A method for planning the movement path of people in a building

Abstract

The present invention discloses a method for planning the path of movement of people in a building in the field of pedestrian flow simulation, including importing architectural drawings, constructing a navigation grid-based architectural data model; adding people to the architectural drawing data model, the people are provided with action logic, the generation of action nodes of people in the architectural scene is based on a number of points selected in the navigation grid and interaction rules, and the shortest path nodes between adjacent points are generated in sequence; the movement of people between nodes is based on the steering force algorithm of steering force pathfinding combined with the ORCA optimal mutual collision avoidance algorithm, the movement route of people is a path area with width, the path area is generated between each node according to the width passed, the path area generated at the adjacent edge of the navigation grid is provided with a deviation value, the people move in the direction of the nearest node one by one, the action of people is simulated through the architectural data model, and the path planning is optimized according to the simulation result. The scheme can simulate the action of people more realistically.

Description

Technical Field

[0001] The invention belongs to the field of pedestrian flow simulation, and in particular is a method for planning a movement path of people in a building. Background Art

[0002] With the rapid development of social material and spiritual civilization, large-scale gatherings held in various public places are gradually increasing. The crowds in these places are often dense and unstable, which can easily lead to safety accidents such as crowding and trampling. In addition to the unreasonable design of public buildings, the more important cause of the accident is the inadequate emergency management measures in the face of emergencies, which leads to avoidable serious congestion during the evacuation of the crowd, causing huge losses of life and property to the evacuated people. Therefore, how to improve the management of dense crowd evacuation is a key task in public safety research.

[0003] In order to solve the above problems, patent publication number CN109190325B discloses a crowd evacuation path planning simulation method based on pedestrian congestion analysis, including: obtaining evacuation crowd data and determining the current constraint status of pedestrians; calculating the constraint force of pedestrians according to the current constraint status of pedestrians; performing congestion assessment on the area where pedestrians are located according to the congestion constraint force to which pedestrians are subject, and calculating the probability of pedestrians choosing a certain evacuation path when evacuating according to the assessment value, and providing the M evacuation paths with the highest probability to the evacuated crowd, thereby realizing the planning of crowd evacuation simulation paths.

[0004] The above-mentioned crowd evacuation path planning simulation method based on pedestrian congestion analysis calculates the probability of choosing a certain evacuation path when people evacuate by evaluating the congestion value of the area. However, in actual situations, the walking route of people will not be a perfect straight line or arc, but a floating line within a certain range; at the same time, at the corners, people move in line with the corners, but keep a distance from the corners to reduce the probability of scratches at the corners after people turn around. Summary of the invention

[0005] In order to solve the problem of low degree of restoration of actual walking status of personnel in the prior art, an object of the present invention is to provide a method for planning the movement path of personnel in a building, which can simulate the movement of personnel more realistically.

[0006] In order to achieve the above object, the technical solution of the present invention is as follows: comprising the following steps:

[0007] Step 1: Import architectural drawings and build an architectural data model. The architectural drawings include dxf and dwg format files. Add simulation facilities to the architectural drawings according to the actual situation. The simulation facilities are equipped with interactive rule logic that fits the actual situation. The triangulation algorithm and convex polygon solving algorithm are used to generate the navigation grid.

[0008] Step 2: Add personnel to the architectural drawing data model, and set action logic for the personnel;

[0009] The action logic is to generate action nodes of personnel in the building scene according to several points selected in the navigation grid, and generate the shortest path nodes between adjacent points in turn; the group movement of personnel between nodes is based on the ORCA optimal mutual collision avoidance algorithm combined with the steering force algorithm of steering force pathfinding:

[0010] ORCA formula:

[0011]

[0012]

[0013]

[0014] A and B are two different people, r is the shape parameter, v is the speed value, v opt is the speed preference value near the opt coordinate position, max is the limit, u is the pointing vector, and D represents the circular range;

[0015] Steering force formula:

[0016]

[0017] in is the new speed, m δ is the steering quality, is the desired velocity, Δt is the time step, is the current speed;

[0018]

[0019] in For the new speed, is the current speed, is the steering acceleration, is the tangential acceleration;

[0020] The steering force algorithm processes the preferred speed of personnel, and ORCA solves obstacle avoidance for groups of personnel and between personnel and obstacles.

[0021] The movement route of personnel is a path area with width. The path area is generated between each node according to the width passed. The path area generated at the adjacent edge of the navigation grid is set with a deviation value. The deviation value follows the normal distribution. Personnel move towards the nearest node one by one.

[0022] Step three: construct a simulation scene in the building drawing data model, set the personnel's movement target and interaction rules with the target according to the current path planning, simulate the personnel's actions through the building data model, and optimize the path planning according to the simulation results.

[0023] The following beneficial effects are achieved by adopting the above scheme: users can obtain the simulated movement results of the current scheme by performing personnel movement route simulation in the building data model, optimize the current scheme according to the simulated movement results, reduce the congestion of various areas in the building, and improve the smoothness of personnel movement.

[0024] Import simulated architectural drawings, which are equipped with simulated facilities and obstacles that interact with people. The simulated facility handrails include elevators and gates, and the obstacles are walls and isolation belts. The obstacles are expanded to the size of the person, and the expanded architectural drawings are triangulated. The triangulation algorithm is used to generate calibrated movement routes, but the divided triangular areas are too many, and the computing performance is wasted. Therefore, the convex polygon optimization solution is performed on the triangulated building drawings, and several triangles that can be merged are merged into one polygon. Reduce the number of divided areas and reduce the computing pressure.

[0025] The personnel are equipped with a steering force algorithm based on the ORCA optimal mutual collision avoidance algorithm. By combining the guided path finding to simulate the acceleration, deceleration, steering, collision, avoidance, blocking and queuing of personnel during the action, the movement process of personnel is more in line with the real movement behavior of personnel. In the movement of personnel, by expanding the path line to the path area, dividing the nodes in the path area, the personnel move with the nodes as the target in turn, and the nodes are randomly fluctuated in the path area to simulate the human walking route and restore the real movement behavior of personnel. The path area generated at the corner in the architectural drawing is set with a deviation value to simulate the human habit when turning. People usually leave a certain amount of space when turning. The deviation value is fluctuating. By setting the value that follows the normal distribution, the deviation value is more in line with reality.

[0026] Users can optimize path planning step by step through simulation and iterate to plan a better course of action.

[0027] Compared with the existing technology, the steering force algorithm enables personnel to restore the real human movement during the action process and simulates various interactions of the crowd under the physical rules; by setting the movement route as a path area with width, the offset of human walking is simulated to further restore human behavior; by setting the deviation value, human logical habits are fitted, and by following the normal distribution of values, it can restore the reality.

[0028] Furthermore, the building data model is also used to add obstacles and interactive facilities within the building data model in real time without interrupting the simulation.

[0029] Beneficial effects: Obstacles can prevent people from moving toward that location, and are used to simulate various emergencies that occur in buildings, or to correct the movement routes of people, change the direction of the crowd, and enable the execution of the planning scheme to be changed dynamically.

[0030] Furthermore, personnel are given random collision volumes and accelerations.

[0031] Beneficial effects: People's physical fitness varies, resulting in different volumes and athletic abilities. By setting random personnel attributes, the actions of personnel can be further simulated.

[0032] Further, the personnel assess and correct the speed based on the personnel density at the expected location, and the speed correction amount is calculated based on the density-speed correlation function.

[0033] Beneficial effects: When there is congestion, it will affect the movement of people, making it difficult for them to move at a normal speed, thus causing speed attenuation. When there is congestion, people at the edge of the crowd will not be affected by the congestion when moving outward. The expected position is used to estimate the next position of the person, so that the speed attenuation of the congestion value acts on the movement orientation of the person, making the simulation more realistic.

[0034] Furthermore, the action logic also includes sideways logic. When the cross-section of the moving space in the path area is smaller than the length of the person but larger than the width of the person, the person rotates until the projection of the person on the cross-section of the moving space is smaller than the cross-section of the moving space.

[0035] Beneficial effects: Humans usually have a shoulder distance greater than the thickness of their body and can move sideways, so they can pass through smaller spaces sideways. The sideways logic can simulate the sideways movement of a person, and will stop when the person's projection on the cross-section of the moving space is smaller than the cross-section of the moving space, simulating the logical judgment logic of humans to speed up their own passing speed.

[0036] Furthermore, after the person rotates, a speed attenuation of 0% to 50% is obtained according to the angle difference between the person and the initial state after the rotation.

[0037] Beneficial effect: Humans move more slowly when they move sideways, so the speed of the person is corrected based on the angle difference between the rotation and the initial state.

[0038] Furthermore, after the personnel set the moving target, the personnel are displayed in different colors according to different moving targets.

[0039] Beneficial effect: Depending on the moving target, people are displayed in different colors, so that users can know their approximate moving direction by the color of the people during the simulation, estimate the movement of the people, and facilitate real-time correction of the movement trajectory of the people.

[0040] Furthermore, the simulation includes pause, fast forward and rewind functions.

[0041] Beneficial effects: Pause, fast forward and rewind enable users to modify planning schemes in real time and verify them quickly. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Schematic diagram of an embodiment of the present invention.

[0043] Figure 2 Schematic diagram of the algorithm used to generate the navigation mesh

[0044] Figure 3 A schematic diagram of the route area.

[0045] Figure 4 Schematic diagram of the offset of intersections according to distribution probability.

[0046] Figure 5 A schematic diagram of a navigation grid.

[0047] Figure 6 Schematic diagram of the density-velocity correlation function.

[0048] Figure 7 A schematic diagram of the action logic. DETAILED DESCRIPTION

[0049] The following is further described in detail through specific implementation methods:

[0050] Embodiment 1

[0051] The embodiment is basically as shown in the attached Figure 1 To Attachment Figure 7 As shown:

[0052] A method for planning a movement path of a person in a building, comprising the following steps:

[0053] Step 1: Import architectural drawings and build an architectural data model. The architectural drawings include dxf and dwg format files. Add simulation facilities to the architectural drawings according to the actual situation. The simulation facilities are equipped with interactive rule logic that fits the actual situation. The navigation grid is generated using the triangulation algorithm and convex polygon solving algorithm.

[0054] Step 2: Add personnel to the architectural drawing data model, and set action logic for the personnel;

[0055] The action logic is to generate action nodes of personnel in the building scene according to several points selected in the navigation grid, and generate the shortest path nodes between adjacent points in turn; the group movement of personnel between nodes is based on the ORCA optimal mutual collision avoidance algorithm combined with the steering force algorithm of steering force pathfinding:

[0056] ORCA formula:

[0057]

[0058]

[0059]

[0060] A and B are two different people, r is the shape parameter, v is the speed value, v opt is the speed preference value near the opt coordinate position, max is the limit, u is the pointing vector, and D represents the circular range;

[0061] Steering force formula:

[0062]

[0063] in is the new speed, m δ is the steering quality, is the desired velocity, Δt is the time step, is the current speed;

[0064]

[0065] in For the new speed, is the current speed, is the steering acceleration, is the tangential acceleration;

[0066] The movement route of personnel is a path area with width. The path area is generated between each node according to the width passed. The path area generated at the adjacent edge of the navigation grid is set with a deviation value. The deviation value follows the normal distribution. Personnel move towards the nearest node one by one.

[0067] Step three: construct a simulation scene in the building drawing data model, set the personnel's movement target and interaction rules with the target according to the current path planning, simulate the personnel's actions through the building data model, and optimize the path planning according to the simulation results.

[0068] The specific implementation process is as follows: users can perform personnel movement route simulation in the building data model to obtain the simulated movement results of the current plan, optimize the current plan according to the simulated movement results, reduce the congestion of various areas in the building, and improve the smoothness of personnel movement.

[0069] Import simulated architectural drawings, which are equipped with simulated facilities and obstacles that interact with people. The simulated facility handrails include elevators and gates, and the obstacles are walls and isolation belts. The obstacles are expanded to the size of the person, and the expanded architectural drawings are triangulated. The triangulation algorithm is used to generate calibrated movement routes, but the divided triangular areas are too many, and the computing performance is wasted. Therefore, the convex polygon optimization solution is performed on the triangulated building drawings, and several triangles that can be merged are merged into one polygon. Reduce the number of divided areas and reduce the computing pressure.

[0070] The personnel are equipped with a steering force algorithm based on the ORCA optimal mutual collision avoidance algorithm. By combining the guided path finding, the acceleration, deceleration, steering, collision, avoidance, blocking and queuing of the personnel in the process of action are simulated. Make the movement process of personnel more in line with the real movement behavior of personnel. In the movement of personnel, the actual human body structure is not completely symmetrical. There will be slight deviations when walking. At the same time, the human body needs to maintain balance when standing. The change of balance state will also cause the steps to deviate from the route; when there is no clear reference or pattern on the road surface, the human body movement route will always be offset. By expanding the path line to the path area and dividing the nodes in the path area, the personnel move with the nodes as the target in turn, and the nodes are randomly fluctuated in the path area, so that the personnel will have a directional deviation after moving a straight distance, so as to simulate the human walking route and restore the real movement behavior of personnel. The path area generated at the corner in the architectural drawing is set with a deviation value to simulate the human habit when turning. People usually leave a certain space when turning. The deviation value is fluctuating. By setting the value that follows the normal distribution, the deviation value is more in line with reality.

[0071] Users can optimize path planning step by step through simulation and iterate to plan a better course of action.

[0072] The invention uses a steering force algorithm to enable personnel to more closely replicate realistic human movements during their movements, simulating various interactions among people under physical rules. It also simulates the offset of human walking by setting the movement route as a path area with width, further restoring human behavior. It also fits human logical habits by setting deviation values, and restores reality by following the values ​​of the normal distribution.

[0073] Embodiment 2

[0074] The difference from the above embodiment is that the building data model is also used to add obstacles and interactive facilities in the building data model in real time without interrupting the simulation.

[0075] The specific implementation process is as follows: obstacles can prevent people from moving towards that place, which is used to simulate various emergencies occurring in the building, or to correct the movement route of the crowd, change the direction of the crowd, and enable the execution of the planning scheme to change dynamically.

[0076] Embodiment 3

[0077] The difference from the above-mentioned embodiment is that the person is provided with a random collision volume and acceleration.

[0078] The specific implementation process is as follows: There are differences in the physical fitness of the human body, which leads to different volumes and movement abilities. By setting random personnel attributes, the actions of personnel can be further simulated.

[0079] Embodiment 4

[0080] The difference from the above-mentioned embodiment is that the personnel evaluate and correct the speed according to the personnel density at the expected position, and the speed correction amount is calculated according to the density-speed correlation function.

[0081] The specific implementation process is as follows: when there is congestion, it will affect the movement of people, making it difficult for them to move at a normal speed, thus causing speed attenuation. When there is congestion, people at the edge of the crowd will not be affected by the congestion when moving outward. This expected position is used to estimate the next position of the person, so that the speed attenuation of the congestion value acts on the movement orientation of the person, making the simulation more realistic.

[0082] Embodiment 5

[0083] The difference from the above embodiment is that the action logic also includes sideways logic. When the cross-section of the moving space in the path area is smaller than the length of the person but larger than the width of the person, the person rotates until the projection of the person on the cross-section of the moving space is smaller than the cross-section of the moving space.

[0084] The specific implementation process is as follows: Humans usually have a shoulder distance greater than the thickness of their body and can move sideways, so they can pass through a smaller space sideways. The sideways logic can simulate the sideways movement of a person, and will stop when the person's projection on the cross-section of the moving space is smaller than the cross-section of the moving space, simulating the logical judgment logic of humans to speed up their own passing speed.

[0085] Embodiment 6

[0086] The difference from the above embodiment is that after the person rotates, a speed attenuation of 0% to 50% is obtained according to the angle difference between the rotation and the initial state.

[0087] The specific implementation process is as follows: humans move more slowly when moving sideways, so the speed of the person is corrected according to the angle difference between the rotation and the initial state after the rotation.

[0088] Embodiment 7

[0089] The difference from the above embodiment is that after the personnel sets the moving target, the personnel are displayed in different colors according to different moving targets.

[0090] The specific implementation process is as follows: according to different moving targets, personnel are displayed in different colors, so that users can know their approximate moving direction by the color of the personnel during the simulation process, estimate the movement of the personnel, and facilitate real-time correction of the personnel's movement trajectory.

[0091] Embodiment 8

[0092] The difference from the above embodiment is that the simulation includes pause, fast forward and rewind functions.

[0093] The specific implementation process is as follows: Pause, fast forward and rewind can facilitate users to modify the planning scheme in real time and verify it quickly.

[0094] The above is only an embodiment of the present invention. The common sense such as the known specific structure and characteristics in the scheme is not described in detail here. The ordinary technicians in the relevant field know all the common technical knowledge in the technical field of the invention before the application date or priority date, can obtain all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, which will not affect the effect of the implementation of the present invention and the practicality of the patent. The protection scope required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for planning a path for people moving in a building, characterized by: The following steps are involved: Step 1: Import architectural drawings and build an architectural data model. The architectural drawings include dxf and dwg format files. Add simulation facilities to the architectural drawings according to the actual situation. The simulation facilities are equipped with interactive rule logic that fits the actual situation. The triangulation algorithm and convex polygon solving algorithm are used to generate the navigation grid. Step 2: Add personnel to the architectural drawing data model, and set action logic for the personnel; The action logic is to generate action nodes of personnel in the building scene according to several points selected in the navigation grid, and generate the shortest path nodes between adjacent points in turn; the group movement of personnel between nodes is based on the ORCA optimal mutual collision avoidance algorithm combined with the steering force algorithm of steering force pathfinding: ORCA formula: A and B are two different people, r is the shape parameter, v is the speed value, v opt is the speed preference value near the opt coordinate position, max is the limit, u is the pointing vector, and D represents the circular range; Steering force formula: in is the new speed, m δ is the steering quality, is the desired velocity, Δt is the time step, is the current speed; in For the new speed, is the current speed, is the steering acceleration, is the tangential acceleration; The steering force algorithm processes the preferred speed of personnel, and ORCA solves the obstacle avoidance of groups of personnel and personnel and obstacles; The movement route of personnel is a path area with width. The path area is generated between each node according to the width passed. The path area generated at the adjacent edge of the navigation grid is set with a deviation value. The deviation value follows the normal distribution. Personnel move towards the nearest node one by one. Step three: construct a simulation scene in the building drawing data model, set the personnel's movement target and interaction rules with the target according to the current path planning, simulate the personnel's actions through the building data model, and optimize the path planning according to the simulation results.

2. The method for planning a movement path of people in a building according to claim 1, characterized in that: The building data model is also used to add obstacles and interactive facilities within the building data model in real time without interrupting the simulation.

3. The method for planning a movement path of people in a building according to claim 2, characterized in that: Personnel have random collision volumes and accelerations.

4. The method for planning a movement path of people in a building according to claim 3, characterized in that: Personnel assess and correct speed based on the personnel density at the expected location, and the speed correction is calculated based on the density-speed correlation function.

5. The method for planning a movement path of people in a building according to claim 4, characterized in that: The action logic also includes sideways logic. When the cross-section of the moving space in the path area is smaller than the length of the person but larger than the width of the person, the person rotates until the projection of the person on the cross-section of the moving space is smaller than the cross-section of the moving space.

6. The method for planning a movement path of people in a building according to claim 5, characterized in that: After the person rotates, a speed attenuation of 0% to 50% is obtained according to the angle difference between the initial state and the rotation state.

7. The method for planning a movement path of people in a building according to claim 6, characterized in that: After setting the moving target, the personnel will be displayed in different colors according to the moving target.

8. The method for planning a movement path of people in a building according to claim 7, characterized in that: The simulation includes pause, fast forward and rewind capabilities.

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