749 results about "Vehicle identification" patented technology

The invention relates to the technical field of vehicle image recognition, and discloses a multi-modal data enhanced vehicle recognition method and system based on a generative adversarial network, and the method comprises the steps: collecting vehicle multi-modal data (a visible light image, a thermal infrared image and three-dimensional laser point cloud data), and constructing a vehicle multi-modal data set; performing preprocessing and feature alignment on the data set to obtain standardized multi-modal data; constructing a cross-modal generator based on an adaptive attention mechanism, learning inter-modal feature association through a dynamic weight distribution module, and generating vehicle feature fusion data; designing a dual discriminator structure consisting of a perception consistency discriminator and a semantic fidelity discriminator, and optimizing the generator by adopting an alternate adversarial training strategy; generating a supplementary data sample for the complex scene by using the optimized generator, and constructing an enhanced data set; and constructing a multi-mode cooperative vehicle identification model based on the enhanced data set, and realizing high-precision vehicle attribute identification.

The invention belongs to the field of computer vision, and particularly relates to a high-robustness vehicle identification method based on target area constraint, which comprises the following steps of: acquiring original vehicle image data, and obtaining a training image data set with a vehicle target area mask; constructing an improved convolutional neural network as a classification backbone network, and inputting the training image data set into the network for feature extraction to obtain a feature map; constructing an attention region constraint module, and forming attention region constraint loss by calculating Dice loss between the target vehicle mask and the attention thermodynamic diagram; the feature map is input to a target guide segmentation module, a segmentation prediction map of the target vehicle is generated, and segmentation loss is calculated; and updating parameters of the improved convolutional neural network by using a total loss function obtained by combining constraint loss and segmentation loss, thereby realizing high-robustness vehicle identification. According to the method, the robustness and accuracy of vehicle identification under the foreground vehicle shielding condition are effectively improved while high-efficiency calculation of the model is kept.

The invention provides a vehicle target automatic labeling method and system based on deep learning. The method comprises the steps of obtaining video stream monitoring data collected by a road camera; based on the video stream monitoring data, utilizing a pre-trained vehicle identification model to automatically label a vehicle target in the video stream monitoring data to obtain a preliminary labeling result; performing uncertainty evaluation on the preliminary labeling result through an adversarial sample generation strategy to obtain an uncertainty score; when the uncertainty score does not exceed a preset threshold value, taking the preliminary labeling result as a final labeling result of the vehicle target; according to the invention, by using the pre-trained convolutional neural network model, large-scale video stream data can be quickly processed and preliminary annotation can be completed, so that the dependence on manual annotation is reduced; uncertainty evaluation is carried out on the preliminary labeling result through an adversarial sample generation strategy, errors or low-confidence-coefficient areas possibly existing in the labeling result can be effectively recognized, and the accuracy of the final labeling result can be guaranteed.

The invention provides a vehicle energy management method and system and a vehicle, and belongs to the technical field of vehicles, and the method comprises the steps: transmitting vehicle identification information to a cloud after the vehicle is powered on, enabling the cloud to obtain the historical driving data of the vehicle based on the vehicle identification information to obtain driving route data, and training a preset first energy management model, target model parameters are obtained, and then a target energy management model is obtained; and determining a target driving route from the plurality of historical driving routes through the application core based on the route data of the plurality of historical driving routes and the current working condition data of the vehicle, and obtaining control parameters for controlling the vehicle to drive on the target driving route based on the target energy management model and the feature data corresponding to the target driving route, and the control parameters are sent to the execution core so that the execution assembly can control the vehicle based on the control parameters. Through the method provided by the invention, the inherent defects of a traditional fixed strategy and a single deployment architecture can be overcome.

Systems and methods for automated unmanned aerial vehicle recognition. A multiplicity of receivers captures RF data and transmits the RF data to at least one node device. The at least one node device comprises a signal processing engine, a detection engine, a classification engine, and a direction finding engine. The at least one node device is configured with an artificial intelligence algorithm. The detection engine and classification engine are trained to detect and classify signals from unmanned vehicles and their controllers based on processed data from the signal processing engine. The direction finding engine is operable to provide lines of bearing for detected unmanned vehicles.

The invention discloses a vehicle identification method and system based on dynamic behavior analysis and federated learning, the method is applied to local ends in one-to-one correspondence with pre-detection areas, and the method comprises the steps: detecting a vehicle entering the pre-detection area, and obtaining the first multi-mode perception data of the vehicle; inputting the first multi-modal perception data of the vehicle into a trained exclusive identification model to extract exclusive identification features of the vehicle for identification to obtain an identification result of the vehicle, the identification result being used for indicating a model type, a behavior type and a virtual identity label of the vehicle; wherein the exclusive recognition model and the universal recognition model are jointly trained under the constraint of a total loss function, basic parameters of the universal recognition model are standard universal parameters issued by the cloud end at the last time, and the standard universal parameters are obtained by the cloud end through aggregating fuzzy network parameters of all the universal recognition models based on federal learning. The method is better in recognition effect and has no risk of privacy leakage.

Systems and methods for automated unmanned aerial vehicle recognition. A multiplicity of receivers captures RF data and transmits the RF data to at least one node device. The at least one node device comprises a signal processing engine, a detection engine, a classification engine, and a direction finding engine. The at least one node device is configured with an artificial intelligence algorithm. The detection engine and classification engine are trained to detect and classify signals from unmanned vehicles and their controllers based on processed data from the signal processing engine. The direction finding engine is operable to provide lines of bearing for detected unmanned vehicles.

The invention belongs to the field of risk prediction, and relates to a wet and slippery road risk assessment method based on vehicle trajectory identification by an unmanned aerial vehicle, which comprises the following steps: extracting video data of a vehicle driving trajectory of a dangerous road section by adopting a mode of carrying a holder on a high-resolution unmanned aerial vehicle, and mapping the video data to obtain a vehicle ground trajectory sequence; calculating parameter indexes of the normal sample and the wet and slippery road according to the reference trajectory curve, comparing the wet and slippery road with the normal sample, and outputting a distribution difference; self-adaptively adjusting a threshold index under the wet and slippery road based on the meteorological coefficient, and calculating a weighted threshold proportion and duration exceeding 95% quantile of normal weather; and judging the adjacency consistency and the intra-segment space coherence, and finally forming a segment-level risk score and a risk level. The accuracy, the real-time performance and the interpretability of risk early warning are remarkably improved, and the false alarm rate caused by environment misjudgment or equipment errors is effectively reduced.

The invention belongs to the technical field of radio frequency identification, and particularly relates to an unmanned aerial vehicle radio frequency fingerprint identification method for different signal-to-noise ratios based on a time-frequency graph. The invention provides a radio frequency fingerprint identification method for different signal-to-noise ratios based on a time-frequency graph and a YOLOV8 image identification technology on the basis of a traditional unmanned aerial vehicle radio frequency signal fingerprint identification technology, and the method mainly comprises the steps: constructing a new training data structure, specifically, converting an IQ signal into the time-frequency graph, and carrying out the recognition of the time-frequency graph and the YOLOV8 image identification technology. And then performing arrangement according to the unmanned aerial vehicle type (J), the signal bandwidth (B), the signal frequency band (F) and the signal-to-noise ratio (R) to obtain a structured data set, and performing identification by using an improved YOLOV8 model. Compared with the prior art, the method further improves the accuracy and speed of unmanned aerial vehicle identification, and is a scheme for identifying the model of the unmanned aerial vehicle on the hardware level without information matching. According to the method, high sensitivity of the model to a low-signal-to-noise-ratio signal is kept, and meanwhile, a large amount of misrecognition caused by low confidence is effectively inhibited.

The invention relates to a label extraction model training method and device, a vehicle label extraction method and device and electronic equipment, and the method comprises the steps: inputting a current sample vehicle image into a preset multi-modal model, and obtaining a sample description text; performing grammar analysis processing on the sample description text to obtain at least one segmented word, the part-of-speech of the at least one segmented word and the dependency relationship between the at least one segmented word; performing noun merging based on the at least one segmented word, the part of speech of the at least one segmented word and the dependency relationship among the at least one segmented word to obtain a sample description tag; based on a preset filtering word, filtering the sample description label to obtain a real description label; and training a to-be-trained label extraction model based on the current sample vehicle image and the real description label to obtain a label extraction model. According to the embodiment of the invention, the method can meet the multi-dimensional demands of vehicle recognition, is adaptive to a multi-label scene, greatly improves the readability of a vehicle recognition result, and reduces the decision errors.

According to an embodiment, it is a system comprising, a sensor and a processor, wherein the processor storing instructions in a non-transitory memory that, when executed, cause the processor to detect, via the sensor of a host vehicle, a target vehicle; capture, via the sensor, one or more images of one or more regions of the target vehicle comprising a characteristic of the target vehicle; analyze, the images using an image processing module; and determine, an identity of the target vehicle; assign, based on the identity and the second identity, a confidence level on the target vehicle; and plan, a behavior for the host vehicle in real-time, based on the confidence level on the target vehicle, and wherein the system is configured for adaptive behavior based on the identity.

The invention discloses a vehicle emission identification system and method based on artificial intelligence, and relates to the technical field of vehicle emission detection. The snapshot recognition module is provided with a multispectral camera array and a synchronous controller, dynamically adjusts imaging parameters in combination with a laser radar and a millimeter wave radar, and supports multi-type vehicle recognition; the AI algorithm processing module adopts a heterogeneous computing power architecture to allocate resources; the vehicle anomaly judgment module constructs a multi-dimensional anomaly feature chain, calls OBD data to study and judge anomaly and identifies blacklist vehicles; the data storage and calling module adopts block chain evidence storage and hierarchical storage; the mobile law enforcement adaptation module supports end-side cloud collaboration; the multi-source data fusion module realizes data space-time alignment; the dynamic model self-updating module optimizes the model based on federal learning. The method improves the comprehensiveness of vehicle identification and abnormity determination, guarantees the law enforcement data to be legal and credible, relieves the computing force pressure of a mobile terminal, supports model continuous adaptation technology iteration, assists efficient mobile law enforcement, and provides powerful support for environmental protection law enforcement.

The invention discloses an unmanned aerial vehicle detection and directional interference system based on an artificial intelligence technology, which belongs to the technical field of unmanned aerial vehicle detection interference and comprises a data acquisition module, an unmanned aerial vehicle identification module, a geographic information correction module, a trajectory prediction module and a directional interference module. The data acquisition module is used for acquiring geographic information data of a target area based on a geographic information system, comprises a radar, an acoustic sensor and a radio frequency spectrum detection sensor, and is used for acquiring feature data of a moving object in the target area; and the unmanned aerial vehicle identification module obtains feature data of a moving object in the target area based on the data acquisition module. According to the unmanned aerial vehicle detection and directional interference system based on the artificial intelligence technology, the obstacle in the target area is identified, the personalized behavior of the unmanned aerial vehicle is analyzed, the trajectory prediction and interference strategy of the unmanned aerial vehicle is optimized in combination with topographic features, and the trajectory prediction precision and the directional interference effect are improved.

The invention discloses a road traffic blocking identification and accident site positioning method. The method comprises the following steps: acquiring expressway vehicle running data in real time through an ETC door frame; data preprocessing and data correction are carried out by comparing timestamps of adjacent records; extracting the identification information of the vehicles with the same journey in the front and rear gantries, and calculating the average running speed of the vehicles on each journey; drawing a vehicle space-time diagram of each portal section; and taking an intersection point of the spatial-temporal trajectories of the fast vehicle and the slow vehicle at the starting moment and the ending moment of the abnormal time difference as a blocking ending spatial-temporal position judgment point. No extra monitoring equipment needs to be deployed, the data reliability problem and the real-time problem of the jump door frame data are solved only based on the ETC door frame data, and second-level road blocking and accident site positioning are achieved.

The invention discloses an NVH (Noise Vibration and Harshness) detection method, device, system and equipment, a medium and a product. The NVH detection method comprises the following steps: obtaining identification information of a to-be-detected vehicle; acquiring an NVH detection parameter configuration information set corresponding to the identification information of the to-be-detected vehicle; sending the NVH detection parameter configuration information set to the to-be-detected vehicle, so that the to-be-detected vehicle collects audio data corresponding to the detection time configuration information when in each detection working condition, and determining a target sound pressure level corresponding to each detection working condition according to the audio data and correction parameter configuration information corresponding to each detection working condition, determining an NVH detection result corresponding to each detection working condition according to the difference between the standard parameter configuration information corresponding to each detection working condition and the target sound pressure level; and receiving an NVH detection result corresponding to each detection working condition sent by the to-be-detected vehicle. According to the technical scheme provided by the embodiment of the invention, the comprehensiveness and accuracy of NVH detection are improved.

The invention discloses an intelligent management method for a parking lot, and the method comprises the following steps: S1, vehicle recognition: collecting the image information of a vehicle when the vehicle enters an entrance of the parking lot, and recognizing the identity information of the vehicle through a license plate recognition system; s2, vacancy detection: acquiring the occupation state of each parking space in the parking lot in real time, and uploading the vacancy parking space information to the central management system; s3, path recommendation: the central management system calculates an optimal parking path according to the vehicle entrance position, the real-time parking space information and the traffic flow direction of the parking lot, and pushes guide information to a navigation display screen at the entrance or a user mobile phone terminal; and S4, charging and payment: before the vehicle leaves, the parking fee is automatically calculated according to the entrance time and the exit time, the problems of inaccurate identification, inflexible guidance, inconvenient payment, opaque management and the like in the prior art are solved, and the user experience and the parking resource utilization efficiency are remarkably improved.

The invention discloses a first-level road non-ETC vehicle toll collection method and system based on multi-source data fusion, and the method comprises the steps: scanning a vehicle in a radar sensing recognition region in real time through a radar, so as to obtain radar scanning information; generating a vehicle tracking record based on the radar scanning information, wherein the vehicle tracking record comprises the target vehicle and first vehicle information corresponding to the target vehicle; in response to the target vehicle entering the visual identification area, an image acquisition instruction is sent to image identification equipment by using a radar, and the image identification equipment comprises license plate identification equipment and vehicle body identification equipment; acquiring second vehicle information acquired by the image recognition equipment; fusing the first vehicle information and the second vehicle information to obtain vehicle fusion information; and determining identification information of the target vehicle based on the vehicle fusion information, and determining a charging amount of the target vehicle according to the identification information. According to the invention, through fusion processing of the multi-source data, the accuracy of vehicle identification is effectively improved, and the conditions of misidentification and missed identification are reduced.

The invention provides a multi-modal fusion unmanned aerial vehicle identification method and system, and the method comprises the steps: obtaining a radio signal, an image signal and a sound signal of an unmanned aerial vehicle; performing feature extraction on the radio signal based on a blind source separation algorithm and adaptive filtering to obtain radio features; performing feature extraction on the image signal based on image super-resolution reconstruction and a double-flow feature extraction network to obtain image features; performing feature extraction on the sound signal based on spectral analysis and harmonic feature extraction to obtain an audio feature; fusing the radio feature, the image feature and the audio feature to form a multi-modal fusion feature; and performing identification based on the multi-modal fusion features to obtain an unmanned aerial vehicle category identification result. By adopting the scheme, the accuracy, reliability and environmental adaptability of unmanned aerial vehicle identification can be remarkably improved.

The invention provides an unmanned aerial vehicle recognition method, device and equipment, a medium and a computer program product, and the method comprises the steps: inputting a multi-dimensional feature vector of a to-be-detected terminal into a motion mode recognition model, and outputting a motion mode type; if the type of the motion mode shows that the to-be-detected terminal is suspected to be an unmanned aerial vehicle object, performing spatial matching on the multi-dimensional feature vector and GIS data to obtain a geographic information model; and determining whether the to-be-detected terminal is an unmanned aerial vehicle or not according to a comparison result of the real-time motion state of the to-be-detected terminal and the geographical environment characteristics. The unmanned aerial vehicle identification method provided by the invention not only considers the motion information of the to-be-detected object, but also combines GIS information, especially auxiliary verification of actual road network information and building information, can distinguish the unmanned aerial vehicle and other targets easy to confuse with the unmanned aerial vehicle in a complex environment, can realize accurate identification of the unmanned aerial vehicle, and improves the detection accuracy of the unmanned aerial vehicle. And decision accuracy is improved.

The invention relates to the technical field of sensor sensing, and discloses a microspur sensing method and system for an MEMS ultrasonic sensor array, and the method comprises the steps: carrying out the phase calibration of the MEMS ultrasonic sensor array, and obtaining a phase calibration parameter; performing near-field spherical wave phase compensation on the phase calibration parameter based on the macro target distance and the incident angle to obtain a near-field compensation parameter; controlling the MEMS ultrasonic sensor array to execute beam forming of wide beam scanning and narrow beam focusing according to the near-field compensation parameter to obtain a macro focusing sound field; transmitting ultrasonic waves to a rear vehicle target through the micro-distance focusing sound field and receiving a reflection signal, and performing vehicle feature recognition on the reflection signal to obtain a vehicle recognition result; and positioning calculation of the target angle and distance is carried out based on the vehicle identification result to obtain rear vehicle positioning data, and the angle resolution and the distance measurement precision in a microspur range are improved.

The invention provides an intelligent optimization method and system for a parking lot system. Wherein parking space pressure change data is collected through a parking space pressure sensor, vehicle size data of a vehicle identification system is synchronized, and a three-dimensional thermodynamic diagram containing parking demands is constructed based on spatial distribution; the stand column displacement sensing array monitors the height of a vehicle chassis, a dynamic vehicle size database is constructed by combining size data, and parking space physical constraints are matched; in cooperation with the parking demand and the constraint condition, screening and matching an idle parking space set according to the size of the entering vehicle and the thermodynamic diagram prediction trend; and a vehicle guiding path is generated by combining the incidence relation between the historical parking path and the thermodynamic diagram and integrating the parking space position and the path occupation state, so that intelligent optimization of the parking lot is realized. According to the technical scheme provided by the invention, precise parking space matching and dynamic path guiding of vehicle size self-adaption are realized, and the turnover efficiency and the space utilization rate of the parking lot are improved.

Systems and methods for causing a first autonomous vehicle (e.g., a drone or other aerial vehicle) to utilize a LIDAR pattern being emitted by a LIDAR device of a second autonomous vehicle (e.g., a ground vehicle) to determine one or more characteristics of the second vehicle. The one or more characteristics may comprise, for example, (i) a make and / or model of a manufacturer of the LIDAR device and / or the second vehicle; and (ii) a particular unit of the LIDAR device and / or the second vehicle. The first vehicle may further utilize the LIDAR pattern of the second vehicle to perform other operations, such as mapping a 3-D model of the environment being traversed by the second vehicle.

This invention discloses a signal post-processing method and apparatus for improving the accuracy of train passing identification, belonging to the field of high-speed railway condition monitoring technology. Based on candidate train passing events initially identified by existing methods, the method first performs time-domain waveform screening to eliminate events whose time-domain waveforms do not conform to a preset impact pattern. Then, for the screened events, the frequency domain features of their vibration signals are extracted. Finally, a second screening is performed based on the comparison results of the frequency domain features and a preset threshold to distinguish real train passing events. This invention, through a two-stage screening architecture of first the time domain and then the frequency domain, can effectively capture and utilize the essential differences in time-domain waveforms and frequency-domain energy distribution between transient train passing impacts and persistent interference, thereby significantly improving the accuracy and reliability of train passing identification and producing a high-purity train passing event dataset.

The invention provides an unmanned aerial vehicle identification and tracking method and system based on multi-modal fusion and trajectory modeling, and relates to the technical field of target detection and tracking. Infrared / visible light images and audio signals are synchronously collected in an airport key area, visual and acoustic features are extracted through a deep network after time alignment, and the target detection and tracking accuracy is improved. Outputting respective unmanned aerial vehicle category probabilities and candidate target frames; afterwards, weights are distributed adaptively according to confidence coefficients of audio and visual classification results, category fusion is carried out on a probability level, and unified multi-modal fusion representation is constructed on a feature level, so that relatively high recognition reliability is still kept in a small-target, weak-texture and strong-noise scene; on the basis, the multi-modal fusion feature sequence serves as input, the position and speed of the unmanned aerial vehicle in each time step are constructed into a trajectory state sequence, a sequence generation model outputs a complete trajectory in an autoregressive mode under conditional constraints, and continuous tracking, occlusion interval complementation and future trend prediction of the motion of the unmanned aerial vehicle are achieved.

The invention discloses an unmanned aerial vehicle identification method and device based on category specific modeling, equipment and a storage medium, and relates to the technical field of mode identification based on digital data processing, and the method comprises the steps: obtaining a radio frequency signal of an unmanned aerial vehicle, and generating a time-frequency graph through short-time Fourier transform; inputting the time-frequency graph into a target residual network, and extracting feature vectors of the unmanned aerial vehicle; calculating a log-likelihood value of the feature vector under each model by using a Gaussian mixture model constructed for the known class of each unmanned aerial vehicle in the training stage, and obtaining a preliminary judgment result according to the maximum log-likelihood value and a preset adaptive threshold value; and for the samples which are preliminarily judged as the unknown class, calculating the minimum feature distance between the samples and all samples in the known class unmanned aerial vehicle sample feature matrix, and if the distance is smaller than a preset recovery threshold value, re-classifying the samples as the known class unmanned aerial vehicle to which the corresponding nearest neighbor sample belongs. According to the invention, known unmanned aerial vehicles can be accurately identified in an open environment, and unknown unmanned aerial vehicles can be effectively distinguished.

The invention provides a vehicle real-time speed measurement method and system, electronic equipment and a storage medium, and belongs to the technical field of port vehicle identification, and the method comprises the steps: carrying out the camera calibration of a camera in a target monitoring region, and obtaining the vehicle video data in the target monitoring region based on the calibrated camera; extracting a key video frame from the vehicle video data, and converting a target vehicle in the key video frame from an original coordinate system to a world coordinate system; and determining the driving distance of the target vehicle according to the change of the coordinate value between the original coordinate system and the world coordinate system, and determining the speed of the target vehicle according to the driving distance of the target vehicle and the corresponding time. According to the vehicle real-time speed measurement method and system, the electronic equipment and the storage medium provided by the invention, the accuracy of vehicle real-time speed measurement in a complex scene can be improved.

The invention relates to the field of low-altitude security and protection, and discloses an intelligent identification and interference countering system of a low-altitude aircraft, which comprises a radar detection module, a radio detection module, a data fusion module, a data processing module and an early warning countering module, through cooperative detection of radar and radio, the micro-motion, track and signal fingerprint features of a target are obtained; performing deep analysis and time sequence verification on the fused multi-dimensional features by using a pre-trained unmanned aerial vehicle identification model to realize high-confidence unmanned aerial vehicle identification, model identification and threat level evaluation; and then, according to the dynamically calculated comprehensive threat index, a countering process including dynamic review and effect evaluation is started, countering means can be adaptively selected and switched, and accurate, layered and effective disposal of the suspicious unmanned aerial vehicle is realized. According to the method, the problems of low identification accuracy, high false alarm rate and single and rigid countering measures in the prior art are solved, and the effectiveness of low-altitude security and protection is improved.

The embodiment of the invention provides a configuration method and device for a vehicle control unit, a vehicle and a storage medium, and the method comprises the steps: responding to a received configuration instruction of the vehicle control unit corresponding to the vehicle, and obtaining the vehicle identification information of the vehicle; determining target difference configuration information corresponding to the vehicle identification information from a second storage area based on the vehicle identification information; based on the basic configuration information and the target difference configuration information, the vehicle control unit is configured, a configuration result is obtained, and the configuration result is used for representing whether the vehicle control unit is successfully configured or not. According to the invention, the technical problem of low efficiency of storing and dynamically switching multi-vehicle configuration in the prior art is solved.

The invention discloses a vehicle risk identification method and system, electronic equipment and a storage medium, and relates to the technical field of vehicle risk identification, and the method comprises the steps: building a conversion relation between a radar coordinate system used by a radar and an image coordinate system used by a camera device; based on the conversion relation, constructing a state space model between the radar data of the same road surface area acquired by the radar and the camera device and the road surface area image; using the state space model to perform data fusion on target radar data of the target road surface area acquired by the radar and the camera device and the target road surface area image to obtain fusion data; and carrying out risk identification on vehicles in the target road surface area according to the fusion data. According to the invention, through a cooperation mechanism of data fusion and a state space model, the advantages of radar high-precision distance detection and video precise vehicle identification are fully exerted, and efficient and accurate vehicle risk assessment is realized.

The invention relates to the technical field of vehicles, and provides a vehicle-mounted terminal network equipment post-replacement management method and device, and the method comprises the steps: carrying out the network access verification of old piece equipment based on a preset first white list and a logic network address carried in a request under the condition that a network connection request of the old piece equipment is received, the first white list stores logic network addresses of offline old pieces; and under the condition that the network verification result indicates that the old piece equipment is allowed to access the in-vehicle domain network, vehicle binding relationship verification is performed on the old piece equipment based on a preset second white list, the equipment identification code of the old piece equipment and the change condition of the vehicle identification code bound with the old piece equipment, and the equipment identification code of the offline old piece is stored in the second white list. According to the method and the device provided by the invention, a traditional mode which completely depends on manual recording, judgment and operation is replaced, so that human errors are reduced, and accurate and automatic management of the life cycle state of the equipment is realized.