14 results about "Behavior space" patented technology

The invention discloses an attack trapping and analyzing method and system based on a honeynet, and relates to the technical field of attack analysis, and the method comprises the steps: collecting attack interaction data and an associated attack chain trajectory set; dividing attack behavior units, and constructing a behavior space attitude matrix; constructing a two-dimensional behavior relationship graph, performing interpolation enhancement of sub-time granularity, and raising the dimension of the interpolated behavior node into a three-dimensional semantic space; performing attack path trend analysis on the attack behavior unit, and constructing an attack path bending rule model; and inputting parameters such as the attack chain trajectory set and behavior nodes in the three-dimensional semantic space into the attack path bending rule model, outputting a trapping response strategy, and dynamically adjusting the honeynet environment. According to the invention, the type and position of the honeypot can be automatically adjusted according to the attack behavior change, the structure definition and processing precision of the attack behavior data are effectively improved, and the attack behavior identification result can be conveniently and directly used for trapping strategy optimization.

The present application relates to the technical field of abnormal behavior recognition, and more particularly to an abnormal behavior monitoring and early warning method based on a space-time graph neural network, medium and equipment, which converts abstract behavior data into a graph structure containing three kinds of nodes and three kinds of edges by extracting the object-space-time ternary relationship contained in the behavior data of the target monitoring object, and quantifies the correlation strength through the edge weight, and carries out deep feature extraction and embedding representation for the object, space and time nodes respectively, converts the abstract graph node into a high-dimensional vector rich in semantic information, and gradually integrates the spatial semantic features and the time dynamic features into the object behavior features, and finally generates a space-time enhanced representation vector that can reflect the individual behavior mode of the object and the influence of the space-time environment, breaking through the limitations of a single object node graph, solving the problems of insufficient behavior space-time context modeling and fragmented processing of spatial features and time features, and realizing accurate identification and early warning of space-time coupled abnormal behavior.

The invention relates to the field of artificial intelligence, robot technology and aided navigation, in particular to a multi-robot collaborative navigation system based on intention reasoning, which constructs a Riemannian manifold multi-modal feature space based on a differential geometry theory, maps a voice instruction, a gesture operation and an emotional state of a user to a unified feature space, and provides a multi-robot collaborative navigation system based on intention reasoning. In combination with historical behavior data of a user, the user intention is accurately understood, the system adopts a mapping theory in differential geometry to establish a mapping relationship between an intention space and a behavior space, smooth transition from an abstract intention to a specific behavior is realized, and an optimal navigation path is calculated on a joint manifold formed by intention-environment-emotion through a geodesic optimization algorithm. The system can dynamically adjust the navigation strategy according to the emotional state of the user and provide personalized navigation experience, the multi-robot collaborative navigation unit decomposes the navigation strategy into a plurality of subtasks, evaluates the ability index of each robot, optimizes the distribution scheme, realizes the collaborative work of multiple robots, and improves the intention understanding accuracy.

The application belongs to the technical field of data analysis, and specifically discloses a user behavior analysis method, system and program product based on big data. Behavior data of a user in each time period is collected based on big data technology and processed to construct a behavior data matrix by using quantitative behavior parameters. The behavior data matrix is then converted and processed to obtain a standard data matrix. Based on the standard data matrix, convergence calculation and behavior space matrix construction analysis are performed to determine the highlight index of each behavior type. Finally, several behavior types with the highest highlight index are selected as highlight behavior types to construct a user behavior portrait. The application can efficiently and accurately analyze the highlight behavior types of a user based on the behavior data of the user in each time period, thereby constructing a high-matching-degree behavior portrait for the user. This can improve the user experience by accurately classifying and providing targeted services for the user in the future.

The invention provides an intelligent research journey recommendation method and system based on collaborative filtering and LSTM fusion, and the method comprises the steps: taking a multi-modal behavior time sequence as the input of a pre-constructed research behavior space-time attention network, and obtaining a time sequence preference implicit vector; calculating the matching degree of the time sequence preference implicit vector and the dynamic constraint factor, and correcting the time sequence preference implicit vector by using the constraint factor feature vector based on a calculation result to obtain a scene adaptive time sequence preference vector; extracting a time sequence behavior feature vector of the current research journey group, and performing weighted fusion on the time sequence behavior feature vector and the scene adaptive time sequence preference vector to obtain a final time sequence preference vector; the final time sequence preference vector is used for outputting a personalized research and study journey recommendation list. According to the multi-dimensional intelligent recommendation method, the group preference, the personal time sequence behavior, the research education attribute and the real-time resource state can be fused, so that the recommendation accuracy and the user experience are improved.

The application provides a multi-element information collaborative decision method based on intention recognition for an autonomous underwater robot in a weak communication and partial observation environment, so as to improve the adaptability and intelligent decision-making ability of an AUV group in a complex dynamic environment. Specifically, the application provides an intention recognition method based on decision consensus. Firstly, a decision consensus among AUVs is constructed through fuzzy reasoning, and key features such as a packet loss rate, a distance, and a number of partners are mapped to a behavior space, so that a preliminary intention inference is efficiently realized with a minimum amount of data exchange. Subsequently, a residual convolutional neural network is used to further strengthen feature extraction, and precise intention prediction is realized. Finally, by completing global state information and optimizing decision input, a dynamic perception-intention reasoning-collaborative decision closed loop is constructed, and more efficient task planning and cooperation efficiency of the AUV group are realized.

The present disclosure relates to the field of animal behavior and pattern recognition technology, and particularly relates to an animal behavior abnormal pattern recognition method, system, device and medium. The present disclosure constructs a complex behavior pattern expression based on a probability generation model, greatly improving the adaptability and accuracy of the model. The present disclosure uses a Gaussian mixture model or other probability generation model to describe the "normal behavior space", rather than using a rigid fixed threshold. This enables the model to automatically learn and express various normal sub-patterns of animal behavior, such as foraging and resting, and their dynamic changes. This data-driven modeling approach can better adapt to behavior changes under different species, different seasons and different environments, avoiding the high false alarm rate problem caused by the "one-size-fits-all" approach of traditional rule engines, making the abnormality judgment more scientific and accurate.

The application discloses a Q network control method and device and a readable storage medium, and relates to the electronic information field. In the Q network control method provided by the application, a preset behavior is a behavior randomly selected in a behavior space A or an optimal behavior selected in the behavior space A by using a database operator. A plurality of experience samples are extracted from an experience pool, and corresponding expected benefits are calculated based on the database operator. Finally, the parameters and vector indexes of the Q network and the parameters and vector indexes of a target Q network are updated according to the expected benefits. The database operator is an operator realized based on an approximate nearest neighbor search technology. Compared with the previous training control method, the approximate nearest neighbor search technology is introduced in the scheme to obtain the database operator, so that the search range is greatly reduced. Compared with the previous DQN which traverses all the action spaces, the training and decision efficiency is increased, and the scheme can be applied to large-scale discrete behavior scenes.

The application provides an API behavior prediction and security policy management method based on machine learning, relates to the field of computer network security, and comprises the following steps: acquiring API original data in real time, preprocessing the original data to obtain preprocessed original data, acquiring API dimension features in real time based on the preprocessed original data, and fusing to obtain unstructured data; a dynamic characteristic validity verification rule is established to preprocess the unstructured data, and a unified dimension standardization feature tensor is obtained; the joint modeling of API behavior space-time features is performed through the collaborative architecture of a multi-head time attention mechanism and a dynamic graph neural network, API behavior prediction is performed, API dynamic security management and control strategies are generated in real time through a strategy engine, strategy execution is performed, the security strategy execution effect is monitored in real time, and iterative optimization is performed. The application solves the problems of fixed and rigid security management and control strategies of a traditional API gateway, and the difficulty in dynamically adjusting security strategies according to real-time access conditions and behaviors.

The application discloses a kind of attack trapping and analysis method and system based on honeynet, it is related to attack analysis technical field, including: attack interaction data and associated attack chain trajectory set are collected;Divide attack behavior unit, construct behavior space posture matrix;Two-dimensional behavior relationship atlas is constructed, interpolation enhancement is carried out to sub-time granularity, and the behavior node after interpolation is upgraded to three-dimensional semantic space;Attack path trend analysis is carried out to attack behavior unit, and attack path bending law model is constructed;Attack chain trajectory set, the behavior node in three-dimensional semantic space and the like parameter are input into attack path bending law model, and output is trapping response strategy, and dynamically adjusts honeynet environment.The application can automatically adjust honeypot type and position according to attack behavior change, effectively improve the structural clarity and processing precision of attack behavior data, and facilitate to directly use attack behavior identification result for trapping strategy optimization.

The invention provides a timing training anti-cheating system and a timing training anti-cheating method based on a trusted root and a behavior space-time chain, which can fuse time, space and behavior multi-dimensional information, ensure credibility from a data source and realize all-around and high-reliability cheating prevention through intelligent association analysis, and comprises a timing training terminal with a built-in trusted execution environment TEE module; the trusted execution environment TEE module is used for generating environment digital fingerprints and encrypting and signing the collected vehicle state sensing terminal data; the system further comprises a cloud collaborative judgment platform which is used for receiving a signed chain type data packet, and the chain type data packet comprises a plurality of data units which are linked according to a time sequence and signed by TEE; based on a preset physical rule model, performing logic consistency verification on the spatio-temporal trajectory data and the vehicle behavior data in the chained data packet; and judging whether simulation positioning cheating exists through a multi-mode causal analysis module.

The invention provides an intelligent terminal network intrusion behavior identification method and device based on deep learning, and the method comprises the steps: building a dynamic behavior space-time atlas comprising a network address node and an internal operation node through obtaining the original network flow and operation log data of a terminal in a preset time period; in the map, a behavior evolution relationship is represented through a time sequence edge, and co-occurrence association of the network and an operation behavior is represented through an association edge. And inputting the atlas into a pre-trained intrusion behavior prediction deep network, simulating propagation evolution of intrusion on the atlas by using a space-time propagation prediction layer, and outputting intrusion probability distribution of each node and an intrusion propagation path graph among the nodes. And performing intrusion behavior sequence reconstruction based on the result, generating a candidate intrusion behavior sequence, calculating a credibility score, finally screening out a target sequence with the highest score, and generating a response instruction containing a terminal identifier and an intrusion time period. According to the method, hidden intrusion behavior modes can be deeply mined, and a complete attack chain can be reconstructed.

The application relates to the technical field of edge AI computing systems of graph neural networks, and discloses a behavior space-time prediction method and system of an edge AI chip. The method comprises the following steps: extracting customers and shelf entities in real time based on a video stream, and constructing a dynamic space-time node set; generating a sparse heterogeneous space-time high-order correlation structure based on the node set; and performing behavior prediction and loss prevention response with topological perception based on the structure through a topological graph neural network after knowledge distillation. The application is aimed at solving the problems of high memory occupation in traditional unstructured data processing, difficulty of traditional graph structure in capturing multi-body interaction, low behavior discrimination precision of an edge model, and lag of a loss prevention response. Through model quantization pruning, dynamic sparse pruning, and multi-dimensional loss prevention evidence fusion, the application adapts to resource constraints of an edge chip, improves the fine behavior discrimination precision and real-time response speed, and realizes accurate loss prevention and shelf layout optimization in an unmanned sales scene.

The invention discloses a social user behavior recommendation system based on deep reinforcement learning, and the system comprises the following modules: a data collection and preprocessing module which is used for collecting and standardizing social original data to generate a user behavior feature vector; the vector mapping module is used for mapping the user behavior feature vector to a potential behavior space; the causal graph construction module is used for calculating the distance between the sample points based on the Minkowski distance and establishing a causal graph; the energy field construction module is used for calculating potential behavior energy corresponding to each node and forming a behavior energy field through an energy transfer mechanism; the force field construction module is used for performing Laplace optimization on the behavior energy field to obtain a behavior influence force field; and the personalized recommendation generation module is used for dividing different user groups through a DBSCAN algorithm according to the behavior influence field and generating user behavior recommendation content. According to the method, the Minkowski distance and the DBSCAN algorithm are combined to realize accurate personalized social behavior recommendation.