A method, system, device and medium for urban ventilation analysis based on LBM

Abstract

This invention discloses a method, system, equipment, and medium for urban ventilation analysis based on LBM (Laser-Based Boltzmann Model), relating to the field of urban planning. The method includes: inputting a three-dimensional building dataset, height extrema, and background wind speed of the study area; dividing the study area into several units and calculating the ventilation potential of each unit based on the three-dimensional building dataset and height extrema to obtain a ventilation potential matrix; initializing the airflow component matrix of the lattice Boltzmann model based on the background wind speed to obtain an initial state airflow component matrix; iteratively performing airflow migration and airflow collision processes on the initial state airflow component matrix until a set number of iterations is reached to obtain a final state airflow component matrix; calculating the wind speed data of the study area based on the final state airflow component matrix and exporting it as a result set to represent the relative wind speed of each unit in the study area. This invention enables urban ventilation environment analysis with high analytical accuracy, high operational efficiency, and low operating costs.

Description

Technical Field

[0001] This invention relates to the field of urban planning, and in particular to an LBM-based urban ventilation analysis method, system, equipment, and medium. Background Technology

[0002] With rapid urbanization and increasing population density, the urban heat island effect has become a significant negative factor affecting the quality of urban life. Urban ventilation has a significant effect on mitigating the urban heat island effect. To enable governments and urban planners to make accurate and intuitive decisions, a suitable method for analyzing urban ventilation environments based on urban buildings is urgently needed. Traditional ventilation analysis methods include wind tunnel experiments, computational fluid dynamics (CFD), and morphological analysis methods based on Geographic Information System (GIS) technology. Wind tunnel experiments can objectively and realistically observe the details of airflow changes as it passes through the observed object, but their high cost and limited scope have prevented widespread application. CFD methods are suitable for small-scale, high-resolution fluid simulations and can be used for building structural design and urban wind environment analysis. However, this method requires a huge amount of computation; therefore, in urban wind environment simulation, it is often used for individual buildings or small-scale building complexes. Morphological methods based on GIS technology do not rely on fluid dynamics principles. They approximate the analysis of urban ventilation environment by calculating the surface roughness of the underlying surface. This method is currently widely used. Its significant feature is high operating efficiency but low analysis accuracy.

[0003] Therefore, there is an urgent need for an urban ventilation analysis method that combines the advantages of CFD methods and GIS-based methods for urban ventilation environment analysis in urban planning. Summary of the Invention

[0004] The purpose of this invention is to provide an urban ventilation analysis method, system, equipment, and medium based on LBM, so as to achieve urban ventilation environment analysis with high analysis accuracy, high operating efficiency, and low operating cost.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A method for urban ventilation analysis based on LBM includes:

[0007] Input the 3D building dataset, height extrema, and background wind speed of the study area; the 3D building dataset includes: geodetic coordinate system information, projected coordinate system information, and building information; the building information includes: the base outline and height attributes of each independent building;

[0008] The study area is divided into several units, and the ventilation potential of each unit is calculated based on the three-dimensional building dataset and the height extreme value to obtain a ventilation potential matrix;

[0009] The airflow component matrix of the lattice Boltzmann model is initialized based on the background wind speed to obtain the initial state airflow component matrix; the number of rows and columns of the airflow component matrix is ​​equal to the number of rows and columns of the ventilation potential matrix;

[0010] The airflow component matrix of the initial state is iteratively subjected to airflow migration and airflow collision processes until a set number of iterations is reached, and the airflow component matrix of the final state is obtained.

[0011] The wind speed data of the study area is calculated based on the airflow component matrix of the terminated state and exported as a result set; the wind speed data of the study area includes: the horizontal velocity component, the vertical velocity component, and the magnitude of the airflow velocity of each unit in the study area; the result set is used to represent the relative wind speed of each unit in the study area.

[0012] A city ventilation analysis system based on LBM includes:

[0013] The data input module is used to input the three-dimensional building dataset, height extreme values, and background wind speed of the study area; the three-dimensional building dataset includes: geodetic coordinate system information, projected coordinate system information, and building information; the building information includes: the bottom outline and height attributes of each independent building;

[0014] The ventilation potential matrix determination module is used to divide the study area into several units and calculate the ventilation potential of each unit based on the three-dimensional building dataset and the height extreme value to obtain the ventilation potential matrix;

[0015] The initial state airflow component matrix determination module is used to initialize the airflow component matrix of the lattice Boltzmann model according to the background wind speed, so as to obtain the initial state airflow component matrix; the number of rows and columns of the airflow component matrix is ​​equal to the number of rows and columns of the ventilation potential matrix;

[0016] The airflow component matrix determination module for the terminated state is used to iteratively perform airflow migration and airflow collision processes on the airflow component matrix of the initial state until a set number of iterations is reached to obtain the airflow component matrix of the terminated state.

[0017] The result set export module is used to calculate the wind speed data of the study area based on the airflow component matrix of the termination state and export it as a result set; the wind speed data of the study area includes: the horizontal velocity component, vertical velocity component and magnitude of airflow of each unit in the study area; the result set is used to represent the relative wind speed of each unit in the study area.

[0018] An electronic device includes a memory and a processor, the memory storing a computer program, and the processor running the computer program to enable the electronic device to perform the LBM-based urban ventilation analysis method described above.

[0019] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described LBM-based urban ventilation analysis method.

[0020] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0021] The present invention provides an urban ventilation analysis method based on the Lattice Boltzmann Method (LBM), comprising: dividing the study area into several units, and calculating the ventilation potential of each unit based on a three-dimensional building dataset and height extrema to obtain a ventilation potential matrix; initializing the airflow component matrix of the Lattice Boltzmann model based on the background wind speed to obtain an initial state airflow component matrix; iteratively performing airflow migration and airflow collision processes on the initial state airflow component matrix until a set number of iterations is reached to obtain a final state airflow component matrix; calculating the wind speed data of the study area based on the final state airflow component matrix and deriving a result set representing the relative wind speed of each unit in the study area. This invention combines the advantages of CFD methods and GIS-based methods, enabling urban ventilation environment analysis with high analytical accuracy, high operational efficiency, and low operating costs. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A flowchart of the LBM-based urban ventilation analysis method provided by this invention;

[0024] Figure 2 This is an overview diagram of the study area according to an embodiment of the present invention;

[0025] Figure 3 This is a schematic diagram showing the volume ratio of buildings within a unit in an embodiment of the present invention;

[0026] Figure 4 This is a schematic diagram of the ventilation potential matrix according to an embodiment of the present invention;

[0027] Figure 5A schematic diagram of the direction and weight of gas components provided by the present invention;

[0028] Figure 6 This is a schematic diagram of gas migration under the influence of ventilation potential provided by the present invention;

[0029] Figure 7 The diagram shows the analysis results of an embodiment of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] The purpose of this invention is to provide an urban ventilation analysis method, system, equipment, and medium based on LBM, so as to achieve urban ventilation environment analysis with high analysis accuracy, high operating efficiency, and low operating cost.

[0032] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0033] like Figure 1 As shown, the LBM-based urban ventilation analysis method provided by this invention includes:

[0034] Step S1: Input the 3D building dataset, height extrema, and background wind speed of the study area.

[0035] Taking a street in Wuhan as an example, see Figure 2 The three-dimensional building dataset A includes: geodetic coordinate system information CS geo Projected coordinate system information (CS) proj And building information; the building information includes: the base outline and height attributes of each individual building. The maximum height h = 100m. The background wind speed. This is vector data, representing a northerly wind with a speed of 0.2l, or 2 m / s.

[0036] Step S2: Divide the study area into several units, and calculate the ventilation potential of each unit based on the three-dimensional building dataset and the height extreme value to obtain a ventilation potential matrix.

[0037] Step S2 specifically includes:

[0038] Step S21: Divide the study area into several units using a square grid with a set side length.

[0039] like Figure 2 As shown, with the upper left corner of the study area boundary as the origin O, the east direction as the X-axis, and the south direction as the Y-axis, the study area is divided into several units using a square grid G ​​with a side length of l = 10m.

[0040] Step S22: Determine the three-dimensional spatial unit and the three-dimensional building data within the three-dimensional spatial unit based on the cells in the grid and the height extreme values.

[0041] In this embodiment, any cell g in the grid G ​​and its height extremum h = 100m form a ground-level three-dimensional spatial cell, denoted as V. The three-dimensional spatial cell V is used to spatially clip the three-dimensional building dataset A to obtain the three-dimensional building data A within the cell. g ,like Figure 3 As shown.

[0042] Step S23: Calculate the volume of the three-dimensional spatial unit based on the set side length and the extreme height.

[0043] Specifically, using the area a of unit g g =l 2 =100m 2 Given a height extremum h = 100, calculate the volume of a three-dimensional spatial unit V, v. g =a g ×h=10000m 3 ;

[0044] Step S24: Calculate the volume of the three-dimensional building data based on the bottom contour and the height attribute.

[0045] Specifically, the three-dimensional building data A g The volume is denoted as v g，a ,like Figure 3 In the example shown, v g，a =2000m 3 .

[0046] Step S25: Calculate the ratio r between the volume of the three-dimensional building data and the volume of the three-dimensional spatial unit. g =v g，a / v g The ventilation potential of the unit was determined.

[0047] In this embodiment, formula r is used. g =v g，a / v g =0.2% of the 3D building volume within calculation unit g, r g , will r g Ventilation potential as unit g.

[0048] Step S26: Determine the ventilation potential matrix Orr based on the ventilation potential of all units in the study area.

[0049] After traversing each cell in the rule grid G, integrate the r... g Transformed into a ventilation potential matrix Orr, the spatial distribution of Orr is as follows: Figure 4 As shown.

[0050] Step S3: Initialize the airflow component matrix of the lattice Boltzmann model according to the background wind speed to obtain the initial state airflow component matrix; the number of rows and columns of the airflow component matrix is ​​equal to the number of rows and columns of the ventilation potential matrix.

[0051] Step S3 specifically includes:

[0052] Step S31: Define the direction vector and direction weights according to the classical lattice Boltzmann model.

[0053] like Figure 5 As shown, based on the classic LBM model, nine direction vectors are defined.

[0054] Define 9 directional weights w i Their values ​​are respectively

[0055] Step S32: Obtain the number of rows and columns of the ventilation potential matrix, and denote them as Rows and Cols, respectively. In this embodiment, Rows = 300, Cols = 400.

[0056] Step S33: Establish an airflow component matrix based on the direction vector, the direction weight, and the number of rows and columns of the ventilation potential matrix.

[0057] Establish an airflow component matrix N with Rows = 300 and Cols = 400. Each cell of N stores a floating-point array of length 9, where the array index i is an integer from 0 to 9, corresponding to... The component of airflow in a particular direction.

[0058] Step S34: Search for the boundary of the airflow component matrix in the opposite direction of the background wind speed to determine the cells at the boundary.

[0059] Specifically, along Search for the boundary of matrix N in the opposite direction of the vector, and denote the element at the boundary as N. edge ,like Figure 2 As shown.

[0060] Step S35: Based on the classical lattice Boltzmann model, determine the initial value of the standard state of airflow distribution of each unit in the airflow component matrix according to the background wind speed, the direction vector and the direction weight, and obtain the initial state airflow component matrix.

[0061] In this embodiment, let any element n in matrix N be denoted as . As Standard state of directional airflow distribution, according to the classical LBM model, satisfy in Will The value is used as the initial value for each element of matrix N.

[0062] Step S4: Iteratively execute the airflow migration and airflow collision process on the airflow component matrix of the initial state until the set number of iterations is reached to obtain the airflow component matrix of the final state.

[0063] Step S4 specifically includes:

[0064] Step S41: For the t-th iteration, the airflow component matrix after the (t-1)-th iteration is used as the starting state matrix of the migration process in the t-th iteration; where t is a positive integer starting from 1, and when t=1, the airflow component matrix of the initial state is used as the starting state matrix of the migration process in the first iteration.

[0065] Specifically, let the iteration number be t and the initial value be 1. In the t-th iteration, let matrix N... t-1 The transition process is executed as the initial state; specifically, let N... t-1＝0 =N.

[0066] Step S42: Perform the airflow migration process on the starting state matrix of the migration process in the t-th iteration, and calculate the ending state matrix of the migration process in the t-th iteration.

[0067] Specifically, the airflow migration process is performed on the initial state matrix of the migration process in the t-th iteration, and the final state matrix mN of the migration process in the t-th iteration is calculated using the following formula. t For the airflow migration process, see Figure 6 n j，pass and n i，bounce These represent the gas components moving from lattice n along... When the flow direction is towards cell m, the amount entering cell m and the amount bounced off the edge... The direction returns the quantity of cell n, m i，pass and m j，bounce These represent the gas components moving from lattice m along... When the flow direction is towards cell n, the amount entering cell n and the amount bounced off the edge... The direction returns the amount of cell m.

[0068]

[0069]

[0070] in, This indicates that after the t-th iteration, element m moves along... directional airflow component, This indicates that after the t-th iteration, the n-th cell moves along... directional airflow component, Indicates that the m-th element before the t-th iteration... directional airflow component, Indicates the n-th cell before the t-th iteration. directional airflow component, o m Indicates the ventilation potential of unit m, o n Let m represent the ventilation potential of cell n. Cell m (i.e., m grid) and cell n (i.e., n grid) are any two adjacent cells in the airflow component matrix. Cell n is within cell m. Direction, m element in n element direction, and They are in opposite directions.

[0071] Step S43: Perform an airflow collision process on the termination state matrix of the migration process in the t-th iteration, update the data of each cell and modify the value of the cell at the boundary, and obtain the airflow component matrix after the t-th iteration.

[0072] Specifically, the classic LBM model collision algorithm is used to terminate the state matrix mN of the transition process in the t-th iteration. t Perform the airflow collision process and update the data of each unit using the following formula:

[0073]

[0074] in, The data is the cell data after the collision. This is the cell data before the collision. Before the collision The standard state of directional airflow distribution is given, with τ as the relaxation factor. Let the air viscosity at room temperature be v = 0.01834 mPa·s. The relaxation factor τ is calculated using the formula τ = 3 * v + 0.5 = 0.55502, and is a dimensionless value.

[0075] Next, modify the value of the element at the boundary (i.e., N). edge The values ​​of each cell at the location are ), thus obtaining the airflow component matrix N after the t-th iteration. t .

[0076] Step S44: Determine whether the current iteration count has reached the set iteration count; if the set iteration count has not been reached, update the current iteration count and return to step S41; if the set iteration count has been reached, determine the airflow component matrix after the t-th iteration as the airflow component matrix of the termination state.

[0077] In this embodiment, steps S41 to S43 are repeated until the current iteration number t = num = 200, at which point the airflow component matrix is ​​denoted as N. t＝num .

[0078] Step S5: Calculate the wind speed data of the study area based on the airflow component matrix of the terminated state and export it as a result set; the wind speed data of the study area includes: the horizontal velocity component, vertical velocity component, and magnitude of airflow velocity of each unit in the study area; the result set is used to represent the relative wind speed of each unit in the study area.

[0079] Step S5 specifically includes:

[0080] Step S51: Calculate the horizontal velocity component, vertical velocity component, and magnitude of the airflow velocity of each unit in the study area based on the airflow component matrix of the terminated state, and obtain the wind speed data of the study area.

[0081] Specifically, for a cell n at any position in the grid, its horizontal velocity component of the airflow is:

[0082]

[0083] For any cell n at any position in the grid, its vertical velocity component of the airflow is:

[0084]

[0085] For a cell at any location in the grid, the modulus of its airflow velocity is:

[0086]

[0087] Step S52: Using the geodetic coordinate system information CS geo and the projected coordinate system information CS proj Establish a raster dataset R for the spatial reference coordinate system.

[0088] Step S53: Transfer the wind speed data u of the study area x u y , Write it into the raster dataset R and export it as a result set.

[0089] The method of this invention (hereinafter referred to as "this method") is compared and analyzed with the results of CFD and classical GIS-based methods. See [link to relevant documentation]. Figure 7 Here, FAI represents the windward index-based classification method, LCP represents the minimum cost path analysis method, and CFD represents the computational fluid dynamics-based method. The CFD method has high accuracy but low operating efficiency; in this comparison, CFD simulation results are used as relative true values. For example... Figure 7 As shown, the FAI results cannot distinguish between different wind directions, resulting in insufficient dynamic accuracy; the LCP method can simulate changes in wind direction, but the LCP path can only identify a few major ventilation paths, leading to insufficient coverage accuracy of the analysis results; this method can effectively achieve dynamic simulation under different wind directions, and the analysis results cover the entire region. Using the classic geographically weighted regression method, the correlation coefficient R between this method and the CFD analysis results is calculated. 2 =0.86, indicating that the proposed method and CFD analysis results show good consistency. The CFD analysis in this comparative experiment took approximately 10 hours, while the proposed method took approximately 0.1 hours. In summary, the method proposed in this invention integrates the advantages of CFD and GIS-based methods, possessing good simulation accuracy and high operational efficiency.

[0090] To implement the above methods and achieve the corresponding functions and technical effects, an LBM-based urban ventilation analysis system is provided below, which includes:

[0091] The data input module is used to input the three-dimensional building dataset, height extreme values, and background wind speed of the study area; the three-dimensional building dataset includes: geodetic coordinate system information, projected coordinate system information, and building information; the building information includes: the bottom outline and height attributes of each independent building.

[0092] The ventilation potential matrix determination module is used to divide the study area into several units and calculate the ventilation potential of each unit based on the three-dimensional building dataset and the height extreme value to obtain the ventilation potential matrix.

[0093] The initial state airflow component matrix determination module is used to initialize the airflow component matrix of the lattice Boltzmann model according to the background wind speed to obtain the initial state airflow component matrix; the number of rows and columns of the airflow component matrix is ​​equal to the number of rows and columns of the ventilation potential matrix.

[0094] The airflow component matrix determination module for the terminated state is used to iteratively perform airflow migration and airflow collision processes on the airflow component matrix of the initial state until a set number of iterations is reached to obtain the airflow component matrix of the terminated state.

[0095] The result set export module is used to calculate the wind speed data of the study area based on the airflow component matrix of the termination state and export it as a result set; the wind speed data of the study area includes: the horizontal velocity component, vertical velocity component and magnitude of airflow of each unit in the study area; the result set is used to represent the relative wind speed of each unit in the study area.

[0096] The present invention also provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor runs the computer program to enable the electronic device to perform the above-described LBM-based urban ventilation analysis method. The electronic device may be a server.

[0097] In addition, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described LBM-based urban ventilation analysis method.

[0098] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0099] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for urban ventilation analysis based on LBM, characterized in that, include: Input the 3D building dataset, height extrema, and background wind speed of the study area; The three-dimensional building dataset includes: geodetic coordinate system information, projected coordinate system information, and building information; the building information includes: the base outline and height attributes of each individual building; The study area is divided into several units, and the ventilation potential of each unit is calculated based on the three-dimensional building dataset and the height extreme value to obtain a ventilation potential matrix; The airflow component matrix of the lattice Boltzmann model is initialized based on the background wind speed to obtain the initial state airflow component matrix; the number of rows and columns of the airflow component matrix is ​​equal to the number of rows and columns of the ventilation potential matrix; The airflow component matrix of the initial state is iteratively subjected to airflow migration and airflow collision processes until a set number of iterations is reached, resulting in the airflow component matrix of the final state. The airflow migration process is then performed on the starting state matrix of the migration process in the t-th iteration, and the final state matrix of the migration process in the t-th iteration is calculated. Specifically, this includes performing the airflow migration process on the starting state matrix of the migration process in the t-th iteration, and using the formula... and Calculate the termination state matrix of the transition process in the t-th iteration; where, Indicates the first After the second iteration unit along directional airflow component, Indicates the first After the second iteration unit along directional airflow component, Indicates the first Before the next iteration unit along directional airflow component, Indicates the first Before the next iteration unit along directional airflow component, express The ventilation potential of the unit express The ventilation potential of the unit Unit and A unit is any two adjacent units in the airflow component matrix. Unit in unit direction, Unit in unit direction, and They are opposite directions; The wind speed data of the study area is calculated based on the airflow component matrix of the terminated state and exported as a result set; the wind speed data of the study area includes: the horizontal velocity component, the vertical velocity component, and the magnitude of the airflow velocity of each unit in the study area; the result set is used to represent the relative wind speed of each unit in the study area.

2. The urban ventilation analysis method based on LBM according to claim 1, characterized in that, The study area is divided into several units, and the ventilation potential of each unit is calculated based on the three-dimensional building dataset and the height extreme values ​​to obtain a ventilation potential matrix, which specifically includes: The study area is divided into several units using a square grid with a set side length; The three-dimensional spatial unit and the three-dimensional building data within the three-dimensional spatial unit are determined based on the cells in the grid and the height extreme values. The volume of the three-dimensional spatial unit is calculated based on the set side length and the extreme height. Calculate the volume of the three-dimensional building data based on the bottom contour and the height attribute; The ratio of the volume of the three-dimensional building data to the volume of the three-dimensional spatial unit is determined as the ventilation potential of the unit; A ventilation potential matrix was determined based on the ventilation potential of all units within the study area.

3. The urban ventilation analysis method based on LBM according to claim 1, characterized in that, The airflow component matrix of the lattice Boltzmann model is initialized based on the background wind speed to obtain the initial state airflow component matrix, specifically including: The direction vector and direction weights are defined based on the classical lattice Boltzmann model; Obtain the number of rows and columns of the ventilation potential matrix; An airflow component matrix is ​​established based on the direction vector, the direction weight, and the number of rows and columns of the ventilation potential matrix; Search the boundary of the airflow component matrix in the opposite direction of the background wind speed to determine the cells at the boundary; Based on the classical lattice Boltzmann model, the initial values ​​of the standard state of airflow distribution of each unit in the airflow component matrix are determined according to the background wind speed, the direction vector and the direction weight, thus obtaining the initial state airflow component matrix.

4. The urban ventilation analysis method based on LBM according to claim 1, characterized in that, The airflow component matrix of the initial state is iteratively subjected to airflow migration and airflow collision processes until a set number of iterations is reached, resulting in the airflow component matrix of the final state, specifically including: For the t-th iteration, the airflow component matrix after the (t-1)-th iteration is used as the starting state matrix of the migration process in the t-th iteration; where t is a positive integer starting from 1, and when t=1, the airflow component matrix of the initial state is used as the starting state matrix of the migration process in the first iteration. Perform the airflow migration process on the initial state matrix of the migration process in the t-th iteration, and calculate the final state matrix of the migration process in the t-th iteration; For the termination state matrix of the migration process in the t-th iteration, perform the airflow collision process, update the data of each cell and modify the value of the cell at the boundary, and obtain the airflow component matrix after the t-th iteration. Determine if the current iteration count has reached the set iteration count; If the set number of iterations has not been reached, update the current number of iterations and return to step "For the t-th iteration, use the airflow component matrix after the (t-1)-th iteration as the starting state matrix of the migration process for the t-th iteration"; If the set number of iterations is reached, the airflow component matrix after the t-th iteration is determined as the airflow component matrix of the final state.

5. The urban ventilation analysis method based on LBM according to claim 4, characterized in that, For the termination state matrix of the migration process in the t-th iteration, an airflow collision process is performed to update the data of each element and modify the values ​​of the elements at the boundary, resulting in the airflow component matrix after the t-th iteration, specifically including: For the t-th iteration of the migration process, a flow collision process is performed on the final state matrix, using the formula... Update the data of each unit and modify the values ​​of the units at the boundary to obtain the airflow component matrix after the t-th iteration; in, The data is the cell data after the collision. This is the cell data before the collision. Before the collision Standard state of directional airflow distribution It is a relaxation factor.

6. The urban ventilation analysis method based on LBM according to claim 1, characterized in that, The wind speed data of the study area is calculated based on the airflow component matrix of the terminated state and exported as a result set, specifically including: Based on the airflow component matrix of the terminated state, the horizontal velocity component, vertical velocity component, and magnitude of the airflow velocity of each unit in the study area are calculated to obtain the wind speed data of the study area. A raster dataset is established using the geodetic coordinate system information and the projected coordinate system information as a spatial reference coordinate system; The wind speed data of the study area is written into the raster dataset and exported as a result set.

7. A city ventilation analysis system based on LBM, characterized in that, include: The data input module is used to input the 3D building dataset, height extrema, and background wind speed of the study area; The three-dimensional building dataset includes: geodetic coordinate system information, projected coordinate system information, and building information; the building information includes: the base outline and height attributes of each individual building; The ventilation potential matrix determination module is used to divide the study area into several units and calculate the ventilation potential of each unit based on the three-dimensional building dataset and the height extreme value to obtain the ventilation potential matrix; The initial state airflow component matrix determination module is used to initialize the airflow component matrix of the lattice Boltzmann model according to the background wind speed, so as to obtain the initial state airflow component matrix; the number of rows and columns of the airflow component matrix is ​​equal to the number of rows and columns of the ventilation potential matrix; The module for determining the airflow component matrix in the final state is used to iteratively perform airflow migration and airflow collision processes on the airflow component matrix in the initial state until a set number of iterations is reached to obtain the airflow component matrix in the final state. Specifically, it performs an airflow migration process on the starting state matrix of the migration process in the t-th iteration and calculates the final state matrix of the migration process in the t-th iteration. This includes: performing an airflow migration process on the starting state matrix of the migration process in the t-th iteration and using the formula... and Calculate the termination state matrix of the transition process in the t-th iteration; where, Indicates the first After the second iteration unit along directional airflow component, Indicates the first After the second iteration unit along directional airflow component, Indicates the first Before the next iteration unit along directional airflow component, Indicates the first Before the next iteration unit along directional airflow component, express The ventilation potential of the unit express The ventilation potential of the unit Unit and A unit is any two adjacent units in the airflow component matrix. Unit in unit direction, Unit in unit direction, and They are opposite directions; The result set export module is used to calculate the wind speed data of the study area based on the airflow component matrix of the termination state and export it as a result set; the wind speed data of the study area includes: the horizontal velocity component, vertical velocity component and magnitude of airflow of each unit in the study area; the result set is used to represent the relative wind speed of each unit in the study area.

8. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the LBM-based urban ventilation analysis method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the LBM-based urban ventilation analysis method as described in any one of claims 1 to 6.

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