GIS-based roof photovoltaic resource evaluation method and system

Abstract

The invention relates to a GIS-based roof photovoltaic resource evaluation method and system, and belongs to the technical field of photovoltaic resource evaluation. The method comprises the steps of obtaining a regional roof remote sensing image, constructing a regional roof remote sensing data set through preprocessing, and obtaining a roof recognition model through an improved FCN segmentation network according to the regional roof remote sensing data set; obtaining a roof remote sensing image of a to-be-evaluated region, obtaining a roof region segmentation image through the roof recognition model, and calculating a roof available area through an available roof area proportion according to the roof region segmentation image; the solar radiation amount of a to-be-measured area is obtained through GIS solar radiation analysis, the inclined plane irradiation amount and the maximum installation number of the roof photovoltaic modules are obtained through calculation of the installation inclination angles of the photovoltaic modules, the roof photovoltaic power generation amount of the area is obtained through roof photovoltaic resource evaluation calculation, the roof photovoltaic resources are accurately evaluated, investment decisions are optimized, and the power generation efficiency is improved. Comprehensive support is provided for roof photovoltaic projects, and sustainable development of the photovoltaic industry is promoted.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic resource assessment, and in particular relates to a rooftop photovoltaic resource assessment method and system based on GIS. Background Art

[0002] Rooftop photovoltaic power generation refers to installing solar panels on the roof of a building to convert solar energy into electricity for use in the building or to connect to the grid for power generation. Rooftop photovoltaic power generation has the advantages of saving land resources, reducing transmission losses, and improving energy efficiency. With the transformation of the global energy structure and the advancement of the low-carbon economy, rooftop photovoltaic power generation, as a form of distributed energy, has become an important force in promoting energy transformation. Accurate evaluation of rooftop photovoltaic resources is the key to achieving efficient and stable operation of photovoltaic power generation projects. However, the benefits of rooftop photovoltaic power generation are affected by many factors, such as the available area of ​​the roof, the tilt angle of photovoltaic modules, and local solar radiation. Although the existing rooftop photovoltaic resource assessment methods provide references for project planning and decision-making to a certain extent, the existing assessment methods often rely on limited field surveys and data collection, which makes it difficult to cover a wide range of rooftop resources. Moreover, most assessment methods use simplified models or empirical formulas, which makes it difficult to fully consider the impact of complex external factors on rooftop photovoltaic power generation, resulting in large errors in the assessment results.

[0003] GIS is an information system that integrates geographic spatial data, attribute data and spatial analysis functions. It can efficiently manage, analyze and display spatial data and provide data support for rooftop photovoltaic resource assessment. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the present invention proposes a rooftop photovoltaic resource assessment method and system based on GIS.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: A GIS-based rooftop photovoltaic resource assessment method, including: Acquire a regional rooftop remote sensing image and construct a regional rooftop remote sensing dataset through preprocessing, and obtain a rooftop recognition model through an improved FCN segmentation network according to the regional rooftop remote sensing dataset; Acquire a remote sensing image of the roof of the area to be evaluated and obtain a segmented image of the roof area through the roof recognition model, and calculate the available roof area by using the available roof area ratio according to the segmented image of the roof area; The solar radiation of the area to be measured is obtained by GIS solar radiation analysis, and the irradiation of the inclined surface and the maximum number of photovoltaic modules installed on the roof are calculated according to the solar radiation of the area to be measured and the inclination angle of the photovoltaic modules; The regional rooftop photovoltaic power generation is calculated based on the inclined surface radiation and the maximum number of installed rooftop photovoltaic components through rooftop photovoltaic resource assessment.

[0006] A further improvement of the present invention is that the step of acquiring a regional rooftop remote sensing image and constructing a regional rooftop remote sensing data set through preprocessing comprises: The rooftop remote sensing images of the area are all orthographic projection images; The preprocessing includes data expansion and image annotation. The data expansion includes brightness adjustment and random rotation. The image annotation includes continuous point and line annotation through labelme software.

[0007] A further improvement of the present invention is that the roof recognition model obtained by improving the FCN segmentation network according to the regional roof remote sensing data set includes: The improved FCN segmentation network includes a full convolution stage and a deconvolution stage; The full convolution stage includes replacing the original backbone network as the feature extraction network by improving the lightweight MobileNetV3, and the improved lightweight MobileNetV3 is to introduce CBAM attention in MobileNetV3; Extracting a roof area feature thumbnail from the regional roof remote sensing image through the feature extraction network; The deconvolution stage is to upsample the feature map extracted in the full convolution stage, and the upsampling is performed by bilinear interpolation through deconvolution of a fixed convolution kernel; Obtaining a roof area segmentation image of original size by upsampling according to the roof area feature thumbnail; The improved FCN segmentation network is iteratively trained according to the regional rooftop remote sensing dataset to obtain a rooftop recognition model.

[0008] A further improvement of the present invention is that the calculation of the available roof area by using the available roof area ratio according to the roof area segmentation image comprises: Obtaining the roof area by pixel statistics calculation based on the roof area segmentation image; Preset the ratio of available roof area in the area to be assessed; The product of the roof area and the ratio of the available roof area in the area to be evaluated is the available roof area.

[0009] A further improvement of the present invention is that the step of obtaining the roof area by pixel statistical calculation based on the roof area segmentation image comprises: Obtaining a roof area binary image by binarization processing according to the roof area segmentation image; The binarization process is to mark the pixels in the roof area as white and the pixels in the non-roof area as black; The number of pixels in the roof area in the binary image of the roof area is counted, and the product of the number of pixels in the roof area and the actual representative area of ​​a single pixel is the roof area.

[0010] A further improvement of the present invention is that the calculation of the inclined surface irradiation and the maximum number of rooftop photovoltaic modules installed according to the solar radiation in the measured area and the photovoltaic module installation inclination angle comprises: Preset the photovoltaic module installation angle; The inclined surface irradiance is calculated by converting the horizontal surface irradiance according to the photovoltaic module installation inclination angle; The mathematical expression for the horizontal plane radiation conversion is:

[0011] Among them, Fx is the irradiance of the inclined surface, Fs is the solar radiation of the area to be measured, and cosα is the cosine value of the installation inclination angle of the photovoltaic module; The maximum number of photovoltaic modules installed on the roof is calculated based on the photovoltaic module installation inclination angle and the available area of ​​the roof through the photovoltaic module capacity.

[0012] A further improvement of the present invention is that the mathematical expression for rooftop photovoltaic resource evaluation is:

[0013] Among them, E is the regional rooftop photovoltaic power generation, Amax is the maximum number of rooftop photovoltaic modules installed, P is the nominal power of a single photovoltaic module, H is the inclined surface irradiation, and μ is the photovoltaic system performance ratio.

[0014] A rooftop photovoltaic resource assessment system based on GIS, comprising a regional rooftop remote sensing data set construction module, a rooftop identification model construction module, a rooftop regional segmentation module and a rooftop photovoltaic resource assessment module; The regional rooftop remote sensing data set construction module is used to obtain regional rooftop remote sensing images and construct a regional rooftop remote sensing data set by preprocessing the remote sensing images; The roof recognition model building module is used to build a roof recognition model by improving the FCN segmentation network according to the regional roof remote sensing data set; The roof area segmentation module is used to obtain the remote sensing image of the roof of the area to be evaluated and obtain the roof area segmentation image through the roof recognition model; The rooftop photovoltaic resource assessment module is used to evaluate the rooftop photovoltaic resources and calculate the regional rooftop photovoltaic power generation according to the available roof area, the photovoltaic module installation inclination angle, the inclined surface irradiation, and the maximum number of rooftop photovoltaic modules installed.

[0015] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the GIS-based rooftop photovoltaic resource assessment method when executing the program.

[0016] A storage medium containing computer executable instructions, which are used to perform the steps of the GIS-based rooftop photovoltaic resource assessment method when executed by a computer processor.

[0017] Compared with the prior art, the present invention has at least the following beneficial technical effects: The present invention provides a GIS-based rooftop photovoltaic resource assessment method and system. The method identifies the roof area by improving the FCN segmentation network and obtains the available roof area by calculating the ratio of the available roof area. The irradiation of the inclined surface and the maximum number of rooftop photovoltaic components installed are calculated according to the solar radiation in the area to be measured and the installation inclination angle of the photovoltaic components. The regional rooftop photovoltaic power generation is calculated by the rooftop photovoltaic resource assessment calculation, which is helpful to accurately assess the rooftop photovoltaic resources, optimize investment decisions, provide comprehensive support for rooftop photovoltaic projects, and help promote the sustainable development of the photovoltaic industry. By introducing CBAM attention in MobileNetV3 and then replacing the original backbone network of the FCN segmentation network with the improved lightweight MobileNetV3 as the feature extraction network, the feature extraction capability can be enhanced, the model segmentation accuracy can be improved, and the model calculation amount can be reduced. By upsampling the feature map extracted in the full convolution stage in the deconvolution stage, a segmented image of the roof area of ​​the original size can be obtained, which is convenient for subsequent area calculation. By fusing the global information of the last layer with the local information of the shallow layer through the skip-level structure, a more accurate segmentation prediction result can be obtained, and the information loss caused by sampling can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 The present invention is a flowchart of a GIS-based rooftop photovoltaic resource assessment method.

[0020] Figure 2 The present invention is a structural block diagram of a GIS-based rooftop photovoltaic resource assessment system. DETAILED DESCRIPTION

[0021] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0022] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0023] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an" and "the" are intended to include plural forms unless the context clearly indicates otherwise.

[0024] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0025] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0026] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0027] Example 1 like Figure 1 As shown, the present invention provides a GIS-based rooftop photovoltaic resource assessment method, comprising: S1: Acquire a regional rooftop remote sensing image and construct a regional rooftop remote sensing dataset through preprocessing, and obtain a rooftop recognition model through an improved FCN segmentation network based on the regional rooftop remote sensing dataset; S2: Acquire a remote sensing image of the roof of the area to be evaluated and obtain a segmented image of the roof area through the roof recognition model, and calculate the available roof area by using the available roof area ratio according to the segmented image of the roof area; S3: obtaining the solar radiation of the area to be measured through GIS solar radiation analysis, and calculating the irradiation of the inclined surface and the maximum number of photovoltaic modules installed on the roof according to the solar radiation of the area to be measured and the installation inclination angle of the photovoltaic modules; S4: Calculate the regional rooftop photovoltaic power generation based on the inclined surface radiation and the maximum number of installed rooftop photovoltaic components through rooftop photovoltaic resource assessment.

[0028] In this embodiment, the acquisition of the regional rooftop remote sensing image and the construction of the regional rooftop remote sensing dataset through preprocessing are specifically implemented by the following steps: The rooftop remote sensing images of the area are all orthographic projection images; The preprocessing includes data expansion and image annotation. The data expansion includes brightness adjustment and random rotation. The image annotation includes continuous point and line annotation through labelme software.

[0029] In this embodiment, the roof recognition model is obtained by improving the FCN segmentation network according to the regional roof remote sensing data set, and is specifically implemented by the following steps: The improved FCN segmentation network includes a full convolution stage and a deconvolution stage; The full convolution stage includes replacing the original backbone network as the feature extraction network by improving the lightweight MobileNetV3, and the improved lightweight MobileNetV3 is to introduce CBAM attention in MobileNetV3; Extracting a roof area feature thumbnail from the regional roof remote sensing image through the feature extraction network; The deconvolution stage is to upsample the feature map extracted in the full convolution stage, and the upsampling is performed by bilinear interpolation through deconvolution of a fixed convolution kernel; Obtaining a roof area segmentation image of original size by upsampling according to the roof area feature thumbnail; The improved FCN segmentation network is iteratively trained according to the regional rooftop remote sensing dataset to obtain a rooftop recognition model.

[0030] Specifically, in the full convolution stage, the last layer of the feature extraction network uses a convolution layer instead of a fully connected layer. In the deconvolution stage, the upsampling obtains the prediction result by fusing the global information of the last layer with the local information of the shallow layer through a skip structure.

[0031] In this embodiment, the method of calculating the available roof area by using the available roof area ratio according to the roof area segmentation image is specifically implemented by the following steps: S201: Obtaining the roof area by pixel statistics calculation based on the roof area segmentation image; S201-1: Obtain a roof area binarized image by binarizing the roof area segmented image, wherein the binarization process is to mark pixels in the roof area as white and pixels in the non-roof area as black; S201-2: Count the number of pixels in the roof area in the binary image of the roof area, and the product of the number of pixels in the roof area and the actual representative area of ​​a single pixel is the roof area; S202: Preset the ratio of available roof area in the area to be evaluated; S203: The product of the roof area and the ratio of the available roof area in the area to be evaluated is the available roof area.

[0032] In this embodiment, the calculation of the inclined surface irradiation and the maximum number of rooftop photovoltaic modules installed according to the solar radiation of the measured area and the photovoltaic module installation inclination angle is specifically implemented by the following steps: S301: Preset the photovoltaic module installation inclination angle; S302: Calculating the inclined surface radiation amount by converting the horizontal surface radiation amount according to the photovoltaic module installation inclination angle; The mathematical expression for the horizontal plane radiation conversion is:

[0033] Among them, Fx is the irradiance of the inclined surface, Fs is the solar radiation of the area to be measured, and cosα is the cosine value of the installation inclination angle of the photovoltaic module; S303: Calculate the maximum number of photovoltaic modules installed on the roof based on the photovoltaic module installation inclination angle and the available area of ​​the roof through the photovoltaic module capacity.

[0034] Specifically, the photovoltaic module capacity is the quotient of the available roof area and the actual occupied area of ​​a single photovoltaic module after considering the influence of the inclination angle, that is, the maximum number of installed roof photovoltaic modules.

[0035] In this embodiment, the calculation of the regional rooftop photovoltaic power generation according to the inclined surface irradiation and the maximum number of rooftop photovoltaic components installed by rooftop photovoltaic resource evaluation includes: The mathematical expression of the rooftop photovoltaic resource assessment is:

[0036] Among them, E is the regional rooftop photovoltaic power generation, Amax is the maximum number of rooftop photovoltaic modules installed, P is the nominal power of a single photovoltaic module, H is the inclined surface irradiation, and μ is the photovoltaic system performance ratio.

[0037] Example 2 like Figure 2As shown, the present invention provides a GIS-based rooftop photovoltaic resource assessment system, including a regional rooftop remote sensing data set construction module, a rooftop recognition model construction module, a rooftop area segmentation module and a rooftop photovoltaic resource assessment module; The regional rooftop remote sensing data set construction module is used to obtain regional rooftop remote sensing images and construct a regional rooftop remote sensing data set by preprocessing the remote sensing images; The roof recognition model building module is used to build a roof recognition model by improving the FCN segmentation network according to the regional roof remote sensing data set; The roof area segmentation module is used to obtain the remote sensing image of the roof of the area to be evaluated and obtain the roof area segmentation image through the roof recognition model; The rooftop photovoltaic resource assessment module is used to evaluate the rooftop photovoltaic resources and calculate the regional rooftop photovoltaic power generation according to the available roof area, the photovoltaic module installation inclination angle, the inclined surface irradiation, and the maximum number of rooftop photovoltaic modules installed.

[0038] Example 3 The present invention provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the GIS-based rooftop photovoltaic resource assessment method when executing the program.

[0039] The electronic device may also include one or more of a multimedia component, an input / output (I / O) interface, and a communication component.

[0040] The processor is used to control the overall operation of the electronic device to complete all or part of the steps in the storage medium sharing method. The memory is used to store various types of data to support the operation of the electronic device, which may include instructions for any application or method used to operate on the electronic device, as well as application-related data, such as contact data, messages sent and received, pictures, audio, video, etc. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving an external audio signal. The received audio signal may be further stored in a memory or sent through a communication component. The audio component also includes at least one speaker for outputting an audio signal. The I / O interface provides an interface between the processor and other interface modules, and the above-mentioned other interface modules may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component is used for wired or wireless communication between the electronic device and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component may include: Wi-Fi module, Bluetooth module, NFC module.

[0041] In an exemplary embodiment, the electronic device can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the storage medium sharing method.

[0042] Example 4 The present invention provides a storage medium containing computer executable instructions, which are used to execute the steps of the GIS-based rooftop photovoltaic resource assessment method when executed by a computer processor.

[0043] The computer storage medium of the present embodiment can adopt any combination of one or more computer-readable media. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, computer-readable storage media can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0044] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0045] The program code included on the computer readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF, etc., or any suitable combination of the above. The computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or their combinations, and the programming language includes object-oriented programming languages-such as Java, Smalltalk, C++, and also includes conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0046] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0047] In addition, it should be understood that although this specification is described in accordance with the implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation modes that can be understood by those skilled in the art. The above content is only to illustrate the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A GIS-based rooftop photovoltaic resource assessment method, characterized in that: include: Acquire a regional rooftop remote sensing image and construct a regional rooftop remote sensing dataset through preprocessing, and obtain a rooftop recognition model through an improved FCN segmentation network according to the regional rooftop remote sensing dataset; Acquire a remote sensing image of the roof of the area to be evaluated and obtain a segmented image of the roof area through the roof recognition model, and calculate the available roof area by using the available roof area ratio according to the segmented image of the roof area; The solar radiation of the area to be measured is obtained by GIS solar radiation analysis, and the irradiation of the inclined surface and the maximum number of photovoltaic modules installed on the roof are calculated according to the solar radiation of the area to be measured and the inclination angle of the photovoltaic modules; The regional rooftop photovoltaic power generation is calculated based on the inclined surface radiation and the maximum number of installed rooftop photovoltaic components through rooftop photovoltaic resource assessment.

2. The GIS-based rooftop photovoltaic resource assessment method according to claim 1, characterized in that: The acquiring of the regional rooftop remote sensing image and constructing the regional rooftop remote sensing dataset through preprocessing comprises: The rooftop remote sensing images of the area are all orthographic projection images; The preprocessing includes data expansion and image annotation. The data expansion includes brightness adjustment and random rotation. The image annotation includes continuous point and line annotation through labelme software.

3. The GIS-based rooftop photovoltaic resource assessment method according to claim 1, characterized in that: The roof recognition model obtained by improving the FCN segmentation network according to the regional roof remote sensing data set includes: The improved FCN segmentation network includes a full convolution stage and a deconvolution stage; The full convolution stage includes replacing the original backbone network as the feature extraction network by improving the lightweight MobileNetV3, and the improved lightweight MobileNetV3 is to introduce CBAM attention in MobileNetV3; Extracting a roof area feature thumbnail from the regional roof remote sensing image through the feature extraction network; The deconvolution stage is to upsample the feature map extracted in the full convolution stage, and the upsampling is performed by bilinear interpolation through deconvolution of a fixed convolution kernel; Obtaining a roof area segmentation image of original size by upsampling according to the roof area feature thumbnail; The improved FCN segmentation network is iteratively trained according to the regional rooftop remote sensing dataset to obtain a rooftop recognition model.

4. The GIS-based rooftop photovoltaic resource assessment method according to claim 1, characterized in that: The calculating the available roof area by using the available roof area ratio according to the roof area segmentation image comprises: Obtaining the roof area by pixel statistics calculation based on the roof area segmentation image; Preset the ratio of available roof area in the area to be assessed; The product of the roof area and the ratio of the available roof area in the area to be evaluated is the available roof area.

5. The GIS-based rooftop photovoltaic resource assessment method according to claim 4 is characterized in that: The step of obtaining the roof area by pixel statistics calculation based on the roof area segmentation image comprises: Obtaining a roof area binary image by binarization processing according to the roof area segmentation image; The binarization process is to mark the pixels in the roof area as white and the pixels in the non-roof area as black; The number of pixels in the roof area in the binary image of the roof area is counted, and the product of the number of pixels in the roof area and the actual representative area of ​​a single pixel is the roof area.

6. The GIS-based rooftop photovoltaic resource assessment method according to claim 1, characterized in that: The calculation of the inclined surface irradiation and the maximum number of rooftop photovoltaic modules installed according to the solar radiation in the measured area and the photovoltaic module installation inclination angle includes: Preset the photovoltaic module installation angle; The inclined surface irradiance is calculated by converting the horizontal surface irradiance according to the photovoltaic module installation inclination angle; The mathematical expression for the horizontal plane radiation conversion is: Among them, Fx is the irradiance of the inclined surface, Fs is the solar radiation of the area to be measured, and cosα is the cosine value of the installation inclination angle of the photovoltaic module; The maximum number of photovoltaic modules installed on the roof is calculated based on the photovoltaic module installation inclination angle and the available area of ​​the roof through the photovoltaic module capacity.

7. The GIS-based rooftop photovoltaic resource assessment method according to claim 1, characterized in that: The mathematical expression of the rooftop photovoltaic resource assessment is: Among them, E is the regional rooftop photovoltaic power generation, Amax is the maximum number of rooftop photovoltaic modules installed, P is the nominal power of a single photovoltaic module, H is the inclined surface irradiation, and μ is the photovoltaic system performance ratio.

8. A GIS-based rooftop photovoltaic resource assessment system, characterized in that: It includes regional rooftop remote sensing dataset construction module, rooftop recognition model construction module, rooftop area segmentation module and rooftop photovoltaic resource assessment module; The regional rooftop remote sensing data set construction module is used to obtain regional rooftop remote sensing images and construct a regional rooftop remote sensing data set by preprocessing the remote sensing images; The roof recognition model building module is used to build a roof recognition model by improving the FCN segmentation network according to the regional roof remote sensing data set; The roof area segmentation module is used to obtain the remote sensing image of the roof of the area to be evaluated and obtain the roof area segmentation image through the roof recognition model; The rooftop photovoltaic resource assessment module is used to evaluate the rooftop photovoltaic resources and calculate the regional rooftop photovoltaic power generation according to the available roof area, the photovoltaic module installation inclination angle, the inclined surface irradiation, and the maximum number of rooftop photovoltaic modules installed.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the GIS-based rooftop photovoltaic resource assessment method as described in any one of claims 1-7 are implemented.

10. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are used to execute the steps of the GIS-based rooftop photovoltaic resource assessment method as described in any one of claims 1-7 when executed by a computer processor.

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