Marine photovoltaic power generation facility development trend prediction method and device and storage medium

Abstract

The invention relates to the technical field of remote sensing, in particular to an offshore photovoltaic power generation facility development trend prediction method, device and equipment and a computer storage medium. According to the method, multi-source remote sensing data are collected and integrated, the multi-source remote sensing data comprise data from different remote sensing platforms, the remote sensing data and geographic information data are fused, a unified geographic space database is established, space-time dynamic analysis is carried out on the offshore photovoltaic power generation facility, and the offshore photovoltaic power generation facility is analyzed. According to the invention, by introducing remote sensing data of a long time sequence and a space-time dynamic analysis method, comprehensive dynamic monitoring of the whole process from construction to operation of the offshore photovoltaic power generation facility is realized, so that not only can subtle changes in the construction process of the facility be accurately captured, but also the expansion condition and the operation state of the facility can be continuously monitored, and the monitoring accuracy is improved. The comprehensive dynamic monitoring capability helps to improve the management level of the facility, reduce the maintenance cost, and guarantee the long-term stable operation of the facility.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing technology, and in particular to a method, device, equipment and computer storage medium for predicting the development trend of offshore photovoltaic power generation facilities. Background Art

[0002] As the global demand for clean energy continues to grow, offshore photovoltaic power generation, as a highly promising renewable energy utilization method, is gradually becoming a research hotspot in the energy field. The ocean is rich in solar energy resources, and offshore photovoltaic power generation has the advantages of not occupying land resources and relatively high power generation efficiency. However, the construction and operation of offshore photovoltaic power generation facilities face many complex challenges. The marine environment is complex and changeable, including high humidity, high salinity, strong winds and waves, complex marine ecosystems and other factors, which puts higher requirements on the design, construction, maintenance and management of photovoltaic power generation facilities.

[0003] However, in the existing technologies, for the monitoring and analysis of offshore photovoltaic power generation facilities, especially in terms of spatiotemporal dynamic analysis, the existing remote sensing technology only relies on optical remote sensing data when acquiring data on offshore photovoltaic power generation facilities, which affects the temporal continuity and integrity of the data. Secondly, in terms of feature extraction and analysis, the existing methods are difficult to accurately extract the subtle changes in the characteristics of offshore photovoltaic power generation facilities during construction, expansion and operation, and it is difficult to accurately evaluate the changing trend of the facility's power generation efficiency over time and its impact on the surrounding marine ecological environment.

[0004] In summary, existing technical means have many defects in the spatiotemporal dynamic analysis of offshore photovoltaic power generation facilities and cannot meet the needs of the rapid development of the offshore photovoltaic power generation industry. Summary of the invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the problem of inaccurate spatiotemporal dynamic analysis and facility development trend prediction in the prior art.

[0006] In order to solve the above technical problems, the present invention provides a method for predicting the development trend of offshore photovoltaic power generation facilities, comprising:

[0007] Collect multi-source remote sensing data and integrate geographic information data to establish a geospatial database;

[0008] Based on the geospatial database, a spatiotemporal dynamic analysis is performed on the offshore photovoltaic power generation facilities, wherein the spatiotemporal dynamic analysis includes a construction phase analysis, an expansion phase analysis, and an operation status analysis to obtain the construction progress, expansion status, and operation status;

[0009] Based on the geospatial database, extract multi-dimensional characteristic variables related to the development trend of offshore photovoltaic power generation facilities from long-term series remote sensing data and auxiliary data;

[0010] Building a prediction model based on the multi-dimensional feature variables in combination with the geospatial database, the expansion situation and the operation status;

[0011] The trained prediction model is used to perform multi-scenario predictions on the development trend of offshore photovoltaic power generation facilities, and the prediction output results are integrated with the construction progress, the expansion situation and the operation status to obtain the target prediction results.

[0012] Preferably, the collecting of multi-source remote sensing data and fusing of geographic information data to establish a geospatial database comprises:

[0013] Collecting multi-source remote sensing data from different remote sensing platforms and preprocessing the multi-source remote sensing data;

[0014] The preprocessed multi-source remote sensing data is integrated with geographic information data to establish a geospatial database.

[0015] Preferably, the spatiotemporal dynamic analysis of the offshore photovoltaic power generation facilities is performed based on the geospatial database, and the spatiotemporal dynamic analysis includes construction phase analysis, expansion phase analysis and operation status analysis, and the construction progress, expansion status and operation status obtained include:

[0016] Extracting feature information of remote sensing images at different stages of the facility construction process based on the geospatial database, analyzing the difference between the actual construction situation and the design planning data based on the feature information, and evaluating the construction progress;

[0017] Based on the geospatial database, remote sensing images of different periods are compared, and combined with historical expansion conditions, the direction, scope and speed of future expansion are determined to obtain future expansion conditions;

[0018] Based on the geospatial database, the spectral feature information of the remote sensing images at different periods during the operation of the facility is extracted, and the surface state of the photovoltaic panels of the facility is monitored according to the spectral feature information. Combined with the historical operation status of the facility, the change trend of the facility's power generation efficiency over time is determined, thereby determining the future operation status of the facility.

[0019] Preferably, the spatiotemporal dynamic analysis of the offshore photovoltaic power generation facilities is performed based on the geospatial database, and the spatiotemporal dynamic analysis includes construction phase analysis, expansion phase analysis and operation status analysis, and after obtaining the construction progress, expansion status and operation status, it also includes:

[0020] According to the construction progress, combined with marine engineering knowledge and on-site construction conditions, analyze the causes of construction deviations, and output improvement suggestions based on the causes of construction deviations;

[0021] Based on the expansion situation, the rationality of the expansion plan is analyzed in combination with marine environmental factors and changes in energy demand, and the relationship between the expanded facilities and the surrounding marine ecological environment is analyzed using spatial analysis technology, and the expansion plan is adjusted based on the analysis results;

[0022] In combination with the above operation conditions, monitor the changes in the marine ecological environment around the facility, analyze the real-time monitoring results, and output corresponding improvement measures for facility construction or operation.

[0023] Preferably, the constructing of a prediction model based on the multi-dimensional feature variables in combination with the geospatial database, the expansion situation and the operation status includes:

[0024] Selecting and reducing the dimension of the multi-dimensional feature variables;

[0025] A prediction model is constructed based on the selected multiple characteristic variables in combination with the geospatial database, the expansion situation and the operation status.

[0026] Preferably, the use of the trained prediction model to perform multi-scenario prediction on the development trend of offshore photovoltaic power generation facilities, and integrating the prediction output results with the construction progress, the expansion situation and the operation status, and obtaining the target prediction results also includes:

[0027] Performing anomaly detection on the target prediction result according to a preset threshold;

[0028] When an anomaly is detected, an alarm is triggered.

[0029] Preferably, when an abnormality is detected, triggering an alarm includes:

[0030] determining an abnormality severity score for the detected anomaly;

[0031] Based on the abnormality score, according to a preset severity threshold, determining an alarm level corresponding to the detected abnormality;

[0032] An alarm of a corresponding type is triggered according to the alarm level.

[0033] The present invention also provides a device for predicting the development trend of offshore photovoltaic power generation facilities, comprising:

[0034] Data collection and fusion module, used to collect multi-source remote sensing data, fuse geographic information data, and establish a geospatial database;

[0035] A spatiotemporal dynamic analysis module, used to perform spatiotemporal dynamic analysis on offshore photovoltaic power generation facilities based on the geospatial database, wherein the spatiotemporal dynamic analysis includes construction phase analysis, expansion phase analysis and operation status analysis, and obtains construction progress, expansion status and operation status;

[0036] A multidimensional feature extraction module, for extracting multidimensional feature variables related to the development trend of offshore photovoltaic power generation facilities from long-term series of remote sensing data and auxiliary data based on the geospatial database;

[0037] A prediction model building module, used to build a prediction model based on the multi-dimensional feature variables, in combination with the geospatial database, the expansion situation and the operation status;

[0038] The prediction result output module is used to use the trained prediction model to perform multi-scenario prediction on the development trend of offshore photovoltaic power generation facilities, and integrate the prediction output results with the construction progress, the expansion situation and the operation status to obtain the target prediction results.

[0039] The present invention also provides a device for predicting the development trend of offshore photovoltaic power generation facilities, comprising:

[0040] Memory for storing computer programs;

[0041] A processor is used to implement the above-mentioned steps of the method for predicting the development trend of offshore photovoltaic power generation facilities when executing the computer program.

[0042] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for predicting the development trend of offshore photovoltaic power generation facilities are implemented.

[0043] The above technical solution of the present invention has the following advantages compared with the prior art:

[0044] The method for predicting the development trend of offshore photovoltaic power generation facilities described in the present invention realizes comprehensive dynamic monitoring of the entire process from construction to operation of offshore photovoltaic power generation facilities by introducing long-term series remote sensing data and spatiotemporal dynamic analysis methods. This can not only accurately capture subtle changes in the facility construction process, but also continuously monitor the expansion and operation status of the facility, so as to timely discover and warn of potential problems. This comprehensive dynamic monitoring capability helps to improve the management level of the facility, reduce maintenance costs, and ensure the long-term stable operation of the facility. The prediction model constructed based on multi-dimensional characteristic variables of the present invention can accurately predict the development trend of the facility in the future. Through multi-scenario prediction, the growth of facility scale, technology update direction, changes in power generation efficiency and the impact on the surrounding marine ecological environment can be evaluated. The prediction results are presented in the form of intuitive charts and reports to provide a scientific basis for decision makers. At the same time, the early warning mechanism can automatically issue an alarm when an abnormal situation is detected, helping relevant personnel to take countermeasures in time, thereby improving the accuracy and response speed of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein:

[0046] Figure 1 It is a flow chart for realizing a method for predicting development trend of offshore photovoltaic power generation facilities provided by the present invention;

[0047] Figure 2 It is a framework diagram of the implementation process of a method for predicting development trends of offshore photovoltaic power generation facilities provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The core of the present invention is to provide a method, device, equipment and computer storage medium for predicting the development trend of offshore photovoltaic power generation facilities. It can provide comprehensive and accurate data support and decision-making basis for the planning, construction, operation and maintenance of offshore photovoltaic power generation facilities through in-depth spatiotemporal dynamic analysis and accurate prediction models, and can formulate more scientific and reasonable development strategies based on the historical development of the facilities and future trend forecasts, avoid blind investment and waste of resources, and improve the success rate and economic benefits of the project.

[0049] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0050] Please refer to Figure 1 , Figure 1 This is a flow chart of the implementation of a method for predicting the development trend of offshore photovoltaic power generation facilities provided by the present invention; the specific operation steps are as follows:

[0051] S101: Collect multi-source remote sensing data, integrate geographic information data, and establish a geospatial database;

[0052] S102: performing spatiotemporal dynamic analysis on the offshore photovoltaic power generation facilities based on the geospatial database, wherein the spatiotemporal dynamic analysis includes construction phase analysis, expansion phase analysis and operation status analysis to obtain construction progress, expansion status and operation status;

[0053] S103: Based on the geospatial database, extracting multi-dimensional characteristic variables related to the development trend of offshore photovoltaic power generation facilities from long-term series remote sensing data and auxiliary data;

[0054] S104: constructing a prediction model based on the multi-dimensional feature variables in combination with the geospatial database, the expansion situation and the operation status;

[0055] S105: Use the trained prediction model to perform multi-scenario prediction on the development trend of offshore photovoltaic power generation facilities, and integrate the prediction output results with the construction progress, the expansion situation and the operation status to obtain the target prediction results.

[0056] Based on the above embodiments, this embodiment describes step S101 in detail:

[0057] Collect multi-source remote sensing data from different remote sensing platforms and pre-process the multi-source remote sensing data;

[0058] Among them, multi-source remote sensing data include data from different remote sensing platforms;

[0059] In the first specific application, the required remote sensing data sources are determined, including optical satellites, radar satellites, and hyperspectral satellites, etc. For example, optical satellites with high spatial resolution are selected to obtain the appearance and layout information of offshore photovoltaic power generation facilities, and the ability of radar satellites to penetrate clouds and fog is used to obtain facility data under severe weather conditions, and the rich spectral information provided by hyperspectral satellites is used to analyze the material properties of the facilities.

[0060] In the second specific application, multiple data sources such as optical satellites, radar satellites and hyperspectral satellites are selected. Optical satellite data is used to obtain intuitive image information of offshore photovoltaic power generation facilities and the surrounding marine environment, such as the location and scale of the facilities and some basic characteristics of the surrounding ocean surface. Radar satellite data can penetrate clouds and fog and provide information about the structure of facilities and the state of the ocean surface under severe weather conditions, which is very important for continuous monitoring of facilities and the marine environment. Hyperspectral satellite data provides rich spectral information, which can be used to deeply analyze the details of the marine ecological environment, such as water quality conditions, the distribution of marine biological communities, etc., and pre-process various types of remote sensing data collected. For optical satellite data, radiation calibration is performed to ensure the physical accuracy of the data, atmospheric correction is performed to eliminate atmospheric interference with the spectrum, and geometric correction is performed to make the data accurately correspond in geographic space. Similarly, corresponding pre-processing steps are also performed for radar satellite and hyperspectral satellite data.

[0061] ·Fuse the pre-processed multi-source remote sensing data with geographic information data to establish a geospatial database.

[0062] Specifically, the collected remote sensing data are preprocessed and data fused to fuse remote sensing data from different sources and types to obtain D integrated =f(D1, D2, ..., D n ; G), where Dintegrated represents the integrated geospatial database, D i represents the dataset of the ith remote sensing platform, G represents geographic information data, and f is a comprehensive function used to fuse multiple data sources and geographic information data. integrated The specific form is: Where (x, y) represents the geographic location coordinates, t represents the time point, and w i It represents the weight coefficient of the i-th remote sensing data source, which is used to adjust the contribution of different data sources in the fusion process. α represents the weight coefficient of geographic information data, which is used to adjust the contribution of geographic information data in the fusion process.

[0063] In the first specific application, for optical satellite data, radiation calibration is performed to convert the digital quantization values ​​obtained by the sensor into radiation brightness or reflectivity values ​​with physical meaning. Atmospheric correction eliminates the absorption and scattering effects of the atmosphere on electromagnetic radiation and restores the true spectral characteristics of the ground objects. Geometric correction corrects the geometric deformation of the image by establishing a mapping relationship between the image coordinates and the geographic coordinates. Similarly, the radar satellite and hyperspectral satellite data are also preprocessed accordingly, and the preprocessed data are fused. Let the optical satellite data be D1, the radar satellite data be D2, the hyperspectral satellite data be D3, and the geographic information data be G. According to the formula D i nintegrated = f(D1, D2, D3; G), where D integrated represents the integrated geospatial database, f is a comprehensive function, and its specific form is Here (x, y) represents the geographic location coordinates, t represents the time point, w1, w2, and w3 are the weight coefficients of the optical satellite, radar satellite, and hyperspectral satellite data sources, respectively, and α is the weight coefficient of the geographic information data. For example, based on the data quality and its importance to facility analysis, w1=0.4, w2=0.3, w3=0.2, and α=0.1 are determined. In this way, remote sensing data from different sources and of different types are integrated with geographic information data to establish a unified geospatial database, providing a comprehensive data basis for subsequent analysis.

[0064] In the second specific application, the collected data are fused, that is, the optical satellite data is D1, the radar satellite data is D2, the hyperspectral satellite data is D3, and the geographic information data is G. According to the formula D integrated =f(D1, D2, D3; G) for data fusion, where D integrated is the integrated geospatial database, f is a comprehensive function, and its specific form is Here (x, y) represents the geographic location coordinates, t represents the time point. According to the characteristics of the scene and data requirements, w1=0.3, w2=0.3, w3=0.2, α=0.2 are determined. In this way, data from different sources are integrated into a comprehensive geospatial database, providing a solid data foundation for subsequent analysis.

[0065] Based on the above embodiments, this embodiment describes step S102 in detail:

[0066] Extracting feature information of remote sensing images at different stages of facility construction based on the geospatial database, analyzing the difference between actual construction conditions and design planning data based on the feature information, and evaluating construction progress;

[0067] Specifically, from the integrated geospatial database D integratod (x, y, t) to extract the characteristic information of the facility construction process, and evaluate the construction progress by calculating the difference between the actual construction situation and the design planning data, that is, Among them C progress (x, y, t) represents the construction progress at the geographical location (x, y) and time point t, D integratod (x, y, t) represents the integrated geospatial database, P design (x, y) represents the design planning data, ki represents the weight coefficient of the i-th feature, which is used to adjust the importance of different features in the evaluation, and m represents the number of features.

[0068] In the first specific application, the integrated geospatial database D integratod (x, y, t) to extract characteristic information during the construction of facilities. By comparing remote sensing images at different times and using image recognition and analysis technology, we can determine the location, shape, size and other parameters of the infrastructure (such as support piles). For photovoltaic panels, we can identify their installation angles and layout changes, and evaluate the construction progress. According to the formula Among them C progress (x, y, t) represents the construction progress at the geographical location (x, y) and time point t, D integratod (x, y, t) represents the integrated geospatial database, P desigm (x, y) represents the design planning data, k i Represents the weight coefficient of the i-th feature. For example, assume that the weight coefficient of the infrastructure location is k1 = 0.3, the shape weight coefficient is k2 = 0.2, the size weight coefficient is k3 = 0.2, the photovoltaic panel installation angle weight coefficient is k4 = 0.2, and the layout change weight coefficient is k5 = 0.1. By calculating the difference between the actual construction situation and the design planning data, the quantitative index of the construction progress is obtained.

[0069] In the second specific application, during the facility construction phase, by comparing remote sensing images at different times and using image recognition and analysis technology, we should not only pay attention to the extraction of feature information on the progress of the facility's own construction, but also consider the impact of the construction process on the surrounding marine ecological environment. For example, during the construction of infrastructure (such as support piles), analyze whether the activities of marine life around the construction area are disturbed, and evaluate by monitoring the changes in the distribution of marine life and the disturbance of water bodies. For the installation of photovoltaic panels, consider the possible impact of changes in lighting conditions in the surrounding sea areas on marine life. For example, the photosynthesis of some plankton may be affected by changes in light intensity and time. When evaluating the progress of construction, The idea is to take ecological impact factors into consideration.

[0070] Based on the geospatial database, remote sensing images of different periods are compared, and combined with historical expansion conditions, the direction, scope and speed of future expansion are determined to obtain future expansion conditions;

[0071] Specifically, by comparing remote sensing images from different periods, the direction, range and speed of expansion are determined, namely, Ee xpansion (x, y, t) = ΔD integrated (x, y, t)+λ·E prev (x, y, t-1), where E expansion (x, y, t) represents the expansion at the geographical location (x, y) and time point t, ΔD integrated (x, y, t) represents the data difference between the current time point and the previous time point, E prev (x, y, t-1) represents the expansion situation at the previous time point, and λ represents the smoothing coefficient of the expansion trend.

[0072] In the first specific application, the expansion of facilities is continuously monitored, and the direction, scope and speed of expansion are determined by comparing remote sensing images of different periods. Assume that the current time point is t, and the previous time point is t-1. According to formula E expansion (x, y, t) = ΔD integrated (x, y, t)+λ·E prev (x, y, t-1), where E expansiom (x, y, t) represents the expansion at the geographical location (x, y) and time point t, ΔD integrated (x, y, t) represents the data difference between the current time point and the previous time point, E prev (x, y, t-1) represents the expansion situation at the previous time point, and λ represents the smoothing coefficient of the expansion trend. For example, when λ=0.5, if ΔD integrated (x, y, t) shows that the facility area of ​​a certain area has increased by 1000 square meters during this period.prev (x, y, t-1) indicates that the expansion of the area in the previous period was 500 square meters, so E expansiom (x, y, t) = 1000 + 0.5 × 500 = 1250 square meters, that is, the expansion of the area during this time period is an increase of 1250 square meters.

[0073] Extracting spectral feature information of remote sensing images at different periods during the operation of the facility based on the geospatial database, and monitoring the surface status of the photovoltaic panels of the facility based on the spectral feature information, and determining the change trend of the power generation efficiency of the facility over time in combination with the historical operation status of the facility, thereby determining the future operation status of the facility.

[0074] Among them, the surface condition of the photovoltaic panels of the facility includes the dirt, aging and damage on the surface of the photovoltaic panels;

[0075] Specifically, based on the spectral characteristics of remote sensing data, the dirt, aging, and damage of the photovoltaic panel surface are monitored, and combined with time series data, the changing trend of the power generation efficiency of the facility over time is evaluated, that is, status (x, y, t) = μ·D integrated (x, y, t)+(1-μ)·O prev (x, y, t-1), O status (x, y, t) represents the operating status at the geographical location (x, y) and time point t, μ represents the weight coefficient of the current data, which is used to adjust the impact of the current data and historical data, O prev (x, y, t-1) represents the operating status at the previous time point.

[0076] In the first specific application, the dirt, aging, and damage on the surface of photovoltaic panels are monitored based on the spectral characteristics of remote sensing data. For example, through hyperspectral remote sensing data, the reflectivity changes of photovoltaic panels in different bands are analyzed. Dirt will cause the reflectivity of photovoltaic panels to increase in certain bands, while aging and damage may cause abnormal changes in reflectivity in other bands. Combined with time series data, the change trend of the power generation efficiency of the facility over time is evaluated. Combined with time series data according to the formula, the change trend of the power generation efficiency of the facility over time is evaluated. According to formula O status (x, y, t) = μ·D integrated (x, y, t)+(1-μ)·O prev (x, y, t-1), where O status (x, y, t) represents the operating status at the geographical location (x, y) and time point t, μ represents the weight coefficient of the current data, O prev (x, y, t-1) represents the operation status at the previous time point. For example, if μ = 0.6, if the current D integrated(x, y, t) shows that the power generation efficiency of the photovoltaic panel may be 80% under the combined influence of the spectral characteristics and environmental factors, and the operating status of the previous time period is O prev (x, y, t-1) shows that the power generation efficiency is 82%, so O status (x, y, t)=0.6×80%+(1-0.6)×82%=80.8%, that is, the current estimated power generation efficiency is 80.8%.

[0077] · Analyze the causes of construction deviations based on the construction progress, combined with marine engineering knowledge and on-site construction conditions, and output improvement suggestions based on the causes of construction deviations;

[0078] In the first specific application, according to the construction progress, combined with marine engineering knowledge and on-site construction conditions, the reasons for construction deviation are analyzed. For example, if the construction progress of the supporting piles in the area is found to be lagging behind, the factors such as whether the seabed geological conditions are complex, whether there are any faults in the construction equipment, whether it is affected by bad weather (such as typhoons, heavy rains, etc.), the technical level of the construction personnel and whether the manpower arrangement is reasonable are analyzed. Based on the analysis results, improvement suggestions are provided, such as adjusting the construction equipment, optimizing the construction plan or increasing the number of construction personnel.

[0079] In the second specific application, if it is found that the construction process has caused significant interference to a certain important marine ecological area, even if the facility construction progress is in line with the design plan, the construction plan needs to be adjusted to reduce the impact on the ecological environment. Through this comprehensive assessment, it is ensured that the facility construction meets the project progress requirements while protecting the marine ecological environment to the greatest extent.

[0080] ·Based on the expansion situation, analyze the rationality of the expansion plan in combination with marine environmental factors and changes in energy demand, and use spatial analysis technology to analyze the relationship between the expanded facilities and the surrounding marine ecological environment, and adjust the expansion plan based on the analysis results;

[0081] In the first specific application, the rationality of the expansion plan is evaluated by considering marine environmental factors (such as ocean currents, wind speed, waves, etc.) and changes in energy demand. For example, if the facility is expanded in a certain direction, it may be impacted by strong ocean currents. The impact of the current on the facility structure and the impact on power generation efficiency are analyzed. At the same time, combined with energy demand forecasts, if the energy demand in the area grows rapidly in the future and the expansion direction meets the requirements of marine environmental conditions, then the expansion plan has a certain rationality; on the contrary, if the expansion direction will cause great damage to the marine ecological environment and the energy demand growth is not obvious, it is necessary to re-evaluate the expansion plan and analyze the relationship between the expanded facilities and the surrounding marine ecological environment. Using spatial analysis technology, calculate the distance and spatial overlap between the facilities and important ecological areas such as marine protected areas, fish migration channels, and coral reefs to ensure ecological balance. If it is found that the expanded facilities overlap with the marine protected areas, the expansion plan needs to be adjusted to avoid damage to the ecological environment.

[0082] Based on the above operation conditions, monitor the changes in the marine ecological environment around the facility, analyze the real-time monitoring results, and output corresponding improvement measures for facility construction or operation.

[0083] In the first specific application, the changes in the marine ecological environment around the facility are monitored synchronously, such as water quality parameters (temperature, salinity, pH, dissolved oxygen, etc.) and the structure and diversity of marine biological communities, etc., using hyperspectral remote sensing data and biological indicator analysis methods, for example, by analyzing the spectral characteristics of seawater to invert water quality parameters, and by using marine biological survey data to evaluate changes in biological community structure and diversity. If it is found that the water quality of the surrounding sea area has deteriorated or the structure of the biological community has changed significantly after the operation of the facility, further analysis of the reasons is needed. It may be due to the release of harmful substances from the materials of the photovoltaic panels, or the construction and operation of the facilities have changed the water flow and light conditions of the ocean, etc., and corresponding measures should be taken to improve them.

[0084] In the second specific application, the spectral characteristics of remote sensing data are used to monitor changes in the marine ecological environment: the rich spectral information of hyperspectral remote sensing data is used to analyze water quality parameters (such as temperature, salinity, pH, dissolved oxygen, etc.). By establishing a relationship model between spectral characteristics and water quality parameters, water quality information can be inverted from remote sensing data. For example, there is a specific relationship between the spectral reflectance of certain bands and the dissolved oxygen content in the water. By monitoring the reflectance changes of these bands, the changes in the dissolved oxygen content can be understood in real time. At the same time, the biological indicator analysis method is used to monitor the structure and diversity of marine biological communities in combination with remote sensing data. For example, by analyzing the spectral characteristics of the ocean surface and some specific spectral markers related to the distribution of marine organisms, the changes in the distribution range and species composition of marine biological communities can be inferred. The impact of facility operation on the marine ecological environment is monitored based on remote sensing data: by comparing remote sensing data before and after facility construction and at different operation stages, the impact of facility operation on the marine ecological environment is analyzed. For example, by observing the changes in spectral characteristics of the sea area surrounding the facility, if it is found that the reflectance of certain bands in a certain area has changed significantly after the facility is operated, it may mean that the water quality or marine biological community in that area has been affected. Combined with time series data, we further analyzed the changing trend of this impact over time, and combined with O status (x, y, t) = μ·D integrated (x, y, t)+(1-μ)·O prev (x, y, t-1), which reflects the dynamic evaluation idea of ​​the operation status, can more accurately grasp the impact of facility operation on the ecological environment.

[0085] Based on the above embodiments, this embodiment describes step S103 in detail:

[0086] Specifically, from the long-term remote sensing data D integrated The multi-dimensional characteristic variables F related to the development trend of offshore photovoltaic power generation facilities are extracted from the auxiliary data i ;

[0087] In the first specific application, from the long-term series of remote sensing data D integrated The multi-dimensional characteristic variables F related to the development trend of offshore photovoltaic power generation facilities are extracted from the auxiliary data (such as meteorological data, marine environment data, energy market data, etc.) i For example, the spatial distribution change characteristics of facilities, image texture characteristics, wind speed, wind direction, rainfall, solar radiation intensity and other characteristics in meteorological data, ocean temperature, salinity, wave height, ocean tidal speed and direction and other characteristics in marine environment data, electricity price, market demand, policy and regulatory changes and other characteristics in energy market data.

[0088] Based on the above embodiments, this embodiment describes step S104 in detail:

[0089] ·Selecting and reducing the dimension of the multi-dimensional feature variables;

[0090] · Building a prediction model based on the selected multiple feature variables in combination with the geospatial database, the expansion situation and the operation status.

[0091] Specifically, based on the selected feature variable F i Combined with the integrated geospatial database D integrated 、Extension Case E expansion and operating status O status , build prediction model M predict ,Right now Among them, M predict represents the prediction model, F i represents the i-th characteristic variable, D integrated (x, y, t) represents the integrated geospatial database, E expansion (x, y, t) represents the expansion situation, O status (x, y, t) represents the operating state, θ i represents the weight coefficient of the i-th characteristic variable, η, ζ, ξ represent the weight coefficients of the integrated geospatial database, expansion status and operation status in the prediction model respectively, and t represents the prediction time. By weighted summation, the influence of each characteristic variable and data source is combined to obtain the prediction results at different times.

[0092] In the first specific application, based on the selected characteristic variable F i Combined with the integrated geospatial database D integrated 、Extension Case E expansion and operating status O status , build prediction model M predict , according to the formula M predict represents the prediction model, F i represents the i-th feature variable, θ i represents the weight coefficient of the i-th characteristic variable, η, ζ, ξ represent the weight coefficients of the integrated geospatial database, expansion status and operation status in the prediction model, and t represents the prediction time. For example, by analyzing the data characteristics and the degree of influence on the prediction results, it is determined that θ1 = 0.2, η = 0.3, ζ = 0.2.ξ = 0.3.

[0093] In the second specific application, multi-dimensional characteristic variables related to offshore photovoltaic power generation facilities and marine ecological environment are extracted from long-term series of remote sensing data and auxiliary data (such as marine ecological research reports, marine environmental monitoring data, etc.). These variables include the construction progress of the facilities, the impact indicators of the operating status on the ecological environment, and the changing characteristics of the marine ecological environment itself (such as the trend of water quality parameters, the change of biological community diversity, etc.). Based on these characteristic variables, a decision model is constructed to evaluate the feasibility and effectiveness of different ecological protection measures. For example, a model based on cost-benefit analysis is constructed, considering the implementation cost of ecological protection measures (such as the construction cost of artificial reefs, the engineering cost of adjusting the layout of facilities, etc.) and the expected benefits of improving the marine ecological environment (such as the degree of biodiversity recovery, the improvement of water quality, etc.). Although there is no direct corresponding formula, the idea of ​​constructing the decision model is similar to that of constructing the prediction model, which comprehensively considers the impact of multiple factors on the decision results. Through this decision model, various possible ecological protection measures are evaluated and compared, and the optimal protection measure is selected.

[0094] Based on the above embodiments, this embodiment describes step S105 in detail:

[0095] Specifically, using the trained prediction model M predict The development trend of the facility is predicted in multiple scenarios, including the growth of facility scale, technology update direction, power generation efficiency change and the impact on the surrounding marine ecological environment, namely P trend (t+Δt)=M predict (t+Δt)+κ·C progress (x, y, t)+v·E expansion (x, y, t)+ω·O status (x, y, t), where P trend (t+Δt) represents the development trend forecast result at the time point t+Δt, M predict (t+Δt represents the output result of the prediction model, κ, v, ω represent the weight coefficients of construction progress, expansion status and operation status in the prediction results respectively, and the prediction results are presented in the form of charts and reports to better understand and use the prediction results.

[0096] In the first specific application, the trained prediction model is used to make multi-scenario predictions on the development trend of the facility. The prediction content includes the growth of the facility scale, the direction of technology update, the change of power generation efficiency and the impact on the surrounding marine ecological environment. According to the formula P trend (t+Δt)=M predict (t+Δt)+κ·C progress (x, y, t)+v·E expansion (x, y, t)+ω·O status(x, y, t), where P trend (t+Δt) represents the development trend forecast result at the time point t+Δt, M predict (t+Δt represents the output result of the prediction model, κ, v, ω represent the weight coefficients of construction progress, expansion status and operation status in the prediction result respectively. Finally, the prediction results are presented in the form of charts and reports. For example, for the growth of facility scale, a line graph can be drawn with time as the horizontal axis and facility scale (such as installed capacity, floor area, etc.) as the vertical axis; for the direction of technological update, a bar graph can be used to represent the possibility of different technologies in different scenarios; for the change of power generation efficiency, a curve can be drawn to show the change trend of power generation efficiency over time in different scenarios; for the impact on the surrounding marine ecological environment, charts can be used to show the changes in water quality parameters and biodiversity index, etc.

[0097] In the second specific application, the power generation benefits of offshore photovoltaic power generation facilities and their impact on the marine ecological environment are comprehensively considered to establish a scientific sustainable development evaluation index system, for example, including the ratio of energy output to ecological and environmental impact, marine biodiversity retention rate, water quality compliance rate and other indicators. Through long-term monitoring and analysis of these indicators, the sustainable development status of the project is evaluated, and the sustainable development status of the project is evaluated regularly. According to the evaluation results, the operation and ecological protection measures of the facilities are adjusted and optimized. For example, if it is found that the ratio of energy output to ecological and environmental impact continues to decline, it means that the sustainable development of the project faces challenges, and further analysis of the reasons is needed. It may be that the impact on the ecological environment during the operation of the facility is too large, or the power generation efficiency is reduced. For these problems, take corresponding measures, such as optimizing the facility operation strategy, strengthening ecological protection measures, etc., and then evaluate again to form a dynamic evaluation and feedback mechanism to ensure that the project can achieve sustainable development.

[0098] Based on the above embodiment, step S105 further includes:

[0099] ·Performing anomaly detection on the target prediction result according to a preset threshold;

[0100] Specifically, by setting the threshold T threshold , detection prediction result P trend The abnormal situation in (t+Δt), that is, Where W alert (t+Δt) represents the alarm state at time point t+Δt, P trend (t+Δt) represents the development trend forecast result at the time point t+Δt, is anomaly detection function.

[0101] When anomalies are detected, an alarm is triggered:

[0102] Specifically, for the alarm state When the prediction result is greater than the set threshold, it is marked as abnormal, otherwise it is marked as normal, that is, when an abnormal situation is detected, W alert (t+Δt)=1, an alarm is automatically issued to notify relevant personnel.

[0103] In another specific embodiment, the alarms are divided into different levels according to the severity of the anomaly, specifically:

[0104] Determine the abnormality score S of the detected anomaly severity ;

[0105] Based on the abnormality score and a preset severity threshold, determine an alarm level corresponding to the detected abnormality; and trigger an alarm of a corresponding type according to the alarm level.

[0106] Specifically, according to the severity of the abnormality S severity , classify the alarms into different levels, namely Where W level (t+Δt) represents the alarm level at time point t+Δt, Represents the warning level division function, its warning level division function The discriminant of is: Among them, S1 and S2 are severity thresholds used to divide different alarm levels. level When (t+Δt)=1, it is a low-level warning. level When (t+Δt)=2, it is a medium-level warning. level When (t+Δt)=3, it is a high-level warning.

[0107] like Figure 2 As shown, Figure 2 A framework diagram of the implementation process of a method for predicting development trends of offshore photovoltaic power generation facilities provided in one embodiment of the present invention.

[0108] In summary, in the first specific application, the application process of the offshore photovoltaic power generation facility development trend prediction method in the offshore photovoltaic power plant construction and monitoring scenario is demonstrated in detail, which effectively improves the management level of offshore photovoltaic power generation facilities, ensures the smooth progress and long-term stable operation of the project, and also provides strong technical support for the sustainable development of the offshore photovoltaic power generation industry. In the second specific application, the comprehensive application of the offshore photovoltaic power generation facility development trend prediction method in the coordinated development scenario of offshore photovoltaic power generation and marine ecological protection is demonstrated. Through the effective integration of multi-source remote sensing data and spatiotemporal dynamic analysis, the accurate monitoring and evaluation of the ecological and environmental impacts during the construction and operation of facilities is achieved. Based on these analysis results, a decision-making model is constructed to formulate ecological protection measures, and through the sustainable development evaluation index system and feedback mechanism, the development strategy of the project is continuously optimized to ensure that offshore photovoltaic power generation and marine ecological protection can work together to achieve the goal of sustainable development. This method provides strong technical support and practical guidance for the offshore photovoltaic power generation industry in terms of ecological and environmental protection.

[0109] The present invention realizes comprehensive dynamic monitoring of the entire process of offshore photovoltaic power generation facilities from construction to operation by introducing long-term series remote sensing data and spatiotemporal dynamic analysis methods. This can not only accurately capture subtle changes in the facility construction process, but also continuously monitor the expansion and operation status of the facility, so as to timely discover and warn of potential problems. This comprehensive dynamic monitoring capability helps to improve the management level of the facility, reduce maintenance costs, and ensure the long-term stable operation of the facility.

[0110] The prediction model constructed by the present invention based on multi-dimensional characteristic variables can accurately predict the development trend of facilities in the future. Through multi-scenario prediction, it can evaluate the growth of facility scale, technology update direction, changes in power generation efficiency and the impact on the surrounding marine ecological environment. The prediction results are presented in the form of intuitive charts and reports to provide a scientific basis for decision makers. At the same time, the early warning mechanism can automatically issue an alarm when an abnormal situation is detected, helping relevant personnel to take countermeasures in time, thereby improving the accuracy and response speed of decision-making.

[0111] An embodiment of the present invention provides a device for predicting the development trend of offshore photovoltaic power generation facilities; the specific device may include:

[0112] Data collection and fusion module, used to collect multi-source remote sensing data, fuse geographic information data, and establish a geospatial database;

[0113] A spatiotemporal dynamic analysis module, used to perform spatiotemporal dynamic analysis on offshore photovoltaic power generation facilities based on the geospatial database, wherein the spatiotemporal dynamic analysis includes construction phase analysis, expansion phase analysis and operation status analysis, and obtains construction progress, expansion status and operation status;

[0114] A multidimensional feature extraction module, for extracting multidimensional feature variables related to the development trend of offshore photovoltaic power generation facilities from long-term series of remote sensing data and auxiliary data based on the geospatial database;

[0115] A prediction model building module, used to build a prediction model based on the multi-dimensional feature variables, in combination with the geospatial database, the expansion situation and the operation status;

[0116] The prediction result output module is used to use the trained prediction model to perform multi-scenario prediction on the development trend of offshore photovoltaic power generation facilities, and integrate the prediction output results with the construction progress, the expansion situation and the operation status to obtain the target prediction results.

[0117] The offshore photovoltaic power generation facility development trend prediction device of this embodiment is used to implement the aforementioned offshore photovoltaic power generation facility development trend prediction method. Therefore, the specific implementation method of the offshore photovoltaic power generation facility development trend prediction device can be seen in the implementation method part of the offshore photovoltaic power generation facility development trend prediction method mentioned above, for example, the data collection and fusion module, the spatiotemporal dynamic analysis module, the multidimensional feature extraction module, the prediction model construction module, and the prediction result output module are respectively used to implement steps S101, S102, S103, S104 and S105 in the above-mentioned offshore photovoltaic power generation facility development trend prediction method. Therefore, its specific implementation method can refer to the description of the corresponding each part of the implementation method, which will not be repeated here.

[0118] A specific embodiment of the present invention also provides a device for predicting the development trend of offshore photovoltaic power generation facilities, including: a memory for storing a computer program; and a processor for implementing the steps of the above-mentioned method for predicting the development trend of offshore photovoltaic power generation facilities when executing the computer program.

[0119] A specific embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for predicting the development trend of offshore photovoltaic power generation facilities are implemented.

[0120] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0121] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0122] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0124] Obviously, the above embodiments are merely examples for the purpose of clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the present invention.

Claims

1. A method for predicting the development trend of offshore photovoltaic power generation facilities, characterized in that: include: Collect multi-source remote sensing data and integrate geographic information data to establish a geospatial database; Based on the geospatial database, a spatiotemporal dynamic analysis is performed on the offshore photovoltaic power generation facilities, wherein the spatiotemporal dynamic analysis includes a construction phase analysis, an expansion phase analysis, and an operation status analysis to obtain the construction progress, expansion status, and operation status; Based on the geospatial database, extract multi-dimensional characteristic variables related to the development trend of offshore photovoltaic power generation facilities from long-term series remote sensing data and auxiliary data; Building a prediction model based on the multi-dimensional feature variables in combination with the geospatial database, the expansion situation and the operation status; The trained prediction model is used to perform multi-scenario predictions on the development trend of offshore photovoltaic power generation facilities, and the prediction output results are integrated with the construction progress, the expansion situation and the operation status to obtain the target prediction results.

2. The method for predicting the development trend of offshore photovoltaic power generation facilities according to claim 1, characterized in that: The collecting of multi-source remote sensing data, fusing geographic information data, and establishing a geospatial database includes: Collecting multi-source remote sensing data from different remote sensing platforms and preprocessing the multi-source remote sensing data; The preprocessed multi-source remote sensing data is integrated with geographic information data to establish a geospatial database.

3. The method for predicting the development trend of offshore photovoltaic power generation facilities according to claim 1, characterized in that: The spatiotemporal dynamic analysis of the offshore photovoltaic power generation facilities based on the geospatial database includes construction phase analysis, expansion phase analysis and operation status analysis, and the construction progress, expansion status and operation status are obtained including: Extracting feature information of remote sensing images at different stages of the facility construction process based on the geospatial database, analyzing the difference between the actual construction situation and the design planning data based on the feature information, and evaluating the construction progress; Based on the geospatial database, remote sensing images of different periods are compared, and combined with historical expansion conditions, the direction, scope and speed of future expansion are determined to obtain future expansion conditions; Based on the geospatial database, the spectral feature information of the remote sensing images at different periods during the operation of the facility is extracted, and the surface state of the photovoltaic panels of the facility is monitored according to the spectral feature information. Combined with the historical operation status of the facility, the change trend of the facility's power generation efficiency over time is determined, thereby determining the future operation status of the facility.

4. The method for predicting the development trend of offshore photovoltaic power generation facilities according to claim 1, characterized in that: The spatiotemporal dynamic analysis of the offshore photovoltaic power generation facilities based on the geospatial database includes construction phase analysis, expansion phase analysis and operation status analysis. After obtaining the construction progress, expansion status and operation status, the following is also included: According to the construction progress, combining marine engineering knowledge and on-site construction conditions, analyze the causes of construction deviations, and output improvement suggestions based on the causes of construction deviations; Based on the expansion situation, the rationality of the expansion plan is analyzed in combination with marine environmental factors and changes in energy demand, and the relationship between the expanded facilities and the surrounding marine ecological environment is analyzed using spatial analysis technology, and the expansion plan is adjusted based on the analysis results; In combination with the above operation conditions, monitor the changes in the marine ecological environment around the facility, analyze the real-time monitoring results, and output corresponding improvement measures for facility construction or operation.

5. The method for predicting the development trend of offshore photovoltaic power generation facilities according to claim 1, characterized in that: The constructing of a prediction model based on the multi-dimensional feature variables in combination with the geospatial database, the expansion situation and the operation status includes: Selecting and reducing the dimension of the multi-dimensional feature variables; A prediction model is constructed based on the selected multiple characteristic variables in combination with the geospatial database, the expansion situation and the operation status.

6. The method for predicting the development trend of offshore photovoltaic power generation facilities according to claim 1, characterized in that: The method further comprises: using the trained prediction model to perform multi-scenario prediction on the development trend of offshore photovoltaic power generation facilities, and integrating the prediction output results with the construction progress, the expansion situation and the operation status, and obtaining the target prediction results. Performing anomaly detection on the target prediction result according to a preset threshold; When an anomaly is detected, an alarm is triggered.

7. The method for predicting the development trend of offshore photovoltaic power generation facilities according to claim 1, characterized in that: When an abnormality is detected, triggering an alarm includes: determining an abnormality severity score for the detected anomaly; Based on the abnormality score, according to a preset severity threshold, determining an alarm level corresponding to the detected abnormality; An alarm of a corresponding type is triggered according to the alarm level.

8. A device for predicting the development trend of offshore photovoltaic power generation facilities, characterized in that: include: Data collection and fusion module, used to collect multi-source remote sensing data, fuse geographic information data, and establish a geospatial database; A spatiotemporal dynamic analysis module, used to perform spatiotemporal dynamic analysis on offshore photovoltaic power generation facilities based on the geospatial database, wherein the spatiotemporal dynamic analysis includes construction phase analysis, expansion phase analysis and operation status analysis, and obtains construction progress, expansion status and operation status; A multidimensional feature extraction module, for extracting multidimensional feature variables related to the development trend of offshore photovoltaic power generation facilities from long-term series remote sensing data and auxiliary data based on the geospatial database; A prediction model building module, used to build a prediction model based on the multi-dimensional feature variables, in combination with the geospatial database, the expansion situation and the operation status; The prediction result output module is used to use the trained prediction model to perform multi-scenario prediction on the development trend of offshore photovoltaic power generation facilities, and integrate the prediction output results with the construction progress, the expansion situation and the operation status to obtain the target prediction results.

9. An offshore photovoltaic power generation facility development trend prediction device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of a method for predicting the development trend of offshore photovoltaic power generation facilities as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for predicting development trends of offshore photovoltaic power generation facilities as described in any one of claims 1 to 7.

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