A method, equipment, and medium for determining the impact of reflected light from photovoltaic modules on highway slopes.

Abstract

This application provides a method, device, and medium for determining the impact of reflected light from photovoltaic (PV) modules on highway slopes, relating to the field of PV reflected light technology. The method includes: determining a target area on a pre-set map based on the highway section where PV modules are installed; determining, based on a pre-set image processing algorithm, whether potential light-sensitive targets (residential buildings, libraries, art galleries, etc.) exist within the target area; determining the processing priority of candidate objects according to a pre-set priority determination method; determining the impact degree of light-sensitive targets according to the processing priority, and identifying the target light-sensitive targets based on the impact degree; and determining the PV module deployment scheme to be optimized based on the impact degree of the reflected light from the target objects. This application can formulate personalized adjustment strategies for different degrees of light environment impact, effectively reducing the impact of PV module reflected light on sensitive targets.

Description

Technical Field

[0001] This application relates to the field of photovoltaic reflected light technology, and in particular to a method, equipment and medium for determining the influence of reflected light from photovoltaic modules on highway slopes. Background Technology

[0002] With the rapid development of the photovoltaic industry, photovoltaic modules are increasingly widely used in various scenarios. However, during the use of photovoltaic modules, reflected light may have adverse effects on the surrounding environment, especially on light-sensitive buildings such as residences, libraries, and art galleries. Currently, there is often a lack of systematic identification and assessment methods for potential light-sensitive targets in the surrounding environment when deploying photovoltaic modules. Traditional practices have not fully considered the differences in light sensitivity among different buildings, nor have they developed targeted and reasonable photovoltaic module deployment plans. This results in reflected light from photovoltaic modules potentially interfering with residents' lives, affecting the reading environment in places such as libraries, and even damaging exhibits in art galleries. This not only reduces the experience of users in surrounding buildings but also triggers a series of social and environmental problems, limiting the sustainable development of the photovoltaic industry. Summary of the Invention

[0003] To address the aforementioned technical problems, this application provides a method, equipment, and medium for determining the impact of reflected light from photovoltaic modules on highway slopes, which at least partially solves the problems existing in the prior art.

[0004] In a first aspect of this application, a method for determining the impact of reflected light from photovoltaic modules on highway slopes is provided, the method comprising:

[0005] S100, based on the highway section where the photovoltaic module is installed, determine the target area corresponding to the photovoltaic module on a preset map; wherein, the target area is the area affected by the reflected light of the photovoltaic module;

[0006] S200 determines whether there are potential light-sensitive targets within the target area based on a preset image processing algorithm;

[0007] S300, if present, determines the processing priority for each potential light-sensitive target according to a preset priority determination method;

[0008] S400: The impact of potential light environment sensitive targets is determined in descending order of processing priority, and the target light environment sensitive target is determined based on the impact of each potential light environment sensitive target; wherein, the target light environment sensitive target is a potential light environment sensitive target whose impact is greater than a preset impact threshold.

[0009] S500 determines the optimal deployment scheme of photovoltaic modules based on the impact of the target light environment sensitive target.

[0010] In a second aspect of this application, a non-transitory computer-readable storage medium is provided, wherein at least one instruction or at least one program is stored in the storage medium, and the at least one instruction or at least one program is loaded and executed by a processor to implement the aforementioned method for determining the influence of reflected light from photovoltaic modules on highway slopes.

[0011] In a third aspect of this application, an electronic device is provided, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0012] This application has at least the following beneficial effects:

[0013] The method for determining the impact of reflected light from photovoltaic modules on highway slopes provided in this application first determines the affected area (target area) of the photovoltaic modules. Then, within this target area, a preset image processing algorithm is used to determine whether potential light-sensitive targets exist. Here, potential light-sensitive targets are light-sensitive buildings, such as residences, libraries, and art galleries. The impact degree of potential light-sensitive targets is determined sequentially according to processing priority, from high to low. Higher priority, i.e., higher light sensitivity, is processed first. The impact degree of a potential light-sensitive target is the degree of influence of the reflected light from the photovoltaic modules on the target. Potential light-sensitive targets with an impact degree greater than a preset impact degree threshold are identified as target light-sensitive targets. Here, target light-sensitive targets are those where the reflected light from the photovoltaic modules has a significant impact on the potential light environment, requiring adjustment or processing of the corresponding photovoltaic modules. Finally, the photovoltaic modules corresponding to the target light environment sensitive targets need to be adjusted. Therefore, based on the impact degree of the target light environment sensitive targets, an optimized deployment scheme for the corresponding photovoltaic modules is determined. In this embodiment, potential light environment sensitive targets are determined based on a preset image processing algorithm. This method can accurately identify light-sensitive buildings such as residences, libraries, and art galleries. Compared to relying on manual experience, this greatly improves the accuracy and reliability of identification and reduces the risk of missing potential light environment sensitive targets. The impact degree of potential light environment sensitive targets is determined sequentially according to processing priority from high to low. This method fully considers the differences in light sensitivity among different buildings. Prioritizing targets with high light sensitivity ensures timely attention and processing of key areas, avoiding resource waste and poor results due to improper processing order. Potential light environment sensitive targets with an impact degree greater than a preset impact degree threshold are identified as target light environment sensitive targets, clearly filtering out buildings with a large impact from photovoltaic module reflection. This allows subsequent work to focus on key issues, avoiding excessive investment in areas with less impact and improving resource utilization efficiency. Finally, based on the impact degree of the target light environment sensitive targets, an optimized deployment scheme for the corresponding photovoltaic modules is determined. This application can formulate personalized adjustment strategies for different levels of light environment impact, effectively reduce the impact of photovoltaic module reflected light on sensitive targets, improve the quality of the surrounding environment, ensure the normal use of residents' lives and special places, and promote the sustainable development of the photovoltaic industry. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A flowchart illustrating the method for determining the influence of reflected light from photovoltaic modules on highway slopes, as provided in this application embodiment. Detailed Implementation

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0017] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0018] It should be noted that the following description covers various aspects of embodiments within the scope of the appended claims. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this application, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0019] Please refer to Figure 1 As shown, embodiments of this application provide a method for determining the impact of reflected light from photovoltaic modules on highway slopes, the method comprising:

[0020] S100, based on the highway section where the photovoltaic module is installed, determine the target area corresponding to the photovoltaic module on a preset map; wherein, the target area is the area affected by the reflected light of the photovoltaic module.

[0021] Specifically, the refractive index of semiconductor materials such as silicon and glass covers in photovoltaic modules differs from that of the surrounding air. When light enters these material surfaces from the air, some of the light is reflected. Even seemingly smooth photovoltaic module surfaces have a certain degree of roughness at the microscopic scale. These tiny undulations and unevenness cause diffuse reflection of light, resulting in some light being scattered in different directions. This can cause some interference with building appearance and urban landscape, and may even lead to light pollution. Here, the target area corresponding to the photovoltaic modules is a rectangular area with the length of the photovoltaic modules as its length and a width of 400 meters outside the highway boundary line facing due south of the photovoltaic modules.

[0022] S200 determines whether there are potential light-sensitive targets within the target area based on a preset image processing algorithm.

[0023] Specifically, after determining the target area, a preset image processing algorithm is used to determine whether there are potential light-sensitive targets within the target area. Here, potential light-sensitive targets are light-sensitive buildings, such as residences, libraries, and art galleries.

[0024] S300, if present, determines the processing priority for each potential light-sensitive target according to a preset priority determination method.

[0025] Specifically, since different potential light-sensitive targets have different sensitivities to the reflected light from photovoltaic modules, the building type of each potential light-sensitive target is obtained, and the processing priority of each potential light-sensitive target is obtained according to a preset priority mapping table. The preset priority mapping table includes each building type and its corresponding light sensitivity. The higher the light sensitivity, the higher the processing priority of the corresponding potential light-sensitive target; and vice versa.

[0026] S400, determine the impact of potential light environment sensitive targets in descending order of processing priority, and determine the target light environment sensitive target based on the impact of each potential light environment sensitive target; wherein, the target light environment sensitive target is a potential light environment sensitive target whose impact is greater than a preset impact threshold.

[0027] Specifically, the impact of potential light-sensitive targets is determined sequentially from highest to lowest processing priority. Higher priority targets, i.e., those with higher light sensitivity, are processed first. The impact of a potential light-sensitive target is the degree to which the light reflected from the photovoltaic module affects that target. Potential light-sensitive targets with an impact greater than a preset threshold are designated as target light-sensitive targets. Here, target light-sensitive targets are those where the light reflected from the photovoltaic module has a significant impact on the potential light environment, requiring adjustment or treatment of the corresponding photovoltaic module.

[0028] S500 determines the optimal deployment scheme of photovoltaic modules based on the impact of the target light environment sensitive target.

[0029] Specifically, the photovoltaic modules corresponding to the target light environment sensitive targets need to be adjusted. Therefore, based on the influence of the target light environment sensitive targets, the optimal deployment scheme of the corresponding photovoltaic modules is determined. Here, if the influence of the target light environment sensitive targets is different, the optimal deployment scheme of the corresponding photovoltaic modules will be different. The optimal deployment scheme of photovoltaic modules can be determined manually, or it can be determined according to relevant reference documents, or it can be determined according to a preset mapping table of optimal deployment of photovoltaic modules.

[0030] In this embodiment, the influence area (target area) of the photovoltaic module is first determined. Then, within this target area, a preset image processing algorithm is used to determine whether there are potential light-sensitive targets. Here, potential light-sensitive targets are light-sensitive buildings, such as residences, libraries, and art galleries. The influence degree of potential light-sensitive targets is determined sequentially according to processing priority from high to low. The higher the priority, the higher the light sensitivity, and the earlier it is processed. The influence degree of a potential light-sensitive target is the degree of influence of the light reflected from the photovoltaic module on the potential light-sensitive target. Potential light-sensitive targets with an influence degree greater than a preset influence degree threshold are identified as target light-sensitive targets. Here, target light-sensitive targets are those where the light reflected from the photovoltaic module has a significant impact on the potential light environment sensitivity, requiring adjustment or processing of the corresponding photovoltaic module. Finally, the photovoltaic modules corresponding to the target light-sensitive targets need to be adjusted. Therefore, based on the influence degree of the target light-sensitive targets, an optimized deployment scheme for the corresponding photovoltaic modules is determined. In this embodiment, potential light-sensitive targets are identified based on a preset image processing algorithm. This method can accurately identify light-sensitive buildings such as residences, libraries, and art galleries. Compared to previous methods relying on manual experience, this significantly improves the accuracy and reliability of identification and reduces the risk of missing potential light-sensitive targets. The impact of potential light-sensitive targets is determined sequentially according to processing priority, from high to low. This method fully considers the differences in light sensitivity among different buildings. Prioritizing targets with high light sensitivity ensures timely attention and processing of key areas, avoiding resource waste and poor results due to improper processing order. Potential light-sensitive targets with an impact greater than a preset impact threshold are identified as target light-sensitive targets, clearly filtering out buildings with a significant impact from photovoltaic module reflections. This allows subsequent work to focus on key issues, avoiding over-investment in less affected areas and improving resource utilization efficiency. Finally, based on the impact of the target light-sensitive targets, an optimized deployment scheme for the corresponding photovoltaic modules is determined. This embodiment can formulate personalized adjustment strategies for different degrees of light environment impact, effectively reduce the impact of photovoltaic module reflected light on sensitive targets, improve the quality of the surrounding environment, ensure the normal use of residents' lives and special places, and promote the sustainable development of the photovoltaic industry.

[0031] In one exemplary embodiment of this application, a potential light-sensitive target is determined according to the following steps:

[0032] S210, determine the building type corresponding to each building within the target area based on a preset image processing algorithm.

[0033] Specifically, the system first identifies each building within the target area based on a preset image processing algorithm, and then determines the building type corresponding to each building.

[0034] S220, based on the building type corresponding to each building in the target area and the preset list of light environment sensitive building types, determine whether each building in the target area is a potential light environment sensitive target; wherein, if the building type corresponding to any building in the target area is the same as any preset light environment sensitive building type in the preset list of light environment sensitive building types, then the building is a potential light environment sensitive target.

[0035] Specifically, the list of preset light environment sensitive building types contains several preset light environment sensitive building types; if the building type corresponding to any building in the target area is the same as any preset light environment sensitive building type in the list of preset light environment sensitive building types, then the building is a potential light environment sensitive target.

[0036] In one exemplary embodiment of this application, step S210 further includes:

[0037] S211, Based on the remote sensing image of the target area and the building recognition model, obtain the first building area identifier list J = (J1, J2, ..., J...). i , ..., J n ); i = 1, 2, ..., n; where n is the number of buildings contained in the target area; J i This is the region identifier for the building area corresponding to the i-th building within the target area in the remote sensing image of the target area; the remote sensing image of the target area is an RGB image.

[0038] Specifically, remote sensing images of the target area are acquired. These images are products of information obtained from various sensors and serve as information carriers for remotely detected targets. Information about the target object is obtained by recording its characteristics such as reflection and emission of electromagnetic waves. Furthermore, the remote sensing images of the target area are RGB images. RGB images are based on the RGB color mode, generating various colors through the variation and superposition of the three color channels: red (R), green (G), and blue (B). These three colors are called the three primary colors of light, and their mixing is similar to the superposition of the light from three red, green, and blue lamps. The brightness of the mixed colors is equal to the sum of the brightness of the three; the more they are mixed, the higher the brightness, which is an additive mixing process. The remote sensing images of the target area are then input into a building recognition model to identify the building regions corresponding to each building within the target area. Here, the building region can be the outline information of the corresponding building on the remote sensing image.

[0039] It should be noted that the building recognition model is any model that can identify the area where a building is located in a remote sensing image, which can be selected by those skilled in the art. The building recognition model has been pre-trained, and its corresponding training set and test set can be images of target areas corresponding to different photovoltaic modules that have been manually annotated.

[0040] S212, perform a masking operation on each building area contained in J on the remote sensing image of the target area, and obtain the key area with the target area after the masking operation as the area center; wherein, the target area is contained within the key area, and the area of ​​the key area is larger than the area of ​​the target area.

[0041] Specifically, each building region contained in J is masked, that is, each building region contained in J is erased on the remote sensing image. Then, the key region is obtained with the target region after the masking operation as the region center. That is, the key region is obtained by uniformly expanding outward from the target region. The target region is the region center of the key region. The target region is contained within the key region, and the area of ​​the key region is larger than the area of ​​the target region.

[0042] S213, input the remote sensing images of the key areas into the non-building facility identification model to obtain a list set of non-building facility types F = (F1, F2, ..., F j F m ); j = 1, 2, ..., m; where m is the number of types of non-building facilities included in the key area; F j F is a list of identifiers for the j-th type of non-building facility contained within the critical area. j =(F j，1 F j，2 F j，a F j，f(j) ); a = 1, 2, ..., f(j); f(j) is the number of the j-th type of non-building facility; F j，a This is the facility area identifier for the a-th and j-th type of non-building facility.

[0043] Specifically, non-building facilities in key areas are identified; here, non-building facilities can be playgrounds, parking lots, machinery, etc.; and the number of each type of non-building facilities in the key area is obtained; in this embodiment, non-building facilities are identified in the key area that is larger than the area affected by the reflected light of the photovoltaic modules (target area) in order to obtain as many non-building facilities as possible around each building in the target area, so as to help determine the building type.

[0044] It should be noted that the non-building facility identification model can be any model that can identify the types and regional outlines of non-building facilities in remote sensing images, which can be selected by those skilled in the art, and the non-building facility identification model has been pre-trained.

[0045] S214, input the remote sensing images corresponding to the key areas into the building recognition model to obtain the second building area identifier list E = (E1, E2, ..., E x , ..., E y ); x = 1, 2, ..., y; where y is the number of buildings within the key area excluding the target area; E x This is the area identifier for the x-th building within the key area (excluding the target area) on the remote sensing image of the key area.

[0046] Specifically, obtain the building area of ​​each building within the key area, excluding the target area.

[0047] S215, perform a masking operation on each non-building facility in F and each building area in E on the remote sensing image of the key area, and input the remote sensing image of the key area after the masking operation into the terrain and geomorphology feature extraction model to obtain the terrain and geomorphology features D corresponding to the key area.

[0048] Specifically, on the remote sensing image of the key area, the building area of ​​each non-building facility and each building outside the target area is erased, and the remote sensing image of the key area after the masking operation is input into the topographic feature extraction model to obtain the topographic features corresponding to the key area. This is because topographic features can affect the layout and site selection of buildings and take into account the restrictions of geological conditions on building types. Therefore, obtaining the topographic features corresponding to the key area can help determine the building type of each building in the target area.

[0049] S216, extract RGB features for each building area corresponding to J to obtain a building RGB feature list T = (T1, T2, ..., T...). i ,…,T n ); where T i The features are obtained by extracting RGB features from the remote sensing image of the i-th building contained in the target area.

[0050] Specifically, by performing RGB feature extraction on each building area corresponding to J, we can intuitively obtain the pixel distribution of different color values ​​in the image. We can also extract the contours of objects in the image through edge detection algorithms. The basic information such as the color and contour of each building area can be obtained through the features obtained by RGB feature extraction.

[0051] S217, extract spectral features for each building area corresponding to J to obtain a spectral feature list set G = (G1, G2, ..., G...). i , ..., G n ); where G iG is a list of spectral features corresponding to the i-th building within the target area; i =(G i，1 G i，2 , ..., G i，b , ..., G i，c b = 1, 2, ..., c; c is the number of spectral ranges used for spectral feature extraction; G i，b Let b be the spectral feature corresponding to the i-th building contained in the target area; each spectral feature has a corresponding spectral range; any two spectral features have different spectral ranges.

[0052] Specifically, for RGB images, feature extraction at different spectral ranges yields different features. Extracting features separately across multiple spectral ranges allows for a complete representation of the image's true color information. By combining the red, green, and blue channels, the colors of various objects in the image can be accurately reproduced. Compared to single-spectral-range extraction, multispectral extraction provides richer and more accurate color features, making the image appear more natural and realistic. Furthermore, the relationships between different spectral ranges can be utilized to identify objects. For example, by comparing the color values ​​in the red, green, and blue channels, different types of objects can be distinguished. Multispectral extraction enhances the ability to identify and differentiate different objects in complex scenes.

[0053] S218, Based on D, T, G, and F, obtain the target feature vector list M = (M1, M2, ..., M...). i M n ); where M i M is the target feature vector corresponding to the i-th building within the target area; i =(D,T) i G i ,(f(1),L(1,i)),(f(2),L(2,i)),…,(f(j),L(j,i)),…,(f(m),L(m,i)));L(j,i) is the average distance from the j-th non-building facility to the i-th building contained in the target area.

[0054] Specifically, based on the aforementioned features, a target feature vector is obtained for each building. This vector includes the topographic features of the corresponding key area, the building's color, basic outline features, and characteristics of the building under different spectra. Additionally, the target feature vector contains the number of non-building facilities of each category surrounding the building within the corresponding key area, as well as the average distance from each category of non-building facility to the building. In summary, the target feature vector includes information such as the building's color and outline, the topographic features of the area where the building is located, and the distribution of non-building facilities surrounding the building. The average distance can be obtained from the distance between the geometric center of each non-building facility of the same category on the remote sensing image and the geometric center of the corresponding building on the remote sensing image.

[0055] S219, Based on M and the building type classification model, obtain the classification result list B = (B1, B2, ..., B...). i B n ); where B i The building type is the i-th building within the target area.

[0056] Specifically, the target feature vector of each constructed building is input into the building type classification model to obtain the building type to which each building belongs. Here, building types include preset light environment sensitive building types (residential buildings, libraries, etc.) and non-preset light environment sensitive building types (some factories, etc.); that is, the building type can determine whether the corresponding building is a potential light environment sensitive target, i.e., whether further processing is required.

[0057] In this embodiment, a target feature vector is obtained for each building. This vector includes the topographic features of the corresponding key area, the building's color, basic outline features, and features under different spectra. Additionally, the target feature vector contains the number of non-building facilities of each category surrounding the building within the corresponding key area, as well as the average distance from each category of non-building facility to the building. In summary, the target feature vector includes information such as the building's color and outline, the topographic features of the area where the building is located, and the distribution of non-building facilities around the building. This embodiment includes not only the building's basic features, such as color and outline, but also the topographic features of the area where the building is located, and the types and distances of surrounding non-building facilities, describing the building from multiple perspectives to comprehensively reflect the characteristics of the building and its environment. Furthermore, by using features under different spectra and information such as the number and distance of non-building facilities within the key area, the description of the building is further refined, making the feature vector richer and more detailed, which helps to more accurately identify building types. This embodiment comprehensively considers multiple factors, enabling more accurate differentiation between different types of buildings. For example, a library may have a specific association with surrounding cultural facilities or green spaces, while residential areas may have different distributions of surrounding facilities and topographical requirements. This detailed feature information helps classification models better capture the differences between different building types, thereby improving classification accuracy. Furthermore, multi-dimensional feature vectors can reduce the possibility of misclassification due to similarity in a single feature. Even if some buildings have similar exterior colors or outlines, differences in their terrain, surrounding facilities, etc., can serve as important distinguishing factors, enabling classification models to more accurately determine building types.

[0058] In one exemplary embodiment of this application, M i =(D,T) i G i ,(f(1),Z(1,i)),(f(2),Z(2,i)),…,(f(j),Z(j,i)),…,(f(m),Z(m,i)));Z(j,i) is the shortest distance from the j-th non-building facility to the i-th building contained in the target area.

[0059] In this embodiment, the average distance from each category of non-building facilities to the building contained in the target feature vector is replaced with the shortest distance, which better reflects the non-building facilities of each category that are closest to each building. This allows for a more accurate acquisition of the types of non-building facilities around each building and their closest distances.

[0060] In one exemplary embodiment of this application, after step S211, the method further includes:

[0061] S001, Based on the remote sensing image of the target area and the primary building type classification model, the primary classification result CB = (CB1, CB2, ..., CB) is obtained. i , ..., CB n ); among which, CB i This represents the initial classification result for the i-th building within the target area.

[0062] S002, if each primary classification result in CB indicates that the confidence level of each building type is less than the preset confidence threshold, then proceed to step S212.

[0063] In this embodiment, a preliminary classification is first performed using an initial building type classification model with lower precision. If classification fails, a higher precision building type classification model is then used to obtain the building type for each building. If the initial building type classification model with lower precision successfully classifies some buildings, the subsequent steps are skipped. Therefore, setting up an initial building type classification model for preliminary classification can save the model's computational power and the time required to determine the building type to some extent.

[0064] In one exemplary embodiment of this application, the influence of a potential light-sensitive target includes the continuous residence time of reflected light and the brightness of reflected light; the preset influence threshold meets the following conditions:

[0065] When the angle between the reflected light from the photovoltaic module and the horizontal plane is [0°, 45°], the threshold for the continuous residence time of the reflected light is 30 minutes.

[0066] When the angle between the reflected light from the photovoltaic module and the horizontal plane is [0°, 30°], the reflected light brightness threshold is 3000 cd / m². 2 .

[0067] The target light environment sensitive target is a potential light environment sensitive target whose continuous residence time of reflected light and / or reflected light brightness is greater than the corresponding preset influence threshold.

[0068] Specifically, if any of the above conditions are not met, the target is considered a light environment sensitive target.

[0069] In an exemplary embodiment of this application, an electronic device capable of implementing the above-described method is also provided.

[0070] Those skilled in the art will understand that various aspects of this application can be implemented as a system, method, or program product. Therefore, various aspects of this application can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, collectively referred to herein as a "circuit," "module," or "system."

[0071] An electronic device according to this embodiment of the present application. The electronic device is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.

[0072] Electronic devices are manifested in the form of general-purpose computing devices. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and buses connecting different system components (including memory and processor).

[0073] The memory stores program code that can be executed by a processor, causing the processor to perform the steps described in the "Exemplary Methods" section above, according to various exemplary embodiments of this application.

[0074] The storage may include readable media in the form of volatile storage, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0075] The storage may also include programs / utilities having a set (at least one) of program modules, including but not limited to: an operating system, one or more applications, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0076] A bus can represent one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus that uses any of the various bus architectures.

[0077] The electronic device can also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication can be achieved through input / output (I / O) interfaces. Furthermore, the electronic device can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. As shown in the figure, the network adapter communicates with other modules of the electronic device via a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0078] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the method according to the embodiments of this application.

[0079] In exemplary embodiments of this application, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible implementations, various aspects of this application may also be implemented as a program product including program code, which, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of this application described in the "Exemplary Methods" section above.

[0080] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0081] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0082] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0083] Program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0084] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this application, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0085] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0086] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for determining the impact of reflected light from photovoltaic modules on highway slopes, characterized in that, The method includes: S100, based on the highway section where the photovoltaic module is installed, determine the target area corresponding to the photovoltaic module on a preset map; wherein, the target area is the area affected by the reflected light of the photovoltaic module; S200 determines whether there are potential light-sensitive targets within the target area based on a preset image processing algorithm; S300, if present, determines the processing priority for each potential light-sensitive target according to a preset priority determination method; S400: The impact of potential light environment sensitive targets is determined in descending order of processing priority, and the target light environment sensitive target is determined based on the impact of each potential light environment sensitive target; wherein, the target light environment sensitive target is a potential light environment sensitive target whose impact is greater than a preset impact threshold. S500 determines the optimal deployment scheme of photovoltaic modules based on the impact of the target light environment sensitive target; Potential light-sensitive targets are identified using the following steps: S210, determine the building type corresponding to each building within the target area based on a preset image processing algorithm; S220, based on the building type corresponding to each building in the target area and the preset list of light environment sensitive building types, determine whether each building in the target area is a potential light environment sensitive target; wherein, if the building type corresponding to any building in the target area is the same as any preset light environment sensitive building type in the preset list of light environment sensitive building types, then the building is a potential light environment sensitive target. Step S210 also includes: S211, Based on the remote sensing image of the target area and the building recognition model, obtain the first building area identifier list J = (J1, J2, ..., J...). i , ..., J n ); i = 1, 2, ..., n; where n is the number of buildings contained in the target area; J i This is the region identifier for the building area corresponding to the i-th building within the target area in the remote sensing image of the target area; the remote sensing image of the target area is an RGB image; S212, On the remote sensing image of the target area, each building area contained in J is masked, and the key area is obtained with the masked target area as the center; wherein, the target area is contained within the key area, and the area of ​​the key area is larger than the area of ​​the target area. S213, input the remote sensing images of the key areas into the non-building facility identification model to obtain a list set of non-building facility types F=(F1, F2, ..., F j F m ); j=1,2,…,m; where m is the number of types of non-building facilities contained in the key area; F j F is a list of identifiers for the j-th type of non-building facility contained within the critical area. j =(F j，1 F j，2 F j，a F j，f(j) ); a = 1, 2, ..., f(j); f(j) is the number of the j-th type of non-building facility; F j，a This is the facility area identifier for the a-th and j-th type of non-building facility; S214, Input the remote sensing images corresponding to the key areas into the building recognition model to obtain the second building area identifier list E=(E1, E2, ..., E x , ..., E y ); x = 1, 2, ..., y; where y is the number of buildings within the key area excluding the target area; E x The region identifier for the building region corresponding to the x-th building within the key region (excluding the target region) on the remote sensing image of the key region. S215, perform a masking operation on each non-building facility in F and each building area in E on the remote sensing image of the key area, and input the remote sensing image of the key area after the masking operation into the topographic feature extraction model to obtain the topographic feature D corresponding to the key area. S216, extract RGB features for each building area corresponding to J to obtain a building RGB feature list T=(T1, T2, ..., T i ,…,T n ); where T i The features are obtained by extracting RGB features from the remote sensing image of the i-th building contained in the target area; S217, extract spectral features for each building area corresponding to J to obtain a spectral feature list set G=(G1, G2, ..., G... i , ..., G n ); where G i G is a list of spectral features corresponding to the i-th building within the target area; i =(G i，1 G i，2 , ..., G i，b , ..., G i，c b = 1, 2, ..., c; c is the number of spectral ranges used for spectral feature extraction; G i，b Let b be the spectral feature corresponding to the i-th building within the target area; each spectral feature has a corresponding spectral range; any two spectral features have different spectral ranges. S218, Based on D, T, G, and F, obtain the target feature vector list M = (M1, M2, ..., M...). i M n ); where M i M is the target feature vector corresponding to the i-th building within the target area; i =(D,T i G i ,(f(1),L(1,i)),(f(2),L(2,i)),…,(f(j),L(j,i)),…,(f(m),L(m,i)));L(j,i) is the average distance from the j-th non-building facility to the i-th building contained in the target area; S219, Based on M and the building type classification model, obtain the classification result list B=(B1, B2, ..., B i B n ); where B i The building type is the i-th building within the target area.

2. The method for determining the influence of reflected light from photovoltaic modules on highway slopes according to claim 1, characterized in that, The target area corresponding to the photovoltaic modules is a rectangular area with the length of the photovoltaic modules as the length and the width as 400 meters outside the boundary of the highway land on the south side facing the photovoltaic modules.

3. The method for determining the influence of reflected light from photovoltaic modules on highway slopes according to claim 1, characterized in that, M i =(D,T i G i ,(f(1),Z(1,i)),(f(2),Z(2,i)),…,(f(j),Z(j,i)),…,(f(m),Z(m,i)));Z(j,i) is the shortest distance from the j-th non-building facility to the i-th building contained in the target area.

4. The method for determining the influence of reflected light from photovoltaic modules on highway slopes according to claim 1, characterized in that, After step S211, the method further includes: S001, Based on the remote sensing image of the target area and the primary building type classification model, the primary classification result CB=(CB1, CB2, ..., CB) is obtained. i , ..., CB n ); among which, CB i This represents the initial classification result for the i-th building within the target area; the accuracy of the initial building type classification model is lower than that of the building type classification model. S002, if each primary classification result in CB indicates that the confidence level of each building type is less than the preset confidence threshold, then proceed to step S212.

5. The method for determining the influence of reflected light from photovoltaic modules on highway slopes according to any one of claims 1-4, characterized in that, The impact of potential light-sensitive targets includes the continuous residence time and intensity of reflected light; the preset impact threshold meets the following conditions: When the angle between the reflected light from the photovoltaic module and the horizontal plane is [0°, 45°], the threshold for the continuous residence time of the reflected light is 30 minutes. When the angle between the reflected light from the photovoltaic module and the horizontal plane is [0°, 30°], the reflected light luminance threshold is 3000 cd / m². 2 .

6. The method for determining the influence of reflected light from photovoltaic modules on highway slopes according to claim 5, characterized in that, The target light environment sensitive target is a potential light environment sensitive target whose continuous residence time of reflected light and / or reflected light brightness is greater than the corresponding preset influence threshold.

7. A non-transitory computer-readable storage medium, wherein at least one instruction is stored therein, characterized in that, The at least one instruction is loaded and executed by the processor to implement the method as described in any one of claims 1-6.

8. An electronic device, characterized in that, Includes a processor and the non-transitory computer-readable storage medium as described in claim 7.

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