Heat island effect relieving, regulating and controlling method and system for porous ecological pavement

Abstract

The invention relates to the technical field of supervision and control, and provides a porous ecological pavement heat island effect relieving regulation and control method and system. The method comprises the following steps: constructing a temperature distribution matrix by executing grid-level surface temperature analysis on a target area; calling building distribution information from the GIS by taking the boundary coordinates as constraints; registering the temperature matrix and the building information to obtain a building feature matrix; based on the temperature matrix and the building characteristics, carrying out porous ecological pavement coupling analysis, and outputting a construction matrix; on the basis of decision consistency, morphological optimization is executed on the construction matrix, and a paving topological graph is obtained; and after pavement is executed according to the topological graph, ground surface cooling tracking is carried out, and cooling compensation optimization is triggered. According to the method and the device, the technical problem of unstable heat island relieving efficiency caused by lack of land surface temperature dynamic tracking and building feature coupling analysis capability in heat island effect regulation and control is solved, and the technical effect of improving the heat island relieving efficiency persistence and the resource utilization efficiency is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of supervisory control, in particular to a heat island effect mitigation and regulation method and system for porous ecological paving. BACKGROUND

[0002] Under the background of increasingly intensified urban heat island effect, traditional paving technology lacks the ability to supervise and control the dynamic changes of the thermal environment, making it difficult to achieve precise and continuous heat island effect mitigation. Existing methods mostly rely on static paving design, without considering the stress effect of building space layout on ground temperature distribution, nor establishing a closed-loop supervisory control mechanism for post-paving effectiveness, resulting in degradation problems such as pore clogging and evaporation decay that cannot be responded in a timely manner. In addition, existing heat island effect mitigation measures mostly focus on the application of greening, cooling systems and reflective materials, however, these methods often have limited effectiveness, complex implementation, and high maintenance costs. Therefore, there is an urgent need for an intelligent paving solution based on real-time data analysis and adaptive adjustment, to more accurately and efficiently regulate the heat island effect. SUMMARY

[0003] The present application provides a heat island effect mitigation and regulation method and system for porous ecological paving, aiming to solve the technical problem of lack of dynamic tracking of ground temperature and coupling analysis of building characteristics in heat island effect regulation, resulting in unstable heat island mitigation effectiveness, to achieve precise dynamic matching of paving strategy and thermal environment characteristics through grid-level real-time supervisory control, and to improve the technical effect of heat island mitigation effectiveness and resource utilization efficiency.

[0004] The first aspect of the present application provides a heat island effect mitigation and regulation method for porous ecological paving, the method comprising: constructing a ground temperature distribution matrix by performing grid-level ground temperature fluctuation analysis on a target regulation area; calling regional building distribution information from a GIS platform with the boundary coordinates of the target regulation area as the calling constraint; after spatial registration of the ground temperature distribution matrix and regional building distribution information, obtaining a building feature matrix through grid mapping segmentation; performing coupling analysis of porous ecological paving construction based on the ground temperature distribution matrix and building feature matrix, and outputting a gridded paving construction matrix; performing morphological adjacent merging optimization on the gridded paving construction matrix based on paving construction decision consistency, and obtaining a porous paving construction topology graph; after performing porous ecological paving on the target regulation area according to the porous paving construction topology graph, performing dynamic tracking of ground cooling on the target regulation area, and triggering cooling compensation optimization according to the tracking results.

[0005] In another aspect of the present disclosure, a heat island effect mitigation regulation system for porous ecological pavement is provided, which comprises: a temperature analysis module, which constructs a ground temperature distribution matrix by performing grid-level ground temperature fluctuation analysis on a target regulation area; a building distribution calling module, which calls regional building distribution information from a GIS platform with the boundary coordinates of the target regulation area as a calling constraint; a spatial registration module, which performs spatial registration on the ground temperature distribution matrix and the regional building distribution information, and then obtains a building feature matrix via grid mapping segmentation; a coupling analysis module, which performs coupling analysis on the ground temperature distribution matrix and the building feature matrix to output a gridded pavement construction matrix; a merging and optimization module, which performs morphological adjacent merging and optimization on the gridded pavement construction matrix based on the consistency of pavement construction decisions to obtain a porous pavement construction topology; and a cooling compensation module, which performs dynamic tracking of ground cooling on the target regulation area after performing porous ecological pavement on the target regulation area according to the porous pavement construction topology, and triggers cooling compensation optimization according to the tracking results.

[0006] The one or more technical solutions provided in the present disclosure have at least the following technical effects or advantages: The above-mentioned heat island effect mitigation regulation method for porous ecological pavement first constructs a ground temperature distribution matrix by performing grid-level ground temperature fluctuation analysis on a target area. Then, building distribution information is obtained from a GIS platform with the boundary coordinates of the target area as a constraint, and the ground temperature distribution matrix and the building information are spatially registered to obtain a building feature matrix. Subsequently, coupling analysis on the construction of porous ecological pavement is performed based on the two matrices to output a gridded pavement construction matrix. Then, morphological adjacent merging is performed through consistent decision optimization to generate a porous pavement construction topology. After the implementation of ecological pavement, dynamic tracking of ground temperature is performed, and cooling compensation optimization is triggered according to the tracking results, thereby improving the pavement effect and mitigating the heat island effect.

[0007] The above description is only a summary of the technical solutions of the present disclosure. In order to more clearly understand the technical means of the present disclosure, the specific embodiments of the present disclosure can be implemented in accordance with the content of the description, and in order to make the above and other purposes, features and advantages of the present disclosure more obvious and easy to understand, the following specific embodiments of the present disclosure are described. BRIEF DESCRIPTION OF DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0009] Figure 1 A flowchart of a heat island effect mitigation method for a porous ecological pavement in an embodiment.

[0010] Figure 2 A system architecture diagram of a heat island effect mitigation system for a porous ecological pavement in an embodiment.

[0011] Legend: temperature analysis module 11, building distribution calling module 12, space registration module 13, coupling analysis module 14, merging optimization module 15, and cooling compensation module 16. DETAILED DESCRIPTION

[0012] The embodiments of the present application provide a heat island effect mitigation method for a porous ecological pavement and a system thereof, solve the technical problem that the heat island effect mitigation lacks dynamic tracking of ground surface temperature and coupling analysis of building features, and leads to unstable heat island mitigation efficiency, and achieve the technical effect of precise dynamic matching of pavement strategies and heat environment features through grid-level real-time supervision and control, and improved heat island mitigation efficiency sustainability and resource utilization efficiency.

[0013] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0014] It should be noted that the terms "comprising" and "having" and any variations thereof are intended to cover not exclusive inclusion, for example, a process, method, system, product, or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules that are not clearly listed or inherent to the process, method, product, or device.

[0015] Embodiment one, as shown in the present application provides a heat island effect mitigation method for a porous ecological pavement, which comprises: Figure 1 Through grid-level ground surface temperature fluctuation analysis on the target control area, a ground surface temperature distribution matrix is constructed. Through grid-level ground surface temperature fluctuation analysis on the target control area, a ground surface temperature distribution matrix is constructed.

[0016] In the embodiments of the present application, first, the ground surface temperature of the target control area is collected, and the collected temperature data is analyzed at the grid level using a preset grid scale, so as to divide the target control area into a plurality of small units (grids). For each divided grid, a plurality of temperature extreme values are extracted, each temperature extreme value corresponding to a grid. By splicing and restoring these temperature extreme values according to geographical positions, a ground surface temperature distribution matrix is constructed. Each element in the matrix represents the temperature extreme value of the corresponding grid, providing basic data support for subsequent analysis of temperature distribution.

[0017] Further, the present application provides a method for constructing a ground surface temperature distribution matrix by performing grid-level ground surface temperature fluctuation analysis on a target control area, the method comprising: Based on a preset monitoring period, the target control area is scanned by a thermal infrared remote sensing instrument to obtain P sets of time-series thermal environment data. After meteorological bias compensation of the P sets of time-series thermal environment data, grid-level ground surface temperature distribution analysis is performed to output the ground surface temperature distribution matrix.

[0018] Preferably, within a preset monitoring period, the target control area is scanned by a thermal infrared remote sensing instrument. The thermal infrared remote sensing instrument can capture thermal environment information by sensing infrared radiation emitted from the ground surface, thereby obtaining P sets of time-series thermal environment data, each data point representing the temperature distribution of the area at a time. Since the P sets of time-series thermal environment data may be affected by meteorological factors (such as wind speed, humidity, etc.), meteorological bias compensation is needed for the P sets of time-series thermal environment data. Meteorological bias compensation can be achieved by weighted and optimized fusion of the P sets of time-series thermal environment data and meteorological data collected by a UAV, so as to eliminate the interference of meteorological fluctuations on the data, thereby ensuring that the obtained data more truly reflects the changes in ground surface temperature. Subsequently, the P sets of spatially continuous temperature fields obtained after meteorological bias compensation are analyzed at the grid level, i.e., using a preset grid scale to divide the P sets of spatially continuous temperature fields into a plurality of grid ground surface temperature units. For each grid ground surface temperature unit, the corresponding ground surface temperature extreme value is extracted as the temperature of the grid ground surface temperature unit. Then, all the obtained ground surface temperature extreme values are spliced according to the geographical coordinates of the target control area to construct a ground surface temperature distribution matrix. Each element of the matrix represents the ground surface temperature value of each grid, so as to show the ground surface temperature distribution of the target area within the monitoring period, providing data support for subsequent heat island effect mitigation measures.

[0019] Further, the present application provides that the method further comprises: According to the building distribution of the target regulation region, a spatial flight trajectory is fitted based on a building outer contour convex hull algorithm; when the target regulation region is scanned by thermal infrared remote sensing based on a preset monitoring period, the spatial flight trajectory is used to control a UAV to synchronously perform meteorological data collection, so that P sets of compensation thermal environment data are obtained, wherein the UAV is equipped with a thermal infrared imager and a miniature weather station; according to the spatial flight trajectory, SLAM real-time positioning splicing is performed on the P sets of compensation thermal environment data, so that P sets of regional thermal environment data are generated; the P sets of time-series thermal environment data and the P sets of regional thermal environment data are subjected to meteorological deviation weighted optimization fusion, and P sets of spatial continuous temperature fields are output.

[0020] Optionally, first, according to the building distribution information of the target control area extracted from the GIS (Geographic Information System) platform, the boundary of the building is extracted to obtain the outline of the building in the region, and then the building outline is processed using the building outline convex hull algorithm. The convex hull algorithm calculates the edge points of the building, selects the outermost points to build a smallest convex hull to surround all the building outlines. This outline can be regarded as the boundary of the UAV flight area. Subsequently, a safe flight area is set on the convex hull boundary of the building outline to ensure that the flight trajectory line is at least 50 meters (or a larger safety distance) away from the building facade. This can be achieved by expanding or offsetting the convex hull boundary to keep the flight path away from the building and ensure safety. After determining the safe flight area, the spatial flight trajectory of the UAV is designed using a route planning algorithm (such as A* algorithm, Dijkstra algorithm) based on the safe flight area. The spatial flight trajectory should follow the outer contour as much as possible, fly along the periphery of the building distribution area, and ensure that the UAV completes the flight task in the most efficient way while meeting the requirements of flight height, safety distance, etc. Then, when performing thermal infrared remote sensing scanning of the target control area within the preset monitoring period, the designed spatial flight trajectory is used to control the flight route of the UAV. During the flight along the fitted trajectory, the UAV uses the onboard thermal infrared imager and miniature weather station to collect meteorological data. The thermal infrared imager is used to collect ground temperature data of the target area, and the miniature weather station collects real-time meteorological data (such as temperature, humidity, wind speed, etc.) to provide support for subsequent meteorological bias compensation. After the UAV completes the collection of real-time meteorological data, the collected meteorological data is transmitted back to the system to form P sets of compensation thermal environment data, which include temperature, humidity, and other thermal environment information. Then, based on the planned spatial flight trajectory and the P sets of compensation thermal environment data collected by the UAV in real time, SLAM (Simultaneous Localization and Mapping) technology is used for data stitching. Specifically, before the UAV takes off, the SLAM system is initialized. The SLAM system can obtain the position of the UAV and the surrounding environment data in real time through the sensors of the UAV (such as IMU, GPS, laser radar, camera, etc.). During flight, the SLAM system continuously obtains real-time positioning information through the sensors of the UAV. Whenever the UAV collects a new thermal environment data, the SLAM system records the current position and performs positioning and stitching of the data, i.e., determines the region corresponding to the data based on the current position of the UAV, and real-time stitches these data into the map, so that each set of thermal environment data can accurately reflect the temperature distribution information of the region where it is located. After all the compensation thermal environment data is stitched, P sets of regional thermal environment data are obtained, which constitute the thermal environment distribution of the target area at different positions.Finally, the P time series thermal environment data and the P regional thermal environment data are fused, that is, the corresponding meteorological data is obtained from the P regional thermal environment data according to the preset meteorological influence factor, and then the data is weighted and fused with the thermal environment data at the corresponding position in the P time series thermal environment data, so that the influence of the meteorological influence factor is corrected, the interference to the temperature data is eliminated, and P spatial continuous temperature fields are obtained, which represent the ground temperature distribution of the target control region at different time points and different positions, and provide high-quality environmental data support for subsequent heat island effect mitigation strategies.

[0021] Further, the application provides that after the P time series thermal environment data is meteorologically compensated, grid-level ground temperature distribution analysis is performed, and the ground temperature distribution matrix is output. The method comprises: After the P spatial continuous temperature fields are spatially registered and overlapped, the P spatial continuous temperature fields are segmented based on a preset grid scale to obtain a plurality of grid ground temperature units; time series extreme value extraction is performed on the plurality of grid ground temperature units to obtain a plurality of grid-level ground temperature extreme values; the plurality of grid-level ground temperature extreme values are mapped and spliced to restore the target control region according to the geographical coordinate system, and the ground temperature distribution matrix is output.

[0022] Preferably, after obtaining P spatially continuous temperature fields, spatial registration is performed on the P spatially continuous temperature fields, i.e., the spatial data of each spatially continuous temperature field is superimposed and aligned according to coordinates, to ensure that the temperature data collected at different times and different locations can accurately correspond to the actual spatial position of the target region. By superimposing multiple spatially continuous temperature fields, each temperature data can be accurately matched in a unified coordinate system. Subsequently, the P spatially continuous temperature fields after superposition are rasterized and segmented according to a preset grid scale (such as 10 m x 10 m, 20 m x 20 m, etc.), to obtain multiple groups of grid surface temperature units, each grid surface temperature unit representing a small unit of the target control region, and each small unit containing a certain range of temperature data. Then, time series extreme value analysis is performed on the temperature data contained in each obtained grid surface temperature unit, to extract the temperature maximum and temperature minimum of each grid surface temperature unit, forming multiple grid-level surface temperature extremes, which represent the temperature fluctuation range of the region during the monitoring period. Then, according to the geographic coordinate system of the target control region, the multiple grid-level surface temperature extremes are spliced and restored, i.e., through the mapping of map coordinates, the extracted extreme value information of each grid is corresponded to the actual geographic location. The purpose of this step is to restore the grid data from the grid coordinate system to the actual geographic spatial data, to ensure that the temperature extreme value of each grid can correspond to the actual geographic location of the target region. Finally, the multiple grid-level surface temperature extremes after splicing and restoration are summarized and constructed into a complete surface temperature distribution matrix, each element of the matrix representing the temperature extreme value of a grid unit during the monitoring period, so as to obtain the surface temperature distribution of the entire target control region. The surface temperature distribution matrix provides basic data support for the subsequent heat island effect mitigation strategy.

[0023] The boundary coordinates of the target control region are taken as a calling constraint to call the regional building distribution information from the GIS platform.

[0024] In one embodiment, first, the specific region range to be analyzed is determined by obtaining the boundary coordinates of the target control region. Subsequently, these boundary coordinates are input into the GIS platform as a calling constraint to extract the building distribution information related to the region from the GIS platform. The GIS platform provides a digital geographic spatial data system, which contains the position, shape, size and other related information of various buildings in the region. By calling these data, the detailed distribution of buildings in the target region can be obtained, to provide spatial data basis of building layout for subsequent analysis.

[0025] After spatial registration is performed on the surface temperature distribution matrix and the regional building distribution information, an architectural feature matrix is obtained through grid mapping segmentation.

[0026] In one embodiment, the land surface temperature distribution matrix and the regional building distribution information are first spatially registered by matching the location of the building and the location of the temperature data, thereby aligning the land surface temperature data and the building distribution data so that they have consistent spatial positions under the same geographic coordinate system, ensuring that the temperature value of each grid correctly corresponds to the corresponding building information. After completing the spatial registration, the registered data is processed by grid mapping segmentation, dividing the entire region into multiple grid units, each grid unit representing a specific part of the region, containing the land surface temperature information and building feature information of that part. Finally, through this gridding segmentation, a building feature matrix is obtained, each element of which represents the building features (such as building density, building height, etc.) of a grid unit, which will be used for subsequent paving construction analysis and optimization.

[0027] Further, the present application provides a method for obtaining a building feature matrix from the land surface temperature distribution matrix and the regional building distribution information after performing spatial registration, the method comprising: According to the geographic coordinate system of the target control area, the land surface temperature distribution matrix is projected to the regional building distribution information, and the regional building distribution information is divided into a grid building unit matrix according to the grid unit of the land surface temperature distribution matrix; a multivariate feature parameter calculation is performed on the grid building unit matrix to obtain a grid multivariate feature matrix; and a multidimensional vector conversion is performed on the grid multivariate feature matrix to obtain the building feature matrix.

[0028] Preferably, first, each grid cell in the land surface temperature distribution matrix is associated with the building distribution information of the target regulation area by matching the location of the building and the location of the temperature data according to the geographic coordinate system of the target regulation area, ensuring that the temperature data and the building data correspond in the same coordinate system, and ensuring the consistency of the data space. After the land surface temperature distribution matrix is projected to the building distribution information, the regional building distribution information is segmented according to the grid cells of the land surface temperature distribution matrix, that is, the regional building distribution information (such as the location, shape, etc. of the building) is also divided into a corresponding grid building cell matrix according to the grid cells of the temperature matrix, and each element of the grid building cell matrix represents the building characteristics of a small cell in the target area. Then, multi-dimensional feature calculation is performed on each grid building cell in the grid building cell matrix to obtain the multi-dimensional features of the building in each grid cell, such as building density, building height, and building material reflectivity. By summarizing these multi-dimensional features, a grid multi-dimensional feature matrix containing multiple building characteristics is generated for each grid building cell. Then, the building characteristics of each grid building cell are extracted from the grid multi-dimensional feature matrix of each grid building cell, and the building characteristics are spliced according to a preset vector template, so as to convert each grid multi-dimensional feature matrix into a multi-dimensional vector, each multi-dimensional vector representing the building characteristics of a grid building cell, and the dimension of the vector is the same as the number of calculated building characteristics. Finally, the multi-dimensional vectors of all grid building cells are summarized according to the corresponding positions in the land surface temperature distribution matrix to form a building characteristic matrix, each row in the matrix representing a building characteristic vector of a grid building cell, and the entire matrix displays the building characteristics of all grid building cells in the target regulation area. The building characteristic matrix will be used for further ecological paving construction coupling analysis to optimize the heat island effect mitigation strategy.

[0029] Further, the present application provides that each grid multi-dimensional feature consists of building density characteristics, building height characteristics, building material reflectivity characteristics, building type distribution characteristics, and building space three-dimensional characteristics.

[0030] Optionally, each grid multi-feature in the matrix of grid building units includes a building density feature, a building height feature, a building material reflectivity feature, a building type distribution feature, and a building space three-dimensional feature. Among them, the building density feature represents the spatial proportion of buildings in the grid area, reflecting the density of buildings in the area, and the calculation method is the ratio of the building floor area in the grid to the total area of the grid. The building height feature refers to the average height level of buildings in the grid area, and the calculation method is to multiply the height of each building by its floor area, then sum the weighted height of all buildings, and then divide by the total area of all buildings in the grid. The building material reflectivity feature represents the reflection ability of the outer surface material of the building, reflecting the ability of the building to absorb and reflect solar radiation, and the calculation method is to multiply the material reflectivity of each building by its floor area, then sum the weighted material reflectivity of all buildings, and then divide by the total area of all buildings in the grid. The building type distribution feature describes the area proportion of different types of buildings (such as residential buildings, commercial buildings, industrial buildings, etc.) in the grid area, and the calculation method is the ratio of the floor area of each type of building to the total area of all buildings in the grid. The building space three-dimensional feature refers to the floor condition of the building in the vertical direction, reflecting the three-dimensional space utilization degree of the buildings in the area, and the calculation method is the ratio of the total vertical projection area of all buildings in the grid to the total area of the grid.

[0031] Based on the ground temperature distribution matrix and the building feature matrix, a porous ecological paving construction coupling analysis is performed, and a gridded paving construction matrix is output.

[0032] In one embodiment, after obtaining the ground temperature distribution matrix and the building feature matrix, the ground temperature distribution matrix and the building feature matrix are coupled with the historical strategies in the paving construction coupling information base as input data, and the best paving strategy under the current temperature and building characteristics is identified, thereby forming a gridded paving construction matrix to achieve the effect of relieving the heat island effect. The gridded paving construction matrix outputs a specific paving construction scheme for each grid building unit, which can be different types of porous materials, paving thickness or other construction parameters, aiming to optimize the effect of relieving the heat island effect, and providing specific guidance for subsequent construction, so that the paving scheme can be fine-tuned according to the actual situation of different areas.

[0033] Further, the application provides a method for performing a porous ecological paving construction coupling analysis based on the ground temperature distribution matrix and the building feature matrix, and outputting a gridded paving construction matrix, the method comprising: The local call multiple historical porous paving strategies and multiple historical strategy execution areas are called. The historical temperature fluctuation of the multiple historical strategy execution areas of the multiple historical porous paving strategies is solved to obtain multiple sample ground surface temperatures. After calling the multiple sample building distribution information of the historical strategy execution area, the multiple sample building multidimensional vectors are obtained by executing the multivariate characteristic parameter vectorization processing. The data association storage of the multiple historical porous paving strategies, the multiple sample ground surface temperatures and the multiple sample building multidimensional vectors is carried out based on the knowledge graph to construct a paving construction coupling information library. A dynamic matching engine is established to execute the heat stress weight priority matching of the ground surface temperature distribution matrix and the building feature matrix by traversing the paving construction coupling information library element by element to output the rasterized paving construction matrix.

[0034] Preferably, first, a plurality of historical multi-porous paving strategies are called from the local database, which are effective paving schemes that have been verified and have been implemented historically and achieved significant results, each strategy has an implementation area associated with it, which has used these paving strategies and the paving effects and temperature fluctuations of these areas are recorded in the historical data. Then, the temperature data in these historical strategy implementation areas is analyzed to calculate a plurality of average values and standard deviations of a plurality of historical strategy implementation areas, and then the temperature data of each historical strategy implementation area is filtered using the average value plus or minus two or three standard deviations to remove outliers. After cleaning the temperature data of each historical strategy implementation area, the temperature extreme values are selected from the temperature data of each historical strategy implementation area to form a plurality of sample ground surface temperatures, which represent the temperature changes of these areas when implementing a specific paving strategy. Then, the building distribution information related to these historical strategy implementation areas is called, and the same multi-dimensional feature vector processing as described above is used to generate a multi-dimensional feature vector for each sample, thereby obtaining a plurality of sample building multi-dimensional vectors to support subsequent calculations and analyses. Then, the knowledge graph is used to store the data of the plurality of historical multi-porous paving strategies, the plurality of sample ground surface temperatures and the plurality of sample building multi-dimensional vectors, the knowledge graph establishes a graph structure by taking each data point (such as paving strategy, temperature sample and building feature) as a node and the relationship between each data point as an edge. By storing this graph structure, a paving construction coupling information library is constructed, which provides strong data support for the temperature influence of different paving strategies in different building environments. In order to achieve accurate matching, a dynamic matching engine is constructed, which matches the new ground surface temperature distribution matrix and building feature matrix with the data in the coupling information library by traversing the temperature and building features of each grid cell by unit, dynamically matching the most suitable historical paving strategy. In the process of dynamic matching, the Euclidean distance is used to calculate the building similarity between each multi-dimensional vector in the building feature matrix and the sample building multi-dimensional vector traversed, and the Euclidean distance is used to calculate the temperature similarity between the ground surface temperature in the ground surface temperature distribution matrix and the sample ground surface temperature traversed. Then, according to the pre-set weight, a larger thermal stress weight is assigned to the temperature similarity, and a smaller thermal stress weight is assigned to the building similarity. By weighting and summing these two similarities, the matching degree of each strategy in the paving construction coupling information library is obtained. Finally, the matching degrees are sorted in descending order to select the paving strategy with the highest matching degree to form a gridded paving construction matrix, which provides the optimal paving strategy recommendation for each grid cell to guide subsequent construction, so that the paving scheme can maximize the alleviation of the heat island effect and improve the thermal comfort of the area.

[0035] Based on the consistency of the paving construction decision, morphological adjacent merging optimization is performed on the gridded paving construction matrix to obtain a porous paving construction topology map.

[0036] In one embodiment, the gridded paving construction matrix is first processed according to the consistency of the paving construction decision, which means using the same or similar paving strategy in adjacent areas to ensure the integrity of the area and the coordination of the paving effect. Subsequently, the gridded paving construction matrix is processed by morphological adjacent merging optimization, that is, adjacent grid cells with similar paving decisions are merged into a larger area. The merged area will select the strategy with the optimal temperature regulation effect in the sub-area as the merged strategy, thereby reducing unnecessary paving type changes, improving construction efficiency, and ensuring the continuity of the paving area. After morphological adjacent merging optimization, the result obtained is a more simplified and optimized porous paving construction topology map, which shows the paving distribution and construction path of each area and can clearly guide the actual construction operation, ensuring the consistency and integrity of the paving effect, while avoiding excessive boundary changes and paving strategy switching.

[0037] After performing porous ecological paving on the target control area according to the porous paving construction topology map, dynamic tracking of the ground surface temperature of the target control area is performed, and cooling compensation optimization is triggered according to the tracking results.

[0038] In one embodiment, after obtaining the porous paving construction topology map, porous ecological paving construction is performed on the target control area according to the porous paving construction topology map, and ecological paving materials are applied to the designated area. After paving is completed, the dynamic tracking of the ground surface temperature of the target control area is entered, and the real-time change of the cooling effect is tracked. If it is found that the cooling effect of some areas does not meet the expectations, cooling compensation optimization is automatically triggered to obtain multiple porous paving construction topology maps, such as adding supplemental water sources, optimizing paving materials, or adjusting paving thickness, to further enhance the cooling effect of the area and ensure that the ideal heat island effect mitigation target is achieved.

[0039] Further, the present application provides that after performing porous ecological paving on the target control area according to the porous paving construction topology map, dynamic tracking of the ground surface temperature of the target control area is performed, and cooling compensation optimization is triggered according to the tracking results, the method comprising: According to the area division of the porous paving construction topology map, the ground temperature of the M construction topology sub-areas in the target control area is dynamically tracked to obtain M real-time ground temperature distributions; according to the M matching porous paving strategies of the M construction topology sub-areas, M sample heat island effect alleviation targets and M sample cooling compensation strategies are associated and called; according to the cooling energy efficiency deviation of the M real-time ground temperature distributions and the M sample heat island effect alleviation targets, the M sample cooling compensation strategies are matched to obtain M hierarchical cooling compensation strategies; and the M hierarchical cooling compensation strategies are used to execute cooling compensation optimization of the porous ecological paving.

[0040] Preferably, after the completion of the porous ecological paving, according to the area division of the porous paving construction topology map, the target control area is divided into M construction topology sub-areas, and in each sub-area, ground temperature dynamic tracking is performed to monitor and record the ground temperature change of each sub-area in real time to obtain M real-time ground temperature distributions. Subsequently, according to the actual porous paving strategy of each construction topology sub-area, the sample heat island effect alleviation target and the sample cooling compensation strategy matched with each sub-area are called from historical data, which are based on the cooling data and paving strategies of similar areas in the past to provide reference data for optimizing the cooling effect of the current area. By associating the paving strategies of the M sub-areas with the sample targets and compensation strategies, a specific cooling compensation plan can be developed for each sub-area. For each construction topology sub-area, the difference between the real-time ground temperature distribution and the target temperature in the sample heat island effect alleviation target is compared, and the cooling energy efficiency deviation is calculated, which reflects the gap between the actual cooling effect and the expected effect. Then, the calculated cooling energy efficiency deviation is compared with the cooling compensation strategy deviation table to match the corresponding sample cooling compensation strategy, such as increasing water supply, high-pressure flushing, etc., to improve the cooling effect. Finally, based on the M hierarchical cooling compensation strategies, the corresponding compensation strategy is applied to each construction topology sub-area to optimize the cooling effect after paving, ensuring that the cooling effect of the overall area reaches the expected value, thereby effectively alleviating the heat island effect and improving the thermal comfort of the area.

[0041] Further, the application provides that the method further comprises: tracking the cooling compensation optimization effect of the M construction topology sub-areas to obtain M sets of cooling energy efficiency deviation logs and M sets of hierarchical cooling compensation strategies; mapping and packaging the M sets of cooling energy efficiency deviation logs and M sets of hierarchical cooling compensation strategies into M temporary cooling compensation containers; and scheduling the M temporary cooling compensation containers to execute cooling compensation optimization response according to the cooling energy efficiency deviation of the M updated ground temperature distributions and the M sample heat island effect alleviation targets.

[0042] Optionally, after the cooling compensation optimization of each construction topology sub-area is performed, the cooling effect of each sub-area is continuously tracked, the cooling energy efficiency change of each sub-area is monitored and recorded, the cooling energy efficiency deviation of each sub-area is calculated, M sets of cooling energy efficiency deviation logs are formed, each log records the gap between the cooling effect and the expected target of the corresponding sub-area. At the same time, according to the cooling deviation of each sub-area, M sets of hierarchical cooling compensation strategies are generated, which record the specific optimization measures taken for each sub-area. Subsequently, the M sets of cooling energy efficiency deviation logs and the M sets of hierarchical cooling compensation strategies are mapped and encapsulated into M temporary cooling compensation containers. These containers are equivalent to storage modules and can store the cooling compensation data of each sub-area, including the cooling energy efficiency deviation and the corresponding compensation strategy. Each temporary cooling compensation container contains the historical compensation strategy and energy efficiency feedback for a specific sub-area, ensuring that the system can schedule and execute compensation operations at any time. Then, based on the M updated ground temperature distribution and the cooling energy efficiency deviation of the M sample heat island effect mitigation targets, the M temporary cooling compensation containers are scheduled to execute compensation optimization response, that is, according to the difference between the new ground temperature data and the target mitigation effect, the corresponding cooling compensation strategy is extracted from the temporary container, and the corresponding optimization operation is performed for each sub-area, such as triggering high-pressure flushing, starting the rainwater recycling system to supplement water to the paving layer, etc. In this way, real-time temperature changes can be dynamically responded to and appropriate compensation measures can be taken to ensure that the cooling effect in the region is continuously optimized.

[0043] In summary, the embodiments of the present application have at least the following technical effects: The embodiments of the present application first perform grid-level ground temperature fluctuation analysis on the target control area to construct a ground temperature distribution matrix. Then, the boundary coordinates of the target control area are used as a call constraint to call the regional building distribution information from the GIS platform. After that, the ground temperature distribution matrix and the regional building distribution information are spatially registered, and the building feature matrix is obtained through grid mapping segmentation. Further, based on the ground temperature distribution matrix and the building feature matrix, multi-pore ecological paving construction coupling analysis is performed to output a gridded paving construction matrix. Then, based on the consistency of the paving construction decision, the gridded paving construction matrix is optimized by morphological adjacent merging to obtain a multi-pore paving construction topology graph. Finally, after performing multi-pore ecological paving on the target control area according to the multi-pore paving construction topology graph, the ground cooling of the target control area is tracked dynamically, and the cooling compensation optimization is triggered according to the tracking result. These technical effects collectively solve the technical problem of lack of ground temperature dynamic tracking and building feature coupling analysis capability in heat island effect control, resulting in unstable heat island mitigation efficiency. The technical effects of accurately matching the paving strategy with the thermal environment characteristics through real-time supervision and control at the grid level are achieved, and the persistence of heat island mitigation efficiency and resource utilization efficiency are improved.

[0044] Embodiment two, based on the same inventive concept as the heat island effect mitigation method of one of the preceding embodiments, as shown in Figure 2 As shown in the accompanying drawings, the present application provides a heat island effect mitigation system for porous ecological pavement, which comprises: a temperature analysis module 11: a surface temperature distribution matrix is constructed by performing grid-level surface temperature fluctuation analysis on the target control area; a building distribution calling module 12: the boundary coordinates of the target control area are used as calling constraints to call regional building distribution information from the GIS platform; a spatial registration module 13: after performing spatial registration on the surface temperature distribution matrix and regional building distribution information, the building feature matrix is obtained by grid mapping segmentation; a coupling analysis module 14: based on the surface temperature distribution matrix and the building feature matrix, the coupling analysis of the porous ecological pavement construction is performed, and the gridded paving construction matrix is output; a merging and optimization module 15: based on the consistency of the paving construction decision, the morphological adjacent merging optimization is performed on the gridded paving construction matrix, and the porous paving construction topology map is obtained; a cooling compensation module 16: after performing the porous ecological pavement on the target control area according to the porous paving construction topology map, the surface cooling dynamic tracking is performed on the target control area, and the cooling compensation optimization is triggered according to the tracking result.

[0045] Further, the temperature analysis module 11 is also used to perform the following method: Based on the preset monitoring period, the target control area is scanned by thermal infrared remote sensing, P sets of time-series thermal environment data are obtained, after the P sets of time-series thermal environment data are compensated for meteorological deviation, the grid-level surface temperature distribution analysis is performed, and the surface temperature distribution matrix is output.

[0046] Further, the temperature analysis module 11 is also used to perform the following method: According to the building distribution of the target control area, the spatial flight trajectory is fitted based on the building outer contour convex hull algorithm; when the target control area is scanned by thermal infrared remote sensing based on the preset monitoring period, the spatial flight trajectory is used to control the unmanned aerial vehicle to synchronously perform meteorological data collection, P sets of compensated thermal environment data are obtained, wherein the unmanned aerial vehicle is equipped with a thermal infrared imager and a miniature weather station; according to the spatial flight trajectory, P sets of compensated thermal environment data are performed by SLAM real-time positioning splicing, P sets of regional thermal environment data are generated; the P sets of time-series thermal environment data and the P sets of regional thermal environment data are weighted and optimally fused for meteorological deviation, and P sets of spatial continuous temperature fields are output.

[0047] Further, the temperature analysis module 11 is also used to perform the following method: After the P spatially continuous temperature fields are spatially registered and overlapped, the P spatially continuous temperature fields are segmented based on a preset grid scale to obtain a plurality of groups of grid surface temperature units; time series extreme value extraction is performed on the plurality of groups of grid surface temperature units to obtain a plurality of grid-level surface temperature extreme values; and the plurality of grid-level surface temperature extreme values are mapped and spliced to restore the target regulation region according to a geographical coordinate system of the target regulation region, and the surface temperature distribution matrix is output.

[0048] Further, the spatial registration module 13 is further configured to perform the following method: According to the geographical coordinate system of the target regulation region, the surface temperature distribution matrix is projected to the regional building distribution information, and the regional building distribution information is segmented into a grid building unit matrix according to the grid unit of the surface temperature distribution matrix; the grid building unit matrix is subjected to multivariate feature parameter calculation to obtain a grid multivariate feature matrix; and the grid multivariate feature matrix is subjected to multidimensional vector conversion to obtain the building feature matrix.

[0049] Further, the spatial registration module 13 is further configured to perform the following method: Each grid multivariate feature is composed of a building density feature, a building height feature, a building material reflectivity feature, a building type distribution feature, and a building space stereoscopic feature.

[0050] Further, the coupling analysis module 14 is further configured to perform the following method: A plurality of historical porous paving strategies and a plurality of historical strategy execution regions are locally called; historical temperature fluctuations of the plurality of historical strategy execution regions of the plurality of historical porous paving strategies are solved to obtain a plurality of sample surface temperatures; a plurality of sample building distribution information of the historical strategy execution region is called, and a multivariate feature parameter vectorization process is performed to obtain a plurality of sample building multidimensional vectors; data association storage of the plurality of historical porous paving strategies, the plurality of sample surface temperatures, and the plurality of sample building multidimensional vectors is performed based on a knowledge graph to construct a paving construction coupling information library; and a dynamic matching engine is established to perform thermal stress weight priority matching on the paving construction coupling information library by traversing the surface temperature distribution matrix and the building feature matrix unit by unit, and output the grid paving construction matrix.

[0051] Further, the cooling compensation module 16 is further configured to perform the following method: According to the area division of the porous pavement construction topology map, the ground temperature of M construction topology sub-areas in the target control area is dynamically cooperated and tracked to obtain M real-time ground temperature distributions; according to M matching porous pavement strategies of the M construction topology sub-areas, M sample heat island effect relieving targets and M sample cooling compensation strategies are called in association; according to the cooling energy efficiency deviation of the M real-time ground temperature distributions and the M sample heat island effect relieving targets, the M sample cooling compensation strategies are matched to obtain M hierarchical cooling compensation strategies; and the M hierarchical cooling compensation strategies are used to execute cooling compensation optimization of the porous ecological pavement.

[0052] Further, the cooling compensation module 16 is also used to execute the following method: The cooling compensation optimization effect of the M construction topology sub-areas is tracked to obtain M groups of cooling energy efficiency deviation logs and M groups of hierarchical cooling compensation strategies; the M groups of cooling energy efficiency deviation logs and the M groups of hierarchical cooling compensation strategies are mapped and packaged into M temporary cooling compensation containers; and according to the cooling energy efficiency deviation of M updated ground temperature distributions and the M sample heat island effect relieving targets, the M temporary cooling compensation containers are dispatched to execute cooling compensation optimization response.

[0053] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The above describes a specific embodiment of the present application. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0054] The above only describes the preferred embodiments of the present application and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0055] The present specification and drawings are only exemplary descriptions of the present application, and are considered to cover any and all modifications, changes, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art can make various modifications and changes to the present application without departing from the scope of the present application. Thus, if these modifications and changes of the present application belong to the scope of the present application and its equivalents, the present application intends to include these modifications and changes.

Claims

1. A method for mitigating and controlling the heat island effect of porous ecological pavement, characterized in that: The method comprises: By performing grid-level surface temperature fluctuation analysis on the target control area, a surface temperature distribution matrix is ​​constructed; Using the boundary coordinates of the target control area as a call constraint, call regional building distribution information from the GIS platform; After performing spatial registration on the surface temperature distribution matrix and the regional building distribution information, a building feature matrix is ​​obtained through grid mapping segmentation; Conducting a coupled analysis of multi-porous ecological pavement construction based on the surface temperature distribution matrix and the building characteristic matrix, and outputting a gridded pavement construction matrix; Based on the consistency of pavement construction decisions, a morphological neighbor merging optimization is performed on the gridded pavement construction matrix to obtain a multi-porous pavement construction topology map; After the multi-porous ecological paving is performed on the target control area according to the multi-porous paving construction topology map, dynamic tracking of surface cooling is performed on the target control area, and cooling compensation optimization is triggered according to the tracking results.

2. The method for mitigating the heat island effect of porous ecological pavement according to claim 1, characterized in that: Based on the surface temperature distribution matrix and the building characteristic matrix, a multi-porous ecological pavement construction coupling analysis is performed to output a gridded pavement construction matrix. The method includes: Locally call multiple historical multi-porous paving strategies and multiple historical strategy execution areas; Solving historical temperature fluctuations for multiple historical strategy execution areas of the multiple historical multi-porous paving strategies to obtain multiple sample surface temperatures; After calling the distribution information of multiple sample buildings in the historical strategy execution area, a multidimensional vector of multiple sample buildings is obtained by performing multivariate feature parameter vectorization processing; Based on the knowledge graph, data association and storage of the multiple historical multi-porous paving strategies, multiple sample surface temperatures, and multiple sample building multi-dimensional vectors are performed to construct a paving construction coupling information library; A dynamic matching engine is established to traverse the surface temperature distribution matrix and the building feature matrix unit by unit through the pavement construction coupling information library to perform heat stress weight priority matching and output the rasterized pavement construction matrix.

3. The method for mitigating the heat island effect of porous ecological pavement according to claim 1, characterized in that: By performing a grid-level surface temperature fluctuation analysis on the target control area, a surface temperature distribution matrix is ​​constructed, the method comprising: Perform thermal infrared remote sensing scanning on the target control area based on a preset monitoring period to obtain P time-series thermal environment data; After performing meteorological deviation compensation on the P time-series thermal environment data, a grid-level surface temperature distribution analysis is performed to output the surface temperature distribution matrix.

4. The method for mitigating the heat island effect of porous ecological pavement according to claim 3, characterized in that: The method further comprises: According to the building distribution in the target control area, the spatial flight trajectory is fitted based on the building outer contour convex hull algorithm; When performing a thermal infrared remote sensing scan of the target control area based on a preset monitoring period, the space flight trajectory is used to control the UAV to synchronously perform meteorological data collection to obtain P groups of compensated thermal environment data, wherein the UAV is equipped with a thermal infrared imager and a micro-meteorological station; According to the space flight trajectory, SLAM real-time positioning and splicing are performed on P groups of compensated thermal environment data to generate P regional thermal environment data; The P time-series thermal environment data and the P regional thermal environment data are subjected to weighted optimal fusion of meteorological deviations to output P spatially continuous temperature fields.

5. The method for mitigating and controlling the heat island effect of porous ecological pavement according to claim 4, characterized in that: After performing meteorological bias compensation on the P time-series thermal environment data, performing grid-level surface temperature distribution analysis and outputting the surface temperature distribution matrix, the method includes: After spatial registration and overlapping of the P spatially continuous temperature fields, the P spatially continuous temperature fields are segmented based on a preset grid scale to obtain multiple groups of grid surface temperature units; performing time series extreme value extraction on the plurality of groups of grid surface temperature units to obtain a plurality of grid-level surface temperature extreme values; The plurality of grid-level surface temperature extremes are mapped, spliced ​​and restored according to the geographic coordinate system of the target control area, and the surface temperature distribution matrix is ​​output.

6. The method for mitigating and controlling the heat island effect of porous ecological pavement according to claim 1, characterized in that: After performing spatial registration on the surface temperature distribution matrix and the regional building distribution information, a building feature matrix is ​​obtained through grid mapping segmentation. The method includes: After projecting the surface temperature distribution matrix onto the regional building distribution information according to the geographic coordinate system of the target control area, the regional building distribution information is divided into a grid building unit matrix according to the grid units of the surface temperature distribution matrix; Performing multivariate characteristic parameter calculation on the grid building unit matrix to obtain a grid multivariate characteristic matrix; Performing multi-dimensional vector conversion on the grid multivariate feature matrix to obtain the building feature matrix.

7. The method for mitigating and controlling the heat island effect of porous ecological pavement according to claim 6, characterized in that: The multivariate features of each grid are composed of building density features, building height features, building material reflectivity features, building type distribution features and building space three-dimensional features.

8. The method for mitigating and controlling the heat island effect of porous ecological pavement according to claim 2, characterized in that: After performing multi-porous ecological paving on the target control area according to the multi-porous paving construction topology map, performing dynamic tracking of surface cooling on the target control area, and triggering cooling compensation optimization based on the tracking results, the method includes: Based on the regional division of the porous pavement construction topology map, the surface temperature drop of M construction topology sub-areas in the target control area is dynamically and collaboratively tracked to obtain M real-time surface temperature distributions; According to the M matching multi-porous paving strategies of the M construction topology sub-areas, M sample heat island effect mitigation targets and M sample cooling compensation strategies are associated and called; According to the cooling energy efficiency deviations of the M real-time surface temperature distributions and the M sample heat island effect mitigation targets, the M sample cooling compensation strategies are matched to obtain M graded cooling compensation strategies; The M graded cooling compensation strategies are used to perform cooling compensation optimization of the multi-porous ecological pavement.

9. The method for mitigating and controlling the heat island effect of porous ecological pavement according to claim 8, characterized in that: The method further comprises: Tracking the cooling compensation optimization effect of the M construction topology sub-areas to obtain M groups of cooling energy efficiency deviation logs and M groups of hierarchical cooling compensation strategies; Map and encapsulate the M groups of cooling energy efficiency deviation logs and the M groups of hierarchical cooling compensation strategies into M temporary cooling compensation containers; According to the cooling energy efficiency deviations between the M updated surface temperature distributions and the M sample heat island effect mitigation targets, the M temporary cooling compensation containers are scheduled to perform a cooling compensation optimization response.

10. A heat island effect mitigation and control system for porous ecological pavement, characterized in that: The system is used to implement the heat island effect mitigation and control method of porous ecological pavement according to any one of claims 1 to 9, and the system includes: Temperature analysis module: Builds a surface temperature distribution matrix by performing grid-level surface temperature fluctuation analysis on the target control area; Building distribution calling module: using the boundary coordinates of the target control area as the calling constraint, calling the regional building distribution information from the GIS platform; Spatial registration module: after performing spatial registration on the surface temperature distribution matrix and the regional building distribution information, a building feature matrix is ​​obtained through grid mapping segmentation; Coupling analysis module: performs coupling analysis of multi-porous ecological pavement construction based on the surface temperature distribution matrix and the building characteristic matrix, and outputs a rasterized pavement construction matrix; Merging optimization module: Based on the consistency of pavement construction decisions, morphological neighbor merging optimization is performed on the gridded pavement construction matrix to obtain a multi-porous pavement construction topology map; Cooling compensation module: After performing multi-porous ecological paving on the target control area according to the multi-porous paving construction topology map, dynamic tracking of surface cooling is performed on the target control area, and cooling compensation optimization is triggered according to the tracking results.

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