Outdoor thermal comfort evaluation method, device, equipment and storage medium

Abstract

The present invention relates to the field of thermal comfort assessment technology, and specifically to an outdoor thermal comfort assessment method, device, equipment and storage medium. The present invention first collects street view images of outdoor areas, then extracts vegetation attribute information on the street view images, and calculates the average radiation temperature of the outdoor area based on the vegetation attribute information, and finally obtains the thermal comfort evaluation result of the outdoor area based on the average radiation temperature. From the above analysis, it can be seen that the present invention takes vegetation information into consideration when calculating the average radiation temperature, and the vegetation information affects the average radiation temperature at the pedestrian layer, and this part of the temperature is related to thermal comfort. Therefore, the thermal comfort evaluation result obtained using the average radiation temperature has a higher accuracy, that is, using vegetation information to evaluate thermal comfort can improve the accuracy of the evaluation result.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal comfort assessment, and in particular to an outdoor thermal comfort assessment method, device, equipment and storage medium. Background Art

[0002] The thermal comfort level of outdoor areas can reflect the comprehensive impact of the meteorological environment of the outdoor area on the thermal balance and comfort level of the human body. The indicators for evaluating thermal comfort include physiological equivalent temperature, new standard effective temperature, universal thermal climate index and other indicators. The thermal comfort of outdoor areas is not only related to the surface temperature and the distribution of buildings, but also to vegetation data. When evaluating the thermal comfort of outdoor areas, the existing technology is limited by the lack of vegetation data and does not consider the impact of vegetation data on thermal comfort, resulting in low accuracy of the evaluation results.

[0003] In summary, the evaluation results of thermal comfort in the existing technology have low accuracy.

[0004] Therefore, the prior art still needs to be improved and enhanced. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides an outdoor thermal comfort evaluation method, device, equipment and storage medium, which solves the problem of low accuracy of thermal comfort evaluation results in the prior art.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides an outdoor thermal comfort evaluation method, which comprises:

[0008] Collecting street view images of outdoor areas, and extracting vegetation attribute information related to outdoor temperature on the street view images;

[0009] Determining the average radiation temperature of the outdoor area according to the vegetation attribute information;

[0010] A thermal comfort evaluation result of the outdoor area is obtained according to the average radiation temperature.

[0011] In one implementation, extracting vegetation attribute information related to outdoor temperature on the street view image includes:

[0012] Extracting vegetation structure data on the street view image;

[0013] Extracting vegetation target position data on the street view image, and using the vegetation structure data and the vegetation target position data as vegetation attribute information.

[0014] In one implementation, extracting vegetation structure data on the street view image includes:

[0015] The tree height, crown width, crown height and under-crown height of the trees on the street view image are extracted, and the tree height, crown width, crown height and under-crown height are used as vegetation structure data.

[0016] In one implementation, extracting vegetation target location data on the street view image includes:

[0017] Determine a first image and a second image containing the same target vegetation in the street view image, and obtain a first acquisition position corresponding to the first image and a second acquisition position corresponding to the second image, wherein the first acquisition position and the second acquisition position are located on the same acquisition horizontal plane;

[0018] determining, based on the second image, a horizontal distance between the target vegetation and the second acquisition position on the acquisition horizontal plane;

[0019] Acquire positioning error data between a measured position and an actual position of the vegetation, and determine a horizontal expansion distance based on the positioning error data and the horizontal distance;

[0020] Identify each vegetation on the first image, and determine a straight line formed by each vegetation and the first acquisition position;

[0021] Filter out pre-selected straight lines from various straight lines that intersect with the line segment where the horizontal expansion distance is located;

[0022] Determine the intersection point of each of the preselected straight lines and the line segment where the horizontal extension distance is located;

[0023] When the position difference between the intersection point and the target vegetation along the line segment is less than a preset value, the vegetation target position data of the target vegetation is determined based on the intersection point.

[0024] In one implementation, determining the horizontal extension distance according to the positioning error data and the horizontal distance includes:

[0025] Determine an average positioning error and a maximum positioning error in the positioning error data, wherein the average positioning error is an error of a collection device, and the maximum positioning error is a maximum error of a monocular depth estimation model, wherein the monocular depth estimation model is used to extract a depth value of the street view image, and the depth value is used to calculate the horizontal distance;

[0026] A horizontal expansion distance is determined according to the maximum positioning error, the average positioning error and the horizontal distance.

[0027] In one implementation, determining the average radiation temperature of the outdoor area according to the vegetation attribute information includes:

[0028] Acquiring meteorological data, solar radiation data, land type information and digital elevation model data of the outdoor area;

[0029] The average radiation temperature of the outdoor area is determined based on the vegetation attribute information, the meteorological data, the solar radiation data, the land type information and the digital elevation model data.

[0030] In one implementation, obtaining a thermal comfort evaluation result of the outdoor area according to the average radiation temperature includes:

[0031] Acquiring meteorological data of the outdoor area;

[0032] Obtaining a general thermal climate index for the outdoor area based on the meteorological data and the average radiant temperature;

[0033] A thermal comfort evaluation result of the outdoor area is obtained according to the universal thermal climate index.

[0034] In a second aspect, an embodiment of the present invention further provides an outdoor thermal comfort evaluation device, wherein the device comprises the following components:

[0035] An information extraction module, used to collect street view images of outdoor areas and extract vegetation attribute information related to outdoor temperature on the street view images;

[0036] An average radiation temperature analysis module, used to determine the average radiation temperature of the outdoor area according to the vegetation attribute information;

[0037] The evaluation module is used to obtain a thermal comfort evaluation result of the outdoor area according to the average radiation temperature.

[0038] In a third aspect, an embodiment of the present invention further provides a terminal device, wherein the terminal device includes a memory, a processor, and an outdoor thermal comfort evaluation program stored in the memory and executable on the processor, and when the processor executes the outdoor thermal comfort evaluation program, the steps of the outdoor thermal comfort evaluation method described above are implemented.

[0039] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which an outdoor thermal comfort evaluation program is stored. When the outdoor thermal comfort evaluation program is executed by a processor, the steps of the outdoor thermal comfort evaluation method described above are implemented.

[0040] Beneficial effect: The present invention first collects street view images of outdoor areas, then extracts vegetation attribute information on the street view images, and calculates the average radiation temperature of the outdoor area based on the vegetation attribute information, and finally obtains the thermal comfort evaluation result of the outdoor area based on the average radiation temperature. From the above analysis, it can be seen that the present invention takes vegetation information into account when calculating the average radiation temperature, and the vegetation information affects the average radiation temperature at the pedestrian layer, which is related to thermal comfort. Therefore, the thermal comfort evaluation result obtained using the average radiation temperature has a higher accuracy, that is, using vegetation information to evaluate thermal comfort can improve the accuracy of the evaluation result. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is the overall flow chart of the present invention;

[0042] Figure 2 is a schematic diagram of thermal comfort evaluation in an embodiment of the present invention;

[0043] Figure 3 A tree structure diagram in an embodiment of the present invention;

[0044] Figure 4 A side view of a tree shape detection in an embodiment of the present invention;

[0045] Figure 5 A top view of tree shape detection in an embodiment of the present invention;

[0046] Figure 6 is a schematic diagram of the geographical location of a computing tree in an embodiment of the present invention;

[0047] Figure 7 A structural diagram of an outdoor thermal comfort evaluation device provided by the present invention;

[0048] Figure 8 This is a block diagram of the internal structure principle of the terminal device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following is a clear and complete description of the technical solution of the present invention in combination with the embodiments and the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] The thermal comfort level of outdoor areas can reflect the comprehensive impact of the meteorological environment of the outdoor area on the thermal balance and comfort level of the human body. The indicators for evaluating thermal comfort include physiological equivalent temperature, new standard effective temperature, universal thermal climate index and other indicators. The thermal comfort of outdoor areas is not only related to the surface temperature and the distribution of buildings, but also to vegetation data. When evaluating the thermal comfort of outdoor areas, the existing technology is limited by the lack of vegetation data and does not consider the impact of vegetation data on thermal comfort, resulting in low accuracy of the evaluation results.

[0051] In order to solve the above technical problems, the present invention provides an outdoor thermal comfort evaluation method, device, equipment and storage medium, which solves the problem of low accuracy of thermal comfort evaluation results in the prior art. In specific implementation, firstly, a street view image of an outdoor area is collected, and vegetation attribute information related to the outdoor temperature on the street view image is extracted; and based on the vegetation attribute information, the average radiation temperature of the outdoor area is determined; finally, based on the average radiation temperature, the thermal comfort evaluation result of the outdoor area is obtained.

[0052] The thermal comfort evaluation results of the present invention can be used to assist in the construction and renewal of buildings and landscape facilities in cities. The specific application scenarios are as follows:

[0053] Collect panoramic images of streets (streets are outdoor areas), identify roadside trees from the panoramic images, and calculate the location information, tree height, crown width, crown height and under-crown height of the roadside trees through image processing technology. The location information, tree height, crown width, crown height and under-crown height of the roadside trees are vegetation attribute information. The average radiation temperature of the street is calculated based on the vegetation attribute information. Finally, the thermal comfort evaluation result of the street is obtained according to the average radiation temperature. The construction of buildings and landscape facilities on the street is guided according to the thermal comfort evaluation results to reduce the street temperature. For example, more facilities with shading effects are added in areas where cooling is required, such as roof greening, canopies, pergolas, etc., or trees or vertical greening are appropriately increased.

[0054] The outdoor thermal comfort evaluation method of this embodiment can be applied to a terminal device, which can be a terminal product with a street view image processing function, such as a computer. Figure 1 As shown in , the outdoor thermal comfort evaluation method specifically includes the following steps:

[0055] S100, collecting a street view image of an outdoor area, and extracting vegetation attribute information related to outdoor temperature on the street view image;

[0056] S200, determining the average radiation temperature of the outdoor area according to the vegetation attribute information;

[0057] S300: Obtain a thermal comfort evaluation result of the outdoor area according to the average radiation temperature.

[0058] Embodiment 1: In this embodiment, step S100 includes the following specific steps S101 to S109:

[0059] S101, extracting the heights of trees on the street view image , Crown Width , Crown height and crown height The tree height, the crown width, the crown height and the under-crown height are used as vegetation structure data.

[0060] For the tree, For the tree crown, In this embodiment, the vegetation is trees, and the trees are roadside trees. The vegetation may also be other trees. The street view image is a panoramic street view image, such as Figure 2 As shown, the panoramic street view image is input into the street tree data acquisition module to extract the street tree structure (the street tree structure is the vegetation structure data) and coordinates (the coordinates are the vegetation target location data).

[0061] Tree Height , Crown Width , Crown height , Height below crown like Figure 3 As shown. Among them, the tree height The calculation formula is as follows:

[0062] ;

[0063] In the formula, Figure 4 As shown in the figure, D is the horizontal distance between the acquisition device (the acquisition device is the camera) and the trees (the trees are the vegetation). is the vertical offset angle of the top of the tree relative to the height plane where the collection equipment is located, such as Figure 3 ( Figure 3 The image width direction is the horizontal coordinate, and the image height direction is the vertical coordinate), the pixel coordinates of the edge in the street view image are , is the height of the acquisition equipment, The conversion factor corresponding to the top of the tree, , .

[0064] ;

[0065] in, , ;

[0066] is the distance between the collection equipment and the base of the tree, which can be called the actual distance, e.g. Figure 3 As shown, the bottom of the tree is the lowest point of the tree, that is, the coordinates of the bottom of the tree are , is the horizontal coordinate of the tree base, is the ordinate of the tree base, is the top coordinate of the tree. All horizontal and vertical coordinates in this embodiment are pixel coordinates on the street view image. B is the baseline of the acquisition device. F is the focal length of the camera acquisition device. is the disparity between all objects including trees in the street view image, is the depth value represented by the depth map of the street view image. C is the calibration parameter of the acquisition device, which is obtained by the following method: randomly select street view images, and compare the average value of the ratio of the distance from the camera to the tree predicted by the actual measurement and the Monodepth2 model (Monodepth2 model is a monocular depth estimation model) as the calibration parameter H is the total number of pixels in the street view image (panoramic street view image) in the height direction, such as Figure 4 As shown, is the angle between the horizontal line where the collection equipment is located and the connecting line, which is the connecting line between the collection equipment and the bottom of the tree, that is, is the vertical offset angle from the height plane where the acquisition device is located to the bottom of the tree. The street view image in this embodiment is the street view image after the bottom street view image block is removed from the original street view image. is the vertical angle of the street view image, is the total number of pixels in the height direction of the original street view image The ratio of the total number of pixels in the height direction to the street view image (the street view image is the street view image after the bottom street view image block is removed). Street view image blocks, then equal ( , ), for .

[0067] Height below crown :

[0068] ;

[0069] ;

[0070] like Figure 3 As shown, is the ordinate of the dividing line between the crown and the trunk. In this embodiment, Python (a computer programming language) is used to identify the detailed tree structure of the single tree mask street view image. Resampling is performed every 7 rows, and the minimum derivative of the number of pixels of adjacent resampled rows is .

[0071] like Figure 4 As shown, is the angle between the horizontal line where the collection equipment is located and the dividing line (the dividing line is the dividing line between the crown and the trunk), that is, is the vertical offset angle of the dividing line relative to the height plane of the acquisition equipment, is the conversion factor corresponding to the dividing line, .

[0072] Crown height :

[0073] ;

[0074] Crown Width :

[0075] ;

[0076] ;

[0077] ;

[0078] ;

[0079] like Figure 5 As shown, is the horizontal offset angle of the tree crown edge relative to the center of the tree crown (using masks to represent trees on street view images), is the horizontal offset angle of one edge of the tree canopy relative to the center of the street view image, is the horizontal offset angle of the other edge of the tree canopy relative to the center of the street view image, is the conversion factor corresponding to the two ends of the tree, . is the total number of pixels in the width direction of the street view image (panoramic street view image). Figure 3 As shown, is the horizontal coordinate of one edge of the crown, is the horizontal coordinate of the other edge of the crown, where one edge of the crown is higher than the other.

[0080] S102: Determine a collection location of each of the street view images.

[0081] like Figure 6As shown, after the collection device (collection device, that is, camera) placed on the vehicle collects the street view image at the collection position S1, the vehicle travels along the traveling direction, and after traveling a certain distance, arrives at the collection position S2. The collection device collects the street view image again at the collection position S2, and then continues to travel until it reaches the last collection position. Some of the distances between the collection positions are less than 70 meters.

[0082] The direction of travel in real time is determined as follows:

[0083] The shooting points of two adjacent street view images are used to construct vector segments according to the street view image serial numbers. When the distance between the two points is less than 70m, its direction is the vehicle travel direction of the previous point; for points with a relatively long remaining distance (the distance is greater than 70m), the direction of the vector segment from any of its nearest remaining points to the point whose travel direction has been obtained is calculated. If the difference between this direction and the travel direction of the nearest point is less than or equal to 5°, this direction is used as the vehicle travel direction of the remaining points; the above steps are repeated to determine the vehicle travel direction of all street view images, and points that do not meet the above conditions are eliminated.

[0084] S103, combining the street view images in pairs to obtain groups of image combinations, each group of the image combination includes a first image and a second image, and a first acquisition position of the first image and a second acquisition position of the second image are located on the same acquisition horizontal plane.

[0085] like Figure 6 As shown, the acquisition device acquires street view images at three acquisition positions. If the three street view images are A, B, and C, A and B form an image combination, B and C form an image combination, and A and C form an image combination. And the three acquisition positions are all located on the horizontal plane where the height of the acquisition device is located.

[0086] S104, for each group of the image combinations, determining a first predicted geographical location of vegetation on the first image; and determining a second predicted geographical location of vegetation on the second image.

[0087] The coordinates of the shooting point, the distance from the camera to the tree, the relative angle of the tree in the street view image to the camera at the shooting point, and the direction of travel of the vehicle are used to predict the geographical location of the vegetation in each street view image. The predicted geographical location is Figure 6 The initial positions of the six trees tr11, tr12, tr21, tr22, tr31, and tr32, Figure 6 Not all the sampled trees are marked in FIG. 1 . For the convenience of explaining this embodiment, only the initial positions of these six trees are marked.

[0088] S105: Determine a horizontal distance D between the second predicted position and the second acquisition position.

[0089] The vegetation in this embodiment is trees, and the trees are roadside trees. The collection horizontal plane is the horizontal plane where the collection device is located. Through the above step S101, the horizontal distance D between the vegetation and the corresponding collection position on the horizontal plane is calculated, that is, Figure 6 The horizontal distance D between the tree tr11 and the collection position S1 is Figure 6 The horizontal distance D between the predicted position tr21 of the tree and the collected position S2.

[0090] S106, determining the average positioning error in the positioning error data and the maximum positioning error , the average positioning error is the error of the acquisition device, the maximum positioning error is the maximum error of a monocular depth estimation model, wherein the monocular depth estimation model is used to extract a depth value of the street view image, and the depth value is used to calculate the horizontal distance.

[0091] The error of the acquisition equipment mainly comes from the positioning error of the positioning device on the vehicle supporting the acquisition equipment, so the acquisition equipment will cause certain errors in the calculation of the horizontal distance D. When calculating the horizontal distance D between the vegetation and the acquisition equipment, the depth value of the image is used, and the depth value of the image is calculated by the monocular depth estimation model. Therefore, the monocular depth estimation model will also cause certain errors in the calculation of the horizontal distance D.

[0092] S107, based on the maximum positioning error and the average positioning error And the horizontal distance D, determine the horizontal extension distance L:

[0093] ;

[0094] In the formula, is the buffer threshold after considering the error, The value of can be 0.66, The value can be 3 meters.

[0095] S108, determining a first straight line formed by the first predicted geographical location and the first collection location; determining a second straight line formed by the second predicted geographical location and the second collection location; and determining an intersection point between the first straight line and the second straight line.

[0096] S109: Filter out candidate intersection points from the intersection points according to the horizontal expansion distance and the position difference between the position of the intersection point and the second predicted position.

[0097] The distance between the alternative intersection point and the second collection position is smaller than the horizontal expansion distance. Since the horizontal distance D is the distance between the second predicted position and the second collection position, and the horizontal expansion distance L is a distance extended on the basis of the horizontal distance D, the distance between the alternative intersection point and the second collection position is smaller than the horizontal expansion distance L, that is, the alternative intersection point is located within the line segment where the horizontal expansion distance starting from the second collection position is located.

[0098] The candidate intersection point must not only be located within the horizontal expansion distance, but also be the intersection point closest to the second predicted position among all the intersection points.

[0099] S1010, obtaining vegetation target position data according to the candidate intersection points corresponding to each group of the image combinations.

[0100] A candidate intersection point can be screened out through the two predicted geographic locations on the two street view images in each group of images. Several candidate intersection points can be obtained from all image combinations. By clustering several candidate intersection points, vegetation target location data can be obtained. In other words, the predicted geographic locations obtained before are not the actual geographic locations of the vegetation. By clustering the intersection points, vegetation target location data that is closer to the actual geographic location can be obtained.

[0101] For example, Figure 6 Taking steps S102 to S1010 as an example, the following are described:

[0102] The camera (collection device) is placed on the vehicle. The vehicle passes through the collection position S1, collection position S2, collection position S3 until the Nth collection position SN. The shooting direction of the camera is perpendicular to the direction of the vehicle's travel, corresponding to Figure 3 The angle of the tree position corresponds to 0° Figure 3 The angle corresponding to the position of the trees in the image.

[0103] A street view image is collected at each collection position, for example, street view image A is collected at collection position S1, street view image B is collected at collection position S2, and street view image C is collected at collection position S3.

[0104] By combining street view image A, street view image B, and street view image C in pairs, we obtain image combination A-B, image combination A-C, and image combination B-C.

[0105] Taking image combination A and B as an example, street view image A in the combination (street view image collected at collection position S1) is defined as the first image, and street view image B in the combination (street view image collected at collection position S2) is defined as the second image.

[0106] Identify street view image A, obtain predicted locations tr11 and tr12 of vegetation (in image combination A and B, tr11 and tr12 are first predicted geographic locations), identify street view image B, obtain predicted locations tr21 and tr22 of vegetation (in image combination A and B, predicted locations tr21 and tr22 are second predicted geographic locations).

[0107] Connect the predicted position tr11 with the collected position S1 to get a straight line , connect the predicted position tr12 with S1 and get the straight line ,straight line and straight line That is, the first straight line corresponding to the image combination A and B; the predicted position tr21 is connected with the acquisition position S2 to obtain the straight line , connect the predicted position tr22 with the collected position S2 to get the straight line ,straight line and straight line That is the second straight line corresponding to the image combination A and B.

[0108] straight line With straight line ,straight line There are intersection points O211 and O212 respectively. Since the intersection point O212 is closest to the predicted position tr21, if the distance between the intersection point O212 and the acquisition position S2 is less than the horizontal expansion distance L (the horizontal expansion distance L is the horizontal expansion distance corresponding to the predicted position tr21 and the acquisition position S2), then the intersection point O212 is the candidate intersection point corresponding to the image combination A and B. With straight line ,straight line There is also an intersection point, but this intersection point is not the closest intersection point of the line connecting S3 and S2 for the same tree, so this intersection point cannot be used as an alternative intersection point.

[0109] Similarly, for image combination B and C, a line is established between the predicted position tr22 and the acquisition position S2 to obtain the straight line ; Establish a line between the predicted position tr21 and the acquisition position S2 to obtain a straight line ; Establish a line between the predicted position tr32 and the acquisition position S3 to obtain a straight line .straight line With straight line ,straight line There are intersection points O321 and O322 respectively. Since the intersection point O322 is the closest to the predicted position tr32 (the predicted position tr32 is located on the straight line If the distance between the intersection point O322 and the acquisition position S3 is smaller than the horizontal expansion distance L (the horizontal expansion distance L is the horizontal expansion distance corresponding to the predicted position tr32 and the acquisition position S3), then the intersection point O322 is the candidate intersection point corresponding to the image combination B and C.

[0110] Using the same sampling method, the intersection point O132 is selected from the intersection point O131 and the intersection point O132 as the candidate intersection point, and the candidate intersection point corresponding to the image combination A and C is obtained.

[0111] By clustering the intersection points O212, O322, and O132 (the clustering distance is 3 meters), the vegetation target location data is obtained, that is, Figure 6 The location of the box in the figure can be considered to have vegetation. The final tree structure parameters are calculated by using the mean after removing outliers using the interquartile range method for tree height, crown height and crown diameter.

[0112] Embodiment 2 is based on embodiment 1. In this embodiment, step S200 includes the following specific steps: obtaining meteorological data, solar radiation data, land type information and digital elevation model data of the outdoor area; determining the average radiation temperature of the outdoor area based on the vegetation attribute information, the meteorological data, the solar radiation data, the land type information and the digital elevation model data.

[0113] This embodiment calculates the average radiation temperature based on any one or more of the meteorological data, solar radiation data, land type information, and digital elevation model data of the outdoor area.

[0114] like Figure 2 As shown, the average radiation temperature calculation module calculates the average radiation temperature based on meteorological data, solar radiation data, land type information, and digital elevation model data.

[0115] The resolution of meteorological data is , meteorological data includes temperature, relative humidity, wind speed, wind direction, atmospheric pressure, and rainfall data. The resolution of the digital elevation model data is 30m*30m. Land type information includes trees, grass, buildings, water bodies, bare land (exposed soil), and hard pavement. Since different types of land have different absorptivity and reflectivity to thermal radiation, they will have different effects on the environment or human body. Therefore, when calculating radiation data, considering land type information can improve the accuracy of calculation. Use ArcGIS 10.8 (ArcGIS 10.8 is the 10.8th generation global data platform) to obtain solar radiation data.

[0116] The geographical location and structural parameters of the street trees obtained in the previous stage, the digital elevation model data of the entire domain, the land use classification data, and the building height and location data obtained in the first step were split into small blocks with an overlap of 120 m using ArcGIS 10.8. The meteorological data (air temperature, relative humidity, wind speed, atmospheric pressure, rainfall) and solar radiation data of the entire domain were obtained using python and ArcGIS 10.8 to obtain the data of each small block after cutting.

[0117] The data of the above-mentioned small blocks were preprocessed using the plug-ins UMEP (UMEP, i.e., urban multi-scale environmental prediction model) and UMEP for processing (UMEP forprocessing, i.e., urban multi-scale environmental prediction processing model) of the software QGIS (QGIS, i.e., open source geographic information system) to generate sky visibility files, wall height files, and direction files. After converting other data into a format recognizable by UMEP, the average radiation temperature with a resolution of 2m*2m was calculated in the SOLWEIG (SOLWEIG, i.e., solar and long-wave environmental radiation illumination geometric model) module in UMEP; the average radiation temperature raster data of all the small blocks calculated were merged into the average radiation temperature with a resolution of 2m*2m for the entire study area in ArcGIS 10.8.

[0118] Embodiment 3, based on embodiment 1 or embodiment 2, in this embodiment, step S300 includes the following specific steps: obtaining meteorological data of the outdoor area; obtaining a general thermal climate index of the outdoor area based on the meteorological data and the average radiation temperature; obtaining a thermal comfort evaluation result of the outdoor area based on the general thermal climate index.

[0119] like Figure 2 As shown, the outdoor thermal comfort assessment module calculates the universal thermal climate index (UTCI) based on meteorological data and the average radiant temperature, and then obtains the thermal comfort evaluation result of the outdoor area based on the UTCI.

[0120] The meteorological data in this embodiment include wind speed, temperature, relative humidity. The wind speed, temperature, relative humidity and the average radiation temperature in the second embodiment are integrated into the same resolution, and the wind speed, temperature, relative humidity and average radiation temperature of the same resolution are applied to the software BioKlima 2.6 (BioKlima 2.6 is a bioclimatic and thermal physiology calculation software) to obtain UTCI of the same resolution, and the heat stress in the outdoor area is evaluated according to the original UTCI scale. When UTCI is in the range of -13 to 0, the thermal comfort evaluation result is moderate cold stress; when UTCI is in the range of 0 to 9, the thermal comfort evaluation result is mild cold stress; when UTCI is in the range of 9 to 26, the thermal comfort evaluation result is no heat stress; when UTCI is in the range of 26 to 32, the thermal comfort evaluation result is moderate heat stress; when UTCI is in the range of 32 to 38, the thermal comfort evaluation result is strong heat stress; when UTCI is in the range of 38 to 46, the thermal comfort evaluation result is very strong heat stress; when UTCI is greater than 46, the thermal comfort evaluation result is extreme heat stress.

[0121] In summary, the present invention uses high-precision multi-source heterogeneous data related to the urban thermal environment to calculate the urban average radiation temperature with high temporal and spatial resolution, and then combines it with meteorological data to quickly calculate the high-precision (2m*2m) outdoor thermal comfort at the urban scale, so as to realize effective high-precision outdoor thermal comfort detection.

[0122] This embodiment also provides an outdoor thermal comfort evaluation device, such as Figure 7 As shown, the device comprises the following components:

[0123] The information extraction module 01 is used to collect street view images of outdoor areas and extract vegetation attribute information related to outdoor temperature on the street view images;

[0124] The average radiation temperature analysis module 02 is used to determine the average radiation temperature of the outdoor area according to the vegetation attribute information;

[0125] The evaluation module 03 is used to obtain a thermal comfort evaluation result of the outdoor area according to the average radiation temperature.

[0126] Based on the above embodiments, the present invention further provides a terminal device, whose principle block diagram can be shown as follows: Figure 8As shown. The terminal device includes a processor, a memory, a network interface, and a display screen connected through a system bus. Among them, the processor of the terminal device is used to provide computing and control capabilities. The memory of the terminal device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the terminal device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an outdoor thermal comfort evaluation method is implemented. The display screen of the terminal device can be a liquid crystal display screen or an electronic ink display screen.

[0127] Those skilled in the art will understand that Figure 8 The principle block diagram shown in the figure is only a block diagram of a partial structure related to the scheme of the present invention, and does not constitute a limitation on the terminal device to which the scheme of the present invention is applied. The specific terminal device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0128] In one embodiment, a terminal device is provided. The terminal device includes a memory, a processor, and an outdoor thermal comfort evaluation program stored in the memory and executable on the processor. When the processor executes the outdoor thermal comfort evaluation program, the following operation instructions are implemented:

[0129] Collecting street view images of outdoor areas, and extracting vegetation attribute information related to outdoor temperature on the street view images;

[0130] Determining the average radiation temperature of the outdoor area according to the vegetation attribute information;

[0131] A thermal comfort evaluation result of the outdoor area is obtained according to the average radiation temperature.

[0132] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. 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 embodiments of the present invention.

Claims

1. A method for evaluating outdoor thermal comfort, characterized in that: include: Collecting street view images of outdoor areas, extracting vegetation attribute information related to outdoor temperature on the street view images, and extracting vegetation target position data on the street view images; Determining the average radiation temperature of the outdoor area according to the vegetation attribute information; According to the average radiation temperature, a thermal comfort evaluation result of the outdoor area is obtained, and the construction of street landscape facilities is guided according to the thermal comfort evaluation result to reduce the street temperature; Extracting vegetation target location data on the street view image, including: Determining a collection location of each of the street view images; Combining the street view images in pairs to obtain groups of image combinations, each group of the image combination includes a first image and a second image, and a first acquisition position of the first image and a second acquisition position of the second image are located on the same acquisition horizontal plane; For each group of the image combinations, determining a first predicted geographical location of vegetation on the first image; and determining a second predicted geographical location of vegetation on the second image; determining a horizontal distance between the second predicted position and the second acquisition position; Acquire positioning error data between a measured position and an actual position of the vegetation, and determine a horizontal expansion distance based on the positioning error data and the horizontal distance; Determine a first straight line formed by the first predicted geographical location and the first acquisition location; determine a second straight line formed by the second predicted geographical location and the second acquisition location; determine an intersection point between the first straight line and the second straight line; Filtering candidate intersection points from the intersection points according to the horizontal expansion distance and the position difference between the position of the intersection point and the second predicted position; The vegetation target position data is obtained according to the candidate intersection points corresponding to each group of the image combinations.

2. The outdoor thermal comfort evaluation method according to claim 1, characterized in that: Extracting vegetation attribute information related to outdoor temperature on the street view image, including: Extracting vegetation structure data on the street view image, and using the vegetation structure data and the vegetation target position data as vegetation attribute information.

3. The outdoor thermal comfort evaluation method according to claim 2, characterized in that: Extracting vegetation structure data on the street view image, including: The tree height, crown width, crown height and under-crown height of the trees on the street view image are extracted, and the tree height, crown width, crown height and under-crown height are used as vegetation structure data.

4. The outdoor thermal comfort evaluation method according to claim 1, characterized in that: Determining a horizontal expansion distance according to the positioning error data and the horizontal distance includes: Determine an average positioning error and a maximum positioning error in the positioning error data, wherein the average positioning error is an error of a collection device, and the maximum positioning error is a maximum error of a monocular depth estimation model, wherein the monocular depth estimation model is used to extract a depth value of the street view image, and the depth value is used to calculate the horizontal distance; A horizontal expansion distance is determined according to the maximum positioning error, the average positioning error and the horizontal distance.

5. The outdoor thermal comfort evaluation method according to claim 1, characterized in that: Determining the average radiation temperature of the outdoor area according to the vegetation attribute information includes: Acquiring meteorological data, solar radiation data, land type information and digital elevation model data of the outdoor area; The average radiation temperature of the outdoor area is determined based on the vegetation attribute information, the meteorological data, the solar radiation data, the land type information and the digital elevation model data.

6. The outdoor thermal comfort evaluation method according to claim 1, characterized in that: According to the average radiation temperature, a thermal comfort evaluation result of the outdoor area is obtained, including: Acquiring meteorological data of the outdoor area; Obtaining a general thermal climate index for the outdoor area based on the meteorological data and the average radiant temperature; A thermal comfort evaluation result of the outdoor area is obtained according to the universal thermal climate index.

7. An outdoor thermal comfort evaluation device, characterized in that: The device comprises the following components: An information extraction module is used to collect street view images of outdoor areas, extract vegetation attribute information related to outdoor temperature on the street view images, and extract vegetation target location data on the street view images; An average radiation temperature analysis module, used to determine the average radiation temperature of the outdoor area according to the vegetation attribute information; An evaluation module, used to obtain a thermal comfort evaluation result of the outdoor area according to the average radiation temperature, and guide the construction of street landscape facilities according to the thermal comfort evaluation result to reduce the street temperature; Extracting vegetation target location data on the street view image, including: Determining a collection location of each of the street view images; Combining the street view images in pairs to obtain groups of image combinations, each group of the image combination includes a first image and a second image, and a first acquisition position of the first image and a second acquisition position of the second image are located on the same acquisition horizontal plane; For each group of the image combinations, determining a first predicted geographical location of vegetation on the first image; and determining a second predicted geographical location of vegetation on the second image; determining a horizontal distance between the second predicted position and the second acquisition position; Acquire positioning error data between a measured position and an actual position of the vegetation, and determine a horizontal expansion distance based on the positioning error data and the horizontal distance; Determine a first straight line formed by the first predicted geographical location and the first acquisition location; determine a second straight line formed by the second predicted geographical location and the second acquisition location; determine an intersection point between the first straight line and the second straight line; Filtering candidate intersection points from the intersection points according to the horizontal expansion distance and the position difference between the position of the intersection point and the second predicted position; The vegetation target position data is obtained according to the candidate intersection points corresponding to each group of the image combinations.

8. A terminal device, characterized in that: The terminal device includes a memory, a processor, and an outdoor thermal comfort evaluation program stored in the memory and executable on the processor. When the processor executes the outdoor thermal comfort evaluation program, the steps of the outdoor thermal comfort evaluation method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an outdoor thermal comfort evaluation program, and when the outdoor thermal comfort evaluation program is executed by the processor, the steps of the outdoor thermal comfort evaluation method according to any one of claims 1 to 6 are implemented.

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