CN114494830A - Multi-source information photovoltaic map generation method and device - Google Patents
Multi-source information photovoltaic map generation method and device Download PDFInfo
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Abstract
The invention relates to the technical field of remote sensing, and particularly provides a multi-source information photovoltaic map generation method, which comprises the steps of designing a photovoltaic facility identification model and a photovoltaic facility laying recommendation model based on a remote sensing image top view by utilizing the correlation among remote sensing data multiband and combining actual operation data of a photovoltaic energy management platform, and acquiring photovoltaic laying inquiry analysis results by utilizing an image character photovoltaic analysis identification model through inquiring photovoltaic laying related data from internet images; and integrating the multi-party data to perform target identification and mutual cross validation, and constructing a photovoltaic map based on the remote sensing image map additional real-time operation data and photovoltaic laying recommendation to form more accurate photovoltaic system operation and planning. Compared with the prior art, the photovoltaic energy management platform is used for constructing the photovoltaic map, so that more precise photovoltaic system operation is formed, and the platform operation efficiency is improved.
Description
Technical Field
The invention relates to the technical field of remote sensing, and particularly provides a multi-source information photovoltaic map generation method and device.
Background
With the rapid development of deep learning technology and the support of mass data and high-efficiency computing power in the times of internet and cloud computing, a large-scale neural network similar to a human brain structure is obtained by training and constructing the deep learning technology represented by a CNN convolutional neural network, so that breakthrough progress is made in the fields of computer vision, speech recognition, natural language understanding and the like, and subversive changes are being brought to the whole society.
In recent years, the remote sensing technology is more widely applied, multispectral images and full-color images obtained by satellite shooting form remote sensing images with higher spatial resolution and spectral resolution through image fusion, and the remote sensing image has more advantages than other technical means in the aspects of obtaining basic geographic data, resource information and emergency disaster data and is widely applied to the fields of national economy and military.
The global development and utilization of traditional fossil energy sources on a large scale have increasingly highlighted problems of environmental pollution, climate change and the like, and solar energy is the cleanest, safe and reliable energy source in the future, and the development and utilization of solar energy has been planned for a long time as the main content of energy revolution. Photovoltaic (photo voltaic) is a short name for Solar Photovoltaic power generation systems (Solar power systems), and is a novel power generation system which directly converts Solar radiation energy into electric energy by utilizing the Photovoltaic effect of a Solar cell semiconductor material. The photovoltaic system has the advantages of being small in distribution and scale, uncertain in power output, random and the like, controllability of the photovoltaic system is remarkably reduced, the photovoltaic system needs to be managed and distributed more finely, meanwhile, safety of the photovoltaic system, light pollution and other factors need to be comprehensively considered, higher requirements are provided for operation management of the photovoltaic system, a photovoltaic map is built by means of a remote sensing technology, reasonable planning and accurate management of the photovoltaic system become a research hotspot, and through displaying information such as sunshine and shadow states of a roof, photovoltaic resources and development mode selection, guiding service is provided for development contractors, and meanwhile unified and accurate management of photovoltaic facilities based on positions and maps is achieved.
Under the circumstance, how to effectively fuse technologies such as big data, remote sensing technology and deep learning to generate a photovoltaic map and provide support for photovoltaic rational planning and accurate management becomes a problem which needs to be solved urgently.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a strong-practicability multi-source information photovoltaic map generation method
The invention further aims to provide a multi-source information photovoltaic map generation device which is reasonable in design, safe and applicable.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a multi-source information photovoltaic map generation method comprises the steps of designing a photovoltaic facility identification model and a photovoltaic facility laying recommendation model based on a remote sensing image top view by utilizing correlation among remote sensing data multiband and combining actual operation data of a photovoltaic energy management platform, and acquiring photovoltaic laying inquiry analysis results by utilizing an image character photovoltaic analysis identification model through inquiring photovoltaic laying related data from internet images;
target identification and mutual cross validation are carried out by integrating multi-party data, a photovoltaic map is constructed based on the remote sensing image map and the additional real-time operation data and photovoltaic laying recommendation, and more accurate photovoltaic system operation and planning are formed.
Further, the remote sensing image is an image picture generated by image fusion of a multispectral image and a full-color image obtained by satellite shooting; the photovoltaic energy management platform is deployed in a cloud data center and used for providing photovoltaic facility data acquisition, and the cloud data center meets the resource requirement required by the operation of the photovoltaic energy management platform;
the photovoltaic facility identification model RS-PVI-Det core is a target detection model of the CNN convolutional neural network, and a target area of the photovoltaic facility is identified by inputting a remote sensing image for target detection;
the recommended model RS-PVI-Pred core for photovoltaic facility laying is a target detection model based on a CNN convolutional neural network, and based on remote sensing images, the recommended model RS-PVI-Pred core outputs areas suitable for photovoltaic laying and the degree suitable for laying photovoltaic facilities;
the IMG-PVI-Det core is a network model which is based on text recognition and image recognition and performs comprehensive analysis, semantic data in the IMG-PVI-Det core is extracted, and comprehensive analysis and recognition are performed by combining image recognition results to obtain photovoltaic paving query analysis results;
performing Cross validation to obtain a Cross validation model, wherein the Cross validation model Cross-Check performs judgment to form a judgment result based on an output result from the photovoltaic facility identification model RS-PVI-Det and the image character photovoltaic analysis identification model IMG-PVI-Det based on the same coordinate position area by adopting a mode of combining a preset rule with a neural network;
the photovoltaic Map generation module PVI-Map-Gen is used for constructing a photovoltaic Map by adding operation data such as real-time acquisition data and power generation amount of photovoltaic facilities on the basis of the identification result and data of actual photovoltaic facilities managed by the photovoltaic energy management platform on the basis of a remote sensing image Map, providing recommendation suggestions of a photovoltaic laying area and displaying planning prediction data such as expected income and power generation amount.
Further, the training for the photovoltaic facility identification model comprises:
s101, collecting remote sensing image data and marking photovoltaic facilities;
s102, designing a photovoltaic facility identification model RS-PVI-Det based on a target detection model;
s103, carrying out image cutting on the remote sensing image data according to the input size of the photovoltaic facility identification model RS-PVI-Det;
s104, training the photovoltaic facility identification model RS-PVI-Det to form the photovoltaic facility identification model RS-PVI-Det.
Further, training of the photovoltaic facility laying recommendation model comprises the following steps:
training of a photovoltaic facility laying recommendation model, comprising:
s201, collecting photovoltaic facility related data of actual planning operation provided by a photovoltaic energy management platform;
s202, marking a remote sensing image provided by a photovoltaic energy management platform and used before laying of an actually operated photovoltaic facility based on an actual laying condition;
s203, performing data annotation on the remote sensing image on the photovoltaic facility area planned and constructed by the photovoltaic energy management platform;
s204, designing the recommended model RS-PVI-Pred for laying the photovoltaic facility;
s205, image cutting is carried out on the remote sensing image data according to the input size of the recommended model RS-PVI-Pred laid by the photovoltaic facility;
s206, training the recommended model RS-PVI-Pred for photovoltaic facility laying to form the recommended model RS-PVI-Pred for photovoltaic facility laying.
Further, training of the image character photovoltaic analysis recognition model comprises:
s301, collecting related data with coordinate position data on the digital media through an Internet photovoltaic information query module IE-PVI-Search;
s302, cleaning, filtering and grouping the collected data, and labeling the data;
s303, designing the image character photovoltaic analysis recognition model IMG-PVI-Det;
and S304, taking the marked keyword data as input, performing combined training on the model, and forming an image character photovoltaic analysis recognition model IMG-PVI-Det.
Further, the cross-validation model training comprises:
s401, forming a basic input data set by the photovoltaic facility identification model RS-PVI-Det and the image character photovoltaic analysis identification model IMG-PVI-Det based on the output result of the same coordinate position area based on remote sensing image data and internet digital media data;
s402, performing cross validation result judgment data annotation on the basic input data set to form a data set;
s403, designing the Cross-validation model Cross-Check;
s404, fixing network parameters of the photovoltaic facility identification model RS-PVI-Det and the image character photovoltaic analysis identification model IMG-PVI-Det, and performing combined training on a Cross validation model Cross-Check;
s405, extracting and setting business rules according to the actual photovoltaic facility data of the photovoltaic energy platform, and combining a Cross-Check-verification model with joint training to form the Cross-Check-verification model.
Further, the step of generating the photovoltaic map comprises:
s501, setting a map generation area position range;
s502, identifying through the photovoltaic facility identification model RS-PVI-Det based on remote sensing image data and a set region position range, and identifying a photovoltaic facility target region;
s503, searching digital media in a set area through an Internet photovoltaic information query module IE-PVI-Search in remote sensing image data and the set area range, and analyzing and identifying based on the image character photovoltaic analysis identification model IMG-PVI-Det to obtain a target result;
s504, judging results output by the photovoltaic facility identification model RS-PVI-Det and the image character photovoltaic analysis identification model IMG-PVI-Det according to data in the same area and actual photovoltaic facility data of the photovoltaic energy platform through the Cross validation model Cross-Check;
s505, based on the remote sensing image data and the set region position range, outputting a recommended photovoltaic facility laying region and a recommended degree value by using the recommended photovoltaic facility laying model RS-PVI-Pred;
and S506, generating a photovoltaic map based on the remote sensing image data and the position data and by combining the actual photovoltaic facility data and the planning data of the photovoltaic energy platform according to the output from the step S502 to the step S505.
Further, the application and optimization of the photovoltaic map comprise:
s601, acquiring data of a photovoltaic facility in real time by using a photovoltaic energy platform, attaching the data to a map for displaying, and continuously updating;
s602, acquiring data of photovoltaic facilities in real time by a photovoltaic energy platform, attaching the data to a map for display, distinguishing the data through colors, and providing related estimation data for decision planning;
s603, an external photovoltaic energy platform is docked, data are accessed to the platform, are added to a photovoltaic map according to position data and are displayed, and the data are distinguished according to colors;
s604, according to different concerns, performing personalized data display, displaying information such as roof sunshine and shadow states, photovoltaic resource and development mode selection and the like, collecting actual feedback, continuously optimizing the model, and generating a more reasonable and accurate photovoltaic map.
A multi-source information photovoltaic map generation device, comprising: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor is used for calling the machine readable program and executing a multi-source information photovoltaic map generation method.
Compared with the prior art, the multi-source information photovoltaic map generation method and device have the following outstanding beneficial effects:
according to the method, the spectral characteristic features of remote sensing images and the conventional selected positions of photovoltaic laying are fully considered, a photovoltaic facility identification model and a photovoltaic facility laying recommendation model are designed based on a remote sensing image top view and in combination with a deep learning target detection algorithm, and operation planning data of a photovoltaic energy management platform are considered, so that a more accurate photovoltaic map is formed, reasonable photovoltaic planning and accurate management are realized, photovoltaic illegal building projects are actively discovered, the laying safety of photovoltaic facilities is guaranteed, and light pollution is reduced.
Compared with the traditional remote sensing image analysis and identification mode, the image identification mode has lower cost, effectively utilizes the internet data position information and the image character information, and improves the identification accuracy through mutual verification of multi-source fusion; the accuracy of the photovoltaic map is further improved by adopting the actual photovoltaic facility data of the photovoltaic energy management platform, and the processing efficiency is improved.
The photovoltaic facility is laid and recommends and adopt the degree of depth learning model, and the intrinsic relation that exists between the photovoltaic region of discovery that can be better is laid, discerns the photovoltaic region of laying, combines illumination information estimation area and photovoltaic installed capacity, provides effectual support for accurate planning. In addition, based on multi-source data, photovoltaic facility data updated in real time and external platform docking data, personalized data display is achieved based on a photovoltaic map, models are optimized continuously, more precise photovoltaic system operation is formed, and platform operation efficiency is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow diagram of a multi-source information photovoltaic map generation method.
Detailed Description
The present invention will be described in further detail with reference to specific embodiments in order to better understand the technical solutions of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A preferred embodiment is given below:
as shown in fig. 1, in the multi-source information photovoltaic map generation method in this embodiment, correlation between remote sensing data multiple bands is fully considered, a photovoltaic facility identification model and a photovoltaic facility laying recommendation model are designed based on a remote sensing image top view by combining actual operation data of a photovoltaic energy management platform, photovoltaic laying related data including images from the internet is queried, and a photovoltaic laying query analysis result is obtained by using an image character photovoltaic analysis identification model.
Target identification and mutual cross validation are carried out by integrating multi-party data, a photovoltaic map is constructed based on the remote sensing image map and the additional real-time operation data and photovoltaic laying recommendation, and more accurate photovoltaic system operation and planning are formed.
The remote sensing image is an image picture generated by fusing a multispectral image and a full-color image which are obtained by satellite shooting; the photovoltaic energy management platform is deployed in a cloud data center, functions of real-time data acquisition, asset management, instruction control, OTA (over the air) upgrading, information display and the like of photovoltaic facilities are provided, infrastructure services such as calculation, storage and network are provided by the cloud data center, the resource requirements required by the operation of the photovoltaic energy management platform are met, and meanwhile, service of the Internet of things, database service, AI (Artificial intelligence) service, deep learning model training and the like are provided.
The photovoltaic facility identification model RS-PVI-Det core is a target detection model of the CNN convolutional neural network, and a target area of the photovoltaic facility is identified by inputting a remote sensing image to perform target detection. The recommended model RS-PVI-Pred core for photovoltaic facility laying is a target detection model based on a CNN convolutional neural network, and based on remote sensing images, the region suitable for photovoltaic laying and the degree suitable for laying photovoltaic facilities are output.
The internet photovoltaic laying related information is photovoltaic laying related data containing images of digital media such as microblogs, blogs and news from the internet, and is searched and obtained through an internet photovoltaic information query module. The internet photovoltaic information query module IE-PVI-Search queries digital media such as microblogs, blogs, news and the like based on position coordinate information or photovoltaic laying character keywords by means of a digital media Search engine, and acquires the internet photovoltaic laying related information.
The image character photovoltaic analysis and recognition model IMG-PVI-Det core is a network model which is based on text recognition and image recognition and carries out comprehensive analysis, semantic data in the network model are extracted, and comprehensive analysis and recognition are carried out by combining image recognition results to obtain photovoltaic paving query analysis results.
The Cross verification model Cross-Check is used for judging a formed judgment result based on the output results of the photovoltaic facility identification model RS-PVI-Det and the image character photovoltaic analysis identification model IMG-PVI-Det based on the same coordinate position area by adopting a mode of combining a preset rule with a neural network.
The photovoltaic Map generation module PVI-Map-Gen is on the remote sensing image Map, adds operation data such as real-time acquisition data and power generation amount of the photovoltaic facilities to construct a photovoltaic Map based on the identification result and the data of the actual photovoltaic facilities managed by the photovoltaic energy management platform, provides recommendation suggestions of a photovoltaic laying area, and displays planning prediction data such as expected income and power generation amount.
Training for photovoltaic facility identification models includes:
s101, collecting remote sensing image data and marking photovoltaic facilities;
s102, designing a photovoltaic facility identification model RS-PVI-Det based on a target detection model;
s103, carrying out image cutting on the remote sensing image data according to the input size of the photovoltaic facility identification model RS-PVI-Det;
s104, training the photovoltaic facility identification model RS-PVI-Det to form the photovoltaic facility identification model RS-PVI-Det.
The training of the photovoltaic facility laying recommendation model comprises the following steps:
s201, collecting photovoltaic facility related data of actual planning operation provided by a photovoltaic energy management platform;
s202, marking a remote sensing image provided by a photovoltaic energy management platform and used before laying of an actually operated photovoltaic facility based on an actual laying condition;
s203, performing data annotation on the photovoltaic facility region planned and constructed by the photovoltaic energy management platform on a remote sensing image;
s204, designing the recommended model RS-PVI-Pred for laying the photovoltaic facility;
s205, image cutting is carried out on the remote sensing image data according to the input size of the recommended model RS-PVI-Pred laid by the photovoltaic facility;
s206, training the recommended model RS-PVI-Pred for photovoltaic facility laying to form the recommended model RS-PVI-Pred for photovoltaic facility laying.
The training of the image character photovoltaic analysis recognition model comprises the following steps:
s301, collecting relevant data, such as photovoltaics, roofs, top views, unmanned planes and the like, with coordinate position data on digital media, such as microblogs, blogs, news and the like, from the Internet through an Internet photovoltaic information query module IE-PVI-Search;
s302, cleaning, filtering and grouping the collected data, and labeling the data;
s303, designing the image character photovoltaic analysis recognition model IMG-PVI-Det;
s304, taking the marked images and characters, combining data such as coordinates, shooting dates and search keywords as input, and performing combined training on the model to form an image character photovoltaic analysis recognition model IMG-PVI-Det.
The cross validation model training comprises:
s401, based on remote sensing image data and internet digital media data, forming a basic input data set by the photovoltaic facility identification model RS-PVI-Det and the image character photovoltaic analysis identification model IMG-PVI-Det based on output results of the same coordinate position area;
s402, performing cross validation result judgment data annotation on the basic input data set to form a data set;
s403, designing the Cross-validation model Cross-Check;
s404, fixing network parameters of the photovoltaic facility identification model RS-PVI-Det and the image character photovoltaic analysis identification model IMG-PVI-Det, and performing combined training on a Cross validation model Cross-Check;
s405, extracting and setting business rules according to the actual photovoltaic facility data of the photovoltaic energy platform, and combining a Cross-Check-verification model with joint training to form the Cross-Check-verification model.
The steps in generating the photovoltaic map include:
s501, setting a map generation area position range;
s502, identifying through the photovoltaic facility identification model RS-PVI-Det based on remote sensing image data and a set region position range, and identifying a photovoltaic facility target region;
s503, searching digital media such as microblogs, blogs and news in a set area through an Internet photovoltaic information query module IE-PVI-Search in the remote sensing image data and the set area range, and analyzing and identifying based on the image character photovoltaic analysis and identification model IMG-PVI-Det to obtain results such as a target image area, a geographic position coordinate, semantics and confidence coefficient;
s504, judging results output by the photovoltaic facility identification model RS-PVI-Det and the image character photovoltaic analysis identification model IMG-PVI-Det according to data in the same area and actual photovoltaic facility data of the photovoltaic energy platform through the Cross validation model Cross-Check;
s505, based on the remote sensing image data and the set region position range, outputting a recommended photovoltaic facility laying region and a recommended degree value by using the recommended photovoltaic facility laying model RS-PVI-Pred;
and S506, generating a photovoltaic map based on the remote sensing image data and the position data and by combining the actual photovoltaic facility data and the planning data of the photovoltaic energy platform according to the output from the step S502 to the step S505.
Application and optimization of a photovoltaic map include:
s601, acquiring data of a photovoltaic facility in real time by using a photovoltaic energy platform, attaching the data to a map for displaying, and continuously updating;
s602, acquiring data of photovoltaic facilities in real time by a photovoltaic energy platform, attaching the data to a map for display, distinguishing the data through colors, and providing related estimation data for decision planning;
s603, an external photovoltaic energy platform is docked, data are accessed to the platform, are added to a photovoltaic map according to position data and are displayed, and the data are distinguished according to colors;
s604, according to different concerns, performing personalized data display, displaying information such as roof sunshine and shadow states, photovoltaic resource and development mode selection and the like, collecting actual feedback, continuously optimizing the model, and generating a more reasonable and accurate photovoltaic map.
Based on the method, the multi-source information photovoltaic map generation device comprises the following steps: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor is used for calling the machine readable program and executing a multi-source information photovoltaic map generation method.
The above embodiments are only specific cases of the present invention, and the scope of the present invention includes but is not limited to the above embodiments, and any suitable changes or substitutions that are consistent with the claims of the multi-source information photovoltaic map generation method and apparatus of the present invention and are made by those skilled in the art shall fall within the scope of the present invention.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (9)
1. A multi-source information photovoltaic map generation method is characterized in that correlation among remote sensing data multiband is utilized, actual operation data of a photovoltaic energy management platform are combined, a photovoltaic facility identification model and a photovoltaic facility laying recommendation model are designed based on a remote sensing image top view, photovoltaic laying related data from an internet image are inquired, and a photovoltaic laying inquiry analysis result is obtained through an image character photovoltaic analysis identification model;
target identification and mutual cross validation are carried out by integrating multi-party data, a photovoltaic map is constructed based on the remote sensing image map and the additional real-time operation data and photovoltaic laying recommendation, and more accurate photovoltaic system operation and planning are formed.
2. The method for generating the multi-source information photovoltaic map according to claim 1, wherein the remote sensing image is an image picture generated by image fusion of a multispectral image and a full-color image obtained by satellite shooting; the photovoltaic energy management platform is deployed in a cloud data center and used for providing photovoltaic facility data acquisition, and the cloud data center meets the resource requirement required by the operation of the photovoltaic energy management platform;
the photovoltaic facility identification model RS-PVI-Det core is a target detection model of the CNN convolutional neural network, and a target area of the photovoltaic facility is identified by inputting a remote sensing image for target detection;
the recommended model RS-PVI-Pred core for photovoltaic facility laying is a target detection model based on a CNN convolutional neural network, and based on remote sensing images, the recommended model RS-PVI-Pred core outputs areas suitable for photovoltaic laying and the degree suitable for laying photovoltaic facilities;
the IMG-PVI-Det core is a network model which is based on text recognition and image recognition and performs comprehensive analysis, semantic data in the IMG-PVI-Det core is extracted, and comprehensive analysis and recognition are performed by combining image recognition results to obtain photovoltaic paving query analysis results;
performing Cross validation to obtain a Cross validation model, wherein the Cross validation model Cross-Check performs judgment to form a judgment result based on an output result from the photovoltaic facility identification model RS-PVI-Det and the image character photovoltaic analysis identification model IMG-PVI-Det based on the same coordinate position area by adopting a mode of combining a preset rule with a neural network;
the photovoltaic Map generation module PVI-Map-Gen is used for constructing a photovoltaic Map by adding operation data such as real-time acquisition data and power generation amount of photovoltaic facilities on the basis of the identification result and data of actual photovoltaic facilities managed by the photovoltaic energy management platform on the basis of a remote sensing image Map, providing recommendation suggestions of a photovoltaic laying area and displaying planning prediction data such as expected income and power generation amount.
3. The multi-source information photovoltaic map generation method of claim 2, wherein training for a photovoltaic facility recognition model comprises:
s101, collecting remote sensing image data and marking photovoltaic facilities;
s102, designing a photovoltaic facility identification model RS-PVI-Det based on a target detection model;
s103, carrying out image cutting on the remote sensing image data according to the input size of the photovoltaic facility identification model RS-PVI-Det;
s104, training the photovoltaic facility identification model RS-PVI-Det to form the photovoltaic facility identification model RS-PVI-Det.
4. The multi-source information photovoltaic map generation method according to claim 3, wherein training of the photovoltaic facility laying recommendation model comprises:
s201, collecting photovoltaic facility related data of actual planning operation provided by a photovoltaic energy management platform;
s202, marking a remote sensing image provided by a photovoltaic energy management platform and used before laying of an actually operated photovoltaic facility based on an actual laying condition;
s203, performing data annotation on the photovoltaic facility region planned and constructed by the photovoltaic energy management platform on a remote sensing image;
s204, designing the recommended model RS-PVI-Pred for laying the photovoltaic facility;
s205, image cutting is carried out on the remote sensing image data according to the input size of the recommended model RS-PVI-Pred laid by the photovoltaic facility;
s206, training the recommended model RS-PVI-Pred for photovoltaic facility laying to form the recommended model RS-PVI-Pred for photovoltaic facility laying.
5. The method for generating the multi-source information photovoltaic map according to claim 4, wherein the training of the image character photovoltaic analysis recognition model comprises:
s301, collecting related data with coordinate position data on the digital media through an Internet photovoltaic information query module IE-PVI-Search;
s302, cleaning, filtering and grouping the collected data, and labeling the data;
s303, designing the image character photovoltaic analysis recognition model IMG-PVI-Det;
and S304, taking the marked keyword data as input, performing combined training on the model, and forming an image character photovoltaic analysis recognition model IMG-PVI-Det.
6. The method of claim 5, wherein cross validation model training comprises:
s401, forming a basic input data set by the photovoltaic facility identification model RS-PVI-Det and the image character photovoltaic analysis identification model IMG-PVI-Det based on the output result of the same coordinate position area based on remote sensing image data and internet digital media data;
s402, performing cross validation result judgment data annotation on the basic input data set to form a data set;
s403, designing the Cross-validation model Cross-Check;
s404, fixing network parameters of the photovoltaic facility identification model RS-PVI-Det and the image character photovoltaic analysis identification model IMG-PVI-Det, and performing combined training on a Cross validation model Cross-Check;
s405, extracting and setting business rules according to the actual photovoltaic facility data of the photovoltaic energy platform, and combining a Cross-Check-verification model with joint training to form the Cross-Check-verification model.
7. The method for generating the multi-source information photovoltaic map according to claim 6, wherein the step of generating the photovoltaic map comprises:
s501, setting a map generation area position range;
s502, identifying through the photovoltaic facility identification model RS-PVI-Det based on remote sensing image data and a set region position range, and identifying a photovoltaic facility target region;
s503, searching digital media in a set area through an Internet photovoltaic information query module IE-PVI-Search in remote sensing image data and the set area range, and analyzing and identifying based on the image character photovoltaic analysis identification model IMG-PVI-Det to obtain a target result;
s504, judging results output by the photovoltaic facility identification model RS-PVI-Det and the image character photovoltaic analysis identification model IMG-PVI-Det according to data in the same area and actual photovoltaic facility data of the photovoltaic energy platform through the Cross validation model Cross-Check;
s505, based on the remote sensing image data and the set region position range, outputting a recommended photovoltaic facility laying region and a recommended degree value by using the recommended photovoltaic facility laying model RS-PVI-Pred;
and S506, generating a photovoltaic map based on the remote sensing image data and the position data and by combining the actual photovoltaic facility data and the planning data of the photovoltaic energy platform according to the output from the step S502 to the step S505.
8. The method for generating the multi-source information photovoltaic map according to claim 7, wherein the application and optimization of the photovoltaic map comprises:
s601, acquiring data of a photovoltaic facility in real time by using a photovoltaic energy platform, attaching the data to a map for displaying, and continuously updating;
s602, acquiring data of photovoltaic facilities in real time by a photovoltaic energy platform, attaching the data to a map for display, distinguishing the data through colors, and providing related estimation data for decision planning;
s603, an external photovoltaic energy platform is docked, data are accessed to the platform, are added to a photovoltaic map according to position data and are displayed, and the data are distinguished according to colors;
s604, according to different concerns, performing personalized data display, displaying information such as roof sunshine and shadow states, photovoltaic resource and development mode selection and the like, collecting actual feedback, continuously optimizing the model, and generating a more reasonable and accurate photovoltaic map.
9. A multi-source information photovoltaic map generation device, comprising: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor, configured to invoke the machine readable program, to perform the method of any of claims 1 to 8.
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