CN114120108A - Roof photovoltaic intelligent identification method based on satellite remote sensing technology - Google Patents
Roof photovoltaic intelligent identification method based on satellite remote sensing technology Download PDFInfo
- Publication number
- CN114120108A CN114120108A CN202111371222.0A CN202111371222A CN114120108A CN 114120108 A CN114120108 A CN 114120108A CN 202111371222 A CN202111371222 A CN 202111371222A CN 114120108 A CN114120108 A CN 114120108A
- Authority
- CN
- China
- Prior art keywords
- remote sensing
- photovoltaic
- roof
- intelligent
- image
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Life Sciences & Earth Sciences (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Probability & Statistics with Applications (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Biology (AREA)
- Biodiversity & Conservation Biology (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- General Engineering & Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a roof photovoltaic intelligent identification method based on a satellite remote sensing technology, which aims to solve the problem that most roof photovoltaic statistical methods are manually investigated and measured in the field and require a large amount of manpower and time. The invention comprises the following steps: step 1: establishing a remote sensing image library; step 2, segmenting the image by using a K-means algorithm; step 3, extracting the building roof based on a line segment analysis method; step 4, identifying by using an intelligent algorithm/model, and deeply learning a network model; step 5, establishing a roof photovoltaic intelligent recognition library; and 6, establishing the intelligent identification system for the roof photovoltaic.
Description
Technical Field
The invention belongs to the technical field of computer vision identification, and relates to a roof photovoltaic intelligent identification method based on a satellite remote sensing technology.
Background
Along with the continuous development and the combination of computer technology and spatial information technology, to present photovoltaic roof installation popularization difficult, the installation dispersion is unfavorable for problems such as management, and the installation urgently needs make full use of digital means, strives to promote the photovoltaic development and gets into the 3.0 times of popular sharing, digital efficient for the novel electric power method of using new forms of energy as the main part is built to the construction, effectively reduces energy field carbon and discharges.
Disclosure of Invention
In order to overcome the problems in the prior art, the invention aims to provide a roof photovoltaic intelligent identification method based on a satellite remote sensing technology, so as to solve the problems that most roof photovoltaic statistical methods in the background technology are investigated and measured manually, although the precision is high, a large amount of manpower and time are needed.
In order to achieve the above purpose, the solution of the invention is as follows:
a roof photovoltaic intelligent identification method based on a satellite remote sensing technology comprises the following steps:
step 1: preparing image data and establishing a remote sensing image library;
step 2: segmenting the image by using a K-means algorithm;
and step 3: extracting the roof of the building based on a line segment analysis method;
and 4, step 4: recognizing by using an intelligent algorithm/model and deeply learning a network model;
and 5: and establishing a roof photovoltaic intelligent identification library.
And in the step 1, geometric correction, light and color evening and mosaic are carried out on the remote sensing image data.
And 2, segmenting the image by using a K-means algorithm, specifically, randomly selecting K points from a remote sensing image library as an initial clustering center, redistributing the sample objects according to the principle of being closest to the clustering center, and finally obtaining the segmented remote sensing image result.
And 3, extracting the building roof based on a line segment analysis method, extracting line segments according to the linear features of the image building area, optimizing the smooth feature line segments through an algorithm, and solving to obtain the target building roof.
In the step 4, the component supports the intelligent automatic interpretation of the roof type solar power plant based on the artificial intelligent algorithm by manually interpreting the roof information and processing the information into the pixel level label library through normalization.
In the step 5, a training data sample set is established through the source domain image with the label and the target domain image without the label, and intelligent identification is carried out on the roof data and the photovoltaic panel data in the remote sensing image through the data in the sample set.
Step 6: and obtaining the roof photovoltaic intelligent identification system which comprises management information and an interface.
The invention has the beneficial effects that: the method is used for intelligently identifying roof data and photovoltaic panel data in remote sensing images, preliminarily estimating the power generation capacity of each site based on the site areas of different types, the sunlight intensity, the sunlight time and the like, obtaining the power consumption cost and the like of the building by combining the types of the building, and measuring the investment return ratio and the like of a user according to a construction scheme, construction cost, grid-connected income, self-use energy conservation and other dimensions, so that the popularization and construction work of a roof-type photovoltaic new energy distributed method in the relevant departments of a power grid can be facilitated, the utilization rate of new energy is improved, and the aim of reducing carbon emission is fulfilled.
Drawings
Fig. 1 is a flowchart of an intelligent identification method for rooftop photovoltaic based on satellite remote sensing technology according to an embodiment.
Fig. 2 is a management interface diagram of a rooftop photovoltaic intelligent recognition method based on a satellite remote sensing technology according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clearly understood, the following describes in detail a roof photovoltaic intelligent recognition method based on satellite remote sensing technology, by way of embodiments, with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, the invention provides a roof photovoltaic intelligent identification method based on a satellite remote sensing technology, which specifically comprises the following steps:
step 1: firstly, different types of image data are prepared, and geometric correction, light and color evening and mosaic are carried out on the image data to obtain a remote sensing image result.
Step 2: and randomly selecting K points from a remote sensing image library as an initial clustering center, then redistributing the sample objects according to the principle of being closest to the clustering center, and finally obtaining the segmented remote sensing image result.
And step 3: and extracting line segments according to the linear features of the image building area, optimizing the smooth feature line segments through an algorithm, and solving to obtain the target building roof.
And 4, step 4: intelligent automatic interpretation of a roof-type solar farm based on an artificial intelligent algorithm is supported by utilizing intelligent algorithm/model recognition, through a deep learning network model, a semantic-based tag library of components, through manual interpretation of roof information, and through normalization processing into a pixel-level tag library.
And 5: establishing a roof photovoltaic intelligent recognition library, establishing a training data sample set through a source domain image with a label and a target domain image without the label, and intelligently recognizing roof data and photovoltaic panel data in a remote sensing image through data in the sample set.
Step 6: the intelligent identification system for the roof photovoltaic is obtained, and as shown in fig. 2, the main functions include basic information management: management of managing basic information such as a venue type, a user type, a power rate type, and the like; photovoltaic panel and manufacturer management: managing basic information of a photovoltaic panel and basic information of a photovoltaic manufacturer; site management: managing all sites suitable for building photovoltaic power plants (including constructable, convertible, constructed, non-constructable); user management: managing basic information of users, all sites suitable for building photovoltaic power stations of the users and the like; and (3) measuring and calculating the investment return: providing a photovoltaic construction scheme for a user, carrying out investment return and income measurement and calculation and the like; visual report forms: providing visual statistics of all photovoltaic power generation assets within the regional power grid jurisdiction range; the special subject report: and providing thematic reports of various dimension types according to construction conditions, inventory conditions, power generation conditions and the like.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (7)
1. A roof photovoltaic intelligent identification method based on a satellite remote sensing technology is characterized by comprising the following steps:
step 1: preparing image data and establishing a remote sensing image library;
step 2: segmenting the image by using a K-means algorithm;
and step 3: extracting the roof of the building based on a line segment analysis method;
and 4, step 4: recognizing by using an intelligent algorithm/model and deeply learning a network model;
and 5: and establishing a roof photovoltaic intelligent identification library.
2. The intelligent identification method for rooftop photovoltaic based on satellite remote sensing technology as claimed in claim 1, wherein in step 1, the remote sensing image data is subjected to geometric correction, light and color evening and mosaic.
3. The intelligent rooftop photovoltaic identification method based on satellite remote sensing technology as claimed in claim 1, wherein in step 2, the image is segmented by using a K-means algorithm, specifically, K points are randomly selected from a remote sensing image library as an initial clustering center, a sample object is redistributed according to a principle that the distance from the initial clustering center is closest to the initial clustering center, and finally, a segmented remote sensing image result is obtained.
4. The intelligent identification method for the photovoltaic of the roof based on the satellite remote sensing technology as claimed in claim 1, wherein in the step 3, the roof of the building is extracted based on a line segment analysis method, a line segment is extracted according to the straight line characteristics of the image building area, a smooth characteristic line segment is optimized through an algorithm, and the roof of the target building is obtained through solving.
5. The intelligent rooftop photovoltaic identification method based on satellite remote sensing technology as claimed in claim 1, wherein in step 4, the component is based on semantic tag library, and supports intelligent automatic interpretation of the rooftop solar farm based on artificial intelligence algorithm by manually interpreting rooftop information and processing into pixel level tag library through normalization.
6. The intelligent rooftop photovoltaic identification method based on satellite remote sensing technology as claimed in claim 1, wherein in step 5, a training data sample set is established through the labeled source domain image and the unlabeled target domain image, and intelligent identification is performed on rooftop data and photovoltaic panel data in the remote sensing image through data in the sample set.
7. The intelligent identification method for the rooftop photovoltaic based on the satellite remote sensing technology as claimed in claim 1, characterized in that, the step 6: and obtaining the roof photovoltaic intelligent identification system which comprises management information and an interface.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111371222.0A CN114120108A (en) | 2021-11-18 | 2021-11-18 | Roof photovoltaic intelligent identification method based on satellite remote sensing technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111371222.0A CN114120108A (en) | 2021-11-18 | 2021-11-18 | Roof photovoltaic intelligent identification method based on satellite remote sensing technology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114120108A true CN114120108A (en) | 2022-03-01 |
Family
ID=80397740
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111371222.0A Pending CN114120108A (en) | 2021-11-18 | 2021-11-18 | Roof photovoltaic intelligent identification method based on satellite remote sensing technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114120108A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114758252A (en) * | 2022-06-16 | 2022-07-15 | 南开大学 | Image-based distributed photovoltaic roof resource segmentation and extraction method and system |
-
2021
- 2021-11-18 CN CN202111371222.0A patent/CN114120108A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114758252A (en) * | 2022-06-16 | 2022-07-15 | 南开大学 | Image-based distributed photovoltaic roof resource segmentation and extraction method and system |
| CN114758252B (en) * | 2022-06-16 | 2022-11-11 | 南开大学 | Image-based distributed photovoltaic roof resource segmentation and extraction method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107784661A (en) | Substation equipment infrared image classifying identification method based on region-growing method | |
| CN111563467B (en) | Solar panel cleaning system based on machine vision | |
| CN105373971A (en) | Method of building energy efficiency management on the basis of big data | |
| CN104166999B (en) | Cloud cluster extracting method based on strength layering of foundation cloud pictures | |
| CN112197218B (en) | Comprehensive energy wisdom street lamp | |
| CN112990558B (en) | Meteorological temperature and illumination prediction method based on deep migration learning | |
| CN110675421B (en) | Depth image collaborative segmentation method based on few labeling frames | |
| Nguyen et al. | Automated quantification of solar photovoltaic potential in cities Overview: A new method to determine a city's solar electric potential by analysis of a distribution feeder given the solar exposure and orientation of rooftops. | |
| CN105894041A (en) | Method of extracting substation information in power distribution system based on hyperspectral remote sensing images | |
| CN103888731A (en) | Structured description device and system for mixed video monitoring by means of gun-type camera and dome camera | |
| CN114120108A (en) | Roof photovoltaic intelligent identification method based on satellite remote sensing technology | |
| CN114120141A (en) | All-weather remote sensing monitoring automatic analysis method and system thereof | |
| Liu et al. | A novel approach for assessing rooftop-and-facade solar photovoltaic potential in rural areas using three-dimensional (3D) building models constructed with GIS | |
| CN117036825A (en) | Solar cell panel detection method, medium and system | |
| Joshi et al. | Rooftop detection for planning of solar PV deployment: a case study in Abu Dhabi | |
| CN212777124U (en) | Comprehensive energy wisdom street lamp | |
| CN115907268A (en) | Integrated forecast meteorological resource integrated management method and system | |
| CN112116569A (en) | Photovoltaic power station power generation power prediction method based on shadow recognition | |
| Tian et al. | An innovative method for evaluating the urban roof photovoltaic potential based on open-source satellite images | |
| Liu et al. | Remote Sensing Big Data Analysis of the Lower Yellow River Ecological Environment Based on Internet of Things | |
| Feng et al. | Image recognition based on water hyacinth controlled breeding monitoring equipment | |
| Hao et al. | Fine-grained PM2. 5 detection method based on crowdsensing | |
| Danqiu et al. | Image retrieval technology of smart archives from the perspective of national reading | |
| CN116739623B (en) | Transaction data tracking analysis method based on blockchain technology | |
| CN113780439B (en) | Multi-cloud identification system of different types of meteorological satellites based on unsupervised domain adaptation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |