30943results about "Electrical testing" patented technology

The invention discloses an electric bicycle lithium battery data real-time monitoring and early warning system and method, and relates to the technical field of battery management, and the system comprises a multi-dimensional data collection module which is used for obtaining a multi-source heterogeneous data set of a lithium battery system; the collaborative feature extraction module is used for generating a comprehensive evaluation parameter set; the dynamic threshold generation module is used for constructing a self-adaptive early warning boundary model according to the comprehensive evaluation parameter set; the intelligent decision module is used for generating a hierarchical control instruction set based on a multi-objective optimization algorithm; and the cloud collaboration module is used for synchronizing the hierarchical control instruction set to the edge computing node and the cloud management platform, and triggering a multi-level linkage protection mechanism based on the game theory when the thermal runaway risk index is detected to exceed a first dynamic threshold value. According to the electric bicycle lithium battery data real-time monitoring and early warning system and method provided by the invention, the safety and reliability of a battery system are improved.

The invention relates to the field of battery fault prediction, in particular to a lithium ion battery fault prediction method and system based on a BMS. The method comprises the following steps: extracting multi-dimensional operation monitoring parameters of a battery through a BMS (Battery Management System), carrying out multi-state evolution perception and label mapping processing, and constructing a global multi-state perception map of the battery; short-term abnormal sudden change detection is carried out according to the multi-dimensional operation monitoring parameters of the battery, and normal characteristic deviation trend analysis is carried out, so that an abnormal fluctuation deviation evolution trajectory is constructed; and performing deep topological correlation learning on the global multi-state sensing map of the battery based on the abnormal fluctuation deviation evolution trajectory, performing heterogeneous node global sensing, performing abnormal behavior causal relationship mining on heterogeneous deviation nodes in the battery, and performing multi-causal fission simulation to generate a battery behavior deterioration chain under an abnormal trend. According to the method, accurate and efficient fault prediction is realized, transfer learning is carried out, and the perspectiveness of subsequent BMS fault prediction is improved.

An apparatus is dedicated to controlling the voltage of a battery comprising at least two modules connected in series and each comprising at least one electrochemical cell, and each coupled to voltage balancing means. The apparatus comprises measurement means for determining first voltages across the terminals of each of the modules, calculation means for measuring a second voltage across the terminals of the battery and for determining a mean voltage per module representative of the second voltage divided by the number of modules, and processor means for comparing each measured first voltage with the mean voltage per module and for delivering to the balancing means signals representative of the result of the comparison whenever the first voltage of a module is greater than the mean voltage, such that the balancing means reduce the voltage across the terminals of the module.

The invention discloses a self-adaptive frequency spectrum monitoring and interference suppression method for a railway power transformer. The method comprises the following steps: S1, collecting original multi-source signal data; s2, performing high-order filtering and Z-score normalization processing on the original multi-source signal; s3, inputting the original multi-source signal into a multi-scale residual fusion time-frequency transformation network, and extracting a time-frequency feature tensor; s4, inputting the time-frequency feature tensor into the interference identification network fused with the attention mechanism; s5, dynamically activating an interference suppression module according to an identification result; s6, constructing a multi-dimensional tensor data structure, extracting sparse dictionary morphological features and spectral domain statistics, and generating a composite feature vector set; s7, inputting the composite feature vector into a health state evaluation module; and S8, uploading the diagnosis result to a remote monitoring platform through the embedded communication module. According to the invention, multi-dimensional perception and adaptive modeling are fused, and intelligent identification and remote monitoring of railway transformer faults are realized.

The invention relates to the technical field of battery detection, and particularly provides a battery fault early warning and diagnosis method, which comprises the following steps: acquiring operation parameters of a battery pack in real time, processing the operation parameters based on a preset judgment condition, generating a corresponding preprocessing signal according to a noise environment state, and extracting spatial-temporal characteristics to perform weak signal enhancement processing, so as to obtain a battery fault early warning and diagnosis result. Generating an enhanced feature set; based on the enhanced feature set, performing space-time fusion calculation according to a preset weight relation to obtain a fault energy accumulation value; dynamically correcting a fault judgment threshold according to the health state of the battery and the real-time environment parameters; and when the fault energy accumulation value exceeds the fault judgment threshold after dynamic correction, outputting a graded early warning signal. By capturing weak fault features of the battery pack and combining spatial domain feature extraction of temperature difference and voltage distribution and a signal enhancement technology, the detection sensitivity of early hidden faults is improved, and in a lithium battery safety early warning scene, the fault detection time is shortened, the false alarm rate is reduced and the like.

The invention discloses a smart power grid line loss intelligent detection method based on Internet of Things big data, and relates to the technical field of smart power grid electric energy metering, and the method comprises the following steps: S1, extracting a voltage signal and a current signal of each electric energy metering node in a set time window, carrying out the frequency domain transformation to obtain a frequency spectrum, calculating the energy concentration index of a high-frequency band, and calculating the energy concentration index of the high-frequency band; and identifying a high-frequency harmonic interference time section according to the change of the energy concentration degree. According to the method, a closed-loop mechanism of high-frequency harmonic interference identification, error traceability, cause discrimination and dynamic correction is constructed, fragment-level and node-level error compensation is realized, and the accuracy and the stability of line loss anomaly identification of the smart grid are improved by combining electric quantity consistency verification and trend model updating; and the reliability and the intelligent level of the system in a complex interference environment are obviously enhanced.

The invention relates to the technical field of abnormity early warning, in particular to a transformer load abnormity early warning method and system, and the method comprises the following steps: obtaining an electrical parameter sequence and correcting a phase, constructing a current and power change rate sequence, extracting a load fluctuation trend factor, calculating a disturbance growth rate and judging whether the disturbance growth rate crosses a boundary, and recognizing a power gradient abrupt change feature. And judging whether periodic overlapping exists or not, and outputting a combined abnormity early warning identification sequence. According to the invention, by introducing phase difference and time base offset calculation, constructing a phase-aligned electrical parameter sequence group, and analyzing a periodic rate mean value and an integral quantity, disturbance trend evaluation is realized, a power gradient sequence is used to identify a section mutation interference signal, and a joint anomaly identification mechanism is constructed on two dimensions of a period and a section. The method achieves the composite judgment of the load disturbance trend and sudden change interference, effectively improves the accuracy and response time efficiency of load abnormity early warning, and reduces the recognition error risk caused by hidden fluctuation or local sudden change.

The invention relates to the technical field of electrical fault diagnosis, and discloses an electrical system fault diagnosis method based on multiple parameters. According to the method, multi-dimensional operation parameters of an electrical system are collected, wherein the multi-dimensional operation parameters comprise voltage waveform data, current waveform data and temperature distribution data; time-frequency conjoint analysis is carried out on the multi-dimensional operation parameters, and multi-scale electrical features are extracted and comprise steady-state feature components and transient feature components; based on a historical fault case library, performing mode matching on the multi-scale electrical characteristics to generate an initial fault type set; performing confidence evaluation on the fault types in the initial fault type set by using a dynamic weight distribution algorithm, and screening out high-confidence fault types; and according to the high-confidence fault type, a fault evolution path model is constructed, and the fault evolution path model is used for describing a time sequence of fault features.

The invention relates to a weak current equipment abnormity judgment method and system based on the Internet of Things. The method comprises the steps that multi-dimensional parameters such as temperature, current, voltage and vibration frequency are collected through nodes of the Internet of Things, and a dynamic tracking identifier is generated; performing dynamic parameter association analysis on edge nodes, and establishing a real-time coupling degree relationship between parameters; according to the analysis result, evaluating the abnormity level in a grading manner, and distinguishing the primary abnormity of single parameter deviation and the advanced abnormity of multi-parameter collaborative deviation; the cloud platform starts a differential verification mechanism for different levels of anomalies, primary anomalies are transversely compared, and advanced anomalies execute full-life-cycle backtracking verification; the system comprises a data acquisition module, an edge calculation module, an analysis and evaluation module and a cloud analysis platform, and can realize the method. According to the invention, through dynamic coupling degree analysis and a hierarchical verification mechanism, the abnormality judgment accuracy is remarkably improved, and false alarms caused by environmental interference are effectively reduced.

An ID token wirelessly communicates a battery voltage to a vehicle gateway via short-range communication. A battery health may be determined based on the current battery voltage and one or more previous battery voltages associated with ID token. Replacement of the ID token may be initiated based on the battery health to avoid communication failures by the ID token due to insufficient battery charge.

The invention relates to the technical field of energy storage battery diagnosis, and discloses an energy storage battery fault diagnosis method and system based on a data fusion algorithm. The method comprises the following steps: acquiring historical operation data and real-time operation data of an energy storage battery in a preset operation period, and generating a historical fault data set according to the historical operation data; performing multi-source feature fusion processing on the historical fault data set to generate a fusion feature parameter set integrating voltage, current, temperature and impedance parameter joint change features; a multi-dimensional fault space is constructed based on the parameter set, coordinate axes of the multi-dimensional fault space correspond to different parameter dimensions, and spatial position coordinates represent parameter change characteristic values; calculating the fault correlation degree of the historical fault event in the multi-dimensional fault space, and determining a fault early warning index set; and extracting real-time characteristic parameters based on the real-time operation data to form a state vector, carrying out space mapping correlation calculation on the state vector and the fault early warning index set in a multi-dimensional fault space, and outputting a real-time fault correlation factor.

The invention relates to the technical field of electric power system intelligent monitoring, and discloses an intelligent power supply system state monitoring and fault early warning method and system. According to the system, power grid operation parameters are collected in real time through a heterogeneous sensor array, multi-dimensional features are extracted through wavelet transform, a fault diagnosis model is constructed based on deep learning, precise early warning is achieved in combination with a dynamic threshold optimization algorithm, an optimal disposal scheme is generated based on an expert knowledge base, and remote data transmission is achieved through dual-channel communication. Real-time monitoring, fault early warning and intelligent decision support of the state of the power supply network are realized, and the operation reliability and the operation and maintenance efficiency of the power grid are remarkably improved.

The invention discloses an operation and maintenance debugging monitoring system suitable for an electric field, and relates to the technical field of operation and maintenance debugging monitoring, and the system comprises a collection unit which collects the operation data of an electric field device at a multi-dimensional monitoring point, the operation data comprises an electrical parameter, a mechanical vibration parameter, an environment parameter and chemical gas component data, and a multi-modal monitoring data set is constructed; the feature analysis module is used for carrying out spatial-temporal feature extraction on the multi-modal monitoring data set, generating a multi-dimensional feature tensor containing a time domain feature, a frequency domain feature and a spatial distribution feature, obtaining a spatial-temporal coupling relationship among the features through a tensor decomposition technology, and sending the spatial-temporal coupling relationship to the multi-modal monitoring data set; according to the method, a whole-process intelligent solution from monitoring, early warning to decision making is provided for electric field operation and maintenance, the equipment fault risk is greatly reduced, the power failure time is shortened, and the safety and economical efficiency of power grid operation are improved.

The invention provides a new energy power distribution fault diagnosis system and method based on multi-source data fusion, and relates to the technical field of power system fault diagnosis. The system comprises a data processing module, a fusion reasoning module, a graph structure diagnosis module and a control response module. The data processing module performs unified structured processing on the multi-source operation data to generate a feature tensor; the fusion reasoning module constructs a dynamic fusion weight based on the information entropy of each data source, and outputs a fusion confidence judgment result; the graph structure diagnosis module is combined with the distribution network topology to construct a graph structure model, and node-level fault positioning is realized through a graph neural network; and the control response module generates a control instruction based on the diagnosis result, and acquires and executes feedback to update graph structure attributes, thereby realizing a diagnosis closed loop. The method has the advantages of high diagnosis precision, strong adaptability, timely response and the like, and is suitable for intelligent fault identification and dynamic processing in a new energy power distribution scene.

The invention relates to the technical field of lithium battery safety monitoring, and discloses a lithium battery pack thermal runaway early warning system based on multi-mode sensing. The system comprises multi-source sensing data acquisition, thermal field feature tensor construction, thermal field reconstruction and thermal coupling association network generation. The multi-source sensing data acquisition module acquires multi-dimensional heterogeneous data from a temperature sensor, a voltage and current monitoring unit, a gas component detector and an acoustic emission sensor, and generates a standardized data bin through timestamp alignment and missing value compensation; the thermal field feature tensor construction module extracts features such as temperature gradient and electrochemical response from the data bin in a multi-scale manner, and constructs a tensor in combination with time continuity; the thermal field reconstruction module generates association diagrams according to the feature space-time distribution and fuses the association diagrams into a lithium battery pack three-dimensional thermal field reconstruction map; and the thermal coupling association network generation module extracts a feature vector cluster, calculates an entropy weight value, and generates a network according to a high-entropy node space adjacency relationship. According to the system, multi-dimensional data fusion is realized, and the thermal runaway evolution law can be comprehensively described.

The invention discloses a battery abnormity identification monitoring method for battery operation and maintenance, and particularly relates to the technical field of battery detection. After a battery enters a station, a temperature difference array, shell micro-deformation, cavity gas indication and end plate near-end temperature are synchronously acquired to form a skin observation vector; applying slight current disturbance and recording voltage response and temperature rise differential to generate a disturbance differential vector; performing event alignment, same-window shadow trajectory depolarization and quantile projection normalization under a unified time reference to obtain a stable and reliable equivalent short window representation vector; performing weighted matching on the vector and historical reference fingerprint quality to obtain a consistency score, calculating a multi-physical disturbance coupling index and a historical trajectory deviation index, outputting a short window credibility coefficient through a discrimination model, generating a risk tag by combining a consistency threshold coefficient after zooming the consistency score, and performing risk identification on the risk tag. And linkage isolation recheck, power limitation and temperature rise limitation or release and archiving are carried out, so that the problem that abnormity in a short time window is easily covered by an environment common mode and artifacts is solved.

The invention discloses a power distribution equipment remote diagnosis method based on edge computing, and particularly relates to the technical field of intelligent monitoring of power equipment, and the method comprises the steps: an edge computing node collects the operation state data of the power distribution equipment in real time; performing diagnosis analysis locally at the node to generate a preliminary diagnosis result and key data; uploading the structured data to a cloud according to a preset strategy, and checking the integrity; and the cloud platform performs association analysis on the multi-node data to identify common anomalies, dynamically optimizes a diagnosis algorithm, automatically triggers alarms and work order distribution in a grading manner, and realizes rapid closed-loop processing in combination with the positions and skills of operation and maintenance personnel. Through cooperation of the edge and the cloud, communication bandwidth occupation is reduced, diagnosis accuracy and real-time performance are improved, fault response time is shortened, and the method is suitable for line-level monitoring and operation and maintenance management of the power distribution network.

The invention relates to the technical field of new energy battery testing, and discloses a new energy battery safety testing system and method, and the system comprises a multi-physical field synchronous collection and analysis module which is used for synchronously collecting electric signals, temperature field distribution and stress-strain data in a battery testing process, and carrying out the multi-field coupling analysis; a self-adaptive test parameter regulation and control module; the thermal runaway risk prediction module is used for analyzing and predicting the risk probability and residual safety time of thermal runaway of the battery based on multi-parameter fusion; the microstructure-macroscopic performance correlation analysis module is used for acquiring microstructure information of the battery through nondestructive testing and establishing a correlation model with macroscopic safety performance, and the new energy battery safety test system realizes collaborative analysis of multiple parameters of electricity, heat and force through the multi-physics field synchronous acquisition and analysis module; the safety state of the battery can be reflected more comprehensively, and the defect that a traditional testing method is single in dimension is overcome.

The invention discloses a battery full life cycle intelligent management method and system based on a large model, and the method comprises the following steps: S1, collecting the operation data of a battery, and carrying out the preprocessing; s2, inputting the pre-processed sample into a pre-trained Transform encoder model, and extracting a time sequence and cross-stage characteristics; s3, executing model reasoning, outputting a state index, and generating a battery state vector; s4, identifying an abnormal category and a position in combination with the working condition information; s5, generating a dynamically optimized battery management strategy based on the battery state index and the abnormity identification result; s6, deploying a lightweight model at the edge device, executing local reasoning and uploading data; s7, the cloud updates the model through self-supervised training and issues the model; and S8, repeatedly executing the steps S1 to S7, and carrying out optimized closed-loop management. According to the invention, large model modeling and an edge cloud cooperation mechanism are fused, and intelligent sensing and dynamic management of the whole life cycle of the battery are realized.

A system for facilitating battery fault prediction is configurable to: (i) access a set of raw data comprising battery sensor data associated with a physical battery and operation data associated with operation of the physical battery; (ii) obtain an estimated battery state for the physical battery by utilizing the battery sensor data as input to a battery digital twin; (iii) obtain a battery fault prediction by utilizing (a) the estimated battery state obtained via the battery digital twin and (b) operation input based on the operation data as input to a battery fault prediction model, and wherein the battery fault prediction comprises one or more likelihood metrics indicating a likelihood of one or more battery faults occurring within one or more predetermined time periods; and (iv) cause presentation of an alert via a battery fault notification system.

The invention discloses an automatic anchor point searching and processing method for grid-connected test data of a photovoltaic inverter, and belongs to the technical field of automatic test of a power system. According to the method, a three-phase voltage and current signal output by a power grid simulator and an inverter power instruction signal are aligned through a high-precision time synchronization device; wavelet transform multi-scale noise reduction and moving average filtering combined preprocessing is adopted to improve the signal-to-noise ratio; identifying a voltage zero crossing point candidate set, a drop starting point candidate set and a recovery termination point candidate set based on a self-adaptive dynamic threshold value; transient energy characteristic verification is introduced for a voltage drop starting point; effective anchor points are confirmed through time window association of power instruction step changes. According to the method, the problems of low efficiency of manual key event point identification, misjudgment caused by noise interference, grid event and inverter response time sequence correlation missing and the like are solved, and the automation degree of test data analysis, anchor point positioning precision and control response time sequence analysis reliability are remarkably improved.

The invention discloses a battery safety risk grade early warning and evaluation method, and relates to the technical field of lithium ion batteries, and the method comprises the steps: collecting battery operation data, carrying out the processing to obtain a capacity attenuation characteristic and an internal resistance growth characteristic, and obtaining a degradation characteristic parameter related to the temperature through an Arrhenius model; estimating capacity loss trends at different temperatures and time; inputting the multi-source time sequence data into a long short-term memory network, and predicting the capacity and internal resistance change of a plurality of cycles in the future; and in combination with a life end criterion, calculating the remaining service life, fusing the remaining service life with the degradation index of the aging model and the degradation index of the capacity model, generating a safety state index, and outputting a battery safety risk grade and corresponding early warning information. According to the invention, overall safety risk assessment can be provided for the whole battery system, accurate monitoring and early warning can be carried out on the single batteries or local modules, and the safety and reliability of battery operation can be improved.

The invention relates to the technical field of storage batteries, in particular to a storage battery monitoring, detection, treatment and evaluation integrated system and a storage battery management method, and the system comprises a plurality of accompanying modules, a collection power distribution control module, a storage battery management capacity checking host and a remote platform; the internal resistance analysis test of the system is based on multi-frequency-point alternating current excitation (0.5 Hz-7. 5kHz), a complex impedance spectrum of not less than 20 frequency points can be constructed, and the internal polarization characteristics of the battery are analyzed; the system adopts a feed network type discharge capacity checking, so that discharge electric energy is fed back to a power grid, and the capacity detection precision is improved to 98%; according to the system, EIS data and an LSTM neural network are fused, and a battery state of health (SOH) prediction model is established; the system is based on a DSP digital power supply technology, and can realize 50A active equalization topology.

The invention discloses an energy storage system real-time diagnosis and networking control method and system based on digital twinning and deep learning, and the method comprises the steps: collecting the electrical, thermal and aging state data of an energy storage battery cluster through a multi-mode sensor, constructing a multi-physics field coupled digital twinborn model by using a graph neural network and a long short-term memory network; performing synchronous mapping on battery cluster operation data acquired in real time and the digital twinborn model to generate a state evolution sequence in the battery cluster with advanced prediction capability; based on the state evolution sequence, predicting a dynamic stability boundary of the key node of the power grid and a possible instability risk time period in the future; and according to the dynamic stability boundary and the instability risk time period, generating a cooperative adjustment instruction of the output voltage amplitude, the phase and the virtual impedance of the network construction type energy storage equipment. According to the embodiment of the invention, the diagnosis reliability, the control foresight and the operation safety of the energy storage system in a complex power grid environment can be improved.

The invention provides a battery life intelligent prediction method fusing a time convolution network and a Mama state space model. The method comprises the following steps: a TCN branch extracts local time sequence characteristics in a battery degradation process by utilizing multi-layer expansion causal convolution, and nonlinear degradation phenomena such as capacity regeneration and the like are effectively identified; the Mama branch is based on a selective state space mechanism, dynamically models a long-period dependency relationship of a battery aging track, and adaptively focuses a key degradation node through weight adjustment of context sensing. In order to enhance model robustness, a variational mode decomposition unit is integrated to carry out noise suppression and mode separation on an original capacity sequence, and the generalization ability of a cross-battery chemical system is improved in combination with normalized constraint of a hidden state matrix. The accuracy of life prediction is remarkably improved, and key features in a complex degradation mode are effectively captured; the method has excellent noise suppression capability and cross-model generalization, is suitable for multiple types of battery systems, and realizes real-time aging state evaluation.

The invention provides a health state monitoring and service life prediction method and system for an energy storage battery pack, relates to the field of energy storage batteries, and solves the problems that prediction models in the prior art mostly adopt a single machine learning algorithm and lack adaptability to a battery degradation mechanism and actual working conditions, and the prediction efficiency is poor. And the battery health state evaluation and residual life prediction precision is low. The method comprises the following steps: preprocessing an original data set, analyzing a multi-dimensional health feature vector, and constructing a health feature matrix; constructing a health state evaluation model based on the improved CNN-LSTM hybrid model and by fusing battery degradation physical mechanism constraints; inputting the health feature matrix into a health state evaluation model, and outputting a current SOH value; and predicting residual life information based on the current SOH value and the load fluctuation correction coefficient. The method is used in the process of health state evaluation and residual life prediction of the energy storage battery pack.

The invention discloses a power generation equipment state fault diagnosis method and system based on artificial intelligence, and the method comprises the steps: actively injecting a mechanical excitation signal of a preset frequency spectrum into a key part according to a physical topological structure of power generation equipment, and carrying out the fusion to generate a time-space-frequency three-dimensional data volume; inputting the three-dimensional data volume into a physical embedded variational auto-encoder, and outputting an equipment state pure feature tensor; inputting the pure feature tensor into a graph space-time causal reasoning network to generate a fault propagation causal graph with probability weight; performing multi-agent diagnosis on the fault propagation causal atlas, and outputting a fault diagnosis report which has a credibility interval and comprises fault positioning and root cause analysis; and mapping the fault diagnosis report to the digital twin of the equipment in real time, and outputting a self-adaptive maintenance strategy sequence which minimizes the expected value of the whole life cycle operation and maintenance cost. According to the embodiment of the invention, the accuracy and anti-interference capability of fault diagnosis can be improved, and the operation and maintenance cost can be effectively reduced.

The present invention relates to the technical field of lithium battery state estimation, and in particular to a fuzzy EKF-AH algorithm-based state of charge (SOC) estimation and correction method for a lithium iron phosphate battery. The method comprises: by taking a certain startup of an energy storage device as a start, a BMS reading an SOC and a state of health (SOH) at the previous shutdown, and on the basis of a standby time, selecting an open circuit voltage (OCV) or an EKF algorithm to correct the SOC; using an EKF-AH algorithm to estimate the SOC, establishing, on the basis of fuzzy control, a fuzzy rule library associated with the SOC and the SOH, and dynamically adjusting a weight and a measurement noise deviation of the EKF-AH algorithm; and calculating estimation differences between the EKF algorithm and an ampere-hour integration method, and if the sum of the estimation differences is greater than a corresponding threshold, issuing an SOH correction warning. The present invention improves the estimation accuracy of the entire life cycle of the lithium iron phosphate battery, and assists the correction of the SOH.

The invention discloses an intelligent power grid operation and maintenance system based on federated learning and edge calculation, and the system comprises an intelligent electric meter enhancement module which is disposed at a power grid monitoring node and is used for collecting electric energy quality parameters and environment data in real time; the edge calculation layer is used for carrying out data preprocessing, power quality parameter anomaly detection and building a federated learning model; the secure communication framework is used for establishing a hybrid communication network and multi-level security protection, carrying out routing decision and carrying out encrypted transmission on interactive data among the modules; and the end analysis platform is used for integrating the multi-source heterogeneous data of the power grid, predicting the state of the power grid and generating a maintenance plan. According to the invention, comprehensive monitoring and predictive maintenance of the operation state of the power grid are realized.

The invention relates to an algorithm fusion and fault early warning system in a battery safety management platform, and aims to realize multi-dimensional risk identification and active response control in a battery operation process. The system comprises a multi-source data acquisition module, a fusion calculation engine, a fault early warning response module and an execution interface module. By collecting voltage, current, temperature, humidity, sound wave signals, electrochemical impedance spectroscopy, gas concentration and other multi-mode operation data, a fusion calculation engine completes time sequence correlation modeling, causal feature recognition and risk trend deduction on the basis of a constructed hierarchical algorithm fusion structure, and a risk response signal reflecting a safety state is generated; the fault early warning response module generates hierarchical control instructions corresponding to risk levels according to the signals, the hierarchical control instructions comprise virtual fusing, cooling scheduling, BMS refreshing and the like, the execution interface module issues the control instructions to a physical controller of the battery system, response behaviors are driven, and therefore intelligent, closed-loop and dynamic battery safety management is achieved.