481 results about "Behavioral analytics" patented technology

The invention relates to the field of log fault analysis, in particular to a log aggregation fault diagnosis method and system based on artificial intelligence. The method comprises the following steps: collecting a multi-modal heterogeneous log, carrying out sliding time sequence slicing processing, carrying out time sequence association sequence reconstruction, and constructing a time sequence reconstruction log data stream; log event deep semantic analysis is carried out on the time sequence reconstruction log data stream, event semantic topological evolution is carried out, and a multi-dimensional event topological representation matrix is constructed; performing routine event behavior analysis and abnormal fault mode inference based on the multi-dimensional event topology representation matrix, and marking abnormal fault points; and the occurrence timestamp and the abnormal propagation rate of the abnormal fault point are calculated, fault space-time diffusion evolution is carried out, and a dynamic fault propagation path map is constructed. Through efficient and accurate fault traceability analysis, the fault diagnosis efficiency is greatly improved, and the stability and reliability of log data are improved.

The invention relates to the technical field of network security, in particular to a dynamic access blocking method based on zero trust. Comprising the following steps: step 1, collecting whole network flow data in real time in a bypass monitoring mode through a flow mirroring function of a network switch, performing deep packet inspection analysis on the collected original flow data, and extracting network flow characteristic parameters; 2, maintaining a dynamic identity information base; 3, performing real-time behavior analysis on each network session; 4, according to the risk assessment result and the real-time security context, generating a dynamic access control strategy based on a minimum permission principle; 5, implementing access control at the network execution point; and 6, continuously monitoring the network flow and the strategy execution effect, collecting feedback data, optimizing the risk assessment model and the strategy generation algorithm based on the feedback data, and forming closed-loop control. By dynamically updating the identity and asset information, the system can identify new assets or changes in real time, so that the adaptability and response capability of a network environment are improved.

The invention provides a data flow monitoring method and system based on a large model, and the method comprises the steps: obtaining a data flow record set generated by a to-be-monitored system in a continuous operation period, carrying out the correlation path construction of the data flow record set, generating a data flow topological graph containing a node interaction relation and a time sequence dependency relation, and carrying out the correlation path construction of the data flow record set; calling a pre-trained circulation behavior analysis large model to perform node sequence pattern recognition on the data circulation topological graph, and generating behavior abnormal confidence and abnormal pattern labels of each node in the data circulation topological graph; and according to the abnormal behavior confidence and the abnormal mode label, screening an abnormal interaction node cluster in the data flow topological graph. According to the method, relevance between abnormal nodes and time sequence relevance are considered, missing detection or false detection is avoided, and the reliability of the monitoring effect is improved.

The invention provides a resource allocation method and system based on edge cloud, electronic equipment and a storage medium. The method comprises the steps that a data acquisition module acquires environment information in an edge cloud environment; wherein the environment information comprises application load information, equipment performance information and user behavior information; the data analysis module analyzes and processes the environment information to obtain an application load prediction result, an equipment capability evaluation result and a behavior analysis result, and determines an initial resource allocation strategy according to the application load prediction result, the equipment capability evaluation result and the behavior analysis result; optimizing the initial resource allocation strategy to obtain a target resource allocation strategy; the resource allocation module allocates the computing resources according to the target resource allocation strategy; and the monitoring feedback module monitors the use state of the computing resources in real time and feeds back the use state to the data analysis module, so that the data analysis module adjusts the target resource allocation strategy according to the use state.

The invention relates to the technical field of radio frequency identification, in particular to a customer commodity interaction behavior identification method and system based on UHF RFID. The method comprises the steps of obtaining a multivariate data set of activated scattering of RFID tags on commodities in a reading area of a retail scene, obtaining a state evaluation index according to power loss of a transmission signal so as to obtain abnormal probability distribution of each RFID tag, and the abnormal probability distribution is used for judging dynamic commodities in the retail scene and obtaining the taking times of the dynamic commodities. The UHF RFID work cards worn by the employees are introduced to confirm the interaction implementers, and the action duration, the action occurrence time sequence and the action type of each interaction action of the customer are determined, so that the interaction behavior type of the customer and the commodity is judged. According to the method, the problems that real interaction behaviors are difficult to distinguish in a complex electromagnetic environment and the anti-interference capability is weak in the prior art can be effectively solved, and reliable technical support is provided for customer behavior analysis, commodity display optimization and intelligent marketing in a retail scene.

The invention discloses a detection method and system for a phishing mail, and relates to the technical field of mail security communication. The detection method for the phishing mail comprises the following steps of dual-stage screening, feature extraction and fusion, confrontation sample verification and detection model test and evaluation. According to the method, the mail sample is input into the phishing mail detection model to obtain the mail sample to be tested, the phishing mail double-stage screening is carried out to judge whether the mail sample is the phishing mail, and if the fine-grained mail screening is carried out, the total feature vector is obtained to carry out dynamic scoring to judge whether deep behavior analysis is carried out or not. The method comprises the following steps of: firstly, carrying out confrontation sample verification to output a test result, judging whether to carry out model parameter optimization, and finally, carrying out detection model test evaluation to feed back the test condition of the phishing mail detection model, thereby improving the accuracy of identifying the phishing mail by the phishing mail detection model. The problem that in the prior art, a phishing mail detection model is low in phishing mail recognition accuracy is solved.

The invention relates to the technical field of pepper breeding regulation and control, and discloses a pepper breeding isolation shed control system based on the Internet of Things. The system comprises an environment data acquisition module, a user behavior analysis module, an isolation shed partition module, a dynamic baseline generation module, an abnormal interaction detection module, a root node decision module and a recommendation chain construction module. The environment data acquisition module acquires temperature, humidity, illumination intensity and carbon dioxide concentration data in the greenhouse in real time; the user behavior analysis module analyzes a user historical regulation and control instruction; the isolation shed partition module divides regulation and control sub-regions according to environmental differences; a dynamic baseline generation module generates a dynamic environment parameter baseline for each sub-region; the abnormal interaction detection module identifies abnormal environment fluctuation and instructions; a root node decision module screens a root node area with most significant fluctuation; the recommendation chain construction module constructs a multi-level regulation and control recommendation chain. According to the system, the accuracy and intelligence of environment regulation and control of the isolation shed are improved, and the pepper breeding requirement is met.

A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user / entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies / threats were previously known. The security platform can include both real-time and batch paths / modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.

The invention provides a method, a system and equipment for improving the computing performance stability of a hardware platform and a medium, and belongs to the technical field of computer system performance optimization. The method comprises the following steps: acquiring architecture characteristic data of a bottom hardware platform through a hardware detection tool, and generating and loading a first-stage system kernel parameter configuration based on the architecture characteristic data; reading the architecture characteristic data, carrying out behavior analysis and type identification on the running process based on the architecture characteristic data, and generating a second-level process scheduling strategy according to the behavior analysis and type identification; reading cache structure information in the architecture characteristic data, and driving a file system to intelligently prefetch and reconstruct a storage layout of memory data based on the cache structure information; and continuously collecting performance index flow data when the system runs, performing real-time analysis on the performance index flow data by using a performance degradation model, generating a feedback control instruction according to an analysis result, and dynamically correcting the kernel parameter configuration of the first-stage system and the scheduling strategy of the second-stage process.

The invention discloses a traffic checkpoint holographic recognition method and system based on multi-mode perception and a medium, and relates to the technical field of traffic management, and the method comprises the steps: collecting an original perception data set in real time through a multi-mode sensor array disposed at a target traffic checkpoint; performing time-space synchronization processing on the original sensing data set to generate a multi-modal data frame sequence; performing fusion processing on the multi-modal data frame sequence by using a deep learning model to generate a holographic feature vector of the target vehicle; and based on the holographic feature vector, target identity recognition and behavior analysis of the target vehicle are carried out, a recognition result is output, and abnormal event early warning is carried out. The technical problems of low traffic recognition precision and insufficient real-time performance caused by the fact that an existing traffic monitoring system cannot efficiently fuse various sensor data for holographic recognition are solved, and the technical effect of improving traffic management efficiency and safety through multi-modal data space-time synchronization and deep feature fusion is achieved.

The invention discloses a burying point analysis system for customer behavior analysis, and relates to the field of customer behavior analysis, the system comprises the following components: a data acquisition module, a digital twin model construction module, a business scene simulation module and a behavior prediction and decision support module; according to the invention, the digital twinborn model which accurately reflects customer behavior characteristics is constructed by using the digital twinborn model construction module and combining a deep learning algorithm and a data mining technology, and the model is imported into a diversified virtual business scene for behavior simulation through the business scene simulation module, so that the customer behavior simulation efficiency is improved. And finally, the behavior prediction and decision support module accurately predicts customer behaviors and generates a personalized decision plan by applying a space-time correlation prediction algorithm and an intelligent decision recommendation algorithm based on a knowledge graph, so that the customer behaviors can be accurately predicted. The functions jointly enhance the insight ability of enterprises for customer behaviors.

The invention provides a cross-platform user behavior analysis method and system based on transfer learning, and relates to the technical field of network security, first, historical network behavior record data of a source domain security platform and real-time network behavior flow data of a target domain security platform are obtained, the historical network behavior record data comprise security behavior sequences of source domain users in different access scenes, and the real-time network behavior flow data are stored in the target domain security platform; the method comprises the following steps of: performing cross-domain security feature extraction on two types of data, constructing a cross-domain security behavior association graph, migrating source domain historical malicious behavior mode knowledge to a target domain through a migration learning model based on the graph, generating cross-platform migration security features, and performing cross-domain security feature extraction on the target domain historical malicious behavior mode knowledge. And calling a security behavior analysis model to carry out joint modeling and time sequence security association analysis, identifying an abnormal security behavior mode of a target domain user, and finally matching a network security disposal rule base according to the abnormal mode, generating and issuing a protection strategy, and realizing real-time risk interception.

The invention relates to the field of post capability modeling, in particular to an intelligent post capability dynamic modeling optimization system and method based on data analysis. The method comprises the following steps: carrying out hierarchical deep analysis on an enterprise organization structure, carrying out enterprise topology network modeling, and constructing an enterprise structure topology network; the method comprises the following steps: collecting enterprise employee multi-dimensional behavior logs based on an internal heterogeneous system of an enterprise, carrying out one-by-one employee behavior analysis and dynamic behavior interaction perception, and constructing a multi-post behavior interaction graph; performing staff performance time sequence calculation based on the multi-post behavior interaction diagram, performing multi-dimensional capability comprehensive evaluation, and constructing a post multi-dimensional capability evaluation model; spatial position calculation is carried out on the enterprise architecture topology network one by one, and a real-time adjustment post topology model is constructed. According to the method, dynamic personnel demand configuration analysis and adjustment are realized, the maximum utilization efficiency of talent resources is kept, and the enterprise personnel configuration flexibility is improved.

The invention provides an intelligent gate control access method and system based on driver identification and data mapping, and is applied to the field of data processing. The gateway edge computing node is arranged in the water conservancy terminal, automatic identification and protocol adaptation of the gate control equipment are achieved, the tedious process of traditional manual configuration of a communication channel and protocol driving is avoided, manual intervention is effectively reduced, after the equipment is accessed, the variable format and the equipment function of the equipment can be analyzed, and the control strategy can be automatically matched and executed; if the strategy cannot be directly applied, dynamic verification and behavior analysis can be carried out based on simulation data, the linkage relation between the equipment and the upstream and downstream areas is further constructed, the strategy adaptability and the equipment universality are enhanced through the mechanism, the intelligent level, the operation reliability and the automation capability of the gate control system can be improved, and the application prospect is wide. The method is especially suitable for intelligent water conservancy scenes of large-scale equipment heterogeneous access and unified control.

The invention relates to a computer examination safety technology, and discloses an AI vision-based answering track cheating detection method, device and equipment and a medium, and the method comprises the steps: obtaining the original data of target examinees of an examination system and a monitoring platform, generating an answering track diagram of each target examinee according to the original data of the target examinees, according to the answering trajectory diagram, trajectory similarity analysis among multiple examinees is carried out on the target examinees to obtain a high-similarity trajectory set, spatial and network association analysis is carried out on examinee behaviors of the high-similarity trajectory set to obtain suspicious synchronous behaviors and communication possibilities of the corresponding target examinees, and the suspicious synchronous behaviors and communication possibilities of the target examinees are obtained. And performing cheating behavior analysis on the target examinee according to the suspicious synchronization behavior and the communication possibility, and generating an answer track cheating detection report of the target examinee. Through trajectory similarity analysis, space and network association analysis and multi-dimensional feature comprehensive judgment among multiple examinees, the cheating behavior recognition efficiency can be improved, and the misjudgment rate can be reduced.

The invention provides an elderly nursing method and system, which is applied to elderly nursing services, and comprises the following steps: receiving a video link generated when a monitoring camera monitors an identification object through a message push service; acquiring user configuration information from a database; sending the video link and the user configuration information to an artificial intelligence cloud service, so that the artificial intelligence cloud service analyzes the dangerous behavior and the daily behavior of the identified object to obtain an analysis result; an analysis result sent by the artificial intelligence cloud service is received, and daily state information of the recognition object is counted according to the analysis result; comparing the daily state information with the user configuration information to obtain a comparison result; through combination of the intelligent camera and AI behavior analysis, abnormal or high-risk behaviors of the elderly can be fed back in real time and pushed to family members in time for first-time processing, so that the family members can comprehensively master the living state of the elderly, potential health risks or living abnormalities can be found in time, and the user experience is improved.

The invention relates to an Internet of Things equipment identification method based on equipment behavior analysis. The method comprises the following steps: extracting periodic communication features and information entropy features of Internet of Things equipment on distributed nodes; the optimal feature subset is selected through chi-square verification, and the recognition accuracy is further improved; capturing time dependence and dynamics of behavior modeling by using an LSTM network, and aggregating local behavior patterns of each node through federated learning to form a global model; training and evaluating the model to obtain an optimal model and outputting a recognition result; according to the method, original data are not shared, only model updating is shared, user privacy is effectively protected, and compared with the prior art, the method has remarkable advantages in the aspects of privacy protection, recognition accuracy, expandability and robustness, is suitable for a complex and changeable Internet of Things environment, and has wide application prospects. The method can be widely applied to the fields of intelligent home, intelligent wearing, intelligent transportation and the like, and has important practical significance and wide application prospects.

The invention discloses a microgrid electrical load prediction method and system, and the method comprises the steps: building a power grid load prediction model based on an energy consumption data classification record, and generating a real-time load prediction result; based on the real-time load prediction result, identifying a power use mode of the user, and generating a user behavior analysis result; optimizing the power grid load prediction model based on the user behavior analysis result, and generating an energy influence evaluation result; generating a node stability evaluation result by considering node positions and connectivity through an energy influence evaluation result; and based on a node stability evaluation result, adjusting abnormal load early warning parameters by considering node stability, predicting a power grid abnormal load in combination with the optimized power grid load prediction model, and generating power grid load prediction data. The monitoring capability and response speed of the power system are improved by analyzing the user power use data in the micro-grid in real time, and the accuracy of the prediction model is enhanced by combining classified processing of the user power data and analysis of the use mode.

The invention discloses a marketing strategy optimization method based on consumer group behavior analysis, and the method comprises the following steps: deeply integrating and analyzing multi-source heterogeneous data in real time, such as biological characteristic data, physical environment and spatial data, social contact and content interaction deep analysis data, and voice and dialogue analysis data; carrying out association modeling on the behavior data, psychological characteristics, physiological states and social environment factors, and constructing a dynamic holographic user portrait; a large language model and a multi-mode generation model are utilized, marketing content with high personalization and situation adaptation is generated in real time based on a user portrait and a real-time context, and an interactive co-creation mode of the user and AI is explored; and according to an evaluation result, performing online updating and optimization on each link by using a machine learning algorithm to form a continuously improved closed loop. According to the invention, a more accurate and personalized marketing strategy can be provided, the sense of participation of users is enhanced, and the marketing effect and enterprise competitiveness are improved.

The invention discloses an engineering vehicle driver behavior analysis system based on federal learning. The system comprises a multi-source sensing layer which forms vehicle-road-person collaborative all-weather scenarized data input; the edge calculation layer is used for capturing a complex space relationship of a driving scene, embedding a lightweight cross-modal attention module, calculating an attention weight, and processing long-sequence driving operation data by adopting a Mamba state space model to capture a long-term dependency relationship of a driver state; the federated learning layer adopts a pFedMe algorithm to realize a driver customized model, during local training, the model learns local data distribution characteristics through a Moreau Envelopes regularization item, the cloud aggregates distribution of adaptive local data of each driver, models a federated learning process as a meta-learning problem, and performs training updating; a privacy and security layer; and differential privacy dynamic adjustment, Paillier improved homomorphic encryption and a Hyperledger Fabric block chain are adopted to prevent a log from being tampered.

The invention discloses an early warning method and an early warning device for a purchaser system enclosing mark, and relates to the technical field of information data processing. The method comprises the steps of performing abnormal behavior risk identification according to current bidding information, and obtaining a behavior risk identification result and a behavior analysis score; according to the historical bidding information, performing historical bid winning risk identification by using a mathematical probability statistical method to obtain a historical risk identification result; based on an IF-IDF algorithm and an NLP style analysis tool, performing file analysis according to the to-be-analyzed bidding information to obtain a file risk identification result; based on an external network, acquiring associated company information of the to-be-analyzed bidding information, and constructing an associated company atlas; bidding company association risk identification is carried out according to the association company atlas, and an association company risk identification result is obtained; and issuing direct early warning information and the risk level of the fund-string bidding, and taking countermeasures according to the risk level of the fund-string bidding. The early warning method provided by the invention is high in efficiency and quick in response aiming at bid enclosing and bidding of the purchasing system.

The invention relates to the technical field of artificial intelligence and education technology cross fusion, and particularly discloses a new media course intelligent learning resource generation system based on generative AI, and the system obtains the multi-dimensional state data of a learner through the means of eye movement tracking, electroencephalogram collection, interaction behavior analysis and the like, and constructs a high-dimensional state space through fusion; differential induction logic programming and a depth feature extraction network are cooperatively compressed into a low-dimensional semantic meta-state space, and the model interpretability and decision-making efficiency are improved; dynamic matching of personalized resource types and difficulty levels is realized based on a graph neural network and reinforcement learning; calling a multi-modal generation model to generate texts, videos or interactive contents as required, and introducing a cross-modal consistency verification mechanism to guarantee the output quality; the system also has an online adaptive optimization capability, combines a symbol rule recombination mechanism and a differential privacy protection strategy, and takes account of user privacy security while ensuring long-term stable operation of the system.

The invention discloses a consumer behavior analysis method based on multi-view image fusion and feature vectorization. The consumer behavior analysis method comprises the steps of image data acquisition, image data preprocessing, feature vectorization processing, analysis decision output and the like. According to the method, deep optimization and customized design are carried out in each link of image preprocessing, model architecture, feature learning, index retrieval, behavior analysis and the like around core pain points (complex environment, shielding deformation, real-time performance, accurate association and low false alarm early warning) of a tobacco retail scene; finally, a consumer behavior analysis method based on multi-view image fusion and feature vectorization with high robustness, high efficiency, high precision and low false alarm is given, long-standing technical bottlenecks in the industry are practically solved, and powerful and reliable technical support is provided for intelligent upgrading, refined operation and strict compliance supervision of tobacco retail.

The invention discloses an intranet penetration traceability method for multi-scale heterogeneous log behavior analysis, and relates to the technical field of network security, and the method comprises the steps: carrying out the standardization processing of an original log stream of a heterogeneous log source, and generating a unified semantic event stream; performing multi-scale time window division on the unified semantic event stream, calculating an event density characteristic index of each window, and identifying a short-time dense subsequence based on the event density characteristic indexes; clustering analysis is carried out on the identified short-time dense subsequences based on semantic weighted entropy, and aggressive dense segments and normal high-load segments are distinguished; based on the semantic relevance of the event, extracting long-time sparse clues from the unified semantic event stream; a long-time sparse clue is used as a time anchor point, an aggressive dense section adjacent to the time anchor point is positioned in an extended time range of the long-time sparse clue, and an intermediate interpolation event sequence is generated through a conditional time sequence model to reconstruct a complete attack chain.

The invention relates to the technical field of data processing, can be applied to the field of science and technology finance, and discloses a credit evaluation method and device for multi-source data co-processing and related equipment. The method comprises the steps of receiving credit evaluation data of at least two types of preset data sources, and performing cleaning, time scale unification and numerical standardization processing on the credit evaluation data to form standardized multi-source feature data; constructing credit portrait data based on the standardized multi-source feature data; performing scoring reasoning on the credit portrait data through a pre-trained credit extension evaluation model, and generating credit extension evaluation result data through a limit mapping rule; receiving the use behavior data and the performance behavior data, performing time sequence analysis and deviation identification, and generating credit use behavior analysis data; and updating credit extension evaluation model parameters based on the credit use behavior analysis data, loading the credit extension evaluation model parameters to a model operation state, outputting credit extension evaluation result data, and recording model parameter updating information. According to the method, the dynamic adaptive capacity of multi-source credit assessment is improved.

The invention relates to the field of video generation, in particular to a new media video automatic generation method and system based on artificial intelligence. The method comprises the following steps: identifying user input information; performing deep semantic analysis on the user input information, and constructing a time sequence demand curve; performing role behavior analysis according to the time sequence demand curve, and constructing a role behavior data packet; performing visual expression demand analysis according to user input information, and constructing a multi-level 3D scene; performing dynamic behavior scene fusion on a multi-level 3D scene by using the role behavior data packet, and outputting a video frame sequence; and performing intelligent compression and dynamic code rate optimization on the video frame sequence, and outputting a finished video. According to the invention, full-process automatic, high-quality and high-efficiency generation of the new media video from content conception to finished product output is realized.

The invention relates to an API intelligent recommendation method and system based on a semantic knowledge graph, and relates to the technical field of cloud computing, the accurate and personalized recommendation of micro-services / APIs is realized by constructing an intelligent recommendation method fusing the semantic knowledge graph and multi-source data, the functional requirements can be understood based on semantic similarity, and the intelligent recommendation method and system based on the semantic knowledge graph can be applied to the intelligent recommendation of the micro-services / APIs. In addition, user behavior analysis can be combined, recommendation accuracy is improved, meanwhile, a service popularity adjustment mechanism is introduced, it is ensured that the recommendation result has individuation and practicability, development complexity is effectively reduced, and the service reuse rate and development efficiency are improved.

The invention discloses a multi-modal fusion short message compliance and security dual auditing method and system, and the method comprises the steps: encrypting account information through employing an irreversible algorithm based on SHA256 and a random salt value, and decomposing the content of a short message into a text stream, a link stream and a symbol stream through employing a regular expression; traversing each character in the short message content by adopting a prefix mode based on a Trie tree in combination with an AC automaton algorithm to detect sensitive words; performing symbol semantic classification mapping and analysis on the special symbol feature data extracted from the symbol stream; carrying out sending behavior analysis on the text feature data extracted from the text stream; performing special detection on link feature data extracted from the link stream; based on the factors corresponding to the sensitive words, the semantics, the behaviors and the links and the weights of the factors, a multi-modal feature fusion decision risk assessment algorithm is adopted to output a risk score value and a risk decision rule of the risk score value so as to execute short message interception operation or short message release operation. According to the invention, full-dimension perception and dynamic defense of risks can be realized.

The invention relates to the technical field of traceability management, and discloses an indoor decoration material traceability management system based on a block chain, which comprises an identifier management unit, a circulation management unit, a verification management unit and a traceability management unit, and is characterized in that basic data and a unique identifier are fused through the identifier management unit, and a source feature hash value is generated and uploaded to the block chain; meanwhile, the circulation management unit collects transportation clock-in data in real time every half an hour, generates a circulation state monitoring vector, a performance behavior analysis value and an abnormal deviation degree in combination with a source feature hash value, forms a circulation hash string through processing, and establishes irreversible association with an initial node, thereby achieving the dynamic and accurate management and control of the whole circulation process. According to the method, specific circulation links of damage and other problems can be accurately backtracked and positioned, a responsibility subject is defined, and meanwhile, a traceability management chain is finally generated, so that the traceability management accuracy is improved, and the quality control and responsibility traceability capabilities of indoor decoration materials are enhanced.

The invention discloses a dynamic access control method and system based on authority graph calculation and strategy switching, and relates to the field of electric digital data processing, and the method comprises the steps: taking an operation relation in an authority graph network and a historical access abnormity statistical score as training data, constructing a user access behavior analysis model, when a target resource access request initiated by a target user carrying the token is received, performing permission range verification on the token in the target resource access request; if the permission range verification is passed, converting the target access request information into a target access request vector, and inputting the target access request vector into a user access behavior analysis model to obtain a risk score; and adjusting the access authority of the target user according to the risk score. By implementing the method, the effectiveness of resource access control can be improved.