CN115017748B - Improved vision field segmentation method based method for simulating people flow in large public building - Google Patents
Improved vision field segmentation method based method for simulating people flow in large public building Download PDFInfo
- Publication number
- CN115017748B CN115017748B CN202210949323.XA CN202210949323A CN115017748B CN 115017748 B CN115017748 B CN 115017748B CN 202210949323 A CN202210949323 A CN 202210949323A CN 115017748 B CN115017748 B CN 115017748B
- Authority
- CN
- China
- Prior art keywords
- square grid
- degree
- flow
- people
- space
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/20—Finite element generation, e.g. wire-frame surface description, tesselation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Accounting & Taxation (AREA)
- Strategic Management (AREA)
- Finance (AREA)
- Development Economics (AREA)
- Geometry (AREA)
- Entrepreneurship & Innovation (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Computer Graphics (AREA)
- Software Systems (AREA)
- Evolutionary Computation (AREA)
- Computer Hardware Design (AREA)
- Game Theory and Decision Science (AREA)
- Economics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method for simulating the pedestrian flow in a large public building based on an improved vision field segmentation method, which comprises the following steps: s1, importing a three-dimensional layout drawing of a building, and drawing square grids of each plane floor and a connecting channel (including stairs, straight ladders and escalators) in the building; s2, calculating the integration degree of the initial vision field of each square grid in the square grids; s3, simulating a people stream running track; s4, calculating the integral degree W of the whole visual field of each square grid i i To represent the crowding degree of the people flow of the square grid i; s5, according to the intensity of crowding degree, the integral degree W of the whole vision of the square grid i is integrated by different shades i And performing grading setting, and marking and early warning the area exceeding the preset congestion degree threshold. The invention introduces a fluid theory, internalizes factors such as visibility, visual range, attraction, sight angle and the like into the measurement of viscosity and the integral degree of the whole visual field, improves the simulation precision, adopts the actual distance to replace the visual field length in the original formula, and improves the scientificity of calculation.
Description
Technical Field
The invention belongs to the technical field of computer aided planning, and particularly relates to a method for simulating the pedestrian flow in a large public building based on an improved vision field segmentation method.
Background
The large public buildings in cities mainly comprise commercial buildings such as shopping malls and shopping centers, scientific and educational and cultural buildings such as libraries and museums, office buildings such as office buildings and office buildings, and traffic buildings such as airports and stations, and the buildings generally have the characteristics of large pedestrian volume and high crowd concentration. With the high-quality development of economic society, the number of large public buildings in cities is increasing day by day, and meanwhile, safety accidents caused by gathering activities of people in places are also endless; the reasonable people stream gathering degree and the space activity degree have great social safety value and economic benefit significance for meeting the people stream density control requirement, exciting economic benefit and guaranteeing public safety.
In the fields of city planning and building design, people stream mode optimization and space activity improvement through space layout planning are important requirements. For example, in commercial buildings, the location of the shops determines the value of each shop. In order to measure the activity of space and the attraction degree to human streams, the traditional method adopts space syntax. As a method of studying spatial structural features and spatial behavior analysis of people therein, spatial Syntax (Space Syntax) is widely used for people stream simulation in urban or architectural spaces. The main research methods of the spatial syntax include an axis method, a convex polygon method, and a view division method. Among them, the basic principle of the visual field segmentation method is to perform spatial analysis based on the accessibility of a visual line, and it is considered that a space with a high degree of integration of visual lines causes human behavior. The traditional vision division method carries out space analysis by using a vision area method, calculates the vision area of each point by selecting a certain number of sight points, forms a relation diagram according to the occlusion relation between the vision areas, and calculates the syntactic variable of each vision area.
However, in the conventional view division method, the study object is a planar space, the calculation of the syntax value is only applied to the planar space, and when the three-dimensional space analysis is performed inside the building, the height factor of the study area has to be ignored and the study area is assumed to be the planar space, so that the use effect is limited to a certain extent.
Secondly, in the building, the space separation and connection mode has certain influence on the people stream running mode, and in addition, the action preference response based on the target object, the action selection based on the interactive experience, the people stream line design and other action preferences also deeply influence the people stream running result. The traditional view division method is based on the principle of 'reach while being visible', focuses on the final target of 'reach', and focuses less on the difficulty and preference of the 'reach' process.
Therefore, the conventional view division method cannot realize accurate and detailed simulation of the pedestrian flow inside the large public building.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for simulating the pedestrian flow in the large public building based on an improved vision field segmentation method, which can be used for more accurately simulating the integration degree of the vision field in the building, realizing three-dimensional quantitative analysis based on visual line visibility and behavior preference selection and enhancing the adaptability of the vision field segmentation method in the aspect of simulating the pedestrian flow in the building.
In order to achieve the above object, the present invention provides a method for simulating the flow of people in a large public building based on an improved view segmentation method, which is characterized in that the method comprises the following steps:
s1 import buildingBuilding a three-dimensional layout, drawing square grids of each plane floor and connecting passage (including stairs, straight ladders and escalators) in a building, establishing a space type attribute field according to building function requirements, marking the space type, the attractors, building entrances and exits and other attributes of each square grid, wherein the space type comprises a mark B 1 Display space of (1), marked B 2 Of (2), marked as B 3 And a rest space marked as B 4 A traffic space of (a);
s2, calculating the integration degree Q (i, m) of the initial vision field of each square grid i:
wherein P is a gravitational coefficient, J is a plane angle coefficient, Z is a vertical angle coefficient, a =0,1,2 \8230;, x represents x attractors visible in the comfortable range of the human eye, m =1,2,3,4 represents four directions of the square grid respectively, and Q is (i,1) ，Q (i,2) ，Q (i,3) ，Q (i,4) Respectively representing the integration degree of the initial vision field in four directions of the square i;
and S3, introducing a fluid theory and simulating a people flow running track. The method comprises the following steps of taking the people flow entering the building as a fluid, taking the building entrance as a fluid movement starting position, taking a square grid as a fluid operation space, and calculating the flow I obtained by each square grid, wherein the method comprises the following specific steps:
s31, marking an entrance position e in the building as a movement starting position, and simulating fluid movement by taking square grids of a traffic space, an interaction space and a rest space as fluid operation spaces. The inlet position is e, e =1, \ 8230;, y, y is a natural number greater than 0, the number of the characterizing inlets, the number of the population of the inlet position is the initial fluid flow, and is marked as I e ；
S32, assuming that the fluid is driven to move forwards, leftwards and rightwards by the visibility of the suction primer, the visibility is characterized by the integration degree of the initial vision field, and the greater the integration degree of the initial vision field, the greater the probability that the fluid moves towards the corresponding direction. The probability is characterized by a fluid flow-splitting coefficient F, and the calculation formula is as follows:
in the formula, m, m' are three moving directions of the fluid, namely forward, leftward and rightward.
S33, setting the flow of the square grid I as I i If the next square grid flow of the square grid I in the m direction is I (i+1) ：
When the flow distribution direction is the direction of the pedestrian flow passing through the square grid or the exit grid of the escalator, measuring 0 by the square grid flow corresponding to the flow distribution direction;
s34, when no square grid which does not pass through exists in the four directions of people stream running, or when the total time of people stream running exceeds a preset value T, simulating the people stream running track to end;
s4, calculating the integral degree W of the whole visual field of each square grid i i To represent the strength of crowding degree of people flow of the square grid i; the method comprises the following specific steps:
s41 setting single-view-field integration degree w of square grid i of non-rest area i And (3) representing the degree of integration of the single visual field of any strand of people flowing through the square grid i:
w i ＝I i *t i
s42, calculating the passing time t of each square grid i i ；
S43, fatigue factor correction is carried out on the human flow movement track;
s44, calculating the degree of integration w of the single view field of the square grid j of the rest area j ；
S45, when the square grids are non-rest area square grids i, integrating degrees w of any strand of single-view domain flowing into each square grid i i The superposition summation is carried out, namely the integral degree W of the whole visual field of the square grid i ：
W i ＝∑w i
When the square grid is a square grid of a rest areaj, degree of integration w for any single view field flowing in each square grid j j Performing superposition summation, namely the integral vision field degree W of the square grid j ：
W j ＝∑w j
The square grid area with high integration degree of the whole vision field is an area with high crowding degree of the people flow, and conversely, the square grid area with high integration degree of the whole vision field is an area with low crowding degree of the people flow;
s5, according to the intensity of crowding degree, integrating degree W of the whole visual field by using different shades i And performing grading setting, and marking and early warning the area exceeding the preset congestion degree threshold.
Preferably, the method for calculating the gravity coefficient P in step S2 is:
in the formula, r is a type factor and is represented by the attraction degree of different attractor types to the stream of people; g is a grade factor and is characterized by the attraction degree of different attractor importance to the human flow; and s is the linear distance between the square grid and the attraction.
Preferably, the method for calculating the plane angle coefficient J in step S2 is:
J＝1-|α/62°|
in the formula, alpha is an included angle between a connecting line of the square grid and the attraction and a projection of a straight front sight line on a horizontal plane, and 62 degrees is a comfortable range of the human eye viewing objects on the plane. When the angle is alpha =0 degrees, J =1, the plane angle coefficient is minimum, the influence of the attraction on the trajectory deflection of the stream of people is maximum, when the plane included angle is alpha =62 degrees, J =0, the plane angle coefficient is maximum, the influence of the attraction on the trajectory deflection of the stream of people is minimum, and when the target is selected under the same condition, the target facing the visual angle is more advantageous than the oblique visual angle.
Preferably, the vertical angle coefficient Z in step S2 is calculated by
In the formula, theta is a visual angle of a horizontal line of sight projected on a vertical plane of a connecting line of the square grid and the attraction, d is an actual distance between the square grid and the attraction, and the comfortable range of the human eye vertical sight is defined by 20 degrees upwards and 30 degrees downwards. When theta =0 degrees, Z =1 is the attraction on the same floor, when theta =20 degrees or-30 degrees, Z =0 is the largest vertical angle coefficient, and the influence of the attraction on the trajectory deflection of the people stream is the smallest.
Preferably, the passing time t of the square grid i in step S42 i The calculation method comprises the following steps:
wherein k is the number of the attraction, j is the number of the rest area, t k Total length of time of stay for attraction by an attraction, r k Is the type factor of k attractants, g k Is a scale factor of k attractions, A k Is the area of interaction space corresponding to k attractions, T 1 For rest area dwell time, A j The area of the rest space corresponding to the j rest area.
Preferably, the method for correcting the fatigue factor of the trajectory of the human flow in step S43 is: setting the time from the beginning of the stream to the sensing of fatigue as T 2 When Σ t i ＜T 2 When the person moves, the person only considers the attraction effect of the attractor on the fluid movement, and when the person moves according to the sigma-delta t i ≥T 2 When the rest space is included in the attraction search range, namely when the rest area j enters a given vision field range, the people stream movement directly jumps to the rest area.
Preferably, the degree of single-view integration w of the rest area is calculated in step S44 i The method comprises the following steps: because the crowdedness of the rest area is related to the flow and the area of the rest area, the calculation formula of the integration degree of the single view field of the rest area is as follows:
w i ＝I m *T 1 /A j
in the formula, T 1 For presetting the total stay time of the stream of people in the rest area j, A j The area of the rest area j.
The invention also proposes a device comprising: at least one processor and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method described above.
The invention further proposes a computer-readable storage medium, in which a computer program is stored, which is characterized in that the computer program realizes the above-mentioned method when being executed by a processor.
On the basis of space separation, the invention improves the integration degree calculation of the traditional view field segmentation method, further considers the people stream path preference selection (such as the attraction force, the running time and the like of the target object), the environment negative effect (such as the actual running distance, the environment crowding degree, the friction loss of the change of the moving direction and the like), and is beneficial to obtaining a more accurate and fine people stream simulation method.
According to the invention, by distinguishing the influence of different spaces and attractors in the building on the human flow running track, a quantifiable human flow simulation method and a quantifiable visual system are established, so that the human flow simulation is realized more accurately and meticulously. The people flow simulation method can guide a large public building to use a space with high integration degree for arranging shops with high commercial value, setting a core display project and the like, and promote the maximization of the space value benefit; the early warning can be implemented in the area where the crowd gathering degree exceeds the safety threshold value, the large public building is guided to form a space organization scheme with more uniform crowd gathering through the modes of adjusting the positions of the attractors or the barriers and the like, and the potential safety and sanitation hazards caused by local crowding of people are avoided.
Compared with the prior art, the invention has the beneficial effects that:
1. on the basis of improving the measure of visibility, sight distance and the like of a sight division method, the influence of the attraction of a target object and the sight angle on the pedestrian flow action track is additionally considered;
2. expanding a vision field segmentation method from a two-dimensional field to a three-dimensional field, increasing the influence of three-dimensional visibility on the integration degree, and considering the influence of the visibility of a connection channel and the visibility of attractors on different floors on the integration degree in the calculation process;
3. considering the influence of population distribution and running resistance on the measurement of the integration degree of the whole visual field in the process of people stream running, introducing a fluid theory into people stream track simulation, internalizing the integration degree of the initial visual field into the measurement of a fluid distribution coefficient, internalizing running time into running resistance, and improving the simulation and precision of simulation;
4. the quantitative people flow simulation method and the visual system generated by the method can provide scientific and accurate reference basis for the internal space layout planning and the rationalization adjustment of the large public buildings.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 (a), 2 (b), 2 (c), and 2 (d) are three-dimensional layout views of one floor, two floors, three floors, and four floors of the building, respectively.
Fig. 3 is a schematic view of a building grid.
Fig. 4 is a view field diagram.
Fig. 5 is a diagram of fluid motion analysis.
Fig. 6 is a schematic diagram of the crowdedness degree of people in the building.
Fig. 7 (a), 7 (b), 7 (c) and 7 (d) show the degree of integration of the entire viewing areas of the first floor, the second floor, the third floor and the fourth floor of the building, respectively.
Fig. 8 is a schematic plan view of a building using a conventional view division method.
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments.
As shown in fig. 1, the method for simulating the people flow in the large public building based on the improved view segmentation method provided by the invention comprises the following steps:
s1, importing a three-dimensional layout drawing of a building, drawing square grids of each plane floor and a connecting channel (including stairs, straight ladders and escalators) in the building, establishing a space type attribute field according to building function requirements, and marking the space type of each square grid and building entrances and exits;
s2, calculating the integration degree Q of the initial vision field of each square grid i in the square grids (i,m) ：
And S3, introducing a fluid theory to simulate a people flow running track. Taking the stream of people entering the interior of the building as a fluid, taking the entrance of the building as an initial position of fluid movement, taking the grids as fluid operation spaces, and calculating the flow I obtained by each grid;
s4, calculating the integral degree W of the whole vision field of each square grid i i To represent the strength of crowding degree of people flow of the square grid i;
s5, according to the intensity of crowding degree, integrating degree W of the whole visual field by using different shades i And (5) performing grading setting, and marking and early warning the area exceeding the preset congestion degree threshold.
Embodiment A flow simulation based on exhibition attraction in exhibition hall
S1, importing a three-dimensional layout drawing of a building of an exhibition hall into an ArcGIS (software system), as shown in a drawing 2 (a), a drawing 2 (b), a drawing 2 (c) and a drawing 2 (d), the three-dimensional layout drawing is respectively a three-dimensional layout drawing of a first layer, a second layer, a third layer and a fourth layer in the building, each plane floor display area and a connecting channel square grid of the exhibition hall are drawn according to 1m × 1m precision, the display area is divided into four types of spaces including a display space, an interaction space, a rest space and a traffic space according to the functional requirements of visitors on visiting, resting and passing according to the positions of the display item, the rest area, stairs (including an escalator and a straight stair) and corresponding attribute fields of 'space type' are added, wherein: the area for displaying the exhibit is a display space marked as B 1 The region of tourist standing appreciation exhibit is interaction space marked as B 2 Providing necessary rest seats, wherein the rest space is a region for the tourists to rest halfway and is marked as B 3 The other areas for the tourists to pass through quickly are traffic spaces marked as B 4 As shown in fig. 3.
S2, improving a view domain segmentation method by utilizing an ArcGIS software platform,calculating the degree of integration Q of the initial vision of each square grid i (i,m) 。
S21, the different standing directions of the people are considered, and the different ranges are considered. Defining initial degree of integration Q of vision i ＝{Q (i,m) }, wherein: m =1,2,3,4, respectively representing four directions of east, west, south and north, Q (i,1) ，Q (i,2) ，Q (i,3) ，Q (i,4) Respectively representing the integration degree of the initial vision field of the square grid i in the four directions of east, west, south and north.
S22, calculating the integration degree Q of the initial vision field in the m direction of the square i grid (i,m) The calculation formula is as follows:
wherein P is gravity coefficient, J is plane angle coefficient, Z is vertical angle coefficient, a =0,1,2 \8230;, x represents x attractors visible in the comfortable range of human eye, m =1,2,3,4 respectively represents four directions of the square grid, Q (i,1) ，Q (i,2) ，Q (i,3) ，Q (i,4) Respectively representing the integration degree of the initial vision in four directions of the square i;
s23, searching for the items of expansion in the m-direction view according to the given views of 62 degrees at two sides of the m-direction plane view and 20 degrees upwards and 30 degrees downwards of the vertical view according to the comfort requirement of the human eyes to see the objects, and recording the number of the items of expansion as a.
S24, calculating a gravity coefficient P: during the process of people moving, the target exhibition item, the stairs and the rest area are attractors influencing the moving track of people. For the target exhibition item, the attraction is related to the importance degree of the exhibit, the exhibition item exhibition form and the space distance between the exhibit and a person, and the calculation formula is as follows:
in this embodiment, r is a type factor, and is self-defined and assigned according to the attraction degree of exhibition forms of exhibits in an exhibition hall to audiences, and the exhibition items of 4 exhibition forms of static exhibition, model exhibition, simulation exhibition, and demonstration exhibition are respectively assigned as 0.5,0.6,0.8, and 1.0.g is a grade factor, the values are assigned according to the importance of exhibition items of the exhibition hall in a self-defining way, and the values of the very important, relatively important and general important exhibition items are respectively 1.0,0.5,0.3 and 0.2.s is the apparent distance between the person and the target show.
| Exhibition and display mode | Means for display |
| Static display | Lamp box, display board, display cabinet, etc |
| Model type exhibition display | Multimedia content output, images, and the like |
| Analog display | Live-action reproduction, live-action sand table, and the like |
| Demonstration type exhibition display | Immersive performance, independent demo, interactive games, and the like |
S25, calculating a plane angle coefficient J: when the target object is selected under the same condition, the target object at the right viewing angle is often more advantageous than the target object at the oblique viewing angle, as shown in fig. 4. The plane angle coefficient is calculated by the formula
J＝1-|α/62°|
Wherein: α is the planar view between the target spread and the direction m. When the angle of the plane is a =62 °, J =0, the angle coefficient of the plane is maximum, and the influence of the target spread term on the trajectory deflection of the human flow is minimum.
S26, calculating a vertical angle coefficient Z: considering that when a person selects a target object, a visual target object on the same floor is often more advantageous than a visual target object on different floors, as shown in fig. 4, and is influenced by whether the visual target object can arrive quickly. The vertical angle coefficient calculation formula is as follows:
wherein: theta is the vertical viewing angle between the person and the target exhibition item, and d is the actual distance between the person and the target exhibition item. When theta =0 degrees, Z =1 is the same-floor target, when theta =20 degrees or-30 degrees, Z =0 is the largest, the vertical angle coefficient is the smallest, and the influence of the target expansion term on the deflection of the pedestrian trajectory is the smallest.
S3, simulating a people stream movement track entering the inside of the exhibition hall based on the integration degree of the initial vision field, and specifically comprising the following steps:
s31, simulating fluid movement by using the stream of people entering the exhibition hall as fluid, the entrance position of the exhibition hall as the starting position of the fluid movement, and the square grids of the traffic space, the interaction space, and the rest space as the fluid operation space, as shown in fig. 5. Marking entrance positions as e, e =1, \ 8230;, y, y are natural numbers larger than 0, representing the number of entrances and exits of the exhibition hall, and the number of the entrances is the initial fluid flow and is marked as I e 。
S32 calculates a fluid split coefficient. The fluid movement is driven by the viscous force, and the next square grid for the fluid movement is 3 square grids which are directly connected with the fluid movement and are arranged forwards, leftwards and rightwards. The larger the viscous force is, the larger the probability that the fluid moves to the corresponding direction is, and the larger the fluid flow rate acquired by the next grid is. And defining the fluid flow distribution coefficient to represent the direction selection probability of fluid movement, wherein the visibility is represented by the initial vision integration degree, and the greater the initial vision integration degree is, the greater the probability of human flow selection is, and the greater the fluid flow distribution coefficient is. The probability is characterized by a fluid flow-splitting coefficient F, and the calculation formula is as follows:
in the formula, m, m' are three moving directions of the fluid, namely forward, leftward and rightward;
s33 flow I of square grid I is set i At an initial fluid flow rate I e Calculating the flow I of each square grid I in the m direction next to the next square grid for the starting quantity (i+1) ：
When the reposition of redundant personnel direction is the pedestrian flow and has not passed square grid, reposition of redundant personnel direction m includes forward, left and right, when the reposition of redundant personnel direction was pedestrian flow and has passed square grid or staircase export net, the degree of integration that corresponds the reposition of redundant personnel direction was got 0.
S34, when no square grid net which does not pass through exists in the four directions of people stream running, or when the total time of people stream running exceeds a preset value T, simulating the people stream running track is finished. And setting T =7200 seconds according to the actual visit time of the visitors in the exhibition hall.
S4, calculating the integral degree W of the whole visual field of each square grid i i To represent the strength of crowding degree of people flow of the square grid i; the method comprises the following specific steps:
s41, the integral degree of the whole visual field is characterized by the product of the fluid flow and the fluid passing time. Single visual field integration degree w for setting square grid i of non-rest area i And (3) representing the degree of integration of the single visual field of any strand of people flowing through the square grid i:
w i ＝I i *t i
s42, calculating the passing time t of each square grid i i 。
The fluid runtime is related to the type of space. The calculation formula of the square grid i transit time is as follows:
in this embodiment, k is the number of the exhibition item, and j is the number of the restInformation area number, t k Visit duration, r, for a visitor to fully visit the target exhibition item k k Is the type factor of k spread terms, g k Rank factor, A, for k spread terms k For the interaction space area corresponding to k spread terms, T 1 For rest area dwell time, A j The area of the rest space corresponding to the j rest area.
An association between the fluid runtime and the type of space is established. Based on experience observation of people's touring behaviors, the passing time of the traffic space square grid is assigned to be 1.5s, and the passing time of the rest space is assigned to be T 1 At 10 minutes (600 seconds), the interactive space transit time is modified based on the total length of time required to fully recognize the attraction, in combination with the attraction type factor, the rating factor, and the assigned interactive space area.
S43, fatigue factor correction is carried out on the human flow motion trail.
The fatigue condition of people in the visiting process and the function of the rest area on relieving fatigue are considered. Defining the fatigue time T 2 =3600 seconds. When Σ t i < 3600, fluid motion only considers the attraction of the target spread to the human flow motion, when Σ t i And when the number of the objects is more than or equal to 3600, bringing the rest space into the target object searching range. When the rest area j enters a given vision field range, the people stream movement directly jumps to the rest area.
S44, calculating the degree of integration w of the single view field of the square grid j of the rest area j . The flow rate of the people stream is consistent with the flow rate of the square grid before the people stream movement position is jumped, and is set as I m . Considering that the crowdedness degree of the rest area is related to the flow and the area of the rest area, and the retention time T 1 Set to 10 minutes (600 seconds) without difference, the single field integration for the rest area is calculated as:
w j ＝600I m /A j 。
s45, when the square grids are square grids i in the non-rest area, the integration degree w of any strand of single-view domain flowing into each square grid i i The superposition summation is carried out, namely the integral degree W of the whole visual field of the square grid i ：
W i ＝∑w i
When squareWhen the net is a rest area square net j, the integration degree w of the single view field of any strand flowing into each square net j j Performing superposition summation, namely the integral vision field degree W of the square grid j ：
W j ＝∑w j
The square grid region with high integration degree of the whole vision field is a region with high crowding degree of the people flow, and conversely, the square grid region with low crowding degree of the people flow is shown in fig. 5;
s5, according to the intensity of crowding degree, the integral degree W of the whole vision of the square grid i is integrated by different shades i The gradation setting is performed as shown in fig. 6. In this embodiment, the degree of congestion is classified into 4 levels, W i When the current value is less than 1500, the current value is set as one stage, the current value is in an extremely uncongested state, and W is more than or equal to 1500 i When the current is less than 3000, the current is set as two stages, the current is not crowded, and W is more than 3000 i At 4500 or below, setting the state as three-stage, W is a relatively crowded state i If > 4500, the number of stages is four, and the state is very crowded. The third level and the fourth level respectively mark areas for yellow and red early warning of crowd of people. The integration degree of the whole vision field of the first floor, the second floor, the third floor and the fourth floor inside the building are respectively unfolded to obtain the schematic view field integration degree of each floor of the building as shown in fig. 7 (a), fig. 7 (b), fig. 7 (c) and fig. 7 (d).
Example two person flow simulation based on store attractions inside a mall
S1, a three-dimensional layout diagram of a mall building is led in a software system ArcGIS, each plane floor display area and a connecting channel square grid of the mall are drawn according to the precision of 1m × 1m, and the inside of the mall is divided into four types of spaces including a display space, an interaction space, a rest space and a traffic space according to the functional requirements of shopping, rest and traffic of consumers by combining the positions of the shop, the rest area, stairs (including an escalator, a straight stair) and the like, and a corresponding attribute field 'space type' is added, wherein: setting the area of the store as display space, marked B 1 The corridor area around the shop is an interaction space marked as B 2 Providing necessary rest chair, wherein the rest space is the area for the consumers to rest halfway and is marked as B 3 The other areas for the consumers to pass through quickly are traffic spaces marked B 4 。
S2, calculating the integration degree Q of the initial vision field of each square grid i (i,m) 。
In this embodiment, when the attraction coefficient P is calculated, the attraction magnitude is related to the level factor and the type factor, and the calculation formula is:
in this embodiment, r is a type factor, and the attraction of the store front business model to the consumer represents the type factor, and is respectively assigned to the model categories of clothing, catering, beauty makeup, general merchandise, interactive entertainment and the like by referring to the classification of the purchasing behavior of the consumer as 1,0.8,0.5 and 0.4.g is a grade factor, the known name of the market shop brand is represented as the gravity of the grade factor, and the values are respectively 1,0.8,0.4 and 0.2 according to the international brand, the domestic brand, the local brand and other four grades. s is the apparent distance between the person and the store.
S3, simulating a people stream movement track in a mall based on the integration degree of the initial vision field, and specifically comprising the following steps:
s31, taking the stream of people entering a market as a fluid, taking the entrance position of the market as a fluid movement starting position, and taking a square grid of a traffic space, an interaction space and a rest space as a fluid operation space, so as to simulate the fluid movement. The entrance position is marked as e, e =1, \ 8230;, y, and represents the number of entrances and exits of the shopping mall, and the number of the entrances and exits of the entrance position is the initial fluid flow I e ，；
S32 calculating the fluid flow-splitting coefficient F (i,m) 。
S33 flow I of square grid I is set i Calculating the flow I of the next square grid in the m direction of the I square grid (i+1) ：
When the shunting direction is the square grid which people have passed through or the escalator exit grid, the square grid flow quantity corresponding to the shunting direction is taken to be 0.
And S34, when no square grid which does not pass through exists in the four directions of people stream running, or when the total time of people stream running exceeds a preset value T, simulating the people stream running track to end. T =3600 seconds is set in conjunction with the empirical value of actual shopping time.
S4, calculating the integral degree W of the whole visual field of each square grid i i To represent the strength of crowding degree of people flow of the square grid i; the method comprises the following specific steps:
s41 setting single-view-field integration degree w of square grid i of non-rest area i And (3) representing the degree of integration of the single visual field of any strand of people flowing through the square grid i:
w i ＝I i *t i
s42, calculating the passing time t of each square grid i i ：
In this embodiment, k is a shop number, j is a rest area number, t k K time required by shop for complete experience of consumer, r k Is a type factor of k stores, g k Is a rank factor of k stores, A k An interactive space area T corresponding to the k stores 1 For rest area dwell time, A j The area of the rest space corresponding to the j rest area.
S43, fatigue factor correction is carried out on the human flow motion trail.
Defining shopping fatigue time T 2 =2700s. When Σ t i < 2700, fluid movement only considers the attraction of the store to fluid movement when Σ t i And when the number is more than or equal to 2700, the rest space is brought into the target object searching range. When the rest area j enters a given field of view, the fluid movement jumps directly to the rest area.
S44, calculating the integration degree w of the single visual field of the rest area j Setting the residence time T 1 For 600 seconds, the single-view integration of the rest area is:
w j ＝I m *T 1 /A j ＝600I m /A j 。
s45, when the square grids are non-rest area square grids i, the integration degree w of any strand of single view domain flowing into each square grid i i The superposition summation is carried out, namely the integral degree W of the whole visual field of the square grid i ；
W i ＝∑w i
When the square grid is a rest area square grid j, the integration degree w of any strand of single view field flowing into each square grid j j The superposition summation is carried out, namely the integral degree W of the whole visual field of the square grid j ：
W j ＝∑w j
The square grid region with the integral degree of the whole vision field is a region with high crowding degree of the people flow, otherwise, the square grid region is a region with low crowding degree of the people flow;
s5, according to the intensity of the crowding degree, the integral degree W of the whole vision field of the opposite grid i is set by different shades i And performing grading setting. The congestion level in this example is classified into 4 levels, W i When the number is less than 3000, the state is set as a first stage and is in a very uncongested state; w is more than or equal to 3000 i If the number is less than 6000, the state is set as second grade, and the state is not crowded; w is not less than 6000 i When the current is less than or equal to 9000, setting the current to be three stages, and setting the current to be a more crowded state; w i When > 9000, the state is very crowded with four stages. The third and fourth levels mark areas for yellow and red early warning of crowd of people, respectively.
Comparing the effect of the method of the invention with that of the traditional view field division method, as shown in fig. 8, the traditional view field division method conceptualizes human visual experience as a polygon drawn on a building plan, and reflects the aggregation degree of one unit space and all other spaces in the system by the view field integration degree; in the view division method adopted by the invention, as shown in fig. 7 (a), the view integration degree grading setting shows stronger correlation with the space type, the target attraction layout, the attribute and the like, the distribution characteristic of the people stream simulation is closer to the real scene, the view integration degree grading result shows thinner patch scale, and the simulation precision is higher.
Based on the above method, the present invention further provides an apparatus, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method described above.
The present invention further provides a computer-readable storage medium storing a computer program, wherein the computer program is configured to implement the method when executed by a processor.
In addition to the above embodiments, the present invention may have other embodiments. All changes, modifications, substitutions, combinations, and simplifications which should be made herein without departing from the spirit and principles of the invention are intended to be embraced within the scope of the invention.
Claims (5)
1. A method for simulating the pedestrian flow in a large public building based on an improved vision field segmentation method is characterized by comprising the following steps: the method comprises the following steps:
s1, importing a three-dimensional layout drawing of a building, drawing square grids of each plane floor and a connecting channel in the building, establishing a space type attribute field according to building function requirements, and marking the space type, the attraction and the building entrance and exit of each square grid in the square grids, wherein the space type comprises a mark B 1 Display space of (1), marked B 2 Is marked as B 3 And a rest space marked as B 4 The traffic space of (a);
s2, calculating the integration degree Q of the initial vision field of each square grid i in the square grids (i,m) ：
Wherein P is gravity coefficient, J is plane angle coefficient, Z is vertical angle coefficient, a =0,1,2 \8230;, x represents x attractors visible in the comfortable range of human eye, m =1,2,3,4 respectively represents four directions of the square grid, Q (i,1) ，Q (i,2) ，Q (i,3) ，Q (i,4) Respectively representing the integration degree of the initial vision in four directions of the square i;
the calculation method of the gravity coefficient P comprises the following steps:
in the formula, r is a type factor and is represented by the attraction degree of different attractors to the stream of people; g is a grade factor and is characterized by the attraction degree of different attractor importance to the human flow; s is the linear distance between the square grid and the attractor;
the plane angle coefficient J is calculated by the following method
J＝1-|α/62°|
In the formula, alpha is an included angle between a connecting line of the square grid and the attraction and a projection of a straight front sight line on a horizontal plane;
the vertical angle coefficient Z is calculated by
In the formula, theta is a visual angle of a connecting line horizontal sight line of the square grid and the attraction projected on a vertical plane, and d is an actual distance between the square grid and the attraction; when theta =0 degrees, Z =1 is the attraction on the same floor, when theta =20 degrees or-30 degrees, Z =0 is the largest, the vertical angle coefficient is the smallest, and the influence of the attraction on the trajectory deflection of the people flow is the smallest;
s3, introducing a fluid theory, simulating a people flow running track, taking people flow entering the building as fluid, taking the building entrance as a fluid motion initial position, taking the grid as a fluid running space, and calculating the flow I obtained by each grid, wherein the method comprises the following specific steps of:
s31, marking an entrance position e in the building as a movement starting position, and simulating fluid movement by taking a square grid of a traffic space, an interaction space and a rest space as a fluid operation space;
s32, driving the fluid to move forwards, leftwards and rightwards by the visibility of the priming primer, wherein the visibility is represented by the integration degree of the initial vision field, and the larger the integration degree of the initial vision field is, the larger the probability that the fluid moves towards the corresponding direction is; the probability is characterized by a fluid flow-splitting coefficient F, and the calculation formula is
In the formula, m, m' are three moving directions of the fluid, namely forward, leftward and rightward;
s33, setting the flow of the square grid I as I i Then the next square grid flow I of the square grid I in the m direction (i+1) Comprises the following steps:
s34, when no square grid net which does not pass through exists in the four directions of people stream running, or when the total time of people stream running exceeds a preset value T, simulating the people stream running track to be finished;
s4, calculating the integral degree W of the whole visual field of each square grid i i To represent the strength of crowding degree of people flow of the square grid i; the method comprises the following specific steps:
s41 setting single-view-field integration degree w of square grid i of non-rest area i And (3) representing the degree of integration of the single visual field of any strand of people flowing through the square grid i:
w i ＝I i *t i
s42, calculating the passing time t of each square grid i i ；
S43, fatigue factor correction is carried out on the human flow motion trail;
s44, calculating the degree of integration w of the single view field of the square grid j of the rest area j Because the crowdedness degree of the rest area is related to the flow and the area of the rest area, the calculation formula of the degree of integration of the single visual field of the rest area is as follows:
W j ＝I m *T 1 /A j
in the formula, T 1 For rest area dwell time, A j Rest space area corresponding to the j rest area, I m The flow of people in the rest area;
s45, when the square grids are non-rest area square grids i, the integration degree w of any strand of single view domain flowing into each square grid i i Performing superposition summation, namely the integral vision field degree W of the square grid i ：
W i ＝∑w i
When the square grid is a rest area square grid j, the integration degree w of any strand of single view field flowing into each square grid j j Performing superposition summation, namely the integral vision field degree W of the square grid j ：
W j ＝∑w j
The square grid area with high integration degree of the whole vision field is an area with high crowding degree of the people flow, and conversely, the square grid area with high integration degree of the whole vision field is an area with low crowding degree of the people flow;
and S5, according to the intensity of the crowding degree, grading and setting the integration degree of the whole visual field by using different shades, and marking and early warning the area exceeding the preset crowding degree threshold.
2. The improved vision field segmentation based method for simulating the flow of people in the large public building according to claim 1, wherein: the passing time t of the square grid i in step S42 i The calculation method comprises the following steps:
wherein k is the number of the attraction, j is the number of the rest area, t k Total length of time of stay for attraction by an attraction, r k Is a type factor of k attraction, g k Is a scale factor of k attractions, A k Is the area of interaction space corresponding to k attractions, T 1 For rest area dwell time, A j The area of the rest space corresponding to the j rest area.
3. The method for simulating the pedestrian flow in the large public building based on the improved view division method according to claim 1, wherein: the method for correcting the fatigue factor of the human stream movement track in the step S43 comprises the following steps: setting the time from the beginning of the stream to the sensing of fatigue as T 2 When Σ t i ＜T 2 When people move, the people only consider the attraction function of the attractor on the fluid movement, when Σ t i ≥T 2 When the rest space is included in the attraction search range, namely when the rest area j enters a given vision field range, the people stream movement directly jumps to the rest area.
4. An apparatus, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 3.
5. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method of any one of claims 1 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210949323.XA CN115017748B (en) | 2022-08-09 | 2022-08-09 | Improved vision field segmentation method based method for simulating people flow in large public building |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210949323.XA CN115017748B (en) | 2022-08-09 | 2022-08-09 | Improved vision field segmentation method based method for simulating people flow in large public building |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115017748A CN115017748A (en) | 2022-09-06 |
| CN115017748B true CN115017748B (en) | 2022-11-18 |
Family
ID=83066196
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210949323.XA Active CN115017748B (en) | 2022-08-09 | 2022-08-09 | Improved vision field segmentation method based method for simulating people flow in large public building |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115017748B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103473114A (en) * | 2013-09-04 | 2013-12-25 | 李乐之 | Method for calculating architectural space activeness through people-stream simulation |
| CN105808852A (en) * | 2016-03-09 | 2016-07-27 | 清华大学 | Indoor pedestrian microscopic simulation method based on cellular automaton |
| CN110648019A (en) * | 2019-09-04 | 2020-01-03 | 武汉市规划编制研究和展示中心 | Improved space syntax-based small-sized civil facility site selection method |
| CN111784830A (en) * | 2020-06-16 | 2020-10-16 | 中国城市规划设计研究院 | Rule-based three-dimensional geographic information model space analysis method and system |
| CN113962494A (en) * | 2021-12-15 | 2022-01-21 | 深圳小库科技有限公司 | Business space people flow simulation method and device based on ABM model |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7188056B2 (en) * | 2002-09-09 | 2007-03-06 | Maia Institute | Method and apparatus of simulating movement of an autonomous entity through an environment |
-
2022
- 2022-08-09 CN CN202210949323.XA patent/CN115017748B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103473114A (en) * | 2013-09-04 | 2013-12-25 | 李乐之 | Method for calculating architectural space activeness through people-stream simulation |
| CN105808852A (en) * | 2016-03-09 | 2016-07-27 | 清华大学 | Indoor pedestrian microscopic simulation method based on cellular automaton |
| CN110648019A (en) * | 2019-09-04 | 2020-01-03 | 武汉市规划编制研究和展示中心 | Improved space syntax-based small-sized civil facility site selection method |
| CN111784830A (en) * | 2020-06-16 | 2020-10-16 | 中国城市规划设计研究院 | Rule-based three-dimensional geographic information model space analysis method and system |
| CN113962494A (en) * | 2021-12-15 | 2022-01-21 | 深圳小库科技有限公司 | Business space people flow simulation method and device based on ABM model |
Non-Patent Citations (3)
| Title |
|---|
| "Visual perception of traditional garden space in Suzhou, China: A case study with space syntax techniques";Zhiming Li 等;《2011 19th International Conference on Geoinformatics》;20110811;第1-4页 * |
| "基于空间句法理论的太原商业综合体内部空间评价研究";张昀琪;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20190815(第08期);C038-302 * |
| "基于空间句法的长沙地铁站域地下商业空间分析与评价";谌轶华;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20180715(第07期);C038-1100 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115017748A (en) | 2022-09-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Vilar et al. | Indoor human wayfinding performance using vertical and horizontal signage in virtual reality | |
| US8005656B1 (en) | Apparatus and method for evaluation of design | |
| Shao et al. | Autonomous pedestrians | |
| Hillnhütter | Stimulating urban walking environments–Can we measure the effect? | |
| El-Darwish | Enhancing outdoor campus design by utilizing space syntax theory for social interaction locations | |
| CN108376198B (en) | Crowd simulation method and system based on virtual reality | |
| Kang et al. | Evaluation of the influence of roadside non-walking spaces on the pedestrian environment of a Japanese urban street | |
| Santosa et al. | Visual quality evaluation of urban commercial streetscape for the development of landscape visual planning system in provincial street corridors in Malang, Indonesia | |
| D’Acci | Mathematize urbes by humanizing them. Cities as isobenefit landscapes: Psycho-economical distances and personal isobenefit lines | |
| Valls et al. | E-learning and serious games: New trends in architectural and urban design education | |
| CN115017748B (en) | Improved vision field segmentation method based method for simulating people flow in large public building | |
| CN112034983B (en) | Eye tracking and virtual reality combined indoor navigation road network construction method | |
| Santosa et al. | Management and Enhancement of Livable Urban Streetscape through the Development of the 3d Spatial Multimedia System a Case Study of Provincial Street Corridors in Malang, Indonesia | |
| Sepe et al. | Improving liveability and attractiveness by preserving place identity in emblematic thoroughfares: A method and a case study | |
| Boustila et al. | Evaluation of factors affecting distance perception in architectural project review in immersive virtual environments | |
| Alexandersson et al. | Pedestrians in microscopic traffic simulation. Comparison between software Viswalk and Legion for Aimsun. | |
| Zhang et al. | Study of effective corridor design to improve wayfinding in underground malls | |
| Schwartz | Evaluating jogging routes in mass models | |
| Usman et al. | Simulation‐as‐a‐Service: Analyzing Crowd Movements in Virtual Environments | |
| JP6834722B2 (en) | Simulation program, simulation method and simulation equipment | |
| Meng et al. | Group behaviors in pedestrian traffic simulation model | |
| De Luca et al. | A user experience analysis for a mobile Mixed Reality application for cultural heritage | |
| Feng et al. | Development of a VR tool to study pedestrian route and exit choice behaviour in a multi-story building | |
| Ong et al. | Dragonfly Algorithm for Crowd NPC Movement Simulation in Metaverse | |
| Anuar et al. | STREETSCAPE AT JALAN TEMENGGUNG, KOTA BHARU, KELANTAN: EVALUATING ITS EFFECTIVENESS TOWARDS THE WALKING CULTURE |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |