76 results about "Pattern vector" patented technology

The invention provides a knowledge graph generation method and system for science and technology project risk control, and the method comprises the steps: obtaining a multi-source heterogeneous data set of a target science and technology project, converting structured index data into a standard vector sequence through a heterogeneous data fusion mechanism, and converting unstructured text data into a semantic vector sequence; converting the time sequence behavior data into a behavior pattern vector sequence, inputting the three into a risk quantitative evaluation model, generating a risk entity feature matrix and a risk association strength matrix, and determining a node distribution topology of the knowledge graph according to entity feature vectors in the risk entity feature matrix; and according to association strength values in the risk association strength matrix, determining an entity relationship topology of the knowledge graph, generating a dynamic knowledge graph of the target science and technology project, and identifying a potential risk propagation path in the dynamic knowledge graph. According to the invention, the risk identification result has the dynamic characteristic of real-time updating, and the traceability of the multi-dimensional risk characteristic is maintained.

The invention relates to the technical field of video behavior recognition, and discloses a behavior recognition and early warning system for a real-time video stream. The system comprises a spatio-temporal feature modeling module, a behavior fragment extraction module, an anomaly propagation modeling module, a risk area positioning module and an early warning strategy generation module. The spatial-temporal feature modeling module builds a dynamic model based on historical data, captures a skeleton key point three-dimensional coordinate sequence, a motion optical flow vector field and a micro-expression intensity spectrum, and outputs a theoretical behavior mode vector; the behavior fragment extraction module generates a multi-modal difference feature tensor through cross-modal difference analysis; the exception propagation modeling module generates an exception propagation path risk probability distribution cloud picture in combination with spatial constraint and trajectory information; the risk area positioning module identifies a high-risk area and marks a boundary; and the early warning strategy generation module dynamically configures monitoring parameters, starts high-frame-rate micro-expression capture for a high-risk area, and performs a track disturbance test on an adjacent area.

The invention discloses a multi-source sensing fusion highway slope geological disaster real-time early warning method, particularly relates to the technical field of safety early warning and monitoring, and is used for solving the problem that an existing fusion system fails to report due to multi-source data false consistency in the early stage of deep hidden catastrophe. By collecting slope multi-source geological parameter time series data, energy conversion efficiency differential entropy mutation is analyzed to generate an abnormal association mark; constructing a co-evolution matrix in a marking state; searching a space-time overlapping region of a main deformation parameter second derivative zero point and an auxiliary parameter frequency domain extreme point in the matrix, and marking a phase change critical state by comparing geological history envelope lines; performing hidden variable decoupling on the critical state matrix, and generating a hidden catastrophe risk index based on the spatial similarity of the main feature vector and the catastrophe mode vector; and when the risk index continuously exceeds the dynamic critical value and the frequency domain characteristic is in an unstable evolution state, an early warning signal is generated, the deep catastrophe critical state is effectively identified, and missing report caused by safety illusion is avoided.

The invention relates to the technical field of PID parameter setting, and provides a PID controller parameter adaptive adjustment method and device, electronic equipment and a medium. Multi-source heterogeneous data collected by a distributed sensor array are acquired in real time, feature quantization processing is performed on the multi-source heterogeneous data to obtain a quantization feature matrix, and working condition pattern recognition based on depth fuzzy clustering is performed on the quantization feature matrix to obtain working condition pattern vectors. Performing parameter space adaptive mapping on the working condition mode vector to obtain a PID parameter initial set and an optimized space, and performing parallel parameter optimization processing on the PID parameter initial set and the optimized space according to a hybrid optimization algorithm to obtain an optimized parameter set, and performing parameter dynamic fusion and robustness enhancement processing on the optimized parameter set to obtain a target PID parameter set. Through combination of data processing, deep learning, algorithm optimization and robustness enhancement, the precision of PID controller parameter adaptive adjustment is improved, and efficient and stable operation of a target system under different working conditions is ensured.

The invention discloses a love and marriage matching system based on hierarchical AI expert decision, relates to the technical field of hierarchical matching, and is used for solving the problems that hierarchical management and a refined hierarchical pushing mechanism are lacked, various recommendation targets are difficult to consider, and recommendation accuracy is reduced. Extracting a user pair feature vector, calling a plurality of field expert agents to generate a multi-dimensional score vector, performing similarity comparison on the feature vector and a success mode vector in the dynamic matching mode knowledge base, calculating a matching degree score, and synthesizing the matching degree score and the multi-dimensional score vector to obtain a matching recommendation level. And dividing the user pairs into corresponding recommendation pools according to the matching recommendation levels, and outputting a recommendation list according to a display strategy, so that multi-stage, layered and dynamic management of love and marriage matching is realized, the matching success rate and the interaction satisfaction degree are improved, and a layered recommendation decision-making mechanism based on multi-source expert knowledge driving is realized.

A computer-implemented method comprising receiving a digital media signal from a creator; segmenting the media signal into sequential discrete segments; selecting a subset of the sequential discrete segments; iteratively, for each discrete segment, (i) generating a segment vector, (ii) generating an encrypted metadata vector based on media signal metadata, (iii) combining the segment vector and the metadata vector, (iv) applying a modulo operation to the combined vector to obtain a hidden pattern vector, (v) generating an encrypted creator vector, (vi) subtracting the hidden pattern vector from the creator vector to obtain a signature vector, (vii) modifying selected values in the discrete segment, based on the signature vector, to obtain a modified discrete segment; and outputting a modified version of the media signal comprising all of the modified discrete segments.

The invention relates to a real-time interaction violation detection method, system and device and a medium. The method comprises the following steps: acquiring an online interaction session original information flow containing a user text sequence, a voice signal, an image file and an interaction behavior timestamp; extracting text semantic vectors, voice acoustic features and image visual content description, and integrating to generate an initial feature vector set; based on the interaction behavior timestamps, constructing an interaction time sequence diagram by taking the initial vectors as nodes, calculating multi-modal association weights among the nodes and updating connection edges to obtain a multi-modal fusion diagram; and inputting the fused graph into a graph neural network, outputting a global graph embedded vector through message passing and node aggregation, matching a preset violation mode vector library to calculate a similarity score, determining a violation type, and generating a risk assessment conclusion containing the violation type and confidence. According to the method, cross-statement and cross-modal context violation association is effectively captured, violation judgment accuracy is improved, and an intervention basis is provided for a platform.

The invention discloses a Thangka generation method based on a structure and pattern two-channel constraint diffusion model, and belongs to the technical field of Thangka image generation, and the method comprises the steps: obtaining a Thangka image, and carrying out the structure labeling and pattern labeling of the Thangka image; constructing a structure vector and a pattern vector; constructing a dual-channel constraint diffusion model; constructing a joint loss function, and training the dual-channel constraint diffusion model; in the training and production process, a structure-pattern adaptive coupling mechanism is introduced, and a structure constraint weight and a pattern constraint weight are dynamically adjusted; and executing a back diffusion generation process, and generating a target Thangka image through iterative denoising under double constraints of a structure vector and a pattern vector. According to the method, the structure normalization, the pattern definition and the overall style uniformity of the generated Thangka image are remarkably improved, and the method can be used for Thangka digital protection, virtual restoration, digital recarving, cultural education, auxiliary drawing and intelligent content generation in the literature and blog industry.

The application discloses a mold control equipment operation state monitoring method and device and a medium, relates to the technical field of intelligent equipment monitoring, and comprises the following steps: carrying out denoising, normalization and outlier rejection processing on a mold operation data set to obtain clean operation data; performing correlation analysis and feature extraction on the clean operation data to generate a sound-vibration coupling mode vector set, and performing feature index calculation and mode deviation evaluation to obtain a sound-vibration state feature set; performing state coding on the sound-vibration state feature set to generate a mold operation state table, and performing health degree calculation and state evaluation to output a mold operation feature set; performing operation level judgment and trend analysis on the mold operation feature set to output an operation state result; and performing information arrangement and instruction generation on the operation state result to generate an operation state report. The mold operation state is quantitatively monitored with high precision.

The invention discloses a mold control equipment operation state monitoring method, equipment and a medium, and relates to the technical field of intelligent equipment monitoring, and the method comprises the steps: carrying out the denoising, normalization and abnormal value elimination processing of a mold operation data set, and obtaining clean operation data; performing correlation analysis and feature extraction on the clean operation data to generate a sound-vibration coupling mode vector set, and obtaining a sound-vibration state feature set through feature index calculation and mode deviation evaluation; state coding is carried out on the sound vibration state feature set, a mold operation state table is generated, health degree calculation and state evaluation are carried out, and a mold operation feature set is output; performing operation grade judgment and trend analysis on the mold operation feature set, and outputting an operation state result; and performing information arrangement and instruction generation on the operation state result to generate an operation state report. And high-precision quantitative monitoring of the operation state of the mold is realized.

The invention relates to the technical field of battery management, and discloses a battery fault diagnosis method and system for a CCS module, and the method comprises the steps: obtaining a multi-dimensional data sequence of the voltage, current and temperature of a battery pack, carrying out the abnormality detection, and obtaining a potential abnormal time period and a multi-dimensional data subset; performing clustering analysis on the multi-dimensional data subset to obtain an abnormal feature clustering center, and performing dynamic association analysis to obtain an association mode vector set; calculating the similarity of each single data and a fault propagation path, and matching the propagation path with a preset mode to obtain a fault propagation score; fusing the clustering center and the propagation score, positioning a fault monomer through a support vector machine regression model, and determining a fault monomer identifier; and carrying out segmented clustering and logic judgment on the data corresponding to the identifier, and determining a fault type. According to the invention, accurate positioning and type determination of fault monomers can be realized.

The invention discloses an internet data service security monitoring method and system based on deep learning, and relates to the technical field of internet data service security monitoring, and the method comprises the steps: collecting a request log of an API interface, extracting access features of the request log, constructing an API access feature sequence, and carrying out aggregation and division according to user dimensions into access fragments with fixed duration; constructing a graph neural network model, mapping an API call relationship in the access fragment into a graph structure, extracting context features of an API call chain through graph convolution operation, generating an API access mode vector, performing density evaluation, and calculating a deviation degree between a current access behavior and a historical normal access mode; and when the deviation degree exceeds a dynamic threshold value, judging that an abnormal access behavior exists, and outputting an authentication bypassing attack alarm. According to the method, the security monitoring of Internet data service is improved by constructing access behavior acquisition, semantic extraction, anomaly recognition and alarm feedback.

The invention provides a metro train dispatching optimization method based on a Markov decision process model. According to the method, a Markov decision model taking a state-action pair as a core is constructed, a train stop mode vector is introduced to accurately represent different jump stop schemes, and the method adapts to typical working conditions such as large and small intersections and non-stop passing. Meanwhile, simulation output data of the four typical parking modes are used for establishing a train running time query table, and the calculation efficiency of the train running time under the variable parking strategy is greatly improved. According to the method, a dual deep Q network algorithm is adopted, an'optimal / worst experience pool 'mechanism is innovatively introduced, and key state transition data which are optimal and worst in the training process are stored respectively, so that the learning capability of high-quality experience and the avoidance capability of low-quality strategies are enhanced. Compared with a traditional single-working-condition optimization scheme, the method shows higher comprehensive performance and practical application potential in a multi-working-condition fusion scheduling task.

The intelligent data processing system comprises a distributed edge probe group, a dynamic fraud strategy engine, an intelligent evidence chain construction module and a credible evidence storage interface, the distributed edge probe group is deployed in a core transaction system, a lightweight protocol analyzer is arranged in the distributed edge probe group, an SWIFT / UnionPay private protocol and a real-time desensitization module are adapted in the analyzer, and transaction account masking and behavior track hash solidification are executed at a data source end; the dynamic fraud strategy engine accesses an abnormal transaction flow of the edge probe group, and stores a cross-mechanism transaction mode vector through a graph neural network fraud feature library; the real-time threat evaluation unit detects the transaction time sequence abnormal probability based on a hidden Markov model; the self-adaptive evidence obtaining controller dynamically activates a targeted evidence obtaining instruction according to the threat value, capital flow of a cross-platform is captured through a high priority, and the evidence storage period of a suspicious session is prolonged.

The invention relates to the technical field of fire-fighting informatization, in particular to a fire-fighting remote control device based on the Internet of Things technology, and the device comprises a distributed abnormal signal monitoring module which collects non-alarm data such as communication abnormality, response timeout and voltage fluctuation; the abnormal feature decoding and risk identification module extracts features such as signal drift and delay vectors and generates risk mode vectors; the equipment layer stability prediction module predicts the stability level and the fault type in the future 30 minutes in combination with historical data and environmental parameters; the linkage consistency evaluation module aggregates prediction results of all the nodes and identifies potential consistency risks under a communication link or a power bus; and the intelligent disposal strategy generation module outputs a disposal suggestion package based on the consistency report. According to the invention, early recognition, trend prediction and cross-node linkage disposal of the fire control system on non-alarm faults are realized, and the system stability and the remote control intelligence level are significantly improved.

The invention discloses a multi-site collaborative energy storage centralized control scheduling method, which relates to the technical field of multi-site collaborative energy storage, and comprises the following steps of: after an ambiguity condition of a scheduling strategy is identified, constructing a response mode vector based on a site historical scheduling behavior, and utilizing a response prediction model fusing a multi-head attention mechanism and a behavior trajectory transformation network to obtain a multi-site collaborative energy storage centralized control scheduling strategy; determining a site response behavior difference under the condition that the scheduling strategy has ambiguity; according to a site response behavior difference result, a strategy analysis prompt instruction is generated through a behavior deviation value mapping mode, the strategy analysis prompt instruction and an original scheduling strategy are combined to construct a structured scheduling strategy with a consistent analysis path, and unified analysis and constraint control of scheduling strategy execution logic are achieved. According to the method, the problem of multi-site response behavior difference caused by an ambiguous scheduling strategy is solved, unified analysis and closed-loop control of the scheduling strategy are realized, and the consistency and stability of cooperative scheduling are guaranteed.

The invention relates to the technical field of fault early warning, and discloses a CFB boiler operation safety domain fault early warning method based on multi-source data fusion, and the method comprises the steps: obtaining CFB boiler multi-source monitoring data, and generating a multi-dimensional spatio-temporal data matrix; analyzing the multi-dimensional spatio-temporal data matrix by using multi-resolution wavelet transform to generate a multi-scale feature tensor; analyzing the multi-scale feature tensor based on a tensor network, calculating a cross-scale coupling weight matrix according to a physical constraint equation, and extracting a cross-scale fault association mode vector through tensor contraction operation; performing similarity matching on the cross-scale fault association mode vector and a fault semantic prototype library to generate hierarchical fault situation information including a summary layer, a detail layer and a depth analysis layer; outputting a self-adaptive layered fault situation awareness result; according to the method, intelligent self-adaptive sensing of complex multi-scale fault situations is realized, the integrity of fault information is ensured, and the man-machine interaction efficiency is improved.

The present invention discloses a multivariate neural decoding method based on OPM-MEG and RSA, which belongs to the field of biomedical engineering technology. The method comprises: obtaining resting-state structural MRI data and OPM-MEG scan images for joint registration; playing auditory stimulation and completing OPM-MEG data acquisition, preprocessing the data, and obtaining segmented data; averaging different trials of the same auditory stimulation at the segmented data level and constructing a neural response pattern vector, calculating the Pearson correlation of all possible pairwise stimulus pairs at each time point, obtaining a representation similarity matrix and an average result of the neural response pattern similarity; determining the time points with differences at each time point; estimating the source activation time series of each subject, and determining the activation area using a T-test. The present invention can reveal the decoding representation process of the brain by calculating the distance of the neural response pattern in the representation space, thereby more comprehensively understanding the response pattern of the brain under specific tasks or conditions.

This invention relates to the field of bioinformatics processing and medical laboratory data analysis, specifically a method for classifying hepatitis B antibody patterns based on plasma protein profiles. The method includes: acquiring host hardware information of the execution environment and extracting central processing unit (CPU) cache parameters; acquiring a high-dimensional sparse one-dimensional array and extracting non-zero feature indices; performing mapping calculations using a locality-sensitive hashing (LSH) algorithm to reconstruct the data into a locally dense two-dimensional matrix; dynamically segmenting the data into independent sub-blocks according to cache parameters and initial segmentation dimensions; performing low-rank tensor decomposition on the independent sub-blocks to extract local latent feature vectors; concatenating the sub-blocks, weighting them through a single-layer attention network, and inputting the result into a classifier function to output a target classification pattern vector; and generating dimension update instructions based on a preset dynamic adjustment mechanism to adjust subsequent segmentation dimensions. This invention achieves lower memory peaks, higher cache hit rates, and stable multi-label pattern output capabilities.

A system and method for improving identity resolution match rates using deep learning techniques that enable privacy-preserving fuzzy matching of personally identifiable information (PII) employs a deep learning model trained with transformer architecture and contrastive learning on third-party identity graph data. Custom tokenizers process different Pll types by leveraging hierarchical structures and domain-specific characteristics. The trained model generates vector embeddings that enable fuzzy matching to account for variations in spellings, typographical errors, and data inconsistencies without requiring adherence to strict data schemas. A vector database stores embeddings for nearest neighbor searches to identify potential identity matches based on distance calculations between embeddings. A match filter applies logic to determine valid matches, and the system can operate without requiring movement of raw Pll data from secure environments.

The application discloses a Thangka generation method based on a structure and pattern double-channel constraint diffusion model, and belongs to the technical field of Thangka image generation, and comprises the following steps: acquiring a Thangka image, and performing structure labeling and pattern labeling on the Thangka image; constructing a structure vector and a pattern vector; constructing a double-channel constraint diffusion model; constructing a joint loss function, and training the double-channel constraint diffusion model; in the training and production process, introducing a structure-pattern adaptive coupling mechanism, and dynamically adjusting a structure constraint weight and a pattern constraint weight; and performing a reverse diffusion generation process, and generating a target Thangka image through iterative denoising under the double constraints of the structure vector and the pattern vector. The application significantly improves the structure standardization, pattern clarity and overall style uniformity of the generated Thangka image, and can be used for Thangka digital protection, virtual restoration, digital replication, cultural education, auxiliary drawing and intelligent content generation in the cultural and museum industry.

The invention provides a law case recommendation method and system based on a large model, and relates to the technical field of intellectual property, and the method comprises the steps: obtaining document data of an intellectual property case, a user operation sequence corresponding to a to-be-analyzed technical scheme, and law text information of a to-be-queried case, and constructing a technical knowledge graph based on the document data; inputting the technical knowledge graph and the user operation sequence into a time sequence graph neural network, and generating a mode vector and weight information; generating technical association data based on the mode vector and the weight information, and processing the technical association data by using a variational auto-encoder to obtain enhanced technical feature data; and finally, inputting the enhanced technical feature data and the legal text information into a large legal model, generating a joint query vector, and performing case retrieval to obtain a class case recommendation result. According to the method, the association accuracy of the class case recommendation result and the technical facts is improved.

The invention provides a mobile printing robot based on Mecanum wheels. The mobile printing robot aims at solving the problems that an existing automatic printing device is limited in printing breadth, poor in moving flexibility and low in complex path printing precision. The robot comprises a robot body, Mecanum wheels, an ink-jet printing assembly, a position sensor, an AI trajectory planning module and a linkage control module. The four Mecanum wheels at the bottom of the robot body are arranged in a four-wheel rectangle mode and are connected with independent driving motors correspondingly, and omnidirectional movement is achieved. And the ink jet head is vertically aligned with the center of motion of the Mecanum wheel. The AI trajectory planning module disassembles a path based on target pattern vector data, plans movement parameters by combining with equipment parameters and performs real-time correction according to feedback of a position sensor; and the linkage control module realizes linkage of the AI algorithm, the driving motor and the ink jet head. The method breaks through the limitation of the printing breadth, effectively reduces the printing error of a complex path, greatly improves the printing efficiency, and is suitable for automatic printing of scenes such as large-area paper surfaces, grounds and wall surfaces.

The application relates to the technical field of fault early warning and discloses a CFB boiler operation safety domain fault early warning method based on multi-source data fusion, which comprises the following steps: acquiring multi-source monitoring data of a CFB boiler to generate a multi-dimensional space-time data matrix; analyzing the multi-dimensional space-time data matrix by using multi-resolution wavelet transform to generate a multi-scale feature tensor; analyzing the multi-scale feature tensor based on a tensor network, calculating a cross-scale coupling weight matrix according to a physical constraint equation, and extracting a cross-scale fault correlation mode vector through tensor contraction operation; carrying out similarity matching on the cross-scale fault correlation mode vector and a fault semantic prototype library to generate layered fault situation information comprising a summary layer, a detail layer and a deep analysis layer; and outputting an adaptive layered fault situation awareness result; the application realizes intelligent adaptive perception of a complex multi-scale fault situation, guarantees the integrity of fault information, and improves the efficiency of man-machine interaction.

The invention relates to the technical field of computer data processing, and discloses a dynamic market research sample intelligent matching method based on multi-source data fusion, which comprises the following steps: performing sentiment analysis processing on social media data to obtain a sentiment feature vector, performing activeness analysis processing on player behavior data to obtain a behavior pattern vector, and matching the behavior pattern vector with the sentiment feature vector; the method comprises the following steps: performing purchase frequency analysis processing on a game purchase record to obtain consumption behavior vectors, uniformly mapping three types of feature vectors to the same dimension space, performing normalization and vector alignment fusion, constructing an initial fusion matrix, and further generating a fusion vector diagram, so that the relation between different data sources under a behavior tag index is subjected to structured expression, and the fusion efficiency is improved. Guiding weighted dynamic fusion processing through a fusion vector diagram, constructing a fusion feature matrix, and inputting the fusion feature matrix and the three types of original data into a deep neural network model to obtain a survey sample data set.

The present invention discloses an information security defense method and device for an automatic fire alarm system, relating to the field of intelligent firefighting quantum security technology. The method includes calculating a normalized coupling coefficient based on dynamic light pattern vectors and current decay rates, outputting a security authentication flag when the normalized coupling coefficient is within a safe range, generating a network attack warning signal when the coefficient is below the safe range, and generating a physical tampering fuse signal when the coefficient is above the safe range. The method also maintains a normal alarm path based on the security authentication flag, shuts down external communication ports and increases monitoring density based on the network attack warning signal, and triggers an electrochemical reaction based on the fuse signal to corrode specific metal circuits and activate an audible and visual alarm device, generating a fuse feedback state. Through electrochemical fusing and quantum unclonable credential generation, the present invention achieves active defense and identity authentication against physical layer attacks, enhancing the information security protection and recovery capabilities of the fire alarm system.

The invention discloses an electromechanical coupling system fault diagnosis method and system based on a multi-level vector library, and belongs to the technical field of fault diagnosis. The electromechanical coupling system is sequentially divided into a plurality of hierarchies according to functional composition of the electromechanical coupling system, each hierarchy comprises a plurality of units with different functions, and the method comprises the following steps: converting a fault mode into a fault mode vector x through an embedded model; the adjacent agent An evaluates and awards the fault reason of the corresponding unit retrieved by the agent An-1 from the PHM multi-level vector library, and retrieves the fault reason of the unit in the PHM multi-level vector library according to the fault mode vector x and the fault reason; and fusing the N fault causes of the intelligent body search through a fusion device. According to the invention, the fault part of the electromechanical coupling system can be accurately positioned.

The invention relates to the technical field of energy storage equipment monitoring, and discloses a fault prediction and early warning system for an energy storage box-type substation. The multi-modal data acquisition and preprocessing module, the macroscopic operation mode vector extraction module, the real-time causal calculation engine module, the real-time dynamic causal entropy map construction module and the working condition causal baseline dynamic mapping module generate a theoretical health baseline map corresponding to the current working condition in real time according to the macroscopic operation mode vector; an atlas topology difference and anomaly detection module generates a difference atlas by comparing the real-time dynamic causal entropy atlas with the theoretical health baseline atlas; the topology fingerprint and fault diagnosis module analyzes the difference atlas to extract topology fingerprint vectors and matches a fault knowledge base to output a diagnosis conclusion. By constructing the health base line which is dynamically adjusted along with the working condition, normal operation fluctuation can be effectively filtered out, the accuracy of fault detection is improved, and diagnosis from abnormal detection to specific fault attribution is realized.

A method is disclosed that includes obtaining (S800) an image. And a film particle pattern vector representing characteristics of film particles contained in the obtained image. A pattern vector is obtained (S802) with the obtained image as an input using a first neural network (S), which is a network trained to output a film particle pattern vector from the image.

The application discloses an AGV digital twin monitoring method based on a three-dimensional simulation model, relates to the technical field of digital twin monitoring, and comprises the following steps: performing multi-dimensional feature extraction on a target AGV to form an AGV behavior mode vector set; generating a nonlinear coupling matrix representing the correlation strength between AGV motion state parameters and environmental constraint parameters based on the vector set and environmental geometric information; dividing a three-dimensional digital twin model into a plurality of simulation units, and independently simulating local AGV disturbance responses of the units according to the matrix; mapping high-frequency motion events and low-frequency task events into the simulation units based on the local AGV disturbance responses to form a nonlinear disturbance coupling field; and dynamically adjusting a virtual AGV state based on the disturbance propagation trend of the disturbance coupling field. The application realizes nonlinear behavior synchronization of a virtual AGV and a physical AGV, thereby solving the problem that virtual and real behaviors of an AGV are difficult to be consistent in the prior art.