996 results about "Time space" patented technology

The invention discloses a cross-regional virtual power plant cooperative scheduling method and device, a medium and a product, and relates to the field of data processing. The method comprises the following steps: acquiring real-time characteristic data such as space-time positions, output / demand prediction and the like of distributed energy resources and loads, and determining dynamic weights of characteristic dimensions based on a global optimization target and data of a current scheduling period; generating a dynamic resource cluster division instruction containing a member list and a coordination constraint condition according to the dynamic weight and the real-time data, and sending an initial cross-regional coordination scheduling instruction containing a net exchange power target value and the like and a compensation price signal to each dynamic resource cluster local agent; after aggregation response boundary information returned by the local agent is received, an instruction and a signal are updated, a target collaborative scheduling instruction is obtained and finally sent to each dynamic resource cluster for execution, and effective control over cross-regional virtual power plant resources is achieved. According to the method, the problem that the adaptability of the cross-regional virtual power plant collaborative scheduling instruction and the actual resource capacity is insufficient can be relieved.

The invention provides a mine abnormal event real-time identification method and system based on time sequence characteristics, and relates to the technical field of mode identification, and the method comprises the steps: carrying out the time-space alignment and semantic annotation of multi-modal monitoring data, and constructing a time sequence knowledge graph; calculating a dynamic association weight between entities, and analyzing a risk propagation path; predicting a risk situation based on a historical evolution rule; and dynamically generating a differential early warning strategy and establishing a closed-loop tracking system. According to the invention, early identification, accurate prediction and efficient disposal of mine safety risks can be realized, and the mine safety management level is improved.

The invention belongs to the technical field of power system load prediction, and discloses a cross-dimension multi-scale fusion load prediction method based on multi-user load time-space correlation, which comprises the following steps of: firstly, preprocessing user load statistical data, extracting time sequence dependence and periodic characteristics in a time sequence, and calculating the time sequence dependence and periodic characteristics of the user load statistical data; introducing a channel attention mechanism to adaptively mine key variable information; then, a multi-scale space-time fusion module is combined with frequency domain analysis and a graph convolutional network to realize depth feature interaction under different time scales and space levels; and finally, outputting a load prediction result under a plurality of time granularities in the future through a linear projection structure. Compared with an existing method, the method has the remarkable advantages in the aspects of capturing a complex load mode, improving model prediction precision and enhancing generalization ability, and is suitable for various application scenes such as power consumer energy consumption management and power grid load dispatching.

The invention relates to the technical field of high-precision satellite navigation and positioning, and discloses a multi-modal sensor space-time synchronization method for high-precision data acquisition, which comprises the following steps: constructing a hardware delay observation loop to calculate physical response delay in real time; establishing a receiver clock drift model and extracting a clock drift coefficient; generating a phase lead trigger instruction in response to the delay mean value and the clock drift coefficient cumulant based on the GNSS whole-second target moment; according to the method, a feed-forward control closed loop is constructed by using GNSS clock difference parameters, the intrinsic alignment of the heterogeneous sensor physical sampling action and GNSS atomic time is realized, the real-time alignment of the sensor physical sampling action and the GNSS atomic time is realized, and the real-time alignment of the sensor physical sampling action and the GNSS atomic time is realized. And a strict physical time reference is provided for high-dynamic multi-source data fusion.

The invention discloses a vehicle-mounted cross-protocol data time sequence cooperative processing method, which comprises the following steps: establishing a unified time reference, and providing an accurate space-time coordinate system for heterogeneous network nodes by electing a global master clock and constructing a clock relation model and a periodic correction mechanism; systematic cognition and management of communication resources are realized, and a verified protocol capability table is generated by automatically identifying protocol types and variants; constructing an automatic mapping system from demand to execution, performing formalized packaging on a target scene by introducing a unified intermediate model, generating a cross-protocol mapping rule based on the model and a protocol capability table, and then forming a global scheduling table in combination with a unified time reference; the cooperative communication of all nodes is finally realized by using the global scheduling table, and the end-to-end delay is constrained within the preset range, so that the problem that the end-to-end time sequence in the vehicle-mounted heterogeneous network is uncontrollable is solved, and the controllability and reliability of the time sequence of the vehicle-mounted network are improved.

The invention relates to the technical field of data processing systems, and discloses a command and dispatch management method and system based on artificial intelligence, and the method comprises the steps: generating a space-time occupancy statement containing an occupancy area, a time window and a task state through an edge node, and broadcasting the space-time occupancy statement to an adjacent node; and a receiver performs local conflict verification according to a state mutual exclusion rule, and triggers priority-based lightweight negotiation to adjust an occupied time window when detecting that static observation and dynamic intervention states coexist in an overlapped space-time. Postward conflict processing of traditional centralized scheduling is converted into beforehand conflict avoidance, the response speed and the operation toughness of the system are remarkably improved, and meanwhile, through physical reachability verification and a channel self-adaption mechanism, the cooperation reliability in a complex environment is guaranteed.

The invention discloses a multi-stage spatial-temporal clustering method and system based on a fused mahalanobis distance. The method comprises the following steps: acquiring a spatio-temporal data set, and determining a spatio-temporal neighbor relation of samples in the data set; calculating a space communication distance and a time decay distance of the sample; fusing the space communication distance and the time decay distance by using a mahalanobis distance to obtain a relative distance of the sample; selecting a class cluster center from the data set according to the local density of the sample and the relative distance; adopting a multi-stage distribution strategy to distribute non-class-cluster center samples to corresponding class clusters; wherein the multi-stage allocation strategy comprises an inevitable allocation stage based on space-time shared neighbor and a similarity allocation stage based on a weighted similarity matrix. According to the method, the key problems that an existing space-time clustering algorithm is insufficient in space-time attribute coupling processing and sensitive to distribution errors are solved, and the clustering accuracy, robustness and practicability in the fields of intelligent traffic analysis, seismic sequence recognition and the like are remarkably improved.

The invention relates to a multivariate time series data abnormal root cause identification method based on a space-time cause and effect diagram, and belongs to the technical field of anomaly detection. According to the method, multi-window expansion causal convolution is adopted for multivariate time series data, mutual information screening is combined, and time embedding covering short-term mutation and long-time dependence at the same time is extracted; non-local space correlation is learned through multi-head self-attention, the directional causal intensity is measured through conditional entropy, and a sparse and interpretable space-time causal graph is generated through normalization-pruning; introducing a causal enhancement graph attention network on the space-time causal graph, and performing multiple rounds of causal propagation updating on node embedding; and calculating a root cause score by integrating the abnormal degree and the causal influence, identifying a key source node in an abnormal propagation path, and realizing accurate root cause positioning of the system abnormality. According to the method, the adaptability to the dynamic behavior mode and the capturing capability to the abnormal driving factor are enhanced, and the modeling precision and the root cause identification capability of the abnormal propagation process are improved.

The invention discloses an engineering machinery fault prediction and intelligent maintenance method based on deep learning, and belongs to the technical field of intelligent operation and maintenance of engineering machinery. The method comprises the following steps: firstly, performing timestamp synchronization and feature enhancement on multi-source sensor data to generate a space-time alignment tensor; fusing the image and time sequence features through a multi-modal feature distillation network, and constructing a cross-modal unified feature vector; thirdly, calculating a fault probability and residual life distribution, and constructing a Markov decision model in combination with a resource state; and finally, dynamically optimizing the maintenance instruction by using deep reinforcement learning, and continuously updating the model through closed-loop feedback. According to the method, early-stage accurate prediction of the fault and dynamic optimization of the maintenance strategy are realized, the problems of inaccurate prediction, decision lag, resource waste and the like in a traditional method are effectively solved, and the availability rate and the maintenance economy of equipment are remarkably improved.

The invention is suitable for the technical field of earth surface monitoring, and provides an earth surface deformation monitoring method and system based on a time sequence InSAR, and the method comprises the following steps: carrying out the SAR image screening and interferogram generation, and collecting corresponding meteorological data and thermal infrared data; performing space-time adaptive atmospheric phase correction, taking the air pressure vertical gradient and the temperature anomaly as driving factors of atmospheric delay, and separating an atmospheric phase through a space-time weighted model; dynamic deformation modeling is carried out, and deformation is decomposed into linear deformation and nonlinear deformation; the method comprises the following steps: taking a mining area road network as a geometric constraint, unwrapping a coherent region by adopting a minimum cost flow algorithm, converting an unwrapping phase into sight-line-direction deformation, and calculating horizontal and vertical deformation components in combination with InSAR sight-line-direction deformation and a digital elevation model. The method adapts to a complex deformation mechanism of a mining area by capturing linear and nonlinear deformation. Through sight line deformation and DEM geometric projection, vertical and horizontal deformation separation is realized, and the deformation direction is determined.

The invention discloses a real-time interaction and intelligent management method for a multi-dimensional data space, and relates to the technical field of data interaction management, and the method comprises the following steps: I, collecting multi-source data, including structured data, unstructured data and spatio-temporal data, in real time through a diversified collection terminal, and dynamically adjusting the sampling frequency based on a data entropy value, carrying out cross-device space-time synchronization calibration and edge end lightweight processing on the acquired data; iI, preprocessing the data flow in real time, fusing the multi-modal data, and the like. According to the method, the sampling frequency is dynamically adjusted based on the data entropy value, and in combination with edge end lightweight processing, invalid data transmission is reduced, and the key data acquisition efficiency is improved; hot data is stored in a time sequence database and a memory database, real-time monitoring of an emergency scene and second-level response of emergency command are supported, dynamic capacity expansion and shrinkage of computing resources are matched, edge-cloud end collaboratively distributes tasks, network delay is reduced, and system stability and response speed in a high-concurrency scene are guaranteed.

The invention provides a multi-dimensional multi-spatio-temporal data base plate construction method and system, and the method comprises the steps: carrying out the standardization processing of obtained original multi-source heterogeneous data, and generating a standardized data set; performing multi-level space fusion and dynamic time weighting processing based on the standardized data set to generate a multi-level fusion spatio-temporal data set; performing space-time grid aggregation and adaptive node screening on the multi-level fusion space-time data set to generate an optimized space-time node set; based on the optimized space-time node set, dynamic association is constructed, and an association model is generated and comprises event rules, topological relations and dynamic weights; and generating a multi-dimensional multi-spatiotemporal data base plate based on the optimized spatiotemporal node set and the association model. By adopting the method, the data base plate which can cover global features and has real-time response capability can be constructed, and the decision-making precision and efficiency of scenes such as water conservancy disaster prevention and control and environment monitoring can be improved.

The invention provides a multi-data center computing power-electric power cooperative scheduling and demand response declaration capacity optimization method, which comprises the following steps of: firstly, acquiring multi-dimensional historical time sequence data of a data center; a machine learning algorithm is used to predict the workload of each data center in each time period of a future single day in a future scheduling period and the power market price of the place where each data center is located, and then a space-time coupling-oriented multi-data center computing power-power cooperation model considering uncertainty is constructed; the collaborative model comprises a joint optimization framework of computing power scheduling and power scheduling, and takes actual profit maximization as a target, then the established collaborative model is converted into a mixed integer linear programming problem and solved, and the optimal declaration capacity of each data center participating in demand response is obtained; and each data center carries out scheduling according to the task load of space migration and time migration, the charging and discharging power of an energy storage system and the power generation amount of renewable energy sources, so that overall optimization of demand response declaration capacity of the multiple data centers is realized.

The invention discloses a universal multi-modal target tracking method based on space-time propagation and modal cooperation, and belongs to the technical field of computer vision. The method comprises the following steps: converting RGB and X modal images into a token form, and constructing initial features in combination with modal specific time tokens; the method comprises the following steps of: extracting a multi-level enhanced feature through a Transform encoder and a Mama collaborative prompt block; generating discriminative fusion features by using a gating fusion and context sensing module; a time-guided attention mechanism is adopted to strengthen search area features, and a result is output through a tracking prediction head; and transmitting the fusion time token as historical information to the next frame, and dynamically updating the template by combining a long-short time template updating strategy. According to the method, complementarity and space-time dependence between modes are effectively mined, tracking robustness and generalization ability in a complex scene are improved, and the method is suitable for various mode combination tasks such as RGB-D, RGB-T and RGB-E.

The invention discloses a server temperature control method and system based on deep learning, and belongs to the technical field of server temperature control, and the method comprises the steps: firstly, synchronously collecting server hardware temperature, business load and environment parameters, capturing a heat conduction relation through combining a thermal imaging technology, and constructing a dynamic relation network through time-space fusion processing; a heat flow prediction model is constructed based on the network, model parameters are dynamically corrected in combination with thermal response characteristic attenuation coefficients generated by service load characteristic analysis, and temperature distribution changes are predicted; generating a candidate control strategy set, screening an optimal strategy through composite scene simulation, and generating a control instruction; and comparing the actual temperature after the control instruction is executed with the model prediction temperature in real time, switching to a safety control mode when the deviation exceeds a threshold value, and extracting an incremental sample to update model parameters after the deviation is recovered, thereby realizing accurate temperature control.

The invention discloses an electric connector fault prediction method, and the method comprises the steps: synchronously obtaining multiple types of time series data of a contact area of a target electric connector in a preset time window based on a preset data collection device, and the time series data comprise temperature spatial and temporal distribution time series data, contact resistance transient time series data and a surface image sequence; performing feature analysis on the multi-source time sequence data by using a preset time sequence coupling sign extraction mechanism to obtain a multi-source phase change feature vector of the target electric connector in the current time window; inputting the multi-source phase change feature vector into a pre-trained electric connector health state prediction model, and outputting a prediction result of the health state of the target connector at the current moment, including a health state index; and performing differential early warning based on the predicted health state index of the target connector. Therefore, early and quantitative accurate prediction of the state of the electric connector is realized.

The invention discloses a time series data lossless compression method and system based on dynamic context awareness, and relates to the field of data lossless compression, and the method comprises the steps: extracting target data from a device port, and carrying out the preprocessing of the target data through a data processing technology according to the business dimension corresponding to the target data, constructing a structured data set based on a preprocessing result; capturing local time sequence features based on the structured data set, distributing attention weights in combination with a hidden state, generating a feature vector used for sensing a context, and outputting a dimension vector by using a three-layer full-connection network; and according to the dimension vector, analyzing the compression performance score value of each compression algorithm in the resource library, sorting the compression performance score values, and selecting the compression algorithm meeting the target requirement to compress the target data. According to the method, the global periodic features and the local mutation features of the time series data are analyzed through the space-time attention mechanism, and real-time driving algorithm switching is achieved.

The embodiment of the invention provides a time-space synchronization method and device, electronic equipment and a medium, which are used for solving the problem of inaccuracy of a local clock in related technologies. In the embodiment of the invention, the electronic equipment combines the average deviation pseudo-range, the RTT jitter and the target frequency offset into the multi-dimensional observation vector, so that the error source of clock synchronization is fully covered. The time deviation and the frequency deviation of the clock are accurately and effectively determined through a Kalman filtering algorithm, recursive processing of observation vectors and dynamic estimation of the time deviation and the frequency deviation of the clock, and high-precision synchronization of the local clock in a complex environment is achieved in combination with the optimization capability of the Kalman filtering algorithm. And the time of the local clock is the time determined according to the time of the multiple clocks, so that the clocks are more accurate.

The invention provides a digital twinning-based scene information reverse transmission method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the space-time alignment and abnormal value processing of collected multi-source data, forming a standardized data sequence, selecting a reference data segment from the standardized data sequence, and carrying out the calculation of the reference data segment; generating two feature subsequences based on the reference data segment, and calculating the dynamic correlation of the two feature subsequences to form a feature interval; selecting a key data unit in the feature interval, and selecting two key data units outside the feature interval; constructing a nonlinear data track according to the time continuity of the three key data units; generating a correction factor according to the nonlinear data track; and jointly inputting the correction factor and the standardized data sequence into a generative adversarial network to generate extreme fault time sequence data under multi-source interference. According to the invention, the adaptability and security of reverse transmission are improved.

The invention discloses a spatio-temporal data compression method and system based on lightweight processing, and relates to the technical field of image data processing, and the method comprises the steps: based on spatio-temporal data to be compressed, establishing standardized data entries, forming a window set through time axis segmentation, carrying out the periodic discrimination of windows according to the frequency domain energy distribution, and constructing spatio-temporal blocks, establishing an error control parameter table based on the global error budget; generating a time coding stream, performing integerization on the three-dimensional coordinates and the attribute values, generating a space coding stream through double difference and run length coding, performing edge folding lightweight and texture compression on the grid data, and generating a joint coding result; and based on a joint coding result, dividing the compressed data into a base layer and a plurality of enhancement layers, and establishing a relevance hierarchical storage structure and a block-level index table to generate the compressed data capable of being transmitted in a streaming manner. According to the method, collaborative compression can be carried out by utilizing spatial-temporal data internal relevance.

The invention provides a structure health early warning method and system based on space-time correlation characteristics and digital twinning, and relates to the technical field of data processing. The method comprises the following steps: acquiring multi-source heterogeneous data of a target structure; performing dynamic sampling alignment and wavelet packet decomposition on the multi-source heterogeneous data to extract energy features to obtain synchronous data, and performing abnormal data filtering on the synchronous data to obtain cleaned fusion data; performing wavelet decomposition on a high-frequency vibration signal in the fused data to obtain a damage impact feature, performing time sequence processing on low-frequency temperature data in the fused data to obtain a temperature time feature, and performing dynamic graph convolutional network processing on strain data in the fused data to obtain a spatial correlation feature; constructing an input vector; calculating a damage degree index; and according to the damage degree indexes, early warning grades are divided, and corresponding control instructions are triggered for different early warning grades. By implementing the technical scheme provided by the invention, the accuracy of structural health early warning can be improved.

The invention relates to the technical field of laser displacement meters, discloses a calibration method of a laser displacement meter for a Beidou device based on multi-data coupling, and effectively solves the problems of time delay compensation and data synchronization of cooperative work of multiple devices in a dynamic environment. According to the invention, when a carrier moves at a high speed or environmental parameters suddenly change, submillimeter-level positioning precision can still be maintained, meanwhile, data processing delay is controlled within a millisecond-level range, the reliability of a measurement system under a complex working condition is remarkably improved, and through double marking of a GNSS timestamp and a local clock, the measurement precision is greatly improved. And an accurate time deviation observation value is provided for a subsequent time delay compensation algorithm, so that a time-space unified data basis is established in a data preprocessing stage, and the feasibility of multi-source heterogeneous data fusion is remarkably improved.

The invention provides a long video understanding method capable of relieving time sequence illusion in a video language large model. The long video understanding method is based on a static bias adaptive frame selection mechanism and a cross-modal feature fusion strategy. According to the static bias mechanism, inter-frame similarity is evaluated through a discriminator, redundant frames are identified, key frames are selected or a complete sequence is reserved, so that calculation overhead is reduced, and spatio-temporal information integrity is kept; a video frame and a text are mapped to a shared semantic space, the single-frame semantic understanding ability is enhanced, then an embedded sequence serves as a soft prompt to be input into a large language model, and a final answer is generated in an autoregression mode. According to the method, the efficiency and accuracy of long video understanding and video question and answer tasks can be remarkably improved; the problem of low training and reasoning efficiency caused by time sequence dependence redundancy and excessive computing resource consumption is effectively relieved; and through a dynamic multi-modal task processing framework and a space-time memory bank compression mechanism, the modeling capability and generalization performance of the model on a long video sequence are further improved.

The invention provides a video depth forgery detection method based on space-time inconsistency and frequency domain analysis, the forgery detection method constructs a video depth forgery detection network of an unknown environment image, and the video depth forgery detection network comprises a space-time inconsistency module, a frequency domain analysis module and a video level classifier. The space-time inconsistency module captures space-time features in video frames, through a double-flow cooperation mechanism integrating space-time inconsistency modeling and frequency domain artifact analysis, the space-time features in videos are efficiently captured, dynamic information interaction between key frames is achieved, and therefore an enough feature basis is provided for deep counterfeiting detection. And the frequency domain analysis module combines a deep neural network with time-frequency analysis and maps video frames to a frequency domain so as to realize end-to-end identification of an unknown tampering mode and provide support for understanding of fine-grained content and improvement of classification precision.

The invention provides a video monitoring abnormal behavior identification and tracking linkage method based on artificial intelligence, which relates to the technical field of artificial intelligence, and comprises the following steps: carrying out space-time registration on a multi-camera video stream, and establishing a unified coordinate system; in the system, static objects are detected, and suspected remnants are marked; constructing a time sequence backtracking window to determine the association between the article and the person in charge; establishing a topological graph model containing a camera switching probability and a spatial adjacency relation; and predicting a motion track of an uncovered area according to the current motion state, dynamically distributing a tracking weight and generating an alarm. According to the invention, the remnant tracking efficiency and accuracy are improved.

Certain aspects of the present disclosure provide flow estimation techniques for dynamic scenes. A method generally includes obtaining a first time series sequence of sample sets of a scene (including a first and a second set of samples) over a first period of time associated with a reference time; processing, with a spatial-temporal attention module, the first time series sequence, to generate a modified first time series sequence of sample sets of the scene comprising the first set and not the second set of samples; processing, with a first neural ordinary differential equation (ODE) and a first ODE solver, the modified first time series sequence to predict a second time series sequence of sample sets; and generating a first output indicating a predicted flow of the scene from the reference time to a first selected time based on the modified first time series sequence and the second time series sequence.

The invention relates to the technical field of construction resource scheduling, in particular to a water conservancy construction equipment scheduling method and system, and the method comprises the following steps: obtaining on-site point cloud and soil data to construct an environmental resistance field; calculating a dynamic work efficiency matrix by combining the equipment power parameters and the resistance field; generating an initial time sequence chain based on the work efficiency matrix, and calculating an overlapping volume of a three-dimensional space envelope body of the equipment; if there is overlap, the job time is adjusted according to the priority to generate a conflict-free scheduling instruction set. According to the method, the rated work efficiency parameters of different machine types in a specific operation area are corrected in real time by constructing the equipment passing resistance model fusing the terrain gradient and the soil moisture content, the problem that static scheduling cannot adapt to environmental changes is solved, and meanwhile space-time conflict detection logic based on the three-dimensional operation envelope surface is introduced; and when it is recognized that the multi-machine moving tracks are overlapped, avoiding or peak shifting instructions with priorities are automatically generated, and conflict-free collaborative operation of the equipment cluster in a complex water conservancy scene is achieved.

The invention discloses a quality monitoring abnormity positioning method and system for multi-source heterogeneous media data, and particularly relates to the technical field of quality monitoring of the multi-source heterogeneous media data.The method comprises the steps that through access and time base normalization, sources such as RTSP, HLS / DASH, offline files and message buses are mapped to a unified time reference, and out-of-order rearrangement and late cutting are carried out; basic quality elements are extracted from A / V / S in a sliding window, a dynamic threshold value is formed by adopting a trimming mean value and a median absolute deviation, and a window-level quality event is generated; using event anchor points as common reference to estimate cross-modal time delay and fuse evidences, outputting scene / mode labels by means of a lightweight online classifier, entering a confidence reduction mode when the evidences are missing, and outputting an availability bitmap and a reason code; and then event confirmation, time-space range refinement and hierarchical root cause posterior determination are completed, influence measurement and priority are calculated, and finally an anomaly positioning and root cause analysis record set including an event timeline, severity / influence degree, root causes and disposal suggestions is formed.

The invention provides a trajectory clustering method based on space-time weighting and a density peak value, and relates to the technical field of trajectory clustering. The method provided by the invention comprises the following steps: based on a minimum description length criterion, fusing an adaptive space-time weight and a space-time geometric distance to segment trajectory data to obtain sub-trajectory segments; calculating a space-time local density, a relative distance and a decision value of each sub-track segment based on a density peak clustering algorithm; performing noise identification on the sub-track segments based on the average space-time geometric distance and the average local space-time density, iteratively selecting a class cluster center based on the decision values of the non-noise sub-track segments and the time-space factor decision values, and generating a class cluster set; and determining a candidate representative trajectory set from the class cluster set based on the spatio-temporal local density and the maximum density, extracting continuous sub-trajectory segments from the candidate representative trajectory set based on spatio-temporal continuity constraints, and sequentially connecting end points of the continuous sub-trajectory segments to generate a representative trajectory. According to the method, the trajectory clustering effect is improved through space-time weighted segmentation and density peak trajectory clustering.

The invention discloses an Internet of Things equipment binding method and system based on AI behavior data, and relates to the technical field of Internet of Things, and the method comprises the steps: defining Internet of Things equipment as nodes, defining a space-time interaction feature set as node attributes, obtaining the weights of edges between the nodes through calculating the behavior relevance, completing the construction of a behavior relevance topological graph, and obtaining a behavior relevance topological graph; and verifying the behavior consistency of the new equipment by using the trained GNN through the behavior association topological graph, obtaining the legality probability of the new equipment, setting a trigger threshold value, collecting implicit bioelectrical impedance data of the new equipment when the legality probability of the new equipment is smaller than the trigger threshold value, performing cross-modal feature fusion, generating a user behavior identification vector, and performing user behavior identification on the user behavior identification vector. Performing final verification to obtain a final verification result of the new equipment; according to the method, the mining of the deep behavior relationship is realized, and the limitation of a static topological structure on the mining of a dynamic interaction mode is solved.