15188 results about "Electric 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 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 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 relates to the technical field of electric power energy storage system planning analysis, and discloses an electric power system planning operation optimization method, system and device and a storage medium. The method comprises the following steps: collecting electrical and equipment states and environmental parameters for feature extraction, and evaluating the health state of equipment; grouping the distributed energy sources based on health state evaluation and weather early warning information to form a virtual power plant resource pool; performing task decomposition according to the characteristics of the resource pool, and formulating a collaborative scheduling strategy; arranging a maintenance plan according to the equipment state and the scheduling strategy; and optimizing system operation parameters based on the scheduling strategy and the maintenance plan to obtain a balance control scheme. According to the invention, under the condition of considering the health state of the equipment and the influence of extreme weather, the dynamic balance of the minimization of the operation cost of the power distribution network and the maximization of renewable energy consumption is realized through the intelligent arrangement technology of the virtual power plant group.

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 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.

A box structure includes a cooling member and a frame. The cooling member includes a main pipe and at least one branch pipe connected to the main pipe. The main pipe is configured to pass a coolant into the branch pipe, and the branch pipe is configured to contact the battery pack to cool the battery pack. An accommodation groove is created at a bottom of the frame, at least a part of the main pipe is located in the accommodation groove, and the main pipe is bonded and fixed to the accommodation groove.

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 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 relates to the technical field of building energy consumption management, in particular to a building energy consumption management system and method for analyzing the load of a power grid, and the system comprises an acquisition processing unit, a load analysis unit and an energy management unit. A load prediction module of the load analysis unit predicts a load by using a long short-term memory network and a graph convolutional neural network, a decomposition traceability module decomposes a traceability load by using an attention mechanism and a hidden Markov model, and an energy management unit regulates and controls adjustable equipment in peak and valley periods according to load prediction. According to the method and the system, the abnormal energy consumption equipment is pre-warned according to the load decomposition and traceability result, multiple technical means are integrated, the traceability load can be accurately predicted and decomposed, the energy management is optimized, the complex building environment is adapted, and the problems that the equipment relationship is difficult to grasp, the load change reason is difficult to track and the energy consumption is difficult to accurately manage in a traditional mode are effectively solved.

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 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 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 invention discloses a power transmission line abnormity early warning and fault positioning system, which belongs to the technical field of line detection, and specifically comprises the following steps: dividing early warning equipment in a line into sub-regions according to distribution through a region delimiting module; when a fault occurs, the region positioning module quickly marks the related sub-regions and equipment as undetermined states; the data acquisition module is used for collecting data of the to-be-determined equipment in a set time window before a fault, and preprocessing the data to judge whether the fault is caused by an electrical problem or not; if yes, collecting historical power consumption data of the to-be-determined area; the fault positioning module normalizes historical data, constructs a time sequence model, extracts a current value change sequence, divides the current value change sequence into a plurality of subsequences and then performs clustering analysis; judging the fault reason according to the offset condition of the category cluster where the undetermined current value change sequence is located in the clustering result; if the offset exceeds a preset threshold value, considering that the electric equipment is abnormal; otherwise, the aging of the line insulating material is caused.

A battery cell, a battery, and an electrical device are disclosed. The battery cell includes an electrode assembly, a shell, an electrode terminal, and a current collecting member. The electrode assembly has a tab. The shell accommodates the electrode assembly. The electrode terminal is disposed at one end of the shell in a first direction. The current collecting member is provided with a hollow region, and the hollow region divides the current collecting member into a first connection region and a second connection region located on two sides of the hollow region. The first connection region is connected to the tab, and the second connection region is connected to the electrode terminal. When the electrode terminal is subjected to stress, the hollow region enables the second connection region to deform relative to the first connection region, so the stress is not easily transmitted to the first connection region.

The invention provides a multi-dimensional electrical equipment insulation analysis and evaluation method, and relates to the technical field of electrical equipment insulation online detection and intelligent diagnosis. According to the method, an electrically isolated direct current detection channel is constructed in a neutral point small resistance grounding system, controllable direct current detection signals are injected, micro direct current leakage response signals are collected, and after zero drift correction, temperature compensation and power frequency ripple suppression are carried out, layered attribution is carried out in combination with primary system topology and an equipment ledger, and a multi-dimensional response matrix is formed. According to the method, environment and working condition normalization is realized through factor rejection and robust pull-back, a mechanism model containing leakage channel equivalent parameters and path constraints is established, multi-working-condition data is fused to calculate equivalent insulation parameters and credibility indication quantity, and a multi-dimensional insulation representation vector is generated. And performing time sequence analysis on the representation vector, extracting degradation and abrupt change characteristics, outputting a health index and a risk level, and realizing quantitative evaluation and intelligent early warning of the insulation state.

The invention relates to the technical field of data processing, in particular to a monitoring method and system for electrical equipment, and the method comprises the steps: enabling real-time data of any monitoring index of the electrical equipment and historical data in a historical time period to form a data sequence, and according to the change rule of each piece of data in the data sequence under different conditions, obtaining a data sequence; obtaining three abnormal characteristic values of each piece of data; for any abnormal characteristic value, obtaining similar historical data similar to any abnormal characteristic value of the real-time data, and obtaining an abnormal reaction degree of any abnormal characteristic value according to relevance between the real-time data and each similar historical data under other abnormal characteristic values; according to the abnormal reaction degree of each abnormal characteristic value and the three abnormal characteristic values of each piece of data in the data sequence, the abnormal degree of any monitoring index is obtained, abnormal early warning is carried out on the electrical equipment according to the abnormal degree of each monitoring index, and the accuracy of abnormal early warning on the electrical equipment is improved.

The invention provides a lottery store violation detection method and system based on multi-modal data fusion, and relates to the technical field of intelligent monitoring, and the method comprises the steps: detecting a current abnormal event, carrying out time sequence perception target detection on monitoring video data, and recognizing violation electric equipment in a video image frame based on adaptive feature enhancement and multi-target tracking. The method comprises the following steps: acquiring time sequence data of current abnormity and illegal electric equipment detection, constructing a time sequence incidence relation of current abnormity, equipment detection and an electric state based on a dynamic causal network, calculating a time-varying weight and instantaneous causal intensity by utilizing conditional entropy increment, acquiring optimal time lag in combination with eigenvector conversion and a dynamic programming algorithm, and determining the current abnormity and illegal electric equipment detection according to the optimal time lag. And weighting the integral of the instantaneous causal intensity and the exponential function of the optimal time delay to obtain an event matching score, and distinguishing a temporary power utilization event and a continuous illegal power utilization event based on the event matching score.

The invention discloses an electrical equipment state monitoring system and method based on the Internet of Things, and belongs to the field of the Internet of Things, and the method comprises the following steps: installing an intelligent sensor node with a built-in edge calculation and self-diagnosis function, and carrying out the self-adaptive adjustment of frequency collection data according to the working state and historical data of electrical equipment; an edge server is added between the gateway device and the cloud server, and hierarchical data analysis is carried out; training the historical data of the electrical equipment by using a machine learning algorithm, constructing an equipment health state model, and performing fault prediction and health assessment; an early warning condition value is set according to the prediction result, and the intelligent alarm system carries out fault processing according to the early warning condition value. The maintenance cost can be reduced, and the efficiency and accuracy of maintenance work can be improved.

The invention discloses a high-voltage power distribution cabinet heat dissipation system based on big data, which relates to the technical field of heat dissipation of electrical equipment and comprises a data acquisition module, a processing analysis module, a man-machine interaction module and a data storage module. An existing heat dissipation mode usually depends on a static fan or air conditioning equipment, dynamic adjustment cannot be carried out according to environment changes and load conditions, and the problems of high energy consumption and slow response exist. The system processes data, eliminates abnormal values, fills missing data, calculates correlation among temperature, humidity, current and fan rotating speed, predicts change trends of the temperature, the humidity and the current through a time sequence model, and displays key data through a graphical interface by a man-machine interaction module, so that the equipment management efficiency is improved; and precious historical data is provided for subsequent fault analysis and tracking, so that optimization of a maintenance strategy of the system and reduction of potential operation risks are facilitated.

The invention provides a method for monitoring and analyzing electrical equipment through linkage of a camera and a monitoring system, and the method comprises the steps: firstly, enabling a patrol system to define a key region needing to be monitored, and enabling the camera to carry out the continuous monitoring in a non-patrol task period; and then acquiring a video stream for analysis, extracting key features by using a computer vision technology, identifying the abnormality of the electrical equipment by means of a trained deep learning model, and immediately alarming and recording related information once the abnormality is found. And when the electrical equipment monitoring system perceives a potential problem, the alarm signal triggers the inspection host, and the camera is controlled to jump to a preset position to collect a field condition. And after the collected data is stored, the authenticity and severity of the abnormity are deeply evaluated by using the intelligent analysis algorithm again. And finally, an analysis result is fed back to an operator to assist in decision making, and the operation process and result are recorded in detail so as to facilitate review and performance optimization. According to the method, all-around, accurate and efficient monitoring analysis of the electrical equipment can be realized, the operation and maintenance level is improved, and safe and stable operation of a power system is guaranteed.

A battery cell includes an electrode assembly, a case, an end cover assembly, and a current collecting component. The electrode assembly includes a first tab. The case is used for accommodating the electrode assembly, and the case has an opening. The end cover assembly covers the opening and is provided with an electrode lead-out portion. The current collecting component includes a tab connecting region and an end cover connecting region. The tab connecting region is used for connecting to the first tab. The end cover connecting region is welded to the inner side of the electrode lead-out portion and forms a first welded portion. The penetration depth of the first welded portion formed on the electrode lead-out portion is smaller than or equal to the thickness of the electrode lead-out portion.

An embodiment heat pump system for a vehicle includes a first circuit including a radiator and a first valve in a first line, a second circuit including a first water pump, a second valve, and a third valve provided in a second line, a third circuit including electrical equipment provided in a third line, a fourth circuit including second water pump and a fourth valve provided in a fourth line, a fifth circuit including a battery module provided in a fifth line, a sixth circuit including a third water pump and a cooler provided in a sixth line, a seventh circuit including a fourth water pump and a heater provided in a seventh line, and a centralized energy module including a condenser, a chiller, and an evaporator.

The invention relates to the technical field of image processing, in particular to a high-voltage electrical equipment surface defect identification method based on image processing. The method comprises the following steps: analyzing the gradient of pixel points in a to-be-analyzed image of a to-be-detected area on the surface of the high-voltage electrical equipment to obtain the weight of each pixel point; weighting the gray value of each pixel point in the to-be-analyzed image subjected to Laplacian filtering by using the weight of each pixel point of the to-be-analyzed image to obtain a first feature map; calculating a local standard deviation of each pixel point in the to-be-analyzed image so as to obtain a first parameter and a second parameter; constructing two Gaussian kernels based on the first parameter and the second parameter to filter the to-be-analyzed image to obtain a second feature map; fusing the first feature map and the second feature map of the to-be-analyzed image to obtain a defect saliency map of the to-be-analyzed image; and recognizing a surface defect area of the high-voltage electrical equipment based on the defect saliency map of each to-be-analyzed image. According to the invention, the accuracy of high-voltage electrical equipment surface defect identification can be improved.

The invention discloses a cylindrical battery, a battery pack and electric equipment. The cylindrical battery comprises an electrode assembly and a shell, the electrode assembly is contained in the shell, the shell comprises a side wall arranged around the electrode assembly, the side wall comprises a side wall main body and a thinning part connected to the side wall main body, the thinning part extends in the circumferential direction of the cylindrical battery, the minimum thickness of the thinning part is T1, the thickness of the side wall main body is T2, and T1 / T2 is larger than or equal to 0.2 and smaller than or equal to 0.4. Therefore, the shell has relatively high strength, and the thinned part can be broken when the internal air pressure of the cylindrical battery is too large so as to timely relieve pressure, so that the safety performance is improved while the structural stability of the cylindrical battery is balanced.