58 results about "Crowd density" patented technology

The invention discloses a video crowd counting method based on a cascaded cross-domain feature interaction network. The method comprises the following steps: carrying out data enhancement processing of random cutting and horizontal flipping on a current frame and front and back frames of the current frame; and constructing a cross-domain feature interaction network composed of a spatial domain branch and a frequency domain branch. The frequency domain branch extracts frequency domain feature output of different stages through a high and low frequency signal aggregation module and a feature encoder based on adjacent frames; the spatial domain branch is based on a single-frame image, and static spatial semantic features are extracted through a feature encoder. Cascade fusion is carried out on the double-branch features on multiple scales, two-way channel cross attention is utilized to reconstruct time sequence correlation frequency domain features of a current frame, and fusion and reconstruction of the two domain features are achieved through a cross-domain feature mutual modulation module. And after the reconstructed double-branch features are processed by the fusion network, outputting a crowd density map of the current frame by a density regression head. And after training is completed, storing the optimal model for video crowd counting. According to the invention, through cross-domain feature cascade and bidirectional time sequence modeling, the accuracy and robustness of crowd counting in a video scene are effectively improved.

The invention discloses a robot hierarchical path planning method and device in a complex environment, a medium and a product, and the method comprises the steps: responding to a path planning request of a robot in an airport terminal, dividing the airport terminal into a plurality of dynamic Voronoi regions according to an underlying environment grid map and people flow density information, a minimum path cost algorithm with people flow density weighting is adopted to construct a global path anchor point sequence, the global path anchor point sequence is further split into a plurality of local path planning requests, a double-heuristic cost function is constructed, an improved algorithm is adopted to generate local sub-paths, and according to surrounding environment information collected by a robot in real time, the local sub-paths are calculated. And optimizing each local sub-path by adopting a reinforcement learning algorithm, generating motion control instructions for the robot at different control time points, and realizing dynamic obstacle avoidance of the robot. The path planning efficiency and the motion safety of the mobile robot in a super-large-scale dynamic environment are remarkably improved, and the technical problems that a traditional method is high in calculation complexity and poor in real-time performance are effectively solved.

The invention discloses a crowd density detection method fusing optical flow and texture features, and relates to the technical field of computer vision, and the method comprises the following steps: collecting a real-time video stream of a camera, and carrying out graying, Gaussian filtering and perspective correction preprocessing; performing motion compensation by using image registration and offset transformation; modeling based on a Gaussian mixture model and extracting a foreground to generate a binary mask; analyzing a foreground coverage rate, an optical flow and texture features; pre-defining a multi-ROI and a density early warning standard; inputting the fusion features into a regression model and outputting initial density; dynamically calibrating and correcting the deviation; and generating a thermodynamic diagram superposition video to realize visualization. According to the invention, the optical flow and texture features are fused to improve density estimation precision, illumination resistance and dynamic background interference resistance; the dynamic calibration maintains long-term accuracy, and the dynamic ROI adapts to scene change; the thermodynamic diagram can quickly identify risks, is adaptive to multiple scenes, and meets real-time monitoring requirements.

The invention provides a commercial complex crowd management system based on a real-time LoT sensor, and relates to the technical field of intelligent management. The management main system comprises a data acquisition module, a data processing module, a dynamic environment optimization module, a people flow guidance and excitation module, a resource scheduling module, a safety monitoring and response module, an external cooperation module, a space value optimization module, a user feedback and experience module and a control flow management module; the real-time LoT sensor is deployed to comprehensively monitor the people flow density, the environmental parameters and the equipment state in the commercial complex, the problems of crowded areas and uneven resource allocation in the peak period can be found in time, and user experience decline and potential safety hazards caused by delayed response are effectively avoided. The system achieves minute-level density monitoring in peak hours, and the real-time performance and response efficiency of crowd management are remarkably improved.

The invention relates to the technical field of computer vision, and discloses a competition people stream monitoring and early warning method based on multi-dimensional feature fusion video analysis, which comprises the following steps: acquiring an original video stream, receiving the acquired multi-path high-definition original video stream, and carrying out parallel processing on the original video stream through a first path and a second path respectively; based on the obtained crowd kinetic energy field, crowd entropy field and high-precision density map, constructing a quantitative risk model fusing multi-dimensional features to realize risk decision; and explainable crowd fluid state semantics and risk levels are output to a user through a four-level early warning decision-making mechanism based on a comprehensive risk index given by the quantitative risk model fusing the multi-dimensional features. According to the method, core vision algorithms such as dense optical flow calculation, crowd density estimation of a deep convolutional neural network and crowd behavior modeling based on statistical fluid mechanics are comprehensively utilized, and real-time and quantitative perception and dynamic risk early warning of the microscopic state of dense crowds in scenes such as large stadiums and competition sites are achieved.

The invention relates to the technical field of security and protection monitoring, in particular to a large-scale exhibition crowd density monitoring and abnormal behavior recognition method, which comprises the following steps: acquiring real-time video data acquired by a plurality of monitoring devices in an exhibition venue, and performing crowd density estimation on the real-time video data to generate a real-time crowd density distribution map; acquiring historical crowd flow data, acquiring a crowd dynamic evolution trend in a future preset time period according to the real-time crowd density distribution diagram and the historical crowd flow data, and identifying and acquiring a potential bottleneck region based on the crowd dynamic evolution trend; according to the potential bottleneck area and the real-time crowd density distribution diagram, a space attention thermodynamic diagram is generated, real-time mastering of the crowd state in the exhibition venue, risk pre-judgment and accurate abnormity disposal can be achieved, management and control measures in a large exhibition high-density crowd scene are more timely, and the response efficiency is higher.

The application discloses a kind of multi-modal crowd density prediction methods based on time convolution network, comprising the following steps: video information module obtains image from monitoring camera, the image obtained is carried out crowd counting by crowd counting model, obtains the crowd density of each camera every time, and is organized into time series data;The time series data of crowd density is respectively extracted into the current time hidden vector for each sub-region by time convolution network;Itinerary planning module extracts corresponding itinerary information from itinerary planning table for multi-modal prediction;The features of fusion module and video information module are fused, and the crowd density prediction value of each sub-region in the future is obtained.This application can improve the prediction accuracy of the model by fusing multi-modal information, and can give a smooth prediction result when facing low SNR data, and can also respond in time when the crowd density changes suddenly.

The application provides a building space energy consumption control method and system based on computer vision, comprising the following steps: S1: partitioning a public area of a building space and constructing a correspondence relationship between the partition and a camera and an energy consumption device; S2: mapping the partition to a digital plane grid and constructing a mapping relationship between the grid and a camera field of view; S3: collecting and processing video stream data of each camera in real time, inputting the video stream data into a lightweight crowd density estimation model, and calculating a crowd density value in the field of view of each camera; S4: based on the real-time value of the crowd density value in the field of view of each camera and the mapping relationship between the grid and the camera field of view, a weighted fusion algorithm is used to calculate a crowd density grade of each partition; S5: according to the real-time crowd density grade of the partition, combining preset upper and lower threshold values and a control strategy, corresponding device control instructions are dynamically generated; and S6: the control instructions are sent to terminal devices such as lighting and air conditioning for execution, so that dynamic and fine energy consumption control of the partition is achieved.

The application provides a crowd density estimation method and system based on feature-aware weighted contrast learning, relates to the technical field of crowd density estimation methods, and comprises the following steps: inputting a basic feature map into a constructed multi-level parallel hole convolution layer respectively, normalizing an importance score to obtain a fusion weight coefficient of each branch, and obtaining a final fusion feature map; constructing a regression network to generate a low-resolution density map, inputting the density map into a lightweight convolution network to generate a scaling mask, multiplying the density map after bilinear upsampling with the scaling mask element by element, and generating a predicted density map. The multi-level parallel hole convolution layer enables the network to simultaneously capture local details and wide-range context information, the channel attention automatically strengthens the feature channels related to the crowd, the spatial attention accurately locates the high-density area, the density map is calibrated to the actual number of people for each pixel through a loss function, and the number of people in the whole image can be directly obtained by summation.

The invention discloses a distribution method and system based on crowd density monitoring in disaster emergency hedging transfer. The method comprises the following steps: acquiring personnel position and moving speed data in real time through a sensor network deployed at a hedging channel and a temporary gathering point; identifying and quantifying a personnel gathering area, and positioning an excessive gathering area; the boundary, the people flow direction and the number of people of the excessive gathering area are extracted, and a plurality of alternative shunting paths leading to the standby gathering point are generated in combination with the topological structure of the peripheral channel, the available width and the obstacle information; simulating a shunting process to predict the future load of each path and the gathering point, and selecting a final shunting path from the alternative paths according to a load balancing principle to form a shunting scheme; and finally, integrating into a global transfer path diagram, generating an updated evacuation path, and executing guidance. According to the invention, real-time sensing, dynamic path planning and global load optimization of the crowd gathering risk are realized, so that the evacuation efficiency and the overall safety are improved.

The invention belongs to the technical field of retail equipment, and particularly relates to a self-service retail cabinet commodity identification method and system based on environmental adaptation, and the method comprises the steps: monitoring retail cabinet environmental parameters in real time through an environmental sensor network comprising a photosensitive sensor, a TOF sensor and a gravity sensor, and dynamically adjusting a data collection strategy based on a monitoring result; controlling a distributed visual perception network to acquire commodity image data through the generated data acquisition strategy; the sensing network comprises a top panoramic node, an interlayer focusing node and an import and export verification node, and cooperatively obtains multi-modal commodity image data; according to the invention, through a multi-source environment sensing network constructed by the photosensitive sensor, the TOF sensor and the gravity sensor, the illumination intensity, the people flow density and the commodity placement state are monitored in real time, and the image acquisition strategy is dynamically adjusted based on the fusion data, so that the problem that the image quality is reduced in a complex environment by a traditional fixed parameter acquisition strategy is avoided; and the robustness of data acquisition is obviously improved.

The invention discloses a pedestrian dynamic modeling method and device considering panic emotion and a medium, and the method comprises the steps: firstly completing spatial discretization, static field calculation and obstacle processing through scene initialization, determining the distance from a grid to an exit through Euclidean distance, generating a static field value, and determining the spatial constraint of pedestrian motion; then, in a pedestrian motion calculation link, a multi-dimensional dynamic coupling panic factor quantification system is innovatively constructed, crowd density, environmental stimulation intensity, distances between pedestrians and stimulation sources and the like are synthesized, accurate quantification of panic emotions is achieved through a distance attenuation and time attenuation mechanism, and the panic emotions are deeply bound with motion parameters. According to the method, through deep fusion of the panic emotion and the OSM model, irrational behaviors and differentiated movement tracks of pedestrians in an emergency scene are effectively reproduced, the simulation precision of a complex evacuation scene is remarkably improved, a scientific quantitative basis can be provided for evacuation scheme optimization and crowd management and control strategy formulation, and the method is of great significance in improving the public safety management level.

This invention provides a multimodal crowd counting method based on multi-scale alignment and fusion. First, a multimodal crowd scene dataset is acquired and divided into training, validation, and test sets. Preprocessed RGB images and multimodal auxiliary images are input into a VGG16 backbone network to extract multi-stage high-level feature maps. These are then sequentially fed into a density-sharing local contrastive learning module, a local feature fusion module, and an adaptive Mamba context-aware fusion module to achieve cross-modal fine-grained alignment, local feature fusion, and global context modeling. Subsequently, the global fused features are input into a dynamically upsampled multi-scale feature decoder to generate a high-resolution crowd density map. Supervised training is performed using a composite loss function, and the optimal model is saved for testing. This invention adopts an "align-then-fuse" architecture, effectively mitigating cross-modal heterogeneity and density fluctuation problems through multi-module collaborative design, significantly improving the accuracy and robustness of crowd counting in complex scenes.

The present application belongs to the field of computer vision, and particularly relates to a video anomaly detection method, system and device based on scene classification. The steps of the present application method are as follows: step S1, performing gray processing on original video frames, using background subtraction method and frame difference method to extract foreground targets, and extracting pixel features through Double-Canny algorithm; step S2, using a pre-trained model to detect the number of people in the video frames and generate a heat map of the video frames; step S3, using K-means clustering algorithm to classify scenes of the video frames, and dividing the video frames into two categories of dense scenes and sparse scenes; and step S4, using an anomaly detection module to respectively perform anomaly detection on the dense scenes and the sparse scenes. The present application can select different network structures for anomaly detection according to scenes with different crowd densities, is better applied to complex real scenes, and achieves better anomaly behavior detection effect.

The present application provides a crowd density detection method and device, electronic equipment and medium, wherein the method comprises: acquiring a target picture collected by a camera device; identifying the target picture to determine a first pixel unit belonging to the ground and a second pixel unit belonging to the head center position from the target picture; converting the position of the first pixel unit in the picture coordinate system to obtain the first position in the ground coordinate system according to the target conversion relationship between the picture coordinate system and the ground coordinate system of the target picture; converting the position of the second pixel unit in the picture coordinate system to obtain the second position relative to the ground coordinate system according to the target conversion relationship, so as to determine the standing position of the person on the ground based on the second position; and determining the crowd density according to the first position and the standing position. Thus, the crowd density is effectively determined based on the standing position of the person and the first position of the first pixel unit belonging to the ground in the ground coordinate system in the target picture.

The invention discloses a station long-time abnormal behavior identification method based on multi-granularity spatio-temporal context fusion, and the method comprises the steps: collecting multi-source sensing data, carrying out the spatio-temporal alignment, and generating a multi-modal data flow of a unified coordinate system; constructing an atomic event code based on the attitude sequence, generating a dynamic scene graph according to a target-environment relationship, and forming a dual-channel feature primitive; injecting the feature elements into a short-term memory layer STM, abstracting a middle-term behavior pattern, storing the abstracted middle-term behavior pattern into a middle-term memory layer MTM, and fusing a cross-camera scene graph to construct a long-term memory layer LTM to form a layered space-time memory library; performing cross-level retrieval on the associated memory in the STM / MTM / LTM through a deformable space-time attention lens, and outputting context features of multi-granularity fusion; and calculating short / medium / long-term abnormal scores based on the fusion features, and dynamically adjusting a threshold value in combination with the crowd density to realize collaborative judgment. According to the method, the problem of fragmentation of long-time behavior understanding is effectively solved, and instantaneous anomaly and long-time mode anomaly can be accurately identified at the same time.

The invention discloses a real-time people flow density monitoring and dynamic early warning method based on multi-source data fusion, and relates to the technical field of monitoring and early warning. The method comprises the following steps: constructing a digital grid map, carrying out space-time alignment on multi-source data, and dynamically calculating vision and signal confidence according to the physical coverage state of a sensor in each grid, so as to distinguish a calibration reference grid and a grid to be calibrated; and the density estimation value of the signal dominant grid is dynamically corrected by using a reference conversion coefficient calculated in real time in the reference grid, early warning is triggered by calculating the residual bearing capacity pressure difference between the abnormal high-density grid and the peripheral grid, and a dredging instruction pointing to the high-residual bearing capacity grid is automatically generated. Through dynamic calibration transmission and pressure difference diversion based on gridding data confidence, the effects of sensing people flow density with high precision and generating an intelligent dredging instruction in a shielding environment are realized, and the problems of insufficient monitoring precision and disjunction of early warning and dredging in a complex environment in the prior art are solved.

The invention discloses a data management method and system based on the Internet of Things, and the method comprises the steps: collecting regional crowd image data and crowd wifi connection data, carrying out the processing of the regional crowd image data in different environments through employing different improved CSRNet networks, and improving the robustness and accuracy of obtained crowd density data; according to the crowd wifi connection data, analyzing different areas through which the connection device passes, and generating a device movement track; generating a crowd real-time crowd density map according to crowd density data acquired by the CSRNet network, and judging a crowd moving direction trend in combination with an equipment moving track to predict the crowd density at the next moment; and according to the regional real-time crowd density and the crowd density at the next moment, a worker is prompted to perform regional crowd shunting guide work and dynamically adjust a free region function, so that regional crowd management efficiency is improved, and dangerous situations caused by excessive crowd density are avoided.

The invention discloses a gymnasium people flow density monitoring and emergency dredging management system, which comprises the following steps: collecting people flow data through a multi-sensor network deployed in a gymnasium, carrying out fusion processing on the data, and constructing a graph structure model of a gymnasium space; the data is preprocessed to calculate a people flow density index of each region, and a high-risk congestion region is identified; a space-time diagram convolutional network model is used for predicting people flow density distribution in a future time period, the prediction process combines the output of a diagram convolutional neural network and an adaptive diffusion term based on current density distribution, and the prediction precision is enhanced through a diffusion coefficient which is dynamically calculated; constructing a multi-objective optimization problem according to a prediction result, solving an optimal grooming strategy through a gradient descent algorithm, and determining an exit open state and guiding path intensity; and executing a grooming strategy, realizing people stream guidance through an electronic display screen, a broadcasting system and intelligent lighting equipment, monitoring an execution effect, and regenerating a strategy when a deviation exceeds a threshold value.

The application relates to a crowd density anomaly early warning and safety emergency response method, belonging to the field of public safety. The application can obtain the number of people in a target region of a crowd density map by integrating and summing the target region of the crowd density map, can reduce the complexity of calculation, and can improve the efficiency and accuracy of calculation. The marked dense sub-regions are subjected to expansion operation, corrosion and connection processing, the continuous regions are combined into one whole, the marked dense sub-regions are more compact and continuous, the possibility of misjudgment is reduced, and the accuracy of subsequent processing is improved. The application is suitable for the field of public transportation and the like, can quickly and automatically identify and mark a crowd dense region, greatly improves the efficiency and accuracy of a monitoring system, helps monitoring personnel to timely master the situation of the crowd dense region, and timely takes measures to guarantee public safety.

The invention belongs to the field of computer simulation and optimization algorithms, and provides an indoor evacuation layout optimization method and system oriented to guide layout. Firstly, an evacuation model including a space boundary, an exit position and width and pedestrian distribution is constructed; under the condition that the outlet layout is not changed, the position, the size and the orientation of a guide object are used as optimization variables, and space and safety constraints are set; and generating a guide layout scheme based on a myxomycete algorithm, and comprehensively evaluating the evacuation time, the crowd density and the exit utilization rate through simulation. The method can provide a basis for indoor design and emergency evacuation optimization by optimizing guide arrangement, improving a crowd flowing structure, reducing the crowdedness degree and improving the overall evacuation efficiency.

The application provides an anti-overlapping waiting passenger flow perception method based on escalator door state linkage and manifold constraint, and belongs to the technical field of waiting passenger flow detection. It solves the problems of missed detection and false detection existing in the current waiting passenger number detection. The anti-overlapping waiting passenger flow perception method based on escalator door state linkage and manifold constraint comprises the following steps: collecting continuous image frames and solving a dense optical flow field, extracting real area motion vectors and mirror reflection area motion vectors; calculating cosine similarity and amplitude deviation, identifying reflection artifacts and performing weight attenuation; inputting the visual feature information stream after weight attenuation processing into a deep convolutional neural network to generate an original crowd density probability graph; dynamically correcting the original crowd density probability graph based on an escalator door opening and closing state signal; and performing a weighted accumulation operation on the corrected crowd density probability graph by using a perspective gain function to obtain a waiting passenger flow count value. The application improves the stability and precision of waiting passenger flow statistics on each floor.

The invention discloses a campus riding detection method based on crowd density estimation, and the method comprises the steps: obtaining an original video stream of the detection of a rider in a campus, obtaining an original image, and generating a twin image; inputting the original image and the twinborn image into a twinborn neural network at the same time, and performing feature extraction on the sample pair image to obtain a rider feature and a shelter feature; the extracted features are predicted, the center point, the size and the position offset of the rider are predicted, the crowd density is estimated, and the center point of the rider is predicted for the extracted features in the twin image; constructing a self-adaptive mapping relation between the crowd local density value and an adjusting threshold value in a non-maximum suppression algorithm, and dynamically adjusting shielding among the riders; a contrast learning module is used to limit the characterization difference obtained after the sample pair image is input into the twin neural network, and the feature expression of the visible part of the rider is enhanced. And according to the empirical value, the MR-2 index is reduced to 8.4%, so that the problem of shielding among riders is solved.

The present application relates to the technical field of electric digital data processing, in particular to a data-driven campus safety management level evaluation method and system, the method comprising: determining the crowd change degree of a target area and determining the high-density aggregation area by using the crowd density data of the target area at each time; determining the evacuation confusion degree in the high-density aggregation area by using the movement direction of the target students in the high-density aggregation area; determining the evacuation risk factor in the high-density aggregation area by using the crowd change degree and the evacuation confusion degree; determining the congestion correction coefficient of the high-density aggregation area by using the evacuation risk factor in the process of multiple drills; and determining the campus safety management level evaluation result by using the congestion correction coefficients of multiple high-density aggregation areas. Through the technical scheme of the present application, the rationality of the current evacuation scheme can be more accurately evaluated, and the campus safety management capability level in real emergency situations can be more accurately evaluated.

The invention discloses an experimental method based on crowd disaster video data and virtual scene reconstruction, and the method comprises the steps: carrying out the frame-by-frame analysis of an obtained disaster monitoring video, dividing the disaster monitoring video into a close-shot region and a long-shot region, and extracting the two-dimensional coordinate positions capable of recognizing individual pedestrians through employing a target detection technology, the crowd density estimation technology is adopted to obtain overall density distribution data, and a particle image velocity measurement tool is utilized to extract the velocity and direction of pixel motion frame by frame. Establishing a three-dimensional virtual scene model corresponding to a real accident scene, mapping the processed crowd position and motion information into three-dimensional data matched with a virtual scene through a coordinate projection tool, endowing the three-dimensional data with a unique identity label, and carrying out cross-frame association and trajectory optimization processing according to a time sequence and a spatial proximity relationship; and generating a three-dimensional pedestrian continuous track file for virtual reality simulation, driving the dynamic position and posture change of a virtual pedestrian entity, and realizing credible reproduction of a crowd disaster situation and human in-loop experiment evaluation.

The invention relates to the technical field of computer vision, in particular to a crowd counting method based on multi-feature fusion VMama, and the method comprises the steps: obtaining a to-be-counted crowd image data set, carrying out the resolution normalization processing of an image, and constructing an end-to-end crowd counting model with VMama as a backbone network, extracting four-stage multi-scale features of the image through a VMama backbone network; performing fusion operation through a multi-feature fusion module to generate multi-scale fusion features; inputting the fusion features into an integration attention module, and outputting target enhancement features through feature weight distribution of channel attention and spatial positioning enhancement of coordinate attention; inputting the target enhanced features into an ellipse constraint deformable convolution module, and executing expansion convolution processing on output features of the ellipse constraint deformable convolution module to generate a crowd density map; and the pixel values of the density map are summed to obtain a final crowd counting result, so that the crowd counting effect is improved.

The invention discloses a multi-target vehicle dynamic path planning method and system based on a space-time crowd density field. The method comprises the following steps: collecting crowd distribution data in real time through a distributed sensor, abstracting a physical road network into a directed graph, and further calculating a real-time crowd density value of each edge in the graph; based on the density value, a dynamic weight is calculated for each edge through a non-linear utility function containing a logarithmic observation and performance income item and an exponential safety penalty item, and the weight presents a trend of first decreasing and then increasing along with the change of the crowd density; and finally, on the basis of the dynamic weight and in combination with vehicle physical constraints, an A * path-finding algorithm is adopted to search a path with the minimum cost from the current position to the target node. According to the method, the technical problem that the special vehicle cannot give consideration to viewing benefit and traffic safety in a crowded area is solved, and a path which can actively tend to a proper crowd to maximize viewing coverage and can avoid a high-density congestion risk area can be dynamically planned through the nonlinear weight model.

The application discloses a kind of based on local crowd density and flow direction perception four-legged robot active crowd control method, belong to public security and intelligent robot technical field.Four-legged robot is based on the personnel position information in effective perception field of view obtained based on self sensor;Based on continuous multiple frame information, local dynamic probability grid map is constructed with robot as center, and crowd density of each grid unit is estimated;Personnel target is tracked across frame, and local crowd mainstream direction and average flow velocity are calculated;Risk level is calculated by fusing density, flow velocity and flow direction conflict factor, and future density trend is predicted;When meeting active intervention condition, optimal crowd control strategy is selected from preset candidate strategy set based on current local situation information and executed by robot.The application overcomes the limitation of prior art depending on global view, so that four-legged robot can realize prospective risk assessment and active crowd control only by relying on its own local perception, effectively improve the safety and crowd control efficiency of key node.

The application discloses a landscape lighting light simulation test control system, relates to the technical field of light control, and is used for solving the problem of poor intelligent light control optimization; the application constructs structured time sequence data through holiday identification functions, weather states, sunshine information and crowd density, performs nonlinear mapping after unified sampling and sliding window alignment, and generates tensor type input data sets; a gating cycle unit is adopted to combine a space situation embedding vector to perform state recursion and context adjustment, and to output future multi-time illumination, color temperature and control level prediction sequences; control segment paths are constructed according to illumination change trends and color temperature sign functions, deviation information is generated by collecting current illumination, crowd density and equipment response states, a feedforward and feedback fusion strategy is executed, and a control instruction set containing illumination, color temperature, lamp opening and closing states and mode labels is output, so that adaptive and dynamic light control is realized.

The application discloses a kind of real-time data-driven crowd behavior modeling simulation methods for digital twinning, comprising: constructing three-dimensional twin static scene structure and two-dimensional simulation scene structure;Real-time acquisition of the dynamic changes of the crowd in dynamic scene structure in multiple time windows Video data;Obtain the macroscopic crowd movement flow and crowd density distribution of each time window;Macroscopic crowd movement flow and crowd density distribution are distributed to each unit area, obtain the movement flow set and cumulative density error of each unit area;Obtain the migration adjustment demand and migration adjustment amount of the agent in each unit area;The preferred speed of the agent in each unit area is obtained, the obstacle avoidance speed is obtained, and the three-dimensional dynamic crowd corresponding to the dynamic scene structure time is generated in three-dimensional twin static scene structure.The application can fully meet the application demand of digital twinning scene, with high fidelity, high restoration characteristics.