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

Abstract

Disclosed are a method and system for urban ventilation analysis based on Lattice Boltzmann Method (LBM), a device, and a medium. The method includes: inputting a three-dimensional building dataset, a

Description

[0001] The present disclosure relates to the field of urban planning, and in particular, to a method and system for urban ventilation analysis based on Lattice Boltzmann Method (LBM), a device, and a medium. BACKGROUND

[0002] With the rapid process of urbanization and the continuous increase in population density, the urban heat island effect has become an important adverse factor affecting the quality of urban life. Urban ventilation, on the other hand, has a significant impact on mitigating the urban heat island effect. In order for governments and urban planners to make accurate decisions intuitively, there is an urgent need for a suitable method to analyze the urban ventilation environment based on urban architecture. Traditional ventilation analysis methods include wind tunnel experiments, Computational Fluid Dynamics (CFD) methods, and morphological analysis methods based on the Geographic Information System (GIS) technology. In the wind tunnel experiments, detailed changes in air flow when passing through the observed object can be observed objectively and realistically, but the experiments are expensive and have limited experimental scope and are not widely used. The CFD method is suitable for small-scale, high-resolution fluid simulations and can be used for building structure design, urban wind environment analysis, and so on. However, this method requires massive computational resources and are therefore commonly used for wind environment simulations of individual buildings or small-scale building clusters in urban wind environments. The morphological method based on the GIS technology does not rely on fluid mechanics principles. Instead, it approximately analyzes the urban ventilation environment by calculating the surface roughness of the underlying surface. This method is currently more widely used, characterized by high operational efficiency but low analysis accuracy.

[0003] Therefore, there is an urgent need for an urban ventilation analysis method that combines the advantages of the CFD method and GIS-based method, to analyze urban ventilation environments in urban planning. SUMMARY

[0004] An objective of the present disclosure is to provide a method and system for urban ventilation analysis based on LBM, a device, and a medium to achieve urban ventilation environment analysis with higher analysis accuracy, higher operational efficiency, and lower operational costs.

[0005] To achieve the above objective, the present disclosure provides the following technical solutions.

[0006] A method for urban ventilation analysis based on LBM is provided, including:

[0007] inputting a three-dimensional building dataset, a height extreme value, and a background wind speed of a research area, where the three-dimensional building dataset includes geodetic coordinate system information, projection coordinate system information, and building information; and the building information includes base outlines and height attributes of individual buildings; 10008] dividing the research area into a plurality of cells and calculating ventilation potential of each cell according to the three-dimensional building dataset and the height extreme value, to obtain a ventilation potential matrix;

[0009] initializing an airflow component matrix of a lattice Boltzmann model according to the background wind speed to obtain an initial-state airflow component matrix, where the number of rows and the number of columns of the airflow component matrix are equal to the number of rows and the number of columns of the ventilation potential matrix, respectively;

[0010] iteratively performing an airflow migration process and an airflow collision process on the initial-state airflow component matrix until a predetermined iteration count is reached, to obtain a final-state airflow component matrix; and

[0011] calculating wind speed data of the research area according to the final-state airflow component matrix and exporting the wind speed data as a result set, where the wind speed data of the research area includes horizontal airflow velocity components, vertical airflow velocity components, and airflow velocity magnitudes of the cells in the research area, and the result set represents relative wind speeds of the cells in the research area.

[0012] A system for urban ventilation analysis based on LBM is provided, including: 10013] a data input module configured to input a three-dimensional building dataset, a height extreme value, and a background wind speed of a research area, where the three-dimensional building dataset includes geodetic coordinate system information, projection coordinate system information, and building information; and the building information includes base outlines and height attributes of individual buildings;

[0014] a ventilation potential matrix determining module configured to divide the research area into a plurality of cells and calculate ventilation potential of each cell according to the three-dimensional building dataset and the height extreme value, to obtain a ventilation potential matrix;

[0015] an initial-state airflow component matrix determining module configured to initialize an airflow component matrix of a lattice Boltzmann model according to the background wind speed to obtain an initial-state airflow component matrix, where the number of rows and the number of columns of the airflow component matrix are equal to the number of rows and the number of columns of the ventilation potential matrix, respectively;

[0016] a final-state airflow component matrix determining module configured to iteratively perform an airflow migration process and an airflow collision process on the initial-state airflow component matrix until a predetermined iteration count is reached, to obtain a final-state airflow component matrix; and 10017] a result set export module configured to calculate wind speed data of the research area according to the final-state airflow component matrix and export the wind speed data as a result set, where the wind speed data of the research area includes horizontal airflow velocity components, vertical airflow velocity components, and airflow velocity magnitudes of the cells in the research area, and the result set represents relative wind speeds of the cells in the research area.

[0018] An electronic device is provided, including a memory and a processor, where the memory is configured to store a computer program, and the processor runs the computer program to enable the electronic device to execute the foregoing method for urban ventilation analysis based on LBM.

[0019] A computer-readable storage medium storing a computer program is provided, where the foregoing method for urban ventilation analysis based on LBM is implemented when the computer program is executed by a processor.

[0020] According to specific embodiments provided in the present disclosure, the present disclosure has the following technical effects:

[0021] The method for urban ventilation analysis based on LBM provided by the present disclosure includes the following steps: dividing a research area into a plurality of cells and calculating ventilation potential of each cell according to a three-dimensional building dataset and a height extreme value, to obtain a ventilation potential matrix; initializing an airflow component matrix of a lattice Boltzmann model according to a background wind speed to obtain an initial-state airflow component matrix; iteratively performing an airflow migration process and an airflow collision process on the initial-state airflow component matrix until a predetermined iteration count is reached, to obtain a final-state airflow component matrix; and calculating wind speed data of the research area according to the final-state airflow component matrix and exporting the wind speed data as a result set representing the relative wind speeds of the cells in the research area. The present disclosure combines the advantages of the CFD method and the GIS-based method, allowing for urban ventilation environment analysis with higher analysis accuracy, higher operational efficiency, and lower operational costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] To describe the technical solutions in embodiments of the present disclosure or in the prior art more clearly, the accompanying drawings required for the embodiments are briefly described below. Apparently, the accompanying drawings in the following description show merely some embodiments of the present disclosure, and those of ordinaiy skill in the art may still derive other accompanying drawings from these accompanying drawings without creative efforts.

[0023] FIG. 1 is a flowchart of a method for urban ventilation analysis based on LBM according to the present disclosure;

[0024] FIG. 2 is a sketch map of a research area according to an embodiment of the present disclosure;

[0025] FIG. 3 is a schematic diagram showing a building volume proportion in a cell according to an embodiment of the present disclosure;

[0026] FIG. 4 is a schematic diagram of a ventilation potential matrix according to an embodiment of the present disclosure;

[0027] FIG. 5 is a schematic diagram showing directions and weights of airflow components according to the present disclosure;

[0028] FIG. 6 is a schematic diagram of airflow migration under the influence of ventilation potential according to the present disclosure; and

[0029] FIG. 7 shows an analysis result according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] The technical solutions of the embodiments of the present disclosure are clearly and completely described below with reference to the drawings in the embodiments of the present disclosure. Apparently, the described embodiments are merely a part rather than all of the embodiments of the present disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of the present disclosure without creative efforts shall fall within the protection scope of the present disclosure.

[0031] An objective of the present disclosure is to provide a method and system for urban ventilation analysis based on LBM, a device, and a medium to achieve urban ventilation environment analysis with higher analysis accuracy, higher operational efficiency, and lower operational costs.

[0032] In order to make the above objective, features and advantages of the present disclosure clearer and more comprehensible, the present disclosure will be further described in detail below in combination with accompanying drawings and particular implementation modes.

[0033] As shown in FIG. 1, the method for urban ventilation analysis based on LBM according to the present disclosure includes the following steps:

[0034] Step SI: Input a three-dimensional building dataset, a height extreme value, and a background wind speed of a research area.

[0035] Using a block of Wuhan as an example, referring to FIG. 2, the three-dimensional building dataset A includes geodetic coordinate system information CSgeo, projection coordinate system information CSprOj, and building information; and the building information includes base outlines and height attributes of individual buildings. The height extreme value h = 100m. The background wind speed u = (0,0.21,), which is vector data indicating a northerly wind with a magnitude of 0.21, i.e., 2m / s.

[0036] Step S2: Divide the research area into a plurality of cells and calculate ventilation potential of each cell according to the three-dimensional building dataset and the height extreme value, to obtain a ventilation potential matrix.

[0037] Step S2 specifically includes the following steps:

[0038] Step S21: Divide the research area into a plurality of cells by using a regular square grid with a specified side length.

[0039] As shown in FIG. 2, with the upper-left comer of the boundary of the research area as the origin O, eastward direction as the positive X-axis, and the southward direction as the positive Y-axis, the research area is divided into a plurality of cells by using a regular square grid G with a side length of 1 = 10m.

[0040] Step S22: Determine three-dimensional spatial cells and three-dimensional building data within the three-dimensional spatial cells according to the cells in the grid and the height extreme value.

[0041] In this embodiment, for any cell g in the grid G and the height extreme value h = 100m, a terrestrial three-dimensional spatial cell is formed, denoted as V. Spatial clipping is performed on the three-dimensional building dataset A by using the three-dimensional spatial cell V to obtain three-dimensional building data Ag within the cell, as shown in FIG. 3.

[0042] Step S23: Calculate a volume of the three-dimensional spatial cell according to the specified side length and the height extreme value.

[0043] Specifically, the volume of the three-dimensional spatial cell V is calculated using the area ag = I2 = 100m2 of the cell g and the height extreme value h=100: vg = ag x h = 10000m3.

[0044] Step S24: Calculate a volume of the three-dimensional building data according to the base outlines and the height attributes.

[0045] Specifically, the volume of the three-dimensional building data Ag is denoted as vg a. In the example shown in FIG. 3, vga = 2000m3. v

[0046] Step S25: Determine a ratio rg = *" / Vg between the volume of the three-dimensional building data and the volume of the three-dimensional spatial cell as ventilation potential of the cell.

[0047] In this embodiment, a proportion rg of the three-dimensional building volume within the cell g is calculated using the formula rg = / v = 0.2 , and rg is considered as the ventilation potential of the cell g.

[0048] Step S26: Determine the ventilation potential matrix Orr based on the ventilation potential of all the cells within the research area.

[0049] After the traversal of each cell in the regular square grid G, the calculated rg values are integrated to form the ventilation potential matrix Orr, with the spatial distribution of Orr as shown in FIG. 4.

[0050] Step S3: Initialize an airflow component matrix of a lattice Boltzmann model according to the background wind speed to obtain an initial-state airflow component matrix, where the number of rows and the number of columns of the airflow component matrix are equal to the number of rows and the number of columns of the ventilation potential matrix, respectively.

[0051] Step S3 specifically includes the following steps:

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

[0053] As shown in FIG. 5, according to the classical LBM model, 9 direction vectors are defined: = (0,0), = (1,0), ej = (0,1), = ( - 1,0), ej = (0, - 1), = (1,1), = ( — 1,1) , = ( — 1,1) , and = (1, — 1) ; and 9 direction weights w, are defined, with 4 1 1 values as follows: w0 =-, = w2 = w3 = w4 = - and w5 = w6 = w7 = w8 = —

[0054] Step S32: Obtain the number of rows and the number of columns of the ventilation potential matrix, which are denoted as Rows and Cols. In this embodiment, Rows=300 and Cols=400.

[0055] Step S3 3: Establish an airflow component matrix according to the direction vectors, the direction weights, as well as the number of rows and the number of columns of the ventilation potential matrix.

[0056] An airflow component matrix N is established, with the number of rows being Rows=300 and the number of columns being Cols=400. Each cell in N stores a floating-point array having a length of 9, where values of the array index i are integers from 0 to 9, corresponding to airflow components in the direction o’.

[0057] Step S34: Search along a reverse direction of the background wind speed to find a boundary of the airflow component matrix, and determine cells at the boundary

[0058] Specifically, searching is performed along the reverse direction of the vector u to find the boundary of the matrix N, and cells at the boundary are labeled as Nedge, as shown in FIG. 2.

[0059] Step S35: Based on the classical lattice Boltzmann model, determine initial values in an airflow distribution standard state for the cells of the airflow component matrix by using the background wind speed, the direction vectors, and the direction weights, to obtain the initial-state airflow component matrix.

[0060] In this embodiment, for any cell n in the matrix N , n®q is taken as the airflow distribution standard state in the direction . According to the classical LBM model, n^ satisfies n^ = pw, + 3e; ■ u +1 (ej ■ u)2 — | |u|2j, where p = E$=o nj. The n®q values are used as the initial values for the cells in the matrix N.

[0061] Step S4: Iteratively perform an airflow migration process and an airflow collision process on the initial-state airflow component matrix until a predetermined iteration count is reached, to obtain a final-state airflow component matrix.

[0062] Step S4 specifically includes the following steps:

[0063] Step S41: For the t-th iteration, use an airflow component matrix obtained after the (t-l)-th iteration as a starting-state matrix for a migration process of the t-th iteration, where t is a positive integer starting from 1, and when t=l, the initial-state airflow component matrix is used as a starting-state matrix for the migration process of the first iteration.

[0064] Specifically, an iteration count is denoted as t, with an initial value set to 1. For the t-th iteration, the matrix Nt-1 is used as the starting state to execute the migration process. In the special case, Nt-1=0 = N.

[0065] Step S42: Perform the airflow migration process on the starting-state matrix of the migration process in the t-th iteration, and calculate a final-state matrix of the migration process in the t-th iteration.

[0066] Specifically, the airflow migration process is performed on the starting-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 by using the following formula. The airflow migration process is described in FIG. 6, where n)iPass represents an amount flowing into lattice m when airflow components flow from lattice n to lattice m along the direction e*, while nibounce represents an amount reflected back to lattice n along the direction ej; m, pass represents an amount flowing into lattice n when airflow components flow from lattice m to lattice n along the direction ej, while m) bounce represents an amount reflected back to lattice m along the direction e(. m- = n--1 x om + m'-1 x (1 — on) ni = x on + n^1 x (1 - om)

[0067] m- represents an airflow component along the direction e( for cell m after the t-th iteration, n- represents an airflow component along the direction e* for cen n after the t-th iteration, mf1 represents an airflow component along the direction e( for cell m before the t-th iteration, n'-1 represents an airflow component along the direction for cell n before the t-th iteration. om represents ventilation potential of cell m. on represents ventilation potential of cell n. Cell m (i.e., lattice m) and cell n (i.e., lattice n) are any two adjacent cells in the airflow component matrix. Cell n is in the direction ej of cell m, and cell m is in the direction ej’ of cell n. c, and q are opposite directions.

[0068] Step S43: Perform the airflow collision process on the final-state matrix of the migration process in the t-th iteration, update data of each cell, and modify values of the cells at the boundary to obtain an airflow component matrix after the t-th iteration.

[0069] Specifically, the airflow collision process is performed on the final-state matrix mN' of the migration process in the t-th iteration by using a collision algorithm of the classical LBM model, and the data of each cell is updated using the following formula: nnew = nold+l(neq_nold}

[0070] n”ew is cell data after collision, n1old is cell data before collision, n^ is an airflow distribution standard state along the direction ej before collision, t is a relaxation factor. Air viscosity at room temperature is v = 0.01834mPa. s. The relaxation factor t is calculated using the formula t = 3 * v + 0.5 = 0.55502, and is dimensionless.

[0071] Afterward, the values of cells at the boundary are modified (i.e., the values of cells at 8 position Nedge are set to n^), to obtain the airflow component matrix Nl after the t-th iteration.

[0072] Step S44: Determine whether a current iteration count has reached the predetermined iteration count; if the current iteration count has not reached the predetermined iteration count, update the current iteration count and return to step S41; and if the current iteration count has reached the predetermined iteration count, determine the airflow component matrix after the t-th iteration as the final-state airflow component matrix.

[0073] In this implementation, steps S41 to S43 are repeated until the current iteration count t = num = 200. The airflow component matrix at the end of the iteration process is denoted as [sjt=num

[0074] Step S5: Calculate wind speed data of the research area according to the final-state airflow component matrix and export the wind speed data as a result set, where the wind speed data of the research area includes horizontal airflow velocity components, vertical airflow velocity components, and airflow velocity magnitudes of the cells in the research area, and the result set represents relative wind speeds of the cells in the research area.

[0075] Step S5 specifically includes the following steps:

[0076] Step S51: Calculate the horizontal airflow velocity components, vertical airflow velocity components, and airflow velocity magnitudes of the cells in the research area according to the final-state airflow component matrix, to obtain the wind speed data of the research area.

[0077] Specifically, for any cell n at any position within the grid, the horizontal airflow velocity component is calculated as follows: 01 + n5 + n8) - (n3 + n6 + n7) ux =-----------—------------- In,

[0078] For any cell n at any position within the grid, the vertical airflow velocity component is calculated as follows: _ (n2 + n5 + n6) - (n4 + n7 + n8) Uy

[0079] For any cell at any position within the grid, the airflow velocity magnitude is calculated as follows:

[0080] Step S52: Establish a grid dataset R by using the geodetic coordinate system information CSgeo and the projection coordinate system information CSprOj as spatial reference coordinate systems.

[0081] Step S53: Write the wind speed data ux, uy, and |u^| of the research area into the grid dataset R and export the wind speed data as the result set.

[0082] Results of the method of the present disclosure (referred to as this method) are compared with results of the CFD and classical GIS-based methods for analysis, as shown in FIG. 7. In FIG. 7, FAI represents a method based on frontal area index grading, LCP represents an analysis method based on the least cost path, and CFD represents a method based on computational fluid dynamics. The CFD method has high accuracy but low operational efficiency, and the CFD simulation result is used as a reference true value in the comparison. As shown in FIG. 7, the FAI method cannot distinguish results for different wind directions, and the dynamic accuracy is insufficient. The LCP method can simulate results as the wind direction changes, but it can only identify a small number of major ventilation paths, and the coverage accuracy of the analysis results is inadequate. This method can effectively achieve dynamic simulations under different wind directions, and the analysis results achieve comprehensive coverage. The measurement based on the classical geographically weighted regression method shows that this method has a correlation coefficient R2 = 0.86 with the CFD analysis results, indicating good consistency between this method and the CFD analysis results. In this comparative experiment, the CFD analysis took approximately 10 hours, while this method only took about 0.1 hours. In summary, the method proposed in the present disclosure combines the advantages of the CFD method and the GIS-based method, providing good simulation accuracy and high operational efficiency.

[0083] In order to perform the foregoing method to achieve the corresponding functions and technical effects, a system for urban ventilation analysis based LBM is provided below. The system includes a data input module, a ventilation potential matrix determining module, an initial-state airflow component matrix determining module, a final-state airflow component matrix determining module, and a result set export module.

[0084] The data input module is configured to input a three-dimensional building dataset, a height extreme value, and a background wind speed of a research area, where the three-dimensional building dataset includes geodetic coordinate system information, projection coordinate system information, and building information; and the building information includes base outlines and height attributes of individual buildings.

[0085] The ventilation potential matrix determining module is configured to divide the research area into a plurality of cells and calculate ventilation potential of each cell according to the three-dimensional building dataset and the height extreme value, to obtain a ventilation potential matrix.

[0086] The initial-state airflow component matrix determining module is configured to initialize an airflow component matrix of a lattice Boltzmann model according to the background wind speed to obtain an initial-state airflow component matrix, where the number of rows and the number of columns of the airflow component matrix are equal to the number of rows and the number of columns of the ventilation potential matrix, respectively.

[0087] The final-state airflow component matrix determining module is configured to iteratively perform an airflow migration process and an airflow collision process on the initial-state airflow component matrix until a predetermined iteration count is reached, to obtain a final-state airflow component matrix.

[0088] The result set export module is configured to calculate wind speed data of the research area according to the final-state airflow component matrix and export the wind speed data as a result set, where the wind speed data of the research area includes horizontal airflow velocity components, vertical airflow velocity components, and airflow velocity magnitudes of the cells in the research area, and the result set represents relative wind speeds of the cells in the research area.

[0089] The present disclosure further provides an electronic device, including a memory and a processor, where the memory is configured to store a computer program, and the processor runs the computer program to enable electronic device to execute the foregoing method for urban ventilation analysis based on LBM. The electronic device may be a server.

[0090] The present disclosure further provides a computer-readable storage medium that stores a computer program, where the computer program is executed by a processor to implement the foregoing method for urban ventilation analysis based on LBM.

[0091] Each embodiment in the description is described in a progressive mode, each embodiment focuses on differences from other embodiments, and references can be made to each other for the same and similar parts between embodiments.

[0092] Particular examples are used herein for illustration of principles and implementation modes of the present disclosure. The descriptions of the above embodiments are merely used for assisting in understanding the method of the present disclosure and its core ideas. In addition, those of ordinary skill in the art can make various modifications in terms of particular implementation modes and the scope of application in accordance with the ideas of the present disclosure. In conclusion, the content of the description shall not be construed as limitations to the present disclosure.

Claims

1. A method for urban ventilation analysis based on Lattice Boltzmann Method (LBM), comprising:inputting a three-dimensional building dataset, a height extreme value, and a background wind speed of a research area, wherein the three-dimensional building dataset comprises geodetic coordinate system information, projection coordinate system information, and building information; and the building information comprises base outlines and height attributes of individual buildings;dividing the research area into a plurality of cells and calculating ventilation potential of each cell according to the three-dimensional building dataset and the height extreme value, to obtain a ventilation potential matrix;initializing an airflow component matrix of a lattice Boltzmann model according to the background wind speed to obtain an initial-state airflow component matrix, wherein the number of rows and the number of columns of the airflow component matrix are equal to the number of rows and the number of columns of the ventilation potential matrix, respectively;iteratively performing an airflow migration process and an airflow collision process on the initial-state airflow component matrix until a predetermined iteration count is reached, to obtain a final-state airflow component matrix; andcalculating wind speed data of the research area according to the final-state airflow component matrix and exporting the wind speed data as a result set, wherein the wind speed data of the research area comprises horizontal airflow velocity components, vertical airflow velocity components, and airflow velocity magnitudes of the cells in the research area, and the result set represents relative wind speeds of the cells in the research area.

2. The method for urban ventilation analysis based on LBM according to claim 1, wherein the dividing the research area into a plurality of cells and calculating ventilation potential of each cell according to the three-dimensional building dataset and the height extreme value to obtain a ventilation potential matrix specifically comprises:dividing the research area into a plurality of cells by using a regular square grid with a specified side length;determining three-dimensional spatial cells and three-dimensional building data within the three-dimensional spatial cells according to the cells in the grid and the height extreme value;calculating a volume of the three-dimensional spatial cell according to the specified sidelength and the height extreme value;calculating a volume of the three-dimensional building data according to the base outlines and the height attributes;determining a ratio between the volume of the three-dimensional building data and the volume of the three-dimensional spatial cell as ventilation potential of the cell; anddetermining the ventilation potential matrix based on the ventilation potential of all the cells within the research area.

3. The method for urban ventilation analysis based on LBM according to claim 1, wherein the initializing an airflow component matrix of a lattice Boltzmann model according to the background wind speed to obtain an initial-state airflow component matrix specifically comprises:defining direction vectors and direction weights according to a classical lattice Boltzmann model;obtaining the number of rows and the number of columns of the ventilation potential matrix;establishing an airflow component matrix according to the direction vectors, the direction weights, as well as the number of rows and the number of columns of the ventilation potential matrix;searching along a reverse direction of the background wind speed to find a boundary of the airflow component matrix, and determining cells at the boundary; andbased on the classical lattice Boltzmann model, determining initial values in an airflow distribution standard state for the cells of the airflow component matrix by using the background wind speed, the direction vectors, and the direction weights, to obtain the initial-state airflow component matrix.

4. The method for urban ventilation analysis based on LBM according to claim 1, wherein the iteratively performing an airflow migration process and an airflow collision process on the initial-state airflow component matrix until a predetermined iteration count is reached, to obtain a final-state airflow component matrix specifically comprises:for the t-th iteration, using an airflow component matrix obtained after the (t-l)-th iteration as a starting-state matrix for a migration process of the t-th iteration, wherein t is a positive integer starting from 1, and when t=l, the initial-state airflow component matrix is used as a starting-state matrix for the migration process of the first iteration;performing the airflow migration process on the starting-state matrix of the migration process in the t-th iteration, and calculating a final-state matrix of the migration process in the t-th iteration;performing the airflow collision process on the final-state matrix of the migration process in the t-th iteration, updating data of each cell, and modifying values of the cells at the boundary to obtain an airflow component matrix after the t-th iteration;determining whether a current iteration count has reached the predetermined iteration count;if the current iteration count has not reached the predetermined iteration count, updating the current iteration count and returning to the step of "for the t-th iteration, using an airflow component matrix obtained after the (t-l)-th iteration as a starting-state matrix for a migration process of the t-th iteration"; andif the current iteration count has reached the predetermined iteration count, determining the airflow component matrix after the t-th iteration as the final-state airflow component matrix.

5. The method for urban ventilation analysis based on LBM according to claim 4, wherein the performing the airflow migration process on the starting-state matrix of the migration process in the t-th iteration, and calculating a final-state matrix of the migration process in the t-th iteration specifically comprises:performing the airflow migration process on the starting-state matrix of the migration process in the t-th iteration, and calculating a final-state matrix of the migration process in the t-th iteration by using the following formulas: m- = nJ1 x om + mfy1 x (1 — on) and n[ = mfy1 x on + np1 x (1 - om);wherein m- represents an airflow component along a direction e* for cell m after the t-th iteration; n- represents an airflow component along a direction ej for cell n after the t-th iteration; mj-1 represents an airflow component along the direction ej for cell m before the t-th iteration; n’1 represents an airflow component along the direction cj for cell n before the t-th iteration; om represents ventilation potential of cell m; on represents ventilation potential of cell n; cell m and cell n are any two adjacent cells in the airflow component matrix; cell n is in the direction ej of cell m, and cell m is in the direction ej of cell n; and ej and ej are opposite directions.

6. The method for urban ventilation analysis based on LBM according to claim 4, wherein the performing the airflow collision process on the final-state matrix of the migration process inthe t-th iteration, updating data of each cell, and modifying values of the cells at the boundary to obtain an airflow component matrix after the t-th iteration specifically comprises:performing the airflow collision process on the final-state matrix of the migration process in the t-th iteration, updating the data of each cell, and modifying the values of the cells at the boundary by using the following formula: n"ew = n"ld + (n®q — n°ld), to obtain the airflow component matrix after the t-th iteration;wherein, npew is cell data after collision; n°ld is cell data before collision; n®q is an airflow distribution standard state along a direction fy before collision; and r is a relaxation factor.

7. The method for urban ventilation analysis based on LBM according to claim 1, wherein the calculating wind speed data of the research area according to the final-state airflow component matrix and exporting the wind speed data as a result set specifically comprises:calculating the horizontal airflow velocity components, vertical airflow velocity components, and airflow velocity magnitudes of the cells in the research area according to the final-state airflow component matrix, to obtain the wind speed data of the research area;establishing a grid dataset by using the geodetic coordinate system information and the projection coordinate system information as spatial reference coordinate systems; andwriting the wind speed data of the research area into the grid dataset and exporting the wind speed data as the result set.

8. A system for urban ventilation analysis based on Lattice Boltzmann Method (LBM), comprising:a data input module configured to input a three-dimensional building dataset, a height extreme value, and a background wind speed of a research area, wherein the three-dimensional building dataset comprises geodetic coordinate system information, projection coordinate system information, and building information; and the building information comprises base outlines and height attributes of individual buildings;a ventilation potential matrix determining module configured to divide the research area into a plurality of cells and calculate ventilation potential of each cell according to the three-dimensional building dataset and the height extreme value, to obtain a ventilation potential matrix;an initial-state airflow component matrix determining module configured to initialize an airflow component matrix of a lattice Boltzmann model according to the background wind speedto obtain an initial-state airflow component matrix, wherein the number of rows and the number of columns of the airflow component matrix are equal to the number of rows and the number of columns of the ventilation potential matrix, respectively;a final-state airflow component matrix determining module configured to iteratively perform an airflow migration process and an airflow collision process on the initial-state airflow component matrix until a predetermined iteration count is reached, to obtain a final-state airflow component matrix; anda result set export module configured to calculate wind speed data of the research area according to the final-state airflow component matrix and export the wind speed data as a result set, wherein the wind speed data of the research area comprises horizontal airflow velocity components, vertical airflow velocity components, and airflow velocity magnitudes of the cells in the research area, and the result set represents relative wind speeds of the cells in the research area.

9. An electronic device, comprising a memory and a processor, wherein the memory is configured to store a computer program, and the processor runs the computer program to enable the electronic device to implement the method for urban ventilation analysis based on Lattice Boltzmann Method (LBM) according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the method for urban ventilation analysis based on Lattice Boltzmann Method (LBM) according to any one of claims 1 to 7 is implemented.

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