Distributed photovoltaic installed capacity calculation method, device, equipment and medium

Abstract

The invention relates to the technical field of new energy, and discloses a distributed photovoltaic installed capacity calculation method, device, equipment and medium, and the distributed photovoltaic installed capacity calculation method comprises the steps: obtaining the orthoimage data of a satellite under a preset spatial scale; performing photovoltaic module segmentation on the orthoimage data to obtain a plurality of segmented images including each distributed photovoltaic module; performing edge detection on each segmented image to obtain a target area corresponding to each distributed photovoltaic module; performing photovoltaic module identification on the target areas based on a preset target detection rule to obtain photovoltaic module types corresponding to the target areas; performing distributed photovoltaic installed capacity calculation based on the area of the target region corresponding to each photovoltaic module type and the capacity corresponding to each photovoltaic module type to obtain a capacity calculation result; the distributed photovoltaic installed capacity in the custom field can be evaluated intelligently and automatically, and distributed photovoltaic management is facilitated.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a method, device, computer equipment and storage medium for calculating distributed photovoltaic installed capacity. Background Art

[0002] Due to the fragmented spatial distribution and small scale of distributed photovoltaics, as well as the different sizes of component types and whether components are missing, it is difficult to systematically and accurately assess the installed capacity of distributed photovoltaics in a region. When it is impossible to accurately assess the installed capacity of distributed photovoltaics, it plays a vital role in the scheduling of photovoltaic power systems, the advance planning of photovoltaic power stations, and the management of new energy photovoltaics.

[0003] However, in related technologies, capacity calculation for distributed photovoltaics usually adopts manual statistical methods; manual statistical methods are time-consuming and prone to errors, and cannot meet the accuracy assessment requirements of automatic scanning, range coverage, and intelligent evaluation.

[0004] Therefore, it is urgent to propose a method for calculating distributed photovoltaic installed capacity to solve the problems existing in the manual statistical methods used in related technologies. Summary of the invention

[0005] In view of this, the present invention provides a distributed photovoltaic installed capacity calculation method, device, equipment and medium to solve the problems existing in the manual statistical method used in the related art.

[0006] In a first aspect, the present invention provides a distributed photovoltaic installed capacity calculation method, the distributed photovoltaic installed capacity calculation method comprising: obtaining orthophoto data of a satellite at a preset spatial scale; wherein the preset spatial scale includes the geographical location of each of the distributed photovoltaic components; performing photovoltaic component segmentation on the orthophoto data to obtain a plurality of segmented images including each of the distributed photovoltaic components; performing edge detection on each of the segmented images to obtain a target area corresponding to each of the distributed photovoltaic components; wherein the boundary of the target area is used to represent the contour edge of the distributed photovoltaic component; performing photovoltaic component identification on the target area based on a preset target detection rule to obtain a photovoltaic component type corresponding to each of the target areas; wherein the target detection rule includes a correspondence between preset category features and preset feature labels of each type of photovoltaic panel; performing distributed photovoltaic installed capacity calculation based on the area of ​​the target area corresponding to each type of photovoltaic component and the capacity corresponding to each type of photovoltaic component to obtain a capacity calculation result.

[0007] As an exemplary embodiment, the target detection is performed on the target area based on the preset target detection rule to obtain the component type corresponding to each target area, including: obtaining the correspondence between each category feature and the feature label in the preset target detection rule; performing target detection on the target area based on the category feature to obtain the component type corresponding to each target area.

[0008] As an exemplary embodiment, the edge detection is performed on each of the segmented images to obtain the target area of ​​each of the distributed photovoltaic components, including: Gaussian filtering is performed on each of the segmented images to obtain a filtered segmented image; gradient calculation is performed on the filtered segmented image based on the Sobel operator to obtain a gradient calculation result; based on the gradient calculation result, the Canny algorithm is used to sequentially perform non-maximum suppression and double-threshold hysteresis threshold processing on the filtered segmented image to obtain the target area.

[0009] As an exemplary embodiment, before calculating the distributed photovoltaic installed capacity based on each of the target areas and the corresponding photovoltaic component types, the method also includes: performing status detection on each of the target areas to obtain status detection results; and removing abnormal areas in abnormal states from the target areas.

[0010] As an exemplary embodiment, the state detection is performed on each of the target areas to obtain the state detection result, including: performing semantic recognition on the boundaries of each of the target areas to obtain actual semantic features of the pixels constituting the boundaries of each target area; comparing the actual semantic features with preset semantic features to obtain comparison results; wherein the preset semantic features are used for the outline of the photovoltaic module; based on the comparison results, the actual proportion of normal pixels representing the outline of the photovoltaic module relative to the pixels constituting the boundaries of each target area is calculated; and the target area whose actual proportion is not greater than the preset proportion is marked as an abnormal area.

[0011] As an exemplary embodiment, the state detection of each of the target areas is performed to obtain the state detection result, and further includes: obtaining preset geometric features of the photovoltaic component type corresponding to each of the target areas; extracting actual geometric features of each of the target areas; wherein the actual geometric features are used to represent the boundary shape of the boundary constituting the target area; comparing the actual geometric features with the preset geometric features to obtain a comparison result; based on the comparison result, marking the target area whose actual geometric features do not meet the preset geometric features as an abnormal area.

[0012] As an exemplary embodiment, the state detection is performed on each of the target areas to obtain the state detection result, and further includes: dividing the pixels based on the semantic features of the pixels in each of the target areas to obtain abnormal pixels whose semantic features do not represent photovoltaic components; calculating the actual abnormal proportion of the abnormal pixels in the target area; and marking the target area where the actual abnormal proportion is greater than the preset abnormal proportion as an abnormal state.

[0013] In a second aspect, the present invention provides a distributed photovoltaic installed capacity calculation device, the distributed photovoltaic installed capacity calculation device comprising: a satellite data acquisition module, used to acquire orthophoto data of a satellite at a preset spatial scale; wherein the preset spatial scale includes the geographical location of each of the distributed photovoltaic components; an image segmentation module, used to perform photovoltaic component segmentation on the orthophoto data to obtain a plurality of segmented images including each of the distributed photovoltaic components; an edge detection module, used to perform edge detection on each of the segmented images to obtain a target area corresponding to each of the distributed photovoltaic components; wherein the boundary of the target area is used to represent the contour edge of the distributed photovoltaic component; a photovoltaic component identification module, used to perform photovoltaic component identification on the target area based on a preset target detection rule to obtain a photovoltaic component type corresponding to each of the target areas; wherein the target detection rule includes a correspondence between preset category features and preset feature labels of each type of photovoltaic panel; a capacity calculation module, used to perform distributed photovoltaic installed capacity calculation based on the area of ​​the target area corresponding to each of the photovoltaic component types and the capacity corresponding to each of the photovoltaic component types to obtain a capacity calculation result.

[0014] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the distributed photovoltaic installed capacity calculation method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the distributed photovoltaic installed capacity calculation method of the first aspect or any corresponding embodiment thereof.

[0016] The present invention provides a distributed photovoltaic installed capacity calculation method, the distributed photovoltaic installed capacity calculation method comprising: obtaining orthophoto data of a satellite at a preset spatial scale; wherein the preset spatial scale includes the geographical location of each of the distributed photovoltaic components; performing photovoltaic component segmentation on the orthophoto data to obtain a plurality of segmented images including each of the distributed photovoltaic components; performing edge detection on each of the segmented images to obtain a target area corresponding to each of the distributed photovoltaic components; wherein the boundary of the target area is used to represent the contour edge of the distributed photovoltaic component; performing photovoltaic component identification on the target area based on a preset target detection rule to obtain the type of photovoltaic component corresponding to each of the target areas; wherein the target detection rule includes preset category features and preset feature marks of each type of photovoltaic panel. The corresponding relationship of the signatures; based on the area of ​​the target area corresponding to each photovoltaic component type and the capacity corresponding to each photovoltaic component type, the distributed photovoltaic installed capacity is calculated to obtain the capacity calculation result; the above implementation mode can extract the target area representing each distributed photovoltaic component through the characteristics of the distributed photovoltaic component in the orthophoto data of the geographical location of each distributed photovoltaic component; further, the photovoltaic components of the target area are identified based on the preset target detection rules to obtain the photovoltaic component type of each target area, and further, the distributed photovoltaic installed capacity is calculated according to the area of ​​the target area corresponding to each photovoltaic component type and the capacity corresponding to each photovoltaic component type, which can intelligently and automatically evaluate the distributed photovoltaic installed capacity in the defined field, so as to facilitate the management of distributed photovoltaic. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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.

[0018] Figure 1 is a schematic flow chart of a method for calculating the installed capacity of distributed photovoltaic power generation according to an embodiment of the present invention;

[0019] Figure 2 is a structural block diagram of a distributed photovoltaic installed capacity calculation device according to an embodiment of the present invention;

[0020] Figure 3 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0022] According to an embodiment of the present invention, an embodiment of a method for calculating a distributed photovoltaic installed capacity is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0023] In this embodiment, a method for calculating the installed capacity of distributed photovoltaic power generation is provided. Figure 1 is a flow chart of a method for calculating the installed capacity of distributed photovoltaic power generation according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0024] Step S101, obtaining orthophoto data of a satellite at a preset spatial scale; wherein the preset spatial scale includes the geographical location of each of the distributed photovoltaic components.

[0025] Due to the fragmented spatial distribution and small scale of distributed photovoltaics, as well as the different sizes of component types and whether components are missing, it is difficult to systematically and accurately evaluate the installed capacity of distributed photovoltaics in an area. In order to include all distributed photovoltaics to be evaluated in the orthophoto data, in this embodiment, orthophoto data of the satellite at a preset spatial scale is obtained, and the preset spatial scale includes the geographical location of each of the distributed photovoltaic components.

[0026] Exemplarily, the preset spatial scale may be predefined by an operator, and the defined preset spatial scale includes the geographical location of each of the distributed photovoltaic components.

[0027] The orthophoto data of the satellite is RGB orthophoto data; illustratively, the RGB orthophoto data can be obtained through Beijing No. 2 BJ-2, ​​Gaofen No. 2 GF-2 or Google-earth high-definition satellite maps.

[0028] Step S102 , performing photovoltaic component segmentation on the orthophoto data to obtain a plurality of segmented images including each of the distributed photovoltaic components.

[0029] In this embodiment, a plurality of segmented images of distributed photovoltaic components contained in the orthophoto data are obtained by performing photovoltaic component segmentation on the orthophoto data.

[0030] As a possible implementation method, the segmentation of photovoltaic components can be achieved through an image segmentation algorithm based on deep learning; specifically, the image segmentation algorithm based on deep learning may include MS-GeoNet, Double U-Net or DeepLab, etc.

[0031] Step S103, performing edge detection on each of the segmented images to obtain a target area corresponding to each of the distributed photovoltaic components; wherein the boundary of the target area is used to represent the contour edge of the distributed photovoltaic component.

[0032] In order to further determine the area of ​​the photovoltaic components, edge detection is performed on each segmented image to obtain a target area corresponding to each distributed photovoltaic component; the area of ​​the target area is the area of ​​the photovoltaic component.

[0033] Exemplarily, when edge detection is performed on a segmented image, it can be achieved through the Canny algorithm; specifically, when the Canny algorithm is used to perform edge detection on a segmented image, a Gaussian filter is first used to smooth the image to obtain a filtered segmented image to remove image noise; further, the gradient of the filtered segmented image is calculated based on the Sobel operator to obtain a gradient calculation result; further, based on the gradient calculation result, the Canny algorithm is used to perform non-maximum suppression and double-threshold hysteresis threshold processing on the filtered segmented image in sequence to obtain the target area.

[0034] Step S104, identifying photovoltaic components in the target area based on preset target detection rules to obtain photovoltaic component types corresponding to each target area; wherein the target detection rules include corresponding relationships between preset category features and preset feature labels of each type of photovoltaic panel.

[0035] Photovoltaic module types are divided into single crystal photovoltaic modules, polycrystalline photovoltaic modules, thin-film photovoltaic modules and multi-compound photovoltaic modules. Since different photovoltaic modules have different corresponding rated capacities, after obtaining the target area that characterizes each distributed photovoltaic module, it is necessary to identify the module type of the photovoltaic module, and further calculate the installed capacity of the distributed photovoltaic according to the identified photovoltaic module type and the area of ​​the corresponding target area.

[0036] In this embodiment, the component type of the photovoltaic component can be identified through target detection; specifically, in the process of target detection, the photovoltaic components of the target area are identified based on preset target detection rules to obtain the type of photovoltaic components corresponding to each target area; wherein the target detection rules include the correspondence between preset category features and preset feature labels of each type of photovoltaic panel.

[0037] Exemplarily, the target detection rule includes a correspondence between preset category features and preset feature labels of photovoltaic panels of single crystal photovoltaic modules, multi-crystal photovoltaic modules, thin-film photovoltaic modules and multi-compound photovoltaic modules.

[0038] Among them, as a possible implementation method, the preset target detection rules can be determined by pre-calibration; specifically, the preset segmented images corresponding to the preset single crystal photovoltaic components, the preset polycrystalline photovoltaic components, the preset thin film photovoltaic components and the preset multi-compound photovoltaic components are obtained; the distinguishing features of each of the preset segmented images relative to other segmented images are extracted, and the distinguishing features are used as the preset category features.

[0039] In one embodiment, the distinguishing features may be reflected by different colors of each photovoltaic component represented in the orthophoto data.

[0040] In one embodiment, photovoltaic component identification of the target area based on preset target detection rules can be achieved through a deep learning target detection model; specifically, after obtaining preset segmented images corresponding to preset single crystal photovoltaic components, preset polycrystalline photovoltaic components, preset thin film photovoltaic components and preset multi-compound photovoltaic components, the preset segmented images are labeled to obtain preset category features corresponding to each preset segmented image; the preset segmented images with labeled preset category features are used as input features, and the photovoltaic component types corresponding to each preset segmented image are used as labels as training sets, which are input into a pre-built deep learning target detection model for model training; during the model training process, the model parameters are continuously adjusted so that the deep learning target detection model can extract the preset category features, and output the corresponding photovoltaic component types based on the preset category features to obtain the deep learning target detection model.

[0041] Exemplarily, the deep learning target detection model can be built based on YOLOv8 or RetinaNet.

[0042] Step S105 , calculating the installed capacity of distributed photovoltaic power generation based on the area of ​​the target region corresponding to each photovoltaic component type and the capacity corresponding to each photovoltaic component type, to obtain a capacity calculation result.

[0043] In this embodiment, after the target area corresponding to each photovoltaic component type is obtained, the capacity calculation result is obtained by calculating the area of ​​the target area and the rated capacity corresponding to the photovoltaic component.

[0044] The present embodiment adopts a distributed photovoltaic installed capacity calculation method, which includes: obtaining orthophoto data of a satellite at a preset spatial scale; wherein the preset spatial scale includes the geographical location of each of the distributed photovoltaic components; performing photovoltaic component segmentation on the orthophoto data to obtain a plurality of segmented images including each of the distributed photovoltaic components; performing edge detection on each of the segmented images to obtain a target area corresponding to each of the distributed photovoltaic components; wherein the boundary of the target area is used to represent the contour edge of the distributed photovoltaic component; performing photovoltaic component identification on the target area based on a preset target detection rule to obtain the type of photovoltaic component corresponding to each of the target areas; wherein the target detection rule includes preset category features and preset feature labels of each type of photovoltaic panel corresponding relationship; based on the area of ​​the target area corresponding to each photovoltaic component type and the capacity corresponding to each photovoltaic component type, the distributed photovoltaic installed capacity is calculated to obtain the capacity calculation result; the above implementation mode can extract the target area representing each distributed photovoltaic component through the characteristics of the distributed photovoltaic component in the orthophoto data of the geographical location of each distributed photovoltaic component; further, the photovoltaic component is identified in the target area based on the preset target detection rule to obtain the photovoltaic component type of each target area, and further, the distributed photovoltaic installed capacity is calculated according to the area of ​​the target area corresponding to each photovoltaic component type and the capacity corresponding to each photovoltaic component type, which can intelligently and automatically evaluate the distributed photovoltaic installed capacity in the defined field, so as to facilitate the management of distributed photovoltaic.

[0045] As an exemplary embodiment, the target detection is performed on the target area based on the preset target detection rule to obtain the component type corresponding to each target area, including: obtaining the correspondence between each category feature and the feature label in the preset target detection rule; performing target detection on the target area based on the category feature to obtain the component type corresponding to each target area.

[0046] In this embodiment, the correspondence between each category feature and feature label in the preset target detection rule can be obtained by pre-calibration; specifically, the preset segmented images corresponding to the preset single crystal photovoltaic module, the preset polycrystalline photovoltaic module, the preset thin film photovoltaic module and the preset multi-compound photovoltaic module are obtained; the distinguishing features of each of the preset segmented images relative to other segmented images are extracted, and the distinguishing features are used as the category features.

[0047] As an exemplary embodiment, the edge detection is performed on each of the segmented images to obtain the target area of ​​each of the distributed photovoltaic components, including: Gaussian filtering is performed on each of the segmented images to obtain a filtered segmented image; gradient calculation is performed on the filtered segmented image based on the Sobel operator to obtain a gradient calculation result; based on the gradient calculation result, the Canny algorithm is used to sequentially perform non-maximum suppression and double-threshold hysteresis threshold processing on the filtered segmented image to obtain the target area.

[0048] In this embodiment, when the Canny algorithm is used to perform edge detection on the segmented image, Gaussian filtering is first used to smooth the image to obtain a filtered segmented image to remove image noise; further, the gradient of the filtered segmented image is calculated based on the Sobel operator to obtain a gradient calculation result; further, based on the gradient calculation result, the Canny algorithm is used to perform non-maximum suppression and double-threshold hysteresis threshold processing on the filtered segmented image in sequence to obtain the target area.

[0049] The above-mentioned embodiment of the distributed photovoltaic installed capacity calculation method can realize the calculation of distributed photovoltaic installed capacity; however, in the actual use of distributed photovoltaics, there are some photovoltaic components in abnormal states such as broken, blocked, broken, missing, etc. due to hail, branches, aging and other factors during use. When calculating the distributed photovoltaic installed capacity, taking the photovoltaic components in the above-mentioned damaged state as the normal state to participate in the photovoltaic installed capacity calculation will cause deviations in the calculation results. Therefore, when calculating the photovoltaic installed capacity, it is necessary to eliminate the above-mentioned distributed photovoltaic components in abnormal states.

[0050] Based on this, as an exemplary embodiment, before calculating the distributed photovoltaic installed capacity based on each of the target areas and the corresponding photovoltaic component types, the method also includes: performing status detection on each of the target areas to obtain status detection results; and removing abnormal areas in abnormal states from the target areas.

[0051] In one embodiment, for photovoltaic components in abnormal states due to breakage, obstruction, fragmentation, missing, etc., there are characteristics of incomplete edges or missing photovoltaic components in the area; therefore, based on the above characteristics, a state detection can be performed on each of the target areas to obtain a state detection result, and further, the abnormal area in the abnormal state can be removed from the target area.

[0052] Exemplarily, after edge detection is performed on each of the segmented images to obtain the target area corresponding to each of the distributed photovoltaic components, the region boundary of the target area can be judged to determine whether the photovoltaic component corresponding to the target area is complete, and the target area with incomplete photovoltaic components can be marked as an abnormal area.

[0053] Specifically, when the photovoltaic panel is blocked, the boundary of the photovoltaic panel is affected, and the actual semantic features of the boundary pixels do not appear as the preset semantic features that characterize the contour of the photovoltaic component. Based on this, after obtaining the target area corresponding to each of the distributed photovoltaic components, the boundary of each target area can be semantically identified to obtain the actual semantic features of the pixels constituting the boundary of each target area; further, the actual semantic features are compared with the pre-marked preset semantic features that characterize the contour of the photovoltaic component to obtain the comparison results; based on the comparison results, the actual proportion of normal pixels that characterize the contour of the photovoltaic component relative to the pixels that constitute the boundary of each target area is calculated; and the target area whose actual proportion is not greater than the preset proportion is marked as an abnormal area.

[0054] In one embodiment, since photovoltaic components in a normal state usually have a fixed geometric shape, for photovoltaic components in an abnormal state due to being broken, blocked, broken, missing, etc., the geometric features of their boundaries are different from the fixed geometric shapes of photovoltaic components in a normal state; for example, photovoltaic components in a normal state usually have a shape such as a rectangle, and the flatness of their boundaries is relatively straight. Therefore, after obtaining the target area corresponding to each of the distributed photovoltaic components, it can be determined whether the photovoltaic components corresponding to the target area are complete based on the geometric features of the boundaries of the target area.

[0055] Specifically, after obtaining the target area corresponding to each of the distributed photovoltaic components, the photovoltaic components of the target area can be further identified based on the preset target detection rules to obtain the type of photovoltaic components corresponding to each of the target areas; the preset geometric features are determined based on the photovoltaic component type; wherein the preset geometric features are obtained by collecting photovoltaic components in a normal and non-missing state; further, the actual geometric features of each of the target areas are extracted; the actual geometric features are compared with the preset geometric features, and the target areas whose actual geometric features do not meet the preset geometric features are marked as abnormal areas.

[0056] In an embodiment, whether the photovoltaic assembly corresponding to the target area is complete may also be determined by detecting semantic features contained in pixels within the target area.

[0057] Specifically, after obtaining the target area corresponding to each distributed photovoltaic component, semantic recognition is performed on the pixels constituting each target area to obtain the semantic features of each pixel; the pixels in each target area are divided based on the semantic features of the pixels to obtain abnormal pixels whose semantic features do not represent photovoltaic components; the actual abnormal proportion of the abnormal pixels in the target area is calculated; the preset abnormal proportion is determined based on the photovoltaic component type in the target area; and the target area where the actual angle does not meet the preset angle is marked as an abnormal state.

[0058] Based on this, as an exemplary embodiment, the state detection is performed on each of the target areas to obtain the state detection result, including: semantic recognition is performed on the boundaries of each of the target areas to obtain the actual semantic features of the pixels constituting the boundaries of each target area; the actual semantic features are compared with the preset semantic features to obtain the comparison results; wherein the preset semantic features are used for the outline of the photovoltaic module; based on the comparison results, the actual proportion of normal pixels representing the outline of the photovoltaic module relative to the pixels constituting the boundaries of each target area is calculated; and the target area whose actual proportion is not greater than the preset proportion is marked as an abnormal area.

[0059] In this embodiment, the abnormal area detection is achieved by performing semantic recognition on the boundaries of each target area.

[0060] Among them, as a possible implementation method, the preset ratio is 0.95.

[0061] As an exemplary embodiment, the state detection of each of the target areas is performed to obtain the state detection result, and further includes: obtaining preset geometric features of the photovoltaic component type corresponding to each of the target areas; extracting actual geometric features of each of the target areas; wherein the actual geometric features are used to represent the boundary shape of the boundary constituting the target area; comparing the actual geometric features with the preset geometric features to obtain a comparison result; based on the comparison result, marking the target area whose actual geometric features do not meet the preset geometric features as an abnormal area.

[0062] In this embodiment, the detection of abnormal areas is achieved by detecting the geometric features of each target area.

[0063] Among them, as a possible implementation method, when the photovoltaic panel is in a broken, blocked, shattered, or missing state, the boundary of the photovoltaic panel is affected and cannot maintain its original flatness. Therefore, in one embodiment, the geometric feature includes the actual flatness of the boundary of the target area; specifically, the actual geometric feature of each target area can be obtained by calculating the actual flatness of the boundary of each target area, and further, a preset flatness is determined as a preset geometric feature based on the photovoltaic component type of the target area, and finally, the actual flatness is compared with the preset flatness, and the target area whose actual straightness does not meet the preset flatness is marked as an abnormal state.

[0064] As another possible implementation, the state detection is performed on each of the target areas. When the photovoltaic panel is in a broken, blocked, shattered or missing state, the boundary of the photovoltaic panel is affected and cannot maintain the original angle. Therefore, in one embodiment, the geometric feature includes the actual angle between two intersecting area boundaries in the target area; specifically, the actual geometric feature of each target area can be obtained by calculating the actual angle between the two intersecting area boundaries in each of the target areas; further, a preset angle is determined based on the type of photovoltaic module in the target area; finally, the actual angle is compared with the preset angle, and the target area whose actual straightness does not meet the preset straightness is marked as an abnormal state.

[0065] As an exemplary embodiment, the state detection is performed on each of the target areas to obtain the state detection result, and further includes: dividing the pixels based on the semantic features of the pixels in each of the target areas to obtain abnormal pixels whose semantic features do not represent photovoltaic components; calculating the actual abnormal proportion of the abnormal pixels in the target area; determining a preset abnormal proportion based on the photovoltaic component type of the target area; and marking the target area where the actual angle does not meet the preset angle as an abnormal state.

[0066] This embodiment provides a distributed photovoltaic installed capacity calculation device, such as Figure 2 As shown, including:

[0067] The satellite data acquisition module 201 is used to acquire orthophoto data of satellites at a preset spatial scale; wherein the preset spatial scale includes the geographical location of each of the distributed photovoltaic components;

[0068] An image segmentation module 202 is used to segment the orthophoto data into photovoltaic components to obtain a plurality of segmented images including each of the distributed photovoltaic components;

[0069] The edge detection module 203 is used to perform edge detection on each of the segmented images to obtain a target area corresponding to each of the distributed photovoltaic components; wherein the boundary of the target area is used to represent the contour edge of the distributed photovoltaic component;

[0070] The photovoltaic component identification module 204 is used to identify the photovoltaic components of the target area based on the preset target detection rules to obtain the photovoltaic component type corresponding to each target area; wherein the target detection rules include the correspondence between the preset category features and the preset feature labels of each type of photovoltaic panel;

[0071] The capacity calculation module 205 is used to calculate the distributed photovoltaic installed capacity based on the area of ​​the target area corresponding to each photovoltaic component type and the capacity corresponding to each photovoltaic component type to obtain a capacity calculation result.

[0072] It should be noted here that the examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the contents disclosed in the above embodiments.

[0073] It should be noted that the above modules as part of the device can be implemented by software or hardware, wherein the hardware environment includes a network environment.

[0074] An embodiment of the present invention also provides a computer device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus, the memory is used to store a computer program; the processor is used to execute a method in any of the above embodiments by running the computer program stored in the memory.

[0075] Figure 3 is a structural block diagram of an optional computer device according to an embodiment of the present application, such as Figure 3 As shown, it includes a processor 10, a communication interface 20, a memory 30 and a communication bus 40, wherein the processor 10, the communication interface 20 and the memory 30 communicate with each other through the communication bus 40, wherein:

[0076] A memory 30, for storing computer programs;

[0077] The processor 10 is used to implement the distributed photovoltaic installed capacity calculation method as described in any of the above embodiments when executing the computer program stored in the memory 30.

[0078] Optionally, in this embodiment, the communication bus may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0079] The communication interface is used for communication between the above-mentioned computer device and other devices.

[0080] The memory may include RAM, or may include non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0081] The above-mentioned processor can be a general-purpose processor, which can include but not be limited to: CPU (Central Processing Unit), NP (Network Processor), etc.; it can also be DSP (Digital Signal Processing), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0082] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment will not be described in detail here.

[0083] It can be understood by those skilled in the art that Figure 3 The structure shown is for illustration only. The device for implementing any one of the methods in the above embodiments may be a terminal device, which may be a smart phone (such as an Android phone, an IOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (Mobile Internet Devices, MID), a PAD, or other terminal devices. Figure 3 It does not limit the structure of the above electronic device. For example, the terminal device may also include Figure 3 More or fewer components (such as network interfaces, display devices, etc.) shown in, or having Figure 3Different configurations shown.

[0084] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, which can include: a flash drive, ROM, RAM, a magnetic disk or an optical disk, etc.

[0085] As an exemplary embodiment, the present application also provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute any one of the method steps of the present embodiment when running.

[0086] Optionally, in this embodiment, the above-mentioned storage medium can be used to execute the program code of the method steps of the embodiment of the present application.

[0087] Optionally, in this embodiment, the storage medium may be located on at least one network device among a plurality of network devices in the network shown in the above embodiment.

[0088] Optionally, in this embodiment, the storage medium is configured to store data for executing the method in the above embodiment.

[0089] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, which will not be described in detail in this embodiment.

[0090] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media that can store program codes, such as a USB flash drive, a ROM, a RAM, a mobile hard disk, a magnetic disk, or an optical disk.

[0091] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0092] If the integrated units in the above embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in the above computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling one or more computer devices (which may be personal computers, servers or network devices, etc.) to execute all or part of the steps of the method in the above embodiments.

[0093] In the several embodiments provided in the present application, it should be understood that the disclosed client can be implemented in other ways. Among them, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0094] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution provided in this embodiment.

[0095] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0096] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0097] The above are only preferred implementations of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for calculating the installed capacity of distributed photovoltaic power generation, characterized in that: The distributed photovoltaic installed capacity calculation method includes: Obtaining orthophoto data of a satellite at a preset spatial scale; wherein the preset spatial scale includes the geographical location of each of the distributed photovoltaic components; Performing photovoltaic component segmentation on the orthophoto data to obtain a plurality of segmented images including each of the distributed photovoltaic components; Performing edge detection on each of the segmented images to obtain a target area corresponding to each of the distributed photovoltaic components; wherein the boundary of the target area is used to represent the contour edge of the distributed photovoltaic component; Based on the preset target detection rules, the photovoltaic components of the target area are identified to obtain the photovoltaic component type corresponding to each target area; wherein the target detection rules include the corresponding relationship between the preset category features and the preset feature labels of each type of photovoltaic panel; The distributed photovoltaic installed capacity is calculated based on the area of ​​the target area corresponding to each photovoltaic component type and the capacity corresponding to each photovoltaic component type to obtain a capacity calculation result.

2. The distributed photovoltaic installed capacity calculation method according to claim 1, characterized in that: The performing target detection on the target area based on the preset target detection rule to obtain the component type corresponding to each target area includes: Obtaining the corresponding relationship between each category feature and feature label in the preset target detection rule; The target areas are subjected to target detection based on the category features to obtain component types corresponding to the target areas.

3. The distributed photovoltaic installed capacity calculation method according to claim 1, characterized in that: The performing edge detection on each of the segmented images to obtain a target area of ​​each of the distributed photovoltaic components includes: Performing Gaussian filtering on each of the segmented images to obtain a filtered segmented image; Based on the Sobel operator, the gradient of the filtered segmented image is obtained to obtain a gradient result; Based on the gradient calculation result, the Canny algorithm is used to sequentially perform non-maximum suppression and double-threshold hysteresis threshold processing on the filtered segmented image to obtain the target area.

4. The distributed photovoltaic installed capacity calculation method according to claim 1, characterized in that: Before calculating the distributed photovoltaic installed capacity based on each of the target areas and the corresponding photovoltaic module types, the method further includes: Performing status detection on each of the target areas to obtain status detection results; The abnormal area in an abnormal state is removed from the target area.

5. The distributed photovoltaic installed capacity calculation method according to claim 4, characterized in that: The performing status detection on each of the target areas to obtain a status detection result includes: Performing semantic recognition on the boundaries of each target area to obtain actual semantic features of pixels constituting the boundaries of each target area; Comparing the actual semantic feature with the preset semantic feature to obtain a comparison result; wherein the preset semantic feature is used for the photovoltaic module outline; Calculate the actual proportion of normal pixels representing the outline of the photovoltaic module relative to the pixels constituting the boundary of each target area based on the comparison result; The target area whose actual proportion is not greater than the preset proportion is marked as an abnormal area.

6. The distributed photovoltaic installed capacity calculation method according to claim 4, characterized in that: The performing status detection on each of the target areas to obtain a status detection result also includes: Acquire preset geometric features of the photovoltaic module type corresponding to each of the target areas; Extracting actual geometric features of each of the target areas; wherein the actual geometric features are used to represent the boundary shape constituting the boundary of the target area; Comparing the actual geometric features with the preset geometric features to obtain a comparison result; Based on the comparison result, the target area whose actual geometric features do not meet the preset geometric features is marked as an abnormal area.

7. The distributed photovoltaic installed capacity calculation method according to any one of claims 5 or 6, characterized in that: The performing state detection on each of the target areas to obtain a state detection result also includes: Dividing the pixels in each of the target areas based on semantic features of the pixels to obtain abnormal pixels whose semantic features do not represent photovoltaic components; Calculate the actual abnormal proportion of the abnormal pixels in the target area; The target area where the actual abnormality ratio is greater than the preset abnormality ratio is marked as an abnormal state.

8. A distributed photovoltaic installed capacity calculation device, characterized in that: The distributed photovoltaic installed capacity calculation device comprises: A satellite data acquisition module, used to acquire orthophoto data of satellites at a preset spatial scale; wherein the preset spatial scale includes the geographical location of each of the distributed photovoltaic components; An image segmentation module is used to segment the orthophoto data into photovoltaic components to obtain a plurality of segmented images including each of the distributed photovoltaic components; An edge detection module is used to perform edge detection on each of the segmented images to obtain a target area corresponding to each of the distributed photovoltaic components; wherein the boundary of the target area is used to represent the contour edge of the distributed photovoltaic component; A photovoltaic component identification module, used to identify photovoltaic components in the target area based on a preset target detection rule to obtain the photovoltaic component type corresponding to each target area; wherein the target detection rule includes a correspondence between preset category features and preset feature labels of each type of photovoltaic panel; The capacity calculation module is used to calculate the distributed photovoltaic installed capacity based on the area of ​​the target area corresponding to each photovoltaic component type and the capacity corresponding to each photovoltaic component type to obtain a capacity calculation result.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the distributed photovoltaic installed capacity calculation method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the distributed photovoltaic installed capacity calculation method according to any one of claims 1 to 7.

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