Data-driven uphill pedestrian double-foot gait and social force coupling simulation method

Abstract

The invention provides a data-driven uphill pedestrian double-foot gait and social force coupling simulation method, and belongs to the technical field of pedestrian simulation, and the method comprises the steps: 1, collecting gait dynamic data; step 2, constructing a biped social force coupling model; step 21, simulation initialization is carried out; step 22, step-by-step triggering judgment is carried out; step 23, calculating social force and gait parameters; step 24, planning a foothold and responding to collision; step 25, updating the position of the center of mass; and step 26, simulation circulation and termination. The invention aims to realize high-precision and high-fidelity simulation of pedestrian movement from a microscopic gait to a macroscopic flow rate under different slope conditions through a complete technical chain from experiment to simulation.

Description

Technical Field

[0001] This invention relates to the field of pedestrian simulation technology, and in particular to a data-driven simulation method for the coupling of bipedal gait and social forces of pedestrians going uphill. Background Technology

[0002] Global urbanization has led to an increase in vertical buildings, and ramps (such as those in subway stations, shopping malls, and stadiums) have become common facilities in public spaces. When pedestrians go uphill, they experience a backward tilting moment due to gravity and need to adjust their gait (such as leaning forward or adjusting stride length) to maintain balance. Such scenarios can lead to increased physiological load and a higher risk of loss of control, and may even trigger mass safety accidents (such as the Itaewon ramp stampede). Therefore, there is an urgent need for accurate pedestrian simulation technology to support safety assessments.

[0003] Defects of traditional technology: Modeling limitations: Mainstream social force models simplify pedestrians to point masses, which cannot characterize key biomechanical features such as stride length, stride frequency, and trunk posture when going uphill, resulting in a large deviation from real walking behavior; Poor slope adaptability: Traditional models are mostly designed for flat terrain and do not consider the nonlinear effects of slope on pedestrians' expected speed and movement adjustment ability. The output of macroscopic speed, flow rate, etc., has a systematic deviation from the real slope scene. Low parameter reliability: The model parameters rely heavily on empirical settings and lack the driving force and verification of real biomechanical experimental data. The biomechanical rationality is insufficient and it cannot reproduce the adaptive behavior of humans in response to slope. Summary of the Invention

[0004] This invention provides a data-driven simulation method for the coupling of bipedal gait and social forces of pedestrians on an uphill slope. It aims to achieve high-precision and high-fidelity simulation of pedestrian movement from micro gait to macro flow rate under different slope conditions through a complete technology chain from experiment to simulation.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A data-driven simulation method for coupling uphill pedestrian bipedal gait with social forces includes: Step 1: Collect gait dynamics data of pedestrians at different uphill slopes; Step 2: Based on gait dynamics data, construct a bipedal social force coupling model. The simulation process of this bipedal social force coupling model includes: Step 21: Set the geometric parameters of the simulation scene, initialize the pedestrian group, set the total simulation time and simulation time step, and set the initial gait parameters and initial foot state for each pedestrian based on gait dynamics data. Step 22: In each simulation time step, traverse all pedestrians and determine whether the stepping conditions are met based on the principle of bipedal gait dynamics. If they are met, execute steps 23 and 24 to update the gait parameters and bipedal states. Otherwise, proceed directly to step 25 and use the historical gait parameters and historical bipedal states of the current pedestrian. Step 23: Calculate the social forces and key gait parameters acting on the pedestrian. The key gait parameters are used as the latest gait parameters for the current time step. Step 24: Based on the latest gait parameters, plan the target footing point of the swinging foot, detect whether the target footing point collides with obstacles or other pedestrians. If a collision occurs, trigger a deceleration strategy and replan the footing point until the collision is resolved. Then update the foot status to obtain the latest foot status of the current time step. Step 25: If the pedestrian meets the stepping conditions, calculate the pedestrian's forward displacement and lateral displacement based on the latest gait parameters and the latest foot state; if the pedestrian does not meet the stepping conditions, calculate the pedestrian's forward displacement and lateral displacement based on the current pedestrian's historical gait parameters and historical foot state; wherein, during the initial simulation, the pedestrian's forward displacement and lateral displacement are calculated based on the current pedestrian's initial gait parameters and initial foot state. The centroid coordinates of the pedestrian are updated based on the forward displacement and lateral displacement. Step 26: Determine whether the simulation time has reached the preset total simulation duration. If it has, end the simulation; otherwise, update the simulation time and record the gait parameters and foot states of the current time step as historical data, then return to step 22.

[0006] In this specification, the data-driven simulation method for bipedal gait and social force coupling of pedestrians on slopes also includes: Step 3, using an optimization algorithm to calibrate the key parameters of the bipedal social force coupling model, and verifying the model's micro gait reproduction ability and macro dynamic characteristics in multi-slope scenarios.

[0007] In this specification, step 1, collecting gait dynamics data of pedestrians under different uphill slopes, specifically includes: conducting constant-speed walking experiments under preset uphill slopes and collecting data in real time; the gait dynamics data includes at least the pedestrian's stride length, stride duration, stride frequency, stride width, support phase duration, sway phase duration, double support phase duration, ground reaction force statistics, and step direction angle for each step.

[0008] In this specification, step 21, which sets initial gait parameters and initial foot states for each pedestrian based on gait dynamics data, specifically includes: Initial gait parameter settings: The initial step direction angle is set along the uphill direction of the slope, referring to the statistical value of the average walking direction of pedestrians on the same slope; the initial step length is the statistical average of the step length of pedestrians on the same slope; the initial step width is the statistical average of the step width of pedestrians on the same slope. Initial foot position settings: The initial support foot position and the pedestrian's initial center of gravity position are perpendicularly corresponding in the ramp plane, which conforms to the natural positional relationship between the support foot and the center of gravity when the human body is standing; the initial swing foot position is set on the symmetrical side of the initial support foot based on the initial step width and the initial step direction angle, and after setting, it is necessary to detect whether the initial foot position collides with obstacles or other pedestrians. If a collision occurs, the relative positions of the initial support foot and the swing foot are readjusted until the collision is resolved.

[0009] In this manual, the core basis for determining whether the stepping conditions are met in step 22, based on the principle of bipedal gait dynamics, is the relative relationship between the projection position of the pedestrian's center of mass on the ramp plane and the position of the front end of the current supporting foot.

[0010] In this specification, step 23, calculating the social forces acting on the pedestrian, includes calculating the pedestrian's self-driving force, the interaction force between the pedestrian and other individuals, and the interaction force between the pedestrian and the wall. Specifically, the calculation of the pedestrian's self-driving force incorporates an adaptive relaxation time model, which comprehensively reflects the biomechanical load caused by uphill walking, and includes: The degree of forward tilt of the torso is correlated and quantified by the sine function of the slope angle; The nonlinear growth of friction demand is correlated and quantified by using the exponential form of the tangent function of the slope angle; The impact of weight on motion response is quantified by correlating the ratio of a pedestrian's actual weight to a reference weight. The fatigue effect is correlated and quantified by the cumulative motion height of pedestrians during the active walking phase. The cumulative motion height is reset to the baseline value when the pedestrian's instantaneous speed is lower than the minimum motion speed threshold. The reference weight and the baseline value are determined based on experimental data statistics.

[0011] In this specification, the key gait parameters in step 23 include the step direction angle, step length, and step width. The step length is calculated as follows: the step length equals the product of the pedestrian's current instantaneous velocity and the single-step time. The single-step time is influenced by both acceleration and adaptive relaxation time. Specifically, it is based on the experimentally calibrated average single-step time, with the acceleration influence factor and relaxation time influence factor superimposed. The acceleration influence factor is calculated by the ratio of the acceleration reference value to the pedestrian's current instantaneous acceleration, and the relaxation time influence factor is calculated by the ratio of the current adaptive relaxation time to the relaxation time reference value. Both the acceleration reference value and the relaxation time reference value are determined based on statistical experimental data.

[0012] In this specification, the logic for setting the stride width in step 23 is as follows: the stride width follows a standard normal distribution, the mean of which is the statistical mean of the stride width of pedestrians on the same slope, and the standard deviation is the statistical standard deviation of the stride width of pedestrians on the same slope; the specific value of the stride width is the product of the mean plus or minus the standard deviation and the standard normal distribution random variable, the standard normal distribution random variable follows a distribution with a mean of 0 and a variance of 1, in order to simulate the individual differences in the stride width of different pedestrians.

[0013] In this specification, step 24, triggering the deceleration strategy, specifically includes: reducing the pedestrian's current instantaneous speed to a preset safe speed range, the safe speed range being set based on statistical values ​​of the pedestrian's low-speed adjustment state when walking uphill; shortening the step length by reducing the instantaneous speed, thereby adjusting the position of the swing foot's target foothold until it is detected that the target foothold does not collide with obstacles or other pedestrians; the updating of the foot state specifically involves: updating the original supporting foot to the new swing foot, updating the re-planned, collision-free swing foot target foothold to the new supporting foot position, and ensuring that the updated foot state ensures that the feet do not overlap within the slope plane and conform to the natural posture of bipedal walking.

[0014] In this specification, the key parameters of the bipedal social force coupling model in step 3 include: the posture adaptation coefficient, friction sensitivity coefficient, mass influence coefficient, and fatigue sensitivity coefficient in the adaptive relaxation time model, as well as the acceleration sensitivity coefficient and relaxation time sensitivity coefficient in the step length calculation; the optimization objective of the optimization algorithm is to minimize the sum of squared errors between the model simulation step length and the experimentally acquired step length, and the values ​​of all key parameters are limited to the range of 0 to 1; the verification of the model's micro-gait reproduction capability and macro-dynamic characteristics specifically includes: comparing the consistency between the simulated step length and the experimental step length at the micro level, comparing the consistency between the density-velocity relationship obtained from the simulation and the basic diagram of the pedestrian group dynamics experiment on the slope at the macro level, and verifying that the scenario covers all preset uphill slopes. In summary, the present invention has at least the following beneficial effects: This invention organically integrates biomechanical experiments, bipedal kinematic modeling, and social force simulation to construct a complete technical framework from data acquisition to model validation. It overcomes the limitations of existing models that simplify pedestrians to point masses and fail to characterize gait features. Furthermore, it incorporates the biomechanical adaptation of pedestrians uphill into the simulation, achieving high-precision simulation of pedestrian movement uphill. Specific technical effects are as follows: (1) A data-driven simulation paradigm has been established, which significantly improves the biomechanical realism. Traditional models simplify pedestrians to point masses, which cannot reflect key gait changes such as shortened stride length and adjusted stride frequency when walking uphill. Based on real gait data obtained from controlled treadmill experiments, this invention constructs a bipedal kinematic framework and establishes a coordinated mechanism of center of mass movement and alternating foot movements, which can naturally generate gait trajectories that conform to biomechanical laws, making the simulation of pedestrians walking uphill more realistic.

[0015] (2) This invention innovates the slope adaptive mechanism and realizes dynamic simulation across slopes. Existing methods are mostly designed for flat terrain and lack adaptability to continuous slope changes. This invention quantifies the biomechanical load caused by slope by introducing a slope-dependent expected velocity function and an improved adaptive relaxation time model. It can accurately reflect the dynamic law of pedestrian speed and gait parameters changing with slope and realize continuous adaptive simulation from gentle to steep slopes.

[0016] (3) A complete simulation methodology system has been formed, providing a reliable foundation for engineering applications. This invention integrates experimental measurement, model construction, parameter calibration, and multi-slope verification into an organic whole, providing a novel simulation framework for bipedal pedestrians going uphill. This framework not only ensures the reliability of model parameters through optimized algorithm calibration, but also proves its accuracy in micro-gait reproduction and macro-flow rate analysis through multi-scale verification, providing effective technical means for engineering applications such as ramp facility design and pedestrian safety assessment. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the data-driven simulation method for the coupling of bipedal gait and social forces of pedestrians going uphill, which is involved in this invention.

[0019] Figure 2 This is a schematic diagram of the treadmill walking experiment scenario involved in this invention.

[0020] Figure 3 This is a schematic diagram of the simulation execution process of the bipedal social force model based on gait involved in this invention.

[0021] Figure 4 This is a schematic diagram illustrating the principle of pedestrian gait generation and foot placement calculation involved in this invention.

[0022] Figure 5This is a schematic diagram showing the comparison between the simulated step length and the experimentally measured step length under different slopes involved in this invention.

[0023] Figure 6 This is a schematic diagram showing the comparison and verification of the basic simulation diagram and experimental data obtained in this invention at slopes of 0°, 17° and 27° (simulation results, experimental results 1, experimental results 2).

[0024] Figure 7 This is a schematic diagram showing the comparison and verification of the basic simulation diagram and experimental data obtained in this invention at slopes of 0°, 17° and 27° (simulation results, experimental results 3).

[0025] Figure 8 This is a schematic diagram showing the comparison and verification of the basic simulation diagram and experimental data obtained in this invention at slopes of 0°, 17° and 27° (simulation results, experimental results 4). Detailed Implementation

[0026] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the embodiments of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0027] The following disclosure provides many different implementations or examples for carrying out different structures of the embodiments of the present invention. To simplify the disclosure of the embodiments of the present invention, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the embodiments of the present invention. Furthermore, reference numerals and / or reference letters may be repeated in different examples of the embodiments of the present invention; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various implementations and / or arrangements discussed.

[0028] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0029] like Figure 1 As shown, this embodiment provides a data-driven simulation method for the coupling of bipedal gait and social forces of pedestrians going uphill, including: Step 1: Collect gait dynamics data of pedestrians at different uphill slopes; Step 2: Based on gait dynamics data, construct a bipedal social force coupling model. The simulation process of this bipedal social force coupling model includes: Step 21: Set the geometric parameters of the simulation scene, initialize the pedestrian group, set the total simulation time and simulation time step, and set the initial gait parameters and initial foot state for each pedestrian based on gait dynamics data. Step 22: In each simulation time step, traverse all pedestrians and determine whether the stepping conditions are met based on the principle of bipedal gait dynamics. If they are met, execute steps 23 and 24 to update the gait parameters and bipedal states. Otherwise, proceed directly to step 25 and use the historical gait parameters and historical bipedal states of the current pedestrian. Step 23: Calculate the social forces and key gait parameters acting on the pedestrian. The key gait parameters are used as the latest gait parameters for the current time step. Step 24: Based on the latest gait parameters, plan the target footing point of the swinging foot, detect whether the target footing point collides with obstacles or other pedestrians. If a collision occurs, trigger a deceleration strategy and replan the footing point until the collision is resolved. Then update the foot status to obtain the latest foot status of the current time step. Step 25: If the pedestrian meets the stepping conditions, calculate the pedestrian's forward displacement and lateral displacement based on the latest gait parameters and the latest foot state; if the pedestrian does not meet the stepping conditions, calculate the pedestrian's forward displacement and lateral displacement based on the current pedestrian's historical gait parameters and historical foot state; wherein, during the initial simulation, the pedestrian's forward displacement and lateral displacement are calculated based on the current pedestrian's initial gait parameters and initial foot state. The centroid coordinates of the pedestrian are updated based on the forward displacement and lateral displacement. Step 26: Determine whether the simulation time has reached the preset total simulation duration. If it has, end the simulation; otherwise, update the simulation time and record the gait parameters and foot states of the current time step as historical data, then return to step 22.

[0030] In some embodiments, the data-driven simulation method for bipedal gait and social force coupling of pedestrians on slopes further includes: step 3, using an optimization algorithm to calibrate the key parameters of the bipedal social force coupling model, and verifying the model's micro-gait reproduction ability and macro-dynamic characteristics in multi-slope scenarios.

[0031] In some embodiments, the collection of pedestrian gait dynamics data under different uphill slopes in step 1 specifically includes: setting up an experimental platform using a treadmill with a controllable slope, recruiting healthy adult volunteers to conduct a constant-speed walking experiment under preset uphill slopes, and collecting data in real time using a pressure and gait measurement system; the gait dynamics data includes at least the pedestrian's stride length, stride duration, stride frequency, stride width, support phase duration, swing phase duration, double support phase duration, and ground reaction force statistics; the pressure and gait measurement system is the Zebris FDM-T system in the prior art, and the preset different uphill slopes at least cover 0° flat ground, 5°-12° gentle slopes, and 17°-27° steep slopes.

[0032] In some embodiments, the step 21 of setting initial gait parameters and initial foot states for each pedestrian based on gait dynamics data specifically includes: Initial gait parameter settings: The initial step direction angle is set along the uphill direction of the slope, referring to the statistical value of the average walking direction of people descending the same slope in step 1; the initial step length is the statistical average of the step length of people descending the same slope in step 1; the initial step width is the statistical average of the step width of people descending the same slope in step 1. Initial foot position settings: The initial support foot position and the pedestrian's initial center of gravity position are perpendicularly corresponding in the ramp plane, which conforms to the natural positional relationship between the support foot and the center of gravity when the human body is standing; the initial swing foot position is set on the symmetrical side of the initial support foot based on the initial step width and the initial step direction angle, and after setting, it is necessary to detect whether the initial foot position collides with obstacles or other pedestrians. If a collision occurs, the relative positions of the initial support foot and the swing foot are readjusted until the collision is resolved.

[0033] In some embodiments, the core basis for determining whether the stepping condition is met in step 22 based on the principle of bipedal gait dynamics is the relative relationship between the projection position of the pedestrian's center of mass on the ramp plane and the position of the forehand end of the current supporting foot; the specific judgment logic is as follows: 1. Centroid projection calculation In an uphill scenario, the ramp has a slope angle θ. To determine the stepping conditions in the ramp plane (two-dimensional coordinate system), the three-dimensional coordinates of the pedestrian's centroid need to be projected onto this plane. To simplify the calculation, the ramp is considered as a two-dimensional inclined plane, and the pedestrian... At any moment The coordinates of the centroid are , and They represent pedestrians. The center of mass is in Moment coordinates and coordinate.

[0034] 2. Determining the center point of the support leg Similar to the center of mass location, the spatial reference position of the support foot is defined as the center point of that foot on the ramp plane. Its coordinates on the ramp plane are: , and They represent pedestrians. The supporting legs are Moment coordinates and Coordinates. These coordinates are calculated based on subsequent steps.

[0035] 3. Exceeding the judgment conditions In each simulation step, the projection point of the centroid on the ramp plane is determined. Coordinates greater than the center point of the supporting foot When the coordinates are equal, the following conditions must be met: ; At this point, it is determined that the center of gravity has "surpassed" the current supporting foot, triggering a new step.

[0036] 4. Judgment Process In each step of the simulation, the calculation of each pedestrian's information is performed in real time. and The above comparison is then performed. If the conditions are met, steps S23 and S24 are executed to update the gait parameters and foot states; otherwise, the process proceeds directly to step S25 to continue using the historical state.

[0037] In some embodiments, calculating the social forces acting on the pedestrian in step 23 includes calculating the pedestrian's self-driving force, the interaction force between the pedestrian and other individuals, and the interaction force between the pedestrian and the wall; wherein, when calculating the pedestrian's self-driving force, an adaptive relaxation time model is introduced, which comprehensively reflects the biomechanical load caused by walking uphill, specifically including: The degree of forward tilt of the torso is correlated and quantified by the sine function of the slope angle; The nonlinear growth of friction demand is correlated and quantified by using the exponential form of the tangent function of the slope angle; The impact of weight on motion response is quantified by correlating the ratio of a pedestrian's actual weight to a reference weight. The fatigue effect is correlated and quantified by the cumulative motion height of pedestrians during the active walking phase. The cumulative motion height is reset to the baseline value when the pedestrian's instantaneous speed is lower than the minimum motion speed threshold. The reference weight and the baseline value are determined based on the experimental data statistics in step 1.

[0038] In some embodiments, the key gait parameters in step 23 include the step direction angle, step length, and step width; wherein, the calculation logic of the step length is: the step length is equal to the product of the pedestrian's current instantaneous velocity and the single-step time; the single-step time is affected by both acceleration and adaptive relaxation time, specifically: the average single-step time calibrated in step 1 is used as a benchmark, and an acceleration influence factor and a relaxation time influence factor are superimposed; the acceleration influence factor is calculated by the ratio of the acceleration benchmark value to the pedestrian's current instantaneous acceleration, and the relaxation time influence factor is calculated by the ratio of the current adaptive relaxation time to the relaxation time benchmark value, and both the acceleration benchmark value and the relaxation time benchmark value are determined based on the experimental data statistics in step 1.

[0039] In some embodiments, the logic for setting the step width in step 23 is as follows: the step width follows a standard normal distribution, the mean of which is the statistical mean of the step width of pedestrians on the same slope in step 1, and the standard deviation is the statistical standard deviation of the step width of pedestrians on the same slope in step 1; the specific value of the step width is the product of the mean plus or minus the standard deviation and the standard normal distribution random variable, the standard normal distribution random variable follows a distribution with a mean of 0 and a variance of 1, in order to simulate the individual differences in the step width of different pedestrians.

[0040] In some embodiments, the triggering deceleration strategy in step 24 specifically includes: reducing the pedestrian's current instantaneous speed to a preset safe speed range, the safe speed range being set based on the statistical value of the pedestrian's low-speed adjustment state when walking uphill in step 1; shortening the step length by reducing the instantaneous speed, thereby adjusting the position of the swing foot's target foothold until it is detected that the target foothold does not collide with obstacles or other pedestrians; the updating of the foot state specifically includes: updating the original supporting foot to a new swing foot, updating the re-planned, collision-free swing foot target foothold to a new supporting foot position, and ensuring that the updated foot state ensures that the feet do not overlap in the slope plane and conform to the natural posture of bipedal walking.

[0041] In some embodiments, the key parameters of the bipedal social force coupling model in step 3 include: the posture adaptation coefficient (related to trunk forward lean), friction sensitivity coefficient (related to friction demand), mass influence coefficient (related to body weight influence), and fatigue sensitivity coefficient (related to fatigue effect) in the adaptive relaxation time model, as well as the acceleration sensitivity coefficient and relaxation time sensitivity coefficient in the step length calculation; the optimization algorithm is a differential evolution algorithm in the prior art, such as the differential evolution algorithm. In a typical implementation, the parameter configurations that can be adopted include, but are not limited to: population size (NP) of 30, mutation operator (F) of 0.4, and crossover probability (CR) of 0.1. Those skilled in the art will understand that the above parameters can be adjusted according to the scale of the optimization problem. The optimization objective is to minimize the sum of squared errors between the model simulation step size and the experimental data collection step size in step 1, with all key parameters limited to values ​​between 0 and 1. The verification of the model's micro-gait reproducibility and macro-dynamic characteristics specifically includes: at the micro-level, comparing the consistency between the simulation step size and the experimental step size in step 1; and at the macro-level, comparing the consistency between the density-velocity relationship (basic graph) obtained from the simulation and the publicly available basic graph data of pedestrian group dynamics on slopes obtained through controlled experiments. For example, the velocity-density relationship experimental data under different slopes reported by Huang Zhongyi et al. in *PHYSICAL REVIEW E* serves as the benchmark for macro-level verification. The verification scenario covers all uphill slopes preset in step 1.

[0042] The technical concept of this invention is as follows: This invention achieves coupled simulation of bipedal gait and social forces of pedestrians going uphill through a full-link design of data acquisition, model building, and calibration verification. The core process is as follows: Data foundation construction: Through experiments on treadmills with controllable incline, gait dynamics data of pedestrians walking on different uphill slopes were collected to provide quantitative basis for model building and parameter calibration; Bipedal social force model construction: Integrating social force-driven center of mass movement with bipedal gait generator, an improved adaptive relaxation time model is introduced (quantifying the effects of trunk forward lean, friction demand, body weight, fatigue, etc.) to dynamically generate uphill gait that conforms to biomechanical laws, while ensuring the rationality of movement through foot placement planning and collision response mechanism. Model accuracy assurance: Optimization algorithms are used to calibrate key parameters of the model, and the model's micro gait reproduction capability and macro dynamic characteristics are verified in multi-slope scenarios. Ultimately, data-driven operation is achieved from experiment to simulation, overcoming the shortcomings of traditional models and improving the reliability and applicability of uphill pedestrian simulation.

[0043] Specifically as follows: Step S1, Biomechanical Experiment and Data Acquisition: Through a controlled treadmill ramp walking experiment, gait dynamics data of pedestrians under different uphill slopes were collected to provide quantitative basis for model establishment and parameter calibration.

[0044] Step S2, Gait-based bipedal social force simulation process: Using the data obtained in step S1, construct a bipedal social force coupling model, dynamically generate gait through the center of mass movement and bipedal alternation mechanism, and realize the simulation of uphill walking.

[0045] Step S3, Model Parameter Calibration and Multi-slope Validation: The key parameters of the model are calibrated using an optimization algorithm, and the accuracy and robustness of the model in terms of step size reproduction and basic graph prediction are verified in multi-slope scenarios.

[0046] In some embodiments, step S2 specifically includes the following steps: Step S21, Simulation Initialization: Set the geometric parameters of the simulation scene, initialize the pedestrian group, and set the total simulation duration and simulation time step.

[0047] S211. Set the geometric parameters of the simulation scene, including the ramp length. ramp width Slope angle And the location coordinates of the obstacles; S212. Initialize the pedestrian group, and for each pedestrian... Set the following initial state: (1) Position and velocity: Initial position coordinates of the center of mass , and They represent pedestrians. The center of mass is in Moment coordinates and Coordinates. Initial position coordinates of the supporting leg. , and They represent pedestrians. The supporting legs are Moment coordinates and Coordinates. Initial position coordinates of the swinging foot. and initial velocity vector , and They represent pedestrians. swinging foot Moment coordinates and coordinate.

[0048] (2) Individual physical attributes: mass ,shoulder width .

[0049] (3) Expected speed According to the slope angle The correction is made based on the Lorentz function model disclosed in the prior art, which describes the saturation relationship between motion parameters and slope, and serves as the baseline for the desired speed. For example, the Lorentz-type speed-slope correction model proposed by Michael J. Campbell et al. in the journal *Applied Geography* can be referenced.

[0050] S213, Set the total simulation duration and simulation time step Initialize simulation time .

[0051] Step S22, Step Trigger Judgment: Within each simulation time step, all pedestrians are traversed to determine whether the stepping condition is met. The stepping judgment mechanism is based on the principle of bipedal gait dynamics. If the stepping condition is met, steps S23 and S24 are executed to update the position of the two feet; otherwise, the process proceeds directly to step S25 to update the position of the center of mass.

[0052] In some embodiments, step 22, which determines whether the stepping condition is met based on the principle of bipedal gait dynamics, specifically includes: using the time interval from the end of the previous step to the current simulation time step, the single-step time threshold, and the instantaneous speed as the basis for judgment; the single-step time threshold is initially referenced to the statistical average of pedestrians walking on the same slope, and is subsequently dynamically adjusted as gait parameters are updated during the simulation; when the time interval reaches the single-step time threshold and the pedestrian's instantaneous speed is not lower than a preset minimum movement speed threshold, the stepping condition is determined to be met; the minimum movement speed threshold is set based on the statistical value of the speed boundary between the pedestrian's stationary and walking states.

[0053] In some embodiments, the social forces in step S23 include the pedestrian's self-driving force, the interaction force between the pedestrian and other individuals, the interaction force between the pedestrian and the wall, and the calculation of the pedestrian speed based on Newton's second law; the gait parameters include the calculation of the step direction angle, stride length, and stride width. The steps include: S231, the pedestrian self-driving force as follows: ; In the formula, pedestrian Expected speed, pedestrian exist The speed of time pedestrian exist The expected direction at any moment pedestrian The quality. One of the key innovations of this invention is the adaptive relaxation time. Its model comprehensively reflects the effects of trunk leaning, friction demand, body weight, and fatigue revealed in step S1 on motion response characteristics. The calculation formula is as follows: ; In the formula, The degree of forward tilting of the torso, which gradually intensifies with increasing slope, was quantified. Represents the posture adaptation coefficient; The nonlinear increase in frictional demand during uphill walking was simulated. Represents the friction sensitivity coefficient; Describes the mass relative to the reference mass ,weight Add information on how to extend relaxation time; γ is the mass influence coefficient; fatigue term. This reflects the fatigue effect caused by continuous motion, where δ is the fatigue sensitivity coefficient. The calculation formula is as follows: ; In the formula, The stage of pedestrians actively walking ( The cumulative height of movement means that pedestrians will feel tired after walking for a long time, and the relaxation time will increase accordingly. When pedestrians stop (or rest)... Reset to baseline This simulates the recovery phase during exercise rest, during which the relaxation time is reduced.

[0054] In some embodiments, the interaction forces between the pedestrian and other individuals as follows: ; In the formula, The coefficient representing the intensity of psychological repulsion. The coefficient representing the range of influence of psychological repulsion. For pedestrians and pedestrians The sum of the radii, For pedestrians and pedestrians The actual distance between the centers of mass, Indicates from pedestrians Pointing to pedestrians The unit normal vector, k represents the stiffness coefficient of the physical contact. The coefficient of sliding friction in physical contact. pedestrian with pedestrians The relative speed between them Indicates perpendicular to The unit tangent vector.

[0055] In some embodiments, the interaction force between the pedestrian and the wall as follows: ; In the formula, pedestrian Distance from the wall, pedestrian The shortest distance from the center of mass to the obstacle W. This indicates a direction from obstacle W to the pedestrian. The unit normal vector, pedestrian speed, Indicates perpendicular to The unit tangent vector.

[0056] In some embodiments, the pedestrian speed based on Newton's second law is updated over time as follows: ; In the formula, pedestrian Self-driving force pedestrian and pedestrians The interaction forces between them pedestrian Repulsive force against the wall.

[0057] S232, the stepping direction angle as follows: ; In the formula, pedestrian exist The unit vector along the y-axis at any given time. pedestrian exist The unit vector along the x-axis at any given time.

[0058] In some embodiments, the single-step time The motion is influenced by both acceleration and relaxation time. Acceleration reflects the urgency of the pedestrian's speed adjustment, while relaxation time reflects the response characteristics to changes in motion state. The calculation is as follows: ; In the formula, The average single-step time obtained from the experimental calibration, As the acceleration influencing factor, The relaxation time factor is defined as follows: ; In the formula, This represents the acceleration sensitivity coefficient. This represents the relaxation time sensitivity coefficient. Indicates the reference value of acceleration. pedestrian exist Instantaneous acceleration at a given moment pedestrian exist Improved relaxation time in uphill scenarios This represents the baseline value for relaxation time.

[0059] In some embodiments, the step size as follows: ; In the formula, pedestrian exist The single-step time of a moment. pedestrian exist The instantaneous velocity at a given moment.

[0060] In some embodiments, the step width as follows: ; In the formula, obey For the mean, is a standard normal distribution with standard deviation.

[0061] Step S24: Landing point planning and collision response. Specifically, step S24 includes the following: Based on the calculation, the target foot's foot position is as follows: ; ; In the formula, For pedestrians exist The x-coordinate value of the supporting foot at all times. For pedestrians exist The y-coordinate value of the supporting foot at all times. For pedestrians exist The step size of time, For pedestrians exist The step direction angle at any given moment, For pedestrians The stride width.

[0062] In some embodiments, existing technologies are used to detect whether the target footing point collides with an obstacle or other pedestrian. Collision detection can be achieved through geometric overlap determination. For example, the pedestrian's footing area and the obstacle area can be abstracted as polygons; if the two polygons overlap, a collision is determined. A specific method for determining polygon overlap is: if any vertex of one polygon is located inside another polygon, it is considered an overlap; whether a point is inside a polygon can be determined using a ray intersection method. The basic principle is to emit a ray from that point and determine the parity of the number of intersections with the polygon edges. If a collision is detected, a deceleration strategy is triggered and the footing point is replanned until the collision is resolved. After calculation, the foot state is updated: the original supporting foot is updated to a swinging foot, and the original swinging foot target position is updated to the new supporting foot position.

[0063] Step S25: Pedestrian Status Update. Specifically, step S25 is as follows: Calculate pedestrians exist Displacement in the direction of travel at any given moment and lateral displacement as follows: ; In the formula, pedestrian exist The step size of time, where t represents the current time. This indicates the time point at which step k begins. pedestrian exist The single-step time of a moment. pedestrian stride width, This represents the maximum displacement of the center of mass during lateral oscillation.

[0064] Pedestrian updates based on displacement and step direction angle exist The centroid coordinates at time t are as follows: ; ; In the formula, pedestrian exist The x-coordinate of the supporting foot at that moment. pedestrian exist The y-coordinate of the supporting foot at any given moment. pedestrian exist Displacement in the direction of movement at any given moment. pedestrian exist Lateral displacement at time t, pedestrian exist The step direction angle at any given moment.

[0065] Step S26 Simulation Loop and Termination: The specific steps are as follows: Determine the simulation termination condition, if... End the simulation; otherwise, end the simulation. Return to step S22.

[0066] In one specific embodiment, such as Figure 1 As shown, the data-driven simulation method provided in this embodiment of the invention mainly includes three core steps: biomechanical experiments and data acquisition (S1), gait-based bipedal social force simulation process (S2), and model calibration and multi-slope verification (S3). Firstly, step S1 provides quantitative basis for model establishment and calibration, and its specific implementation is as follows: Step S1: Biomechanical experiments and data acquisition. In a preferred embodiment of the present invention, such as... Figure 2 As shown, an instrumented treadmill experimental platform was built in a laboratory environment. Thirteen healthy adult volunteers were recruited as experimental subjects and walked at a constant speed on treadmills with incline angles of 0°, 5°, 7°, and 12°. The experiment used the Zebris FDM-T pressure and gait measurement system to collect high-precision gait dynamics data of each pedestrian at each incline in real time and synchronously. The key gait parameters collected are shown in Table 1.

[0067] Table 1. Gait Dynamics Data Statistics ; Statistical analysis of the above experimental data quantitatively revealed the systemic biomechanical adaptation patterns triggered by uphill walking, mainly manifested in two synergistic aspects: Spatiotemporal and kinematic adjustments: As the slope increases, pedestrians exhibit significantly shorter stride length, lower stride frequency, longer stride duration, and a general increase in the duration of each gait phase. The fundamental purpose is to improve dynamic stability during walking.

[0068] Posture and dynamic strategies: To counteract the backward tilting moment caused by the slope, pedestrians adopt a compensatory posture of leaning forward and flexing their knees. This change in posture directly leads to changes in dynamic characteristics such as the peak ground reaction force.

[0069] The quantitative data obtained in this step and the patterns it reveals provide an irreplaceable data foundation for the establishment of the simulation model in the subsequent S2 step and the accurate calibration of key parameters in the S3 step, ensuring the data-driven characteristics and biomechanical rationality of the entire simulation method.

[0070] Based on the pedestrian uphill kinematics and dynamics adjustment characteristics revealed in step S1, this invention constructs a gait-based bipedal social force simulation process, decomposing pedestrian motion into a coupled process of center of mass (COM) motion driven by social forces and bipedal alternating motion based on gait parameters. This process is as follows: Figure 3 As shown, the specific steps are as follows: Step S21, Simulation Initialization: Set the geometric parameters of the simulation scene, initialize the pedestrian group, and set the total simulation duration and simulation time step.

[0071] S211, Scene Parameter Settings: In this embodiment, the ramp length L = 30 meters, the ramp width W = 0.8 meters, and the slope angle is set to... The obstacle is the two walls defined by the ramp boundary.

[0072] S212. Initialize the pedestrian group, setting the number of pedestrians to 1, and assigning each pedestrian... Set the following initial state: (1) Position and velocity: Initial position coordinates of the center of mass The initial coordinates of the support feet are randomly generated within the ramp area. Initial position coordinates of the swinging foot Based on the center of mass position, the system checks for overlap between the two foot regions; if overlap is found, it regenerates the initial velocity vector. It follows a normal distribution with a mean of 0.2 m / s and a standard deviation of 0.05 m / s.

[0073] (2) Individual physical attributes: mass Shoulder width follows a normal distribution with a mean of 70 kg and a standard deviation of 5 kg. Determined based on the area of ​​both feet.

[0074] (3) Expected speed According to the slope angle A correction is made based on the Lorentz function model in existing technology, which serves as the baseline for the desired velocity. In this embodiment, the desired velocity is generated by a uniform distribution between the 5th and 15th percentiles of this model. .

[0075] S213, Set the total simulation duration For 200 and simulation time step The initial simulation time is 0.01. .

[0076] Step S22, Step Trigger Judgment: Within each simulation time step, all pedestrians are traversed to determine whether the stepping condition is met. The stepping judgment mechanism is based on the principle of bipedal gait dynamics. If the stepping condition is met, steps S23 and S24 are executed to update the position of the two feet; otherwise, the process proceeds directly to step S25 to update the position of the center of mass.

[0077] Step S23, Gait and Social Force Calculation: When a step is triggered, the social forces acting on the pedestrian are calculated, and the gait parameters for the next step are determined. Specifically, the social forces in step S23 include the pedestrian's self-driving force, the interaction force between the pedestrian and other individuals, the interaction force between the pedestrian and the wall, and the calculation of the pedestrian's speed based on Newton's second law; the gait parameters include the calculation of the step direction angle, stride length, and stride width. This includes the following steps: S231, the pedestrian self-driving force as follows: ; In the formula, pedestrian Expected speed, pedestrian exist The speed of time pedestrian exist The expected direction at any moment pedestrian The quality. One of the key innovations of this invention is the adaptive relaxation time. Its model comprehensively reflects the effects of trunk leaning, friction demand, body weight, and fatigue revealed in step S1 on motion response characteristics. The calculation formula is as follows: ; In the formula, The degree of forward tilting of the torso, which gradually intensifies with increasing slope, was quantified. The posture adaptation coefficient, in this embodiment, represents the posture adaptation coefficient. Set to 0.039; Simulate the nonlinear growth of frictional demand. Representing the friction sensitivity coefficient, in this embodiment, Set to 0.237; Describes the mass relative to the reference mass ,weight To increase the relaxation time, γ is the mass influence coefficient, which is set to 0.807 in this embodiment; fatigue term. This reflects the fatigue effect caused by continuous motion, where δ is the fatigue sensitivity coefficient. In this embodiment, δ is set to 0.567. The calculation formula is as follows: ; In the formula, The stage of pedestrians actively walking ( The cumulative height of movement means that pedestrians will feel tired after walking for a long time, and the relaxation time will increase accordingly. When pedestrians stop (or rest)... Reset to baseline This simulates the recovery phase during exercise rest, during which relaxation time is reduced. In this embodiment, Set to 0.1 m / s. Set to 0.

[0078] , The calibration process for γ and δ is described in detail in step S3.

[0079] The interaction forces between pedestrians and other individuals as follows: ; In the formula, The coefficient representing the intensity of psychological repulsion, in this embodiment... Set it to 2000. In this embodiment, the coefficient representing the range of effect of psychological repulsion is... Set to 0.08. For pedestrians and pedestrians The sum of the radii, For pedestrians and pedestrians The actual distance between the centers of mass, This indicates a pointer from pedestrian j to pedestrian j. The unit normal vector, k represents the stiffness coefficient of the physical contact, and in this embodiment, k is set to 120000. The coefficient of sliding friction, representing the physical contact, in this embodiment, Set it to 240000. pedestrian and pedestrians The relative speed between them Indicates perpendicular to The unit tangent vector.

[0080] The interaction force between the pedestrian and the wall as follows: ; In the formula, pedestrian Distance from the wall, pedestrian The shortest distance from the center of mass to the obstacle W. Indicates pointing from an obstacle to a pedestrian. The unit normal vector, pedestrian speed, Indicates perpendicular to The unit tangent vector.

[0081] The pedestrian speed based on Newton's second law is updated over time as follows: ; In the formula, pedestrian Self-driving force pedestrian and pedestrians The interaction forces between them Indicates the wall is facing pedestrians The repulsive force.

[0082] S232, the stepping direction angle as follows: ; In the formula, pedestrian exist The unit vector along the y-axis at any given time. pedestrian exist The unit vector along the x-axis at any given time.

[0083] The single-step time The motion is influenced by both acceleration and relaxation time. Acceleration reflects the urgency of the pedestrian's speed adjustment, while relaxation time reflects the response characteristics to changes in motion state. The calculation is as follows: ; In the formula, In this embodiment, the average single-step time obtained from experimental calibration is used as an example. It follows a normal distribution with a mean of 0.49s and a standard deviation of 0.02s. As the acceleration influencing factor, The relaxation time factor is given by the following formula: ; In the formula, This represents the acceleration sensitivity coefficient, in this embodiment, Set to 0.006. This represents the relaxation time sensitivity coefficient, in this embodiment, Set to 0.04. This represents the acceleration reference value, in this embodiment, Set it to 0.98. pedestrian exist Instantaneous acceleration at a given moment pedestrian exist Improved relaxation time in uphill scenarios This represents the relaxation time reference value, in this embodiment, Set it to 0.5. and All results were obtained through experimental calibration, and the calibration process is described in detail in step S3.

[0084] The step size The calculation is as follows: ; In the formula, pedestrian exist The single-step time of a moment. pedestrian exist The instantaneous velocity at a given moment.

[0085] The step width The calculation is as follows:

[0086] In the formula, obey For the mean, The standard normal distribution has a standard deviation. In this embodiment, the step width follows a standard normal distribution with a mean of 0.13m and a standard deviation of 0.02m.

[0087] Step S24: Landing point planning and collision response. Specifically, step S24 includes the following: S241, such as Figure 4 As shown, the calculated position of the swing foot's foothold is as follows: ; ; In the formula, For pedestrians exist The x-coordinate value of the supporting foot at all times. For pedestrians exist The y-coordinate value of the supporting foot at all times. For pedestrians exist The step size of time, For pedestrians exist The step direction angle at any given moment, For pedestrians The stride width.

[0088] S242. Collision Detection and Response. Detects whether the target footing collides with an obstacle or other pedestrian using existing technology. If a collision is detected, a deceleration strategy is triggered and the footing is replanned until the collision is resolved. After calculation, the foot state is updated: the original supporting foot is updated to a swinging foot, and the original swinging foot's target position is updated to the new supporting foot position.

[0089] Step S25: Pedestrian Status Update. Specifically, step S25 is as follows: S251, Calculating pedestrians exist Displacement in the direction of travel at any given moment and lateral displacement as follows: ; In the formula, pedestrian exist The step size of time, where t represents the current time. This indicates the time point at which step k begins. pedestrian exist The single-step time of a moment. pedestrian stride width, This represents the maximum displacement of the centroid's lateral oscillation. In this embodiment, Set to 0.02m.

[0090] S252, Update pedestrian information based on displacement and step direction angle. exist The centroid coordinates at time t are as follows: ; ; In the formula, pedestrian exist The x-coordinate of the supporting foot at that moment. pedestrian exist The y-coordinate of the supporting foot at any given moment. pedestrian exist Displacement in the direction of movement at any given moment. pedestrian exist Lateral displacement at time t, pedestrian exist The step direction angle at any given moment.

[0091] Step S26: Loop Progression and Termination Judgment. Determine if the simulation time has reached the preset total duration. Determine the simulation termination condition if... End the simulation; otherwise, end the simulation. Then return to step S22 to continue the simulation loop for the next time step.

[0092] Step S3: Model parameter calibration and multi-slope verification. This involves calibrating key parameters of the adaptive relaxation time model proposed in step S231 (including the forward tilt sensitivity coefficient α, friction demand sensitivity coefficient β, weight correction coefficient γ, and fatigue correction coefficient δ) and step size influence parameters (acceleration sensitivity coefficient). and relaxation time sensitivity coefficient The model was calibrated, and based on simulations of a single-column ramp walking scenario, its universality and accuracy under multi-slope conditions were verified from two levels: microscopic gait parameters and macroscopic basic graphs. The specific steps are as follows: S31. Parameter Calibration. Using the simulation environment established in step S21, to maintain continuous flow, pedestrians are set to automatically cycle back to the starting point on the left after leaving the right end. This invention uses an existing optimization algorithm to calibrate the six key parameters. The calibration process is based on the stride length data obtained from the treadmill experiment in step S1, and the following optimization problem is constructed: Minimize: ; Constraints: ; in, and These represent slopes respectively. The average step size obtained from experiments and simulations. After calibration, the optimal values ​​for each parameter are as follows: ; S32. Multi-slope verification. Using the simulation environment established in step S21, the dynamic characteristics of a single-column pedestrian walking uphill under different slopes are simulated. The specific steps are as follows: S321. Stride length reproduction verification. By simulating the stride length of a freely walking pedestrian at different inclines, the model's ability to reproduce microscopic gait parameters is evaluated. For example... Figure 5 As shown, the simulated step length and the experimental data are in good agreement, with average relative errors of 6.03% and 4.28% respectively compared with experimental results 1 and 2. This indicates that the model can effectively capture the key biomechanical characteristic of step length shortening with increasing slope.

[0093] S322. Reproduction and Verification of the Basic Graph. The basic graph (density-velocity relationship) is a key basis for evaluating the macroscopic performance of the crowd dynamics model. Figure 6 , Figure 7 , Figure 8 The simulation results show a comparison between the basic graphs obtained from the model simulations at slopes of 0°, 17°, and 27° and the experimental data. The simulation results consistently show that the average speed decreases with increasing density, consistent with empirical findings. Quantitative error analysis shows that, under 0° flat terrain conditions, the root mean square error (RMSE) of the simulation results compared to experimental results 1 and 2 are 0.091 and 0.055, respectively; under 17° and 27° uphill conditions, the RMSEs compared to experimental results 3 and 4 are 0.102 and 0.067, respectively. Overall, the model successfully reproduces the basic changing trends of downhill pedestrian flow at different slopes, verifying its applicability and robustness under various slope scenarios.

[0094] The embodiments described above are for illustrative purposes only and are not intended to limit the invention. Therefore, any changes in numerical values ​​or substitutions of equivalent elements should still fall within the scope of this invention.

[0095] The above detailed description will enable those skilled in the art to understand that the present invention can indeed achieve the aforementioned objectives and has complied with the provisions of the Patent Law.

[0096] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention. The above descriptions are merely preferred embodiments of the invention and are not intended to limit the invention. It should be noted that any modifications, equivalent substitutions, and improvements made within the spirit and principles of the invention should be included within the scope of protection of the invention.

[0097] It should be noted that the above description of the process is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to the process under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0098] The basic concepts have been described above. Obviously, for those skilled in the art who have read this application, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore, such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this application.

[0099] Furthermore, this application uses specific terms to describe its embodiments. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different positions in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application can be appropriately combined.

[0100] Furthermore, those skilled in the art will understand that aspects of this application can be described and illustrated through several patentable types or situations, including any new and useful combination of processes, machines, products, or substances, or any new and useful improvements thereof. Therefore, aspects of this application can be implemented entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. All of the above hardware or software can be referred to as a “unit,” “module,” or “system.” Furthermore, aspects of this application can take the form of a computer program product embodied in one or more computer-readable media, wherein computer-readable program code is contained therein.

[0101] The computer program code required for the operation of each part of this application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, and Python; general programming languages ​​such as C; Visual Basic, Fortran2103, Perl, COBOL2102, PHP, and ABAP; dynamic programming languages ​​such as Python, Ruby, and Groovy; or other programming languages. This program code can run entirely on the user's computer, or as a standalone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer via any network, such as a local area network (LAN) or wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as Software as a Service (SaaS).

[0102] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this application are not intended to limit the order of the processes and methods of this application. Although some currently considered useful embodiments of the invention have been discussed in the foregoing disclosure by way of various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the substance and scope of the embodiments of this application. For example, although the implementation of the various components described above can be embodied in a hardware device, it can also be implemented as a purely software solution, such as an installation on an existing server or mobile device.

[0103] Similarly, it should be noted that, in order to simplify the description of the present application and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of the embodiments of the present application sometimes combines multiple features into a single embodiment, drawing, or description thereof. However, this approach of the present application should not be construed as reflecting an intention that the claimed subject matter requires more features than expressly recited in each claim. Rather, the subject of the invention should possess fewer features than in any single embodiment described above.

Claims

1. A data-driven simulation method for coupling uphill pedestrian bipedal gait with social forces, characterized in that, include: Step 1: Collect gait dynamics data of pedestrians at different uphill slopes; Step 2: Based on gait dynamics data, construct a bipedal social force coupling model. The simulation process of this bipedal social force coupling model includes: Step 21: Set the geometric parameters of the simulation scene, initialize the pedestrian group, set the total simulation time and simulation time step, and set the initial gait parameters and initial foot state for each pedestrian based on gait dynamics data. Step 22: In each simulation time step, traverse all pedestrians and determine whether the stepping conditions are met based on the principle of bipedal gait dynamics. If they are met, execute steps 23 and 24 to update the gait parameters and bipedal states. Otherwise, proceed directly to step 25 and use the historical gait parameters and historical bipedal states of the current pedestrian. Step 23: Calculate the social forces and key gait parameters acting on the pedestrian. The key gait parameters are used as the latest gait parameters for the current time step. Step 24: Based on the latest gait parameters, plan the target footing point of the swinging foot, detect whether the target footing point collides with obstacles or other pedestrians. If a collision occurs, trigger a deceleration strategy and replan the footing point until the collision is resolved. Then update the foot status to obtain the latest foot status of the current time step. Step 25: If the pedestrian meets the stepping conditions, calculate the pedestrian's forward displacement and lateral displacement based on the latest gait parameters and the latest foot state; if the pedestrian does not meet the stepping conditions, calculate the pedestrian's forward displacement and lateral displacement based on the current pedestrian's historical gait parameters and historical foot state; wherein, during the initial simulation, the pedestrian's forward displacement and lateral displacement are calculated based on the current pedestrian's initial gait parameters and initial foot state. The centroid coordinates of the pedestrian are updated based on the forward displacement and lateral displacement. Step 26: Determine whether the simulation time has reached the preset total simulation duration. If it has, end the simulation; otherwise, update the simulation time and record the gait parameters and foot states of the current time step as historical data, then return to step 22.

2. The data-driven simulation method for coupling uphill pedestrian bipedal gait with social forces according to claim 1, characterized in that, It also includes: Step 3, using an optimization algorithm to calibrate the key parameters of the bipedal social force coupling model, and verifying the model's micro gait reproduction ability and macro dynamic characteristics in multi-slope scenarios.

3. The data-driven simulation method for coupling uphill pedestrian bipedal gait with social forces according to claim 1, characterized in that, Step 1, which involves collecting gait dynamics data of pedestrians at different uphill slopes, specifically includes: conducting constant-speed walking experiments at preset uphill slopes and collecting data in real time; the gait dynamics data includes at least the pedestrian's stride length, stride duration, stride frequency, stride width, support phase duration, sway phase duration, double support phase duration, ground reaction force statistics, and step direction angle for each step.

4. The data-driven simulation method for coupling uphill pedestrian bipedal gait with social forces according to claim 1, characterized in that, Step 21, based on gait dynamics data, sets initial gait parameters and initial foot states for each pedestrian, specifically including: Initial gait parameter settings: The initial step direction angle is set along the uphill direction of the slope, referring to the statistical value of the average walking direction of pedestrians on the same slope; the initial step length is the statistical average of the step length of pedestrians on the same slope; the initial step width is the statistical average of the step width of pedestrians on the same slope. Initial foot position settings: The initial support foot position and the pedestrian's initial center of gravity position are perpendicularly corresponding in the ramp plane, which conforms to the natural positional relationship between the support foot and the center of gravity when the human body is standing; the initial swing foot position is set on the symmetrical side of the initial support foot based on the initial step width and the initial step direction angle, and after setting, it is necessary to detect whether the initial foot position collides with obstacles or other pedestrians. If a collision occurs, the relative positions of the initial support foot and the swing foot are readjusted until the collision is resolved.

5. The data-driven simulation method for coupling uphill pedestrian bipedal gait with social forces according to claim 1, characterized in that, In step 22, the core basis for determining whether the stepping condition is met based on the principle of bipedal gait dynamics is the relative relationship between the projection position of the pedestrian's center of mass on the ramp plane and the position of the front end of the current supporting foot.

6. The data-driven simulation method for coupling uphill pedestrian bipedal gait with social forces according to claim 1, characterized in that, Step 23 calculates the social forces acting on the pedestrian, including calculating the pedestrian's self-driving force, the interaction forces between the pedestrian and other individuals, and the interaction forces between the pedestrian and the wall. The calculation of the pedestrian's self-driving force incorporates an adaptive relaxation time model, which comprehensively reflects the biomechanical load caused by uphill walking, specifically including: The degree of forward tilt of the torso is correlated and quantified by the sine function of the slope angle; The nonlinear growth of friction demand is correlated and quantified by using the exponential form of the tangent function of the slope angle; The impact of weight on motion response is quantified by correlating the ratio of a pedestrian's actual weight to a reference weight. The fatigue effect is correlated and quantified by the cumulative motion height of pedestrians during the active walking phase. The cumulative motion height is reset to the baseline value when the pedestrian's instantaneous speed is lower than the minimum motion speed threshold. The reference weight and the baseline value are determined based on experimental data statistics.

7. The data-driven simulation method for coupling uphill pedestrian bipedal gait with social forces according to claim 1, characterized in that, The key gait parameters in step 23 include the step direction angle, step length, and step width. The step length is calculated as follows: the step length equals the product of the pedestrian's current instantaneous velocity and the single-step time. The single-step time is influenced by both acceleration and adaptive relaxation time. Specifically, it is based on the experimentally calibrated average single-step time, with the acceleration influence factor and relaxation time influence factor superimposed. The acceleration influence factor is calculated as the ratio of the acceleration reference value to the pedestrian's current instantaneous acceleration, and the relaxation time influence factor is calculated as the ratio of the current adaptive relaxation time to the relaxation time reference value. Both the acceleration reference value and the relaxation time reference value are determined based on statistical analysis of experimental data.

8. The data-driven simulation method for coupling uphill pedestrian bipedal gait with social forces according to claim 7, characterized in that, The logic for setting the stride width in step 23 is as follows: the stride width follows a standard normal distribution, the mean of which is the statistical mean of the stride width of pedestrians on the same slope, and the standard deviation is the statistical standard deviation of the stride width of pedestrians on the same slope; the specific value of the stride width is the product of the mean plus or minus the standard deviation and the standard normal distribution random variable, which follows a distribution with a mean of 0 and a variance of 1, to simulate the individual differences in the stride width of different pedestrians.

9. The data-driven simulation method for coupling uphill pedestrian bipedal gait with social forces according to claim 1, characterized in that, The deceleration strategy triggered in step 24 specifically includes: reducing the pedestrian's current instantaneous speed to a preset safe speed range, which is set based on the statistical value of the pedestrian's low-speed adjustment state when walking uphill; shortening the step length by reducing the instantaneous speed, thereby adjusting the position of the swing foot's target foothold until it is detected that the target foothold does not collide with obstacles or other pedestrians; the update of the foot status specifically includes: updating the original supporting foot to the new swing foot, updating the re-planned non-collision swing foot's target foothold to the new supporting foot position, and ensuring that the updated foot status ensures that the feet do not overlap in the slope plane and conform to the natural posture of bipedal walking.

10. The data-driven simulation method for coupling uphill pedestrian bipedal gait with social forces according to claim 2, characterized in that, The key parameters of the bipedal social force coupling model in step 3 include: the posture adaptation coefficient, friction sensitivity coefficient, mass influence coefficient, and fatigue sensitivity coefficient in the adaptive relaxation time model, as well as the acceleration sensitivity coefficient and relaxation time sensitivity coefficient in the step length calculation; the optimization objective of the optimization algorithm is to minimize the sum of squared errors between the model simulation step length and the experimentally acquired step length, and the values ​​of all key parameters are limited to between 0 and 1; the verification of the model's micro-gait reproduction capability and macro-dynamic characteristics specifically includes: comparing the consistency between the simulated step length and the experimental step length at the micro level, comparing the consistency between the density-velocity relationship obtained from the simulation and the basic diagram of the pedestrian group dynamics experiment on the slope at the macro level, and verifying that the scenario covers all preset uphill slopes.

Images