310 results about "Behavioral analytics" patented technology

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 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 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 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 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 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.

The invention discloses a network equipment dynamic data authority grading authentication management platform, and relates to the field of data authority. The platform comprises a network equipment position area sensing module, a network equipment multi-modal data analysis module, a network equipment compliance security authentication module, a multi-authority historical access behavior analysis module, a network equipment dynamic authority affirmation marking module and an access control execution module. The method comprises the following steps: performing multi-dimensional position secret-related grading according to a physical position, analyzing data content of network equipment in real time, identifying secret-related data flow of the network equipment in real time, performing multi-dimensional data secret-related grading, and performing compliance security authentication on the network equipment at the same time. According to the method, the confidential level of the network equipment is evaluated by combining a feedback mechanism of historical access behavior analysis of multi-authority personnel, a corresponding authority strategy is bound based on a position, data and equipment security ternary framework, and dynamic authority grading authentication is intelligently performed on the network equipment.

A system and method for implementing user and entity behavioral analytics (UEBA) in a cybersecurity analytics platform. An example method includes receiving, by one or more processing devices of a security analytics platform, security data associated with a specified entity; generating, based on at least a subset of the security data, one or more security signals associated with the specified entity and occurring within a specified time window; computing, for each security signal of the one or more security signals, a respective risk score associated with the specified time window; computing, by aggregating risk scores associated with the one or more security signals, a risk score associated with the specified entity for the specified time window; and modifying, based on an attribute of a security watchlist associated with the specified entity, the risk score of the specified entity.

The invention relates to the technical field of information integrated management, in particular to a hospital information integrated management system, which comprises the following steps of: acquiring communication time delay, bandwidth and queuing cost among service nodes, and performing comprehensive calculation and optimal path screening on multiple paths by using a Dikstra algorithm; according to the method, cost-minimized dynamic path planning is realized, a shortest cost path can be efficiently determined under a complex network topology, and failure range calculation and multicast synchronization are executed through hit judgment and a conflict key value updating algorithm in combination with a segmented transmission and block allocation strategy. In the access behavior analysis, trend prediction is performed by counting the access frequency of medical records and prescription sequences and combining a long-short-term memory network model, and the access priority and the capacity threshold are dynamically adjusted, so that the balance of task scheduling and the real-time performance of response are kept during load fluctuation, a traceable cluster recovery index is formed, and the service life of the cluster is prolonged. And stable transmission and intelligent scheduling of cross-system data can still be kept in a complex environment.

The invention relates to the technical field of power system monitoring, in particular to an electricity stealing identification method and system based on multi-source data, electronic equipment and a medium. The method comprises the following steps: acquiring a running data stream and a local timestamp to obtain a plurality of groups of time sequence data sets; extracting an electrical quantity sudden change event or a non-commanding switch action event in each group of time sequence data set; constructing a multi-constraint model based on the topological connection relationship of the power line, the Kirchhoff's law and the energy conservation law; iteratively calculating the clock offset of each monitoring device relative to the system logic time; and inversely solving node current unbalance and loop power unbalance so as to output a judgment result of an electricity stealing event. Through the mode, the technical problem of cyclic dependence between event causal inference and timestamp calibration under the condition of multi-source asynchronous monitoring data in the prior art is solved, and the reliability, the accuracy and the automation level of electricity stealing behavior analysis are improved.

As the number of AI agents in operation has increased, it has become difficult to analyze and search registries of AI agents. Disclosed embodiments enable a holistic view of the behaviors of AI agents, including governance and / or human-like behaviors, both individually and in relation to the behaviors of other AI agents. In particular, a profiling service may embed each of a plurality of behaviors of each AI agent into the same vector space by feeding heterogeneous data about the AI agent through an AI model, converting the output of the AI model into an embedding vector, and storing the embedding vector in a vector database in association with the AI agent. The behaviors that are embedded into the vector space may comprise governance behaviors, human-like behaviors, and / or any other category of behavior. This provides scalability and explainable searches and comparisons of AI agents, behavior-wise or by any set of behaviors.

The invention discloses a safety monitoring system and method based on construction site AI. The system comprises an edge end, a transmission layer and a cloud platform. The edge end comprises an image acquisition module, a lightweight reasoning module and a local alarm module; the transmission layer encrypts and compresses the preprocessed image and the alarm information output by the edge end and transmits the preprocessed image and the alarm information to the cloud platform; the cloud platform comprises an image storage module, a depth reasoning module, a visual management module and a database. According to the method, the lightweight model is fixedly deployed on the edge side and is responsible for real-time target and posture recognition, and first-time response to potential safety hazards is ensured; complex analysis tasks such as an SORT trajectory tracking algorithm and the like are placed in the cloud, and deep behavior analysis is carried out on continuous image frames. The response of the system to the real-time requirement is guaranteed, frequent switching and maintenance of special models in multiple stages are avoided, and the overall stability and execution efficiency of the system are improved.

The invention discloses a user behavior analysis method and system.The method comprises the steps that on the basis of normal behavior data of a user, frequent item sets and rare item sets of normal behaviors are mined from mass unlabeled behavior data, and an accurate basis is provided for data classification; a rare item set is used as a standard to divide majority class and minority class data sets, and minority class data is anchored based on rare features, so that the processing accuracy is improved; performing N times of conversion on the majority class data set based on the rare item set, classifying the converted data into a minority class data set, dynamically balancing the two classes of data, and improving the sensitivity of the model to a minority class normal behavior mode; and constructing a user behavior graph and a graph neural network learning model based on the balanced training data, carrying out structured representation on a user complex behavior pattern, inputting graph neural network learning to obtain a graph structure, nodes and edge features of a normal behavior pattern, inputting actual user behavior data into the trained model, and outputting an accurate user behavior analysis result.

The invention relates to the technical field of data analysis, in particular to a user learning interest-based behavior analysis method and system. The method comprises the following steps: constructing a cognitive map associated with culture resources according to culture resource data to generate a culture resource cognitive map; performing user culture resource cognitive behavior analysis according to the user culture resource interaction log and the culture resource cognitive map to generate user culture resource cognitive behavior data; performing evolution characteristic analysis of user interest content increment according to the user culture resource cognitive behavior data, and generating user culture resource interest evolution characteristic data; and performing learning interest behavior prediction processing on the user culture resources based on the user culture resource interest evolution characteristic data to generate user culture resource learning interest behavior prediction data. The intelligent analysis of the learning interest behaviors of the user can be accurately realized through the information characteristics of the cultural resources and the behavior characteristics of the user.

The application relates to the technical field of driving behavior analysis, and particularly provides a multi-resolution driving working condition identification and classification method. A plurality of time length sliding windows are set to perform multi-scale division on a multi-channel driving time sequence data stream, and a multi-resolution analysis system is constructed; longitudinal driving working conditions are identified and classified according to a yaw angular velocity energy index; a dynamic time warping algorithm is used to match data segments with transverse working condition templates, so that the identification and classification of the transverse driving working conditions are realized; and key driving events are calibrated, thereby providing fine data support for subsequent driving style analysis. The application combines the multi-scale sliding window and time sequence similarity measurement technology, effectively improves the driving working condition identification precision, and significantly reduces the misjudgment rate in complex scenes.

The invention discloses an AI-based tobacco consumer behavior analysis and personalized marketing method and system, and relates to the technical field of data processing, and the method comprises the steps: collecting a multi-modal real-time behavior data stream; performing dynamic noise filtering on the multi-modal real-time behavior data stream to obtain an effective behavior sequence; extracting a plurality of spatio-temporal feature vectors of the effective behavior sequence, fusing the spatio-temporal feature vectors and compressing dimensions to obtain a target feature tensor; inputting the target feature tensor into an incremental learning engine for incremental learning; according to the public opinion event, performing matching in a preset marketing strategy library to obtain alternative actions, and then pushing differentiated marketing strategies; feedback behavior data is collected, and then a strategy utility index is calculated according to the feedback behavior data, and the strategy utility index is transmitted back to the incremental learning engine to serve as a regularization constraint term of next round of updating. According to the method, the incremental learning engine, the public opinion event and the marketing strategy mapping mechanism are combined, and dynamic optimization and precise marketing of the incremental learning engine are achieved.

The application relates to an artificial intelligence-based safe intelligent privacy protection area management server, a method thereof and a privacy protection detection device, the privacy protection area management server comprises a detection information collection unit, an information analysis unit and a processor for controlling the operation of a detection area service processing unit, the processor receives the detection area state information containing detection area identification information transmitted from the privacy protection detection device, uses a pre-learned artificial intelligence model, masters the existence of a dangerous condition by behavior analysis on the detection area state information, analyzes whether a dangerous condition exists by judging whether the behavior state of a user in the detection area is consistent with a preset dangerous state, and outputs dangerous alarm information containing relevant detection area identification information when the analysis result exists a dangerous condition.

The invention provides a method and device for carrying out cost optimization prediction through multi-dimensional business travel behavior analysis, and belongs to the technical field of enterprise business travel management.The method comprises the steps that business travel data are collected, cleaning, duplicate removal and entity alignment operation are carried out, and a business travel behavior data set is constructed; executing a preset query script on the business travel behavior data set, extracting characteristic indexes from preset dimensions, executing descriptive analysis, diagnostic analysis and clustering analysis, and generating a business travel behavior analysis report; and inputting the business travel behavior data set and the business travel behavior analysis report into a cost optimization prediction model to carry out business travel demand prediction, business travel price prediction and business travel travel optimization, generating an optimal travel travel scheme set, pushing the optimal travel travel scheme set to the employee user, collecting a feedback result of the employee user on the optimal travel travel scheme set, updating the business travel behavior data set, and obtaining a business travel result. And incremental training and iterative optimization are carried out on the cost optimization prediction model. Through multi-dimensional analysis and prediction, the travel cost is optimized, and the management efficiency is improved.

The application relates to the technical field of communication, and discloses an Internet of Things multi-protocol compatible communication system and a device identity dynamic authentication method. The method comprises the following steps: converting original data of different protocols into a unified internal message format through a protocol adaptation layer; mapping into a logical identity identifier by an identity abstraction layer; generating a 300-second valid session-level temporary credential based on a challenge-response mechanism by a dynamic authentication engine; continuously evaluating the credibility of a device by a Gaussian mixture model by a device behavior analysis unit; and dynamically adjusting access permissions and bandwidth quotas by a security policy execution unit. The system comprises a protocol adaptation layer, an identity abstraction layer, a dynamic authentication engine, a device behavior analysis unit and a security policy execution unit. The application realizes cross-protocol unified authentication, dynamic key update and continuous trust evaluation, and significantly improves the security and manageability of an Internet of Things system.

The invention provides an intelligent malicious crawler defense method based on Java. The method comprises the following steps: collecting multi-dimensional metadata of a user access request; performing structured processing and feature engineering on the multi-dimensional metadata to generate feature vectors for behavior analysis; constructing and applying a double-layer recognition mechanism fusing a rule engine and a machine learning model, and calculating a probability value P of the user access behavior belonging to a malicious crawler based on the feature vector; setting an initial judgment threshold T according to statistical analysis of historical data, and dynamically optimizing and adjusting the judgment threshold T based on a misjudgment rate and a missed judgment rate; and comparing the probability value P with the current judgment threshold T, and continuously optimizing the machine learning model and the feature weight based on the feedback of the defense result. The problems that an existing crawler prevention method is poor in protection effect, high in misjudgment rate, poor in adaptability and the like are solved, accurate recognition and efficient interception of malicious crawlers are achieved, and meanwhile good access experience of normal users is guaranteed.

The invention discloses a driving behavior dynamic monitoring system based on an intelligent model, which relates to the technical field of intelligent driving assistance and comprises a data acquisition module, a feature processing module and a driving behavior analysis module. The feature processing module is connected with the data acquisition module; and the driving behavior analysis module is connected with the feature processing module, and is used for predicting inertial features corresponding to operations to be executed by a driver based on the scene features, comparing the inertial features with inertial features of actual execution operations, and outputting a judgment result of a driving state in combination with a physiological feature analysis result and an inertial feature comparison result.

The invention relates to the technical field of multi-source data processing and analysis, in particular to an intelligent voice conference behavior analysis method and system based on multi-source perception. The method comprises the following steps: acquiring an audio signal, cooperative behavior data and terminal equipment data, and constructing a time slice index data structure, a voice space positioning sequence and a terminal activation recording sequence; constructing an activation state corresponding matrix, and constructing a multi-source collaborative affiliation graph structure based on the voice space positioning sequence and the terminal activation record sequence to obtain a dominant speaking affiliation result; constructing a conference cooperation graph structure, and calculating the structural contribution strength of the non-voice input behavior to the conference decision content by using a structural coupling degree function to obtain a recessive behavior recognition result; and constructing a conference behavior credibility matrix, and eliminating repeated mapping records of conference participant identity multi-mapping conference decision content under the same time slice to obtain a conference behavior credibility identification result. According to the invention, identification and credibility evaluation of dominant speaking and implicit cooperation behaviors of participants are realized.

The invention discloses a mail system based on a large client load sharing mechanism and a data processing method. The system is constructed based on a three-layer load sharing network architecture of a terminal layer, an intelligent virtualization processing layer and a core server layer. The intelligent virtualization processing layer is deployed between the terminal cluster and the core server layer, and is provided with a plurality of functional modules; comprising an intelligent prefetching cache module, an intelligent attachment cache module, a centralized full-text retrieval preprocessing module, a contact synchronization module, a calendar data pre-calculation module, a mail rule preprocessing module, a batch operation processing optimization module and a mail behavior analysis preprocessing module. According to the scheme, the bearing capacity of the creative mailbox system in a high-concurrency scene is remarkably improved, through distributed caching and intelligent pre-calculation, the core service response time is shortened, the hardware input cost is reduced, and the use experience of large-scale enterprise users is effectively guaranteed.

The invention provides a community shared parking management system and method based on Internet of Things and artificial intelligence, relates to the technical field of intelligent traffic management, and solves the problem of various limitations in community scale shared parking management. In the system, a sensing layer acquires parking space state information, vehicle characteristic information and environment parameter information in real time through multiple types of sensing devices deployed in parking spaces, and generates sensing data; the edge computing layer receives sensing data through an edge computing node, and carries out local data processing, equipment control instruction generation, abnormal state detection and local data caching; the cloud platform layer receives and integrates data from the edge computing layer through a cloud computing platform, and executes parking resource scheduling decision, user behavior analysis and demand prediction, user account management and payment settlement processing. The vehicle and parking space monitoring accuracy can be improved, and the parking experience, the parking space utilization rate and the parking lot passing efficiency of community users are smoothly improved.

The present application relates to the technical field of malware detection, in particular to a malicious code detection method based on behavior analysis, comprising the following steps: monitoring system event flow and process resource occupation data, using fixed time interval sampling to capture network packet arrival time interval, CPU occupation rate fluctuation, target file lock contention state and mouse cursor pixel position, calculating mutual information entropy between different event flows, and revealing hidden associations that cannot be perceived by traditional detection methods. This method is no longer limited to analyzing the isolated behavior of a single process, but captures the weak signals generated when malicious software uses hidden channels for communication or data leakage by quantifying the statistical dependence between seemingly unrelated events.