724 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 provides an advertisement recommendation method and system fused with user dynamic behavior modeling. The method comprises the following steps: acquiring a cross-device operation record of a user on multiple terminal devices, and synchronously acquiring a device unique identification code, high-frequency click area data and an operation timestamp; and constructing a space-time relation graph based on combined analysis of the equipment switching time interval and the click behavior, and generating a feature set reflecting the behavior correlation degree. The trajectory similarity is calculated at the edge calculation node through hardware acceleration, and migration correlation parameters are generated. And inputting the space-time relation graph into a behavior analysis model to extract coherent behavior characteristics, and forming a multi-dimensional scene portrait in combination with geographic position change and interface element distribution. And finally, through identifying a user cross-device interest migration mode, dynamically screening advertisement contents matched with the current scene, and realizing accurate pushing strategy optimization based on behavior continuity. According to the technical scheme provided by the invention, the accuracy of cross-terminal advertisement recommendation and the scene response speed are remarkably improved.

An AI-driven financial planning system for real-time market adjustment, consisting of: a neural inference coprocessor configured to execute deep financial forecasting models, including recurrent neural networks and attention-based encoders, on the device, and wherein the processor dynamically updates portfolio parameters in response to market signals exhibiting volatility differences above a statistical threshold calculated using an exponentially weighted moving standard deviation; a financial data acquisition module configured to continuously receive and analyze heterogeneous data streams, including market indices, interest rates, stock and bond price fluctuations, economic indicators, regulatory updates, and financial news sentiment feeds; a behavioral analytics engine configured to create a dynamically evolving user-specific financial behavior profile based on real-time analysis of transaction history, income-expenditure cycles, psychometric test results, and temporal lifestyle patterns using supervised and unsupervised machine learning algorithms; A goal optimization module configured to transform high-level, user-defined financial goals into quantitatively tracked multi-level goals. It uses a reinforcement learning framework that predicts optimal asset allocations across multiple time horizons. a real-time strategy simulation engine configured to perform Monte Carlo simulations and deep Q-learning-based assessments to simulate the resilience of proposed financial strategies under different macroeconomic regimes and trigger redistribution events based on predefined confidence thresholds; a compliance-aware execution interface configured to interact with financial institutions through encrypted API channels, ensuring policy enforcement using a smart contract validator and a hardware-enabled secure transaction signing unit; and a recommendation display unit configured to render dynamic dashboards for visualizing investments, reallocation warnings, confidence intervals, and sensitivity sliders, and where user interaction with the unit flows back into the behavioral model for real-time learning.

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 traffic participant behavior analysis-based edge calculation signal lamp control method and system, and the method comprises the steps: obtaining video stream data of a target intersection, carrying out the frame-by-frame behavior state analysis of the video stream data through a spatial-temporal feature coding network, extracting the behavior state features of each traffic participant, and carrying out the analysis of the behavior state features of each traffic participant; and inputting the behavior state characteristics into a preset behavior matching model, generating a behavior triggering identifier of the traffic participant in the current time window, generating a signal lamp control instruction of an edge computing node according to the time sequence relevance between the behavior triggering identifier and the phase state of the current signal lamp, and sending the signal lamp control instruction to the edge computing node. And finally, a signal lamp phase switching time sequence of the target intersection is adjusted based on the signal lamp control instruction, so that the traffic participants meeting the traffic rule conflict condition obtain the traffic priority under the signal lamp phase switching time sequence. According to the method, the traffic accident risk is reduced, and meanwhile, the overall traffic efficiency of the intersection is optimized by dynamically adjusting the minimum response period and the phase duration parameters.

The invention discloses a construction site unsafe behavior detection method and system based on image recognition, and relates to the technical field of intelligent visual safety monitoring, and the method comprises the steps: collecting a construction image containing a constructor, carrying out the rectangular frame labeling of unsafe behaviors in the construction image, and generating a structured labeling data set; inputting the structured annotation data set into a YOLOv5 target detection model, performing training through Mosaic data enhancement and an adaptive anchor box generation algorithm, and outputting the trained YOLOv5 target detection model; based on the trained YOLOv5 target detection model, performing 128-dimensional feature vector extraction and track association matching on a detected target through a DeepSORT multi-target tracking algorithm, and outputting a track data table; and the recognition stability and behavior analysis depth of the system in a complex environment are enhanced, so that automation and intelligentization of safety management of a construction site are effectively supported.

The present invention relates to the technical field of electricity applications. Disclosed are a supply-demand interaction game method based on electricity consumption behaviors of user groups, and a system. The method comprises: collecting and preprocessing historical electricity consumption data of smart terminals of user groups; using a clustering analysis method to perform classification on the types of electricity users; by means of the central-limit theorem and Monte-Carlo method, constructing a user electricity consumption behavior analysis model; creating an electricity user group evolutionary game model, so as to analyze long-term evolution patterns of electricity consumption strategies of the user groups; and power supply entities adjusting and formulating, on the basis of the analysis result, an electricity price incentive plan that maximizes benefits, and transmitting, via power lines, the updated plan to smart electricity consumption terminals of the users. By means of using electricity consumption data of user groups collected by a large number of smart terminal devices and combining the data with the evolutionary game theory, the present invention successfully performs mathematical modeling and analysis on electricity consumption behaviors of user groups, thus achieving mutual benefit, win-win outcomes and stable equilibrium between supply-demand parties in long-term supply-demand interaction.

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 an intelligent video monitoring system and method based on deep learning, and belongs to the technical field of video monitoring, and the system comprises a heterogeneous sensing array, a cognitive driving feature fusion module, a dynamic evolvable detection model, a causal reasoning tracking engine, a meta-knowledge enhancement behavior analysis module and a quantum-classical hybrid computing architecture. The heterogeneous sensing array comprises a reconfigurable visible light / infrared dual-mode camera group, a distributed microphone array and a millimeter wave radar, and is configured to generate multi-physical field sensing data; and the cognitive driving feature fusion module adopts a space-time-frequency spectrum joint coding technology, and integrates a three-dimensional convolutional network and a graph attention mechanism to realize cross-modal feature interaction. According to the invention, through multi-dimensional technical innovation, the intelligent video monitoring system with autonomous evolution capability is constructed, the perception capability, reasoning precision, resource efficiency and safety are obviously superior to those of a traditional scheme, and the high-standard requirements of smart cities, security and other scenes are met.

The invention relates to the technical field of safety early warning, in particular to a safety early warning system based on big data, which comprises a sequence behavior analysis module, a frequency fluctuation identification module, an event number generation module, a sensitive grading judgment module and a notification content push module. According to the method, potential abnormal signals in access behaviors are identified through real-time dynamic analysis of the user access sequence and fusion of access interval change and category difference characteristics, risk behaviors are further screened based on a sudden change rule of access frequency, and accurate classification of abnormal events is realized by using unique identification and a mapping mechanism. According to the method, sensitive assets are finely graded by integrating multi-dimensional parameters, a hierarchical early warning basis is generated by adopting a weight quantification mode, push contents are directionally generated by combining different role permissions and identity information, a safety early warning process of data driving, parameter linkage and hierarchical perception is constructed, and the sensitivity of risk monitoring and the accuracy of hierarchical response are improved.

The invention relates to the technical field of corrosion detection, in particular to a grounding grid corrosion intelligent monitoring system which comprises an electrochemical data capture module, a corrosion behavior analysis module, a corrosion probability evaluation module and a monitoring strategy optimization module. According to the method, data errors and abnormal values are filtered and corrected, the overall quality of data is improved, the accuracy of analysis results is ensured, and in corrosion behavior analysis, the future trend of corrosion is effectively predicted through dynamic change detection of electrochemical properties and calculation of the corrosion rate; the time sequence analysis of the electrochemical impedance data not only enhances the prediction accuracy, but also improves the understanding depth of corrosion behaviors, the corrosion probability evaluation module adds consideration of random changes to the whole prediction model through the application of the Markov chain, so that the model is closer to the uncertainty of the real world, and through the optimization of the processing flow, the prediction accuracy is improved. The real-time performance and the early warning capability of the monitoring system are obviously improved, and the reliability and the operation accuracy of the system are enhanced.

The invention discloses a customer data mining exploration method and system based on big data, particularly relates to the technical field of data mining, and is used for solving the problems of feature conflicts and analysis distortion caused by semantic inconsistency of multi-source data in the prior art. A potential semantic conflict path is identified by constructing a cross-channel rule interaction entropy evaluation model, dynamic mapping and weight adjustment of conflict indexes are realized in combination with a domain knowledge base, and finally a customer behavior analysis result with consistent cross-channel semantics is generated. Multi-source behavior indexes are extracted based on business rule definition, a conflict source is accurately positioned through semantic similarity calculation and difference dimension analysis, and a weight fusion strategy is dynamically optimized according to real-time scene features, so that semantic ambiguity between channels is effectively eliminated, and the accuracy and decision support capability of customer portraits are improved.

The invention provides a customer group dynamic division method and system, and relates to the technical field of customer group management.The method comprises the steps that firstly, commodity related data are collected in real time through a multi-channel data collection module, commodity behavior analysis is conducted in combination with service requirements, features are extracted according to analysis results, and commodities are divided into multiple groups; and then, dynamically dividing customers by combining service requirements, generating initial customer group information, and optimizing and adjusting the customer group information according to the updated user portrait information. And finally, performing preference calculation according to the optimized customer group information, and generating a personalized push strategy. According to the method, through dynamic division and optimization of customer group information, unique requirements and behavior characteristics of each customer can be better met, and personalized services are provided.

The invention provides a retail supply chain optimization system and method based on big data analysis, and relates to the technical field of big data analysis. The system comprises a data acquisition module used for acquiring order data, logistics data and user behavior data and generating structured data; the data processing module is used for preprocessing the structured data and establishing a multi-dimensional association relationship among the data based on a preprocessing result; the inventory prediction module is used for predicting future order demands, calculating regional inventory demands and generating inventory distribution data; the inventory optimization module is used for generating replenishment data and an inventory adjustment strategy based on the inventory distribution data; the behavior analysis module is used for generating commodity recommendation data and promotion priorities based on the user behavior data in combination with the adjusted inventory distribution data; and the logistics allocation module is used for dynamically adjusting the logistics distribution path and the distribution priority. According to the technical scheme, the operation efficiency and flexibility of the retail supply chain can be improved.

The invention relates to the technical field of network advertisement promotion, in particular to a network advertisement promotion method and system based on the Internet, and the method comprises the following steps: obtaining user behavior data, analyzing a browsing track, a staying duration, a click frequency and an interactive behavior, calculating the activity of a user in real time, and recognizing the interest point of the user. And updating user tags in real time, classifying user groups, and generating a user tag data set. According to the method, the interest point of the user is identified, the user label is updated in real time, it is ensured that personalized recommendation of the advertisement conforms to the real-time behavior of the user, the active time period of the user is analyzed and predicted, resource waste caused by putting in a fixed time period is avoided, budget allocation is dynamically adjusted according to the adaptation degree between the behavior of the user and the platform, and the user experience is improved. The advertisement putting efficiency is improved, user behavior changes before and after advertisement display are compared, the advertisement putting effect is accurately evaluated, data support is provided for adjustment of advertisement strategies, and the advertisement putting effect and resource configuration are optimized.

The invention discloses an intelligent electric meter electricity utilization behavior interpretability analysis method based on a causal atlas. Through multi-source data fusion and a triple nested time window mechanism, a multi-level causal atlas is constructed; when abnormal power utilization is detected, identifying an affected area by adopting a local influence propagation algorithm, and correcting to realize dynamic map maintenance; a hierarchical lazy index updating mechanism is adopted, paths are divided into key, important and background levels according to importance, and only queried paths are verified; a causal path is quickly reconstructed through a multi-level degradation search strategy, and an interpretable analysis report is generated in combination with anti-factual reasoning. According to the method, the updating complexity and the index maintenance overhead are reduced, the query efficiency is improved, and an efficient interpretable technical scheme is provided for power consumption behavior analysis of the intelligent electric meter.

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 discloses a video monitoring method and system, electronic equipment and a storage medium, and relates to the field of video monitoring. According to the method, a three-dimensional digital twinborn scene is constructed according to the space coordinates of each camera in a target area, and a node relation matrix is constructed according to the space relation between the cameras; according to the three-dimensional digital twinborn scene and the node relation matrix, a dynamic vision field splicing algorithm is adopted, the real-time position and the action path of the target object are determined, and the people flow density of the target area is displayed; determining a behavior mode of the target object through skeleton key point detection and interaction behavior analysis, and predicting an action track of the target object according to the behavior mode; and evaluating an initial risk level of the action track, adjusting the initial risk level in combination with the people flow density to obtain a final risk level, and generating a disposal suggestion according to the final risk level. By implementing the technical scheme provided by the invention, comprehensive and accurate video monitoring and risk management of the target area are realized.

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 provides a smart home intrusion accurate identification and early warning method based on multi-sensor fusion, and relates to the field of intrusion early warning, and the method comprises the steps: firstly obtaining the time sequence distribution of a home switch state and the time sequence distribution of an infrared radiation value, and carrying out the time sequence coding and interaction analysis, so as to obtain a switch-moving state time sequence interaction feature; and then a time sequence of a monitoring image frame is acquired to carry out movement behavior global analysis, and movement behavior analysis is carried out in combination with switch-movement state time sequence interaction characteristics, so that the confidence level is evaluated, and a three-level response mechanism of high-confidence intrusion immediate alarm, medium-confidence artificial review and low-confidence log recording is realized. Thus, the misjudgment problem of a traditional method is solved, the accuracy and environmental adaptability of intrusion recognition are remarkably improved, meanwhile, the user experience is optimized through hierarchical response, and security real-time performance and operation friendliness are both considered.

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.

The invention discloses a computer remote login identification system based on artificial intelligence, and the system comprises a remote login request receiving and processing module which is responsible for receiving a remote login request of a user, and carrying out the preliminary processing and verification; the multi-mode identity authentication module adopts a three-factor verification matrix of biological characteristics, behavior characteristics and equipment fingerprints, and adjusts weights of different authentication dimensions according to security requirements and user requirements; the lock screen state sensing module is responsible for sensing a lock screen state; the dynamic behavior analysis engine is used for monitoring behavior characteristics of the user in real time and comparing the behavior characteristics with a preset abnormal behavior mode so as to find abnormal login behaviors in time; the intelligent unlocking decision tree is used for making an intelligent unlocking decision according to the authentication score, the equipment environment and the behavior abnormality; and the security execution module is responsible for automatically filling a password and executing an unlocking operation after the user identity authentication is passed, and monitoring an abnormal condition in the unlocking process in real time in combination with equipment environment monitoring and an abnormal detection model.

The invention discloses an intelligent interaction method and system based on artificial intelligence, and the method comprises the steps: building an interaction database, collecting the comprehensive data of a sensor in real time, inputting the data into the interaction database, inputting the comprehensive information of a user room into the interaction database, and collecting the historical operation data of a user. Analyzing the historical operation data to obtain an adjustment threshold value corresponding to an operation habit of the user, collecting behaviors of the user after interaction with the smart home, analyzing the behaviors to obtain behavior habit characteristics of the user, analyzing voice characteristics of the user, matching a fuzzy instruction for the voice characteristics to obtain a fuzzy instruction set, and sending the fuzzy instruction set to the smart home; the fuzzy instruction set is screened according to the adjustment threshold value, the influence of the scene feature where the user is located and the behavior habit feature on the fuzzy instructions is analyzed, the priorities of the fuzzy instructions are obtained, and the fuzzy instruction with the highest priority is selected as an interaction instruction, and the method and device have the advantages of improving accuracy and user experience.

The invention relates to a user power consumption behavior portraying method and system based on non-intrusive load monitoring, and belongs to the technical field of residential user power consumption behavior analysis and energy management. According to the technical scheme, a data acquisition module collects user intelligent electric meter data; the user load clustering analysis module adopts a clustering algorithm to classify user load curves; the equipment-level behavior analysis module is used for analyzing the energy consumption ratio, the single-equipment use period preference and the multi-equipment cooperation mode of the equipment type for the user; the user portrait construction module constructs a multi-dimensional user power consumption behavior portrait; and the system application module formulates a personalized energy efficiency optimization scheme. According to the method, user load curve clustering analysis and device-level power consumption data are fused, the types and start-stop time of power consumption devices are identified through the NILM technology, and the total load clustering result is combined, so that fine-grained modeling of user power consumption behaviors is realized, and multi-dimensional mining of the device type energy consumption ratio, the single-device use period preference and the multi-device cooperation mode is carried out.

The invention relates to the technical field of security alarm, in particular to a home security integrated system and method based on artificial intelligence, and the system comprises a multi-source data collection module which collects environment data through an infrared sensor, a thermal imaging camera and a standard video camera, carries out the data fusion, and generates an environment data fusion result. According to the invention, the acquisition module fuses infrared, thermal imaging and video cameras, so that the monitoring coverage is expanded, the capability of capturing environmental details is enhanced, and the detection rate of abnormal behaviors is improved. Standardized monitoring data is generated through data fusion and format unified processing, high-quality input is provided for feature extraction and behavior analysis, data streams are optimized, and information loss is reduced. A pre-trained deep learning model is applied to an intelligent analysis module, recognition of complex behavior modes is enhanced, and the accuracy and timeliness of anomaly detection are improved.

The invention relates to the field of software security, and particularly discloses a software supply chain security analysis method which comprises the following steps: S1, collecting data of components of a software supply chain, and calculating a historical security risk weight for each component in the supply chain; s2, collecting behavior data of the software during operation, and performing association analysis in combination with the supply chain data; s3, preprocessing the collected behavior data and supply chain data, and extracting key features; according to the software supply chain security analysis method, through dynamic behavior analysis, runtime threats, such as zero-day vulnerabilities and hidden backdoor threats, which cannot be detected by static analysis can be found, the coverage rate of threat detection is remarkably improved, meanwhile, historical security risk weights are introduced, historical security problems of supply chain components are quantified, and the security of the software supply chain is improved. And intelligent risk assessment and threat traceability are realized by combining a knowledge graph and a graph neural network technology.

The invention provides a member data management system, which belongs to the technical field of data management, captures member behavior changes in real time through a time sequence behavior analysis engine, solves the problem of cross-system data islands by utilizing a data consanguinity tracking module, and realizes second-level updating of member portraits by adopting a real-time portrait updating component. The system further comprises a dynamic right and interest matching device and an intelligent content generation unit, personalized right and interest recommendation can be generated according to real-time portraits and external event data, and compliant marketing content can be automatically produced. According to the invention, the technical problems of static label management, data integration difficulty, portrait updating lagging and the like of the existing member management system are solved, and accurate dynamic management of the whole life cycle of members is realized.

The invention discloses a person and post matching method and system based on talent evaluation. The method comprises the following steps: step 1, setting a talent evaluation standard; 2, establishing an occupational quality model of the personnel; 3, creating a post model; 4, developing a post matching algorithm; 5, internal employee behavior analysis; step 6, a post recommendation system; step 7, quantitative evaluation and demand analysis; 8, a data processing technology; compared with the prior art, the method has the advantages that the scientificity, objectivity, efficiency and accuracy of personnel and post matching are improved, personalized recommendation of employees is realized, the efficiency of talent recognition is improved, and the method has remarkable superiority.

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.