fire hazard type is identified; according to a fire hazard type identification result, in combination with a use environment of electrical equipment and a threshold determination algorithm, performing quantitative evaluation on a fire hazard risk to obtain a corresponding risk level, and when the risk level exceeds a set multi-layer threshold, automatically starting a linkage control mechanism; therefore, the problems of response lagging, high labor cost, high intermittent fault omission ratio and the like in the prior art are solved.

The invention provides a diaphragm, a battery comprising the diaphragm, an energy storage device and electric equipment, the diaphragm comprises a base membrane and a point coating glue layer, the point coating glue layer is arranged on at least one side surface of the base membrane, the point coating glue layer comprises a plurality of glue points distributed in a dot matrix shape, the glue points have a first annular structure, and the first annular structure is arranged on the surface of at least one side of the base membrane. The distance between the outer edge of the first annular structure and the center of the glue point is R1, the distance between the inner edge of the first annular structure and the center of the glue point is R2, and 1-R2 / R1 is larger than or equal to 0.15 and smaller than or equal to 0.35; the point coating adhesive layer comprises a polymer binder and an auxiliary agent, and the auxiliary agent comprises at least one of a water-soluble coupling agent and a phosphate auxiliary agent.

The invention relates to an electrical equipment fault monitoring and positioning method and system based on data analysis, and belongs to the technical field of electrical equipment monitoring. The method comprises the following steps: sensing a transient traveling wave signal of a fault current by adopting a traveling wave detection technology to determine an initial fault area; acquiring state data of the secondary equipment in the fault area through mapping of the secondary equipment in the fault area; constructing a secondary loop connection model based on the configuration file, and designing an action sequence rule base to verify the action logic and time sequence matching of a regional data centralized protection device, a circuit breaker and a communication link in real time; based on logic verification, action triggering conditions are extracted to be fused with the regional data set; and inputting the fault feature vector into a classification model for fault identification by obtaining the classification model. According to the invention, through traveling wave detection, EEMD decomposition, data synchronization and logic verification, feature information related to the fault is accurately extracted, and through data fusion and real-time learning, the fault identification precision and efficiency are improved.

The invention relates to the technical field of mine safety, and discloses a coal mine underground gas monitoring method and system. The method comprises the following steps: acquiring methane absorption intensity data by adopting a tunable diode laser absorption spectrum, converting the methane absorption intensity data into gas concentration based on a Lambert-Beer law, performing temperature and humidity compensation, calculating a transmission priority according to concentration deviation to form a data packet, and establishing a spatial interpolation model by utilizing multipoint data to predict gas concentration distribution. Calculating a risk assessment index based on the predicted distribution, automatically adjusting the rotating speed of the ventilator, and controlling the electrical equipment to be powered off. According to the method, the technical problems of low detection precision, incomplete monitoring coverage, low data transmission efficiency and lack of prediction and early warning capabilities in the existing underground coal mine gas monitoring method are solved. And the accuracy of gas concentration detection and the intelligent level of gas monitoring are improved.

The present application belongs to the field of battery design technology, and in particular, relates to a battery box cover, a battery box, a battery and an electrical device. The battery box cover includes a panel body and a panel structure, the panel structure is fixedly connected to the circumferential edge of the panel body, the panel structure is used to cover the box opening of the battery box body, and the panel body is provided with a plurality of convex rib structures on the side facing the box body, and the plurality of convex rib structures cross to form a plurality of grid spaces distributed in an array, the grid spaces are used to avoid the convex structures of the battery cells, and at least part of the convex rib structures are away from the end of the panel body to support the battery cells. The application of this technical solution is intended to solve the problem of how to improve the main frequency and mode of the battery box cover to reduce the vibration and abnormal sound of the box cover.

The invention discloses a partial discharge positioning method, system and device based on an acoustic-electric combined signal, and relates to the technical field of electrical device detection, and the method comprises the steps: deploying a plurality of sensor nodes based on a target electrical device, and collecting an electromagnetic signal and a sound wave signal generated by partial discharge at the same time; calculating a preliminary positioning result of the partial discharge source based on the collected acoustic-electric combined signal; environment parameter data, historical operation data and electrical equipment structure information in the electrical equipment are collected and input into a preset error correction model, and a corresponding error correction interval is obtained; when a certain preliminary positioning result exceeds the corresponding error correction interval, obtaining a timestamp corresponding to the preliminary positioning result; performing multi-source information fusion positioning based on the environmental parameter data, the historical operation data, the electrical equipment structure information and the corresponding timestamps, and analyzing and determining final positioning coordinates of the partial discharge source; the accuracy of partial discharge signal positioning is improved, and the positioning efficiency is improved.

The invention discloses an electrical equipment defect identification and alarm method and system based on deep learning. The method comprises the following steps: synchronously collecting and registering visible light and infrared thermal imaging images on the surface of power equipment, and constructing an instance segmentation network comprising a lightweight feature extraction network, a multi-scale feature fusion network and a frequency domain mask prediction branch; enhancing the diversity of training samples by adopting a generative adversarial strategy; based on the graph neural network, analyzing the incidence relation between the defects and the equipment topology and historical records, and deducing the defect causal relation and the risk level; generating interpretable alarm information including the thermodynamic diagram, the natural language report and the maintenance suggestion; real-time detection and deep analysis are realized by adopting an end-side cloud collaborative architecture; and the system performance is continuously improved through a closed-loop optimization mechanism. According to the method, high-precision defect detection under multi-modal data fusion is realized, the robustness and interpretability are high, and the operation and maintenance intelligence level of power equipment is remarkably improved.