49 results about "Social force model" patented technology

The invention relates to the technical field of intelligent driving, and discloses an obstacle avoidance analysis system for intelligent driving. The system comprises a data acquisition module, a road feature extraction module, a lane type judgment module, a virtual marking generation module, an obstacle detection and trajectory prediction module, a dynamic obstacle avoidance decision module and a system verification and optimization module. The method comprises the following steps: acquiring data through a laser radar, a camera and the like; fusing point cloud and an image to extract the width and curvature of a road; judging the type of a lane according to the width; generating a virtual marking line on a marking-free road, fitting by using a B-spline curve when the curvature is large, fusing the point cloud and the image to detect an obstacle, and predicting a track by using Kalman filtering or a social force model. In combination with virtual marking and risk assessment, a safe trajectory is generated through an OccupanyGrid map, an A * algorithm and MPC, and a simulation and real road test optimization system is adopted, so that the problem of marking-free road trajectory planning deviation is solved, and the obstacle avoidance precision and the road condition adaptability are improved.

The invention belongs to the field of artificial intelligence and intelligent traffic systems, particularly relates to a path control system combining multi-modal perception and dynamic trajectory prediction, and aims to solve the problems of insufficient perception fusion, low trajectory prediction precision and control response lag in a complex dynamic environment. The system comprises a multi-modal perception fusion unit, a dynamic interaction modeling unit, a space-time coupling prediction unit, a risk field construction unit and an adaptive path generation unit. Multi-source sensor data are fused through confidence coefficient weighting, an interaction weight matrix under an attention mechanism is constructed, a future trajectory is predicted in combination with individual dynamics and a social force model, a four-dimensional space-time risk field is generated, and a minimum risk path is solved based on an improved fast marching algorithm. The system realizes sensing-prediction-control closed-loop cooperation, the end-to-end delay is less than 250 milliseconds, and the driving safety and comfort in a complex traffic scene are improved.

The invention relates to the technical field of unmanned aerial vehicle trajectory planning, and provides a multi-unmanned aerial vehicle trajectory optimization method and related equipment in post-disaster emergency communication, and the method comprises the steps: carrying out the modeling of the movement condition of each ground user equipment based on the current positions of all ground user equipment, and obtaining a user social force model of each ground user equipment, constructing a reward function according to the user social force model; according to the user social force models of all the ground user equipment and the take-off airport positions of all the unmanned aerial vehicles, constructing unmanned aerial vehicle trajectory constraint conditions; under the constraint of the unmanned aerial vehicle trajectory constraint condition, performing trajectory optimization on the unmanned aerial vehicle based on a reward function to obtain a moving trajectory of the unmanned aerial vehicle in the communication time period; and controlling the unmanned aerial vehicle to move according to the moving trajectory of the unmanned aerial vehicle to realize emergency communication of the ground user equipment. According to the method, the reasonable degree of the unmanned aerial vehicle track of post-disaster emergency communication can be improved.

The invention is suitable for the technical field of crowd simulation and emergency evacuation, and provides an emotional infection and social force model coupling-based crowd evacuation method, which comprises the following steps of: constructing an emotional infection model based on an SEIRS framework, dividing individual emotional states into a susceptible state, an exposed state, an infected state and a recovery state, the infection rate and the recovery rate are dynamically adjusted through a feedback function; establishing a social force model, and embedding an emotion value into calculation of a target driving force and a repulsive force between individuals to realize real-time adjustment of the motion behavior by the emotion; global path planning and an ant colony pheromone mechanism are fused, a density perception cost function and pheromone guiding force are introduced, and dynamic blockage avoidance is achieved; a bidirectional feedback mechanism is designed, and local path reconstruction is triggered when congestion is detected. According to the method, bidirectional coupling of emotion propagation and behavior decision is realized, the authenticity and adaptability of evacuation simulation are improved, and a scientific basis can be provided for emergency management and evacuation strategy optimization in public places.

The invention discloses a crowd evacuation simulation method and system based on panic emotion propagation and hazard source influence, and the method comprises the steps: setting a simulation scene, and modifying the infection rate of infectious disease dynamics into a state transition function affected by an emotion threshold value; after simulation is started, calculating a state transfer function for each individual in the crowd, and establishing a panic emotion propagation model; setting danger repulsive force exclusive to a danger source, establishing a social force model based on the panic emotion propagation model and the danger repulsive force, and simulating crowd evacuation based on the social force model. According to the method, the infection rate of infectious disease dynamics is changed into a dynamic parameter influenced by an emotion threshold value from a static parameter, the dynamic parameter is used as a state transition function of individuals, the function is used as a bridge, a social force model and panic emotion propagation are fused together, individual differences in crowd evacuation are deeply reflected, and the social force model and the panic emotion propagation are integrated. Therefore, simulation of crowd evacuation is realized.

The invention provides a traffic organization method and system based on urban traffic transfer efficiency evaluation, and the method comprises the steps: building a building model based on a pre-obtained urban traffic building space, setting an initial traffic organization scheme of traffic nodes, simulating the movement behaviors of passengers at each traffic node through a social force model, and carrying out the simulation of the passenger movement behaviors at each traffic node; obtaining passenger flow simulation data; setting an initial weight for the factor set based on the evaluation model to obtain a comment set; and dynamically adjusting the initial weight of the factor set based on the passenger flow simulation data and the evaluation model to obtain the target weight of the factor set, calculating the comprehensive association degree in combination with the comment set, and obtaining the urban traffic transfer efficiency evaluation result according to the comprehensive association degree to adjust the initial traffic organization scheme. And obtaining a target traffic organization scheme to organize urban traffic. According to the method, different passenger flow scenes are simulated to dynamically adjust the weight of the evaluation index, the urban traffic transfer efficiency level under the multi-factor interaction influence is quantified, and the reliability of a traffic organization scheme is improved.

The invention discloses a heterogeneous crowd evacuation model fusing multi-dimensional panic emotion characteristics in the technical field of crowd evacuation dynamics, which comprises a multi-dimensional panic emotion analysis unit and an improved social force model unit, the multi-dimensional panic emotion analysis unit is used for quantifying panic emotion influences of three dimensions of environment pressure, individual characteristics and group effect and generating dynamic multi-dimensional emotion pressure factors, and the improved social force model unit is used for adjusting expected speeds of heterogeneous individuals based on the multi-dimensional emotion pressure factors and realizing dynamic simulation of crowd evacuation behaviors. According to the dynamic adjustment mechanism, an evacuation path and a group cooperation strategy are dynamically optimized based on a real-time panic pressure factor, a multi-dimensional panic emotion analysis method and an improved social force model fusing multi-dimensional panic emotion features are innovatively designed, and the influence mechanism of different factors on the panic emotion and evacuation efficiency of pedestrians can be effectively analyzed. Therefore, real simulation of pedestrian evacuation behaviors is realized.

The invention discloses a subway station passenger flow buffer area risk identification and collaborative management method and system, and relates to the field of analog simulation, and the method comprises the steps: building a three-dimensional simulation scene of each subarea passenger flow buffer area of a subway station based on individual motion simulation software Massmotion of a social force model, and obtaining the data of the subway station passenger flow buffer area; adopting a partition independent component analysis PIPCA algorithm to obtain a dominant variable influencing each partition passenger flow system; according to the dominant variable, optimizing the weight and bias parameters of a pre-constructed nonlinear sub-regression neural network NARX through a moss growth algorithm MGO, and obtaining a subway passenger flow buffer area risk identification model for predicting the passenger flow density of each buffer area and a bottleneck period; constructing a target function and a constraint condition, and selecting an optimal control strategy under the constraint condition of meeting the passenger flow volume demand; and selecting the control scheme with the lowest total congestion risk as a final implementation scheme by adopting a congestion risk identification method. The congestion risk of the buffer area is efficiently identified.

The invention relates to the field of artificial intelligence, and discloses a charging robot intelligent scheduling and control method based on visual perception. According to the method, a double-layer architecture integrating global planning and local decision making is constructed; a central scheduling system generates a global conflict-free space-time path; each robot constructs a local three-dimensional semantic map in real time through a visual sensor, and predicts a dynamic target trajectory by using a long and short term memory network fused with a social force model; and when a collision risk is detected, triggering a decentralized negotiation protocol, autonomously re-planning a local obstacle avoidance path according to the dynamic priority, and synchronizing the state to a central system. The system comprises a global path planning module, a visual perception module, a trajectory prediction module, a risk decision module, a conflict negotiation module, a local re-planning module and a state synchronization module. According to the method, millisecond-level local response and global collaborative optimization are realized, and the safety, the task continuity and the system expandability are remarkably improved.

The application discloses a kind of electric bus active suspension control method and system, method includes according to bus parameter and motor distribution establishes the half vehicle dynamics model of bus, constructs the state space equation of model, obtains the active suspension model suitable for bus;Considering the mass distribution change and centroid position change caused by bus passengers getting on and off and walking in the car, a social force model is established;And through the social force model, the relationship between the number of passengers on the bus and the front and rear distribution of passengers is obtained according to the particle swarm optimization algorithm, the passenger distribution law is obtained, and the active suspension model suitable for mass distribution change and centroid position change is established after the range of uncertain parameters is determined;For the direct interference of rear motor vibration, PID control is designed to adapt to the vibration of motor under different working conditions;By deriving the theorem of H2 / H ∞ control, the active suspension feedback control law when the mass distribution changes and the centroid position changes is obtained.The application can effectively improve the comfort of bus.

The application discloses a subway station passenger flow buffer zone risk identification and collaborative management method and system, relates to the field of simulation, and comprises the following steps: based on the individual motion simulation software Massmotion of a social force model, a three-dimensional simulation scene of passenger flow buffer zones of each subzone of a subway station is constructed, and subway station passenger flow buffer zone data is acquired; a partition independent component analysis (PIPCA) algorithm is used to obtain dominant variables affecting passenger flow systems of each subzone; according to the dominant variables, the weight and bias parameters of a pre-constructed nonlinear autoregressive neural network (NARX) are optimized through a moss growth algorithm (MGO), and a subway passenger flow buffer zone risk identification model for predicting passenger flow densities of each buffer zone and a bottleneck period is obtained; a target function and a constraint condition are constructed, an optimal control strategy is selected under the constraint condition of meeting passenger flow requirements, and a congestion risk identification method is used to select a control scheme with the lowest total congestion risk as a final implementation scheme. The application efficiently identifies buffer zone congestion risks.

This invention provides a subway station fire evacuation simulation method and system, belonging to the field of rail transit technology. Based on the lattice-Boltzmann method, the lattice-Boltzmann sublattice model obtained by large eddy simulation is integrated with an obstacle flow simulation model and a smoke diffusion model to construct a lattice-Boltzmann fire model. Sensory, psychological, and physiological models are constructed based on the lattice-Boltzmann fire model, and combined with a social force model to construct an evacuation model. The lattice-Boltzmann fire model is combined with the evacuation model to simulate evacuees' real-time avoidance of fire smoke. This invention achieves a substantial integration of the fire smoke evolution and evacuation processes, simulating their interaction and better recreating actual fire scenarios, providing certain benefits for engineering practice.

The invention relates to the technical field of digital twinning, in particular to a layout generation optimization method and system for a digital twinning body of a space facility, and the method comprises the steps: constructing a two-dimensional data matrix for each layout scheme of the space facility through employing the evaluation indexes of a preset number of channels in a discretization grid; constructing a social force model based on the two-dimensional data matrix, and determining a layout cost index; analyzing evaluation indexes about efficiency safety, energy consumption and a flexible channel in the two-dimensional data matrix to obtain a multi-dimensional evaluation vector, obtaining a dynamic weight vector by adopting a Bayesian network, obtaining a close degree score by utilizing a decision analysis method, obtaining scene adaptation robustness in combination with an element numerical value dispersion degree of the dynamic weight vector, and determining a fitness function, so as to obtain the scene adaptation robustness. And adopting a particle swarm optimization algorithm to obtain an optimal layout scheme. The invention aims to enhance the comprehensive performance of the space facility layout scheme.

The invention belongs to the technical field of building emergency evacuation path planning, and particularly provides an intelligent building-oriented emergency evacuation path dynamic planning method and related equipment thereof, and the method mainly comprises the steps: obtaining building real-time monitoring data, carrying out the preprocessing of the real-time monitoring data, and obtaining building security situation data; performing threat diffusion simulation and region division on the building security situation data to obtain safety region division data; and performing personnel cluster analysis and social force model prediction based on the building security situation data to obtain personnel evacuation behavior prediction data and evacuation bottleneck early warning data. According to the invention, accurate adaptation of the evacuation path with the on-site threat diffusion trend and the real-time change of personnel flow is realized, personnel are prevented from falling into an unavoided threat area, local congestion is reduced, and the overall evacuation time consumption is effectively shortened.

The application discloses an indoor dense crowd emergency evacuation method considering a fragment scattering effect, belongs to the technical field of emergency evacuation, and comprises the following steps: a group intelligence algorithm and a fragment scattering effect social force model are used to calculate different optimal evacuation exit schemes corresponding to a sudden event and partition crowds under different conditions; site, partition crowds and sudden event information are taken as input, and the optimal evacuation exit scheme is taken as output; all the input and output are used to train a decision tree model to obtain a real-time emergency evacuation decision model; indoor partition crowd information under a sudden event is collected, and the information is input into the real-time emergency evacuation decision model together with the site and sudden event information to obtain the optimal evacuation exit scheme; and the optimal target exit of each partition in the optimal evacuation exit scheme is transmitted to a drone of each partition, and the drone uses time-sharing broadcasting to guide the partition crowds to evacuate to the optimal exit.

The application provides a simulation method and device for subway passenger behavior and electronic equipment, and belongs to the field of data processing. The method comprises the following steps: performing grid processing on a subway model scene to obtain a grid map; determining a target moving path in the grid map according to an initial position and a preset destination of a target passenger; obtaining a current state of the target passenger, wherein the current state is used to indicate a boarding process; determining a behavior decision of the target passenger through a preset behavior tree according to the current state, wherein the behavior decision is used to indicate a next state of the target passenger; and simulating the behavior of the target passenger according to the behavior decision, the target moving path and a social force model. The simulation accuracy and authenticity can be improved.

The application relates to a pedestrian motion trajectory evolution method and system based on a social force model, and the method comprises the following steps: setting a simulation environment, obtaining the initial position of pedestrians from a video; obtaining the actual force of the pedestrians at the collision moment and the non-collision moment according to a pedestrian dynamics model, updating the speed of the pedestrians according to the speed change of the pedestrians with time, and then updating the position of the pedestrians; obtaining the pedestrian motion trajectory according to the position of the pedestrians at each moment; wherein the construction process of the pedestrian dynamics model is as follows: obtaining the interaction force between the pedestrians and other individuals and the interaction force between the pedestrians and walls based on the social force model; constructing the social force, constructing the physical force according to the contact or collision action between the pedestrians and other individuals, and constructing the speed change of the pedestrians with time; and constructing the pedestrian dynamics model. Compared with the prior art, the application can more accurately reflect the actual force condition between the pedestrians under different situations, and the dynamics analysis is more in line with the actual situation.

To accurately predict the position of a moving body.SOLUTION: The information processing apparatus includes a controller. In the prediction of the position of the first moving object based on an SFM (SocialForceModel), when it is determined that the plurality of second moving objects are a moving object group in which the plurality of second moving objects are moving together in the same direction, the control unit adjusts a repulsive force received by the first moving object from the plurality of second moving objects to be a repulsive force received from one second moving object.SELECTED DRAWING: Figure 1

The present invention discloses a method for predicting pedestrian crossing trajectories that integrates pedestrian intention and social force models. The method comprises the following steps: obtaining vehicle driving state information, an image in front of the vehicle, and pedestrian motion state information; detecting key points of the pedestrian's posture in the image and fitting a human posture skeleton; selecting key points that can express the pedestrian's intention based on the human posture skeleton, and calculating the distance and angle parameters between the key points; determining the pedestrian's intention; establishing the self-driving force, destination attraction, and mutual repulsion between the pedestrian and the vehicle when crossing the street, and obtaining the resultant force; and predicting the pedestrian's crossing trajectory based on a set time step. The pedestrian crossing trajectory prediction method of the present invention integrates the pedestrian intention recognition process and improves the traditional social force model. It can reflect the actual pedestrian crossing behavior and improve the accuracy of pedestrian crossing trajectory prediction.

The invention discloses an electric bicycle intrusion behavior simulation method based on reinforcement learning parameter self-adaption, and the method comprises the following steps: obtaining environment parameters, initializing a reinforcement learning agent, and enabling the motion space of the reinforcement learning agent to comprise the hyper-parameter adjustment amount of a social force model; iteratively training the reinforcement learning agent until a convergence condition is reached to obtain a simulation model, acquiring all vehicle states and updating a state space by the reinforcement learning agent in each iteration, then calculating a corresponding action space, and meanwhile, calculating an electric bicycle intrusion decision probability according to the state space, and the social force model calculates the social force of the electric bicycle according to the intrusion decision probability of the electric bicycle after adjusting hyper-parameters in the action space, and updates the state of the electric bicycle. According to the method, through deep fusion of reinforcement learning and a social force model, a complete simulation-evaluation-learning closed-loop optimization framework is constructed to realize parameter adaptation, decision dynamics and multi-scene adaptation.

The application is suitable for the field of crowd evacuation simulation technology, and provides a subway fire scene robot-assisted pedestrian flow evacuation simulation method, which is applied to a subway fire scene, and the method comprises the following steps: obtaining a first position of a fire in a subway station and second positions of all pedestrians in a dangerous area of the fire; determining, according to the first position and the second positions, third positions of guide robots for evacuating and shunting the pedestrians in the dangerous area, and controlling the guide robots to move to the third positions; inputting the first position, the second positions and the third positions into an improved social force model for processing to obtain accelerations of each pedestrian in the dangerous area; determining, according to the accelerations of the pedestrians, positions of the pedestrians at a next moment, and guiding the pedestrians to move to the positions; and detecting whether there is an unevacuated person in the dangerous area, if there is, continuing to evacuate the unevacuated person, otherwise ending the simulation. The application has high intelligence and automation levels, and can quickly and effectively evacuate pedestrians.

The present application relates to the technical field of intelligent livestock monitoring, and discloses a flock density dynamic monitoring method based on multi-camera fusion, comprising the following steps: S1, acquiring a flock dynamic image sequence through a plurality of cameras, modeling the interaction force, expected speed and target migration trend between flock individuals based on a social force model, generating group behavior dynamics characteristic data, and dynamically detecting the in-out flow of sheep in the boundary area of the camera field of view, and outputting density distribution data after boundary compensation; S2, the group behavior dynamics characteristic data generated in S1 is input into a space-time joint deep learning compensation model to obtain the density distribution data of the flock in the camera field of view. Through the group behavior dynamics modeling and space-time joint deep learning compensation technical scheme, the present application achieves the technical effect of continuous and stable density estimation in a dynamic scene, and solves the problem of boundary in-out occlusion and significant density jump caused by the rapid movement of the flock, compared with the scheme of relying on fixed camera image splicing and target detection in the prior art.

The invention relates to the related technical field of scene generation, in particular to a scene generation method based on a physical guidance diffusion model, and the method comprises the steps: processing an automatic driving data set, segmenting the data set into a training set, a verification set and a test set, and defining an intelligent agent track to be composed of a state sequence and an action sequence; capturing the time, space and map-level dependency relationship of the trajectory by using a space-time Transform; a track is generated through forward diffusion noise adding and backward diffusion denoising processes, a social strength model containing self-driving force, repulsive force and attraction is injected into the backward diffusion process, and track adjustment is guided to conform to physical laws and human-like driving behaviors. According to the method, the problem that the scene is not real due to the fact that physical constraints are ignored or only post correction is carried out in the prior art is solved, and the generated scene has authenticity and feasibility and can be effectively used for automatic driving system test evaluation.

The invention relates to the technical field of intelligent animal husbandry monitoring, and discloses a sheep flock density dynamic monitoring method based on multi-camera fusion, and the method comprises the steps: S1, collecting a sheep flock dynamic image sequence through multiple cameras, and carrying out the modeling of the interaction force, expected speed and target migration trend among sheep flock individuals based on a social force model, generating group behavior dynamic characteristic data, performing dynamic detection on sheep access flow in a view field boundary area of a camera, and outputting density distribution data after boundary compensation; and S2, performing dynamic feature data on the group behaviors generated in the step S1. According to the method, the technical effect of continuous and stable density estimation in a dynamic scene is achieved through the technical scheme of group behavior dynamics modeling and spatio-temporal joint deep learning compensation; the defects of boundary in-out shielding and obvious density jump caused by rapid movement of the sheep flock are overcome.

This invention relates to a method and system for assessing crowd collision risk in a sloping micro-road network scenario. The method includes the following steps: in the target sloping micro-road network scenario, a multi-view video acquisition network covering the main movement channel of the crowd is used to acquire a crowd movement video stream; pedestrian visual information is extracted from the crowd movement video stream using a target detection algorithm; the pedestrian visual information is input into an improved social force model after local parameter calibration to obtain the total force experienced by each pedestrian in the target sloping micro-road network scenario; a global-level dynamic risk index is calculated based on the total force experienced by each pedestrian; based on the global-level dynamic risk index, the crowd collision risk in the target sloping micro-road network scenario is assessed in real time using preset multi-level risk judgment rules to obtain the assessment result. Compared with the prior art, this invention improves the accuracy, reliability, and practical value of the crowd collision risk assessment results.

This invention discloses a civil aircraft emergency evacuation simulation method based on an improved social force model that considers passenger heterogeneity. The method divides the cabin into seating, aisle, and exit areas and sets independent target point sets. A two-stage screening technique is employed to determine effective action edges, avoiding abnormal repulsive forces at aisle entrances. Differentiated social force model parameters are established to address mechanical deadlock and lateral oscillation issues. A priority passage mechanism triggered by a waiting threshold is used to overcome the problem of permanent waiting. The kinetic energy is kept constant during area transitions through kinetic energy conservation and transformation, addressing the issue of acceleration lag during turns. An aisle boundary constraint protection mechanism is set up to correct positions and velocities exceeding the boundaries in real time, eliminating lateral oscillations. Verified through personnel evacuation tests under different operating conditions, the average error between the simulation results and the actual evacuation time is controlled within 10%, significantly improving the realism and reliability of civil aircraft emergency evacuation simulations and providing a scientific calculation tool for airworthiness certification and cabin safety assessment.

The application discloses an intelligent intersection pedestrian protection system based on trajectory prediction and intention recognition, and through deployment of a perception layer, an edge computing layer and a communication layer, a perception network of multi-source data fusion is constructed to realize accurate detection and continuous tracking of intersection pedestrians and vehicles; a multi-agent trajectory prediction model is established based on a social force model, target driving force, pedestrian interaction and vehicle avoidance factors are comprehensively considered, and future motion trajectories of pedestrians and vehicles are generated; a pedestrian's crossing intention is identified by analyzing the pedestrian's multi-modal behavior characteristics; a collision risk level is evaluated based on the trajectory prediction and intention recognition results; and a differentiated active intervention measure is taken by an execution layer according to the risk level, including signal timing optimization, sound and light warning and vehicle braking instruction sending, so that intelligent active protection of the vulnerable groups at the intersection is realized. The application realizes a paradigm shift from passive protection to active protection and provides all-round and intelligent safety protection for the vulnerable groups at the intersection.

The application relates to a non-motor vehicle track reconstruction method in a motor-non-motor shared space, and the method steps comprise the following: extracting an initial track through a CV algorithm; adopting an LSTM to predict and complete a track missing due to occlusion, and matching the track before and after occlusion through a Hungarian algorithm to complete a complete track; adopting a three-layer cyclic social force particle filtering algorithm constructed by integrating a social force model representing non-motor vehicle driving behavior into a particle filtering framework to denoise the completed complete track to obtain a reconstructed track. Compared with the prior art, the application can effectively improve the performance of a video detector in acquiring high-precision track data, provide more fine perception data for a holographic intersection and a traffic control system, and has great application value in the industry.

The application provides an improved social force model parameter tuning method, device and equipment and a storage medium, and relates to the technical field of virtual group simulation. The method comprises the following steps: obtaining a virtual group simulation target scene and evaluation indexes determined according to the target scene; setting multi-objective particle swarm optimization algorithm parameters by taking interaction force parameters A, n, n', gamma and lambda in the improved social force model as decision variables and taking the evaluation indexes as a target function; setting virtual group simulation experiment parameters for the target scene; and completing evaluation of particles in the multi-objective particle swarm optimization algorithm through virtual group simulation experiment of the target scene to obtain calibrated values of the A, n, n', gamma and lambda under the optimized target scene. The method can quickly obtain optimal model parameters under different simulation scenes, and virtual group simulation using the optimized model parameters can improve the motion interaction effect of the virtual group.

The application is suitable for the field of crowd simulation and emergency evacuation, and provides a crowd evacuation method based on emotion contagion and social force model coupling, which comprises the following steps: constructing an emotion contagion model based on SEIRS framework, dividing individual emotion state into susceptible state, exposed state, infected state and recovered state, and dynamically adjusting infection rate and recovery rate through feedback function; establishing a social force model, embedding emotion value into target driving force and individual repulsive force calculation, realizing real-time adjustment of emotion on movement behavior; fusing global path planning and ant colony pheromone mechanism, introducing density perception cost function and pheromone guiding force, realizing dynamic congestion avoidance; designing a bidirectional feedback mechanism, triggering local path reconstruction when congestion is detected. The application realizes the bidirectional coupling of emotion transmission and behavior decision, improves the reality and adaptability of evacuation simulation, and can provide a scientific basis for public place emergency management and evacuation strategy optimization.