1146 results about "Stream data" patented technology

The present invention relates to an integrated dispatch decision-making system and method for a hub supporting multi-modal transportation coordination, and a device. The system comprises: a multi-modal hub passenger flow data fusion and analysis module, which is used for arranging and classifying collected multi-modal hub passenger flow data, performing preprocessing, performing conversion and mapping to obtain normalized passenger flow data, and finally extracting a data fusion feature, and using a normalized transport passenger flow model to perform multi-dimensional data fusion and analysis computation; a hub transportation coordination decision-making module, which is used for performing transport capacity and volume assessment on the basis of the fusion and analysis of the multi-modal passenger flow data, and using an optimal decision-making model to compute an optimal strategy result; and a hub coordination dispatch and command application module, which is used for providing an intelligent command assistance function to a rail transit dispatch and command personnel on the basis of information access of hub-line-network, and the normalized transport passenger flow model. Compared with the prior art, the present invention has the advantages of improving the connectivity and coordinated dispatch of multi-modal transportation in a complex hub scenario, etc.

The invention relates to the technical field of data security management, and discloses a blockchain-based forestry data security management system and method, and the system comprises a data collection unit which is used for comprehensively obtaining a multi-dimensional information flow of forest ecology and resource states; the data processing unit is used for carrying out deep cleaning, standardized regulation and feature value extraction on the originally collected mass heterogeneous data; the block chain storage unit is used for constructing a permanent, tamper-proof and distributed secure storage infrastructure platform of the forestry core data; a verification unit; the safety control unit is used for constructing a covering data transmission, storage and access full-chain depth defense system structure model; a user interface unit; an auditing unit; and a network communication unit. The method is reasonable in design, and the intelligent analysis module is used for processing to form a forest interannual growth trend prediction function distribution map state visual output result set.

The invention discloses a cable sheath ring current fault positioning in-loop simulation method and system based on LABVIEW. The method comprises the following steps: collecting ring current data of cable sheath monitoring points in real time under the control of LABVIEW, and carrying out digital display and graphical display; decomposing circulation data to obtain a wavelet coefficient and calculating features, and inputting a classification model to obtain a fault type when the features are abnormal; calculating a compensation coefficient considering the influence of the electromagnetic field, and calculating the position of a fault point in combination with a double-end traveling wave method; the operation model switches a typical working condition simulation mode, prediction of a Bayesian reasoning correction model is carried out, and the full-life-cycle operation state of the cable is simulated; and performing fault early warning through the LSTM model and risk assessment. The method can effectively improve the cable operation and maintenance efficiency and reliability, and is widely applied to cable fault detection and guarantee of safe and stable operation of a power system.

The invention relates to the technical field of computer video processing, and discloses a low-delay video stream real-time processing method and device, and the method comprises the steps: obtaining original video stream data, and processing the original video stream data through employing a lightweight motion prediction method; processing the macro block data set and the predicted coding configuration parameter by adopting multi-thread assembly line coding to obtain a coded data block; establishing a data transmission mechanism to perform data flow control on the unified memory access interface; a heterogeneous task scheduling strategy is adopted to distribute task division results; a lightweight neural network is adopted to carry out parameter adaptive adjustment, and an optimized video stream processing result is obtained; according to the method, a zero-copy data transmission technology is adopted, and optimal configuration and efficient utilization of computing resources are achieved.

The invention discloses a digital human interaction method and device based on multi-modal sentiment analysis and a medium, and relates to the field of artificial intelligence, and the method comprises the steps: collecting multi-modal data of a user in real time through a multi-source sensor device; the multi-modal data comprises face video stream data, voice audio stream data and text dialogue data; calling data analysis engines corresponding to different modalities, and extracting corresponding modal feature sequences; according to the current interaction scene, the modal feature sequence and the historical dialogue context features are fused, and a comprehensive emotion evaluation result is generated; outputting a corresponding multi-modal response data packet based on the modal feature sequence through an interactive response engine corresponding to a comprehensive emotion evaluation result; and executing the multi-modal response data packet. And after feature fusion is carried out in combination with the current interaction scene, the generated response can more accurately fit the current emotion demand and communication context of the user, so that the digital human can be more easily fused into various scenes needing emotion interaction.

The invention provides a photovoltaic power station energy storage management system and method, and relates to the technical field of power system energy storage. The photovoltaic power station energy storage management system comprises the following modules: a data acquisition module, a prediction analysis module, an optimization scheduling module, a health management module, a real-time control module, a power grid interaction module and an energy efficiency evaluation module. And the data acquisition module is used for cleaning photovoltaic array output voltage / current data by adopting an improved Kalman filtering algorithm based on an Internet of Things sensing network, integrating environmental parameters such as irradiance and temperature of a meteorological station through a multi-source data fusion technology, and generating a standardized operation data set. According to the method, photovoltaic array output data is cleaned and integrated through an improved Kalman filtering algorithm and a multi-source data fusion technology, a high-precision standardized data set is constructed, a dual prediction architecture of an attention mechanism long-short term memory network and a time convolution network is combined, and the space-time correlation between photovoltaic output and load demand prediction is remarkably improved.

The invention discloses a scenic spot tourism planning and reception integrated management system, and the system comprises a perception access layer which is responsible for collecting tourist distribution, environment data and facility states in real time, and converting fragmented physical information into a standardized data flow; the data intelligent layer is responsible for processing a data stream input by the perception access layer, integrating dispersed fragmented data by using a space-time knowledge graph, constructing a panoramic three-dimensional thermodynamic diagram, performing dominant implicit information, and constructing a tourist digital portrait based on a multi-dimensional data fusion and machine learning algorithm; the service center layer is responsible for constructing an intelligent scheduling platform on the basis of the panoramic three-dimensional thermodynamic diagram to monitor thermodynamic abnormity in real time and trigger graded early warning, and performing scene customization by applying a personalized recommendation engine in combination with tourist digital portraits and real-time thermodynamic distribution; and the interaction touch layer is responsible for receiving and converting the instruction of the service center layer into ground projection navigation and air suspension prompt visual forms and transmitting the ground projection navigation and the air suspension prompt visual forms to tourists.

The invention discloses a traffic network toughness diagnosis method under flood disasters based on a space-time diagram neural network, and relates to the crossing field of traffic engineering and artificial intelligence. The method comprises the following steps: collecting traffic topology, flood monitoring and traffic flow data, and carrying out space-time alignment; a flood coupling dynamic space-time diagram is constructed, a water depth-traffic capacity response mechanism is introduced, and real-time mapping from a disaster physical state to a network topology is realized by utilizing an attenuation function meeting physical monotonicity constraint and dynamically updating an edge weight of a diagram structure according to real-time water depth; inputting the dynamic graph into a pre-trained space-time graph neural network model, extracting space-time evolution characteristics and outputting a toughness diagnosis result; model training adopts a toughness label generated based on an anti-fact baseline to carry out supervised learning, and introduces a physical constraint loss function. According to the method, the problems of decoupling of disaster features and graph structures and unavailability of toughness labels in the prior art are solved, and the physical consistency and accuracy of diagnosis are improved.

The invention discloses a cross-domain joint authentication and multi-party signing system and method, and belongs to the technical field of cross-domain authentication. The system is characterized in that a cross-domain authentication request module receives initiating domain request information, determines a target domain, obtains a physical identity registration result, derives a temporary identity token, constructs, signs and packages a cross-domain authentication request packet, and sends the cross-domain authentication request packet; the execution stream verification module obtains target domain execution stream data and generates a verifiable execution stream; the consistency verification module matches the evidence of the initiation domain and the target domain based on a strategy rule to obtain a target evidence set passing verification; and the cross-domain verification module performs hierarchical hash on the target evidence set to generate a synthesized hash value, generates a signature result in combination with the request packet and verifies the signature result to obtain a cross-domain signature verification result. According to the scheme, by generating the verifiable execution stream and the verification evidence data, credible tracking and integrity guarantee of cross-domain operation are achieved, and it is ensured that cross-domain authentication is safe, reliable and auditable in combination with hierarchical hash and signature verification.

The invention relates to the technical field of multi-sensor detection, and discloses a multi-sensor fusion coal conveying belt coal blockage detection method and device and a storage medium, and the method comprises the steps: collecting a laser point cloud, a first visual image and millimeter wave volume information of a coal flow of a coal conveying belt transfer point, and carrying out the dust environment restoration of the first visual image, obtaining a second visual image; registering the laser point cloud with the second visual image to obtain first fusion data; performing surface projection transformation on the millimeter wave volume information to obtain surface projection data, and integrating the surface projection data with the first fusion data to obtain second fusion data; performing time compensation on the second fusion data to obtain synchronous coal flow data; the coal flow accumulation abnormity identification is performed based on the synchronous coal flow data, the coal flow blockage early warning signal is generated, the accurate identification of the coal flow accumulation abnormity state is realized, and the reliability and the accuracy of blockage detection are improved.

The invention relates to the technical field of Internet red scenic spot tourist crowd gathering risk monitoring, and discloses an Internet red scenic spot tourist crowd gathering risk intelligent monitoring and early warning system which comprises a central processing unit, a crowd density analysis module and the like. The crowd density analysis module generates basic density distribution based on region division and historical passenger flow data; the environment state acquisition module acquires environment and facility data in real time; the tourist behavior analysis module generates a behavior feature vector through the track and the stay duration; the central processing unit generates a risk early warning instruction through a spatial superposition algorithm; the risk early warning execution module adjusts monitoring parameters according to instructions and feeds back states, and triggers a dynamic adjustment algorithm when abnormity occurs. The system also improves the accuracy and the resource utilization rate through an optimization and adaptation module, achieves the intelligent monitoring and precise early warning of the crowd gathering risk, and guarantees the tourism safety.

The invention discloses an AI-based illumination control system self-adaptive dimming method, which comprises the following steps of: acquiring a two-dimensional coordinate, a motion track curve and voice communication data of a personnel position, and identifying social interaction scene categories of different time slices; determining a social interaction strength score according to the voice communication data, and predicting the most adaptive spectrum configuration data in the current social interaction scene in combination with the social influence factor matrix and the social interaction scene type; according to a historical dimming log and an energy consumption metering curve, identifying a short-term high-frequency dimming segment, and adjusting a real-time response strategy and a disturbance classification processing mechanism of the spectrum configuration data; and adjusting the illumination power distribution values of the high-frequency use area and the low-frequency use area according to the occupation raster data generated by the millimeter-wave radar and the infrared array in combination with the lamp area mapping relation and the power budget constraint. Through multi-source data fusion and a self-adaptive regulation and control mechanism, unification of real-time performance, pertinence and energy-saving performance of spectrum regulation is realized, and the dynamic response capability and the user perception experience of the intelligent illumination system are improved.

A computer system for compacting video data. The system acquires a video stream, reduces redundancy through pre-processing, and analyzes the stream to identify patterns and irregularities. It detects spatial or temporal anomalies in the video and produces three outputs: a conditioned video stream based on statistical analysis, an error stream reflecting adjustments made during conditioning, and an anomaly meta-stream containing metadata about detected anomalies. The system communicates with one or more remote systems to synchronize and negotiate a compatible compression codebook, optionally exchanging compact updates that represent differences between local and remote codebooks. The conditioned video stream is then compressed using the agreed codebook. The system outputs a compacted representation of the video that includes the compressed stream, the error stream, and the anomaly metadata, supporting efficient storage or transmission while maintaining the ability to detect, trace, and reconstruct key information within the video.

The invention discloses a congestion management system and method based on vehicle-road cooperation and dynamic group path optimization, and relates to the technical field of intelligent traffic systems. The method comprises the following steps: S1, collecting original traffic data through a multi-modal sensor array to carry out space-time alignment, and constructing a microscopic traffic flow data set; s2, bidirectional information interaction is carried out through a vehicle-road cooperative communication network; s3, calculating a multi-modal fusion confidence coefficient parameter based on the data quality index, and generating traffic state information and road section real-time saturation; s4, fusing historical and static road network data, constructing a dynamic digital twinborn model and a road network state feature map, and generating a road network load balancing index; and S5, making a decision by adopting a hierarchical-distributed architecture, and generating a group path induction strategy through multi-objective optimization. The efficient collaborative decision is realized through the graph convolutional network and the attention mechanism, and the road network traffic efficiency is comprehensively improved through stable and reliable multi-objective optimization on the premise of guaranteeing the user fairness.

The invention relates to the technical field of monitoring behavior analysis, and discloses an intelligent behavior analysis system and method for a monitoring camera. The system obtains video stream data through a video data acquisition module, extracts a video frame sequence and a timestamp, identifies a moving target and a position coordinate, associates behavior segments and calculates an active value, and generates a target behavior active set; a behavior sequence modeling module extracts active values and coordinates, sorts adjacent behavior segments and marks consistent and conflict sections to obtain a behavior consistency partition marking set; the environment mapping module obtains consistent section target behaviors, extracts illumination and shielding time sequences, evaluates the influence intensity of environment interference on the behaviors in combination with behavior types, and generates an overlay analysis result; the exception screening module recognizes exceptional points with response values larger than the average reference value and located in the conflict section, and a behavior exceptional point set is formed; and the risk output module obtains abnormal point information, marks diffusion risk point locations, and generates behavior detection and risk early warning results.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a service-oriented robot-oriented multi-modal emotion recognition method and system, and the method comprises the steps: collecting audio and video stream data of emotion changes of a user, and separating visual and voice data; extracting visual and voice emotion features through a pre-training model, and calculating prediction probability distribution of each mode; constructing a bimodal confidence quantitative model based on the distribution to obtain each modal confidence; and fusing the features by adopting a sectional type dynamic weight distribution strategy so as to identify the emotional state of the user. Visual and voice modes are fused, feature alignment is realized in combination with dynamic time warping, spatial optimization performance is shared and expressed through a confidence model, a dynamic weight strategy and a cross-modal time sequence cooperation module, and the method has high recognition accuracy, high robustness and real-time processing capacity in a complex environment and is suitable for various service scenes.

The invention belongs to the technical field of deep learning, and discloses a packaging production line early warning method and system under dynamic visual monitoring, and the method comprises the steps: carrying out the continuous image collection of a packaging production line, and obtaining an image sequence of the packaging production line; semantic feature information representing the packaging process state is extracted from the image sequence, a multi-dimensional tensor flow data structure is constructed based on the semantic feature information, and a packaging process semantic map is generated; introducing a semantic behavior curvature theory, performing curvature analysis on the packaging process semantic map, calculating semantic evolution curvature change rates of different time sequence segments, and utilizing a sliding window dynamic curvature detection mechanism to identify an existing abnormal bending region and generate semantic bending abnormal information; according to the method, the defects of a traditional video monitoring method in the aspects of process complexity, abnormal fine granularity recognition and response timeliness are overcome, and a more intelligent, accurate and adaptive early warning decision closed loop is achieved.

The invention belongs to the technical field of power system measurement, and relates to a three-phase power parameter wireless synchronous measurement and vector analysis method and system, and the method comprises the following steps: generating and broadcasting a synchronous pulse code containing triggering phase information; generating a predicted phase angle; obtaining a comprehensive phase deviation value; performing dynamic feature separation processing on the comprehensive phase deviation value to generate a slowly-varying propagation delay feature and a transient prediction error feature; injecting the prediction error characteristics into a local phase predictor for calibration; acquiring phase voltage / current data based on the compensated sampling time, and calculating a fundamental wave effective value and a phase angle relative to a local calibration time reference; and packaging the fundamental wave effective value, the phase angle and the local timestamp into a simplified data packet and transmitting the simplified data packet to an analysis terminal. According to the method, the problems that in a traditional mode, satellite time service rejects signals in a substation indoor or underground scene, and synchronization interruption is easily caused by electromagnetic interference are solved.

The invention relates to the field of ocean current remote sensing, and relates to a vector flow inversion method combining a single-station ground wave radar and a satellite-borne SAR, and the method mainly comprises the steps: calculating the frequency deviation of a first-order Bragg peak of the ground wave radar, carrying out the inversion of the sea surface radial flow of the ground wave radar, and carrying out the inversion of the sea surface radial flow of the SAR through employing a Doppler centroid anomaly method; interpolating a ground wave radar radial flow result to an SAR data grid through bilinear interpolation; inverting a single-station vector flow based on a ground wave radar radial flow result, projecting single-station vector flow data with high confidence coefficient to an SAR range direction, calculating a difference mean value of a projection flow velocity and an SAR radial flow velocity, and further calibrating the SAR radial flow; and combining a ground wave radar radial flow result with the calibrated SAR radial flow to invert a vector flow. According to the method, the defect of ocean current observation through a single means is overcome, high-precision inversion of the combined vector flow is achieved, and the method has important significance on ocean environment monitoring, disaster forecasting, shipping safety and the like.

The invention relates to the technical field of subway passenger flow prediction, and discloses a space-time perception subway passenger flow prediction method based on large model and multi-source information fusion, which comprises the following steps: constructing a data agent based on a large language model and a model context protocol; generating text description of each subway station and a corresponding spatial context embedding vector based on the peripheral interest point data of each subway station by utilizing a data agent; constructing an enhanced graph neural network according to the spatial context embedded vector, and performing spatio-temporal joint prediction based on the historical passenger flow data and the spatial context embedded vector by using the enhanced graph neural network to obtain a preliminary passenger flow prediction result; and constructing a cue word template, and performing domain knowledge calibration based on the cue word template by using the pre-trained large language model to obtain a final passenger flow prediction result. According to the method, external multi-source information is deeply fused, so that the urban rail transit passenger flow prediction task performance, robustness and interpretability are remarkably improved.

The application belongs to the technical field of multimedia information processing, and discloses a kind of monitoring video key frame extraction method, system, medium, equipment and terminal, collect original video stream data, original video stream data is decomposed into image frame set;Image frame set obtained by decomposition is sampled, and the image frame result set obtained by sampling is filtered;The image frame set after filtering is adaptively clustered, and the result after clustering is collected to form a video abstract.In order to better utilize the memory space of storage medium and let the user better quickly browse the general content of original video stream, the application provides a key frame screening algorithm for original video stream by sampling, filtering and clustering.The key frame extraction method of the application screens out similar frames, redundant frames and fuzzy frames in original video stream through key frame screening algorithm for original video data, forms a video abstract storage of a section of original video, thereby greatly reduces the occupied storage space.

The invention discloses a horizontal warehouse operation control terminal, and relates to the field of horizontal warehouse operation control, the horizontal warehouse operation control terminal comprises a networking communication module, an emergency decision module, a grain condition regulation and control module, an intelligent scheduling module and a collaborative protection module, dynamic networking communication analysis is carried out by collecting distribution data of various protocol communication devices in a granary, and total communication energy consumption is obtained; equipment fault occurrence time data is collected for fault emergency decision analysis, cooperative response time delay is obtained, grain condition self-adaptive regulation and control analysis is carried out by obtaining grain temperature impurity airflow data and communication total energy consumption of a grain pile, and a minimum energy consumption curve is obtained. Operation chain intelligent scheduling is performed by acquiring position state task attributes and a minimum energy consumption curve of equipment nodes, an equipment start-stop sequence and a disturbance response index are obtained, safety regulation and control are performed based on collaborative response time delay and the equipment start-stop sequence, an equipment control instruction frame is obtained, and closed-loop risk suppression of ventilation enhancement, load reduction operation and sudden stop grading is realized.

The invention relates to the technical field of power internet of things, in particular to a broadband / narrowband self-adaptive switching power internet of things communication system and a control method, which intelligently identify the priority and quality of service (QoS) requirements of different services by collecting network states, terminal states and service flow data in real time. And an optimal communication mode switching strategy is dynamically generated based on a pre-trained machine learning model, seamless switching of broadband and narrowband communication links is realized, and the decision accuracy is continuously improved through a feedback optimization mechanism. According to the method, the problems that a traditional electric power Internet of Things communication mode is fixed and single and cannot intelligently adapt to services and network states are effectively solved, the system communication reliability is remarkably improved, the transmission time delay and energy consumption are reduced, and high-adaptability and high-reliability intelligent communication guarantee in a complex electric power scene is achieved.

The invention relates to the technical field of energy systems, and discloses an energy system intelligent decision-making method, which comprises the following steps: acquiring four-flow data of an industrial energy system, and constructing a four-flow parameter model based on a production flow topological graph; constructing a multi-agent reinforcement learning environment, taking the four-flow parameter model as a state space of the multi-agent reinforcement learning environment, and determining an action space and a reward function of each agent; performing domain adjustment on the large language model based on the industrial energy system knowledge base; through combination of a multi-agent reinforcement learning model and a large language model, a decision strategy is dynamically adjusted according to real-time data feedback, and decision feasibility verification is performed based on a physical mechanism model. According to the method, the four-flow integrated parameter model is constructed, and a multi-agent reinforcement learning and large language model cooperation mechanism is introduced, so that the problems of data splitting and the like in a traditional energy management system are effectively solved, and the intelligent level of energy system decision making is improved.

The invention relates to the technical field of bamboo forest ecological environment monitoring, in particular to a bamboo forest ecological environment collaborative observation method based on multi-source Internet of Things sensing, and the method comprises the steps: synchronously collecting a multi-source collaborative observation parameter set of a bamboo forest slope through an Internet of Things sensor network, and then constructing a point-surface scale fusion bamboo forest moisture dynamic model; correcting evapotranspiration point cluster data by using a random forest gradient lifting algorithm to generate terrain weighted evapotranspiration, aggregating runoff data according to terrain weight to obtain a total amount of runoff sub-regions, then executing dynamic water balance constraint calculation, optimizing infiltration capacity in combination with a variation assimilation algorithm, redistributing infiltration capacity space proportion according to soil hydraulic conductivity when a residual error exceeds a threshold value, and calculating the total amount of the runoff sub-regions. Finally, according to the residual space distribution thermodynamic diagram, the evapotranspiration point cluster is migrated to the micro-topography representative area, and observation network adaptive optimization is achieved, the problems that in the prior art, the data scale is disjointed, the observation precision is insufficient, and the network is difficult to adapt to change are solved, and the bamboo forest ecological observation precision and reliability are improved.

The invention discloses a cable three-dimensional modeling method and device based on a video stream and a key frame, and belongs to the technical field of three-dimensional modeling of power system cable equipment, and the method comprises the following steps: constructing a dynamic truncation symbol field based on camera video stream data, and extracting a cable surface grid; triggering key frame capture, and establishing a key frame database; performing multi-modal fusion on the video stream features and the key frame features to generate a cable deformable template; obtaining a first-order deformable template model based on the cable deformable template; constructing a staged multi-scale adversarial discriminator, and deploying convolution kernel optimization at different resolution levels to obtain a second-order deformable template model; performing covariance optimal registration on the scene point cloud reconstructed by the video stream and the second-order template point cloud to obtain a third-order optimization template model; and constructing a density sensing gradient field, and dynamically fusing the video stream and the key frame data to generate a cable three-dimensional scene model. According to the invention, the precision and real-time performance of cable network modeling in a complex environment are improved.

The invention relates to the technical field of fire early warning, in particular to an early fire early warning system based on AI image recognition, and the system comprises an image collection module which is used for obtaining the video stream data of a monitoring area in real time; an AI image analysis module which is in communication connection with the image acquisition module and is used for receiving the video stream data and carrying out real-time analysis on video frames based on a pre-trained fire identification model so as to extract visual features related to the fire; and the early warning judgment module is in communication connection with the AI image analysis module and is used for receiving an analysis result of the visual features. According to the early fire early warning system and method based on AI image recognition, through video image analysis, the system can recognize weak flame or smoke characteristics at the initial stage of a fire and when naked eyes do not obviously see the characteristics, and the delay problem that a traditional smoke-sensing and temperature-sensing detector needs to wait for physical parameters to be diffused to the detector to be triggered is solved.

The invention discloses a water pollution event index screening method and device, electronic equipment and a storage medium. The method comprises the steps that a historical monitoring data set and real-time monitoring flow data of a water body are collected, and the data are preprocessed; calculating an initial entropy weight of each monitoring index, and screening out a high-sensitivity candidate index set according to a preset threshold value; for pollutants in the candidate index set, fusing acute toxicity and bio-enrichment risk of the pollutants, constructing a toxicity coefficient, performing normalization processing, and dividing toxicity grades based on the toxicity coefficient; measuring the correlation strength of the initial entropy weight sorting and the normalized toxicity weight sorting, and calculating the self-adaptive comprehensive weight of each candidate index in combination with a reference toxicity contribution threshold value based on a risk threshold value and a water body function correction factor calculated based on a water body national standard limit value; and verifying the decision coefficient of the comprehensive weight and the actual risk entropy so as to screen out a final early warning perception index. The method has no empirical parameters, results can be reproduced, and the method is suitable for various water bodies.

The invention relates to the technical field of data processing, in particular to a relational database intelligent query method based on machine learning, which comprises the following steps of: processing multi-modal flow data through time sequence alignment, generating a unified semantic representation vector, constructing a dynamic psychological state map, and modeling a psychological state evolution track by utilizing a neural common differential equation mechanism. After user query is received, historical dialogue nodes are retrieved from the graph, enhanced query intention representation is generated, the enhanced query intention representation is converted into an execution plan through a neural symbol inference engine, and a graph neural network is adopted to predict execution cost. And finally, a personalized analysis report is generated by combining a causal discovery algorithm, and system adaptive optimization is realized through feedback signals. According to the method, the problems of inconsistent time sequence semantics and strong context dependency of the multi-modal psychological data are effectively solved, and the query accuracy and the personalized level in a psychological dialogue scene are improved.

The invention relates to a human traffic monitoring method and system for a dense sitting posture scene, and the method comprises the steps: S1, obtaining the video stream data of the dense sitting posture scene, constructing an original data set, and optimizing the size distribution of an anchor frame; s2, constructing a human traffic monitoring model which is improved and realized on the basis of a YOLOv7 model: in a backbone network, replacing the four starting CBS modules with ODSConv modules, and replacing the two subsequent ELAN modules with E-ELAN-Sim modules; in the head network, a coordinate attention mechanism is embedded in an SPPCSPC module to obtain a CA-SPPCSPC module, the CA module is added in front of a CBS module connected with an ELAN-H module and a Concat module, and meanwhile, a four-scale detection framework is realized in the head network to enhance the small target detection capability; training the constructed people flow supervision model through the training data set; and S3, inputting real-time video stream data into the trained people flow supervision model, and outputting a people flow supervision quantity. According to the method and the system, accurate detection and real-time statistics of the sitting posture target person in the dense sitting posture scene can be realized.