A method, equipment, and medium for determining measures to mitigate the impact of reflected light from photovoltaic modules to be optimized.

Abstract

This application provides a method, apparatus, and medium for determining measures to mitigate the impact of reflected light from a photovoltaic module to be optimized, relating to the field of photovoltaic reflected light technology. The method includes: acquiring basic data of the photovoltaic module to be optimized; wherein the photovoltaic module to be optimized is a photovoltaic module whose reflected light has an impact greater than a preset threshold on any light-sensitive target in the surrounding environment; acquiring data of the target light-sensitive target corresponding to the photovoltaic module to be optimized; wherein the target light-sensitive target is a light-sensitive target whose impact of reflected light from the photovoltaic module to be optimized is greater than a preset threshold; and determining a target mitigation measure from candidate mitigation measures based on the basic data and the target light-sensitive target data; wherein the target mitigation measure is used to mitigate the impact on the photovoltaic module to be optimized. This application comprehensively considers data from both aspects to determine the target mitigation measure from candidate mitigation measures, significantly improving the effectiveness of the selected target mitigation measure.

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 measures to prevent and control the impact of reflected light on photovoltaic modules to be optimized. Background Technology

[0002] With the widespread adoption of solar energy as a clean and renewable energy source globally, the deployment scale of photovoltaic (PV) modules continues to expand. During the process of converting solar energy into electricity, PV modules inevitably generate reflected light. This reflected light can adversely affect light-sensitive targets in the surrounding environment, such as buildings. When the impact of reflected light on these sensitive targets exceeds a preset threshold, optimization of the PV modules is necessary.

[0003] Currently, while the impact of reflected light on sensitive targets in the surrounding light environment has been recognized in photovoltaic module optimization research, there is still a significant research gap in determining the data selection for optimization. This results in a lack of effective data guidance for optimization work, making it difficult to achieve the desired results. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a method, equipment, and medium for determining measures to mitigate the impact of reflected light from photovoltaic modules that need to be optimized, thereby at least partially solving the problems existing in the prior art.

[0005] In a first aspect of this application, a method is provided for determining measures to mitigate the impact of reflected light from photovoltaic modules to be optimized, the method comprising:

[0006] S100, Obtain the basic data of the photovoltaic module to be optimized; wherein, the photovoltaic module to be optimized is the photovoltaic module whose reflected light has an impact on any light-sensitive target in the surrounding light environment greater than a preset threshold.

[0007] S200, acquire data of the target light environment sensitive target corresponding to the photovoltaic module to be optimized; wherein, the target light environment sensitive target is the light environment sensitive target whose influence of reflected light from the photovoltaic module to be optimized is greater than a preset threshold;

[0008] S300, based on basic data and data on the target light environment sensitive target, determines the target prevention and control measures from the candidate prevention and control measures; among them, the target prevention and control measures are used to prevent and control the photovoltaic modules to be optimized.

[0009] 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 measures to prevent the influence of reflected light on photovoltaic modules to be optimized.

[0010] In a third aspect of this application, an electronic device is provided, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0011] This application has at least the following beneficial effects:

[0012] The method for determining the mitigation measures for reflected light from photovoltaic modules to be optimized provided in this application not only determines the mitigation measures based on the basic data of the photovoltaic modules to be optimized themselves, but also refers to the data of the target light environment sensitive targets affected by the photovoltaic modules to be optimized. By comprehensively considering the data from both aspects, the target mitigation measures are determined from the candidate mitigation measures, which significantly improves the effectiveness of the selected target mitigation measures. Attached Figure Description

[0013] 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.

[0014] Figure 1 A flowchart illustrating a method for determining measures to mitigate the impact of reflected light from photovoltaic modules, as provided in this application embodiment. Detailed Implementation

[0015] 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.

[0016] 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.

[0017] 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.

[0018] Please refer to Figure 1 As shown, embodiments of this application provide a method for determining measures to mitigate the impact of reflected light from photovoltaic modules to be optimized, the method comprising:

[0019] S100, Obtain basic data of the photovoltaic module to be optimized; wherein, the photovoltaic module to be optimized is the photovoltaic module whose reflected light has an impact greater than a preset threshold on any light-sensitive target in the surrounding environment.

[0020] 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 landscapes, and may even lead to light pollution. The photovoltaic modules to be optimized are those whose reflected light has an impact on any light-sensitive target in the surrounding environment that exceeds a preset threshold.

[0021] It should be noted that the impact of any light-sensitive target includes the continuous residence time and brightness of reflected light; the preset threshold meets the following conditions:

[0022] 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.

[0023] 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 .

[0024] If any of the above conditions are not met, the impact is considered to be greater than the preset threshold.

[0025] The basic data for photovoltaic modules to be optimized may include: the size data of the photovoltaic modules, the type data of the photovoltaic modules, and the angle data of the photovoltaic modules.

[0026] It is understood that the photovoltaic module to be optimized has a corresponding target area, which is the area affected by the reflected light from the photovoltaic module. In one embodiment, the target area corresponding to the photovoltaic module is a rectangular area with the length of the photovoltaic module as the length and the width as 400 meters outside the highway land boundary line facing due south of the photovoltaic module.

[0027] S200, acquire data of the target light environment sensitive target corresponding to the photovoltaic module to be optimized; wherein, the target light environment sensitive target is the light environment sensitive target whose influence of the reflected light of the photovoltaic module to be optimized is greater than a preset threshold.

[0028] Specifically, the preset threshold meets the following conditions:

[0029] 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.

[0030] 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 .

[0031] A target light environment sensitive target is a light environment sensitive target whose continuous residence time of reflected light and / or reflected light brightness is greater than the corresponding preset threshold.

[0032] In other words, any light-sensitive target that does not meet any of the above conditions is considered a target light-sensitive target.

[0033] The data for targets sensitive to light environments includes building data, surrounding environment data, and light and shadow data; the data for targets sensitive to light environments is obtained through the following steps:

[0034] S210, acquire images of the target light environment sensitive target at different preset acquisition angles and different preset acquisition distances to obtain a building data list set JT = (JT1, JT2, ..., JT...). i , ..., JT n ); i = 1, 2, ..., n; where n is the number of preset acquisition angles corresponding to the building feature data; JT i For the list of building data obtained at the i-th preset acquisition angle; JT i =(JT i，1 JT i，2 , ..., JT i，a , ..., JT i，b ); a = 1, 2, ..., b; b is the preset number of distances; JT i，a The building image is obtained at the position corresponding to the a-th preset distance from the i-th preset acquisition angle.

[0035] Specifically, the target light environment sensitive target in this application refers to a building. By acquiring images of the target light environment sensitive target at different preset acquisition angles and different preset acquisition distances, i.e., acquiring images of the building target at multiple angles and different distances, more comprehensive features of the target light environment sensitive target can be obtained.

[0036] S220: Acquire remote sensing images of the environment surrounding the target light environment sensitive target to obtain building surrounding environment data WT.

[0037] S230, acquire the light and shadow data GT of the target light environment sensitive target; wherein, the light and shadow feature data includes the continuous residence time data of reflected light and the brightness data of reflected light.

[0038] S300, based on basic data and data on the target light environment sensitive target, determines the target prevention and control measures from the candidate prevention and control measures; among them, the target prevention and control measures are used to prevent and control the photovoltaic modules to be optimized.

[0039] Specifically, based on basic data and data on the target light environment sensitive targets, target prevention and control measures are determined from the candidate prevention and control measures; among them, the target prevention and control measures are used to prevent and control the photovoltaic modules to be optimized.

[0040] This embodiment not only determines prevention and control measures based on the basic data of the photovoltaic module to be optimized itself, but also refers to the data of the target light environment sensitive target affected by the photovoltaic module to be optimized. By comprehensively considering the data from these two aspects, the target prevention and control measures are determined from the candidate prevention and control measures, which significantly improves the effectiveness of the selected target prevention and control measures.

[0041] In one exemplary embodiment of this application, step S300 includes:

[0042] S310: Extract and concatenate the features of the basic data of the photovoltaic module to be optimized and the data of each target light environment sensitive target to obtain the target feature vector MT.

[0043] Specifically, the basic data of the photovoltaic module to be optimized and the data of each target light environment sensitive target are extracted and stitched together to obtain the stitched target feature vector MT.

[0044] S320, Based on MT and the preset feature vector corresponding to each preset prevention and control measure in a number of preset prevention and control measures, obtain the first key prevention and control measure list YG = (YG1, YG2, ..., YG...). j YG m ); j = 1, 2, ..., m; where m is the number of first critical prevention and control measures; YG jLet YMP be the preset feature vector corresponding to the j-th first key prevention and control measure; wherein, the matching degree between any first key prevention and control measure and MT is greater than a preset matching degree threshold; each preset prevention and control measure has a corresponding list of prevention and control sub-measures. j It meets the following characteristics: YMP j =(YG j ·MT) / (|YG j |×|MT|).

[0045] Specifically, each preset prevention and control measure has a corresponding list of sub-measures; that is, each preset prevention and control measure has multiple specific sub-measures. Each preset prevention and control measure also has a corresponding preset feature vector, which is obtained by extracting and concatenating features from the historical basic data of the photovoltaic module to be optimized and the data of each target light environment sensitive target. If the matching degree between the preset feature vector corresponding to the preset prevention and control measure and the MT is higher, it indicates that the preset prevention and control measure is more suitable for the photovoltaic module to be optimized. Conversely, if the matching degree between the preset feature vector corresponding to the preset prevention and control measure and the MT is lower, it indicates that the preset prevention and control measure is less suitable for the photovoltaic module to be optimized. In this embodiment, the key prevention and control measure is the preset prevention and control measure with a matching degree greater than a preset matching degree threshold. That is, at least one preset prevention and control measure that is relatively suitable for the photovoltaic module to be optimized is selected from several preset prevention and control measures.

[0046] S330, Based on YG, the first prevention and control sub-measures list YZ = (YZ1, YZ2, ..., YZ) is obtained. x , ..., YZ y ); x = 1, 2, ..., y; where y is the number of duplicate first prevention measures contained in YG; YZ x This is the measure identifier corresponding to the xth first prevention and control sub-measure after deduplication of all first prevention and control sub-measures contained in YG.

[0047] Specifically, after identifying at least one critical prevention measure, since each critical prevention measure has a corresponding list of sub-measures, duplicates are removed from all the sub-measures corresponding to all critical prevention measures to obtain the first list of sub-measures. Here, deduplication is performed because different pre-designed prevention measures may have one or more identical sub-measures.

[0048] S340, Input MT and YZ into the measure selection model to obtain a preset number of first target prevention and control sub-measures; wherein, the preset number of first target prevention and control sub-measures constitute the target prevention and control measures.

[0049] Specifically, the measure selection model is used to further select a more suitable preset number of prevention and control sub-measures from among many prevention and control sub-measures based on the input vector, so as to obtain a preset number of first target prevention and control sub-measures to optimize the prevention and control measures to be optimized.

[0050] In this embodiment, feature vectors are constructed by extracting features from basic data and data of the target light environment sensitive targets. These vectors are then matched with the preset feature vectors corresponding to each preset prevention and control measure. After selecting several key prevention and control measures, all prevention and control sub-measures corresponding to these key measures are further deduplicated and further filtered. The resulting preset number of first target prevention and control sub-measures are more targeted to the component to be optimized, resulting in better prevention and control effects.

[0051] In one exemplary embodiment of this application, step S300 includes:

[0052] S350, respectively extract features from the basic data of the photovoltaic module to be optimized and the data of each target light environment sensitive target to obtain the target feature list BZ = (CTZ, JTZ, WTZ, GTZ); where CTZ is the feature obtained by extracting features from the basic data of the photovoltaic module to be optimized; JTZ is the feature obtained by extracting features from the building data; WTZ is the feature obtained by extracting features from the building surrounding environment data; and GTZ is the feature obtained by extracting features from the light and shadow data.

[0053] Specifically, in this implementation, feature extraction is performed on the basic data of the photovoltaic module to be optimized and the data of each target light environment sensitive target.

[0054] S360, based on BZ and the preset feature vector corresponding to each preset prevention and control measure in a number of preset prevention and control measures, obtain the second key prevention and control measure list EG = (EG1, EG2, ..., EG2). p , ..., EG q ); p = 1, 2, ..., q; where q is the number of second critical prevention measures; EG p Let EG be the preset feature vector corresponding to the p-th second key prevention and control measure; p =(CTG p JTG p WTG p GTG p ); CTG p This refers to the pre-defined basic data characteristics of the photovoltaic module corresponding to the p-th second key prevention and control measure; JTG p This refers to the preset building data features corresponding to the p-th second key prevention and control measure; WTG p This refers to the preset building surrounding environment data characteristics corresponding to the p-th second key prevention and control measure; GTG pThe preset light and shadow data features are defined for the p-th second key prevention and control measure; each preset prevention and control measure has a corresponding list of prevention and control sub-measures; the comprehensive matching degree EGP between BZ and the p-th second key prevention and control measure is defined. p It meets the following characteristics:

[0055] EGP p =α1×CP p +α2×JP p +α3×WP p +α4×GP p ;

[0056] Wherein, α1 is the weight of photovoltaic basic features; α2 is the weight of building features; α3 is the weight of building surrounding environment features; α4 is the weight of light and shadow features; CP p For CTZ and CTG p The degree of matching between them; JP p For JTZ and JTG p The degree of matching between them; WP p For WTZ and WTG p The degree of matching between them; GP p For GTZ and GTG p The degree of matching between them.

[0057] S370, Based on EG, the second list of prevention and control measures EZ = (EZ1, EZ2, ..., EZ...) is obtained. c , ..., EZ d ); c = 1, 2, ..., d; where d is the number of duplicate second prevention measures included in EG; EZ c This is the measure identifier corresponding to the c-th second prevention and control sub-measure after deduplication of all second prevention and control sub-measures contained in EG.

[0058] S380, Input BZ and EZ into the measure selection model to obtain a preset number of second target prevention and control sub-measures; wherein, the preset number of second target prevention and control sub-measures constitute the target prevention and control measures.

[0059] In this embodiment, each feature (features extracted from the basic data of the photovoltaic module to be optimized and preset features of the basic photovoltaic module data, features extracted from building data and preset features of building data, features extracted from the surrounding environment data and preset features of the surrounding environment data, and features extracted from light and shadow data and preset features of light and shadow data) is matched individually, and a comprehensive matching degree is obtained based on the weight corresponding to each feature. This allows for easy adjustment of the weight corresponding to each feature according to its importance to the final matching degree determination, making the obtained comprehensive matching degree more reasonable. This ensures that the final determined target prevention and control measures are more suitable for the module to be optimized.

[0060] In an exemplary embodiment of this application, α1, α2, α3 and α4 respectively satisfy the following conditions: α1=β1 / S; where S=β1+β2+β3+β4; β1 is the unnormalized photovoltaic foundation feature weight; β2 is the unnormalized building feature weight; β3 is the unnormalized building surrounding environment feature weight; β4 is the unnormalized light and shadow feature weight; α2=β2 / S; α3=β3 / S; α4=β4 / S.

[0061] Specifically, after determining the corresponding weight for each feature, each weight is normalized to make the obtained weights more accurate and better reflect the importance of the corresponding feature in determining the matching degree.

[0062] And β1 is determined according to the following steps:

[0063] S361, β1 is determined according to CTZ and the preset basic feature weight mapping table; wherein, the preset basic feature weight mapping table contains the basic feature vectors of several key photovoltaic modules and the preset basic feature weights corresponding to each key photovoltaic module; β1 is the preset basic feature weight of the key photovoltaic module with the highest matching degree between the corresponding basic feature vector and CTZ; the key photovoltaic module is the photovoltaic module to be optimized within the historical time window; the preset basic feature weight is an integer between [1, 10].

[0064] Specifically, within a historical time window, there are several photovoltaic modules that need to be optimized, namely key photovoltaic modules. Each key photovoltaic module has a corresponding preset basic feature weight. That is, according to the preset basic feature weight mapping table, the preset basic feature weight corresponding to the key photovoltaic module with the highest matching degree with CTZ is determined as β1.

[0065] β2 is determined according to the following steps:

[0066] S362, β2 is determined according to JTZ and the preset building feature weight mapping table; wherein, the preset building feature weight mapping table contains building feature vectors of several key light environment sensitive targets and preset building feature weights corresponding to each key light environment sensitive target; β1 is the preset building feature weight corresponding to the key light environment sensitive target with the highest matching degree between the corresponding building feature vector and JTZ; the key light environment sensitive targets are the target light environment sensitive targets within the historical time window; the preset building feature weights are integers between [1, 10].

[0067] Specifically, within a historical time window, there are several key light environment sensitive targets (i.e., target light environment sensitive targets within the historical time window), and each key light environment sensitive target has a corresponding preset building feature weight. That is, according to the preset building feature weight mapping table, the preset building feature weight corresponding to the key light environment sensitive target with the highest matching degree with JTZ is determined as β2.

[0068] β3 is determined according to the following steps:

[0069] S363, obtain the building density ρ within the target area of ​​the photovoltaic module to be optimized;

[0070] S364, β3 is determined according to ρ and the preset building surrounding environment feature weight mapping table; wherein, the preset building surrounding environment feature weight mapping table contains several preset building density ranges and the preset building surrounding environment feature weights corresponding to each preset building density range; β3 is the preset building surrounding environment feature weight corresponding to the preset building density range to which ρ belongs; the preset building surrounding environment feature weights are integers between [1, 10].

[0071] Specifically, a higher building density ρ indicates a greater number of buildings in the target area for the photovoltaic modules to be optimized. This increases the probability and the likelihood of a larger number of light-sensitive targets appearing. In this embodiment, a higher building density ρ results in a higher β3; conversely, a lower building density ρ results in a lower β3.

[0072] β4 is determined according to the following steps:

[0073] S365, obtain the over-limit value CX of the target light environment sensitive target when it meets the preset conditions; where CX = (FZ-30) + (FL-3000) / 100; where FZ is the continuous residence time of the reflected light corresponding to the target light environment sensitive target; FL is the brightness of the reflected light corresponding to the target light environment sensitive target;

[0074] S366, β4 is determined according to CX and the preset light and shadow feature weight mapping table; wherein, the preset light and shadow feature weight mapping table contains several preset overlimit value ranges and preset light and shadow feature weights corresponding to each overlimit value range; β4 is the preset light and shadow feature weight corresponding to the preset overlimit value range to which CX belongs; the preset light and shadow feature weights are integers between [1, 10].

[0075] Specifically, meeting the preset conditions means that when the angle between the photovoltaic module's reflected light and the horizontal plane is [0°, 45°], the threshold for continuous residence time of reflected light is 30 minutes; and when the angle between the photovoltaic module's reflected light and the horizontal plane is [0°, 30°], the threshold for reflected light brightness is 3000 cd / m². 2The system obtains the out-of-limit values ​​of the target light environment sensitive target when it meets preset conditions. Specifically, it first obtains the out-of-limit values ​​for the continuous residence time of reflected light and the brightness of reflected light. To unify the data dimensions, (FL-3000) / 100 is used. The more out-of-limit values, the greater the influence of light and shadow on the target light environment sensitive target. Therefore, in this embodiment, the more out-of-limit values, the greater the influence of the light and shadow feature weight on the matching degree, and the larger β4 becomes; conversely, the fewer out-of-limit values, the smaller β4 becomes.

[0076] In an exemplary embodiment of this application, an electronic device capable of implementing the above-described method is also provided.

[0077] 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."

[0078] 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.

[0079] 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).

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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 measures to mitigate the impact of reflected light from photovoltaic modules to be optimized, characterized in that, The method includes: S100, Obtain the basic data of the photovoltaic module to be optimized; wherein, the photovoltaic module to be optimized is the photovoltaic module whose reflected light has an impact on any light-sensitive target in the surrounding light environment greater than a preset threshold. S200, acquire data of the target light environment sensitive target corresponding to the photovoltaic module to be optimized; wherein, the target light environment sensitive target is the light environment sensitive target whose influence of reflected light from the photovoltaic module to be optimized is greater than a preset threshold; S300, based on the basic data and the data of the target light environment sensitive target, determine the target prevention and control measures from the candidate prevention and control measures; wherein, the target prevention and control measures are used to prevent and control the photovoltaic modules to be optimized; step S300 includes: S310: Extract and concatenate the features of the basic data of the photovoltaic module to be optimized and the data of each target light environment sensitive target to obtain the target feature vector MT. S320, Based on MT and the preset feature vector corresponding to each preset prevention and control measure in a number of preset prevention and control measures, obtain the first key prevention and control measure list YG=(YG1, YG2, ..., YG...). j YG m ); j=1, 2, ..., m; where m is the number of first critical prevention and control measures; YG j Let be the preset feature vector corresponding to the j-th first key prevention and control measure; wherein, the matching degree between any first key prevention and control measure and MT is greater than the preset matching degree threshold; each preset prevention and control measure has a corresponding list of prevention and control sub-measures; S330, Based on YG, obtain the first prevention and control sub-measures list YZ=(YZ1, YZ2, ..., YZ...). x , ..., YZ y ); x = 1, 2, ..., y; where y is the number of all first prevention and control measures in YG after deduplication; YZ x The measure identifier is the x-th first prevention and control sub-measure after deduplication of all first prevention and control sub-measures contained in YG; S340, Input MT and YZ into the measure selection model to obtain a preset number of first target prevention and control sub-measures; wherein, the preset number of first target prevention and control sub-measures constitute the target prevention and control measures.

2. The method for determining the measures to mitigate the impact of reflected light from photovoltaic modules according to claim 1, characterized in that, Data for targets sensitive to light environments includes building data, data on the surrounding environment, and light and shadow data; this data is obtained through the following steps: S210, acquire images of the target light environment sensitive target at different preset acquisition angles and different preset acquisition distances to obtain a building data list set JT=(JT1, JT2, ..., JT... i , ..., JT n ); i = 1, 2, ..., n; where n is the number of preset acquisition angles corresponding to the building feature data; JT i For the list of building data obtained at the i-th preset acquisition angle; JT i =(JT i，1 JT i，2 , ..., JT i，a , ..., JT i，b ); a = 1, 2, ..., b; b is the preset number of distances; JT i，a The building image is obtained at the position corresponding to the a-th preset distance from the i-th preset acquisition angle; S220: Acquire remote sensing images of the environment surrounding the target light environment sensitive target to obtain building surrounding environment data WT; S230, acquire the light and shadow data GT of the target light environment sensitive target; wherein, the light and shadow feature data includes the continuous residence time data of reflected light and the brightness data of reflected light.

3. The method for determining the measures to mitigate the impact of reflected light from photovoltaic modules according to claim 2, characterized in that, Step S300 includes: S350, feature extraction is performed on the basic data of the photovoltaic module to be optimized and the data of each target light environment sensitive target to obtain the target feature list BZ=(CTZ, JTZ, WTZ, GTZ); where CTZ is the feature obtained by feature extraction from the basic data of the photovoltaic module to be optimized; JTZ is the feature obtained by feature extraction from the building data; WTZ is the feature obtained by feature extraction from the building surrounding environment data; and GTZ is the feature obtained by feature extraction from the light and shadow data. S360, based on BZ and the preset feature vector corresponding to each preset prevention and control measure in a number of preset prevention and control measures, obtain the second key prevention and control measure list EG=(EG1, EG2, ..., EG2). p , ..., EG q ); p = 1, 2, ..., q; where q is the number of second critical prevention measures; EG p Let EG be the preset feature vector corresponding to the p-th second key prevention and control measure; p =(CTG p JTG p WTG p GTG p ); CTG p This refers to the pre-defined basic data characteristics of the photovoltaic module corresponding to the p-th second key prevention and control measure; JTG p This refers to the preset building data features corresponding to the p-th second key prevention and control measure; WTG p This refers to the pre-defined building surrounding environment data features corresponding to the p-th second key prevention and control measure; GTG p The preset light and shadow data features are defined for the p-th second key prevention and control measure; each preset prevention and control measure has a corresponding list of prevention and control sub-measures; the comprehensive matching degree EGP between BZ and the p-th second key prevention and control measure is defined. p It meets the following characteristics: EGP p =α1×CP p +α2×JP p +α3×WP p +α4×GP p ; Wherein, α1 is the weight of photovoltaic basic features; α2 is the weight of building features; α3 is the weight of building surrounding environment features; α4 is the weight of light and shadow features; CP p For CTZ and CTG p The degree of matching between them; JP p For JTZ and JTG p The degree of matching between them; WP p For WTZ and WTG p The degree of matching between them; GP p For GTZ and GTG p The degree of matching between them; S370, Based on EG, the second list of prevention and control measures EZ = (EZ1, EZ2, ..., EZ...) is obtained. c , ..., EZ d ); c = 1, 2, ..., d; where d is the number of duplicate second prevention measures included in EG; EZ c This is the measure identifier corresponding to the c-th second prevention and control sub-measure after deduplication of all second prevention and control sub-measures contained in EG; S380, Input BZ and EZ into the measure selection model to obtain a preset number of second target prevention and control sub-measures; wherein, the preset number of second target prevention and control sub-measures constitute the target prevention and control measures.

4. The method for determining the measures to mitigate the impact of reflected light from photovoltaic modules according to claim 1, characterized in that, The matching degree between the j-th primary critical prevention measure and MT (YMP) j It meets the following characteristics: YMP j =(YG j ·MT) / (|YG j |×|MT|)。 5. The method for determining the measures to mitigate the impact of reflected light from photovoltaic modules according to claim 3, characterized in that, α1, α2, α3 and α4 satisfy the following conditions respectively: α1=β1 / S; where S=β1+β2+β3+β4; β1 is the unnormalized photovoltaic foundation feature weight; β2 is the unnormalized building feature weight; β3 is the unnormalized building surrounding environment feature weight; β4 is the unnormalized light and shadow feature weight; α2=β2 / S; α3=β3 / S; α4=β4 / S.

6. The method for determining the measures to mitigate the impact of reflected light from photovoltaic modules according to claim 5, characterized in that, β1 is determined according to the following steps: S361, β1 is determined according to CTZ and the preset basic feature weight mapping table; wherein, the preset basic feature weight mapping table contains the basic feature vectors of several key photovoltaic modules and the preset basic feature weights corresponding to each key photovoltaic module; β1 is the preset basic feature weight of the key photovoltaic module with the highest matching degree between the corresponding basic feature vector and CTZ; the key photovoltaic modules are the photovoltaic modules to be optimized within the historical time window; the preset basic feature weights are integers between [1, 10]; β2 is determined according to the following steps: S362, β2 is determined according to JTZ and the preset building feature weight mapping table; wherein, the preset building feature weight mapping table contains building feature vectors of several key light environment sensitive targets and preset building feature weights corresponding to each key light environment sensitive target; β1 is the preset building feature weight corresponding to the key light environment sensitive target with the highest matching degree between the corresponding building feature vector and JTZ; the key light environment sensitive targets are the target light environment sensitive targets within the historical time window; the preset building feature weights are integers between [1, 10]; β3 is determined according to the following steps: S363, obtain the building density ρ within the target area of ​​the photovoltaic module to be optimized; S364, β3 is determined according to ρ and the preset building surrounding environment feature weight mapping table; wherein, the preset building surrounding environment feature weight mapping table contains several preset building density ranges and the preset building surrounding environment feature weights corresponding to each preset building density range; β3 is the preset building surrounding environment feature weight corresponding to the preset building density range to which ρ belongs; the preset building surrounding environment feature weights are integers between [1, 10]. β4 is determined according to the following steps: S365, obtain the over-limit value CX of the target light environment sensitive target when it meets the preset conditions; where CX=(FZ-30)+(FL-3000) / 100; where FZ is the continuous residence time of the reflected light corresponding to the target light environment sensitive target; FL is the brightness of the reflected light corresponding to the target light environment sensitive target; S366, β4 is determined according to CX and the preset light and shadow feature weight mapping table; wherein, the preset light and shadow feature weight mapping table contains several preset overlimit value ranges and preset light and shadow feature weights corresponding to each overlimit value range; β4 is the preset light and shadow feature weight corresponding to the preset overlimit value range to which CX belongs; the preset light and shadow feature weights are integers between [1, 10].

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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