699 results about "Behavioral analysis" patented technology

The invention relates to a risk management and control method and system based on real-time behavior analysis, and the method comprises the steps: carrying out the structural processing of multi-source behavior data through lightweight protocol decoding and behavior label embedding, and constructing an original behavior data set of a user and an entity; extracting multi-dimensional behavior characteristics by using a sliding window analysis and sparse representation mechanism, and constructing a user behavior graph by combining graph embedding learning; constructing a time-sensitive behavior trend model through streaming modeling and an incremental learning strategy, identifying an abnormal evolution trajectory in real time, and introducing a dynamic risk threshold regulation and control mechanism; adopting a high-throughput flow data processing and fast similarity matching algorithm to construct a fusion discrimination model, giving risk levels to abnormal behaviors and classifying the abnormal behaviors; and finally, performing closed-loop optimization in combination with a historical treatment effect. The system has the advantages of high real-time performance, high calculation efficiency, adaptability to complex network environments and the like, and the network security protection capability can be effectively improved.

The invention discloses a behavior analysis early warning method and system based on AI situation awareness, and relates to the technical field of data recognition. The method comprises the following steps: collecting multi-source sensing data and extracting a numeralization feature vector; an edge correlation degree dynamic threshold self-learning algorithm is adopted to screen effective edges to construct a dynamic space-time diagram, and space-time correlation features are extracted; a modal weight coefficient is calculated based on historical detection accuracy, data integrity and scene adaptation degree, and multi-modal features are weighted and fused; inputting the deep reinforcement learning model, outputting a threat risk value and a safety response strategy, and executing; and updating the dynamic weight, the threshold value and the strategy parameter according to the early warning accuracy and the threat interception success rate to realize closed-loop optimization. The method solves the technical problems that multi-source heterogeneous data lacks uniform feature representation, cross-modal relevance is lost, a space-time association relationship cannot be dynamically updated, multi-modal feature fusion weight is fixed, fusion effectiveness is affected, threat assessment lacks adaptability, and performance is degraded due to model parameter updating lag.

The invention discloses a driver state sensing system based on physiological indexes and external behavior analysis, which relates to the technical field of vehicle active safety control, and comprises a sensor group integrated in a driver contact or close area and a vehicle control part and used for collecting physiological signals and behavior characteristic signals of a driver in real time, the physiological signals at least comprise heart rate and skin electric signals, and the behavior characteristic signals at least comprise eyelid movement, head posture and steering wheel operation signals. According to the driver state sensing system, the comprehensiveness and accuracy of driver state sensing are remarkably improved by integrating physiological indexes and external behavior analysis, the limitation of single signal judgment is effectively overcome through a multi-source signal fusion mechanism, the reliability of state evaluation is ensured, and the advanced signal preprocessing technology adopted by the system is high in reliability. The data anti-interference capability is enhanced, the feature extraction precision is improved, and the robustness of the model is enhanced.

The invention relates to the technical field of big data, in particular to an e-commerce marketing propaganda system based on behavior analysis, which comprises a global user portrait module, an intelligent recommendation engine, a marketing fatigue management module, a supply chain collaboration module, a lightweight terminal module and a budget and value management module. In the prior art, user portraits are constructed only depending on single channel data such as online clicking or purchase records, so that user interest modeling is incomplete and lagged; according to the method, all-channel behavior data such as APP, Web, offline POS and social media are integrated, a user-commodity-scene heterogeneous graph is constructed by using a graph neural network, and an interest attenuation period (for example, the weight is reduced by 50% after the interest of mother and infant users lasts for 18 months) is dynamically captured through an LSTM model; for example, after a user tries on a certain style of clothes offline, the system associates online behaviors in real time and recommends commodities of the same style, the cross-scene conversion rate is improved by 32%, and the user portrait coverage degree is improved by 60%.

The invention provides an active defense system and method based on multi-protocol dynamic simulation and distributed trapping. The active defense method based on multi-protocol dynamic simulation and distributed trapping comprises the following sub-steps: S1, constructing a multi-protocol dynamic simulation environment; s2, deploying distributed trapping nodes; s3, deep trapping of attack behaviors; s4, attack chain reconstruction and behavior analysis; s5, performing adaptive confusion and adversarial enhancement; s6, automatic threat intelligence production and feedback; by loading the protocol template library and initializing the state machine, the response can be dynamically generated according to the real-time session context, and dynamic simulation of various service protocols is adopted, so that the detection capability on network attacks is improved, potential threats can be captured more quickly, and the risks of missing report and false report are reduced; and through an automatic threat intelligence generation and feedback mechanism, in combination with IOC index identification, structured output and real-time response, a defense strategy can be quickly responded and adjusted.

The invention relates to the technical field of test question generation, in particular to an intelligent test question generation method and system based on learning behavior analysis. The method comprises the following steps: acquiring interactive behavior data and score data of learners in a user online learning platform in real time; inputting the interactive behavior data and the score data into a pre-trained knowledge state analysis model to generate a user knowledge state matrix; based on the knowledge state matrix and in combination with a preset teaching target library, identifying a target knowledge point set which needs to be strengthened currently and a corresponding cognitive training type; according to the target knowledge point set needing to be strengthened and the corresponding cognitive training type, a test question element combination algorithm is called, question stems, interference items and question solving path prompts are dynamically assembled, and personalized test questions are generated. The method has the advantages that full-closed-loop intelligent teaching from behavior analysis of the user to targeted training is achieved, and personalized test questions adaptive to individual cognitive vulnerabilities are dynamically generated.

The invention discloses an intelligent intervention system and method based on multi-mode driving behavior analysis, and belongs to the field of intelligent traffic. The system comprises a multi-dimensional perception module, a behavior analysis engine, a risk assessment matrix and a self-adaptive intervention module. The multi-dimensional sensing module is integrated with a multi-source heterogeneous sensor and is used for acquiring physiological characteristics of a driver, driving operation data and environment information; the behavior analysis engine analyzes multi-modal data based on spatial-temporal feature fusion, and digs a driving behavior mode; the risk assessment matrix judges danger levels according to the multi-dimensional driving risk quantification result level by level; and the self-adaptive intervention module implements a grading progressive correction strategy. The system realizes active prevention and control of driving risks through cross-modal data association, behavior chain prediction and environment-behavior coupling analysis. The system solves the problems that a traditional system is single in sensing dimension, coarse in risk assessment, rigid in intervention strategy and the like, and effectively improves the driving safety.

The invention relates to the technical field of perimeter intrusion monitoring, in particular to a perimeter intrusion monitoring system based on cooperation of infrared thermal imaging and an intelligent algorithm. The perimeter intrusion monitoring system based on infrared thermal imaging and intelligent algorithm cooperation comprises a multi-source sensing layer, an edge computing node, a cloud analysis platform and a response execution layer. According to the perimeter intrusion monitoring system based on cooperation of infrared thermal imaging and an intelligent algorithm, environmental noise interference is effectively suppressed through a dynamic threshold compensation mechanism, and accurate classification of personnel, vehicles, unmanned aerial vehicles and animals is realized by combining multi-modal feature fusion of infrared thermal imaging, visible light vision and millimeter wave radar; according to the method, intention modes of passing, observation, invasion and the like can be identified, threat level dynamic adaptation is realized in combination with a hierarchical response system, the technical bottlenecks of poor environmental adaptability, high target misjudgment rate, behavior analysis deficiency and the like of a traditional system are solved, and the intelligent level and all-weather protection capability of perimeter security and protection are remarkably improved.

The invention discloses a low-power-consumption warning ground pile awakening method and system based on human activity recognition, and belongs to the technical field of image analysis and intelligent security and protection. According to the method, in a micro-power-consumption mode, the environment is monitored through an image sensor, and when environment changes meet awakening conditions, lightweight human body detection is conducted through an edge AI chip. And if the human activity probability exceeds the confidence coefficient, starting a main camera and a high-computing-power AI chip to carry out deep behavior analysis, fusing a behavior analysis result with a geographic position and a timestamp, generating a risk decision result, and triggering a dynamic response. According to the invention, a hierarchical wake-up and multi-source fusion technology is adopted, on-demand work is realized, power consumption is greatly reduced, early warning accuracy is improved through deep behavior analysis, and the problems of high energy consumption and inaccurate early warning of traditional equipment are effectively solved.

The invention discloses an abnormal behavior detection method and device, a nonvolatile storage medium and electronic equipment. The method comprises the following steps: acquiring user behavior data; behavior feature vectors are determined in the user behavior data, and the behavior feature vectors comprise at least one of a mouse behavior feature vector, a keyboard behavior feature vector, a process behavior feature vector and a window interaction feature vector; and analyzing the behavior feature vector by using a machine learning model to obtain an anomaly score output by the machine learning model, the anomaly score being used for representing the degree of deviation of the behavior feature vector from a preset normal behavior feature vector. According to the method and the device, the technical problem that the accuracy of distinguishing the normal user activity from the remote control Trojan abnormal operation is insufficient due to the fact that a related abnormal behavior detection method lacks a refined behavior modeling technology and a dynamic behavior analysis mechanism is solved.

The invention relates to the technical field of user behavior data processing, and discloses a user behavior analysis and personalized recommendation method and system, and the method comprises the steps: collecting multi-dimensional behavior data of a user, and carrying out the feature extraction of the multi-dimensional behavior data; key features are defined based on behavior data, redundant features are reduced through a feature selection algorithm, the dimension of numerical features is unified through normalization processing, and an input feature vector used for a deep learning model is generated; processing the input feature vector by adopting a hybrid deep learning architecture, and generating a user-commodity interaction model in combination with the user behavior sequence and the commodity features; performing real-time prediction on the new user behavior data based on a deep learning model, and dynamically adjusting a recommendation strategy according to a prediction result; and displaying a user behavior analysis result through a visual interface, and continuously optimizing a recommendation strategy in combination with an A / B test framework. According to the method, the defects of a traditional recommendation system in the aspects of multi-source data processing, recommendation individuation and the like are effectively overcome.

The invention discloses a deep network security data protection strategy optimization method fused with behavior analysis, which relates to the technical field of network security data protection, and comprises the following steps of: converting a resource identifier, an access timestamp and an access frequency of each access behavior into three-dimensional quantitative vectors by constructing an access behavior time mapping table; carrying out aggregation matching on the access behaviors with the same resource identifier and the access frequency lower than a set threshold value in different time periods, and determining multiple low-frequency interleaving access behavior characteristics occurring for the same resource object in a discontinuous time period; and constructing a cross-cycle risk assessment path with continuity and context relevance by using time sequence backtracking, a resource object merging strategy and a context semantic nesting mechanism according to the determined multi-time low-frequency interleaving access behavior characteristics. According to the method, the problem that low-frequency interlaced attack behaviors are difficult to identify is solved, and cross-cycle risk modeling and intelligent optimization of a protection strategy are realized.

The invention discloses a scenarized vehicle-mounted advertisement recommendation system for intelligent traffic, and relates to the technical field of intelligent traffic, and the system comprises a data collection module which collects a direction deviation angle, a speed matching degree and a path following rate in real time through a vehicle sensor and a navigation system, and obtains original behavior data; the feature extraction module is used for processing the original behavior data by adopting a time sequence analysis method, extracting dynamic features of steering frequency, pause time difference and signal acquisition frequency, and determining a behavior signal sequence; the correlation analysis module is used for calculating a correlation coefficient according to the matching degree of the behavior signal sequence and the navigation setting information on the aspects of time synchronism and environmental interference factors to obtain a correlation feature vector; according to the intelligent traffic-oriented scenarized vehicle-mounted advertisement recommendation system, through combination of multi-level behavior analysis and a recommendation algorithm, the pertinence of advertisement pushing and the user acceptability are remarkably improved, and an efficient advertisement putting effect is realized.

The invention relates to the technical field of video analysis, and discloses a linkage alarm method, device and equipment based on video analysis and a storage medium, and the method comprises the steps: carrying out the behavior semantic recognition of a video frame collected by a doorbell, obtaining a visitor behavior mode, coding the video frame and the visitor behavior mode, and obtaining a semantic vector; constructing a door front three-dimensional semantic map according to the video frame and tracking visitors to obtain visitor trajectory data; decoding the semantic vector and the visitor trajectory data to obtain a behavior analysis result; according to the behavior analysis result and the visitor track data, a linkage strategy is selected from a preset action space, an alarm instruction is generated, and the alarm instruction is executed through the doorbell, end-to-end real-time response is achieved, the strict requirement of an actual doorbell application scene for the response speed is met, and the user experience is improved. And the accuracy of doorbell collected video analysis and linkage alarm is improved.

The invention discloses an AI intelligent marketing person-reaching recommendation method based on brand requirements, and particularly relates to the technical field of artificial intelligence and brand marketing recommendation. Obtaining a tonality keyword of a target brand and constructing a brand tonality semantic feature vector; mapping the feature vector into a multi-dimensional emotion semantic space to generate a brand semantic positioning model; performing multi-modal feature extraction on the reporter content data and the fan behavior data, and constructing a reporter feature vector and a fan behavior matrix; screening a preliminary arrival person set based on the semantic similarity and a fan interaction resonance coefficient; fusing the human exposure conversion rate, the historical brand integrating degree and the fan resonance coefficient, constructing a weighted scoring function to output a recommendation score, and generating a putting scheme according to the recommendation score; through combination of semantic modeling and behavioral analysis, deep matching of brand tonality, a human-reaching style and fan response is realized, and the method is suitable for intelligent delivery requirements of various brands in a multi-platform environment and has a wide application prospect.

The invention discloses a health monitoring system of an old-age care robot, which relates to the technical field of health monitoring, and comprises the steps of constructing an indoor multi-modal data pool, generating an encrypted feature vector, establishing a steady-state physiological behavior baseline according to a historical encrypted feature vector, and outputting a health risk level and an intervention instruction set. According to the invention, all-time and all-scene health monitoring is realized by integrating a multi-mode sensor, old people do not need to wear equipment, feature compression and homomorphic encryption technologies are adopted, data security is ensured, a threshold value can be updated in real time, and the method is suitable for being applied to the health monitoring of the old people. The health state of the old people is accurately monitored, health risk prediction and personalized intervention are carried out in combination with analysis of behaviors such as falling and static abnormity, the prompt mode is dynamically adjusted according to the ability of the old people, the data processing efficiency is optimized through the edge cloud collaborative architecture, the data privacy and stability are guaranteed, and the life quality and safety are improved.

The invention relates to the technical field of video behavior analysis, in particular to a post-examination illegal behavior detection method based on cross-mirror tracking and identity authentication, and the method comprises the steps: collecting monitoring videos of all cameras in an examination scene; carrying out identity authentication by adopting Reid identity matching, and forming and maintaining a personnel identity mapping relation; determining a target bounding box, dynamically evaluating and enhancing the quality score of the target bounding box, constructing an optimized trajectory feature, and tracking a trajectory through hierarchical clustering to obtain a corresponding identity ID; a historical feature library is established and dynamically updated, identity conflict detection is executed, and an identity ID is bound or reset; inputting a GAN generator to synthesize an enhanced spatiotemporal feature sequence, inputting the enhanced spatiotemporal feature sequence into a Bi-LSTM discriminator in combination with an image block sequence, executing adversarial discrimination and action classification, and identifying illegal behaviors; when the illegal behavior is identified, real-name system alarm information including an identity ID and an illegal behavior type is generated; therefore, the automatic discovery and real-name traceability of illegal behaviors after examination can be improved.

The invention provides a computer network security intelligent monitoring method and system based on behavior analysis, relates to the field of network security monitoring, and solves the technical problems of low network security monitoring accuracy and difficulty in mining deep association between behavior events in the prior art. The method comprises the following steps: collecting behavior event data of a plurality of entities in a network and coding to obtain a spatio-temporal context coding vector; based on the spatio-temporal context coding vector, constructing a spatio-temporal causal graph by using a causal discovery algorithm; on the basis of a space-time causal graph, performing anomaly detection by using the graph neural network model subjected to antagonism training, and identifying an abnormal causal path in the graph; and carrying out risk propagation simulation on the abnormal causal path to restore a complete attack chain to obtain a network attack chain. The method and device are used in the computer network security intelligent monitoring process.

The invention discloses a petroleum drilling machine equipment intelligent alarm method based on video real-time display, and belongs to the technical field of petroleum exploitation equipment monitoring, and the method comprises the steps: obtaining the operation video of a key part of petroleum drilling machine equipment, and transmitting the operation video of the key part to a monitoring center; in the monitoring center, preprocessing the operation video of the key part to eliminate the influence of illumination disturbance, and carrying out key part identification and behavior analysis on the preprocessed operation video of the key part by using an equipment part detection and behavior analysis algorithm based on deep learning to obtain a detection result of the key part; and based on the detection result of the key part, the display alarm and the sound-light alarm of the abnormal type and the occurrence position are triggered in real time, and the alarm information is sent to the handheld terminal of the driller. According to the method, intelligent fault detection and alarm are realized, manual intervention is reduced, the comprehensiveness of equipment monitoring is improved, and the accuracy and timeliness of alarm are improved.

The invention discloses a teaching information management method based on big data, and particularly relates to the field of teaching, comprising data acquisition, behavior characteristic analysis, knowledge mastering evaluation, dynamic feedback adjustment, comprehensive evaluation and management strategy. The teaching behavior analysis depth is improved through multi-dimensional data fusion and non-linear feature analysis, real-time adjustment of an evaluation model along with knowledge mastery is achieved through a dynamic feedback mechanism, limitation of traditional static analysis is broken through, theoretical test and practice project data are integrated through an innovatively constructed three-dimensional evaluation system, and the teaching behavior evaluation efficiency is improved. A comprehensive evaluation model covering knowledge application and innovation ability development is formed, a dynamic path selection mechanism synchronously supports basic ability enhancement and high-order skill development requirements, a resource allocation strategy has instant intervention and long-term optimization characteristics, and adaptability and decision scientificity of a teaching management system are remarkably enhanced.

According to the intelligent recommendation method and device based on driving behavior analysis provided by the embodiment of the invention, accurate analysis of user interests is realized by innovatively constructing a multi-dimensional feature fusion mechanism and integrating registration information, video data, driving data and scene perception data. And designing a trajectory prediction model based on a recurrent neural network, and establishing a feature weighted fusion strategy for intelligent matching in combination with a user interest modeling network of an attention mechanism. And an online learning mechanism is introduced, and model parameters are continuously optimized through clicking, staying duration and visit record data, so that dynamic adjustment of personalized recommendation contents is realized. According to the method, the defects of the traditional technology in the aspects of feature extraction, interest modeling, recommendation strategies and the like are effectively overcome, and the recommendation service level and the user experience in the driving scene are remarkably improved.

The invention discloses a pet behavior data management system and method based on big data, and relates to the technical field of pet behavior intelligent monitoring. Performing time synchronization and time sequence reconstruction; carrying out preprocessing, and carrying out key frame extraction and target detection on the video frame to obtain a pet posture track; on the basis of a state switching point of a hidden Markov model, dividing the data subjected to time sequence reconstruction into a plurality of behavior events, and combining adjacent related behavior events to form behavior blocks; extracting statistical characteristics, behavior frequency and context characteristics of each behavior block, and training an individual behavior baseline model by adopting a multi-scale sliding window and a weight attenuation strategy based on historical behavior data; and comparing the real-time behavior block with the individual behavior baseline, and carrying out anomaly monitoring and warning through an integrated classifier. According to the invention, cross-device pet behavior management and abnormal early warning can be realized, and the reliability and the intelligent level of pet health monitoring and behavior analysis are remarkably improved.

The invention discloses a vehicle identification method and system based on dynamic behavior analysis and federated learning, the method is applied to local ends in one-to-one correspondence with pre-detection areas, and the method comprises the steps: detecting a vehicle entering the pre-detection area, and obtaining the first multi-mode perception data of the vehicle; inputting the first multi-modal perception data of the vehicle into a trained exclusive identification model to extract exclusive identification features of the vehicle for identification to obtain an identification result of the vehicle, the identification result being used for indicating a model type, a behavior type and a virtual identity label of the vehicle; wherein the exclusive recognition model and the universal recognition model are jointly trained under the constraint of a total loss function, basic parameters of the universal recognition model are standard universal parameters issued by the cloud end at the last time, and the standard universal parameters are obtained by the cloud end through aggregating fuzzy network parameters of all the universal recognition models based on federal learning. The method is better in recognition effect and has no risk of privacy leakage.

The invention provides a motion video key clip extraction method based on behavior analysis. The method comprises the following steps: firstly, performing decoding and frame standardization on a match video stream, detecting and positioning a coach target, and obtaining and expanding a bounding box; extracting a candidate skeleton key point set in the extended region, selecting the skeleton with the most key points as the skeleton of the coach, and performing cross-frame tracking; analyzing the skeleton in real time, triggering hand fine analysis and extracting hand key points when a scoring trend appears, comprehensively judging based on geometry, kinematics and time sequence rules, and generating a trigger signal when a specific scoring gesture is detected; and the background thread extracts fragments before and after the triggering moment from the video stream as key fragments, and stores the key fragments in association with metadata such as event types, coach identities and competition states.

The invention relates to the technical field of myopia prevention and control, and discloses a myopia prevention and control management system and method based on an AI technology and pattern classification. The system comprises a data acquisition module which is used for acquiring vision related data and daily eye using behavior data of a user; the data processing module carries out preprocessing and feature extraction on the vision related data and the daily eye using behavior data to obtain AI monitoring processing data; the AI analysis module obtains a risk level from AI monitoring processing data through a myopia risk assessment model, starts a myopia disease type AI detection model to obtain a disease type when the risk is medium and high, and obtains a myopia development trend result and an eye using behavior problem through a myopia development trend prediction model and personalized eye using behavior analysis; and the user service module generates and stores a user vision health file and generates a personalized intervention scheme. According to the invention, accurate evaluation of the myopia risk, scientific prediction of the development trend and personalized intervention are realized, and the pertinence and effectiveness of myopia prevention and control are improved.

The invention discloses a security and protection monitoring group target association extraction and behavior analysis method based on a neural network, particularly relates to the field of security and protection video intelligence, is used for solving the problem of group cross-view association and behavior judgment, and comprises the following steps: projecting a multi-path camera detection and tracking result to a ground coordinate, generating group candidates according to track convergence and separation; forming group description by the track topology and the picture entering sequence; setting passing anchor points at adjacent view angle boundaries according to the group description, and splicing to form a group track; constructing a relation graph by using the group tracks, calculating relation scores according to relative positions and speeds, and continuously compensating shielding positions by using tracks; and inputting the relation graph and the group track into a behavior model to generate labels and roles, generating a deviation correction signal when the labels and the roles are inconsistent, triggering relation edge reconfiguration and anchor point remapping, and realizing stable output of cross-view group association and behavior judgment.

The invention discloses a cross-scene user traffic monitoring behavior analysis method and system based on deep learning. The method comprises the following steps: collecting standard traffic data; establishing a multi-modal depth feature extraction network, extracting time sequence features of a statistical level from packet header information of standard traffic data by using a CNN convolutional neural network, obtaining semantic features from traffic payload by using a Bi-LSTM bidirectional long short-term memory network, and obtaining associated features of network resources based on a GNN graph neural network; fusing the time sequence features, the semantic features and the associated features by using a cross-modal attention mechanism to obtain fused features; and inputting the fusion feature into a behavior recognition model based on a multi-head self-attention mechanism, and outputting a user behavior tag which at least comprises application use, behavior intention and behavior abnormality score. The manual analysis cost is reduced, and the monitoring analysis efficiency and accuracy are improved.

The invention relates to the technical field of agent training, and discloses an agent training system based on a mirror image. The system comprises a training environment mirror image module, a behavior feature reconstruction module and a training decision model module. The training environment mirror image module constructs a virtual mirror image environment which is completely synchronous with a real physical environment, covers an environment state parameter set, an agent interaction record sequence and a performance index time sequence, and can reproduce a real environment complex dynamic scene. And the behavior feature reconstruction module performs three-dimensional behavior trajectory analysis on the agent interaction record sequence, generates a behavior feature tensor containing a decision response delay gradient, an action space coverage degree and a cooperation intention fluctuation coefficient, and realizes multi-dimensional behavior analysis. And the training decision model module calls a pre-training agent evolution model, performs strategy space mapping on the behavior feature tensor, generates an optimization strategy parameter set and a behavior defect area identifier, and assists the agent to efficiently play a role in a real scene.

The invention discloses a human resource production and teaching fusion supply and demand matching system based on a dynamic ability portrait, and relates to the field of human resource management. Comprising a capability analysis module, a structure modeling module, an occupational mapping module, a selection monitoring module, a path generation module, a behavior analysis module, a task matching module, a supply and demand evaluation module, a guide iteration module and a recruitment screening module. According to the method, capability portraits do not depend on subjective resumes any more, the accuracy and credibility are remarkably improved, the problem that a static label model cannot reflect a real growth track is solved, the competency and potential rising space of talents for posts can be more accurately judged, the problem that traditional HR cannot process cross-domain task matching is solved, and the method is suitable for popularization and application. And real-time closed-loop alignment of supply and demand of production and education is realized.

The invention relates to the technical field of livestock and poultry intelligent breeding, and discloses a laying hen body type and weight evaluation method and system based on image recognition, and the method comprises the steps: obtaining laying hen behavior video data and environment parameter data; processing the video data, identifying the behavior mode of the laying hens, analyzing the relevance between the environmental parameter data and behaviors, and establishing state representation; constructing a strategy network, realizing stress state detection and optimal evaluation opportunity automatic selection, and extracting body shape features in a stable attitude; constructing a body weight evaluation model by using the body shape characteristics at the selected time and combining the identified behavior state information; learning feature distribution of a normal evaluation process of the body weight evaluation model, and detecting an abnormal evaluation result through double indexes of reconstruction error and probability deviation; through fusion of behavior analysis, environment perception and quality control, high-precision and high-reliability evaluation of the body type and weight of the laying hen is realized.