Urban layout optimization simulation method, system and equipment for relieving urban heat island effect

Abstract

The invention discloses a city layout optimization simulation method, system and device for relieving a city heat island effect, and the method comprises the steps: S100: carrying out the grid division of a target city region, adding the city surface temperature data and city data to corresponding grid points, and obtaining a grid city model; s200, extracting average city surface temperature data and influence factor data of each grid point in the grid city model, and respectively constructing a surface temperature prediction model of each grid point by adopting a geographically weighted regression model; and S300, taking the average urban surface temperature data and the influence factor data of each grid point as a state space, taking each influence factor and the action type as an action space, performing reinforcement learning, and simulating an optimization scheme of the urban layout. According to the method, the optimal city layout simulation scheme capable of keeping city functions, controlling optimization cost and relieving a heat island effect can be automatically learned, so that scientific guidance is provided for city layout optimization.

Description

Technical Field

[0001] This application belongs to the technical field of urban layout optimization simulation, and particularly relates to an urban layout optimization simulation method, system and device for alleviating the urban heat island effect. Background Art

[0002] The urbanization process is advancing at an unprecedented speed, changing the urban landscape and ecological environment. This rapid urbanization has led to the formation and increasing severity of the urban heat island effect. The urban heat island effect is a phenomenon in which heat accumulates within the urban spatial range due to factors such as urban buildings and human activities. Currently, most methods for alleviating the urban heat island effect include increasing urban greening, improving urban infrastructure, enhancing urban ventilation, and controlling artificial heat sources. However, it is impossible to quantify the impact of different planning measures on the urban thermal environment, and it is difficult to judge the advantages and disadvantages of various planning schemes. Summary of the Invention

[0003] The purpose of this application is to provide an urban layout optimization simulation method, system and device for alleviating the urban heat island effect. This application can quantify the impact of different urban planning schemes on the heat island effect and automatically learn the optimal layout scheme.

[0004] On the one hand, this application provides an urban layout optimization simulation method for alleviating the urban heat island effect, including: S100: Conduct grid division on the target urban area, and add the urban surface temperature data and urban data of the target city to the corresponding grid points to obtain a grid-based urban model; the urban data refers to the urban data related to the causes of the urban heat island effect; S200: Extract the average urban surface temperature data and various influencing factor data of each grid point in the grid-based urban model, and use the geographically weighted regression model to construct the surface temperature prediction model of each grid point respectively. The influencing factors are preset and are quantifiable influencing factors that affect the urban heat island effect and are obtained from the urban data; S300: Use the average urban surface temperature data and influencing factor data of each grid point as the state space, and use each influencing factor and action type as the action space, and adopt ε -greedy strategy and conduct reinforcement learning based on the constructed reward function to simulate the optimization scheme of the urban layout; The reward function is , where: , , are weights, + + = 1; , , respectively represent the surface temperature matrices of grid points before and after the execution of the action, the predicted value using the surface temperature prediction model; represents the sum of all elements in the matrix; is the urban functional area p the urban function change value of, which is the sum of the urban function change values of the grid points included in the urban functional area p ; is the target city P the sum of the urban function change values of the urban functional areas; the calculation of the urban function change value of a grid point is as follows: First, calculate the change values of each influencing factor related to the urban functional area p at the grid point after the execution of the action respectively; then, multiply the change value of each influencing factor by the regression coefficient of each influencing factor at the grid point, and the regression coefficient is obtained from the surface temperature prediction model of the grid point; finally, sum up the products corresponding to all influencing factors, which is the urban function change value of the grid point; is the sum of the land use layout change cost values of the grid points, and the land use layout change cost value of a grid point is: the product of the sum of the change values of each influencing factor at the grid point after the execution of the action, the weight of the grid point, and the current housing price; the weight of the grid point is the comprehensive weight plus 1, and the comprehensive weight is pre-allocated and positively correlated with the centralization degree and economic level of the grid point location.

[0005] In some specific embodiments, step S100 is specifically: using the fishnet tool in ArcGIS to rasterize the target urban area, and then using the extract values to points tool in ArcGIS to add the urban surface temperature data and urban data of the target city to the corresponding grid points.

[0006] In some specific embodiments, the urban data includes urban building and building physical property data, urban ecology and landscape data, urban infrastructure and transportation data, and urban social and economic data.

[0007] In some specific embodiments, the surface temperature prediction model is ; where represents the position of the grid point i ; represents the urban surface temperature of the grid point i ; represents the intercept of the grid point i ; represents the k th influencing factor at the grid point i the regression coefficient; represents the k th influencing factor at the grid point ivalue m represents the number of influencing factors; represents the grid point i of the error term.

[0008] In some specific embodiments, the influencing factors include: ① one or more of the building area, average building height, heat capacity, and thermal conductivity of the grid points extracted from the data of urban buildings and building physical characteristics; ② one or more of the urban landscape index of the grid points extracted from the data of urban ecology and landscape, and the floor area of each type of urban blue-green space; ③ the shortest distance from the grid point extracted from the data of urban infrastructure and transportation to the urban infrastructure; ④ one or more of the population, GDP, and electricity consumption of the grid points extracted from the data of urban social economy.

[0009] In some specific embodiments, the action types include three types of action operations: increasing the influencing factor value of the grid point by a preset amplitude, decreasing the preset amplitude, and remaining unchanged.

[0010] In some specific embodiments, the comprehensive weight of the grid points is assigned by the following method: The target city is divided into several regions with different centrality levels according to the geographical location, and a centrality weight is assigned to each region; the centrality weight is positively correlated with the centrality level of the region where it is located; The target city is divided into several regions with different economic levels according to the economic situation, and an economic level weight is assigned to each region; the economic level weight is positively correlated with the economic level of the region; The product of the centrality weight and the economic level weight of the region where the grid point is located is the comprehensive weight of the grid point.

[0011] In some specific embodiments, step S300 further includes: S310: The agent observes the current state space; S320: The agent uses ε -greedy strategy, based on the current probability value ε to select an action in the action space a ; S330: The agent executes the action a ; S340: The agent interacts with the state space, updates the state space, and feedbacks the reward function value; S350: The agent observes the updated state space and updates the Q value using the reward function value: S360: The agent determines whether the stop condition is met. If it is met, the iteration ends, and the final state space is the optimized urban layout simulation solution; otherwise, steps S310 - S350 are continued.

[0012] On the other hand, this application also provides an urban layout optimization simulation system for alleviating the urban heat island effect, including: The first module is used to divide the target urban area into grids, add the urban surface temperature data and urban data of the target city to the corresponding grid points, and obtain a gridded urban model; the urban data refers to the urban data related to the causes of the urban heat island effect. The second module is used to extract the average urban surface temperature data and data of each influencing factor at each grid point in the gridded urban model, and respectively construct a surface temperature prediction model for each grid point by using a geographically weighted regression model. The influencing factors are preset and are quantifiable influencing factors affecting the urban heat island effect, obtained from the urban data. The third module is used to use the average urban surface temperature data and influencing factor data at each grid point as the state space, use each influencing factor and action type as the action space, and adopt ε - the greedy strategy and perform reinforcement learning based on the constructed reward function to simulate the optimization solution of the urban layout. The reward function is , where: , , are weights, + + = 1; , , respectively represent the surface temperature matrices of the grid points before and after performing the action, adopts the predicted value of the surface temperature prediction model; represents the sum of all elements in the matrix; is the urban function change value of the urban functional area p , which is the sum of the urban function change values of the grid points included in the urban functional area p ; is the sum of the urban function change values of P urban functional areas of the target city; the calculation of the urban function change value of a grid point is as follows: First, calculate the difference between the values after performing the action and the urban functional area pThe change values of the relevant influencing factors at the grid points; then, multiply the change values of each influencing factor by the regression coefficients of each influencing factor at the grid points, where the regression coefficients are obtained from the surface temperature prediction model of the grid points; finally, sum up the products corresponding to all the influencing factors, which is the urban function change value of the grid points; is the sum of the costs of changing the land use layout at the grid points. The cost of changing the land use layout at the grid points is: the product of the sum of the change values of each influencing factor at the grid point after performing the action, the weight of the grid point, and the current housing price. The weight of the grid point is the comprehensive weight plus 1, and the comprehensive weight is pre-allocated and positively correlated with the centralization degree and economic level of the grid point location.

[0013] On the other hand, the present application also provides an urban layout optimization simulation device for alleviating the urban heat island effect, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above method can be implemented.

[0014] Compared with the prior art, the present application has the following advantages and beneficial effects: The present application combines the geographically weighted regression model with reinforcement learning. The geographically weighted regression model is used to construct a surface temperature prediction model, so as to obtain the influence coefficient of each influencing factor on the heat island effect at the grid point (i.e., the regression coefficient of the surface temperature prediction model), and the influence coefficient is introduced into the reward function of reinforcement learning, so as to quantify the influence of the influencing factor on the heat island effect. Combining the reward function, reinforcement learning is used to quantitatively simulate the influence of different urban planning schemes on the heat island effect, so as to automatically learn the optimal urban layout simulation scheme that can alleviate the heat island effect while maintaining urban functions and controlling optimization costs. The obtained optimal urban layout simulation scheme can provide technical support and scientific guidance for urban layout optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is the flowchart of the urban layout optimization simulation method in the embodiment of the present application; Figure 2 In the embodiment of the present application ε - The flowchart of reinforcement learning using the greedy strategy. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions in the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments in the present application belong to the scope of protection of the present application.

[0017] Please refer to Figure 1 , which shows a schematic flow chart of an urban layout optimization simulation method for alleviating the urban heat island effect provided by an embodiment of the present application, including the following specific steps: S100: Conduct grid division on the target urban area, add the urban surface temperature data and urban data of the target city to the corresponding grid points to obtain a grid-based urban model; among them, urban data refers to urban data related to the causes of the urban heat island effect; Specifically, use the fishnet tool in ArcGIS to divide the target urban area into evenly distributed grid points of equal size, for example, divide them into grid points of 50m×50m in size; then use the Extract Values to Points tool in ArcGIS to add the urban surface temperature data and urban data of the target city to the corresponding grid points.

[0018] In this embodiment, the urban surface temperature data is obtained by inverting the remote sensing image of the target city, and its specific process is as follows: Use the ENVI software tool to obtain the near-infrared band and infrared band data from the remote sensing image, and crop and merge the target urban area; in this embodiment, the remote sensing image is the Landsat8 OLI_TIRS remote sensing image; Perform radiometric calibration and atmospheric correction on the remote sensing image of the target city, and then use the single-window algorithm to invert the remote sensing image to obtain the urban surface temperature.

[0019] Furthermore, using the single-window algorithm to invert the remote sensing image includes: Calculate the normalized difference vegetation index for the remote sensing image, estimate the land cover type according to the normalized difference vegetation index, and calculate the land surface emissivity in combination with the normalized difference vegetation index; According to the land surface emissivity, invert the urban surface temperature using the radiative transfer equation and Planck's law.

[0020] In the present application, the urban data can be selected as urban data related to the causes of the urban heat island effect without specific limitation. In this embodiment, the urban data includes urban building and building physical property data, urban ecology and landscape data, urban infrastructure and transportation data, and urban social and economic data. Further, the urban building and building physical property data includes but is not limited to building layout, building area and height, heat capacity and thermal conductivity of the building; the urban ecology and landscape data includes but is not limited to urban landscape index, urban blue-green space area; the urban infrastructure and transportation data includes but is not limited to urban road network, shortest distance from grid points to infrastructure such as parks and bus stops; the urban social and economic data includes but is not limited to population quantity, GDP, electricity consumption. It should be noted that the urban blue-green space is a general term for urban green spaces and urban water bodies, including but not limited to water areas, green spaces, and parks.

[0021] The above urban data can be obtained from multi-source data such as remote sensing images, GIS data, urban planning documents, field survey sampling data, and meteorological satellite data of the target city. In this application, the geographically weighted regression model of grid points is constructed using urban land surface temperature data and urban data. Therefore, the urban land surface temperature data and urban data added to the grid points should be data of the same period.

[0022] S200: Extract the average urban land surface temperature data and various influencing factor data of each grid point in the grid urban model, and use the geographically weighted regression model to construct the surface temperature prediction model of each grid point respectively, where the influencing factors are quantifiable influencing factors that affect the urban heat island effect; When constructing the surface temperature prediction model, the urban land surface temperature is used as the dependent variable, and the influencing factors are used as the independent variables. The constructed surface temperature prediction model is expressed as follows: (1) In formula (1): represents the position of grid point i ; represents the urban land surface temperature of grid point i ; represents the intercept of grid point i ; represents the regression coefficient of the k th influencing factor at grid point i ; represents the value of the k th influencing factor at grid point i ; m represents the number of influencing factors; represents the error term of grid point i ;

[0023] In this embodiment, the regional statistical method is used to calculate and extract the average urban land surface temperature data of each grid point.

[0024] In this application, the influencing factors of grid points are preset, and some quantifiable influencing factors that affect the urban heat island effect are selected, which are the quantitative manifestations of the urban data of grid points.

[0025] In this embodiment, the influencing factors include: ① One or more of the building area, average building height, heat capacity, and thermal conductivity of grid points extracted from urban building and building physical property data; ② One or more of the urban landscape index of grid points and the floor area of each type of urban blue-green space extracted from urban ecological and landscape data; ③ The shortest distance from the grid points extracted from urban infrastructure and transportation data to urban infrastructure, such as the shortest distance from grid points to parks, bus stops, etc.; ④ One or more of the population quantity, GDP, and electricity consumption of the grid points extracted from urban socioeconomic data. The population quantity, GDP, and electricity consumption of the grid points can be obtained through downscaling methods.

[0026] In this embodiment, the impact factor values of the grid points are extracted using ENVI and ArcGIS software tools.

[0027] The building layout, such as the floor area and height of buildings, will change the air circulation in the city, affect the heat dissipation of the city, and thus affect the surface temperature. The heat capacity and thermal conductivity of buildings will change the reflectivity and absorptivity of sunlight, affect the heating rate of the surface, and thus affect the surface temperature. Vegetation and water bodies can alleviate the urban heat island effect. Therefore, the floor area of urban blue-green spaces such as parks, water areas, and green spaces, as well as the urban landscape index, are also impact factors of the urban heat island effect. The heat released during factory production and transportation processes, as well as the heat emissions from electrical equipment such as air conditioners and appliances in buildings, can exacerbate the urban heat island effect. Since the heat emission is positively correlated with the electricity consumption, the electricity consumption is also an impact factor of the urban heat island effect.

[0028] Please refer to Table 1 below, which shows the average urban surface temperature data and some impact factor data of some grid points in this embodiment. Among them, uid represents the unique identifier of the grid point, which is used to locate and distinguish grid points; some impact factors include water area, building area, vegetation index, number of people, and GDP.

[0029] Table 1 Average urban surface temperature data and some impact factor data of some grid points in the embodiment

[0030] S300: Using the average urban surface temperature data and impact factor data of each grid point as the state space, using each impact factor and action type as the action space, establishing a reward function with the goals of reducing surface temperature, maintaining urban functions, and controlling optimization costs, and using ε - The greedy strategy to perform reinforcement learning and simulate the optimization scheme of the urban layout.

[0031] Reinforcement learning belongs to machine learning. It mainly learns the optimal behavior by the interaction between the agent and the environment and according to the reward value feedback by the environment. ε - The greedy strategy is a strategy used to balance exploration and exploitation in reinforcement learning; in the exploration stage, the agent randomly selects actions for exploration with a probability ε randomly, and all possible actions have the same probability of being selected; in the exploitation stage, the agent uses a probability of 1 -ε Selection action, based on the current Q value, select the currently considered optimal action, Q where the value includes a reward value used to reflect future rewards.

[0032] The action types include three types of action operations: increase, decrease, and remain unchanged. Specifically, they are operations to increase, decrease, or keep unchanged the influence factor value of the grid point. The increase and decrease amplitudes are preset. In this embodiment, the amplitude is set to 5% of the original value. It should be noted that when performing an action, it is necessary to ensure that the influence factor value has practical significance after the action is executed.

[0033] The reward function constructed in this application is expressed as follows: (2) Where , , respectively represent the weights of the three goals of reducing surface temperature, maintaining urban functions, and controlling optimization costs, + + = 1; , , are preset according to the degree of emphasis on the three goals of reducing surface temperature, maintaining urban functions, and controlling optimization costs.

[0034] In this embodiment, it is strongly hoped to alleviate the urban heat island effect. Therefore, the weights of the three goals of reducing surface temperature, maintaining urban functions, and controlling optimization costs are set to 0.5, 0.3, and 0.2 respectively.

[0035] In the reward function, is used to describe the change in the surface temperature of the target city after performing an action. Among them, represents the sum of all elements in the matrix ; is the surface temperature change matrix of the grid point, used to reflect whether the surface temperature of each grid point decreases after performing an action; the rows and columns of the surface temperature change matrix correspond to longitude and dimension respectively. Among them, each element is the surface temperature change value of the grid point corresponding to each position after performing an action; specifically, i , , is the surface temperature matrix of the grid point before performing an action, is the surface temperature matrix of the grid point after performing an action; the surface temperature of each grid point after performing an action is the predicted value obtained by the surface temperature prediction model; when the surface temperature decreases after performing an action, a positive value is fed back.

[0036] In the reward function, it is used to describe the change in the urban function of the target city after performing an action, represents the number of urban functional areas in the target city, p represents the p th urban functional area; an urban functional area refers to the agglomeration area of various functional activities in the city, including but not limited to commercial areas, residential areas, industrial areas, public service facilities, and green areas; represents the p th number of impact factors related to this type of urban functional area in the q th urban functional area, q represents the

[0037] The impact factors related to various urban functional areas come from m independent variable impact factors. For commercial areas, the impact factors related to them are building area, average building height, heat capacity and thermal conductivity of buildings, shortest distance from grid points to bus stops, electricity consumption, etc.; for residential areas, the impact factors related to them are building area, average building height, heat capacity and thermal conductivity of buildings, shortest distance from grid points to bus stops, shortest distance from grid points to parks, population quantity, electricity consumption, etc.; for industrial areas, the impact factors related to them are building area, shortest distance from grid points to urban infrastructure, GDP, electricity consumption, etc. in various combinations; for public service facilities, the impact factors related to them are building area, average building height, urban landscape index, floor area of each type of urban blue-green space, shortest distance from grid points to urban infrastructure, population quantity, etc. in various combinations; for green areas, the impact factors related to them are urban landscape index, floor area of each type of urban blue-green space, etc.

[0038] represents the urban function change value of the urban functional area p , then it is the sum of the urban function change values of all urban functional areas in the target city, P is the number of urban functional areas in the target city. The urban function change value of the urban functional area p is the sum of the urban function change values of the grid points included in the urban functional area p .

[0039] The calculation of the urban function change value of a grid point is as follows: First, calculate the values related to the urban functional area p ​The change value of each relevant influencing factor at the grid point, where the change value refers to the value of the influencing factor at the grid point after the execution of the action minus the value before the execution of the action; then, multiply the change value of each influencing factor by the regression coefficient of each influencing factor at the grid point, and the regression coefficient is obtained from the surface temperature prediction model of the grid point, and the regression coefficient is the parameter in formula (1). , which represents the influencing factor k at the grid point i regression coefficient; finally, sum up the products corresponding to all influencing factors, that is, the change value of the urban function at the grid point.

[0040] For this embodiment, the number of influencing factors related to the urban functional area p is Q , then first calculate the change values of Q influencing factors at the grid point after the execution of the action respectively, and then multiply the change values of Q influencing factors by the regression coefficients of the corresponding influencing factors at the grid point to obtain the products corresponding to each influencing factor; finally, sum up Q products.

[0041] In the reward function, represents the cost value of changing the land use layout caused by the execution of the action, which is the sum of the cost values of changing the land use layout at the grid points. The calculation of the cost value of changing the land use layout at the grid point is: the product of the total change value of the influencing factors at the grid point after the execution of the action, the weight of the grid point, and the current housing price. Among them, the total change value of the influencing factors refers to the sum of the change values of each influencing factor at the grid point. Specifically, the change value of each influencing factor is the absolute value of the change amount of the influencing factor after the execution of the action. The weight of the grid point is the comprehensive weight plus 1. The comprehensive weight is used to reflect the centralization degree and economic level of each grid point location. The comprehensive weight is positively correlated with the centralization degree and economic level of the grid point location. When the centralization degree is higher and the economic level is higher, the grid point is assigned a high comprehensive weight; otherwise, the grid point is assigned a low comprehensive weight.

[0042] Next, a method for allocating the comprehensive weight of the grid point in this embodiment will be provided. Obviously, the method for allocating the comprehensive weight is not limited to the following method. The method for allocating the comprehensive weight of the grid point in this embodiment is as follows: (1) Divide the target city into several regions with different centralization degrees according to the geographical location, and allocate the centralization degree weights to each region respectively; the centralization degree weight is positively correlated with the centralization degree of the region where it is located, that is: the higher the centralization degree (that is, the closer to the city center) of the region, the higher the allocated centralization degree weight; In this embodiment, the target city is divided into three regions with different degrees of centrality, namely the central urban area, the sub - central urban area, and the peripheral area, according to the geographical location. The weights of the degrees of centrality assigned to the central urban area, the sub - central urban area, and the peripheral area are 3:2:1, and the sum of the weights of the degrees of centrality assigned to the central urban area, the sub - central urban area, and the peripheral area is 1. In specific implementation, the division can be made with reference to the existing urban ring roads in the target city.

[0043] (2) Divide the target city into several regions with different economic levels according to the economic situation, and assign economic level weights to each region respectively; the economic level weight is positively correlated with the economic level of the region, that is: the higher the economic level of the region, the higher the economic level weight assigned; In this embodiment, the target city is divided into a high - economic region, a medium - economic region, and a low - economic region according to the economic situation. The economic level weights assigned to the high - economic region, the medium - economic region, and the low - economic region are 3:2:1, and the sum of the economic level weights assigned to the high - economic region, the medium - economic region, and the low - economic region is 1.

[0044] Therefore, in this embodiment, 9 types of divided regions can be obtained: the high - economic region in the central urban area, the medium - economic region in the central urban area, the low - economic region in the central urban area, the high - economic region in the sub - central urban area, the medium - economic region in the sub - central urban area, the low - economic region in the sub - central urban area, the high - economic region in the peripheral area, the medium - economic region in the peripheral area, and the low - economic region in the peripheral area. The comprehensive weight of these 9 types of divided regions is the product of the corresponding degree - of - centrality weight and the economic - level weight. For example, for the high - economic region in the central urban area, its comprehensive weight is 1 / 4.

[0045] (3) The product of the degree - of - centrality weight and the economic - level weight of the region where the grid point is located is the comprehensive weight of the grid point.

[0046] Figure 2 As shown ε - The process of reinforcement learning using the greedy strategy includes: S310: The agent observes the current state space S ; S320: The agent uses ε - the greedy strategy to select an action A in the action space a , and the selection of the action is based on the current probability value ε to select based on current knowledge or randomly. Selecting based on current knowledge means selecting Q the action with the maximum value; S330: Execute the selected action a , the agent interacts with the state space S , and in this process, the state space S changes according to the execution of the action; S340: Update the state space S and return the reward function value R ( s , a ), and this reward function value is used to reflect the effect of the currently executed action; the reward function is established with the goals of reducing the surface temperature, maintaining urban functions, and controlling optimization costs. A positive reward is given when the target temperature is reached, and a negative reward is given when deviating from the target; S350: The agent observes the updated state space , and uses the returned reward function value R ( s , a ) to update the Q value: (3) In Equation (3), the initial value of is set to 0, is the learning rate, is the discount factor, and both have a value range of (0, 1). The discount factor is used to weigh the importance of the current reward and future rewards. A higher value means the agent attaches more importance to future rewards. Conversely, a lower value pays more attention to the current reward; represents the maximum value among all possible actions in the updated state space Q , which reflects the best future reward that the agent can obtain under the updated state space ; S360: The agent determines whether the stop condition is met. If it is met, the iteration ends, and the final state space is the optimized urban layout simulation scheme; otherwise, continue to execute steps S310 - S350. The stop condition is preset and includes but is not limited to (1) the urban surface temperature reaches the target value; (2) the preset maximum number of iterations is reached; (3) convergence; (4) the reward function values feedback in consecutive M iterations remain unchanged or decrease, M and the value is preset.

[0047] This application uses the reinforcement learning method to change the state space of the target city by executing actions, uses the reward function value to judge whether the change is beneficial to reducing the surface temperature, maintaining urban functions, and controlling optimization costs, and continuously learns and optimizes using the greedy strategy, so as to quickly simulate an urban layout scheme that can effectively reduce the urban surface temperature. The simulated urban layout scheme can be used as a reference for urban layout optimization.

[0048] Note that the above is only a preferred embodiment of the present application and the technical principles applied. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and various obvious changes, re-adjustments, and substitutions can be made by those skilled in the art without departing from the protection scope of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments only. Without departing from the concept of the present application, more other equivalent embodiments can be included, all of which fall within the protection scope of the present application.

Claims

1. A method for simulating urban layout optimization for mitigating urban heat island effect, characterized in that: include: S100: Gridding the target city area, adding the urban surface temperature data and urban data of the target city to corresponding grid points, and obtaining a gridded city model; the urban data refers to urban data related to the causes of the urban heat island effect; S200: extracting average urban surface temperature data and influencing factor data of each grid point in the gridded urban model, and constructing a surface temperature prediction model for each grid point using a geographically weighted regression model, wherein the influencing factor is pre-set as a quantifiable influencing factor affecting the urban heat island effect, and is obtained from the urban data; S300: The average urban surface temperature data and influencing factor data of each grid point are used as the state space, and each influencing factor and action type are used as the action space. ε - Greedy strategy and reinforcement learning based on the constructed reward function to simulate the optimization plan of urban layout; The reward function is ,in: , , is the weight, + + =1; , , Respectively represent the surface temperature matrix of the grid points before and after the action is executed, Use the predicted values ​​of the surface temperature prediction model; It means to sum all elements in the matrix; For urban functional areas p The urban function change value is the urban function area p The sum of the urban function change values ​​of the included grid points; Target city P The calculation of the urban function change value of the grid point is as follows: First, the calculation of the urban function change value of the grid point after the action is executed is as follows: p The change value of each relevant influencing factor at the grid point; then, the change value of each influencing factor is multiplied by the regression coefficient of each influencing factor at the grid point, and the regression coefficient is obtained from the surface temperature prediction model of the grid point; finally, the products corresponding to all influencing factors are summed up, that is, the change value of the urban function of the grid point; It is the sum of the cost values ​​of changing the land use layout of the grid points. The cost value of changing the land use layout of the grid points is: the sum of the change values ​​of the influencing factors of the grid points after the action is executed, the weight of the grid points, and the product of the current housing price; the weight of the grid point is the comprehensive weight plus 1, and the comprehensive weight is pre-allocated and positively correlated with the centralization degree of the grid point location and the economic level.

2. The urban layout optimization simulation method for alleviating the urban heat island effect according to claim 1 is characterized in that: step S100 specifically includes: using the fishing net tool in ArcGIS to divide the target city area into grids, and then using the value extraction to point tool in ArcGIS to add the urban surface temperature data and urban data of the target city to the corresponding grid points.

3. The urban layout optimization simulation method for alleviating the urban heat island effect according to claim 1 is characterized in that: The urban data include urban buildings and building physical characteristics data, urban ecology and landscape data, urban infrastructure and transportation data, and urban socio-economic data.

4. The urban layout optimization simulation method for alleviating the urban heat island effect according to claim 1 is characterized in that: The surface temperature prediction model is ;in, Represents grid points i location; Represents grid points i urban surface temperature; Represents grid points i The intercept of Indicates k Influencing factors at grid points i The regression coefficient of Indicates k Influencing factors at grid points i The value of m Indicates the number of impact factors; Represents grid points i The error term of .

5. The urban layout optimization simulation method for alleviating the urban heat island effect according to claim 1 is characterized in that: The impact factors include: ① One or more of the building area, average building height, building heat capacity and thermal conductivity of the grid points extracted from the urban building and building physical characteristics data; ② One or more of the following: the urban landscape index of the grid points extracted from the urban ecology and landscape data, and the area of ​​each type of urban blue-green space; ③The shortest distance from the grid points extracted from urban infrastructure and transportation data to the urban infrastructure; ④ One or more of the population, GDP, and electricity consumption of the grid points extracted from the urban socio-economic data.

6. The urban layout optimization simulation method for alleviating the urban heat island effect according to claim 1 is characterized by: The action types include three types of action operations: increasing the influence factor value of the grid point by a preset amplitude, decreasing the influence factor by a preset amplitude, and keeping it unchanged.

7. The urban layout optimization simulation method for alleviating the urban heat island effect according to claim 1 is characterized by: The comprehensive weight of the grid points is allocated using the following method: Divide the target city into several areas with different centrality according to the geographical location, and assign centrality weights to each area; the centrality weight is positively correlated with the centrality of the area's location; The target city is divided into several regions with different economic levels according to the economic situation, and economic level weights are assigned to each region; the economic level weights are positively correlated with the economic level of the region; The product of the centrality weight and the economic level weight of the area where the grid point is located is the comprehensive weight of the grid point.

8. The urban layout optimization simulation method for alleviating the urban heat island effect according to claim 1 is characterized by: Step S300 further includes: S310: The agent observes the current state space; S320: Agent Utilization ε - Greedy strategy, based on the current probability value ε Selecting actions in the action space a ; S330: Agent performs actions a ; S340: The agent interacts with the state space, updates the state space, and feeds back the reward function value; S350: The agent observes the updated state space and updates it using the reward function value Q value: S360: The intelligent agent determines whether the stopping condition is met. If it is met, the iteration ends and the final state space is the optimized urban layout simulation plan; otherwise, continue to execute steps S310~S350.

9. An urban layout optimization simulation system for alleviating urban heat island effect, characterized in that: include: The first module is used to grid the target city area, add the urban surface temperature data and urban data of the target city to the corresponding grid points, and obtain a gridded city model; the urban data refers to the urban data related to the causes of the urban heat island effect; The second module is used to extract the average urban surface temperature data and the data of each influencing factor of each grid point in the gridded urban model, and to construct the surface temperature prediction model of each grid point respectively by using the geographically weighted regression model. The influencing factor is preset and is a quantifiable influencing factor affecting the urban heat island effect, which is obtained from the urban data; The third module is used to use the average urban surface temperature data and influencing factor data of each grid point as the state space, and each influencing factor and action type as the action space. ε - Greedy strategy and reinforcement learning based on the constructed reward function to simulate the optimization plan of urban layout; The reward function is ,in: , , is the weight, + + =1; , , Respectively represent the surface temperature matrix of the grid points before and after the action is executed, Use the predicted values ​​of the surface temperature prediction model; It means to sum all elements in the matrix; For urban functional areas p The urban function change value is the urban function area p The sum of the urban function change values ​​of the included grid points; Target city P The calculation of the urban function change value of the grid point is as follows: First, the calculation of the urban function change value of the grid point after the action is executed is as follows: p The change value of each relevant influencing factor at the grid point; then, the change value of each influencing factor is multiplied by the regression coefficient of each influencing factor at the grid point, and the regression coefficient is obtained from the surface temperature prediction model of the grid point; finally, the products corresponding to all influencing factors are summed up, that is, the change value of the urban function of the grid point; It is the sum of the cost values ​​of changing the land use layout of the grid points. The cost value of changing the land use layout of the grid points is: the sum of the change values ​​of the influencing factors of the grid points after the action is executed, the weight of the grid points, and the product of the current housing price; the weight of the grid point is the comprehensive weight plus 1, and the comprehensive weight is pre-allocated and positively correlated with the centralization degree of the grid point location and the economic level.

10. An urban layout optimization simulation device for alleviating the urban heat island effect, characterized by: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor can implement the method according to any one of claims 1 to 8 when executing the computer program.

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