A method and device for implementing regional thermal environment analysis, and storage medium

Abstract

The present invention discloses a method and device for implementing regional thermal environment analysis, and a storage medium, and relates to the technical field of thermal environment analysis. The method for implementing regional thermal environment analysis of the present invention can, by extending the time dimension, mine the corresponding change trends of different surface cover types in remote sensing data at all adjacent locations, and determine the weight parameters corresponding to each surface cover in combination with all the change trends. The weight parameters can measure the degree of influence of the surface cover on the thermal environment of the target area. Finally, according to the weight parameters corresponding to all surface covers and each surface cover, the thermal environment corresponding to the target area is analyzed. It can be seen that the present invention can fully mine the effective content of remote sensing data, thereby improving the accuracy and reliability of regional thermal environment analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal environment analysis, and in particular to a method and device for implementing regional thermal environment analysis, and a storage medium. Background Art

[0002] With the acceleration of urbanization, regional thermal environmental issues have had a significant impact on human health, urban ecosystems, and energy consumption. Regional thermal environmental analysis involves the comprehensive study of environmental data within a region to reveal the spatiotemporal distribution characteristics of the region's thermal environment and its influencing factors. Accurately analyzing the regional thermal environment is crucial for developing effective urban planning and improvement strategies.

[0003] Regional thermal environment analysis generally involves analyzing spatiotemporal distribution characteristics, thermal environment differences between different functional zones (such as industrial areas, residential areas, and green spaces), thermal environment influencing factors, and the impact of urban functional zones. Practice has shown that existing technologies still have many shortcomings in data sources and analysis methods, limiting the accuracy and reliability of regional thermal environment analysis. For example, current regional thermal environment analysis primarily relies on remote sensing data, such as Landsat satellite imagery, which has limited spatial resolution (e.g., Landsat 8's 30-meter resolution), providing a limited data base for effective thermal environment analysis. In recent years, some studies have attempted to improve the accuracy of regional thermal environment analysis through multi-source data fusion, improved model algorithms, or the introduction of new analytical frameworks. For example, research based on the Local Climate Zone (LCZ) framework can better reflect the impact of urban morphology on the thermal environment. The SOLWEIG model, through high-spatial-resolution three-dimensional radiation calculations, provides insights for thermal environment analysis in complex urban environments. However, existing technologies still lack sufficient data mining, limiting the accuracy and reliability of regional thermal environment analysis. Summary of the Invention

[0004] The present invention provides a method and device for implementing regional thermal environment analysis, and a storage medium, for improving the accuracy and reliability of regional thermal environment analysis.

[0005] In order to solve the above technical problems, the first aspect of the present invention discloses a method for implementing regional thermal environment analysis, the method comprising:

[0006] Obtain remote sensing data corresponding to the target area at multiple preset time points;

[0007] For each of the preset time points, determining a boundary line between different types of surface cover based on the remote sensing data corresponding to the preset time point, wherein the types of surface cover include building types, water body types, and vegetation types; and determining an area within a preset range around each boundary line as a boundary area;

[0008] For each of the preset time points, remote sensing spectrum data corresponding to each boundary area is screened out from the remote sensing data corresponding to the preset time point, and a boundary area spectrum graph corresponding to each boundary area is generated based on the remote sensing spectrum data;

[0009] For adjacent first and second surface covers in the target area, determining corresponding change trends of the first and second surface covers at adjacent locations based on frequency spectra of the boundary areas corresponding to the first and second surface covers at all the preset time points, wherein the change trends include erosion trends, expansion trends, degradation trends, and maintenance trends;

[0010] According to the change trend of each surface cover in all adjacent locations, a weight parameter corresponding to each surface cover is determined, and according to all the surface covers and the weight parameter corresponding to each surface cover, the thermal environment corresponding to the target area is analyzed.

[0011] As an optional embodiment, in the first aspect of the present invention, analyzing the thermal environment corresponding to the target area according to all the surface covers and the weight parameters corresponding to each surface cover includes:

[0012] Determining a building index corresponding to the target area based on all building-type surface coverage within the target area and a weight parameter corresponding to each building-type surface coverage;

[0013] Determining a water body index corresponding to the target area based on all water body surface coverages within the target area and weight parameters corresponding to each water body surface coverage;

[0014] Determining a vegetation index corresponding to the target area based on all vegetation types and weight parameters corresponding to each vegetation type in the target area;

[0015] The thermal environment corresponding to the target area is analyzed according to the building index, the water body index, and the vegetation index of the target area.

[0016] As an optional embodiment, in the first aspect of the present invention, analyzing the thermal environment corresponding to the target area according to the building index, the water index, and the vegetation index of the target area includes:

[0017] The building index, the water body index, and the vegetation index of the target area are input into a pre-trained thermal environment analysis model to obtain surface temperature distribution data output by the thermal environment analysis model. The surface temperature distribution data is used to represent the surface temperature of each location in the target area.

[0018] As an optional embodiment, in the first aspect of the present invention, obtaining remote sensing data corresponding to a target area at a plurality of preset time points includes:

[0019] At each preset time point, obtaining reference remote sensing data corresponding to the target area, the reference remote sensing data including directly obtainable secondary remote sensing data corresponding to the target area; determining reference dividing lines between different types of surface cover based on the reference remote sensing data; for each reference dividing line, determining multiple reference points on the reference dividing line according to a preset reference point selection rule, and calculating the curvature radius corresponding to each reference point on the reference dividing line;

[0020] At each preset time point, the remote sensing detection device is controlled to sample along the reference dividing line to obtain boundary remote sensing data, wherein, during the sampling process of the remote sensing detection device, a larger curvature radius corresponds to a higher sampling density within a preset range around the reference point;

[0021] For each preset time point, the remote sensing data corresponding to the target area at the preset time point is obtained based on the reference remote sensing data and the boundary remote sensing data.

[0022] As an optional embodiment, in the first aspect of the present invention, for each reference dividing line, determining multiple reference points on the reference dividing line according to a preset reference point selection rule, and calculating the curvature radius corresponding to each reference point on the reference dividing line includes:

[0023] For each reference dividing line, a point is selected at a fixed distance as a reference point, and the curvature radius of the reference dividing line at the reference point is calculated.

[0024] As an optional embodiment, in the first aspect of the present invention, controlling the remote sensing detection device to sample along the reference dividing line includes:

[0025] The remote sensing detection device is controlled to move along the reference dividing line. When the distance between the remote sensing detection device and the next reference point is less than or equal to the preset distance, the sampling density of the remote sensing detection device is controlled to be adjusted to the sampling density corresponding to the next reference point, wherein the sampling density corresponding to the reference point with a larger curvature radius is greater.

[0026] As an optional embodiment, in the first aspect of the present invention, determining the corresponding change trends of the first surface cover and the second surface cover at adjacent locations based on the boundary area frequency spectrum corresponding to the first surface cover and the second surface cover at all the preset time points includes:

[0027] For each of the preset time points, determining the band amplitude proportions corresponding to the first surface cover and the second surface cover respectively according to the boundary area frequency spectrum corresponding to the preset time point;

[0028] According to the band amplitude ratio corresponding to the first surface coverage at all the preset time points, the change trend of the first amplitude ratio corresponding to the first surface coverage is determined, and the amplitude ratio change trend is used to represent the change of the corresponding amplitude ratio as the preset time points change sequentially; according to the band amplitude ratio corresponding to the second surface coverage at all the preset time points, the change trend of the second amplitude ratio corresponding to the second surface coverage is determined.

[0029] According to the changing trend of the first amplitude proportion corresponding to the first surface cover, the changing trend corresponding to the first surface cover at the adjacent location is determined; according to the changing trend of the second amplitude proportion corresponding to the second surface cover, the changing trend corresponding to the second surface cover at the adjacent location is determined.

[0030] A second aspect of the present invention discloses a device for implementing regional thermal environment analysis, the device comprising:

[0031] A data acquisition module is used to obtain remote sensing data corresponding to a target area at multiple preset time points;

[0032] a boundary analysis module for determining, for each of the preset time points, a boundary between different types of surface cover based on the remote sensing data corresponding to the preset time point, wherein the types of surface cover include building types, water body types, and vegetation types; and determining an area within a preset range around each boundary as a boundary area;

[0033] a spectrum graph determination module for filtering, for each of the preset time points, remote sensing spectrum data corresponding to each boundary area from the remote sensing data corresponding to the preset time point, and generating a boundary area spectrum graph corresponding to each boundary area based on the remote sensing spectrum data;

[0034] a trend analysis module for determining, for adjacent first and second surface covers in a target area, corresponding change trends of the first and second surface covers at adjacent locations based on frequency spectra of the boundary areas corresponding to the first and second surface covers at all the preset time points, wherein the change trends include erosion trends, expansion trends, degradation trends, and maintenance trends;

[0035] The thermal environment analysis module is used to determine the weight parameter corresponding to each surface cover according to the corresponding change trend of each surface cover in all adjacent areas, and analyze the thermal environment corresponding to the target area based on all the surface covers and the weight parameter corresponding to each surface cover.

[0036] As an optional embodiment, in the second aspect of the present invention, the thermal environment analysis module analyzes the specific operation mode of the thermal environment corresponding to the target area based on all the surface covers and the weight parameters corresponding to each surface cover, including:

[0037] Determining a building index corresponding to the target area based on all building-type surface coverage within the target area and a weight parameter corresponding to each building-type surface coverage;

[0038] Determining a water body index corresponding to the target area based on all water body surface coverages within the target area and weight parameters corresponding to each water body surface coverage;

[0039] Determining a vegetation index corresponding to the target area based on all vegetation types and weight parameters corresponding to each vegetation type in the target area;

[0040] The thermal environment corresponding to the target area is analyzed according to the building index, the water body index, and the vegetation index of the target area.

[0041] As an optional embodiment, in the second aspect of the present invention, the specific operation mode of the thermal environment analysis module for analyzing the thermal environment corresponding to the target area according to the building index, the water index, and the vegetation index of the target area includes:

[0042] The building index, the water body index, and the vegetation index of the target area are input into a pre-trained thermal environment analysis model to obtain surface temperature distribution data output by the thermal environment analysis model. The surface temperature distribution data is used to represent the surface temperature of each location in the target area.

[0043] As an optional embodiment, in the second aspect of the present invention, the specific operation method of the data acquisition module to acquire the remote sensing data corresponding to the target area at multiple preset time points includes:

[0044] At each preset time point, obtaining reference remote sensing data corresponding to the target area, the reference remote sensing data including directly obtainable secondary remote sensing data corresponding to the target area; determining reference dividing lines between different types of surface cover based on the reference remote sensing data; for each reference dividing line, determining multiple reference points on the reference dividing line according to a preset reference point selection rule, and calculating the curvature radius corresponding to each reference point on the reference dividing line;

[0045] At each preset time point, the remote sensing detection device is controlled to sample along the reference dividing line to obtain boundary remote sensing data, wherein, during the sampling process of the remote sensing detection device, a larger curvature radius corresponds to a higher sampling density within a preset range around the reference point;

[0046] For each preset time point, the remote sensing data corresponding to the target area at the preset time point is obtained based on the reference remote sensing data and the boundary remote sensing data.

[0047] As an optional embodiment, in the second aspect of the present invention, the data acquisition module determines, for each reference dividing line, multiple reference points on the reference dividing line according to a preset reference point selection rule, and calculates the curvature radius corresponding to each reference point on the reference dividing line. The specific operation includes:

[0048] For each reference dividing line, a point is selected at a fixed distance as a reference point, and the curvature radius of the reference dividing line at the reference point is calculated.

[0049] As an optional embodiment, in the second aspect of the present invention, the specific operation mode of the data acquisition module controlling the remote sensing detection device to sample along the reference dividing line includes:

[0050] The remote sensing detection device is controlled to move along the reference dividing line. When the distance between the remote sensing detection device and the next reference point is less than or equal to the preset distance, the sampling density of the remote sensing detection device is controlled to be adjusted to the sampling density corresponding to the next reference point, wherein the sampling density corresponding to the reference point with a larger curvature radius is greater.

[0051] As an optional embodiment, in the second aspect of the present invention, the trend analysis module determines the specific operation mode of the change trend of the first surface cover and the second surface cover corresponding to adjacent locations based on the boundary area frequency spectrum corresponding to the first surface cover and the second surface cover at all the preset time points, including:

[0052] For each of the preset time points, determining the band amplitude proportions corresponding to the first surface cover and the second surface cover respectively according to the boundary area frequency spectrum corresponding to the preset time point;

[0053] According to the band amplitude ratio corresponding to the first surface coverage at all the preset time points, the change trend of the first amplitude ratio corresponding to the first surface coverage is determined, and the amplitude ratio change trend is used to represent the change of the corresponding amplitude ratio as the preset time points change sequentially; according to the band amplitude ratio corresponding to the second surface coverage at all the preset time points, the change trend of the second amplitude ratio corresponding to the second surface coverage is determined.

[0054] According to the changing trend of the first amplitude proportion corresponding to the first surface cover, the changing trend corresponding to the first surface cover at the adjacent location is determined; according to the changing trend of the second amplitude proportion corresponding to the second surface cover, the changing trend corresponding to the second surface cover at the adjacent location is determined.

[0055] A third aspect of the present invention discloses another regional thermal environment analysis implementation system, the system comprising:

[0056] a memory storing executable program code;

[0057] a processor coupled to the memory;

[0058] The processor calls the executable program code stored in the memory to execute the regional thermal environment analysis implementation method disclosed in the first aspect of the present invention.

[0059] A fourth aspect of the present invention discloses a computer storage medium, wherein the computer storage medium stores computer instructions. When the computer instructions are called, they are used to execute the regional thermal environment analysis implementation method disclosed in the first aspect of the present invention.

[0060] The prior art directly uses remote sensing data to analyze the thermal environment of a region, but fails to fully explore the connotation of the data. The present invention can, by extending the time dimension, explore the corresponding change trends of different surface cover types in the remote sensing data at all adjacent locations. Combining all the change trends, the weight parameter corresponding to each surface cover is determined. This weight parameter can measure the impact of the surface cover on the thermal environment of the target area. Finally, based on all the surface covers and the weight parameters corresponding to each surface cover, the thermal environment corresponding to the target area is analyzed. It can be seen that the present invention can fully explore the effective content of remote sensing data, thereby improving the accuracy and reliability of regional thermal environment analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0062] Figure 1 This is a flow chart of a method for implementing regional thermal environment analysis disclosed in an embodiment of the present invention;

[0063] Figure 2 This is a schematic structural diagram of a device for implementing regional thermal environment analysis disclosed in an embodiment of the present invention;

[0064] Figure 3It is a structural diagram of a regional thermal environment analysis implementation system disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0066] The terms "first," "second," and so on, in the description and claims of the present invention and the accompanying drawings are used to distinguish between different items, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or end comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or end.

[0067] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0068] The invention discloses a method and device for realizing regional thermal environment analysis, and a storage medium, which are used to improve the accuracy and reliability of regional thermal environment analysis.

[0069] Example 1

[0070] See also Figure 1 , Figure 1 This is a flow chart of a method for implementing regional thermal environment analysis disclosed in an embodiment of the present invention. Figure 1 The regional thermal environment analysis implementation method described can be integrated into a regional thermal environment analysis implementation device, and the regional thermal environment analysis implementation device can be integrated into a cloud server or a local server. Figure 1 As shown, the method for implementing the regional thermal environment analysis may include the following operations:

[0071] Step 101: Acquire remote sensing data corresponding to a target area at multiple preset time points.

[0072] In the embodiments of the present invention, remote sensing data refers to information about an area obtained through remote sensing technology. This data is collected from a distance by various sensors and is generally divided into the following categories:

[0073] (1) Classification by electromagnetic spectrum range:

[0074] Optical remote sensing data: This includes panchromatic, multispectral, and hyperspectral images. Panchromatic images typically record only one wavelength band and have higher spatial resolution. Multispectral images record multiple wavelength bands, such as red, green, blue, and near-infrared, and are used to identify features such as vegetation and water bodies. Hyperspectral images record hundreds of continuous wavelength bands, providing more detailed spectral information for material composition analysis.

[0075] Infrared remote sensing data: including thermal infrared images, which record thermal radiation information of the earth's surface and are used for analysis of temperature distribution, vegetation health, etc.

[0076] Microwave remote sensing data: This includes passive and active microwave remote sensing. Passive microwave remote sensing receives microwave radiation naturally emitted by the Earth's surface and is used to monitor oceans, atmosphere, and surface humidity. Active microwave remote sensing transmits microwaves and receives reflected signals, which are used to penetrate clouds and vegetation to obtain surface information.

[0077] (2) Classification by sensor type

[0078] Optical sensor data: This technology acquires data by detecting reflected or emitted visible light, infrared light, and other electromagnetic waves. It is widely used in vegetation monitoring, urban planning, and other fields.

[0079] Radar sensor data: This includes synthetic aperture radar (SAR) and interferometric synthetic aperture radar (InSAR). SAR uses the phase difference of radar waves to acquire high-resolution images, unrestricted by lighting and weather conditions. InSAR compares the phase difference between two SAR data sets for surface deformation monitoring.

[0080] LiDAR data: By emitting laser pulses and receiving reflected signals, it generates high-precision three-dimensional terrain models for use in topographic mapping, forest structure analysis, and other applications.

[0081] Step 102: For each preset time point, determine the boundary between different types of surface cover based on the remote sensing data corresponding to the preset time point; and determine the area within a preset range around each boundary line as the boundary area.

[0082] In an embodiment of the present invention, the types of surface cover may include building types, water body types, and vegetation types; building types correspond to high-density buildings and impermeable surfaces, which are prone to cause local heat island effects; the lush vegetation indicated by the vegetation type effectively alleviates surface warming by virtue of transpiration and shading; the water body portrayed by the water body type helps to reduce the local surface temperature due to evaporation and higher heat capacity.

[0083] In an embodiment of the present invention, the boundary between different types of surface cover is determined based on the remote sensing data corresponding to the preset time point. For image data, the boundary is directly determined based on image analysis; for spectral data, spectral analysis can be combined, for example, by analyzing the spectral data through a trained artificial intelligence model to determine the boundary.

[0084] In an embodiment of the present invention, to fully exploit the implications of remote sensing data, a demarcation area within a preset range around a demarcation line is determined at each time point. By analyzing the temporal changes in remote sensing spectrum data within the demarcation area, the corresponding trends of different types of land cover can be determined. These trends can include erosion, expansion, degradation, and maintenance. The present invention discovered that trends measure the impact of a land cover on the thermal environment of a target area. For example, high-density buildings and impervious surfaces can easily trigger a local heat island effect. A land cover with an expansion trend often indicates that the building and population density within the land cover is gradually approaching saturation, indicating an increase in the land cover. A land cover with a degradation trend often indicates that the building and population density within the land cover is decreasing, indicating a decrease in the land cover. While land covers with different trends may have similar areas and remote sensing spectrum data, they actually have different impacts on the regional thermal environment. Therefore, the present invention further mines remote sensing data through trends, thereby improving the reliability of data analysis.

[0085] Step 103: For each preset time point, remote sensing spectrum data corresponding to each boundary area is filtered out from the remote sensing data corresponding to the preset time point, and a boundary area spectrum graph corresponding to each boundary area is generated based on the remote sensing spectrum data.

[0086] In this embodiment of the present invention, remote sensing spectrum data refers to electromagnetic radiation information within different spectral ranges on the surface of a target area, acquired through remote sensing technology. This data can be used to analyze the spectral characteristics of surface cover, thereby identifying and classifying it. In this embodiment of the present invention, it is necessary to analyze the spectrum data of the boundary area to determine the changing trends of the surface cover types on both sides of the boundary area.

[0087] Step 104: For adjacent first and second surface covers in the target area, determine the corresponding change trends of the first and second surface covers at adjacent locations based on the frequency spectra of the boundary areas corresponding to the first and second surface covers at all preset time points.

[0088] In the embodiment of the present invention, the change trend may include an erosion trend, an expansion trend, a degradation trend, and a maintenance trend; and the change trend corresponding to different surface covers can be analyzed by analyzing the change of the frequency spectrum of the boundary area over time.

[0089] Step 105: Determine a weight parameter corresponding to each surface cover according to the corresponding change trend of each surface cover in all adjacent locations, and analyze the thermal environment corresponding to the target area according to all surface covers and the weight parameter corresponding to each surface cover.

[0090] In the embodiment of the present invention, the change trend in step 104 can only measure the change trend of a certain surface cover in a boundary area. In actual situations, a surface cover is often adjacent to multiple other surface covers, and a surface cover may have an expansion trend in a certain boundary area, but a degradation trend in another boundary area. Therefore, in step 105, for each surface cover, it is also necessary to comprehensively consider the corresponding change trends of the surface cover in all adjacent areas to measure the impact of the surface cover on the thermal environment of the target area.

[0091] The main analysis of regional thermal environment analysis generally includes analysis of spatiotemporal distribution characteristics, analysis of thermal environment differences, analysis of thermal environment influencing factors, analysis of urban functional area impacts, etc. In the prior art, remote sensing data is directly used to conduct regional thermal environment analysis, and the connotation of the data is not fully explored. The embodiment of the present invention can extend the time dimension to explore the corresponding change trends of different surface cover types in remote sensing data at all adjacent locations, and determine the weight parameters corresponding to each surface cover in combination with all the change trends. The weight parameters can measure the impact of the surface cover on the thermal environment of the target area. Finally, according to the weight parameters corresponding to all surface covers and each surface cover, the thermal environment corresponding to the target area is analyzed. It can be seen that the embodiment of the present invention can fully explore the effective content of remote sensing data, thereby improving the accuracy and reliability of regional thermal environment analysis.

[0092] In an optional embodiment, the building type corresponds to high-density buildings and impermeable surfaces, which are prone to cause local heat island effects and can be specifically measured by the building index; the lush vegetation indicated by the vegetation type effectively alleviates surface warming by virtue of transpiration and shading, which can be specifically measured by the vegetation index; the water body characterized by the water body type helps to reduce the local surface temperature due to evaporation and higher heat capacity, which can be specifically measured by the water body index.

[0093] Optionally, the above-mentioned analysis of the thermal environment corresponding to the target area based on all land covers and the weight parameters corresponding to each land cover may include:

[0094] The building index for the target area is determined based on all building-related land cover within the target area and the weight parameters corresponding to each building-related land cover. Specifically, the basic impact factor of building-related land cover can be measured based on the ratio of the reflectance of the band related to building-related land cover to the total spectral band reflectance in the remote sensing spectral data. This basic impact factor is then applied to the weight parameter, such as multiplication, to ultimately determine the building index. The water index and vegetation index described below can also be determined based on the same principle.

[0095] The water index for the target area is determined based on the surface cover of all water bodies and the weight parameters corresponding to each water body type. The vegetation index for the target area is determined based on the surface cover of all vegetation types and the weight parameters corresponding to each vegetation type. The thermal environment of the target area is analyzed based on the building index, water index, and vegetation index of the target area. This allows for more reliable thermal environment analysis.

[0096] In an optional embodiment, there are many possible results for thermal environment analysis, but surface temperature data is a very direct and concise analysis result. For example, surface temperature distribution can be analyzed based on remote sensing data to achieve surface temperature prediction. Therefore, the above analysis of the thermal environment corresponding to the target area based on the building index, water index, and vegetation index of the target area can include:

[0097] The building index, water index, and vegetation index of the target area are input into a pre-trained thermal environment analysis model to obtain the surface temperature distribution data output by the thermal environment analysis model. The surface temperature distribution data is used to represent the surface temperature of each location in the target area.

[0098] In this optional embodiment, the thermal environment analysis model is a model trained based on a training data set for the target area. It can analyze the target area's surface temperature distribution based on the building index, water index, and vegetation index. For example, the surface temperature distribution can be the average temperature of building-related ground cover, the average temperature of vegetation-related ground cover, and the average temperature of water-related ground cover. Thus, this optional embodiment can determine the target area's surface temperature distribution based on the target area's building index, water index, and vegetation index.

[0099] In one optional embodiment, the target area thermal environment analysis relies on remote sensing data, such as Landsat satellite imagery. This data has limited spatial resolution (e.g., Landsat 8's 30-meter resolution), and thus provides a limited basis for valid thermal environment analysis. In other words, if the remote sensing data comes from existing data held by relevant institutions or individuals, it is effectively secondhand. Because secondhand remote sensing data is often not specifically acquired for the target area's thermal environment analysis, it contains less valid data relevant to regional thermal environment analysis.

[0100] To solve the above problem, in this optional embodiment, obtaining remote sensing data corresponding to a target area at multiple preset time points may include:

[0101] For each preset time point, reference remote sensing data corresponding to the target area is obtained. The reference remote sensing data may include second-hand remote sensing data corresponding to the target area that can be directly obtained; based on the reference remote sensing data, a reference dividing line between different types of surface cover is determined; for each reference dividing line, multiple reference points are determined on the reference dividing line according to a preset reference point selection rule, and the curvature radius corresponding to each reference point on the reference dividing line is calculated.

[0102] At each preset time point, the remote sensing detection device is controlled to sample along the reference dividing line to obtain the dividing remote sensing data. During the sampling process of the remote sensing detection device, the larger the curvature radius, the higher the sampling density within the preset range around the reference point. The curvature radius is an important parameter to measure the curvature of the dividing line. Generally, the more curved the dividing line, the more complex its boundary situation is. Correspondingly, the sampling points should be denser, so as to achieve more accurate sampling. Dividing lines with lower curvature, such as straight dividing lines, are clearer and simpler. In order to reduce the workload, they correspond to lower sampling densities.

[0103] For each preset time point, the remote sensing data corresponding to the target area at the preset time point is obtained based on the reference remote sensing data and the boundary remote sensing data.

[0104] In this optional embodiment, to obtain more effective remote sensing data, a remote sensing device, such as a low-altitude detector, is used to perform remote sensing data specifically for thermal environment analysis. However, if the remote sensing device were to survey the entire target area, the workload would be enormous. Therefore, this optional embodiment combines secondary remote sensing data with primary remote sensing data. For important demarcation lines, the remote sensing device is used to sample more accurate primary data, while for other areas, secondary remote sensing data is used. This improves the effectiveness of the remote sensing data while ensuring work efficiency.

[0105] Furthermore, regarding the calculation of the curvature radius on the dividing line, the dividing line is a virtual line in the computer system with countless points on it, and it is impossible to calculate the curvature radius for each point.

[0106] To solve the above problem, in an optional embodiment, for each reference dividing line, determining multiple reference points on the reference dividing line according to a preset reference point selection rule, and calculating the curvature radius corresponding to each reference point on the reference dividing line may include:

[0107] For each reference dividing line, a point is selected at a fixed distance as a reference point, and the curvature radius of the reference dividing line at the reference point is calculated.

[0108] It can be seen that this optional embodiment adopts a solution of selecting a point as a reference point at a fixed interval and calculating the curvature radius of the reference dividing line at the reference point to realize the calculation of the curvature radius of each reference point on the dividing line.

[0109] In yet another optional embodiment, controlling the remote sensing detection device to sample along the reference dividing line may include:

[0110] The remote sensing detection device is controlled to move along the reference dividing line. When the distance between the remote sensing detection device and the next reference point is less than or equal to the preset distance, the sampling density of the remote sensing detection device is controlled to be adjusted to the sampling density corresponding to the next reference point. The sampling density corresponding to the reference point with a larger curvature radius is greater, thereby sampling more accurate remote sensing data at the dividing line with more complex conditions.

[0111] In yet another optional embodiment, determining the corresponding change trend of the first surface cover and the second surface cover at adjacent locations based on the frequency spectrum of the boundary area corresponding to the first surface cover and the second surface cover at all preset time points may include:

[0112] For each preset time point, the band amplitude proportions corresponding to the first surface coverage and the second surface coverage are determined based on the spectrum diagram of the boundary area corresponding to the preset time point; this optional embodiment only selects the spectrum diagram within the boundary area for analysis, which can most efficiently obtain the change information of different surface coverages in corresponding adjacent areas.

[0113] Based on the amplitude ratio of the first land cover at all preset time points, a first amplitude ratio change trend corresponding to the first land cover is determined. The amplitude ratio change trend is used to represent the change in the corresponding amplitude ratio as the preset time points change sequentially. Based on the amplitude ratio of the second land cover at all preset time points, a second amplitude ratio change trend corresponding to the second land cover is determined. Different types of land cover correspond to different spectral bands, and the amplitudes, or reflectivities, of different bands can reflect the land cover ratio corresponding to each band.

[0114] According to the changing trend of the first amplitude proportion corresponding to the first surface cover, the changing trend corresponding to the first surface cover at the adjacent location is determined; according to the changing trend of the second amplitude proportion corresponding to the second surface cover, the changing trend corresponding to the second surface cover at the adjacent location is determined.

[0115] In this optional embodiment, different bands corresponding to different surface coverages are first determined, and then the changes in the amplitude ratio of the band of each surface coverage over time are analyzed, thereby analyzing the change trend of the corresponding surface coverage.

[0116] Example 2

[0117] See also Figure 2 , Figure 2 This is a schematic diagram of the structure of a device for implementing regional thermal environment analysis disclosed in an embodiment of the present invention. Figure 2 As shown, the regional thermal environment analysis implementation device may include:

[0118] The data acquisition module 201 is used to acquire remote sensing data corresponding to a target area at multiple preset time points;

[0119] Boundary analysis module 202 is configured to determine, for each preset time point, a boundary between different types of surface cover based on the remote sensing data corresponding to the preset time point, where the types of surface cover may include building type, water type, and vegetation type; and to determine an area within a preset range around each boundary as a boundary area;

[0120] The spectrum graph determination module 203 is configured to, for each preset time point, filter out remote sensing spectrum data corresponding to each boundary area from the remote sensing data corresponding to the preset time point, and generate a boundary area spectrum graph corresponding to each boundary area based on the remote sensing spectrum data;

[0121] A trend analysis module 204 is configured to determine, for adjacent first and second surface covers in a target area, a change trend of the first and second surface covers at adjacent locations based on a frequency spectrum of the boundary area corresponding to the first and second surface covers at all preset time points, wherein the change trend may include an erosion trend, an expansion trend, a degradation trend, and a maintenance trend;

[0122] The thermal environment analysis module 205 is used to determine the weight parameter corresponding to each surface cover according to the corresponding change trend of each surface cover in all adjacent places, and analyze the thermal environment corresponding to the target area according to all surface covers and the weight parameters corresponding to each surface cover.

[0123] In an optional embodiment, the thermal environment analysis module 205 analyzes the specific operation mode of the thermal environment corresponding to the target area based on all the surface covers and the weight parameters corresponding to each surface cover, which may include:

[0124] Determine the building index corresponding to the target area based on all building-type surface coverage in the target area and the weight parameters corresponding to each building-type surface coverage;

[0125] Determine the water body index corresponding to the target area based on the surface coverage of all water body types in the target area and the weight parameters corresponding to the surface coverage of each water body type;

[0126] Determine the vegetation index corresponding to the target area based on the surface coverage of all vegetation types in the target area and the weight parameters corresponding to the surface coverage of each vegetation type;

[0127] The thermal environment corresponding to the target area is analyzed based on the building index, water index and vegetation index of the target area.

[0128] In another optional embodiment, the specific operation of the thermal environment analysis module 205 analyzing the thermal environment corresponding to the target area according to the building index, water index, and vegetation index of the target area may include:

[0129] The building index, water index, and vegetation index of the target area are input into a pre-trained thermal environment analysis model to obtain the surface temperature distribution data output by the thermal environment analysis model. The surface temperature distribution data is used to represent the surface temperature of each location in the target area.

[0130] In another optional embodiment, the specific operation method of the data acquisition module 201 acquiring the remote sensing data corresponding to the target area at multiple preset time points may include:

[0131] For each preset time point, obtaining reference remote sensing data corresponding to the target area, the reference remote sensing data may include directly obtainable secondary remote sensing data corresponding to the target area; determining reference dividing lines between different types of surface cover based on the reference remote sensing data; for each reference dividing line, determining multiple reference points on the reference dividing line based on a preset reference point selection rule, and calculating the curvature radius corresponding to each reference point on the reference dividing line;

[0132] At each preset time point, the remote sensing detection device is controlled to sample along the reference dividing line to obtain the dividing remote sensing data, wherein, during the sampling process of the remote sensing detection device, a larger curvature radius corresponds to a higher sampling density within a preset range around the reference point;

[0133] For each preset time point, the remote sensing data corresponding to the target area at the preset time point is obtained based on the reference remote sensing data and the boundary remote sensing data.

[0134] In another optional embodiment, the data acquisition module 201 determines, for each reference dividing line, multiple reference points on the reference dividing line according to a preset reference point selection rule, and calculates the curvature radius corresponding to each reference point on the reference dividing line. The specific operation method may include:

[0135] For each reference dividing line, a point is selected at a fixed distance as a reference point, and the curvature radius of the reference dividing line at the reference point is calculated.

[0136] In another optional embodiment, the specific operation mode of the data acquisition module 201 controlling the remote sensing detection device to sample along the reference dividing line may include:

[0137] The remote sensing detection device is controlled to move along the reference dividing line. When the distance between the remote sensing detection device and the next reference point is less than or equal to the preset distance, the sampling density of the remote sensing detection device is controlled to be adjusted to the sampling density corresponding to the next reference point, wherein the sampling density corresponding to the reference point with a larger curvature radius is greater.

[0138] In another optional embodiment, the trend analysis module 204 determines the change trend of the first and second land covers at adjacent locations based on the frequency spectra of the boundary areas corresponding to the first and second land covers at all preset time points, which may include:

[0139] For each preset time point, determining the band amplitude proportions corresponding to the first surface coverage and the second surface coverage respectively according to the frequency spectrum of the boundary area corresponding to the preset time point;

[0140] According to the amplitude ratio of the first surface coverage band corresponding to all preset time points, the change trend of the first amplitude ratio corresponding to the first surface coverage is determined. The amplitude ratio change trend is used to indicate the change of the corresponding amplitude ratio as the preset time points change sequentially; according to the amplitude ratio of the second surface coverage band corresponding to all preset time points, the change trend of the second amplitude ratio corresponding to the second surface coverage is determined.

[0141] According to the changing trend of the first amplitude proportion corresponding to the first surface cover, the changing trend corresponding to the first surface cover at the adjacent location is determined; according to the changing trend of the second amplitude proportion corresponding to the second surface cover, the changing trend corresponding to the second surface cover at the adjacent location is determined.

[0142] Example 3

[0143] See also Figure 3 , Figure 3 This is a schematic diagram of the structure of a regional thermal environment analysis implementation system disclosed in an embodiment of the present invention. Figure 3 As shown, the regional thermal environment analysis implementation system may include:

[0144] A memory 301 storing executable program code;

[0145] a processor 302 coupled to the memory 301;

[0146] The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the method for implementing regional thermal environment analysis described in the first embodiment of the present invention.

[0147] Example 4

[0148] An embodiment of the present invention discloses a computer storage medium storing computer instructions. When the computer instructions are called, they are used to execute the steps of the method for implementing regional thermal environment analysis described in the first embodiment of the present invention.

[0149] Example 5

[0150] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the regional thermal environment analysis implementation method described in Example 1.

[0151] The device embodiments described above are merely illustrative, wherein the modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, i.e., they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Those skilled in the art can understand and implement the present invention without inventive effort.

[0152] Through the detailed description of the above embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by means of software plus the necessary general hardware platform, or of course, by means of hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disk storage, magnetic disk storage, or magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0153] Finally, it should be noted that the method, device, and storage medium for implementing regional thermal environment analysis disclosed in the embodiments of the present invention are only preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for implementing regional thermal environment analysis, characterized in that: The method comprises: Obtain remote sensing data corresponding to the target area at multiple preset time points; For each of the preset time points, determining a boundary line between different types of surface cover based on the remote sensing data corresponding to the preset time point, wherein the types of surface cover include building types, water body types, and vegetation types; and determining an area within a preset range around each boundary line as a boundary area; For each of the preset time points, remote sensing spectrum data corresponding to each boundary area is screened out from the remote sensing data corresponding to the preset time point, and a boundary area spectrum graph corresponding to each boundary area is generated based on the remote sensing spectrum data; For adjacent first and second surface covers in the target area, determining corresponding change trends of the first and second surface covers at adjacent locations based on frequency spectra of the boundary areas corresponding to the first and second surface covers at all the preset time points, wherein the change trends include erosion trends, expansion trends, degradation trends, and maintenance trends; According to the change trend of each surface cover in all adjacent locations, a weight parameter corresponding to each surface cover is determined, and according to all the surface covers and the weight parameter corresponding to each surface cover, the thermal environment corresponding to the target area is analyzed.

2. The method for implementing regional thermal environment analysis according to claim 1, characterized in that: Analyzing the thermal environment corresponding to the target area according to all the surface covers and the weight parameters corresponding to each surface cover includes: Determining a building index corresponding to the target area based on all building-type surface coverage within the target area and a weight parameter corresponding to each building-type surface coverage; Determining a water body index corresponding to the target area based on all water body surface coverages within the target area and weight parameters corresponding to each water body surface coverage; Determining a vegetation index corresponding to the target area based on all vegetation types and weight parameters corresponding to each vegetation type in the target area; The thermal environment corresponding to the target area is analyzed according to the building index, the water body index, and the vegetation index of the target area.

3. The method for implementing regional thermal environment analysis according to claim 2, characterized in that: Analyzing the thermal environment corresponding to the target area according to the building index, the water index, and the vegetation index of the target area includes: The building index, the water body index, and the vegetation index of the target area are input into a pre-trained thermal environment analysis model to obtain surface temperature distribution data output by the thermal environment analysis model. The surface temperature distribution data is used to represent the surface temperature of each location in the target area.

4. The method for implementing regional thermal environment analysis according to claim 1, characterized in that: The acquiring of remote sensing data corresponding to a target area at a plurality of preset time points includes: At each preset time point, obtaining reference remote sensing data corresponding to the target area, the reference remote sensing data including directly obtainable secondary remote sensing data corresponding to the target area; determining reference dividing lines between different types of surface cover based on the reference remote sensing data; for each reference dividing line, determining multiple reference points on the reference dividing line according to a preset reference point selection rule, and calculating the curvature radius corresponding to each reference point on the reference dividing line; At each preset time point, the remote sensing detection device is controlled to sample along the reference dividing line to obtain boundary remote sensing data, wherein, during the sampling process of the remote sensing detection device, a larger curvature radius corresponds to a higher sampling density within a preset range around the reference point; For each preset time point, the remote sensing data corresponding to the target area at the preset time point is obtained based on the reference remote sensing data and the boundary remote sensing data.

5. The method for implementing regional thermal environment analysis according to claim 4, characterized in that: For each reference dividing line, determining multiple reference points on the reference dividing line according to a preset reference point selection rule, and calculating the curvature radius corresponding to each reference point on the reference dividing line, including: For each reference dividing line, a point is selected at a fixed distance as a reference point, and the curvature radius of the reference dividing line at the reference point is calculated.

6. The method for implementing regional thermal environment analysis according to claim 5, characterized in that: The controlling of the remote sensing detection device to sample along the reference dividing line comprises: The remote sensing detection device is controlled to move along the reference dividing line. When the distance between the remote sensing detection device and the next reference point is less than or equal to the preset distance, the sampling density of the remote sensing detection device is controlled to be adjusted to the sampling density corresponding to the next reference point, wherein the sampling density corresponding to the reference point with a larger curvature radius is greater.

7. The method for implementing regional thermal environment analysis according to claim 1, characterized in that: The determining, based on the boundary area frequency spectra corresponding to the first surface cover and the second surface cover at all the preset time points, corresponding change trends of the first surface cover and the second surface cover at adjacent locations includes: For each of the preset time points, determining the band amplitude proportions corresponding to the first surface cover and the second surface cover respectively according to the boundary area frequency spectrum corresponding to the preset time point; According to the band amplitude ratio corresponding to the first surface coverage at all the preset time points, the change trend of the first amplitude ratio corresponding to the first surface coverage is determined, and the amplitude ratio change trend is used to represent the change of the corresponding amplitude ratio as the preset time points change sequentially; according to the band amplitude ratio corresponding to the second surface coverage at all the preset time points, the change trend of the second amplitude ratio corresponding to the second surface coverage is determined. According to the changing trend of the first amplitude proportion corresponding to the first surface cover, the changing trend corresponding to the first surface cover at the adjacent location is determined; according to the changing trend of the second amplitude proportion corresponding to the second surface cover, the changing trend corresponding to the second surface cover at the adjacent location is determined.

8. A device for realizing regional thermal environment analysis, characterized in that: The device comprises: A data acquisition module is used to obtain remote sensing data corresponding to a target area at multiple preset time points; a boundary analysis module for determining, for each of the preset time points, a boundary between different types of surface cover based on the remote sensing data corresponding to the preset time point, wherein the types of surface cover include building types, water body types, and vegetation types; and determining an area within a preset range around each boundary as a boundary area; a spectrum graph determination module for filtering, for each of the preset time points, remote sensing spectrum data corresponding to each boundary area from the remote sensing data corresponding to the preset time point, and generating a boundary area spectrum graph corresponding to each boundary area based on the remote sensing spectrum data; a trend analysis module for determining, for adjacent first and second surface covers in a target area, corresponding change trends of the first and second surface covers at adjacent locations based on frequency spectra of the boundary areas corresponding to the first and second surface covers at all the preset time points, wherein the change trends include erosion trends, expansion trends, degradation trends, and maintenance trends; The thermal environment analysis module is used to determine the weight parameter corresponding to each surface cover according to the corresponding change trend of each surface cover in all adjacent areas, and analyze the thermal environment corresponding to the target area based on all the surface covers and the weight parameter corresponding to each surface cover.

9. A regional thermal environment analysis implementation system, characterized in that: The system includes: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the regional thermal environment analysis implementation method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that The computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the regional thermal environment analysis implementation method according to any one of claims 1 to 7.

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