51 results about "Multiple signal classification algorithm" patented technology

The invention relates to the technical field of wireless communication, in particular to an angle-distance joint positioning method in a near-field communication system assisted by wave beam squint, which comprises the following steps of: determining a starting point (theta s, rs) and an end point (theta e, re) of a near-field sensing area, and determining a distance-to-angle joint in the near-field sensing area by adjusting parameters of a true time delayer and a phase shifter. Generating a beam squint focusing scanning track for joint angle-distance positioning; the method comprises the following steps: a base station sends a pilot signal, receives an echo signal of a user side on each subcarrier, and reversely deduces an initial angle estimation value and an initial distance estimation value of a target user by calculating receiving power and searching a subcarrier index corresponding to a power peak value; and a local search area is selected near the initial estimation value obtained in the coarse positioning step, angle and distance two-dimensional joint search is carried out based on an improved near-field multiple signal classification algorithm, and a high-precision final angle estimation value and a high-precision final distance estimation value are output.

The invention discloses an intelligent metasurface-assisted multi-target positioning system, and belongs to the field of wireless communication. The system comprises an intelligent metasurface; a receiver; the plurality of wireless access points can periodically transmit detection signals, and the detection signals can reach the intelligent metasurface through a plurality of targets and reach the receiver after being reflected by the intelligent metasurface; the received signal model building module is used for solving expressions of signals received by the intelligent metasurface and the receiver under different time blocks and time slots; the virtual multi-dimensional signal construction module is used for calculating the multi-dimensional signal according to the signal received by the receiver to obtain a virtual steering vector received by the receiver; the spatial information positioning module is used for obtaining a spatial spectrum of the multi-signal classification algorithm; and searching the spatial spectrum to obtain steering vectors corresponding to a plurality of peak values in the spatial spectrum, and obtaining angles and distances of a plurality of targets.

The invention discloses an unmanned aircraft integrated comprehensive supervision system and a method thereof, and relates to the technical field of unmanned aircraft management. The system synchronously broadcasts standard 5G synchronization signal blocks, receives airspace target echoes, and generates a local detection spectrum; carrying out symbol-level fusion processing on the local detection spectrums of the plurality of base stations, carrying out joint spectral deviation minimization processing by adopting an improved spatial smoothing multiple signal classification algorithm, and extracting the position and speed of a target unmanned aerial vehicle; identifying the identity legality and the flight type of the target unmanned aerial vehicle; the sensing capacity is dynamically estimated, and flow scheduling is executed during overload; generating a flight instruction or triggering a security linkage measure according to an identification result; and processing task load data returned by the unmanned aerial vehicle and carrying out anomaly detection. According to the method, the coverage and precision of synchronous sensing of the low-altitude airspace unmanned aerial vehicle can be improved, accurate identification and quick response to illegal flight behaviors are realized, and the airspace safety supervision capability in a complex environment is enhanced.

The invention provides a LoRa positioning method and system based on symbol-level frequency hopping, and the method comprises the steps: a commercial LoRa node generates a LoRa signal, and transmits a signal containing symbol-level frequency hopping through dynamically adjusting the carrier frequency; the receiving gateway receives the symbol-level frequency hopping signal, aligns a demodulation window by using a time-frequency window alignment algorithm and eliminates frequency offset; and the operation server integrates cross-band channel state information of the frequency hopping signals through coherent multi-channel splicing, calculates a power delay spectrum, jointly estimates an angle of arrival and flight time by using a multi-signal classification algorithm, and carries out fusion calculation to obtain a target node position. According to the method, the positioning precision is remarkably improved, and the positioning delay is greatly shortened.

The application relates to the technical field of wireless communication, in particular to a beam squint auxiliary angle-distance joint positioning method in a near field communication system, which comprises the following steps: determining the starting point (theta s ,r s ) and the ending point (theta e ,r e ) of the near field sensing area, generating a beam squint focusing scanning track for joint angle-distance positioning by adjusting the parameters of the true time delay device and the phase shifter; a base station sends a pilot signal, receives echo signals of a user terminal on each subcarrier, calculates the received power, finds the subcarrier index corresponding to the power peak value, and reversely deduces the initial angle estimation value and the initial distance estimation value of the target user; a local search area is selected near the initial estimation value obtained in the coarse positioning step, a two-dimensional joint search of the angle and the distance is carried out based on an improved near field multiple signal classification algorithm, and a high-precision final angle estimation value and a final distance estimation value are output.

The invention discloses a transformer partial discharge multi-target positioning method and system based on an ultrasonic array. The method comprises the following steps: S1, receiving an ultrasonic signal and constructing a data matrix; s2, calculating a covariance matrix and performing characteristic decomposition; s3, estimating the number of partial discharge sources based on a Gerschnoid circle criterion; s4, determining the spatial orientation of the discharge source based on a multi-signal classification algorithm; and S5, calculating space coordinates through multi-platform direction-finding cross positioning: synchronously measuring the azimuth angle theta and the pitch angle of the same discharge source through at least two independently arranged ultrasonic arrays, combining the azimuth angle theta and the pitch angle with the space position coordinates of the arrays, establishing an equation set based on a triangular relationship, and solving the equation set to obtain the three-dimensional coordinates of the discharge source so as to realize multi-target positioning. The method has the advantages of high positioning precision and the like.

The embodiment of the present application provides a sparse linear array design method and device. The method includes: first, determining multiple performance indicators of the sparse linear array to be designed, the multiple performance indicators including the upper limit of aperture, the lower limit of aperture, etc.; then, based on the multiple performance indicators, establishing a gridless sparse optimization model based on sidelobe control, and determining the optimized constant diagonal matrix through the gridless sparse optimization model; then, based on the constant diagonal matrix, using the root-finding multi-signal classification Root‑MUSIC algorithm to estimate the frequency of the atoms, and processing the frequency by the least squares method to obtain the weight of the atoms; then, based on a pre-established mapping relationship, converting the frequency and weight into the array element position and excitation of the designed sparse linear array. In this way, the grid mismatch problem existing in the traditional method is overcome by introducing the gridless sparse optimization method, and the constraint term of the array aperture is added, so that the array element position of the sparse linear array falls within the aperture.

The application provides a LoRa positioning method and system based on symbol level frequency hopping, and the method comprises the following steps: a commercial LoRa node generates a LoRa signal, and sends a symbol level frequency hopping signal by dynamically adjusting a carrier frequency; a receiving gateway receives the symbol level frequency hopping signal, aligns a demodulation window by using a time-frequency window alignment algorithm, and eliminates frequency offset; a calculation server integrates cross-band channel state information of the frequency hopping signal by coherent multi-channel splicing, calculates a power delay spectrum, jointly estimates an angle of arrival and a time of flight by using a multiple signal classification algorithm, and obtains a target node position by fusion calculation. The method of the application significantly improves positioning accuracy and greatly shortens positioning delay.

The invention discloses an indoor positioning method and system, and the method comprises the following steps: carrying out the enhancement processing of original IQ in-phase orthogonal signals collected by multiple antennas through a lightweight neural network, and outputting an anti-multipath interference enhanced IQ signal matrix; and constructing a covariance matrix based on the enhanced signal matrix, and calculating an azimuth angle and a pitch angle by using a MUSIC multiple signal classification algorithm. According to the invention, multipath interference can be effectively suppressed, phase jitter is eliminated, multichannel feature fusion is realized, and the accuracy of indoor positioning is improved.

The invention discloses a transmission optimization method and device for a multi-base-station communication and sensing integrated system and computer equipment, and the method comprises the steps: constructing a multi-base-station novel communication and sensing integrated system for an imperfect channel scene under a complex communication condition in a closed environment, and proposing an optimization algorithm and a passive sensing multiple signal classification algorithm. An imperfect channel model in near field communication and a communication and perception model in a system are constructed, and a cooperative perception performance index Cramer-Rao bound is given. And finally, in order to improve the overall perception performance of the system, constructing a resource allocation optimization problem taking the minimum Cramer-Rao bound as a target function on the premise of ensuring communication requirements. The optimization problem is a non-convex optimization problem which is difficult to solve, so that a non-convex objective function is converted into a convex objective function by using the Schel complement theorem, and communication demand constraints are converted into convex constraints by using the semi-definite relaxation theorem. For a target positioning problem in a sensing task, coordinate estimation is carried out based on a passive sensing MUSIC algorithm of an orthogonal subspace.

The application discloses a millimeter wave radar gesture control method and related devices, and relates to the technical field of data analysis. The method comprises: performing clutter suppression processing on each frame of gesture signal data; performing effective gesture signal extraction and signal reconstruction on each frame of gesture signal data after clutter suppression processing based on modal component screening, to obtain a plurality of frames of target gesture signal data; performing distance-angle graph analysis and enhanced radar spectrum graph analysis on the plurality of frames of target gesture signal data, and performing feature extraction by using a multiple signal classification algorithm, to obtain target feature data; determining target weights of each frame of target gesture signal data, and performing gesture recognition by using a multi-task learning gesture recognition model in combination with the target feature data; and matching operation instructions based on a gesture recognition result to perform device control. The application can improve the reliability of gesture recognition and realize more accurate gesture control.

The application discloses a MIMO-OFDM omnidirectional target parameter estimation method based on segmented echo parameter adaptive matching, and specifically comprises the following steps: a two-stage forward-backward average multiple signal classification algorithm is used to estimate the angle parameter of a potential target in a super-resolution manner; a discrete Fourier transform is performed on the echo signal along the antenna dimension to extract segmented echoes, and a known angle-signal correlation term is reconstructed through the accurate angle parameter estimation value of the target; DFT and cross-correlation operation are performed along the subcarrier dimension, a global peak value is obtained through non-coherent accumulation in the cross-symbol dimension, and the distance parameter of the target is estimated; a cross-symbol complex sequence is taken out and point DFT is performed to obtain a Doppler spectrum, and then the speed parameter of the target is estimated; and the parameter estimation of all targets is completed through the above steps. The application overcomes the defect that the overall performance is degraded due to error propagation in the existing successive estimation framework, and the parameter estimation performance is significantly improved compared with the existing successive estimation method.

The invention discloses a multi-target positioning method and device based on a wireless signal, a medium and a product, and relates to the field of wireless sensing, and the method comprises the steps: carrying out the static noise filtering of a reflected signal; performing FFT operation on the filtered reflection signal on a fast time axis to obtain distance dimension data, and performing FFT operation on a slow time axis based on information in each distance gate corresponding to the distance dimension data to obtain a distance Doppler map; clutter suppression is carried out on the distance Doppler map through a CFAR algorithm, and a target distance index is recorded; constructing a two-dimensional data block based on the distance dimension data of the plurality of channels and the target distance index; on the basis of the two-dimensional data blocks, angle information of multiple targets is obtained through a self-adaptive second-order difference multi-signal classification algorithm; constructing a distance-azimuth spectrogram based on the angle information and the target distance index; therefore, when the multiple targets move in the range smaller than the radar angle resolution, accurate positioning and counting of the targets can be achieved.

The application discloses an unmanned aerial vehicle integrated comprehensive monitoring system and method, and relates to the technical field of unmanned aerial vehicle management. The system synchronously broadcasts a standard 5G synchronization signal block and receives airspace target echoes to generate a local detection spectrum; symbol-level fusion processing is performed on the local detection spectrum of multiple base stations, an improved spatial smoothing multiple signal classification algorithm is used for joint spectrum bias minimization processing, and the position and speed of a target unmanned aerial vehicle are extracted; the identity legality and flight category of the target unmanned aerial vehicle are identified; the perception capacity is dynamically estimated, and traffic scheduling is performed when overload occurs; flight instructions are generated or security linkage measures are triggered according to the identification result; and task load data returned by the unmanned aerial vehicle is processed and abnormality detection is performed. The application can improve the coverage and precision of low-altitude airspace unmanned aerial vehicle synchronous perception, accurately identify illegal flight behavior and quickly respond, and enhance the airspace safety monitoring capacity in complex environments.

The present invention belongs to the field of information and communication technologies and relates to a beam tracking method based on deep active learning. Aiming at the multi-user beam tracking problem, the present invention proposes a codebook-free beam tracking framework based on deep active learning to further improve the tracking performance. The framework includes two modules: a multi-beam generator based on DNN; and an AoA estimator based on the multiple signal classification algorithm. The framework adopts an online tracking strategy, using the AoA distribution of the previous time block as the input feature of the DNN to complete adaptive beam tracking. Experimental simulations prove that the method proposed by the present invention is superior to the codebook-based beam tracking method.

The present invention provides a method and system for re-estimating the track of high-speed target motion based on a compact high-frequency radar, belonging to the field of radar technology. The method comprises: performing coherent accumulation of original radar echo data over a preset time period to obtain a target radial distance value and a target velocity value, and determining a target arrival angle value through a multiple signal classification algorithm; calculating the target original point track based on the target radial distance value and the target arrival angle value, and performing track prediction on the target original point track using the target velocity value to obtain a predicted track set; calculating the average heading of each predicted track in the predicted track set, filtering the predicted track set according to a heading threshold to obtain a filtered predicted track set; and fusing the filtered predicted track set to obtain a final track result. The present invention uses velocity measurement values to re-estimate the target original point track, effectively improving the track accuracy output by the compact high-frequency radar without changing the arrival angle estimation accuracy.

The present invention provides a heart rate estimation method based on millimeter wave radar. The method comprises the following steps: sampling a human body by using a millimeter wave radar to obtain a plurality of frequency modulated continuous wave sweep signals; forming a distance-slow time matrix and locating the distance between the human body and the radar; extracting the human body phase by using an inverse tangent function, unfolding the phase whose phase change exceeds a set threshold by using a phase unwrapping function, obtaining the phase difference of adjacent frames, and smoothing the difference exceeding the threshold by using an interpolation method; removing the noise of the smoothed phase signal by using a wavelet transform, and obtaining a reconstructed phase signal; combining an EEMD-ICA method to separate a heartbeat signal, a respiratory signal, a respiratory harmonic and noise in the reconstructed phase signal, and obtaining a heartbeat waveform; constructing a spatial spectrum function by using a multi-signal classification algorithm, and estimating the heartbeat frequency by searching the spectrum peak; the method can accurately separate the heartbeat signal and greatly improve the accuracy of the heartbeat frequency estimation.

The application discloses a kind of intelligent metasurface assisted multi-target positioning system, belong to wireless communication field.The system includes: intelligent metasurface;Receiver;Multiple wireless access points, can periodically emit probe signal, and the probe signal can reach intelligent metasurface by multiple targets, reach receiver after being reflected by intelligent metasurface;Received signal model construction module is used to obtain the expression of the received signal of intelligent metasurface and receiver under different time block time slot;Virtual multidimensional signal construction module calculates the multidimensional signal according to the received signal of receiver, obtains the virtual steering vector received by receiver;And, spatial information positioning module is used to obtain the spatial spectrum of multiple signal classification algorithm;The steering vector corresponding to multiple peak values in spatial spectrum is obtained by searching the spatial spectrum, and the angle and distance of multiple targets are obtained.

The present invention discloses a method for jointly estimating the AOA and TOA of signals based on a dimensionality reduction multiple signal classification algorithm. The method comprises the following steps: converting the time domain signal of an ultra-wideband multipath signal received by a uniform linear array into a frequency domain form to construct a frequency domain model of the received signal; calculating the covariance matrix of the frequency domain model to obtain a noise subspace, and constructing a two-dimensional MUSIC spectrum peak function based on the orthogonal relationship of the signal steering vectors; performing dimensionality reduction processing on the two-dimensional MUSIC spectrum peak function, estimating the AOA of the ultra-wideband multipath signal through a one-dimensional angle search, and estimating the TOA based on the estimated incident angle to discriminate the main path signal angle of the ultra-wideband multipath signal. The method achieves high-precision TOA and AOA estimation, while significantly reducing the computational complexity compared to a two-dimensional multiple signal classification algorithm. Furthermore, the method can distinguish the main path signal independently of the sampled values ​​of the transmitted signal.

The invention relates to the technical field of wireless sensing, and discloses an RFID (Radio Frequency Identification Device) pose estimation non-line-of-sight shielding influence suppression method based on antenna weight, which comprises the following steps of: S1, constructing a relation model of a label position and a non-line-of-sight shielding error; s2, distributing antenna weights according to the theoretical phase difference and the actual phase difference between each antenna and the reference antenna; s3, constructing an augmented Lagrangian convex optimization algorithm according to the relation model in the S1 and the antenna weight in the S2, and finally obtaining label position information by continuously iteratively inhibiting the influence of non-line-of-sight shielding; and S4, in combination with an antenna weight and a multi-signal classification algorithm, label attitude information is obtained through peak detection. By constructing a relation model of a label position and a non-line-of-sight occlusion error, the error size is quantified instead of being simply regarded as environmental noise; in addition, through an adaptive antenna weight distribution mechanism based on phase residual statistical analysis, the shielding condition of the antenna is automatically identified, and the corresponding weight is reduced.

This invention discloses a switching time-modulated harmonic direction finding device and method, belonging to the field of radio frequency signal direction finding technology. It includes a radio frequency switching unit, a control unit, an ADC acquisition module, and a digital processing module. The radio frequency switching unit achieves signal time modulation through periodic switching of PIN diodes, coupled with a compensation network to optimize impedance matching and isolation performance. The control unit uses a high-speed comparator to output a stable, high-speed control signal. The ADC acquisition module performs analog-to-digital conversion. The digital processing module recovers the equivalent direction finding signal using a signal recovery algorithm based on the time-modulated sequence and estimates the direction of arrival based on a multiple signal classification algorithm (MUSIC). The method includes signal modulation, analog-to-digital conversion, equivalent signal recovery, and direction estimation steps. This invention eliminates the need for high-precision phase shifters and multi-channel parallel complete receiving links, simplifying the system structure and reducing hardware costs while retaining array direction finding capabilities. It is suitable for radar, communication, and other scenarios, balancing hardware simplification and direction finding accuracy.

The invention relates to the technical field of optical fiber sensing, in particular to a rapid space positioning method based on improved space phase shift, which comprises the following steps of: firstly, acquiring a sensing array signal, designing a space phase shift angle by adopting an improved space phase shift algorithm and particularly utilizing a relationship among the space phase shift angle, sampling frequency and acousto-optic modulator frequency shift; and a spatial phase shift matrix is constructed by directly using the intermediate frequency signals to quickly obtain phase signals of each sensing array, and the phase signals are processed through a multiple signal classification algorithm to determine the specific position of a sound source. According to the method, the real-time performance of sound source positioning of the system is effectively improved, in addition, the improved space phase shift algorithm is utilized in the demodulation process, complex frequency mixing filtering operation and additional hardware equipment are reduced, and the system cost is reduced.

The application provides a communication and sensing integrated system sensing assisted beam forming method, and relates to the technical field of communication; the method comprises the following steps: S10, an ISAC base station sends a reference signal, the reference signal comprises a reference signal sent omnidirectionally in an initialization stage and a reference signal sent directionally according to a preset encoding vector f k in a sensing assistance stage; S20, an ISAC receiving end utilizes a return signal to acquire parameters of surrounding targets or users based on orthogonal matching pursuit (OMP) and multiple signal classification (MUSIC) respectively; S30, the distance and angle of the targets or users are obtained by using a sensing time slot, and the encoding vector f k is calculated; S40, steps S20 and S30 are repeated to realize sensing assisted cellular communication beam forming; the application has the beneficial effects that high-precision position information of the ISAC receiving end can be sensed, and more accurate sending beams can be formed.

Provided are a direction of arrival (DOA) estimation method and system for a sparse array based on Vandermonde decomposition reconstruction, relating to the technical field of array signal processing. The method includes: constructing a covariance matrix completion optimization model based on sparse array signals and uniform linear array signals, and performing Vandermonde decomposition by using characteristics of a uniform linear array; introducing a nuclear norm to optimize a rank function in the model, and updating the covariance matrix completion optimization model; and introducing an auxiliary variable to transform the model into a solvable optimization problem, and solving the problem by an alternating direction multiplier method to obtain an optimal estimation value. The DOA is estimated using a root multiple signal classification algorithm.

The invention belongs to the technical field of wireless communication security, and discloses an MIMO system equipment identity authentication method based on mutual coupling and spatial AoA, which is realized through the following steps: a transmitting end communicates with a receiving end, an uplink millimeter wave channel is constructed, a communication model with a bimodal characteristic is established, namely, a signal is received, and the signal is transmitted to the receiving end; the communication model comprises a mutual coupling fingerprint model and an AoA fingerprint statistical model, and the mutual coupling fingerprint model comprises a transmitting array mutual coupling matrix and a receiving array mutual coupling matrix; a multi-signal classification algorithm based on a feature space is adopted to extract MC features and an AoA value of a communication model, a receiving end judges whether a current received signal is from legal equipment or an attacker based on the extracted MC features and the extracted AoA value, and authentication is completed. According to the method, a hardware-level MC effect and space arrival angle characteristics are combined, an efficient and high-robustness bimodal physical layer authentication scheme is provided, and a new solution is provided for a future 6G high-security scene.

The invention belongs to the technical field of sensing positioning, and relates to a non-contact personnel trajectory tracking method, which comprises the following steps of: acquiring original channel state data, and performing phase calibration on the original channel state data to obtain calibrated channel state data; adopting a super-resolution multi-signal classification algorithm to obtain an initial position of a track; establishing a geometrical relationship model between the calibrated channel state data and the reflection path length, and calculating the reflection path length variation of the personnel relative to the tracking equipment based on the geometrical relationship model; and obtaining a personnel trajectory based on the initial position and the reflection path length variation. The invention also provides non-contact personnel trajectory tracking equipment, which comprises an antenna assembly, a shell assembly, a sensing module, a development board module and a power supply assembly, the sensing module, the development board module and the power supply assembly are sequentially stacked and connected from top to bottom along the vertical direction, and the sensing module comprises two network cards working in different channels. According to the invention, people do not need to wear any equipment, and the method has the advantages of wide coverage, high precision, low deployment cost and the like.

The invention discloses a robust adaptive beamforming method based on null broadening and main lobe interference detection and elimination, which uses time decay covariance matrix sampling to improve the real-time performance of an algorithm, reconstructs a covariance matrix to separate a desired signal and interference, uses virtual interference rotation broadening null, and uses the main lobe interference detection and elimination to detect and eliminate the main lobe interference. And main lobe interference detection based on an improved multiple signal classification algorithm is used, and interference in a main lobe is eliminated. According to the beam forming method, the output signal to interference plus noise ratio in a high dynamic environment is effectively improved, and the system performance is improved. Compared with an existing classical beam forming algorithm in a high dynamic environment, the scheme provided by the invention has obvious performance gain.

The application relates to a kind of based on reconstruction subspace's super-resolution two-dimensional signal angle of arrival estimation method, comprising the following steps: S1, covariance matrix is constructed;S2, signal subspace matrix and noise subspace matrix are established;S3, construct two-dimensional multiple signal classification algorithm spatial spectrum expression;S4, based on eigenvalue decomposition obtains multiple eigenvalues and corresponding multiple eigenvectors;S5, based on eigenvalue, eigenvector is sorted, and constitutes to be eliminated matrix;S6, reconstruction subspace matrix is constructed, and S6 is repeated to carry out iterative calculation, and reconstruction subspace matrix is obtained Reconstruction subspace matrix set is formed;S7, simultaneously reconstruction subspace matrix set and spatial spectrum expression, obtain new spatial spectrum function set;S8, to new spatial spectrum function set is jointly estimated, and signal angle of arrival is obtained.Compared with prior art, the application has the advantages of high estimation accuracy.

The invention provides a method for recognizing liquid hydrogen C-type ball valve inner leakage based on acoustic emission frequency characteristics, and belongs to the technical field of liquid hydrogen C-type ball valve inner leakage detection. After self-adaptive multi-scale wavelet packet decomposition, mixed noise reduction and coherent accumulation enhancement are carried out, a material frequency dispersion compensation model is established, parameters are extracted and input into a leakage feature recognition model to obtain a leakage probability value, and when the probability value exceeds a threshold value, rapid independent component analysis and sparse reconstruction are adopted to estimate the number of leakage sources; the space angle of a leakage source is positioned by using a multi-signal classification algorithm, the leakage aperture is calculated through thermodynamics-driven inversion, and fusion judgment is carried out by combining turbulence noise features extracted by a fractal theory. The technical problem that it is difficult to accurately recognize a tiny leakage source and achieve quantitative evaluation of the leakage degree in the strong background noise environment during inner leakage detection of the liquid hydrogen C-type ball valve is solved.

The invention relates to the technical field of fault detection, and discloses a thermal runaway detection method, device and equipment, a storage medium and a program product. According to the method, initial delay summation output corresponding to a plurality of grid points is obtained through a multiple signal classification algorithm to serve as an iteration initial value, a plurality of sound source positions can be effectively positioned, a sound source recognition result is sharpened through multiple iterations, the main lobe width is reduced, side lobe interference is attenuated, the detection resolution is improved, and the detection accuracy is improved. The initial delay summation output corresponding to the multiple grid points is screened through the preset screening condition, the initial delay summation output which does not meet the preset screening condition is replaced with the preset value to directly serve as the output result, the output result does not participate in the subsequent iteration process, the calculation amount can be reduced, the detection efficiency can be improved, and therefore the thermal runaway detection accuracy can be improved when the thermal runaway detection function is achieved. And the resolution ratio and the efficiency are high.