Method and device for processing meteorological data of offshore photovoltaic project, equipment and product

Abstract

This disclosure relates to a meteorological data processing method, apparatus, electronic device, and computer program product for offshore photovoltaic (PV) projects, belonging to the field of computer technology. The method includes: acquiring key meteorological data corresponding to key monitoring points in an offshore PV area; determining a first location factor corresponding to any PV unit within the offshore PV area; determining first meteorological data corresponding to the PV unit based on the key meteorological data and the distance between the PV unit and the key monitoring points; determining a second location factor corresponding to the key monitoring points; and determining second meteorological data corresponding to the PV unit based on the first location factor, the second location factor, and the first meteorological data. This disclosure can accurately calculate the meteorological data for each PV unit with a limited number of key monitoring points, enabling refined monitoring and management of different PV units and facilitating the assessment of the PV unit's operating environment.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and more specifically, to a meteorological data processing method, a meteorological data processing device, an electronic device, and a computer program product for offshore photovoltaic projects. Background Technology

[0002] Offshore photovoltaic (PV) projects differ significantly from conventional onshore PV projects. For example, they involve larger individual support structures, greater spacing between piles, and greater susceptibility of the support structure to the marine environment. To ensure safety throughout the construction and service life of the project, monitoring and research of the on-site environment are necessary.

[0003] Marine meteorology is one of the key monitoring areas, including monitoring of factors such as wind conditions, temperature, air pressure, humidity, rainfall, and sunshine. Due to the large area of ​​offshore photovoltaic (PV) systems and the varying environments of different PV units (for example, wind conditions are highly correlated with the array location of the PV units), obtaining meteorological data for each PV unit is crucial for achieving refined monitoring, given the limited number of monitoring points.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this disclosure is to provide a meteorological data processing method, a meteorological data processing device, an electronic device, a computer-readable storage medium, and a computer program product for offshore photovoltaic projects, thereby overcoming, to at least a certain extent, the problem that meteorological monitoring schemes for offshore photovoltaic projects cannot obtain accurate meteorological data from different locations.

[0006] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0007] According to a first aspect of this disclosure, a meteorological data processing method for offshore photovoltaic projects is provided, comprising: acquiring key meteorological data corresponding to key monitoring points in an offshore photovoltaic area; determining a first location factor corresponding to any photovoltaic unit in the offshore photovoltaic area; determining first meteorological data corresponding to the photovoltaic unit based on the key meteorological data and the distance between the photovoltaic unit and the key monitoring points; determining a second location factor corresponding to the key monitoring points; and determining second meteorological data corresponding to the photovoltaic unit based on the first location factor, the second location factor, and the first meteorological data.

[0008] In one exemplary embodiment of this disclosure, obtaining key meteorological data corresponding to key monitoring points in a marine photovoltaic area includes: determining the key monitoring points, which are determined based on the corner positions of the marine photovoltaic area; obtaining meteorological monitoring data collected by meteorological sensors at each key monitoring point, and using the meteorological monitoring data as the key meteorological data.

[0009] In one exemplary embodiment of this disclosure, determining the first location factor corresponding to any photovoltaic unit within the offshore photovoltaic area includes: determining a first distance parameter between the photovoltaic unit and the overall boundary of the offshore photovoltaic area, wherein the first distance parameter is the distance between the photovoltaic unit and the overall boundary in multiple specified directions; determining the sub-region of the field where the photovoltaic unit is located; determining a second distance parameter between the photovoltaic unit and the sub-region boundary of the sub-region, wherein the second distance parameter is the distance between the photovoltaic unit and the sub-region boundary in multiple specified directions; and performing weighted processing on the first distance parameter and the second distance parameter to obtain the first location factor.

[0010] In one exemplary embodiment of this disclosure, determining the first meteorological data corresponding to the photovoltaic unit based on the key meteorological data and the distance between the photovoltaic unit and the key monitoring point includes: using the distance between the photovoltaic unit and the key monitoring point as the photovoltaic point distance; interpolating the key meteorological data based on the photovoltaic point distance to obtain the first meteorological data; or weighting the key meteorological data based on the photovoltaic point distance to obtain the first meteorological data.

[0011] In one exemplary embodiment of this disclosure, the step of interpolating the key meteorological data based on the distance between photovoltaic points to obtain the first meteorological data includes: obtaining the point weights corresponding to each of the key monitoring points, wherein the point weights are determined based on the distance between the photovoltaic points; determining the point distribution characteristics of the key monitoring points, and determining a corresponding interpolation calculation model based on the point distribution characteristics; and interpolating the key meteorological data based on the point weights using the interpolation calculation model to obtain the first meteorological data.

[0012] In one exemplary embodiment of this disclosure, determining the second meteorological data corresponding to the photovoltaic unit based on the first location factor, the second location factor, and the first meteorological data includes: determining the meteorological data type corresponding to the first meteorological data; determining a matching data correction method based on the meteorological data type; and correcting the first meteorological data based on the data correction method, the first location factor, and the second location factor to obtain the second meteorological data.

[0013] In one exemplary embodiment of this disclosure, the first meteorological data includes initial temperature data, and the second meteorological data includes corrected temperature data. The step of correcting the first meteorological data based on the data correction method, the first location factor, and the second location factor to obtain the second meteorological data includes: when the meteorological data type is a temperature data type, determining the data correction method as a positive correction method; and combining the positive correction method, the first location factor, and the second location factor to correct the initial temperature data to obtain the corrected temperature data.

[0014] In one exemplary embodiment of this disclosure, the first meteorological data includes initial wind speed data, and the second meteorological data includes corrected wind speed data. The step of correcting the first meteorological data based on the data correction method, the first location factor, and the second location factor to obtain the second meteorological data includes: when the meteorological data type is wind speed data type, determining the data correction method as a negative correction method; and correcting the initial wind speed data by combining the negative correction method, the first location factor, and the second location factor to obtain the corrected wind speed data.

[0015] According to a second aspect of this disclosure, a meteorological data processing device for offshore photovoltaic projects is provided, comprising: a key data acquisition module for acquiring key meteorological data corresponding to key monitoring points in an offshore photovoltaic area; a first location factor determination module for determining a first location factor corresponding to any photovoltaic unit in the offshore photovoltaic area; a first meteorological data determination module for determining first meteorological data corresponding to the photovoltaic unit based on the key meteorological data and the distance between the photovoltaic unit and the key monitoring points; and a second meteorological data determination module for determining a second location factor corresponding to the key monitoring points, and determining second meteorological data corresponding to the photovoltaic unit based on the first location factor, the second location factor, and the first meteorological data.

[0016] In one exemplary embodiment of this disclosure, the key data acquisition module includes a key data acquisition unit, configured to: determine the key monitoring points, the key monitoring points being determined based on the corner positions of the offshore photovoltaic area; acquire meteorological monitoring data collected by meteorological sensors at each of the key monitoring points, and use the meteorological monitoring data as the key meteorological data.

[0017] In one exemplary embodiment of this disclosure, the first location factor determination module includes a first location factor determination unit, configured to: determine a first distance parameter between the photovoltaic unit and the overall boundary of the offshore photovoltaic area, wherein the first distance parameter is the distance between the photovoltaic unit and the overall boundary in multiple specified directions; determine the sub-area of ​​the field where the photovoltaic unit is located; determine a second distance parameter between the photovoltaic unit and the sub-area boundary of the sub-area, wherein the second distance parameter is the distance between the photovoltaic unit and the sub-area boundary in multiple specified directions; and perform weighted processing on the first distance parameter and the second distance parameter to obtain the first location factor.

[0018] In one exemplary embodiment of this disclosure, the first meteorological data determination module includes a first meteorological data determination unit, configured to: use the distance between the photovoltaic unit and the key monitoring point as the distance between the photovoltaic points; interpolate the key meteorological data based on the distance between the photovoltaic points to obtain the first meteorological data; or perform weighted processing on the key meteorological data based on the distance between the photovoltaic points to obtain the first meteorological data.

[0019] In one exemplary embodiment of this disclosure, the first meteorological data determination unit includes a meteorological data determination subunit, configured to: obtain the point weights corresponding to each of the key monitoring points, wherein the point weights are determined based on the distance between the photovoltaic points; determine the point distribution characteristics of the key monitoring points, and determine a corresponding interpolation calculation model based on the point distribution characteristics; and perform interpolation processing on the key meteorological data based on the point weights using the interpolation calculation model to obtain the first meteorological data.

[0020] In one exemplary embodiment of this disclosure, the second meteorological data determination module includes a second meteorological data determination unit, configured to: determine the meteorological data type corresponding to the first meteorological data; determine a matching data correction method based on the meteorological data type; and correct the first meteorological data based on the data correction method, the first location factor, and the second location factor to obtain the second meteorological data.

[0021] In one exemplary embodiment of this disclosure, the first meteorological data includes initial temperature data, the second meteorological data includes corrected temperature data, and the second meteorological data determination unit includes a first correction subunit, configured to: determine the data correction method as a positive correction method when the meteorological data type is a temperature data type; and correct the initial temperature data by combining the positive correction method, the first location factor, and the second location factor to obtain the corrected temperature data.

[0022] In one exemplary embodiment of this disclosure, the first meteorological data includes initial wind speed data, the second meteorological data includes corrected wind speed data, and the second meteorological data determination unit includes a second correction subunit, configured to: when the meteorological data type is wind speed data type, determine the data correction method as a negative correction method; and combine the negative correction method, the first location factor, and the second location factor to correct the initial wind speed data to obtain the corrected wind speed data.

[0023] According to a third aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory storing computer-readable instructions, which, when executed by the processor, implement the meteorological data processing method for offshore photovoltaic projects according to any one of the preceding claims.

[0024] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the meteorological data processing method for offshore photovoltaic projects according to any one of the preceding claims.

[0025] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the meteorological data processing method for offshore photovoltaic projects as described in any one of the preceding claims.

[0026] The technical solution provided in this disclosure may include the following beneficial effects:

[0027] The meteorological data processing method for offshore photovoltaic projects in the exemplary embodiments of this disclosure, on the one hand, can accurately calculate the meteorological data of each photovoltaic unit with a limited number of key monitoring points, enabling refined monitoring and management of different photovoltaic units. On the other hand, the accurate meteorological data obtained from different locations can be used to assess the working environment conditions of the photovoltaic units.

[0028] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0029] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0030] Figure 1 A flowchart illustrating a meteorological data processing method for offshore photovoltaic projects according to an exemplary embodiment of the present disclosure is shown schematically.

[0031] Figure 2 A schematic diagram illustrating key monitoring points set according to an exemplary embodiment of the present disclosure is shown.

[0032] Figure 3 A flowchart illustrating the determination of a first location factor corresponding to a photovoltaic unit according to an exemplary embodiment of the present disclosure is shown schematically.

[0033] Figure 4 The flowchart illustrating the process of correcting first meteorological data to obtain second meteorological data according to an exemplary embodiment of the present disclosure is shown in the illustration.

[0034] Figure 5 A block diagram of a meteorological data processing apparatus for offshore photovoltaic projects according to an exemplary embodiment of the present disclosure is shown schematically.

[0035] Figure 6 A block diagram of an electronic device according to an exemplary embodiment of the present disclosure is shown schematically;

[0036] Figure 7 The illustration shows a schematic diagram of a computer-readable storage medium according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0037] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0038] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details described, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known structures, methods, apparatuses, implementations, materials, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0039] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, or in one or more software-hardened modules, or in different network and / or processor devices and / or microcontroller devices.

[0040] In offshore photovoltaic projects, meteorological monitoring at the project site is often limited to representative locations due to the large scale of the offshore photovoltaic field. This method of setting up monitoring points at key locations fails to obtain accurate meteorological data for different locations.

[0041] Based on this, in this example embodiment, a meteorological data processing method for offshore photovoltaic projects is first provided. The meteorological data processing method for offshore photovoltaic projects disclosed herein can be implemented using a server, or it can be implemented using a terminal device. The terminal described in this disclosure may include mobile terminals such as mobile phones, tablets, laptops, handheld computers, and personal digital assistants (PDAs), as well as fixed terminals such as desktop computers. Figure 1 A schematic diagram illustrating a meteorological data processing method flow for offshore photovoltaic projects according to some embodiments of the present disclosure is shown. (Reference) Figure 1 The meteorological data processing method for offshore photovoltaic projects may include the following steps:

[0042] Step S110: Obtain key meteorological data corresponding to key monitoring points in the offshore photovoltaic area;

[0043] Step S120: Determine the first location factor corresponding to any photovoltaic unit within the offshore photovoltaic area;

[0044] Step S130: Determine the first meteorological data corresponding to the photovoltaic unit based on the key meteorological data and the distance between the photovoltaic unit and the key monitoring point.

[0045] Step S140: Determine the second location factor corresponding to the key monitoring point, and determine the second meteorological data corresponding to the photovoltaic unit based on the first location factor, the second location factor and the first meteorological data.

[0046] According to the meteorological data processing method for offshore photovoltaic projects in this example embodiment, on the one hand, with a limited number of key monitoring points, the meteorological data for each photovoltaic unit can be accurately calculated, enabling refined monitoring and management of different photovoltaic units. On the other hand, the accurate meteorological data obtained from different locations can be used to assess the working environment of the photovoltaic units.

[0047] The meteorological data processing method for offshore photovoltaic projects in this example embodiment will be further explained below.

[0048] In one exemplary embodiment of this disclosure, step S110, obtaining key meteorological data corresponding to key monitoring points in the marine photovoltaic area, includes: determining key monitoring points, which are determined based on the corner positions of the marine photovoltaic area; obtaining meteorological monitoring data collected by meteorological sensors at each key monitoring point, and using the meteorological monitoring data as key meteorological data.

[0049] The offshore photovoltaic (PV) area, also known as the offshore PV field area, refers to the area on the sea surface where an offshore PV project is located. Key monitoring points are points formed by setting monitoring points at pre-defined key locations. Corner points refer to the intersections of the sides of a polygon, i.e., the locations of the vertices of the polygon. Meteorological sensors are devices that detect meteorological parameters digitally or analogically, capable of real-time monitoring of elements such as temperature, humidity, air pressure, wind speed, wind direction, and precipitation, providing data support for weather forecasting, climate research, and field management of offshore PV projects. Meteorological monitoring data can be data collected in real-time by meteorological sensors. Key meteorological data can be meteorological monitoring data collected from all key monitoring points within the offshore PV area.

[0050] Because offshore photovoltaic (PV) projects typically involve large areas, key locations are usually identified within these areas, and monitoring points are established at these locations to collect meteorological data. (Reference) Figure 2 , Figure 2 A schematic diagram illustrating key monitoring points set according to an exemplary embodiment of this disclosure is provided. Figure 2 In this approach, monitoring points can be set up based on the corner locations corresponding to the offshore photovoltaic area. For longer sides, monitoring points can be added at the middle positions of the sides. For example, six monitoring points, such as ①-⑥, can be set up based on the corner locations as key monitoring points.

[0051] After identifying key monitoring locations, meteorological monitoring data collected by meteorological sensors at these locations can be obtained. This includes real-time data on wind conditions, temperature, air pressure, humidity, rainfall, and sunshine duration, which serve as key meteorological data. The key meteorological data collected through these steps forms the basis for subsequent calculations of the meteorological data corresponding to each photovoltaic unit.

[0052] In one exemplary embodiment of this disclosure, step S120, determining the first location factor corresponding to any photovoltaic unit within the marine photovoltaic area, includes: determining a first distance parameter between the photovoltaic unit and the overall boundary of the marine photovoltaic area, wherein the first distance parameter is the distance between the photovoltaic unit and the overall boundary in multiple specified directions; determining the sub-region of the field where the photovoltaic unit is located; determining a second distance parameter between the photovoltaic unit and the sub-region boundary of the sub-region, wherein the second distance parameter is the distance between the photovoltaic unit and the sub-region boundary in multiple specified directions; and performing weighted processing on the first distance parameter and the second distance parameter to obtain the first location factor.

[0053] In this context, a photovoltaic (PV) unit refers to a basic component of an offshore PV project that possesses independent power generation capabilities and can be centrally connected to the grid. A PV unit can be a fundamental building block of a PV power plant, also known as a PV unit power generation module. A PV unit can consist of a certain number of PV modules connected in series via a DC combiner box, inverted by an inverter, and stepped up by an isolated substation to produce a power supply that meets the grid's frequency and voltage requirements. The first location factor can be the location factor corresponding to the PV unit. The first distance parameter can be a parameter calculated based on the distance between the location of the PV unit and the boundary of the entire offshore PV area. The second distance parameter can be a parameter calculated based on the distance between the location of the PV unit and the boundary of the sub-region of its location within the PV site.

[0054] refer to Figure 3 , Figure 3 A flowchart illustrating the determination of a first location factor corresponding to a photovoltaic unit according to an exemplary embodiment of the present disclosure is shown. For any photovoltaic unit within an offshore photovoltaic area, the corresponding first location factor can be determined through the following steps:

[0055] In step S310, a first distance parameter is determined between the photovoltaic unit and the overall boundary of the offshore photovoltaic area. The first distance parameter is the distance between the photovoltaic unit and the overall boundary in multiple specified directions. The overall boundary can be the boundary corresponding to the offshore photovoltaic area. For any photovoltaic unit within the offshore photovoltaic field, the position factor of the photovoltaic unit is calculated based on factors such as the number of surrounding photovoltaic units and the distance to the boundary. Specifically, the distances between the photovoltaic unit and the overall boundary corresponding to the entire offshore photovoltaic area in four directions (such as due north, due south, due east, and due west) are obtained, resulting in four first distance parameters.

[0056] In step S320, the sub-region of the photovoltaic unit is determined, and a second distance parameter between the photovoltaic unit and the sub-region boundary is determined. The second distance parameter is the distance between the photovoltaic unit and the sub-region boundary in multiple specified directions. The sub-region boundary can be the boundary of the sub-region obtained by dividing the entire marine photovoltaic area according to the channel. For any photovoltaic unit, the distance between the photovoltaic unit and the sub-region boundary in four directions (such as due north, due south, due east, and due west) is obtained, resulting in four second distance parameters.

[0057] In step S330, the first distance parameter and the second distance parameter are weighted to obtain the first location factor. After obtaining the above eight first and second distance parameters, the eight distance parameters are weighted to obtain the first location factor corresponding to the photovoltaic unit. The larger the first location factor, the closer the photovoltaic unit is to the region center and the farther it is from the boundary. The first location factor calculated through the above steps can be used as the data basis for subsequent location correction processing of meteorological data.

[0058] It will be readily understood by those skilled in the art that in some other exemplary embodiments of this disclosure, other directions may be selected for the distance parameter determination process according to specific needs, such as northeast, southeast, southwest, northwest, etc. This disclosure does not impose any special limitations on the specific direction selected.

[0059] In one exemplary embodiment of this disclosure, for step S130, determining the first meteorological data corresponding to the photovoltaic unit based on key meteorological data and the distance between the photovoltaic unit and the key monitoring point includes: using the distance between the photovoltaic unit and the key monitoring point as the photovoltaic point distance; interpolating the key meteorological data based on the photovoltaic point distance to obtain the first meteorological data; or weighting the key meteorological data based on the photovoltaic point distance to obtain the first meteorological data.

[0060] The distance to the photovoltaic (PV) point can be the distance between the PV unit and the key monitoring point. The first meteorological data can be the meteorological data corresponding to a specific PV unit obtained by interpolating or weighting the key meteorological data based on the PV point distance. Alternatively, the first meteorological data can be the meteorological data for a specific PV unit without location correction.

[0061] For a single photovoltaic unit, assuming its location coordinates are (x0, y0), the coordinates of multiple key monitoring points in the offshore photovoltaic area are respectively (x0, y0). i y i The key meteorological data corresponding to each key monitoring point is M. i (i=1,2,…,n), the distance between the photovoltaic unit and each key monitoring point is calculated using the Euclidean distance formula as shown in Formula 1.

[0062]

[0063] Where d is the distance to the photovoltaic point; (x0, y0) can be the location coordinates of the photovoltaic unit; (x i ,y i () can be the location coordinates of each key monitoring point.

[0064] After calculating the distance to the photovoltaic (PV) points, the acquired key meteorological data can be interpolated based on this distance to obtain the first meteorological data. Alternatively, the key meteorological data can be weighted based on the PV point distance to obtain the first meteorological data. When weighting the key meteorological data, the weight of each key monitoring point can be determined based on the PV point distance, and then multiple key meteorological data points can be weighted together to obtain the first meteorological data. Through these processes, meteorological data corresponding to each PV unit can be obtained, allowing the assessment of the PV unit's operating environment using the meteorological data from each location.

[0065] In one exemplary embodiment of this disclosure, key meteorological data is interpolated based on the distance between photovoltaic monitoring points to obtain first meteorological data. This includes: obtaining the point weights corresponding to each key monitoring point, wherein the point weights are determined based on the distance between photovoltaic monitoring points; determining the point distribution characteristics of the key monitoring points, and determining the corresponding interpolation calculation model based on the point distribution characteristics; and interpolating the key meteorological data based on the point weights using the interpolation calculation model to obtain the first meteorological data.

[0066] Among them, the point weight can be the calculation weight corresponding to the key monitoring point. The point distribution characteristic can be the distribution characteristic of the key monitoring points in the offshore photovoltaic area. For example, the point distribution characteristic can include the linear distribution characteristic and the spatial distribution characteristic, etc. The interpolation calculation model can be a model that calculates the first meteorological data using the interpolation algorithm.

[0067] For each key monitoring point, the point weight corresponding to each key monitoring point can be calculated separately. For example, the point weight can be calculated using the power of the reciprocal of the distance. For example, the point weight is shown in Formula 2.

[0068]

[0069] Among them, ω i can be the point weight corresponding to the i-th key monitoring point; d j is the photovoltaic point distance between the photovoltaic unit and the key monitoring point; p is a constant greater than 0. For example, the value of p can be 2; the closer the key point is, the greater the point weight.

[0070] After determining the point weight of each key monitoring point, the point distribution characteristic of the key monitoring point is determined. For example, the key monitoring points can be linearly distributed. In addition, due to the complexity of the on-site environment of the offshore photovoltaic project, multiple key monitoring points show spatial correlation, etc. According to the point distribution characteristic presented by the key monitoring points, an interpolation calculation model adapted to it is selected.

[0071] For example, when the key monitoring points are linearly distributed, that is, multiple key monitoring points are distributed along a straight line. Given two key monitoring points A(x1, y1) and B(x2, y2), and the corresponding key meteorological data are M1 and M2 respectively, and the photovoltaic unit is located at x0, and x1 < x0 < x2. Then, after determining the point weights ω1 and ω2 corresponding to the two key monitoring points A and B, the first meteorological data M0 corresponding to the photovoltaic unit x0 can be calculated through the linear interpolation model, as shown in Formula 3.

[0072] M0 = ω1M1 + ω2M2 (Formula 3)

[0073] Among them, M0 can be the first meteorological data corresponding to the photovoltaic unit x0; ω1 can be the point weight corresponding to the key monitoring point A; ω2 can be the point weight corresponding to the key monitoring point B; M1 can be the key meteorological data corresponding to the key monitoring point A; M2 can be the key meteorological data corresponding to the key monitoring point B.

[0074] For example, when multiple key monitoring points exhibit spatial correlation, meaning that the locations of these key monitoring points are relatively random but have a certain spatial structure, an interpolation calculation model based on the Kriging interpolation method can be used to perform interpolation calculations to obtain the first meteorological data.

[0075] Specifically, it is necessary to calculate the variability function based on the meteorological data from multiple known key monitoring points to describe the spatial variability of the meteorological data. Then, by solving the Kriging equations, the point weight of each key point is obtained, and finally, the first meteorological data of the photovoltaic unit is calculated. The specific calculation method is shown in Formula 4.

[0076]

[0077] Where M0 can be the first meteorological data corresponding to photovoltaic unit x0; ω i It can be the point weight corresponding to the i-th key monitoring point; M i It can be the key meteorological data corresponding to the i-th key monitoring point.

[0078] While Kriging interpolation is relatively complex, it effectively utilizes the spatial structure information of data and typically boasts high accuracy in meteorological data interpolation. In practical applications, a suitable interpolation method needs to be selected based on factors such as the specific characteristics of the meteorological data, the distribution of key locations, and the required computational accuracy. Furthermore, methods such as cross-validation can be used to evaluate the accuracy of the interpolation results and optimize and adjust the interpolation method. By interpolating key meteorological data, the first meteorological data corresponding to each photovoltaic unit can be obtained.

[0079] In one exemplary embodiment of this disclosure, step S140, determining the second meteorological data corresponding to the photovoltaic unit based on the first location factor, the second location factor, and the first meteorological data, includes: determining the meteorological data type corresponding to the first meteorological data; determining a matching data correction method based on the meteorological data type; and correcting the first meteorological data based on the data correction method, the first location factor, and the second location factor to obtain the second meteorological data.

[0080] refer to Figure 4 , Figure 4 The flowchart illustrating the process of correcting first meteorological data to obtain second meteorological data according to an exemplary embodiment of the present disclosure is shown. Before correcting the first meteorological data, in step S410, the meteorological data type corresponding to the first meteorological data can be determined first. For example, the meteorological data type can be a specific type of meteorological data, which may include, but is not limited to, wind conditions, temperature, air pressure, humidity, rainfall, sunshine, etc.

[0081] In step S420, a matching data correction method is determined based on the meteorological data type. Different data correction methods are required for different meteorological data types. The data correction method can be a specific method for correcting the location of the first meteorological data based on the location factors of the photovoltaic unit and key monitoring points. Therefore, a matching data correction method is determined for each different meteorological data type.

[0082] In step S430, the first meteorological data is corrected based on the data correction method, the first location factor, and the second location factor to obtain the second meteorological data. The selected data correction method is used, and the first meteorological data is corrected according to the location factors of the key monitoring points and the photovoltaic unit to obtain the second meteorological data. By correcting the first meteorological data, accurate meteorological data for the location of each photovoltaic unit can be obtained.

[0083] In one exemplary embodiment of this disclosure, the first meteorological data is corrected based on the data correction method, the first location factor, and the second location factor to obtain the second meteorological data. This includes: when the meteorological data type is a temperature data type, determining the data correction method as a positive correction method; and combining the positive correction method, the first location factor, and the second location factor to correct the initial temperature data to obtain corrected temperature data.

[0084] The initial temperature data can be the uncorrected temperature and meteorological data of a photovoltaic unit. The corrected temperature data can be the temperature and meteorological data of a photovoltaic unit after location correction. The positive correction method can be to correct the meteorological data based on the positive correlation between the location factor and the meteorological data.

[0085] During meteorological data correction, if the meteorological data type is temperature-based, a positive correction method can be used. This means that the corrected meteorological data is positively correlated with the location factor. The initial temperature data is positively corrected based on the first and second location factors to obtain the corrected temperature data. During the correction process, a higher location factor corresponding to the photovoltaic unit or key monitoring point indicates a higher temperature. The temperature correction data calculated through the above steps can be used to assess the environmental conditions of the photovoltaic unit's operating environment.

[0086] In one exemplary embodiment of this disclosure, the first meteorological data is corrected based on the data correction method, the first location factor, and the second location factor to obtain the second meteorological data. This includes: when the meteorological data type is wind speed data type, determining the data correction method as a negative correction method; and combining the negative correction method, the first location factor, and the second location factor to correct the initial wind speed data to obtain corrected wind speed data.

[0087] The initial wind speed data can be the uncorrected wind speed meteorological data for a specific photovoltaic unit. The corrected wind speed data can be the wind speed meteorological data obtained after location correction for a specific photovoltaic unit. The negative correction method can be a method of correcting the meteorological data based on the negative correlation between the location factor and the meteorological data.

[0088] During meteorological data correction, if the meteorological data type is wind speed, a negative correction method can be used. This means that the corrected meteorological data is negatively correlated with the location factor. The initial wind speed data is negatively corrected based on the first and second location factors to obtain the corrected wind speed data. During the correction process, the larger the location factor corresponding to the photovoltaic unit or key monitoring point, the lower the wind speed.

[0089] After the above correction steps, the calculated second meteorological data is used as the final meteorological data of the photovoltaic unit. Thus, for photovoltaic units without monitoring points, meteorological data can be accurately calculated to assess their working environment.

[0090] It should be noted that the terms "first" and "second" used in this disclosure are only for distinguishing different meteorological data and different location factors, and should not impose any limitations on this disclosure.

[0091] In summary, the meteorological data processing method for offshore photovoltaic (PV) projects disclosed herein acquires key meteorological data corresponding to key monitoring points in the offshore PV area; determines a first location factor corresponding to any PV unit within the offshore PV area; determines the first meteorological data corresponding to the PV unit based on the key meteorological data and the distance between the PV unit and the key monitoring points; determines a second location factor corresponding to the key monitoring points; and, based on the first location factor, the second location factor, and the first meteorological data, determines the second meteorological data corresponding to the PV unit. On the one hand, with a limited number of key monitoring points, meteorological data for each PV unit can be accurately calculated, enabling refined monitoring and management of different PV units. On the other hand, the accurate meteorological data obtained from different locations can be used to assess the working environment of the PV units. Furthermore, by correcting the first meteorological data to obtain the second meteorological data, accurate meteorological data for each location can be obtained, providing a data foundation for refined monitoring and management.

[0092] It should be noted that although the steps of the method in this invention are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0093] Furthermore, this example embodiment also provides a meteorological data processing device for offshore photovoltaic projects. (Reference) Figure 5 The meteorological data processing device 500 for offshore photovoltaic projects may include: a key data acquisition module 510, a first location factor determination module 520, a first meteorological data determination module 530, and a second meteorological data determination module 540.

[0094] Specifically, the key data acquisition module 510 is used to acquire key meteorological data corresponding to key monitoring points in the marine photovoltaic area; the first location factor determination module 520 is used to determine the first location factor corresponding to any photovoltaic unit in the marine photovoltaic area; the first meteorological data determination module 530 is used to determine the first meteorological data corresponding to the photovoltaic unit based on the key meteorological data and the distance between the photovoltaic unit and the key monitoring point; and the second meteorological data determination module 540 is used to determine the second location factor corresponding to the key monitoring point, and to determine the second meteorological data corresponding to the photovoltaic unit based on the first location factor, the second location factor and the first meteorological data.

[0095] In one exemplary embodiment of this disclosure, the key data acquisition module 510 includes a key data acquisition unit, used to: determine key monitoring points, the key monitoring points being determined based on the corner positions of the marine photovoltaic area; acquire meteorological monitoring data collected by meteorological sensors at each key monitoring point, and use the meteorological monitoring data as key meteorological data.

[0096] In one exemplary embodiment of this disclosure, the first location factor determination module 520 includes a first location factor determination unit, configured to: determine a first distance parameter between the photovoltaic unit and the overall boundary of the offshore photovoltaic area, wherein the first distance parameter is the distance between the photovoltaic unit and the overall boundary in multiple specified directions; determine the sub-region of the field where the photovoltaic unit is located; determine a second distance parameter between the photovoltaic unit and the sub-region boundary of the sub-region, wherein the second distance parameter is the distance between the photovoltaic unit and the sub-region boundary in multiple specified directions; and perform weighted processing on the first distance parameter and the second distance parameter to obtain a first location factor.

[0097] In one exemplary embodiment of this disclosure, the first meteorological data determination module 530 includes a first meteorological data determination unit, used to: take the distance between the photovoltaic unit and the key monitoring point as the photovoltaic point distance; interpolate the key meteorological data according to the photovoltaic point distance to obtain the first meteorological data; or perform weighted processing on the key meteorological data according to the photovoltaic point distance to obtain the first meteorological data.

[0098] In one exemplary embodiment of this disclosure, the first meteorological data determination unit includes a meteorological data determination subunit, which is used to: obtain the point weights corresponding to each key monitoring point, wherein the point weights are determined based on the distance between photovoltaic points; determine the point distribution characteristics of the key monitoring points, and determine the corresponding interpolation calculation model based on the point distribution characteristics; and perform interpolation processing on the key meteorological data based on the point weights using the interpolation calculation model to obtain the first meteorological data.

[0099] In one exemplary embodiment of this disclosure, the second meteorological data determination module 540 includes a second meteorological data determination unit, configured to: determine the meteorological data type corresponding to the first meteorological data; determine a matching data correction method based on the meteorological data type; and correct the first meteorological data based on the data correction method, a first location factor, and a second location factor to obtain the second meteorological data.

[0100] In one exemplary embodiment of this disclosure, the first meteorological data includes initial temperature data, the second meteorological data includes corrected temperature data, and the second meteorological data determination unit includes a first correction subunit, configured to: determine the data correction method as a positive correction method when the meteorological data type is a temperature data type; and correct the initial temperature data by combining the positive correction method, the first location factor, and the second location factor to obtain the corrected temperature data.

[0101] In one exemplary embodiment of this disclosure, the first meteorological data includes initial wind speed data, the second meteorological data includes corrected wind speed data, and the second meteorological data determination unit includes a second correction subunit, used to: when the meteorological data type is wind speed data type, determine the data correction method as a negative correction method; and combine the negative correction method, the first position factor, and the second position factor to correct the initial wind speed data to obtain the corrected wind speed data.

[0102] The specific details of the virtual modules of the meteorological data processing devices for offshore photovoltaic projects mentioned above have been described in detail in the corresponding meteorological data processing methods for offshore photovoltaic projects. For any undisclosed details, please refer to the implementation methods in the method section, and therefore will not be repeated here.

[0103] It should be noted that although several modules or units of the meteorological data processing device for offshore photovoltaic projects have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0104] An exemplary embodiment of this disclosure also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the aforementioned meteorological data processing method for offshore photovoltaic projects.

[0105] In one implementation, the computer program product may be a tangible product containing a computer program, such as a computer-readable storage medium storing the computer program. (See reference...) Figure 6 , Figure 6 The schematic diagram illustrates a computer-readable storage medium 600 according to an exemplary embodiment of the present disclosure. The computer-readable storage medium 600 can be a storage medium based on electrical, magnetic, optical, electromagnetic, infrared, or other signals, including but not limited to: random access memory (RAM), read-only memory (ROM), magnetic tape, floppy disk, flash memory, hard disk drive (HDD), solid-state drive (SSD), etc. Exemplarily, a computer program product can be implemented as a non-volatile storage medium storing a computer program, such as read-only memory, NAND flash memory, etc.

[0106] In one implementation, the computer program product can be an intangible product containing a computer program. For example, the computer program product can be implemented as a virtual digital product, such as an executable file, installation package, or other digital file storing the computer program.

[0107] Computer program code can be written in one or more programming languages. Examples of programming languages ​​include C, Java, and C++. Program code can execute entirely on the user's computing device, partially on the user's computing device, or as a standalone software package. It can also execute partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, such as a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via an internet connection provided by a mobile network operator).

[0108] Computer programs can be carried or transmitted via signals such as electricity, magnetism, light, electromagnetic fields, and infrared radiation. Electronic devices can convert the signals carrying computer programs into digital signals, thereby running the computer programs. When a computer program runs on an electronic device, its code is used to cause the electronic device to execute (more specifically, to execute) the method steps of various exemplary embodiments of this disclosure, such as the meteorological data processing method for offshore photovoltaic projects described above.

[0109] Exemplary embodiments of this disclosure also provide an electronic device, which may include a processor and a memory. The memory stores executable instructions of the processor, such as a computer program. The processor executes the executable instructions to perform the method steps of various exemplary embodiments of this disclosure. Furthermore, the electronic device may also include a display for displaying a graphical user interface.

[0110] The following is for reference. Figure 7 The electronic device is illustrated by way of a general-purpose computing device. It should be understood that... Figure 7 The electronic device 700 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0111] like Figure 7 As shown, the electronic device 700 may include: a processor 710, a memory 720, a bus 730, an I / O (input / output) interface 740, a network adapter 750, and a display 760.

[0112] The memory 720 may include volatile memory, such as RAM 721 and cache unit 722, and may also include non-volatile memory, such as ROM 723. The memory 720 may also include one or more program modules 724, including but not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. For example, program module 724 may include the modules described above.

[0113] The processor 710 may include one or more processing units, such as an AP (Application Processor), a modem processor, a GPU (Graphics Processing Unit), an ISP (Image Signal Processor), a controller, an encoder, a decoder, a DSP (Digital Signal Processor), a baseband processor, and / or an NPU (Neural-Network Processing Unit).

[0114] The processor 710 can be used to execute executable instructions stored in the memory 720, such as the meteorological data processing method for offshore photovoltaic projects described above.

[0115] Bus 730 is used to connect different components of electronic device 700 and may include a data bus, an address bus and a control bus.

[0116] Electronic device 700 can communicate with one or more external devices 800 (such as keyboard, mouse, external controller, etc.) through I / O interface 740.

[0117] Electronic device 700 can communicate with one or more networks via network adapter 750. For example, network adapter 750 can provide mobile communication solutions such as 3G / 4G / 5G, or wireless communication solutions such as wireless LAN, Bluetooth, and near-field communication. Network adapter 750 can communicate with other modules of electronic device 700 via bus 730.

[0118] The electronic device 700 can display a graphical user interface via a display 760, such as an interface that displays accurate meteorological data corresponding to each photovoltaic unit.

[0119] although Figure 7 As not shown in the diagram, other hardware and / or software modules may also be configured in the electronic device 700, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0120] As can be seen from the above, the technical solutions disclosed herein can be implemented as methods, apparatus, systems, computer program products, storage media, electronic devices, etc. Those skilled in the art will understand that various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be referred to as "circuit," "module," or "system," respectively.

[0121] It should be understood that this disclosure is not limited to the specific methods, steps, or structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. Those skilled in the art will readily conceive of other embodiments based on the specific implementations provided in this disclosure. Therefore, the specific implementations provided in this disclosure are merely exemplary, and the scope and spirit of this disclosure are indicated by the claims, and should cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary technical means in the art not disclosed in this disclosure.

Claims

1. A meteorological data processing method for offshore photovoltaic projects, characterized in that, include: Acquire key meteorological data corresponding to key monitoring points in offshore photovoltaic areas; Determine the first location factor corresponding to any photovoltaic unit within the marine photovoltaic area; Based on the key meteorological data and the distance between the photovoltaic unit and the key monitoring point, the first meteorological data corresponding to the photovoltaic unit is determined; Determine the second location factor corresponding to the key monitoring point, and based on the first location factor, the second location factor and the first meteorological data, determine the second meteorological data corresponding to the photovoltaic unit; Determining the first location factor corresponding to any photovoltaic unit within the offshore photovoltaic area includes: Determine a first distance parameter between the photovoltaic unit and the overall boundary of the marine photovoltaic area, wherein the first distance parameter is the distance between the photovoltaic unit and the overall boundary in multiple specified directions; Determine the sub-region of the field area where the photovoltaic unit is located, and determine a second distance parameter between the photovoltaic unit and the sub-region boundary of the sub-region. The second distance parameter is the distance between the photovoltaic unit and the sub-region boundary in multiple specified directions. The first distance parameter and the second distance parameter are weighted to obtain the first location factor; The step of determining the second meteorological data corresponding to the photovoltaic unit based on the first location factor, the second location factor, and the first meteorological data includes: Determine the type of meteorological data corresponding to the first meteorological data; Determine the matching data correction method based on the meteorological data type; Based on the data correction method, the first location factor, and the second location factor, the first meteorological data is corrected to obtain the second meteorological data; The first meteorological data includes initial temperature data, and the second meteorological data includes corrected temperature data. The step of correcting the first meteorological data based on the data correction method, the first location factor, and the second location factor to obtain the second meteorological data includes: When the meteorological data type is a temperature data type, the data correction method is determined to be a positive correction method; The initial temperature data is corrected by combining the positive correction method, the first position factor, and the second position factor to obtain the corrected temperature data; The first meteorological data includes initial wind speed data, and the second meteorological data includes corrected wind speed data. The step of correcting the first meteorological data based on the data correction method, the first location factor, and the second location factor to obtain the second meteorological data further includes: When the meteorological data type is wind speed data type, the data correction method is determined to be a negative correction method; The initial wind speed data is corrected by combining the negative correction method, the first position factor, and the second position factor to obtain the corrected wind speed data.

2. The method according to claim 1, characterized in that, The acquisition of key meteorological data corresponding to key monitoring points in the offshore photovoltaic area includes: The key monitoring points are determined based on the corner locations of the offshore photovoltaic area; Meteorological monitoring data collected by meteorological sensors at each of the key monitoring points are obtained, and the meteorological monitoring data is used as the key meteorological data.

3. The method according to claim 1, characterized in that, The step of determining the first meteorological data corresponding to the photovoltaic unit based on the key meteorological data and the distance between the photovoltaic unit and the key monitoring point includes: The distance between the photovoltaic unit and the key monitoring point is taken as the distance between the photovoltaic points; The key meteorological data is interpolated based on the distance between the photovoltaic points to obtain the first meteorological data; or The key meteorological data is weighted according to the distance of the photovoltaic points to obtain the first meteorological data.

4. The method according to claim 3, characterized in that, The step of interpolating the key meteorological data based on the distance to the photovoltaic points to obtain the first meteorological data includes: Obtain the point weights corresponding to each of the key monitoring points, and the point weights are determined based on the distances to the photovoltaic points; Determine the location distribution characteristics of the key monitoring points, and determine the corresponding interpolation calculation model based on the location distribution characteristics; The first meteorological data is obtained by interpolating the key meteorological data based on the location weights using the interpolation calculation model.

5. A meteorological data processing device for offshore photovoltaic projects, characterized in that, include: The key data acquisition module is used to acquire key meteorological data corresponding to key monitoring points in the offshore photovoltaic area; The first location factor determination module is used to determine the first location factor corresponding to any photovoltaic unit within the offshore photovoltaic area. The first meteorological data determination module is used to determine the first meteorological data corresponding to the photovoltaic unit based on the key meteorological data and the distance between the photovoltaic unit and the key monitoring point. The second meteorological data determination module is used to determine the second location factor corresponding to the key monitoring point, and to determine the second meteorological data corresponding to the photovoltaic unit based on the first location factor, the second location factor and the first meteorological data; The first location factor determination module is further configured to determine a first distance parameter between the photovoltaic unit and the overall boundary of the marine photovoltaic area, wherein the first distance parameter is the distance between the photovoltaic unit and the overall boundary in multiple specified directions; Determine the sub-region of the field area where the photovoltaic unit is located, and determine a second distance parameter between the photovoltaic unit and the sub-region boundary of the sub-region. The second distance parameter is the distance between the photovoltaic unit and the sub-region boundary in multiple specified directions. The first distance parameter and the second distance parameter are weighted to obtain the first location factor; The second meteorological data determining module is also used to determine the meteorological data type corresponding to the first meteorological data; Determine the matching data correction method based on the meteorological data type; Based on the data correction method, the first location factor, and the second location factor, the first meteorological data is corrected to obtain the second meteorological data; The first meteorological data includes initial temperature data, and the second meteorological data includes corrected temperature data. The step of correcting the first meteorological data based on the data correction method, the first location factor, and the second location factor to obtain the second meteorological data includes: When the meteorological data type is a temperature data type, the data correction method is determined to be a positive correction method; The initial temperature data is corrected by combining the positive correction method, the first position factor, and the second position factor to obtain the corrected temperature data; The first meteorological data includes initial wind speed data, and the second meteorological data includes corrected wind speed data. The step of correcting the first meteorological data based on the data correction method, the first location factor, and the second location factor to obtain the second meteorological data further includes: When the meteorological data type is wind speed data type, the data correction method is determined to be a negative correction method; The initial wind speed data is corrected by combining the negative correction method, the first position factor, and the second position factor to obtain the corrected wind speed data.

6. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the meteorological data processing method for offshore photovoltaic projects as described in any one of claims 1 to 4.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the meteorological data processing method for offshore photovoltaic projects as described in any one of claims 1 to 4.

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