447 results about "Cyber Space" patented technology

The invention discloses a visual language navigation method for cross-modal alignment in a dynamic shielding environment, and the method comprises the steps: collecting multi-modal data through a visual sensor, an inertial measurement unit, a laser radar and the like, and carrying out the preprocessing and time synchronization; sensing the dynamic shielding object through a model composed of a convolutional neural network and a long-short-term memory network, and estimating the future change of the dynamic shielding object in combination with a space-time sequence prediction algorithm; a double-branch convolutional neural network and a Transform based on a dynamic attention mechanism are adopted to respectively extract visual and semantic features and fuse the visual and semantic features; on the basis of occlusion prediction, potential occlusion region features are extracted in advance from a time dimension, an occluded image is repaired by using a generative adversarial network and geometric constraints in a space dimension, and cross-modal feature alignment is optimized through an attention mechanism; planning a path by using a hybrid reinforcement learning algorithm based on a deep Q network-space and a fast exploration random tree, and dynamically adjusting according to real-time shielding; according to the method, the accuracy, adaptability and reliability of visual language navigation in a dynamic shielding environment are improved.

The invention discloses a network space security situation awareness detection analysis system and method, and the system comprises a heterogeneous network flow feature deep extraction unit which obtains a flow feature vector through a distributed probe array and an adaptive sampling strategy; the edge computing gateway multi-dimensional threat feature mapping unit realizes feature space mapping through a multi-dimensional feature mapping matrix; the dynamic weight multi-head attention feature fusion unit fuses features by using an optimized multi-head attention mechanism; the time-space sequence security situation evolution modeling unit constructs an evolution model in combination with the time sequence and the fusion features; the security threat risk quantitative evaluation unit carries out risk quantification; and the heterogeneous security policy collaborative response unit generates a response instruction. According to the method, flow collection, feature mapping, fusion, modeling, quantification and response operation are sequentially executed based on all units of the system. According to the method, efficient perception and accurate response of the network space security situation are realized through a multi-unit cooperation and innovation model.

The invention discloses a network space security risk intelligent identification method and system. The method comprises the following steps: deploying traffic collection equipment at key network nodes and boundary equipment, and collecting network traffic data and log data; extracting network behavior features and log key fields, and constructing a data feature model; performing vectorization processing based on the data feature model, performing traffic classification by using a deep neural network, and identifying a device type and a device identifier; carrying out noise reduction, redundant information removal and standardization processing on flow data in the data feature model to generate standardized data and storing the standardized data in a vector database; entity extraction is carried out on the standardized data, a knowledge graph is constructed, data distribution is analyzed through a Gaussian mixture model, the probability of abnormal behaviors is calculated in combination with historical data, and attack risks are calculated based on Bayesian decision; and generating an alarm according to the calculated attack risk, tracing the historical record of the attack risk in the knowledge graph, sending a blocking instruction, and identifying the security risk of the cyberspace.

The invention discloses a causal association privacy protection cyberspace surveying and mapping threat detection method and system, and relates to the technical field of information security and cyberspace surveying and mapping. The method specifically comprises the following steps: constructing a causal incidence matrix based on a multi-modal network space surveying and mapping data dynamic fusion method of multi-scale convolution and causal reasoning to realize cross-modal surveying and mapping data feature fusion; according to the dynamic Gaussian noise privacy protection method based on GCN importance evaluation, privacy budget is dynamically allocated by utilizing causal association importance, and fusion feature privacy is protected; according to the self-adaptive pre-training model gradient protection method based on the self-supervised dual disturbance mechanism, the privacy and availability of the model are dynamically protected; according to the network space surveying and mapping threat detection method based on the stacked LSTM model, abnormal modes in multi-scale traffic are learned, and high-precision threat detection is achieved. According to the invention, through causal association modeling and dynamic privacy protection, the threat detection capability in a complex network environment is improved while the data security is guaranteed.

The invention provides a data security method and system fusing an MIN and a unified AI theory, and the method comprises the steps: S1, completing the multi-dimensional identification registration and synchronization of a data space in a multi-identification network MIN based on a data space identity identification DSID and an ontology semantic mapping frame; s2, acquiring a data acquisition record from a multi-identifier network MIN, and constructing an access control atlas in combination with a semantic vector of a data subject identifier DSID-Item; s3, analyzing the access request and verifying the authority, identifying the sensitivity level of the access request, and triggering a privacy enhancement mechanism when the sensitivity level is marked as medium-high-level privacy data; and S4, judging whether the data object reaches a value end point in the life cycle of the data object or not by taking a data space identity (DSID) registered in the multi-identifier network (MIN) as a retrieval anchor point, and if so, executing a verifiable multi-stage destruction operation. According to the invention, a full-life-cycle trusted data security technical scheme can be realized, and intelligent, explainable and efficient security management of data streams in a network space is realized.

The invention discloses a network space map surveying and mapping method and system based on multi-source data fusion, and the method comprises the steps: obtaining a multi-source data set in a unified format based on network flow data, equipment information data and geographic position data; obtaining a network asset entity and an incidence relation graph thereof through entity identification and correlation analysis based on the multi-source data set with the uniform format; obtaining a network space three-dimensional map model through three-dimensional space mapping and visual rendering based on the network asset entity and the association relationship map thereof; based on the network space three-dimensional map model, performing dynamic updating according to the accessed real-time data flow to obtain real-time network space situation data; and obtaining a network security risk assessment result through anomaly detection and threat identification based on the real-time network space situation data. According to the invention, visual display and security situation awareness of the network space are realized, and a brand new decision support tool is provided for network security management.

The invention provides a power system network space precision linkage defense control method and device, and belongs to the field of power system management. The method comprises the following steps: generating a network topology structure and a key asset distribution diagram based on real-time dynamic mapping of a power system network space by a space mapping technology, extracting operation state data and potential risk information of network nodes, integrating data sources of different levels of a power system, and performing association analysis by using a deep learning algorithm, so as to obtain a key asset distribution diagram; obtaining a network threat classification result and generating a dynamic defense strategy by integrating the network operation state; wherein the strategy comprises defense path selection, response node scheduling and risk isolation measures; and executing the generated dynamic defense strategy, evaluating the effectiveness of defense measures according to a feedback mechanism, collecting historical defense records, threat features and response effects, and updating a threat knowledge base and a defense rule base based on a data analysis technology. According to the invention, the accuracy and efficiency of network space defense can be improved.

The invention relates to the technical field of network space security, and provides a DoH malicious tunnel traffic detection method and system based on feature fusion, and the method comprises the steps: carrying out the preprocessing of obtained to-be-detected DoH traffic data, carrying out the sequence segmentation processing, obtaining a Token sequence, and extracting features based on a byte sequence feature extractor; statistical features are extracted and standardized, features are extracted through a statistical feature extraction sub-network, and statistical feature vectors are obtained; fusing the byte sequence feature vector with the statistical feature vector to obtain a classification result; the byte sequence feature extractor and the statistical feature extraction sub-network extract features, and training is carried out by adopting a semi-supervised learning framework of dynamic pseudo-tag screening for non-tag training samples. According to the method, multi-modal features are fused, a semi-supervised learning mechanism is introduced, malicious DoH traffic generated by multiple DNS tunnel tools is effectively identified under a limited annotation data set, and the detection precision and generalization ability are improved.

The invention provides a building identification method and system based on three-dimensional point cloud, and particularly relates to the technical field of computer vision and three-dimensional target identification, and the method comprises the steps: carrying out the undersampling, oversampling and data enhancement preprocessing of inputted building roof point cloud data, and carrying out the T-net network space alignment to output the aligned data. Then, an NEWPCT model is obtained by improving a PCT model, aligned data are input into the NEWPCT model, a point cloud feature matrix is generated through a linear coding layer, query, key and value matrixes are obtained through linear transformation, multi-head attention output is calculated by introducing an offset matrix, intermediate correlation features are obtained after splicing and fusion, and a global feature vector is generated through global maximum pooling; and finally, inputting the vector into a classifier, and outputting a building roof type, and the method solves the technical problems of geometric transformation sensitivity and local feature loss while improving the precision and robustness of three-dimensional point cloud building identification, thereby effectively improving the identification precision and robustness.

The invention discloses a network space security intelligent monitoring and analysis system, and relates to the technical field of network security monitoring and analysis, and the system comprises a multi-source data collection module which collects multi-dimensional data in a full-link manner, and the collection frequency is dynamically adjusted along with a network load; the data preprocessing module cleans the fused data and generates a standardized analysis data set; the AI intelligent risk identification module identifies various safety risks in real time through a mixed deep learning model; the real-time response processing module starts differential processing according to a three-level mechanism; the threat traceability analysis module traces an attack link and generates a report; the security situation visualization module displays the security state in multiple dimensions; the data encryption storage module encrypts and protects data and performs double backup; and the system self-optimization module dynamically optimizes the strategy through incremental learning. The method is accurate in risk identification, timely in response processing, reliable in traceability and evidence storage, and efficient in cross-domain cooperation; terminal protection and third-party access control are enhanced, and network space security and stable service operation are comprehensively guaranteed.

The invention relates to the technical field of power network space optimization management, and particularly discloses a power network space optimization analysis method and system based on a power grid GIS, and the method comprises the steps: firstly enabling a power grid GIS port to firstly match the space coordinates of a high-voltage side bus of a power transmission substation and a power distribution feeder starting node, building an electrical connection topology after verification, and evaluating a capacity coupling factor; dividing feeder segments according to topology, calculating a loss rate and a risk index, and correcting the risk index in combination with a capacity coupling factor, thereby realizing accurate quantification of the feeder state and improving risk assessment scientificity; secondly, feature vectors are constructed according to the GIS center points, the loss rates and the risk indexes of the feeder line sections for clustering, GIS layers with labels are output, feeder line clusters in the same state can be recognized conveniently, and support is provided for regional analysis; and finally, detecting a loop gap based on a layer, analyzing connectivity, identifying a weak link and optimizing feedback, accurately positioning a network short board, and providing a targeted reference for an optimization scheme.

The invention discloses a cyberspace advanced long-term threat (APT) trapping method based on a graph neural network, a storage medium and a cyberspace advanced long-term threat trapping device, and relates to the technical field of computers, and the method comprises the steps: carrying out the dimension reduction of original encrypted traffic data based on a hierarchical clustering algorithm, retaining an abnormal traffic cluster, and removing normal traffic redundant data; designing an E-ResGAT model to perform encrypted semantic modeling, and introducing a residual connection technology to retain original traffic features; and a dynamic trapping-protocol inference closed-loop system is further constructed, and a honeypot environment is utilized to simulate attack sample interaction behaviors and capture traffic. According to the scheme, efficiency balance of encrypted traffic data reduction and semantic reasoning is realized through collaborative optimization of hierarchical clustering and a graph neural network, the capture capability of the model on a hidden attack mode is enhanced in combination with a residual attention mechanism, and a multi-dimensional analysis basis is provided for high-security scenes such as a military private network; and an innovative solution is provided for improving the security defense capability of a military information system.

The invention discloses a cyberspace surveying and mapping threat detection method and a cyberspace surveying and mapping threat detection system, and belongs to the technical field of network security. Performing multi-dimensional feature extraction including spatial distribution feature extraction, time distribution feature extraction, protocol interaction feature extraction and load response feature extraction to construct feature vectors, and outputting a prediction feature vector of a next time step according to a plurality of feature vector pairs in a latest time window through a learning model; performing error comparison with the actual feature vector to screen out abnormal features, performing attack intention assessment according to the abnormal features to assess an attack intention comprehensive threat coefficient, and finally assessing a threat risk coefficient in combination with the abnormal features and the attack intention comprehensive threat coefficient. The accuracy of attack intention assessment is improved through the multi-dimensional features, and the assessment accuracy of the threat risk coefficient is improved by combining the abnormal features and the attack intention comprehensive threat coefficient.

The present invention discloses a cyberspace mapping threat detection method and system with causal privacy protection, which relates to the field of information security and cyberspace mapping technology. Specifically, it includes: a dynamic fusion method of multimodal cyberspace mapping data based on multi-scale convolution and causal reasoning, which constructs a causal matrix to realize cross-modal mapping data feature fusion; a dynamic Gaussian noise privacy protection method based on GCN importance evaluation, which dynamically allocates privacy budgets based on causal importance to protect the privacy of fusion features; an adaptive pre-training model gradient protection method based on a self-supervised dual perturbation mechanism, which dynamically protects model privacy and availability; a cyberspace mapping threat detection method based on a stacked LSTM model, which learns abnormal patterns in multi-scale traffic to achieve high-precision threat detection. The present invention improves threat detection capabilities in complex network environments while ensuring data security through causal modeling and dynamic privacy protection.

The invention belongs to the technical field of network security, and particularly relates to a threat intelligence automatic extension method based on network surveying and mapping, which comprises the steps of automatically collecting and preprocessing multi-source threat intelligence by a large model, extracting key network identification information, generating standardized network asset characteristics, and obtaining a multi-dimensional asset characteristic matrix based on a network space surveying and mapping technology. Constructing a network asset fingerprint database, carrying out correlation verification, generating a set of verifiable network asset fingerprints, carrying out feature vectorization on the verifiable network asset fingerprints, carrying out homologous asset analysis through a weighted clustering algorithm, and outputting a threat entity topological graph comprising an IP address cluster, a domain name correlation group and a C2 infrastructure. According to the method, potential threats which are difficult to cover by traditional manual analysis can be automatically identified, an integrated multi-source threat intelligence platform and a network surveying and mapping tool are supported, diversified intelligence formats and feature requirements can be processed, the automation degree is high, threat paths can be rapidly expanded, and response time can be shortened.

The invention discloses a longitudinal federal random forest model training method, system and device based on a Stackelberg game incentive mechanism and a medium, and belongs to the field of cyberspace security, and the model training method completes training of a random forest model through four stages of sample sampling, feature sampling, model training and model fusion. A passive party payment strategy is defined through an active party payment upper limit, then benefits of a decision-making tree model are defined, finally an objective function of an active party is calculated, excitation of participants in a federated learning task is achieved through an excitation mechanism of the two-stage Stackelberg game, and the active party can complete the federated learning task by setting a reasonable excitation strategy and objective. The passive party is stimulated to participate and pay data, so that the overall federal learning effect and the model performance are improved; the system, the equipment and the medium are used for implementing the method. The method is high in safety and fairness, and guarantees the performance of a global model.

The invention discloses a Chinese error correction method and system based on multi-agent collaboration and retrieval enhancement, and belongs to the technical field of network space security. The method comprises the following steps: step 1, constructing a plurality of Chinese error correction expert agents and one Chinese error correction judgment agent based on a large language model and an agent prompt project; step 2, establishing an error correction text knowledge base, converting corpora in the knowledge base into a vector database, and accessing a Chinese error correction expert agent and a judgment agent for information retrieval of the Chinese error correction expert agent and the judgment agent, enhancing context understanding ability and realizing a retrieval enhancement generation function; and step 3, designing a detection prompt, a discrimination prompt and a feedback prompt, constructing a multi-agent closed-loop feedback module, realizing information collaboration, step-by-step reasoning, result verification and iterative optimization of each agent, and completing a Chinese error correction task. According to the method, generalization error correction can be realized under the condition of limited training data, and the adaptability of the model to unknown errors is improved.

The invention provides an efficient task scheduling strategy recommendation method and system for a distributed network surveying and mapping scene, and relates to the crossing field of network space surveying and mapping, network security, distributed scheduling and intelligent optimization recommendation algorithms. Analyzing the task request to extract a task type, a task target network address set, a regional distribution feature, a task execution timeliness requirement and calculation and bandwidth resources required by estimation, and forming a task feature vector; and performing state acquisition and capability evaluation on the distributed surveying and mapping nodes based on task resource requirements defined by the task feature vectors, and performing normalization processing to form node capability vectors. Through multi-source information vectorization and closed-loop optimization, adaptive matching of task scheduling and resource dynamics is realized.

The invention relates to the technical field of network situation awareness, in particular to a network situation intelligent awareness security system based on multi-source data analysis, which accurately acquires a perception threat confidence vector, a service interruption risk vector, an attack chain complete vector and a user behavior deviation vector based on multi-source data fusion and targeted analysis. According to the method, network situation key information can be comprehensively and accurately captured, a reliable basis is provided for subsequent decision making, the comprehensiveness and real-time performance of threat perception are improved, dynamic balance of safety protection, service efficiency and operation and maintenance cost is achieved by relying on a multi-target game mechanism and a network space digital twinning technology, and the safety and reliability of threat perception are improved. A simulation deduction deviation value is introduced as a core index of strategy evaluation, a real network situation is simulated through a digital twin environment, the difference between a strategy execution effect and an expected target is quantified, and in combination with reproduction of an attack scene by the twin environment, deviation value analysis is ensured to be more fit with the real network environment.

The invention discloses a network space asset real-time perception and risk early warning system based on a neural network, and belongs to the field of network security. In order to solve the problems of poor real-time performance, low accuracy, incapability of processing large-scale data, lack of adaptive ability and the like of a traditional cyberspace asset sensing and early warning method, a three-level collaborative module architecture is constructed and covers an asset sensing layer, a feature processing layer and a risk early warning layer. The asset perception layer collects asset information in real time by using a hybrid detection engine and generates a fingerprint vector; the feature processing layer fuses multi-modal data; and the risk early warning layer evaluates the risk and visualizes the attack path. Meanwhile, a reinforcement learning optimization detection strategy is adopted, a cross-modal alignment loss function is designed, and core algorithms such as space-time causal convolution and a causal attention mechanism are applied. Practice of Guangxi power grid company verifies that the asset state update delay is reduced from 30 minutes to 2 minutes, the novel APT attack detection rate is improved to 92%, the bandwidth occupation is reduced by 40%, and the network security protection level is effectively improved.

Provided in the present invention are a method and system for ensuring the trustworthiness and security of cyberspace. The method comprises: step S1, on the basis of a multi-identifier network, implementing multi-identifier addressing having authentication and packet signing embedded therein; step S2, defining digital customs, a digital passport and a digital visa based on the multi-identifier network; step S3, on the basis of time data, a digital visa key and a hash function, performing calculation to obtain the digital visa based on the multi-identifier network; step S4, on the basis of the hash value of the digital visa and a cross-states passport key, performing calculation to obtain the digital passport based on the multi-identifier network; step S5, implementing the digital customs by means of a multi-identifier network router, and using and maintaining an exit border table by means of the digital customs; and step S6, by means of the digital customs, using an arrival border table to verify the digital passport, and updating the arrival border table. The present invention can effectively guarantee the trustworthiness and security of cyberspace, and enable the traceability of blockchain logs, thereby providing a better foundation for ensuring the order, rule of law and peace of cyberspace.

The invention relates to the technical field of intelligent monitoring, and particularly discloses a cyberspace geographic map monitoring system based on big data, which adopts an artificial intelligence technology based on deep learning to carry out semantic understanding on node description of a first node in a cyberspace geographic map, extracts semantic feature representation of the node description, and sends the semantic feature representation to the first node; meanwhile, time sequence analysis is conducted on the multi-source heterogeneous data of the first node, state information of the power grid nodes is mined from the multi-source heterogeneous data, and therefore node description is updated in combination with initial node description and the state information of the power grid nodes. Therefore, the real-time monitoring of the node state in the network space geographic map and the real-time updating of the map can be realized, the latest state of the node in the network space can be reflected in time, and the updating efficiency and accuracy of the network space geographic map are improved.

The invention relates to the technical field of emergency management, in particular to a visual emergency management command system which comprises a network channel judgment module, a broadcast circling adjustment module, a material linkage retrieval module, a bidirectional response matching module and an instruction popup prompt module. And judging the fluctuation condition of the signal-to-noise ratio along with time. According to the method, the signal dynamic change and the communication quality sequence are fused, multi-channel use is optimized in real time, the boundary is dynamically linked according to spatial distribution and equipment motion characteristics in the region circling operation, and multi-dimensional association is realized by combining the spatial distance between a team and a warehouse and a material demand type; the supply-demand relationship is automatically graded according to the actual supply difference and visual identification is realized, and the material gap is displayed through popup window information and geographical highlight linkage, so that multi-link data-driven linkage across network, space and resource dimensions is integrally realized, the fine resource allocation and efficient response capabilities are improved, and the collaborative guarantee in an emergency scene is enhanced.

The invention discloses a multi-dimensional network space security intelligent analysis and early warning platform, which relates to the technical field of network security intelligent analysis and early warning, and comprises the following steps: a multi-source network data acquisition module acquires multi-level data and encrypts and gathers an association identifier; the data preprocessing and standardization module filters invalid data to unify a format, and encrypts sensitive information to generate a verification abstract; the multi-dimensional feature extraction module extracts multiple types of features and maps the features to the same vector space; the intelligent security analysis module analyzes threats in combination with a deep learning model and a rule engine; the grading early warning and response module triggers early warning according to grades; the distributed data storage and traceability module adopts a hybrid architecture to ensure that data cannot be tampered and traced; and the visual interaction and management module presents the state in multiple dimensions. According to the platform, multi-source data are integrated to realize multi-dimensional collaborative analysis, and an early warning threshold is dynamically adjusted to avoid false alarm and missing alarm; strategy dynamic optimization, compliance adaptation and full-process tracing are realized, and the network security protection capability is comprehensively enhanced.

The invention belongs to the technical field of IPv6 situation awareness, and relates to an IPv6 address network space situation awareness method based on data collection, which comprises the following steps: acquiring active IP address and service information, domain name registration information and geographic position data by collecting network data; then the data are preprocessed, the preprocessed data are processed based on a Spark big data analysis tool, and abnormal traffic behaviors are identified from the preprocessed data; on the basis, behaviors of the IPv6 address are classified by using a support vector machine (SVM) model, and potential threats are identified. And further analyzing the spatial and temporal distribution condition of the network event in combination with the geographic position and the timestamp data. And finally, based on the identified abnormal traffic behavior, the potential threat and the spatial-temporal distribution thereof, constructing a dynamic situation awareness chart by using an Echarts visualization tool, and visually displaying the overall security situation of the IPv6 network. According to the method, through multi-level data processing and analysis, the security monitoring and response capability of the IPv6 network is comprehensively improved.

A system implements an emergent web space to bind a host computer to one or more client computers. A host computer generates data aspects of characteristically random data to create emergent web spaces in which website resources can be rendered by one or more client computers intersecting the same emergent web spaces. A binding is established between the host computer and one or more client computers when a block of true random data generated by the host computer is provided to the one or more client computers. A unique block of true random data together with the remote storage of data aspects constitutes a zone. A software application executing on the one or more client computers is configured to retrieve data aspects from within the same zone, and further configured to resolve and then render website resources.

The invention relates to the technical field of computer vision and deep learning, and discloses a cascade diffusion model-based virtual fitting method, which comprises the following steps of: inputting a model image and a target clothing image into a multi-condition diffusion model, and outputting a posture control variable and a depth control variable; integrating the posture control variable and the depth control variable into a de-noising U network through a multi-control injection module, and generating a low-resolution try-on image of the model wearing the target garment; inputting the low-resolution fitting image into the super-resolution diffusion model, and performing feature extraction on the low-resolution fitting image; feature codes corresponding to feature extraction are transmitted to a control network space through a full connection layer, then the feature codes are integrated into a super-resolution diffusion model through a cross attention mechanism, the super-resolution diffusion model is guided to reconstruct a low-resolution try-on image, and a high-resolution try-on image is generated. According to the method, high-fidelity and high-resolution clothing try-on image generation is realized through a multi-condition control and super-resolution reconstruction technology.

The invention discloses an ultra-high-definition video adaptive code rate control method and system based on deep learning, relates to the technical field of video coding transmission, and solves the problem of data congestion or frame loss easily caused by network space fluctuation in the traditional coding behavior. According to the method, the current total cache amount and the to-be-processed video frame flow of the network nodes are comprehensively considered, the characteristic flow is accurately calculated, the network interval which the processing time period needs to reach is further determined, the data volume which can be processed in the processing time period is determined based on the network interval, and the data volume can be processed by analyzing the past data characteristics of the video stream path nodes and other nodes. According to the technical scheme, the interaction flow of the multiple path nodes is accurately controlled, network resources can be reasonably distributed, data congestion caused by too large flow of the network nodes is prevented, it can be ensured that the video code rate reaches the optimal state in the adjustment and verification process, and the video transmission efficiency and quality are improved.

Techniques for improved spatial reuse for wireless local area network (WLAN) networks are described. An access point (AP) may win a contention to a wireless medium and obtain a transmission opportunity (TXOP). The AP may perform a procedure to identify a group of un-managed APs for participation in spatial reuse opportunities for synchronous transmission over the TXOP. The AP may perform the procedure using over-the-air signaling. The AP may transmit a spatial reuse (SR) poll frame to one or more un-managed APs of the network, either sequentially or as part of a multiple-AP procedure. The AP may receive an SR response frame, or directly measure potential interference of a station (STA) serviced by one or more un-managed APs, and select a group of APs for coordinated reuse over the TXOP. The AP may select the group of APs based on one or more criteria for coordinated reuse.

The invention belongs to the technical field of network space security and social platform governance, and particularly provides an agent-driven active interactive social robot detection method, electronic equipment and a storage medium, and the method comprises the steps: obtaining original data of a to-be-detected account, and forming a structured data packet; constructing a preliminary screening evaluation model, and outputting a detection task state based on the structured data packet; generating an initial detection state; obtaining an interaction record; carrying out evidence extraction on the interaction record to obtain an evidence vector of the round; and performing sequential updating on the initial risk score and the uncertainty based on the evidence vector, and outputting a detection result when a stop condition is met. According to the method, an active interactive evidence obtaining mechanism of a detection agent is introduced, and on the basis of passive preliminary screening, probe interaction is automatically selected and initiated to obtain incremental evidence, so that the recognition capability of a high-simulation social robot is improved, and an auditing report containing key evidence and an interactive link is output.