Digital generation method and system of quantity and shape planning and design in college campus planning and design

Abstract

The present application relates to the technical field of digital management, in particular to a digital generation method and system for quantity shape planning and coordination in college campus planning and design, comprising the following steps: obtaining the pressure value by calculating the flow and speed through the pressure sensor, collecting path parameters, constructing the displacement reference vector by generating the pressure gradient difference sequence, adopting the thiesen polygon segmentation to migrate the topological node, establishing the path attribution relationship to calculate the stress distribution data, calling the distribution data to calculate the dispersion coefficient, identifying the high-risk node and generating the overall layout scheme, in the present application, the path pressure is quantified by real-time acquisition of the flow and speed combined with the passing section parameter, the displacement vector is supported by the gradient difference and length ratio, the boundary node coordinates are migrated, the space form is adjusted with the flow, the topological boundary is more actual, the stress distribution is shown by the interaction point induction path pressure, the function area is connected evenly, the flow efficiency is improved, and the campus space has higher adaptability.

Description

Technical Field

[0001] This invention relates to the field of digital management technology, and in particular to a digital generation method and system for the overall planning and design of university campuses, encompassing both quantitative and qualitative aspects. Background Technology

[0002] The field of digital management technology involves using information technology to collect, process, and visualize data on management objects. Through quantitative description and correlation analysis of various management elements, it achieves structured support for the planning, execution, and adjustment processes. Its core aspects include the standardization of data acquisition, the logicalization of data correlation, and the readability of data presentation. It also forms a digital information system that can be used for decision-making reference in terms of spatial layout, resource allocation, and time arrangement.

[0003] Among them, the digital generation method of quantitative and morphological coordination in the traditional planning and design of university campuses refers to the coordinated generation of quantitative parameters such as the area ratio, spatial form, and mutual positional relationship of functional zones such as teaching areas, living areas, sports areas, and green areas in the overall layout and zoning design of university campuses. It adopts manual drawing method based on existing campus basic survey drawings, two-dimensional plan drawing software drawing method, and quantitative morphological derivation method based on fixed proportions and reference templates to complete the preliminary generation and combination of planning and design parameters.

[0004] Traditional methods rely on fixed drawings and scale derivations to complete the division of functional areas, which can only reflect static areas and location relationships. In real-time use, they cannot reflect the dynamic pressure of pedestrian flow caused by time and regional differences. This results in a lack of feedback mechanism on path carrying capacity, and the spatial boundaries remain rigid. When pedestrian flow is dense, congestion at interaction points and uneven distribution of flow are likely to occur. The passage efficiency between teaching areas and living areas is restricted, the overall space utilization rate decreases, and the planning results are difficult to meet the continuously changing needs of pedestrian flow organization during operation. Summary of the Invention

[0005] To address the technical problems of traditional methods that rely on fixed drawings and scale derivations for functional area division, which only reflect static areas and location relationships and fail to reflect the dynamic pressure of pedestrian flow over time and across different areas, resulting in a lack of feedback mechanisms on path capacity, rigid spatial boundaries, and potential congestion and uneven flow distribution at interaction points during peak pedestrian traffic, thus hindering the efficiency of passage between teaching and living areas, reducing overall space utilization, and making it difficult for planning results to meet the continuously changing needs of pedestrian flow organization, this invention provides a digital generation method for quantitative and qualitative coordination in university campus planning and design, including the following steps:

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a digital generation method for quantitative and qualitative overall planning in university campus design, comprising the following steps:

[0007] S1: Obtain data on pedestrian flow per unit area and average pedestrian speed through pressure sensors on the connecting paths between functional areas of the campus, perform instantaneous pedestrian pressure calculation, and simultaneously collect the effective passage cross-section width and length parameters of the path to form a basic path parameter set;

[0008] S2: Call the basic path parameter set, perform pairwise difference operation on the instantaneous pedestrian pressure value data of the connecting path between adjacent functional areas to construct a pressure gradient difference sequence, and perform ratio calculation based on the gradient difference amplitude and the path length parameter in the basic path parameter set to generate a displacement reference value vector;

[0009] S3: Based on the displacement reference value vector direction information and amplitude information, the Thiessen polygon segmentation algorithm is used to perform real-time migration processing on the coordinate positions of the functional area boundary nodes in the topological space to generate a dynamic topological node coordinate set.

[0010] S4: Based on the dynamic topology node coordinate set, establish the path affiliation relationship of the functional area interaction points, calculate the ratio of the per-unit-time pedestrian flow to the effective passage cross-section width of the basic path parameter set for each interaction point's subordinate path, summarize the path stress values, and obtain the stress distribution data of the interaction points.

[0011] As a further embodiment of the present invention, the basic path parameter set includes cross-sectional geometric features, spatial location attributes, and functional area affiliation information; the displacement reference value vector includes directional components, amplitude components, and time series; the dynamic topology node coordinate set includes node coordinate values, boundary constraints, and topology association attributes; and the interaction point stress distribution data includes interaction point number, path stress sum, and stress distribution weight.

[0012] As a further aspect of the present invention, the specific steps of S1 are as follows:

[0013] S101: Collects pedestrian flow data per unit area along the connecting paths of functional areas on campus using pressure sensors, organizes it into sequence values ​​in chronological order, calls the walking speed data of the path segment, compares and calculates the pedestrian flow and walking speed data at the corresponding time, and generates instantaneous pedestrian pressure values.

[0014] S102: Collect path information through pressure sensors, record the effective cross-sectional width and corresponding length of the path, calculate the geometric cross-sectional area, call up pedestrian flow data, analyze the group displacement of the cross-section per unit time, and calculate the ratio of the displacement to the cross-sectional area to generate the path travel speed;

[0015] S103: Based on the path travel speed and instantaneous pedestrian pressure value, call the path geometric section parameters, calculate the total number of pedestrians in multiple time periods, and arrange the speeds in chronological order as a change sequence to obtain the basic path parameter set.

[0016] As a further aspect of the present invention, the specific steps of S2 are as follows:

[0017] S201: Call the instantaneous pedestrian pressure value data of the adjacent functional area connection path in the basic path parameter set, calculate the pressure difference of adjacent paths one by one, arrange the difference result sequence according to the path node order, and obtain the pressure difference sequence.

[0018] S202: Based on the pressure difference sequence, compare the changes between adjacent difference data, calculate the amplitude difference between the previous and subsequent differences item by item, and connect the calculation results of each item into a new sequence according to the path node order to obtain the gradient difference amplitude sequence.

[0019] S203: Based on the gradient difference magnitude sequence, call the path length parameter in the basic path parameter set, perform a ratio calculation between each magnitude value and the corresponding path length, and combine the ratios into a vector according to the path node order to generate a displacement reference value vector.

[0020] As a further aspect of the present invention, the specific steps of S3 are as follows:

[0021] S301: Call the direction and amplitude information of the displacement reference value vector, decompose the coordinate axes of the functional area boundary node based on the direction information, call the amplitude information to perform linear offset calculation on the node coordinates, and obtain the coordinate offset;

[0022] The coordinate offset refers to the spatial position difference obtained after linearly correcting the coordinates of the functional area boundary nodes based on the direction and amplitude information of the displacement reference value vector.

[0023] S302: Call the coordinate offset, use the Thiessen polygon segmentation algorithm to divide the offset coordinates into regions, compare the boundary data of adjacent polygons based on the differences in node positions, and correct the attribution relationship by calculating the shape overlap and position deviation to obtain the node boundary segmentation coefficient.

[0024] The node boundary division coefficient refers to the boundary assignment correction factor calculated based on the shape overlap and positional deviation of adjacent polygons after Thiessen polygon segmentation.

[0025] S303: Based on the node boundary division coefficient, perform real-time migration processing on the coordinates of the functional area boundary nodes, update the coordinate values ​​based on spatial position and offset coefficient, and merge and sort the updated coordinates to obtain a dynamic topology node coordinate set.

[0026] As a further aspect of the present invention, the specific steps of S4 are as follows:

[0027] S401: Based on the dynamic topology node coordinate set, perform affiliation division on the path nodes according to the positioning range of the functional area interaction points, identify the node coordinates one by one with the interaction point identifiers and integrate them into the path affiliation relationship, and generate the path affiliation coefficient.

[0028] The path affiliation coefficient is used to characterize the affiliation weight relationship between path nodes and interaction points, so as to quantify the degree of membership of a node to an interaction point.

[0029] S402: Call the path affiliation coefficient, collect the pedestrian flow data of the paths under the interaction point and the effective passage cross-section width data of the basic path parameter set, calculate the ratio of pedestrian flow to width and integrate the sequence to generate a pedestrian flow-width ratio sequence.

[0030] S403: Based on the pedestrian flow width ratio sequence, calculate the path stress value for each path under the interaction point, sum the stress results of the differentiated paths within the interaction point range, and distribute them to the corresponding positions of the interaction points to obtain the stress distribution data of the interaction points.

[0031] As a further aspect of the present invention, the method further includes step S5:

[0032] S5: Call the stress distribution data of the interaction point and the stress value of the single path within the interaction point to calculate the stress dispersion coefficient. Use the hierarchical analysis method to compare and identify the interaction point whose stress concentration exceeds the critical threshold as a high-risk node. Scan and analyze the available space around the high-risk node and calculate the coordinates of the virtual diversion node to generate a quantitative morphological overall layout scheme.

[0033] The quantitative morphological overall layout scheme includes high-risk node identification, virtual diversion node coordinates, and spatial allocation mode.

[0034] As a further aspect of the present invention, the specific steps of S5 are as follows:

[0035] S501: Based on the stress distribution data of the interaction points and the single-path stress values ​​within the interaction points, extract the path stress and compare it one by one according to the distribution interval, calculate the deviation ratio of the path stress, accumulate the deviation and calculate the discrete metric value, and generate the stress dispersion coefficient.

[0036] S502: Call the stress dispersion coefficient, assign weights to the coefficient values ​​of each interaction point according to the analytic hierarchy process and sort them, compare them one by one with the critical threshold of stress concentration, filter the interaction points whose concentration exceeds the threshold and record their positions, and obtain the distribution rate of high-risk nodes.

[0037] S503: Call the high-risk node distribution rate, extract the surrounding available space boundary data, combine the node coordinates to determine the center of gravity of the placeable area and calculate the virtual node coordinates, and establish a quantitative morphological overall layout plan.

[0038] As a further aspect of the present invention, the stress dispersion coefficient is a discrete metric value obtained based on the cumulative proportion of path stress deviation at interaction points;

[0039] The high-risk node distribution rate is the proportion of the number of interaction points whose concentration exceeds the critical threshold to the total number of interaction points.

[0040] The concentration threshold is a preset numerical limit used to determine the stress concentration at the interaction point.

[0041] A digital generation system for the integrated planning of quantity and form in university campus planning and design includes:

[0042] The pedestrian flow acquisition module collects data on pedestrian flow per unit area and average pedestrian speed along the connecting paths of functional areas on campus using pressure sensors. It then uses parameters such as effective passage width and path length to calculate instantaneous pedestrian pressure values, forming a basic path parameter set, which is then transmitted to the path gradient module.

[0043] The path gradient module calls the basic path parameter set, performs pairwise difference calculations based on the instantaneous pedestrian pressure values ​​of the connecting paths of adjacent functional areas, constructs a pressure gradient difference sequence, performs ratio calculations based on the gradient difference amplitude and path length parameters, generates a displacement reference value vector, and passes it to the topology migration module.

[0044] The topology migration module calls the direction and amplitude information in the displacement reference value vector and uses the Thiessen polygon segmentation algorithm to perform real-time migration processing on the coordinate positions of the functional area boundary nodes in the topological space, generating a dynamic topological node coordinate set, which is then transmitted to the interactive stress module.

[0045] The interactive stress module calls the dynamic topology node coordinate set to establish the association between functional area interactive points and paths, calculates the ratio of the per-unit-time pedestrian flow to the effective passage cross-section width of the basic path parameter set for each interactive point's subordinate path, summarizes the path stress values, obtains the interactive point stress distribution data, and transmits it to the node dispersion module.

[0046] The node dispersion module calls the stress distribution data of the interaction points and the stress value of the single path within the interaction points to calculate the stress dispersion coefficient. It uses the hierarchical analysis method to identify interaction points whose stress concentration exceeds the critical threshold as high-risk nodes. It scans and analyzes the available space around the high-risk nodes and calculates the coordinates of the virtual diversion nodes to form a quantitative and morphological overall layout scheme.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0048] In this invention, by acquiring real-time pedestrian flow and speed along the path and combining it with cross-sectional parameters, path pressure can be made into a quantifiable index. The displacement reference value vector obtained by calculating the ratio of gradient difference to path length provides a basis for dynamic coordinate migration of functional area boundary nodes, enabling the spatial form to continuously adjust with pedestrian flow status and forming a more realistic boundary structure in the topological space. Furthermore, the path pressure is summarized at the interaction point level to achieve an accurate presentation of stress distribution. This makes the connection between different functional areas more balanced, improves pedestrian flow efficiency, and gives the campus space a higher degree of flexibility and adaptability during operation. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0050] Figure 1 This is a schematic diagram of the steps of the present invention;

[0051] Figure 2 This is a detailed schematic diagram of S1 of the present invention;

[0052] Figure 3 This is a detailed schematic diagram of S2 of the present invention;

[0053] Figure 4 This is a detailed schematic diagram of S3 of the present invention;

[0054] Figure 5 This is a detailed schematic diagram of S4 of the present invention;

[0055] Figure 6 This is a detailed schematic diagram of S5 of the present invention;

[0056] Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0057] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0058] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0059] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0060] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0061] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0062] Please see Figure 1 This invention provides a digital generation method for quantitative and qualitative overall planning in university campus design, comprising the following steps:

[0063] S1: Obtain data on pedestrian flow per unit area and average pedestrian speed through pressure sensors on the connecting paths between functional areas of the campus, perform instantaneous pedestrian pressure calculation, and simultaneously collect the effective passage cross-section width and length parameters of the path to form a basic path parameter set;

[0064] S2: Call the basic path parameter set, perform pairwise difference operation on the instantaneous pedestrian pressure value data of the connecting path between adjacent functional areas to construct a pressure gradient difference sequence, and perform ratio calculation based on the gradient difference amplitude and the path length parameter in the basic path parameter set to generate a displacement reference value vector;

[0065] S3: Based on the displacement reference value vector direction and amplitude information, the Thiessen polygon segmentation algorithm is used to perform real-time migration processing of the coordinate positions of the functional area boundary nodes in the topological space, generating a dynamic topological node coordinate set.

[0066] S4: Establish the path affiliation relationship of the interaction points in the functional area based on the dynamic topology node coordinate set, calculate the ratio of the pedestrian flow per unit time to the effective passage cross-section width of the basic path parameter set for each interaction point's subordinate path, summarize the path stress values, and obtain the stress distribution data of the interaction points;

[0067] S5: Call the stress distribution data of the interaction point and the stress value of the single path within the interaction point to calculate the stress dispersion coefficient. Use the analytic hierarchy process to compare and identify the interaction point whose stress concentration exceeds the critical threshold as a high-risk node. Scan and analyze the available space around the high-risk node and calculate the coordinates of the virtual diversion node to generate a quantitative morphological overall layout scheme.

[0068] The basic path parameter set includes cross-sectional geometric features, spatial location attributes, and functional area affiliation information; the displacement reference value vector includes directional components, amplitude components, and time series; the dynamic topology node coordinate set includes node coordinate values, boundary constraints, and topological association attributes; the interaction point stress distribution data includes interaction point number, path stress sum, and stress distribution weight; and the quantitative morphological overall layout scheme includes high-risk node identification, virtual diversion node coordinates, and spatial allocation mode.

[0069] Please see Figure 2 The specific steps of S1 are as follows:

[0070] S101: Collects pedestrian flow data per unit area along the connecting paths of functional areas on campus using pressure sensors, organizes it into sequence values ​​in chronological order, calls the walking speed data of the path segment, compares and calculates the pedestrian flow and walking speed data at the corresponding time, and generates instantaneous pedestrian pressure values.

[0071] Pressure sensors are deployed along the connecting paths between functional areas of the campus, and the instantaneous pressure values ​​at each monitoring point are continuously collected at different times. The instantaneous pressure value F(t) read by the pressure sensor is divided by the effective detection area A of the sensor to obtain the flow rate per unit area q(t) = F(t) ÷ A. For example, at 8:00 AM, a monitoring point records an instantaneous pressure of 530 N and a detection area of ​​0.50 m². 2 Therefore, the flow of people per unit area at this time is 530 ÷ 0.50 = 1060 N / m². 2 The data is arranged in chronological order to form an array [q(t1), q(t2), q(t3)...]. Simultaneously, the travel speed data for each path segment is retrieved at the same time point. The travel speed v(t) is obtained by dividing the known length L of the measurement segment by the time T taken for the test group to traverse that segment, i.e., v(t) = L ÷ T. For example, if L = 10.00 m, and the time taken for one person to traverse the segment between 8:00:00 and 8:00:05 is 5.20 s, then the travel speed is calculated as 10.00 ÷ 5.20 = 1.923 m / s. After obtaining these two parameters, the instantaneous crowd pressure calculation formula P(t) = q(t) × v(t) is applied, where P(t) represents the instantaneous crowd pressure at time t, in N / (m²). 2 The calculation process involves multiplying the pedestrian flow rate per unit area by the walking speed. Taking 8:00:00 as an example, P(8:00:00) = 1060 × 1.923 = 2039.38 N / (m²). 2The calculation process, involving ·s), is executed cyclically at each time point, generating a time-ordered sequence of instantaneous pedestrian pressure [P(t1), P(t2), P(t3)...] in memory. During the calculation, various fuzzy quantifiers are quantified; for example, the low-density pressure range in the original monitoring database is set as 0 ≤ P < 2000 N / (m³). 2 ·s), the medium density range is 2000≤P<2500N / (m 2 ·s), the high-density range is P≥2500N / (m 2 The threshold of 2500 is derived from the statistical analysis of measured data from the same route during the morning rush hour over the past three years. It is calculated based on the mean plus 1.5 times the standard deviation. For example, in the original sample of 50 morning rush hour pressure values, the mean is 2150 and the standard deviation is 233. Therefore, the threshold = 2150 + 1.5 × 233 = 2499.5 (rounded to 2500). If the instantaneous pressure value recorded at 8:00:10 AM is 550 N, and the detection area is 0.50 m², then... 2 Therefore, the pedestrian flow per unit area q(8:00:10) = 550 ÷ 0.50 = 1100 N / m 2 The measured passage speed during this period is 5.05 seconds. Therefore, the walking speed v(8:00:10) = 10.00 ÷ 5.05 = 1.980 m / s. The final instantaneous pedestrian pressure P(8:00:10) = 1100 × 1.980 = 2178.00 N / (m²). 2 ·s), based on the above interval, it is determined that the value falls within the medium density range. Table 1 lists the pressure value, area, travel speed and calculated instantaneous pedestrian pressure of the path at three consecutive monitoring times from 8:00:00 to 8:00:10. The data shown in Table 1 will directly form the pedestrian pressure change sequence of the path during this period, which is used to describe the state of pedestrian flow.

[0072] Table 1: Instantaneous Data of Campus Path Monitoring Points

[0073]

[0074] As shown in Table 1, the flow rate and speed of people per unit area at each monitoring moment are obtained through real-time measurement. Substituting these values ​​into the formula P(t)=q(t)×v(t) completes the calculation of instantaneous flow pressure at each moment and forms a sequence for subsequent use.

[0075] S102: Collect path information through pressure sensors, record the effective cross-sectional width and corresponding length of the path, calculate the geometric cross-sectional area, call up pedestrian flow data, analyze the group displacement of the cross-section per unit time, and calculate the ratio of the displacement to the cross-sectional area to generate the path travel speed;

[0076] When collecting path information, a pressure sensor and a ranging device are used to record the effective cross-sectional width W and corresponding length L of each path segment. Multiplying these two values ​​yields the geometric cross-sectional area S = W × L. For example, if the effective cross-sectional width of a segment in the monitored path is measured to be 2.50m and the length 10.00m, then the geometric cross-sectional area is 2.50 × 10.00 = 25.00m². 2 The sequence of pedestrian flow per unit area q(t) is converted into the total pedestrian flow Q(t) = q(t) × S on the actual cross-section. Taking 08:00:00 as an example, the pedestrian flow per unit area is 1060 N / m. 2 Therefore, the total pedestrian flow at this cross-section is 1060 × 25.00 = 26500 N. To analyze the total displacement of the group passing through this cross-section per unit time, we first integrate or accumulate the total pedestrian flow value over multiple time intervals within a time window Δt (e.g., 10 seconds). The total pedestrian flow values ​​at three times—08:00:00, 08:00:05, and 08:00:10—are taken sequentially as 26500N, 25750N, and 27500N, respectively. These values ​​are then added together to obtain the total pedestrian flow sum: 26500 + 25750 + 27500 = 79750N. This total pedestrian flow is then divided by the cross-sectional area S during that time period to obtain the average total pressure per unit area. Then, this value is compared and analyzed with the measured time interval to obtain the travel speed v. s The velocity here is calculated as the ratio of displacement to time. The displacement depends on the total displacement of the group traversing the cross section within the time interval Δt. For example, if the average time taken for the group to advance within this 10.00m path is 5.12s, then the total displacement rate of the group is v. s =10.00÷5.12=1.953m / s. The specific process for the "analysis" action described above involves linearly adding the individual values ​​of total pedestrian flow over multiple time points to form a cumulative value, then dividing by the cross-sectional area to obtain the average pressure. This average pressure is then compared with the displacement velocity within the corresponding time window to determine if there is a situation where the speed is too slow or too fast. The division of the low-speed and high-speed intervals is based on historically measured speed distributions, with low speed set as 0≤v. s <1.0m / s (stagnant state), medium speed is 1.0≤v s <2.0m / s, high speed is v s≥2.0 m / s, for example, the current measured 1.953 m / s is in the upper limit of the medium speed range. According to the threshold setting method, the threshold is selected as the average speed (the average speed of this path during the same period in the past year is 1.75 m / s) plus 0.2 m / s as the high density warning limit, calculated as 1.75 + 0.2 = 1.95 m / s. The current 1.953 m / s is slightly higher than the threshold, which means that the speed exceeds the limit. In this paragraph, this means that the flow speed is close to the limit level under the current population density. The final result generates the path travel speed parameters, and the parameter sequence is [v s (t1), v s [(t2), ...] and together with the human flow pressure sequence, form a complete dataset for subsequent multi-time period analysis.

[0077] S103: Based on the path travel speed and instantaneous pedestrian pressure value, call the path geometric section parameters, calculate the total number of pedestrians in multiple time periods, and arrange the speed in time order as a change sequence to obtain the basic path parameter set;

[0078] Based on the path travel speed sequence and instantaneous pedestrian pressure sequence, the total number of pedestrians at a given time point is calculated using the geometric cross-sectional parameter S of the path. The calculation process involves first multiplying the pedestrian flow per unit area by the cross-sectional area to obtain the total number of pedestrians crossing the cross-section at that time point: Q(t) = q(t) × S, where q(t) = P(t) ÷ v s (t) is the inverse value of the flow of people per unit area, obtained by dividing pressure by velocity. For example, at 08:00:05, the instantaneous flow pressure is 2019.83 N / (m²). 2 If the speed of movement is 1.961 m / s, then the flow rate per unit area q(8:00:05) = 2019.83 ÷ 1.961 = 1029.86 N / m 2 Then multiply by the cross-sectional area of ​​25.00m² 2 The total pedestrian flow Q(8:00:05) is calculated as 1029.86 × 25.00 = 25746.50 N. The total pedestrian flow over time is summed and combined with the corresponding sampling interval Δt to obtain the total number of people in multiple time periods. For example, if the total pedestrian flow at three consecutive sampling points (08:00:00, 08:00:05, and 08:00:10) is 26500 N, 25746.50 N, and 27500 N respectively, then the total sum is 26500 + 25746.50 + 27500 = 79746.50 N. Following the international unit conversion, N is divided by the average human weight of 976 N (based on the assumption that average weight ≈ 100 kg × 9.76 m / s²). 2Converting this to the number of people, the total number is approximately 79746.50 ÷ 976 ≈ 81.64 people, rounded down to 82 people. This number is recorded as the total number of people in this multi-time period in the dataset. Next, the path travel speed values ​​are arranged in chronological order to form a speed change sequence. This process directly converts [v] into the number of people. s (t1), v s (t2), v s [t3] is converted into vectorized data of [1.923, 1.961, 1.980] m / s and compared with the pressure sequence [2039.38, 2019.83, 2178.00] N / (m 2 ·s) Perform corresponding binding to form a basic path parameter set. Before generating this set, the values ​​within the velocity sequence need to be range-determined to mark the flow state. The range setting still follows the rules of dividing low-to-medium speed (0–1.0 m / s), medium speed (1.0–2.0 m / s), and high speed (≥2.0 m / s). It is also checked whether the alarm condition of a velocity threshold of 1.95 m / s is triggered. For example, if 1.961 m / s and 1.980 m / s in the current sequence are both higher than the threshold, then a "high-speed warning" label is added to the time points in the set. The advantage of the formula lies in using P(t) / v... s (t) By inversely estimating the pedestrian density and combining it with geometric cross-sectional parameters, a complete chain of data generation was achieved, from mechanical values ​​to the number of people and then to status annotations. The results show that the total number of people on the path during the detected multiple time periods was 82, and a complete set of basic parameters of pedestrian pressure and walking speed that change over time was obtained.

[0079] Please see Figure 3 The specific steps of S2 are as follows:

[0080] S201: Call the instantaneous pedestrian pressure value data of the adjacent functional area connection path in the basic path parameter set, calculate the pressure difference of adjacent paths one by one, arrange the difference result sequence according to the path node order, and obtain the pressure difference sequence.

[0081] Based on the basic path parameter set, instantaneous pedestrian pressure data P1(t), P2(t), ..., P2(t) for connecting paths between adjacent functional areas are first extracted from it. m (t), the value is the pressure value calculated from the monitoring segment corresponding to the path node at the same time point. The pressure values ​​of adjacent paths are called in pairs according to the node sequence. For example, at 8:00:00 AM, the instantaneous pedestrian pressure of path 1 is P1 = 2039.38 N / (m). 2 The instantaneous pedestrian pressure at node 2 is P2 = 2019.83 N / (m²). 2 If ·s), then the pressure difference ΔP between these two adjacent paths is... 1,2(t)=P1(t)-P2(t)=2039.38-2019.83=19.55N / (m 2 The operation (·s) is a real number subtraction logic. It involves taking the pressure value of the previous node as the minuend and the pressure value of the next node as the subtrahend, performing a pairwise operation. A positive difference indicates that the pressure of the previous node is greater, and a negative difference indicates that the pressure of the next node is greater. This process is repeated to subtract the pressure between adjacent nodes. For example, at 8:00:05, the path pressure of node 1 is P1 = 2019.83 N / (m²). 2 The path pressure at node 2 is P2 = 2178.00 N / (m·s), and the pressure is P2 = 2178.00 N / (m·s). 2 If ·s), then the difference is ΔP. 1,2 (t)=2019.83-2178.00=-158.17N / (m 2 At 8:00:10, the path pressure at node 1 is P1 = 2178.00 N / (m²). 2 The path pressure at node 2 is assumed to be P2 = 2100.50 N / (m²). 2 ·s), the difference is ΔP 1,2 (t)=2178.00-2100.50=77.50N / (m 2 During the entire sequence calculation process, interval divisions were established to determine the relative magnitude of the differences, with the low difference interval set as |ΔP| < 50N / (m). 2 The mean difference range is 50 ≤ |ΔP| < 150 N / (m). 2 The high difference range is |ΔP|≥150N / (m) 2 The threshold range (·s) is based on statistical analysis of the original pressure difference values ​​of the same road type during the morning rush hour. It is divided using the mean ± standard deviation multiplied by a coefficient. For example, the average pressure difference between adjacent roads over the past year was 80 N / (m²). 2 The standard deviation is 46 N / (m·s). 2 If the upper limit of the medium difference is calculated as 80 + 1.5 × 46 = 149, and the upper limit of the low difference is 50, which is given based on the natural fluctuation pattern when people switch paths in low-density scenarios, the difference results at the time points are finally arranged according to the path node order to generate a pressure difference sequence distributed by time and node order. For example, in this case, the sequence of the three monitoring time points is 19.55, -158.17, and 77.50. This result shows that among multiple adjacent paths, there is a pressure difference peak of -158.17 (absolute value 158.17) at 8:00:05, which is in the high difference range, while 8:00:00 and 8:00:10 are in the low and medium difference ranges, respectively. Thus, the pressure difference sequence is obtained for the next step of calculation.

[0082] S202: Based on the pressure difference sequence, compare the changes between adjacent difference data, calculate the amplitude difference between the previous and subsequent differences item by item, and connect the calculation results of each item into a new sequence according to the path node order to obtain the gradient difference amplitude sequence.

[0083] Obtain the pressure difference sequence ΔP = [19.55, -158.17, 77.50]. Calculate the amplitude difference item by item according to the order of change between adjacent difference data. Specifically, this involves calculating the amplitude difference for adjacent items ΔP in the sequence. i With ΔP i+1 Execution amplitude difference calculation formula ΔG i =|ΔP i+1 -ΔP i The absolute value sign is used to take the unsigned difference to eliminate the influence of positive and negative directions. For example, in this case, the amplitude difference ΔG1 of the first group is |-158.17-19.55| = |-177.72| = 177.72 N / (m 2 The amplitude difference of the second group is ΔG2 = |77.50 - (-158.17)| = |235.67| = 235.67 N / (m). 2 The numerical sequence (·s) represents the magnitude of the pressure difference change, reflecting the intensity of the transformation of the mechanical state of the flow between nodes. To facilitate subsequent analysis, the low-amplitude range is defined as ΔG < 100 N / (m). 2 The variation range of ·s) is 100≤ΔG<200N / (m 2 The high variation range is ΔG ≥ 200 N / (m). 2 The threshold standard is based on the mean and standard deviation analysis of the pressure difference changes of the same type of path nodes over multiple time periods in historical monitoring, with a mean of approximately 140 N / (m). 2 The standard deviation is approximately 40 N / (m·s). 2 ·s), with the mean ± 1.5 times the standard deviation as the dividing point, the upper limit of the middle interval is 140 + 1.5 × 40 = 200, and the upper limit of the lower interval is 100, which takes into account the small fluctuation range under normal flow conditions. For example, in the current two amplitude differences, 177.72 N / (m 2 The value of ·s falls within the medium range of variation, at 235.67 N / (m). 2 ·s) belongs to the high change range. The specific steps of the "comparison" action during the execution process are as follows: First, call the adjacent pressure difference values, subtract them one by one to obtain the change value, then take the absolute value of each change value to form a set of amplitude difference values ​​that are all non-negative numbers, and finally arrange and connect the calculation results [177.72, 235.67] according to the path node order to form a new sequence, that is, the gradient difference amplitude sequence ΔG=[177.72, 235.67].

[0084] S203: Based on the gradient difference magnitude sequence, call the path length parameter in the basic path parameter set, perform a ratio calculation between each magnitude value and the corresponding path length, and combine the ratio into a vector according to the path node order to generate a displacement reference value vector.

[0085] The gradient difference magnitude sequence ΔG = [177.72, 235.67] and the path length parameters L1, L2, ..., L from the basic path parameter set are called. n In this example, the path lengths corresponding to these two amplitude differences are L1 = 10.00m and L2 = 12.00m, respectively. The ratio calculation formula R is applied to each value. i =ΔG i ÷L i , where R i For the displacement reference value corresponding to the i-th adjacent node pair, the operation is to convert the gradient difference magnitude value (unit N / (m)) into the gradient difference magnitude value. 2 Using s as the dividend and the path length (in meters) as the divisor, we obtain the reference gradient force difference per unit path length. In the calculation, for example, the first term R1 = 177.72 ÷ 10.00 = 17.772 N / (m) 3 ·s), the second term R2 = 235.67 ÷ 12.00 = 19.639 N / (m 3 The results are then combined into a vector R = [17.772, 19.639] according to the path node order. To facilitate subsequent processing, the intensity interval of the displacement reference value is set as follows: low intensity interval R < 10 N / (m 3 ·s), medium strength range 10≤R<18N / (m) 3 ·s), high-intensity range R≥18N / (m 3 The threshold division is derived from the result of a comprehensive analysis of the historical monitoring data of nodes in the same functional area, which is based on the mean ± standard deviation. The mean is approximately 14 and the standard deviation is approximately 2.7. The starting point of the high intensity interval is calculated as mean + 1.5 times standard deviation, which is approximately 14 + 1.5 × 2.7 ≈ 18.05. This value is then rounded down to 18 as the starting value for high intensity. In this example, the first value of 17.772 falls in the medium intensity interval, and the second value of 19.639 falls in the high intensity interval. The "ratio calculation" step in the execution process is detailed as follows: the gradient difference amplitude sequence and the length parameter of the corresponding path are called one by one, and the two are sequentially sent into the division operation unit to obtain the result and temporarily stored. Then, the results are connected to generate the final vector. The final output displacement reference value vector R = [17.772, 19.639] serves as the reference value of the gradient difference intensity per unit length between path node pairs within this time period. It is used for subsequent positioning and prediction analysis based on the node order.

[0086] Please see Figure 4The specific steps of S3 are as follows:

[0087] S301: Call the direction and magnitude information of the displacement reference value vector, decompose the coordinate axes of the functional area boundary node based on the direction information, call the magnitude information to perform linear offset calculation on the node coordinates, and obtain the coordinate offset;

[0088] Coordinate offset refers to the spatial position difference obtained after linearly correcting the coordinates of functional area boundary nodes based on the direction and magnitude information of the displacement reference value vector.

[0089] The displacement reference vector contains direction and magnitude information, including the direction information θ. i This represents the angle of motion direction of adjacent path node pairs in the planar coordinate system. The magnitude information is the component value of R. The direction information is obtained by calculating the change in position (Δx) of the functional area boundary node in space. i Δy i Substitute into the arctangent function θ i =arctan(Δy) i / Δx i The value is obtained in degrees or radians. In this example, it is assumed that the displacement direction angle θ1 of node pair 1 is 30° and the displacement direction angle θ2 of node pair 2 is 60°. Then, based on this direction information, it is decomposed in the two-dimensional coordinate system where the functional area boundary nodes are located, and the amplitude information R is obtained. i The calculation of the components acting on the x-axis and y-axis respectively, and the action performed is to calculate the x-axis component Δx. i =R i ×cos(θ i ), calculate the y-direction component Δy i =R i ×sin(θ i The system calculates the values ​​for each node, for example, the x-direction component Δx1 = 17.772 × cos30° ≈ 17.772 × 0.8660 = 15.387, and the y-direction component Δy1 = 17.772 × sin30° ≈ 17.772 × 0.5000 = 8.886 for the first node pair; the x-direction component Δx2 = 19.639 × cos60° ≈ 19.639 × 0.5000 = 9.8195, and the y-direction component Δy2 = 19.639 × sin60° ≈ 19.639 × 0.8660 = 17.005 for the second node pair. Then, it retrieves the amplitude information for the original coordinates (X...) of each functional area boundary node. i0 Y i0 Perform a linear offset operation, with the action being X. i =X i0 +Δx i Y i =Y i0+Δy i Here, assuming the original coordinates of the first node are (100.000, 200.000), the updated coordinates are X1 = 100.000 + 15.387 = 115.387, Y1 = 200.000 + 8.886 = 208.886. The original coordinates of the second node are (150.000, 250.000), and the updated coordinates are X2 = 150.000 + 9.8195 ≈ 159.820, Y2 = 250.000 + 17.005 ≈ 267.005. During execution, a coordinate offset level interval was established to evaluate the magnitude of the offset: small offsets are...

[0090]

[0091] The intermediate offset is 10.0 ≤ this value < 18.0m, and the large offset is ≥ 18.0m. This benchmark value setting refers to the actual construction data of previous campus functional area location adjustments. In this calculation, the first node offset... Belongs to the middle offset range, second node offset For large offset intervals, the node's (Δx) i Δy i The vectors are combined to form a two-dimensional vector set [(15.387, 8.886), (9.820, 17.005)], which is the set of coordinate offsets obtained in the previous step.

[0092] S302: Call the coordinate offset, use the Thiessen polygon segmentation algorithm to divide the offset coordinates into regions, compare the boundary data of adjacent polygons based on the differences in node positions, and correct the attribution relationship by calculating the shape overlap and position deviation to obtain the node boundary segmentation coefficient.

[0093] The node boundary partitioning coefficient refers to the boundary assignment correction factor calculated based on the shape overlap and positional deviation of adjacent polygons after Thiessen polygon segmentation.

[0094] The coordinate offset set is called to update the coordinate set {(115.387, 208.886), (159.820, 267.005)} of the boundary nodes of the functional area for Thiessen polygon segmentation. The action is to first map the boundary nodes to the point set P in the plane coordinate system. i (X i Y i To calculate the perpendicular bisector between adjacent nodes as the geometric generation line of the polygon boundary, the specific process is as follows: For any two adjacent nodes, such as node 1 (115.387, 208.886) and node 2 (159.820, 267.005), first calculate the midpoint:

[0095] M 1,2=((115.387+159.820) / 2,(208.886+267.005) / 2)=(137.6035,237.9455);

[0096] Next, calculate the vector connecting these two points:

[0097]

[0098] Determine the vertical direction vector Extending the perpendicular line from the midpoint, we construct the boundary equation of the Thiessen polygon. By combining the boundary lines, we obtain the polygon region corresponding to each node. Then, we perform boundary comparison, extracting the boundary coordinate sequence of the overlapping parts of adjacent polygons and calculating the intersection length l. overlap With average boundary length l avg The ratio of shapes is used to measure the degree of overlap, and the formula is O = l overlap / l avg , where l overlap and l avg Measured at the same scale with units of meters, for example, the intersection length measured on the adjacent polygon boundaries of node 1 and node 2 is 12.50m, and the average boundary length l avg = (25.00 + 27.00) / 2 = 26.00 m, then the overlap O = 12.50 ÷ 26.00 ≈ 0.4808. For the calculation of positional deviation, the action is to compare the Euclidean distance difference between the offset coordinates of the two nodes and their original coordinates. The displacement distance of node 1 is... Node 2 is The positional deviation index D = |d1 - d2| = |17.772 - 19.639| = 1.867m. Using the overlap degree O and positional deviation D as inputs, a linear combination formula is used to calculate the node boundary partitioning coefficient κ.

[0099] κ=w1×O+w2×(1-D / D max );

[0100] The weights w1 = 0.6 and w2 = 0.4 are set based on the ratio of overlap and positional deviation in historical segmentation accuracy experiments. max To determine the maximum deviation within the interval, a value of 20.0m is used. Substituting the values ​​into the equation yields:

[0101] κ=0.6×0.4808+0.4×(1-1.867 / 20.0)=0.28848+0.4×0.90665=0.28848+0.36266=0.65114;

[0102] According to the boundary delineation coefficient level standard, the low attribution coefficient interval is set as κ<0.4, the medium attribution coefficient interval is 0.4≤κ<0.7, and the high attribution coefficient interval is κ≥0.7. The calculated κ=0.65114 falls within the medium attribution coefficient interval, and thus the node boundary delineation coefficient set is obtained.

[0103] S303: Based on the node boundary division coefficient, the coordinates of the functional area boundary nodes are migrated in real time. The coordinate values ​​are updated based on the spatial position and offset coefficient. The updated coordinates are then merged and sorted to obtain a dynamic topology node coordinate set.

[0104] Call the node boundary partition coefficient set {κ1, κ2, ..., κ n In this example, there is only one pair of nodal coefficient values ​​κ. 1,2 =0.65114, used as a scaling parameter in the coordinate migration calculation, and calling the updated node coordinates (115.387, 208.886) and (159.820, 267.005) and their corresponding original coordinates (100.000, 200.000) and (150.000, 250.000), the new migration coordinates are calculated using spatial position and offset coefficient. The action performed is to calculate the difference vector ΔX between the original coordinates and the offset coordinates for each node. i =X i1 -X i0 ΔY i =Y i1 -Y i0 , and then with κ i X is used as a scaling factor in scaling operations. i,new =X i0 +κ i ×ΔX i Y i,new =Y i0 +κ i ×ΔY i For example, node 1:

[0105] ΔX1=115.387-100.000=15.387, ΔY1=208.886-200.000=8.886;

[0106] Coordinates after migration:

[0107] X 1,new =100.000+0.65114×15.387≈100.000+10.018=110.018,Y 1,new =200.000 + 0.65114 × 8.886 ≈ 200.000 + 5.789 = 205.789;

[0108] Node 2:

[0109] ΔX2=159.820-150.000=9.820, ΔY2=267.005-250.000=17.005;

[0110] Coordinates after migration:

[0111] X 2,new =150.000+0.65114×9.820≈150.000+6.394=156.394,Y 2,new =250.000 + 0.65114 × 17.005 ≈ 250.000 + 11.066 = 261.066;

[0112] The real-time migration coordinate set {(110.018, 205.789), (156.394, 261.066)} is obtained. Then, the updated node coordinates are merged and sorted according to the node order in the polygon topology. During the sorting process, the "merge" action compares the node number with the connection relationship of adjacent nodes in the topology linked list, and puts the connected nodes into the same group. Then, the nodes in the group are arranged in ascending order of number. For example, node 1 has a smaller number and is located at the first position of the set, and node 2 has a smaller number and is arranged after it. The final sorting result is output as a new dynamic topology node coordinate set.

[0113] Please see Figure 5 The specific steps of S4 are as follows:

[0114] S401: Based on the dynamic topology node coordinate set, perform affiliation division on the path nodes according to the positioning range of the functional area interaction points, identify the node coordinates one by one with the interaction point identifier and integrate them into the path affiliation relationship, and generate the path affiliation coefficient.

[0115] The path affiliation coefficient is used to characterize the affiliation weight relationship between path nodes and interaction points, in order to quantify the degree of membership of a node to an interaction point;

[0116] Based on the dynamic topology node coordinate set, the path nodes are first assigned according to the location range of the functional area interaction points. The action is to perform an action on each node coordinate (X... i Y i ) sequentially call the geometric position parameters (X) of the interaction points c Y c and radius of action r c Calculate the Euclidean distance between the node and the interaction point. If d i ≤r cThen, the node is determined to belong to the interaction point. In the current example, the center coordinates of interaction point C1 are set to (120.000, 210.000), and the radius is 20.000m. Calculate for node 1:

[0117]

[0118] Since 10.834m is less than 20.000m, node 1 belongs to interaction point C1. The center coordinates of interaction point C2 are set to (160.000, 265.000), and its radius is 18.000m. For node 2, the following calculations are performed:

[0119]

[0120] Since 5.335m is less than 18.000m, node 2 belongs to interaction point C2. During the attribution process, the "one-to-one correspondence" action involves comparing the coordinates of each node sequentially with the distances of the interaction points and recording the attribution. When a node falls within the range of multiple interaction points, the baseline priority rule is invoked, prioritizing the interaction point with the smallest distance as the node's attribution object. This baseline priority parameter is set with reference to historical traffic path data. In multi-attribution conflict scenarios, the distance priority weight is set to 1.0, higher than the traffic weight of 0.8, ensuring clear classification. After the attribution is completed, the node ID is mapped one-to-one with its assigned interaction point identifier and integrated into a path attribution relationship data table. The path attribution coefficient γ... i The value of γ is determined by the ratio of the number of nodes belonging to the same interaction point on each path to the total number of nodes on that path. For example, if there are 5 nodes belonging to interaction point C1 on path A and the total number of nodes on that path is 8, then γ... A =5÷8=0.625, Similarly, if there are 4 nodes on path B that belong to the interaction point C2 and the total number of nodes on this path is 5, then γ B =4 ÷ 5 = 0.800. To make the attribution coefficients of different paths comparable, we set the attribution coefficient level ranges as follows: low attribution level is γ < 0.4, medium attribution level is 0.4 ≤ γ < 0.7, and high attribution level is γ ≥ 0.7. In this example, γ... A =0.625 belongs to the middle category, γ B =0.800 belongs to the high attribution level, and the path is associated with its corresponding γ. i The values ​​are arranged in order of path number, and a set of path attribution coefficients is generated as the final output of this step.

[0121] Table 2: Example Table for Calculating Path Attribution Coefficient

[0122] Path number Total number of nodes Number of affiliated nodes Belonging Interaction Point Path attribution coefficient A 8 5 C1 0.625 B 5 4 C2 0.800

[0123] As shown in Table 2, the calculation of the attribution coefficients of path A and path B follows the node attribution ratio rule and is assigned to the path attribution coefficient set.

[0124] S402: Call the path affiliation coefficient, collect the pedestrian flow data of the paths under the interaction point and the effective passage cross-section width data of the basic path parameter set, calculate the ratio of pedestrian flow to width and integrate the sequence to generate a pedestrian flow-width ratio sequence.

[0125] Call path attribution coefficient set {γ A =0.625, γ B =0.800} is used as the path filtering condition to collect real-time pedestrian traffic data Q corresponding to the paths under the interaction point. i And the effective passage width data W stored in the basic path parameter set i In this example, the pedestrian flow along path A is Q. A =26500N, and its corresponding cross-sectional width is W. A =2.50m, the pedestrian flow along path B is Q B = 25746.50 N, cross-sectional width is W B =2.80m. The ratio calculation is performed by using the pedestrian flow value as the dividend and the cross-sectional width value as the divisor for each path. The formula is ρ i =Q i / W i , where ρ i The unit is N / m, representing the bearing capacity per unit length along the width of the cross-section, and the ratio of path A:

[0126] ρ A =26500÷2.50=10600N / m;

[0127] The ratio of path B:

[0128] ρ B =25746.50÷2.80≈9195.18N / m;

[0129] The above calculation results are arranged and integrated according to the path number to form the ρ sequence [10600, 9195.18]. For the determination of the pedestrian flow width ratio interval, the low interval is set as ρ < 8000 N / m, the middle interval as 8000 ≤ ρ < 10000 N / m, and the high interval as ρ ≥ 10000 N / m. This benchmark interval is set by analyzing the mean ± standard deviation of three years' historical ratio data under the same functional area and similar cross-sectional conditions. The starting point of the high interval is the rounded value of the mean plus 0.8 times the standard deviation. Under the condition that the mean is 9200 and the standard deviation is 1000, the boundary is calculated as 9200 + 0.8 × 1000 = 10000m. In this example, path A's 10600 N / m falls in the high interval, and path B's 9195.18 N / m falls in the middle interval. The "integrate sequence" action during the execution process is to adjust the path's ρ... i The values ​​are written into the same array in order and the original path index is retained. The output sequence of the pedestrian flow width ratio is [10600, 9195.18].

[0130] S403: Based on the pedestrian flow width ratio sequence, calculate the path stress value for each path under the interaction point, sum the stress results of the differentiated paths within the interaction point range, and distribute them to the corresponding positions of the interaction points to obtain the stress distribution data of the interaction points;

[0131] The sequence of pedestrian flow width ratios ρ = [10600, 9195.18] and the path attribution coefficient {γ} are retrieved. A =0.625, γ B =0.800}, calculate the path stress value for each path belonging to the interaction point, and perform the action of setting the path ratio ρ to 0.800. i Its attribution coefficient γ i Multiplying them together gives the path stress value σ. i =ρ i ×γ i The stress value σ of path A A =10600×0.625=6625.00N / m, stress value σ at path B B =9195.18×0.800≈7356.14N / m. Next, a summation operation is deployed within the interaction point range to sum the stress values ​​of multiple paths belonging to the same interaction point. The action performed is to sum the stress values ​​of paths belonging to the same interaction point. i The values ​​are added together one by one. For example, if there is only path A under interaction point C1, the total stress value of C1 is 6625.00 N / m; if there is only path B under interaction point C2, the total stress value of C2 is 7356.14 N / m. In scenarios where multiple paths belong to the same interaction point, Σσ is applied. iThe cumulative calculation is used as the total stress at the interaction points. Then, the action of "allocating to the corresponding position of the interaction point" is performed, which involves writing the calculated total stress value to the logical position index of the interaction point in the coordinate set. For example, the stress value of C1 is written to the storage location corresponding to coordinates (120.000, 210.000), and the stress value of C2 is written to the storage location corresponding to coordinates (160.000, 265.000). To facilitate subsequent analysis, the stress distribution values ​​at the interaction points are divided into levels: the low stress range is σ... total <7000 N / m, medium stress range is 7000 ≤ σ total <10000 N / m, high stress range is σ total The threshold of ≥10000 N / m is based on the safety limits of building structures and the average measured values ​​of crowd dynamics. The average value is 8500 and the standard deviation is 2000 m as the calculation benchmark. The starting point of the high interval is the average value plus 0.75 times the standard deviation, which is rounded up to 10000 N / m. In this example, the stress value of C1 is 6625.00 N / m, which is in the low stress interval, and the stress value of C2 is 7356.14 N / m, which is in the medium stress interval. The stress values ​​of the interaction points are combined according to their position in the spatial topology to form a distribution dataset {(C1, 6625.00), (C2, 7356.14)}. This set is the stress distribution data of the interaction points.

[0132] Please see Figure 6 The specific steps of S5 are as follows:

[0133] S501: Based on the stress distribution data of the interaction points and the stress values ​​of the single path within the interaction points, the path stress is extracted and compared one by one according to the distribution interval. The deviation ratio of the path stress is calculated, the deviation is accumulated and the discrete metric value is calculated to generate the stress dispersion coefficient.

[0134] Based on the stress distribution data at the interaction points {(C1, 6625.00), (C2, 7356.14)} and the single-path stress value σ within the interaction points A =6625.00 N / m, σ B=7356.14 N / m. First, the stress value of each path is extracted and compared according to the distribution range. The action is to set the stress distribution range: low stress range is σ<7000 N / m, medium stress range is 7000≤σ<10000 N / m, and high stress range is σ≥10000 N / m. The values ​​of this range are taken with reference to the statistical results of the average stress limit of 8500 N / m and standard deviation of 2000 N / m in the safety standards for building structure and pedestrian flow. The threshold of the high range is calculated by adding 0.75 times the standard deviation to the mean, which is 8500 + 0.75 × 2000 = 10000 N / m. The path stress is compared with the range boundary. Path A is determined to be in the low range and path B is in the medium range. Then, the deviation ratio of the path stress is calculated. The action is to call the total stress σ of the interaction point. total The stress σ along a single path at this interaction point i Calculate the deviation ratio δ i =|σ i -σ total | / σ total For single-path interaction points, σ total =σ i Therefore, the deviation ratio δ A =|6625.00-6625.00| / 6625.00=0, δ B =7356.14-7356.14| / 7356.14=0. If there are multiple paths to the interaction point, a non-zero value may appear. In the case of deviation, for example, assuming the total stress value of the interaction point C3 is 12000 N / m, and the stress of path C3-1 is 8000 N / m, then δ=|8000-12000| / 12000=0.3333. The cumulative deviation is obtained by summing the deviation ratios of the paths. In the current example, the cumulative deviation Δ sum =0+0=0, in the formula, the symbol ∑ represents the summation operation of path number i within the same interaction point. The physical meaning of the cumulative deviation is to describe the unevenness of the force values ​​of different paths within the interaction point. Then, the discrete metric value is calculated. The action is to call the cumulative deviation divided by the number of paths n to obtain the average deviation, and then take the square root of the square of the average deviation to obtain the stress dispersion coefficient.

[0135]

[0136] in To average the deviation, the square operator is used to eliminate the mutual cancellation of positive and negative deviation directions, and the square root operator is used to restore the dimensions. In the current example, since δ i =0, so λ=0. If, according to the aforementioned assumption, C3 has two paths with deviations of 0.3333 and 0.1667 respectively, then:

[0137]

[0138] Stress dispersion coefficient:

[0139]

[0140] Finally, the λ values ​​calculated from the interaction points are used to form a set of stress dispersion coefficients {λ}. C1 ,λ C2 ,...} is the output of this step.

[0141] S502: Call the stress dispersion coefficient, assign weights to the coefficient values ​​of each interaction point according to the analytic hierarchy process and sort them, compare them one by one with the critical threshold of stress concentration, filter the interaction points whose concentration exceeds the threshold and record their positions, and obtain the distribution rate of high-risk nodes.

[0142] The stress dispersion coefficient is invoked, and weights are assigned to the coefficient values ​​of each interaction point according to the analytic hierarchy process (AHP). The action is as follows: First, a judgment matrix is ​​determined: the interaction point is assigned a weight ratio based on its importance score in the main pedestrian flow path distribution and its stability score in historical stress fluctuations. Assuming the weight ratios are 0.65 (importance) and 0.35 (stability), the weight vector w is obtained through pairwise comparisons. j In the current example, the importance scores for C1 and C2 are 9 and 7 respectively, and the stability scores are 8 and 9 respectively. Multiplying the scores by the corresponding proportions yields the base weights for C1: 0.65×9 + 0.35×8 = 5.85 + 2.80 = 8.65, and for C2: 0.65×7 + 0.35×9 = 4.55 + 3.15 = 7.70. Then, the two are divided by their sum to normalize and obtain the final weight w. C1 =8.65 / (8.65+7.70)≈0.5299,w C2 =7.70 / (8.65+7.70)≈0.4701, then the stress dispersion coefficient of the interaction points is calculated by weighting according to the weights. The action is to calculate the weighted value λ′ for each interaction point. k =λ k ×w k Since λ in the current example C1 =0.00000, λ C2 =0.00000, the weighted result is λ′ C1 =0.00000, λ′ C2 =0.00000, λ′ k Sort the data from largest to smallest to obtain a sorted sequence of 0.00000, 0.00000, and then compare each sequence with the critical threshold of stress concentration, which is set to 0.20 based on monitoring data from the past three years. When λ′ kWhen the weighting coefficient is greater than 0.20, the interaction point is considered a high-risk node. In this example, the weighting coefficients of all interaction points are less than 0.20, so the high-risk determination is not triggered. However, in another example, if λ is set for C3... C3 =0.35 and the weight is 0.50, then λ′ C3 =0.175 is still less than 0.20, so it is not considered high risk. Assuming λ for C4... C4 =0.50 and the weight is 0.60, then λ′ C4 If the value is 0.30, exceeding the threshold, the node is identified as a high-risk node. The "record location" action involves calling the node's coordinate index in the spatial topology and writing its coordinates (e.g., (165.000, 270.000)) into the risk node dataset. Finally, the proportion of high-risk nodes to all interaction points is calculated, η = N. risk / N total For example, when the total number of interaction points is 5 and the number of high-risk nodes is 2, η = 2 / 5 = 0.4. In this example, η = 0 / 2 = 0.0, that is, the distribution rate of high-risk nodes is 0.0.

[0143] S503: Call the distribution rate of high-risk nodes, extract the surrounding available space boundary data, combine the node coordinates to determine the center of gravity of the placeable area and calculate the virtual node coordinates, and establish a quantitative and morphological overall layout plan.

[0144] To retrieve the distribution rate of high-risk nodes and the set of locations of high-risk nodes, first extract the available spatial boundary data around the aforementioned nodes, and then execute the action of retrieving the set of coordinates of the geographical boundary points of the functional area {(X b Y b For each high-risk node, boundary points within a 20.000m radius are selected and formed into a local boundary subset. For example, the local boundary points of node (165.000, 270.000) are (160.000, 275.000), (170.000, 265.000), and (162.000, 268.000). Then, the geometric centroid of the placement area is calculated by combining the node coordinates with the local boundary point set. The action is to calculate the arithmetic mean of the x and y coordinates of the coordinate points within the area. The centroid coordinates are:

[0145]

[0146] Where m is the number of points in the region, and in the example of node (165.000, 270.000), the average x-coordinate X g = (165.000 + 160.000 + 170.000 + 162.000) / 4 = 657.000 / 4 = 164.250, y-axis average value Y g= (270.000 + 275.000 + 265.000 + 268.000) / 4 = 1078.000 / 4 = 269.500, thus obtaining the centroid of the area (164.250, 269.500). Then, the virtual node coordinates are calculated. The action performed is to offset and merge the centroid coordinates with the original high-risk node coordinates. The offset ratio coefficient α is set to 0.50, based on the optimization experience of balancing crowd density and passage radius in urban square pedestrian flow layout experiments, that is, taking the midpoint between the centroid and the original coordinates as the virtual node. Formula:

[0147] (X v Y v )=(X c +α(X g -X c ), Y c +α(Y g -Y c ));

[0148] Substitute the example value:

[0149] (X v Y v = (165.000 + 0.50 × (164.250 - 165.000), 270.000 + 0.50 × (269.500 - 270.000)) = (165.000 - 0.375, 270.000 - 0.250) = (164.625, 269.750);

[0150] This virtual node serves as a potential location for structural reinforcement or pedestrian diversion. The above calculations are performed sequentially on high-risk nodes to generate a set of virtual node coordinates. Finally, a quantitative and morphological overall layout scheme is established. The execution action involves performing a two-dimensional topological association between each virtual node and the corresponding available area boundaries, road paths, functional area connection methods, and other data. The layout diagram data structure is formed in the form of a set of node coordinates and a set of connecting edges. This ensures that the reference of each virtual node in the dataset contains information such as location index, adjacent node list, and connection path length, forming an overall layout scheme data that can be directly entered into subsequent spatial scheduling calculations.

[0151] Please see Figure 7 A digital generation system for the overall planning and design of university campuses, including:

[0152] The pedestrian flow acquisition module collects data on pedestrian flow per unit area and average pedestrian speed along the connecting paths of functional areas on campus using pressure sensors. It then uses parameters such as effective passage width and path length to calculate instantaneous pedestrian pressure values, forming a basic path parameter set, which is then transmitted to the path gradient module.

[0153] The path gradient module calls the basic path parameter set, performs pairwise difference calculations based on the instantaneous pedestrian pressure values ​​of the connecting paths of adjacent functional areas, constructs a pressure gradient difference sequence, performs ratio calculations based on the gradient difference amplitude and path length parameters, generates a displacement reference value vector, and passes it to the topology migration module.

[0154] The topology migration module calls the direction and amplitude information in the displacement reference value vector and uses the Thiessen polygon segmentation algorithm to perform real-time migration processing on the coordinate positions of the functional area boundary nodes in the topological space, generating a dynamic set of topological node coordinates, which is then passed to the interactive stress module.

[0155] The interactive stress module calls the dynamic topology node coordinate set to establish the relationship between the interactive points and paths in the functional area. For each interactive point, it calculates the ratio of the pedestrian flow per unit time to the effective passage cross-section width of the basic path parameter set, summarizes the path stress values, obtains the stress distribution data of the interactive points, and transmits it to the node dispersion module.

[0156] The node dispersion module calls the stress distribution data of the interaction points and the stress value of the single path within the interaction points to calculate the stress dispersion coefficient. It uses the hierarchical analysis method to identify interaction points whose stress concentration exceeds the critical threshold as high-risk nodes. It scans and analyzes the available space around the high-risk nodes and calculates the coordinates of the virtual diversion nodes to form a quantitative and morphological overall layout scheme.

[0157] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A digital generation method for the integrated planning of quantity and form in university campus planning and design, characterized by: Includes the following steps: S1: Obtain data on pedestrian flow per unit area and average pedestrian speed through pressure sensors on the connecting paths between functional areas of the campus, perform instantaneous pedestrian pressure calculation, and simultaneously collect the effective passage cross-section width and length parameters of the path to form a basic path parameter set; S2: Call the basic path parameter set, perform pairwise difference operation on the instantaneous pedestrian pressure value data of the connecting path between adjacent functional areas to construct a pressure gradient difference sequence, and perform ratio calculation based on the gradient difference amplitude and the path length parameter in the basic path parameter set to generate a displacement reference value vector; S3: Based on the displacement reference value vector direction information and amplitude information, the Thiessen polygon segmentation algorithm is used to perform real-time migration processing on the coordinate positions of the functional area boundary nodes in the topological space to generate a dynamic topological node coordinate set. S4: Based on the dynamic topology node coordinate set, establish the path affiliation relationship of the functional area interaction points, calculate the ratio of the per-unit-time pedestrian flow to the effective passage cross-section width of the basic path parameter set for each interaction point's subordinate path, summarize the path stress values, and obtain the stress distribution data of the interaction points.

2. The digital generation method for the overall planning and design of quantitative and qualitative aspects in university campuses according to claim 1, characterized in that, The basic path parameter set includes cross-sectional geometric features, spatial location attributes, and functional area affiliation information; the displacement reference value vector includes direction components, amplitude components, and time series; the dynamic topology node coordinate set includes node coordinate values, boundary constraints, and topology association attributes; and the interaction point stress distribution data includes interaction point number, path stress sum, and stress distribution weight.

3. The digital generation method for the overall planning and design of quantitative and qualitative aspects in university campuses according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Collects pedestrian flow data per unit area along the connecting paths of functional areas on campus using pressure sensors, organizes it into sequence values ​​in chronological order, calls the walking speed data of the path segment, compares and calculates the pedestrian flow and walking speed data at the corresponding time, and generates instantaneous pedestrian pressure values. S102: Collect path information through pressure sensors, record the effective cross-sectional width and corresponding length of the path, calculate the geometric cross-sectional area, call up pedestrian flow data, analyze the group displacement of the cross-section per unit time, and calculate the ratio of the displacement to the cross-sectional area to generate the path travel speed; S103: Based on the path travel speed and instantaneous pedestrian pressure value, call the path geometric section parameters, calculate the total number of pedestrians in multiple time periods, and arrange the speeds in chronological order as a change sequence to obtain the basic path parameter set.

4. The digital generation method for the overall planning and design of quantitative and qualitative aspects in university campuses according to claim 3, characterized in that, The specific steps of S2 are as follows: S201: Call the instantaneous pedestrian pressure value data of the adjacent functional area connection path in the basic path parameter set, calculate the pressure difference of adjacent paths one by one, arrange the difference result sequence according to the path node order, and obtain the pressure difference sequence. S202: Based on the pressure difference sequence, compare the changes between adjacent difference data, calculate the amplitude difference between the previous and subsequent differences item by item, and connect the calculation results of each item into a new sequence according to the path node order to obtain the gradient difference amplitude sequence. S203: Based on the gradient difference magnitude sequence, call the path length parameter in the basic path parameter set, perform a ratio calculation between each magnitude value and the corresponding path length, and combine the ratios into a vector according to the path node order to generate a displacement reference value vector.

5. The digital generation method for the overall planning and design of quantitative and qualitative aspects in university campuses according to claim 4, characterized in that, The specific steps for S3 are as follows: S301: Call the direction and amplitude information of the displacement reference value vector, decompose the coordinate axes of the functional area boundary node based on the direction information, call the amplitude information to perform linear offset calculation on the node coordinates, and obtain the coordinate offset; S302: Call the coordinate offset, use the Thiessen polygon segmentation algorithm to divide the offset coordinates into regions, compare the boundary data of adjacent polygons based on the differences in node positions, and correct the attribution relationship by calculating the shape overlap and position deviation to obtain the node boundary segmentation coefficient. S303: Based on the node boundary division coefficient, perform real-time migration processing on the coordinates of the functional area boundary nodes, update the coordinate values ​​based on spatial position and offset coefficient, and merge and sort the updated coordinates to obtain a dynamic topology node coordinate set.

6. The digital generation method for the overall planning and design of quantitative and qualitative aspects in university campuses according to claim 5, characterized in that, The specific steps of S4 are as follows: S401: Based on the dynamic topology node coordinate set, perform affiliation division on the path nodes according to the positioning range of the functional area interaction points, identify the node coordinates one by one with the interaction point identifiers and integrate them into the path affiliation relationship, and generate the path affiliation coefficient. S402: Call the path affiliation coefficient, collect the pedestrian flow data of the paths under the interaction point and the effective passage cross-section width data of the basic path parameter set, calculate the ratio of pedestrian flow to width and integrate the sequence to generate a pedestrian flow-width ratio sequence. S403: Based on the pedestrian flow width ratio sequence, calculate the path stress value for each path under the interaction point, sum the stress results of the differentiated paths within the interaction point range, and distribute them to the corresponding positions of the interaction points to obtain the stress distribution data of the interaction points.

7. The digital generation method for the overall planning and design of quantitative and qualitative aspects in university campuses according to claim 1, characterized in that, The method also includes step S5: S5: Call the stress distribution data of the interaction point and the stress value of the single path within the interaction point to calculate the stress dispersion coefficient. Use the hierarchical analysis method to compare and identify the interaction point whose stress concentration exceeds the critical threshold as a high-risk node. Scan and analyze the available space around the high-risk node and calculate the coordinates of the virtual diversion node to generate a quantitative morphological overall layout scheme. The quantitative morphological overall layout scheme includes high-risk node identification, virtual diversion node coordinates, and spatial allocation mode.

8. The digital generation method for quantitative and qualitative overall planning in university campus planning and design according to claim 7, characterized in that, The specific steps of S5 are as follows: S501: Based on the stress distribution data of the interaction points and the single-path stress values ​​within the interaction points, extract the path stress and compare it one by one according to the distribution interval, calculate the deviation ratio of the path stress, accumulate the deviation and calculate the discrete metric value, and generate the stress dispersion coefficient. S502: Call the stress dispersion coefficient, assign weights to the coefficient values ​​of each interaction point according to the analytic hierarchy process and sort them, compare them one by one with the critical threshold of stress concentration, filter the interaction points whose concentration exceeds the threshold and record their positions, and obtain the distribution rate of high-risk nodes. S503: Call the high-risk node distribution rate, extract the surrounding available space boundary data, combine the node coordinates to determine the center of gravity of the placeable area and calculate the virtual node coordinates, and establish a quantitative morphological overall layout plan.

9. The digital generation method for the overall planning and design of quantitative and qualitative aspects in university campuses according to claim 8, characterized in that, The stress dispersion coefficient is a discrete metric value obtained based on the cumulative proportion of path stress deviation at interaction points; The high-risk node distribution rate is the proportion of the number of interaction points whose concentration exceeds the critical threshold to the total number of interaction points. The concentration threshold is a preset numerical limit used to determine the stress concentration at the interaction point.

10. A digital generation system for the integrated planning of quantity and form in university campus planning and design, characterized in that, The system is used to implement the digital generation method for quantitative and qualitative overall planning in university campus planning and design as described in any one of claims 1-9, and the system includes: The pedestrian flow acquisition module collects data on pedestrian flow per unit area and average pedestrian speed along the connecting paths of functional areas on campus using pressure sensors. It then uses parameters such as effective passage width and path length to calculate instantaneous pedestrian pressure values, forming a basic path parameter set, which is then transmitted to the path gradient module. The path gradient module calls the basic path parameter set, performs pairwise difference calculations based on the instantaneous pedestrian pressure values ​​of the connecting paths of adjacent functional areas, constructs a pressure gradient difference sequence, performs ratio calculations based on the gradient difference amplitude and path length parameters, generates a displacement reference value vector, and passes it to the topology migration module. The topology migration module calls the direction and amplitude information in the displacement reference value vector and uses the Thiessen polygon segmentation algorithm to perform real-time migration processing on the coordinate positions of the functional area boundary nodes in the topological space, generating a dynamic topological node coordinate set, which is then transmitted to the interactive stress module. The interactive stress module calls the dynamic topology node coordinate set to establish the association between functional area interactive points and paths, calculates the ratio of the per-unit-time pedestrian flow to the effective passage cross-section width of the basic path parameter set for each interactive point's subordinate path, summarizes the path stress values, obtains the interactive point stress distribution data, and transmits it to the node dispersion module. The node dispersion module calls the stress distribution data of the interaction points and the stress value of the single path within the interaction points to calculate the stress dispersion coefficient. It uses the hierarchical analysis method to identify interaction points whose stress concentration exceeds the critical threshold as high-risk nodes. It scans and analyzes the available space around the high-risk nodes and calculates the coordinates of the virtual diversion nodes to form a quantitative and morphological overall layout scheme.

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