New energy power generation prediction method and platform based on acquired data

Abstract

The invention provides a new energy power generation prediction method and platform based on collected data, and relates to a data processing technology, and the method comprises the steps that a server obtains a first power generation time point when a first power generation group carries out initial power generation, and obtains historical first synchronization data of the first power generation group based on the first power generation time point; decomposing the data volume of the first synchronous data according to periods to obtain first sub-information in different periods, determining a target period based on the current time point, and determining the first sub-information corresponding to the target period to obtain a first prediction sample set; traversing a second power generation group corresponding to the attribute of the first power generation group in the database, and performing data completion on the first prediction sample set based on historical second synchronization data of the second power generation group to obtain a second prediction sample set; and performing power generation prediction on the first power generation group based on the first prediction sample set or the second prediction sample set to obtain a new energy power generation prediction result. According to the invention, the power generation prediction precision of the power generation set can be improved.

Description

Technical Field

[0001] The present invention relates to data processing technology, and in particular to a new energy power generation prediction method and platform based on collected data. Background Art

[0002] As the global demand for renewable energy increases, new energy power generation systems such as photovoltaic and wind power generation have gradually become an important part of power supply. These systems are usually composed of multiple power generation units, each operating in different geographical locations and environmental conditions. In order to make full use of natural resources and improve power generation efficiency, accurate prediction of power generation is essential for grid stability and optimal resource allocation. However, due to the variability of environmental factors and equipment aging, the prediction of new energy power generation faces multiple challenges.

[0003] Most existing renewable energy generation forecasting methods rely on simple statistical models for forecasting. These methods usually fail to fully consider the impact of the deployment time of the power generation unit and environmental changes on the power generation, resulting in low forecasting accuracy.

[0004] Therefore, how to improve the accuracy of power generation prediction for power generation groups has become an urgent problem to be solved. Summary of the invention

[0005] The embodiments of the present invention provide a new energy power generation prediction method and platform based on collected data, which can improve the accuracy of power generation prediction for power generation groups.

[0006] A first aspect of an embodiment of the present invention provides a new energy power generation prediction method based on collected data, comprising: The server obtains a first power generation time point when the first power generation group performs initial power generation, and obtains first historical synchronization data of the first power generation group based on the first power generation time point, where the first synchronization data is the collected data of the first power generation group at a historical moment; Decomposing the data volume of the first synchronization data according to the period to obtain first sub-information under different periods, determining a target period based on the current time point, and determining the first sub-information corresponding to the target period to obtain a first prediction sample set; If it is determined that the prediction sample set does not meet the power generation prediction requirements, traverse the second power generation group corresponding to the attributes of the first power generation group in the database, and complete the first prediction sample set based on the historical second synchronization data of the second power generation group to obtain a second prediction sample set; Based on the first prediction sample set or the second prediction sample set, power generation prediction is performed on the first power generation group to obtain a new energy power generation prediction result.

[0007] Optionally, decomposing the data volume of the first synchronization data according to the period to obtain first sub-information in different periods, determining a target period based on a current time point, and determining the first sub-information corresponding to the target period to obtain a first prediction sample set includes: Extracting the earliest time of the first synchronization data, determining the annual time period between the earliest time and the current time based on the earliest time, and establishing a first annual data axis corresponding to each annual time period; Establish corresponding display slots based on the first-year data axis, place each first-year data axis in the display slot, establish a division slot at the bottom of the last display slot, and establish a division data axis corresponding to the first-year data axis in the division slot; All first-year data axes are divided based on the divided data axes to obtain a target period and retrieve the corresponding first sub-information to obtain a first prediction sample set.

[0008] Optionally, extracting the earliest time of the first synchronization data, determining the annual time period between the earliest time and the current time based on the earliest time, and establishing the first annual data axis corresponding to each annual time period includes: Determine a first number of pixel points corresponding to the first annual data axis; Determine the number of days corresponding to each first-year data axis, normalize the number of days of all first-year data axes to obtain a unified number of days, obtain the day time information corresponding to each group of pixel points based on the first number and the unified number of days, and establish a storage format for the collected data based on the day time information, where each day time information corresponds to a storage unit; According to the order of each group of pixels, corresponding time of day information is added to each group of pixels, and the information in the collected data is stored in correspondence with the corresponding group of pixels according to the time of day information, so that each group of pixels has a corresponding storage unit.

[0009] Optionally, the step of dividing all first-year data axes based on the divided data axes to obtain a target period and adjusting the corresponding first sub-information to obtain a first prediction sample set includes: Determine a target period based on the current time point, each current time point has a preset target period, and extract an initial time point and a deadline time point of the target period; Determine the time information and pixel points corresponding to the initial time point and the end time point in the divided data axis, and obtain the initial pixel point and the end pixel point; All first-year data axes are divided based on the initial pixel point and the cutoff pixel point, and the first sub-information is retrieved based on the storage format of the collected data to obtain a first prediction sample set.

[0010] Optionally, dividing all first-year data axes based on the initial pixel point and the cutoff pixel point, and retrieving the first sub-information based on the storage format of the collected data to obtain the first prediction sample set includes: An initial dividing line perpendicular to the dividing data axis is established upward based on the initial pixel point as a starting point, and a cutoff dividing line perpendicular to the dividing data axis is established upward based on the cutoff pixel point as a starting point; All pixel points of each first-year data axis located within the initial dividing line and the cutoff dividing line are obtained, and the first sub-information of the corresponding storage unit in the storage format is retrieved according to the order of the pixel points to obtain the first prediction sample set.

[0011] Optionally, the step of acquiring all pixel points of each first-year data axis located within the initial dividing line and the cutoff dividing line, and retrieving first sub-information of corresponding storage units in the storage format according to the order of the pixel points to obtain the first prediction sample set includes: If it is determined that the user selects the initial dividing line and / or the cut-off dividing line based on the interactive terminal and moves, the initial dividing line and / or the cut-off dividing line are synchronously moved in response to the user's movement; After determining that the movement of the initial dividing line and / or the cut-off dividing line has terminated, counting the pixel points of the initial dividing line and / or the cut-off dividing line; If it is determined that the user double-clicks at any position of the initial dividing line and / or the cut-off dividing line selected by the interactive terminal, the initial dividing line and / or the cut-off dividing line is segmented at the double-clicked position to obtain multiple moving segmenting lines; If it is determined that the user selects and moves any moving dividing line based on the interactive terminal, the moving dividing line is moved synchronously in response to the user's movement, and the pixel points between the moving dividing line and the moving dividing line, the moving dividing line and the cutoff dividing line, or the initial dividing line and the moving dividing line are counted.

[0012] Optionally, if it is determined that the prediction sample set does not meet the power generation prediction requirements, a second power generation group corresponding to the attribute of the first power generation group in the database is traversed, and the first prediction sample set is supplemented based on the historical second synchronization data of the second power generation group to obtain a second prediction sample set, including: If the current prediction quantity of the first sub-information in the prediction sample set is less than the prediction threshold quantity, it is determined that the prediction sample set does not meet the power generation prediction requirement; Calculate the difference between the current predicted quantity and the predicted threshold quantity to obtain the missing predicted quantity, and traverse the second power generation group in the database corresponding to the attribute of the first power generation group; A display slot and a second annual data axis corresponding to the second power generation group are generated, and the first prediction sample set is supplemented based on the second historical synchronous data of the second power generation group to obtain a second prediction sample set.

[0013] Optionally, the step of calculating the difference between the current predicted quantity and the predicted threshold quantity to obtain the missing predicted quantity and traversing the second power generation group in the database corresponding to the attribute of the first power generation group includes: Acquire attributes of the first power generation group, wherein the attributes include at least a power generation type attribute, a power generation specification attribute, a power generation region attribute, and a power generation environment attribute; Based on the power generation type attribute, determine to filter other power generation groups in the database to determine other power generation groups with the same power generation type; Calculate the fusion similarity values ​​of the other power generation groups and the first power generation group in terms of power generation specification attributes, power generation region attributes, and power generation environment attributes; A second power generation group is determined from other power generation groups based on the fused similarity values ​​and the number of missing predictions, and a corresponding prediction offset label is added to the second power generation group based on each fused similarity value.

[0014] Optionally, the calculating of the fusion similarity values ​​of the other power generation groups and the first power generation group in terms of power generation specification attributes, power generation region attributes, and power generation environment attributes includes: Differentiating and processing the power generation specification attributes of the power generation group to obtain power generation specification coefficients of different dimensions, wherein the power generation specification coefficients at least include rated power generation, area information and / or volume specification information; Quantifying the power generation area attributes of the power generation group to obtain a regional quantification coefficient, wherein the regional quantification coefficient at least includes plains, hills, mountains, and cities; Differentiating and processing the power generation specification attributes of the power generation group to obtain power generation environment coefficients of different dimensions, wherein the power generation environment coefficients at least include a temperature coefficient and a humidity coefficient; Based on the power generation specification coefficient, the regional quantification coefficient and the power generation environment coefficient, a fusion calculation is performed to obtain the fusion similarity value of other power generation groups and the first power generation group.

[0015] Optionally, the fusion calculation based on the power generation specification coefficient, the regional quantization coefficient and the power generation environment coefficient to obtain the fusion similarity value of other power generation groups and the first power generation group includes: The fusion similarity value is calculated by the following formula: ; in, For the c The fusion similarity value of the first power generation group and the first power generation group, is the power generation specification weight, For the i The power generation specification coefficient of each dimension, For the i The specification coefficient weights of the dimensions, n is the upper limit of the dimension of power generation specification, is the regional specification weight, For the m The regional specification coefficient of the dimension, For the m The specification coefficient weights of the dimensions, q is the upper limit of the dimension of the region specification. is the environmental specification weight, For the r Environmental specification coefficients for each dimension, For the r The specification coefficient weights of the dimensions, e The upper limit value of the dimension for the environment specification.

[0016] Optionally, determining the second power generation group from other power generation groups based on the fused similarity value and the missing prediction quantity, and adding a corresponding prediction offset label to the second power generation group based on each fused similarity value, includes: Sort all other power generation groups based on the fused similarity value to obtain a similarity value sequence; Sequentially obtain the second year data axis of the first power generation group in the similar value sequence and add it above the first year data axis, and obtain the pixel points and second sub-information corresponding to the second year data based on the initial dividing line, the cutoff dividing line, and the moving dividing line; If the number of the second sub-information is greater than or equal to the missing prediction number, it is determined that the currently acquired second sub-information meets the requirement, and the second power generation group corresponding to the currently acquired second sub-information is selected, and the second sub-information belongs to the second synchronization data of the corresponding second power generation group; A corresponding prediction offset label is determined based on the fused similarity value of the second power generation group, and each similarity value interval has a preset prediction offset label.

[0017] Optionally, also include: If the amount of the second sub-information is less than the missing prediction amount, selecting another power generation group again according to the similarity value sequence, and adding the second year data axis of the other power generation group to the top of the previous second year data axis; Based on the initial dividing line, the cutoff dividing line and the moving dividing line, the pixel points and the second sub-information corresponding to the newly added second year data are obtained until the number of second sub-information is greater than or equal to the missing prediction number or there are no more unselected power generation groups in the similar value sequence.

[0018] Optionally, performing power generation prediction on the first power generation group based on the first prediction sample set or the second prediction sample set to obtain a new energy power generation prediction result includes: If it is determined that only the first prediction sample set exists, the prediction interval is not generated, and the prediction result of new energy power generation at the prediction target time is calculated based on the first prediction sample set by the following formula: ; ; ; in, To calculate the new energy power generation prediction result at the prediction target time based on the first prediction sample set, is the average power generation result of all first sub-information in the first prediction sample set, The first prediction sample set affects the power generation results. F The weight value of the dimension, The predicted time for the first generating unit is F The value of the dimension, The first prediction sample set affects the power generation results. F The average value of the dimension, B is a constant value, G is the upper limit of the dimension that affects the power generation results, The first prediction sample set The generation result of the first sub-information, is the upper limit value of the first prediction sample, is the number of the first prediction sample, The first prediction sample set The first sub-information is in F The value of the dimension, D is the upper limit value of the first sub-information in the first prediction sample set, X is the quantity value of the first sub-information in the first prediction sample set; If it is determined that the second prediction sample set exists, the second prediction sample set is divided to obtain a first prediction sample subset of the first power generation group and a second prediction sample subset of the second power generation group; The first prediction sample subset is calculated and processed according to the calculation method of only the first prediction sample set to obtain the first sub-power generation result, and the second sub-power generation result of the second prediction sample subset is calculated. The new energy power generation prediction result is obtained based on the fusion of the first sub-power generation result, the second sub-power generation result and the prediction offset label.

[0019] Optionally, the first prediction sample subset is calculated and processed in accordance with the calculation method in which only the first prediction sample set exists to obtain a first sub-power generation result, a second sub-power generation result of the second prediction sample subset is calculated, and a new energy power generation prediction result is obtained based on the first sub-power generation result, the second sub-power generation result, and the prediction offset label fusion, including: The second sub-generation result is calculated by the following formula: ; ; in, The result of generating electricity for the second child is, Based on I The predicted result of the second electronic information of the second power generation group, h is the upper limit value of the second generating set, is the quantity value of the second power generation group, For the I The average power generation value of the second power generation group, For the I The average value of the second generation group in the Tth dimension, is the average value of the first power generation group in the Tth dimension, is the weight value of the Tth dimension, N is the upper limit of the dimension when comparing the first power generation group and the second power generation group, M is the quantitative value of the dimension of the first power generation group and the second power generation group when compared; Calculate the average of the first sub-generation result and the second sub-generation result to obtain a predicted mean, determine the offset coefficients corresponding to each predicted offset label, add them together and weight them to obtain the sum of the offset coefficients, and each offset label has a preset offset coefficient; Based on the sum of the offset coefficients, the predicted mean is offset up and down to obtain the prediction result interval as the new energy power generation prediction result.

[0020] A second aspect of an embodiment of the present invention provides a new energy power generation prediction platform based on collected data, including: A time module, used for the server to obtain a first power generation time point when the first power generation group performs initial power generation, and to obtain first historical synchronization data of the first power generation group based on the first power generation time point, wherein the first synchronization data is the collected data of the first power generation group at a historical moment; A decomposition module, configured to decompose the data volume of the first synchronization data according to the period to obtain first sub-information under different periods, determine a target period based on a current time point, and determine the first sub-information corresponding to the target period to obtain a first prediction sample set; A completion module, for traversing a second power generation group in the database corresponding to the attribute of the first power generation group, and completing the first prediction sample set based on the historical second synchronization data of the second power generation group to obtain a second prediction sample set if it is determined that the prediction sample set does not meet the power generation prediction requirement; The prediction module is used to perform power generation prediction on the first power generation group based on the first prediction sample set or the second prediction sample set to obtain a new energy power generation prediction result.

[0021] According to a third aspect of an embodiment of the present invention, a storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the method of the first aspect of the present invention and various possible designs of the first aspect.

[0022] The present invention forms a complete prediction sample set by collecting and integrating historical data of the first power generation group and similar power generation groups. This process solves the problem of prediction bias caused by insufficient or incomplete data in traditional methods. By integrating historical data of multi-dimensional environmental factors (such as temperature, humidity, geographical attributes, etc.), the present invention effectively improves the accuracy and adaptability of the prediction model and significantly improves the accuracy of the prediction of renewable energy power generation.

[0023] The present invention introduces a dynamic data completion mechanism. When the prediction sample set is insufficient, other power generation group data with similar attributes can be automatically screened out from the database for supplementation. This mechanism ensures the integrity and timeliness of the data set. Combined with the user interaction function, it allows users to flexibly adjust the data range and analysis cycle on the visual interface, so that the prediction system can respond to external environmental changes and user needs in real time, further improving the reliability of the prediction results.

[0024] By establishing annual data axes and display slots, the present invention realizes systematic and standardized management of historical data. The pixel point and dividing line method is used for data segmentation and periodic analysis, making data processing more intelligent and intuitive. Users can display and analyze data intuitively through a friendly interface, effectively supporting multi-dimensional analysis and management of renewable energy power generation. This intelligent data processing method ensures the accurate application of data in each time period and supports more complex prediction models and analysis strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of a new energy power generation prediction method based on collected data provided by an embodiment of the present invention.

[0026] Figure 2 It is a schematic diagram of a display slot provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] See also Figure 1 , is a flow chart of a new energy power generation prediction method based on collected data provided by an embodiment of the present invention, including: S1, the server obtains a first power generation time point when the first power generation group performs initial power generation, and obtains first historical synchronization data of the first power generation group based on the first power generation time point, where the first synchronization data is the collected data of the first power generation group at a historical moment.

[0029] First of all, it should be noted that this solution takes into account the different deployment times of power generation groups, and the corresponding predicted power generation results are different. For example, the predicted power generation results corresponding to the deployment of power generation group A 5 years ago and 10 years ago are different. Therefore, this solution will obtain the first power generation time point for the first power generation group to perform initial power generation, and the first power generation time point is also the deployment time corresponding to the first power generation group. Among them, the first power generation group can be a new energy power generation equipment group such as photovoltaic and wind power.

[0030] After obtaining the first power generation time of the first power generation group, the historical data of the first power generation group can be obtained by combining the current time with the first power generation time, that is, the above-mentioned first synchronization data. It can be understood that the first synchronization data includes collected data, and the collected data can be data such as power generation, temperature, humidity, wind speed, etc.

[0031] S2, decomposing the data volume of the first synchronization data according to the period to obtain first sub-information under different periods, determining a target period based on the current time point, and determining the first sub-information corresponding to the target period to obtain a first prediction sample set.

[0032] Since the amount of the first synchronization data may be large, this solution needs to decompose the amount of the first synchronization data to obtain multiple first sub-information. These periods may be divided according to annual, quarterly, monthly and other time periods. Among them, the target period corresponds to the current time point. For example, when divided by quarter, the current time point is January 20, then the corresponding target period can be the quarter corresponding to January 20.

[0033] In some embodiments, the data volume of the first synchronization data is decomposed according to the period to obtain first sub-information in different periods, a target period is determined based on the current time point, and the first sub-information corresponding to the target period is determined to obtain a first prediction sample set, including: S21, extracting the earliest time of the first synchronization data, determining the annual time period between the earliest time and the current time based on the earliest time, and establishing a first annual data axis corresponding to each annual time period.

[0034] This solution extracts the earliest time from the first synchronization data and determines the annual time period from that time to the current time. For example, if the earliest time is 2020 and the current time is 2023, then the corresponding annual time periods are 2020, 2021, 2022, and 2023. For each annual time period, a corresponding annual data axis is established for further data analysis and processing. Figure 2 , the annual data axis can be a horizontal data axis.

[0035] The extracting the earliest time of the first synchronization data, determining the annual time period between the earliest time and the current time based on the earliest time, and establishing the first annual data axis corresponding to each annual time period include: S211, determining a first number of pixel points corresponding to a first year data axis.

[0036] In order to subsequently divide the first annual data axis in detail, this solution will determine the number of pixels corresponding to each annual data axis, for example, 1,000 pixels.

[0037] S212, determine the number of days corresponding to each first-year data axis, normalize the number of days of all first-year data axes to obtain a unified number of days, obtain the day time information corresponding to each group of pixel points based on the first number and the unified number of days, establish a storage format for the collected data based on the day time information, and each day time information corresponds to a storage unit.

[0038] First, for each annual data axis, the number of days contained in the year must be determined. This may involve considering the different number of days in a common year or a leap year. In order to facilitate subsequent processing, the number of days in all annual data axes is normalized. The goal of normalization is to convert the number of days in different years into a unified standard for easy analysis and comparison. Through this normalization process, data inconsistency problems caused by year differences (such as leap years and common years) can be avoided. Based on the determined first quantity and the unified number of days, the day time information corresponding to each group of pixels is calculated. Use this day time information to establish a storage format for the data, where each day time information corresponds to a storage unit. This ensures that the data is stored in a logical and orderly manner.

[0039] S213, according to the order of each group of pixel points, add corresponding time of day information to each group of pixel points, and store the information in the collected data in correspondence with the corresponding group of pixel points according to the time of day information, so that each group of pixel points has a corresponding storage unit.

[0040] This solution will add the corresponding time of day information to each group of pixels in the order of each group of pixels. This step ensures the accurate positioning of each group of data in the time dimension. Then the information in the collected data is stored in correspondence with the corresponding group of pixels according to the time of day information. Such a correspondence ensures the accuracy and traceability of the data. Each group of pixels (for example, 5 pixels) will have a corresponding storage unit, which can effectively store and manage the data associated with it. Through these steps, the standardization and systematic management of a large amount of collected data can be achieved. This method not only improves the efficiency of data storage, but also lays a good foundation for subsequent data analysis and prediction. Through strict normalization processing and storage format design, the integrity and consistency of the data can be ensured.

[0041] S22, establishing corresponding display slots based on the first-year data axis, placing each first-year data axis in the display slot, establishing a division slot at the bottom of the last display slot, and establishing a division data axis corresponding to the first-year data axis in the division slot.

[0042] This solution will combine the first-year data axis to create corresponding display slots for each year. These display slots are used to visualize and manage the display of annual data. Each first-year data axis is placed in its corresponding display slot to ensure the orderly display of data. This structure facilitates intuitive analysis and comparison of data.

[0043] It should be noted that this solution will also establish a partition slot at the bottom of the last display slot. This slot is used to accommodate and manage the partition data. A partition data axis corresponding to the first annual data axis is established in the partition slot. These partition data axes are used for further data processing and analysis, especially for segmentation and period analysis of data, which will be explained in detail in subsequent processing.

[0044] S23, dividing all first-year data axes based on the divided data axes to obtain a target period and retrieving the corresponding first sub-information to obtain a first prediction sample set.

[0045] This solution will divide all first-year data axes based on the established partitioned data axes. This step is to segment the data according to specific periods or conditions for subsequent analysis. Through division, different data periods can be identified, such as quarters, months, or other periodic data features, so that specific data can be used for accurate analysis. After the division is completed, the target period is identified. This period refers to the time range of interest in forecasting or further analysis. The first sub-information associated with the target period is retrieved to form the first forecast sample set. This sample set is an important basis for subsequent forecast model training and verification.

[0046] The step of dividing all first-year data axes based on the divided data axes to obtain a target period and adjusting the corresponding first sub-information to obtain a first prediction sample set includes: S231, determining a target cycle based on a current time point, each current time point having a preset target cycle, and extracting an initial time point and an end time point of the target cycle.

[0047] This solution determines the target period based on the current time point (such as the current date or a specific time mark). Each current time point has a preset target period, which determines the time range of data analysis. The initial time point (such as January 1st) and the end time point (such as February 29th) are extracted from the target period. These two time points define the start and end boundaries of the data analysis, ensuring that data analysis and prediction are performed within the appropriate time range.

[0048] S232, determining the time information and pixel points corresponding to the initial time point and the end time point in the divided data axis, respectively, to obtain the initial pixel point and the end pixel point.

[0049] On the divided data axis, find the specific day time information corresponding to the initial time point and the end time point. This information may include the specific date, time, and other time-related attributes. At the same time, determine the pixel points corresponding to the initial time point and the end time point on the data axis, that is, the initial pixel point and the end pixel point. These pixel points are used to accurately locate the position of the data on the time axis.

[0050] S233, dividing all first-year data axes based on the initial pixel point and the cutoff pixel point, and retrieving the first sub-information based on the storage format of the collected data to obtain a first prediction sample set.

[0051] All first-year data axes are divided using the determined initial pixel points and cutoff pixel points. Through this division, relevant data within the target period can be intercepted for subsequent analysis and processing. The division process should take into account the integrity and continuity of the data to ensure that the obtained sub-datasets are valid and representative. Based on the storage format of the collected data, the first sub-information is retrieved from the divided data. These sub-information are important inputs for predictive analysis and may include various feature data and indicators. Finally, these first sub-information are summarized and organized to form the first prediction sample set. This sample set will be used for subsequent prediction model training and verification to help generate more accurate prediction results. Through this series of steps, the required data can be accurately located and extracted on the timeline, laying a solid foundation for building an effective data model and making accurate predictions. Using pixel points for data division is a refined data processing method that helps to improve the accuracy and efficiency of data analysis.

[0052] The step of dividing all first-year data axes based on the initial pixel point and the cutoff pixel point, and obtaining the first prediction sample set by retrieving the first sub-information based on the storage format of the collected data includes: Based on the initial pixel point as the starting point, an initial dividing line perpendicular to the data dividing axis is established upward, and based on the cutoff pixel point as the starting point, a cutoff dividing line perpendicular to the data dividing axis is established upward. This scheme will establish a line perpendicular to the data dividing axis upward based on the initial pixel point as the starting point, which is called the initial dividing line. This dividing line is used to determine the starting boundary of the data axis and define the position from which data extraction starts. Similarly, based on the cutoff pixel point as the starting point, a line perpendicular to the data dividing axis is established upward, which is called the cutoff dividing line. The cutoff dividing line is used to define the ending boundary of the data axis and indicate the position at which data extraction ends.

[0053] Get all the pixels between the initial dividing line and the cutoff dividing line for each first-year data axis, and retrieve the first sub-information of the corresponding storage unit in the storage format according to the order of the pixels to obtain the first prediction sample set. This solution will obtain all the pixels between the initial dividing line and the cutoff dividing line for each first-year data axis. These pixels represent the data range within the target period to ensure the accuracy and relevance of data extraction. According to the order of the pixels, retrieve the first sub-information of the corresponding storage unit in the storage format in sequence. The storage format is the way data is organized, which may include data tables, arrays, database records, etc. The information extracted according to the storage format needs to ensure the consistency and integrity of the data. The extracted first sub-information is summarized and organized to form a complete first prediction sample set. This prediction sample set contains all the data features and indicators required for model prediction, and can support subsequent analysis and modeling work.

[0054] In some embodiments, the step of obtaining all pixel points of each first-year data axis located within the initial dividing line and the cutoff dividing line, and retrieving the first sub-information of the corresponding storage unit in the storage format according to the order of the pixel points to obtain the first prediction sample set includes: If it is determined that the user has selected the initial dividing line and / or the cutoff dividing line based on the interactive terminal and moved it, the initial dividing line and / or the cutoff dividing line are synchronously moved in response to the user's movement. In order to meet the needs of users, this solution also provides an interactive mechanism. This interactive mechanism allows users to flexibly select and adjust the data range on the data visualization interface, thereby enhancing the accuracy and flexibility of data analysis. Through an intuitive graphical interface, users can better understand the distribution characteristics of the data and perform further analysis or prediction within the selected data range. Among them, the user can select and move the initial dividing line and / or the cutoff dividing line through an interactive terminal (such as a mouse or a touch screen). When the user moves these dividing lines, the positions of these lines are dynamically updated in response to the user's operation. In the above manner, the distance between the initial dividing line and the cutoff dividing line can be changed.

[0055] After determining that the movement of the initial dividing line and / or the cut-off dividing line has stopped, the pixel points of the initial dividing line and / or the cut-off dividing line are counted. Once the user stops moving the dividing line, the program will count all the pixel points contained in the dividing line. These pixel points are used to extract the corresponding first sub-information from the storage format, thereby forming a prediction sample set.

[0056] If it is determined that the user double-clicks at any position of the initial dividing line and / or the cut-off dividing line selected by the interactive terminal, the initial dividing line and / or the cut-off dividing line are segmented at the double-clicked position to obtain multiple moving dividing lines. The user can perform segmentation by double-clicking at any position of the dividing line. After double-clicking, the dividing line will be segmented into multiple moving dividing lines, so that the user can move these newly generated line segments separately. Thus, the required data segments can be customized.

[0057] If it is determined that the user selects any moving dividing line based on the interactive terminal and moves it, the moving dividing line is synchronously moved in response to the user's movement, and the pixel points between the moving dividing line and the moving dividing line, the moving dividing line and the cutoff dividing line, or the initial dividing line and the moving dividing line are counted. It can be understood that the user can select any moving dividing line and move it. The program will synchronously update the positions of these line segments and count the pixel points between these line segments.

[0058] S3, if it is determined that the prediction sample set does not meet the power generation prediction requirements, traverse the second power generation group corresponding to the attributes of the first power generation group in the database, and complete the first prediction sample set based on the historical second synchronization data of the second power generation group to obtain the second prediction sample set.

[0059] It is understandable that if the current prediction quantity of the first sub-information in the prediction sample set is less than the preset prediction threshold quantity, it is judged that the prediction set does not meet the power generation prediction requirements. The prediction threshold quantity is a predefined standard to ensure that the amount of data in the sample set is sufficient for reliable prediction.

[0060] In some embodiments, if it is determined that the prediction sample set does not meet the power generation prediction requirements, a second power generation group corresponding to the attributes of the first power generation group in the database is traversed, and the first prediction sample set is supplemented based on the historical second synchronization data of the second power generation group to obtain a second prediction sample set, including: S31: If the current predicted quantity of the first sub-information in the prediction sample set is less than the prediction threshold quantity, it is determined that the prediction sample set does not meet the power generation prediction requirement.

[0061] If the amount of data is insufficient and does not meet the requirements, data supplementation is required.

[0062] S32, calculating the difference between the current predicted quantity and the predicted threshold quantity to obtain the missing predicted quantity, and traversing the second power generation group in the database corresponding to the attribute of the first power generation group.

[0063] When completing, this solution needs to first calculate the difference between the current predicted quantity and the predicted threshold quantity to obtain the missing predicted quantity. Then, the database is searched for a second power generation group corresponding to the attributes of the first power generation group. These second power generation groups have similar or related attributes to the first power generation group and may provide useful historical data. The first prediction sample set is completed using the historical second synchronous data of the second power generation group.

[0064] The step of calculating the difference between the current predicted quantity and the predicted threshold quantity to obtain the missing predicted quantity and traversing the second power generation group in the database corresponding to the attribute of the first power generation group includes: S321, obtaining attributes of the first power generation group, wherein the attributes at least include power generation type attributes, power generation specification attributes, power generation region attributes, and power generation environment attributes.

[0065] First, this solution needs to obtain the attributes of the first power generation group. Power generation type attributes: for example, wind power, solar power, nuclear power, etc. Power generation specification attributes: may include power generation capacity, equipment type, etc. Power generation region attributes: the geographical location of the power generation facility. Power generation environment attributes: for example, environmental factors that affect power generation efficiency, such as climate conditions and altitude.

[0066] S322, based on the power generation type attribute, determine to filter other power generation groups in the database to determine other power generation groups with the same power generation type.

[0067] This solution will filter out other power generation groups with the same power generation type in the database based on the power generation type attribute of the first power generation group. This step is to ensure that the selected power generation groups are consistent in basic power generation methods.

[0068] S323, calculating the fusion similarity values ​​of the other power generation groups and the first power generation group in terms of power generation specification attributes, power generation region attributes, and power generation environment attributes.

[0069] Finally, this solution will calculate the fusion similarity value of the selected power generation groups with the first power generation group in terms of power generation specification attributes, power generation regional attributes, and power generation environment attributes. The fusion similarity value is a comprehensive indicator that can be obtained by weighted average or other similarity calculation methods to measure the comprehensive similarity of the two power generation groups. Among the selected power generation groups, those with higher fusion similarity values ​​with the first power generation group are given priority for data completion. These power generation groups with higher similarity can provide more relevant historical data to improve the prediction sample set. In this way, suitable power generation groups can be selected under specific conditions for data completion to ensure the quality and relevance of the completed data, and finally obtain a second prediction sample set that meets the power generation forecasting needs. This method makes full use of the existing data assets in the database to support more accurate power generation forecasts.

[0070] In some embodiments, the calculating of the fusion similarity values ​​of the other power generation groups and the first power generation group in terms of power generation specification attributes, power generation region attributes, and power generation environment attributes includes: The power generation specification attributes of the power generation group are differentiated and processed to obtain power generation specification coefficients of different dimensions, and the power generation specification coefficients at least include rated power generation, area information and / or volume specification information. Differentiate and process the power generation specifications of different dimensions to obtain corresponding power generation specification coefficients. These coefficients may include: Rated power generation: refers to the maximum output power of the power generation equipment under standard conditions. Area information: such as the power generation area of ​​the power generation facility. Volume specification information: such as the volume or size of the equipment.

[0071] The regional attributes of the power generation group are quantified to obtain regional quantification coefficients, and the regional quantification coefficients include at least plains, hills, mountains, and cities. The regional attributes are quantified to obtain regional quantification coefficients. These coefficients can reflect geographical features, including but not limited to: plains, hills, mountains, and cities: these terrain features can affect climate conditions and equipment maintenance. Among them, when quantizing, quantization can be performed in combination with a preset quantization table, and different regions in the preset quantization table have pre-corresponding coefficients.

[0072] The power generation specification attributes of the power generation group are differentiated and processed to obtain power generation environment coefficients of different dimensions, and the power generation environment coefficients at least include temperature coefficient and humidity coefficient. This solution will distinguish different dimensions of the power generation environment and calculate the power generation environment coefficients. These coefficients at least include: Temperature coefficient: reflects the impact of temperature on power generation efficiency. Humidity coefficient: reflects the impact of humidity on equipment operation.

[0073] Based on the power generation specification coefficient, regional quantification coefficient and power generation environment coefficient, a fusion calculation is performed to obtain the fusion similarity value between other power generation groups and the first power generation group. Finally, this scheme will fuse the coefficients based on the above three aspects: power generation specification coefficient, regional quantification coefficient and power generation environment coefficient. Through a certain algorithm, the fusion similarity value between other power generation groups and the first power generation group is calculated. The fusion similarity value is a comprehensive indicator that reflects the similarity of different power generation groups in multi-dimensional attributes. Through the above method, other power generation groups with high fusion similarity values ​​with the first power generation group are selected to facilitate more accurate power generation prediction or data completion. This method ensures that the selected comparison power generation groups have a high degree of match in key attributes, thereby improving the accuracy and reliability of the prediction model.

[0074] The fusion calculation based on the power generation specification coefficient, the regional quantification coefficient and the power generation environment coefficient to obtain the fusion similarity value of other power generation groups and the first power generation group includes: The fusion similarity value is calculated by the following formula: ; in, For the c The fusion similarity value of the first power generation group and the first power generation group, is the power generation specification weight, For the i The power generation specification coefficient of each dimension, For the i The specification coefficient weights of the dimensions, n is the upper limit of the dimension of power generation specification, is the regional specification weight, For the m The regional specification coefficient of the dimension, For the m The specification coefficient weights of the dimensions, q is the upper limit of the dimension of the region specification. is the environmental specification weight, For the r Environmental specification coefficients for each dimension, For the r The specification coefficient weights of the dimensions, e The upper limit value of the dimension for the environment specification.

[0075] It can be understood that in this formula, we calculate the fusion similarity value between different power generation groups and the first power generation group by combining the weights of power generation specifications, regional characteristics and environmental factors. This fusion calculation method can comprehensively consider multiple influencing factors and help to more accurately predict power generation and compare between power generation groups. Among them, the power generation specification weight is used to adjust the influence of the power generation specification in the overall fusion similarity value calculation. The power generation specification coefficient of the i-th dimension represents the power generation capacity or characteristics in this dimension. The specification coefficient weight of the i-th dimension reflects the importance of this dimension in the overall specification. The upper limit value of the power generation specification dimension represents the number of dimensions covered by the power generation specification coefficient. The regional specification weight is used to adjust the influence of regional characteristics in the overall similarity value calculation. The regional specification coefficient of the m-th dimension represents the characteristics of the region in this dimension. The specification coefficient weight of the m-th dimension indicates the importance of the regional characteristics of this dimension. The upper limit value of the regional specification dimension indicates how many regional characteristic dimensions are considered. The environmental specification weight is used to adjust the influence of environmental factors in the overall similarity value calculation. The environmental specification coefficient of the r-th dimension represents the environmental characteristics in this dimension. The weight of the specification coefficient of the rth dimension reflects the importance of the environmental characteristics of this dimension. The upper limit value of the environmental specification dimension indicates the number of dimensions covered by the environmental factors. Through this weighted summary method, the impact of different factors on the performance of the power generation unit can be effectively combined, so as to more comprehensively evaluate the similarities between different power generation units. This is of great significance for optimizing power generation strategies, resource allocation, and improving the overall efficiency of the power system.

[0076] S324, determining a second power generation group from other power generation groups based on the fused similarity values ​​and the number of missing predictions, and adding a corresponding prediction offset label to the second power generation group based on each fused similarity value.

[0077] This solution combines the fusion similarity value and the number of missing predictions to select those generating units that meet the preset requirements as the second generating units. For each qualified second generating unit, a prediction offset label is added according to its fusion similarity value. This label may be used to adjust the prediction model to take into account the differences between different generating units to improve the accuracy of the prediction.

[0078] In some embodiments, the determining of the second power generation group from other power generation groups based on the fused similarity value and the missing prediction quantity, and adding a corresponding prediction offset label to the second power generation group based on each fused similarity value, includes: Based on the fused similarity value, all other power generation groups are sorted to obtain a similarity value sequence. This solution will sort all other power generation groups based on the fused similarity value to obtain a similarity value sequence. This step is to identify which power generation group is most similar to the target power generation group in terms of data characteristics.

[0079] The second year data axis of the first power generation group in the similarity value sequence is obtained in sequence and added above the first year data axis, and the pixel points and second sub-information corresponding to the second year data are obtained based on the initial dividing line, the cutoff dividing line, and the moving dividing line. This scheme will extract the second year data axis starting from the first power generation group according to the similarity value sequence, and add it above the first year data axis. Afterwards, this scheme can extend or fill the initial dividing line, the cutoff dividing line, and the moving dividing line to make it cross the second year data axis to obtain the pixel points and second sub-information corresponding to the second year data.

[0080] If the number of the second sub-information is greater than or equal to the missing predicted number, it is determined that the currently acquired second sub-information meets the requirements, and the second generation group corresponding to the currently acquired second sub-information is selected, and the second sub-information belongs to the second synchronization data of the corresponding second generation group. This scheme checks whether the number of the extracted second sub-information is greater than or equal to the missing predicted number. If the requirements are met, the current second sub-information and the corresponding generation group are selected and marked as the second generation group, where the second sub-information belongs to the second synchronization data of the generation group.

[0081] The corresponding prediction offset label is determined based on the fused similarity value of the second power generation group, and each similarity value interval has a preset prediction offset label. This scheme determines the corresponding prediction offset label based on the fused similarity value of the second power generation group. Each similarity value interval has a preset prediction offset label, which is used to correct the prediction results to better adapt to the situation of a specific power generation group. It should be noted that the prediction offset label can be a prediction offset coefficient. The smaller the fused similarity value, the larger the corresponding prediction offset label needs to be.

[0082] Based on the above embodiment, it also includes: If the number of the second sub-information is less than the missing prediction number, select other power generation groups again according to the similarity value sequence, and add the second year data axis of the other power generation groups to the top of the previous second year data axis. It can be understood that if the number of the second sub-information extracted in the initially selected power generation group is less than the required missing prediction number, it is necessary to further select the data of other power generation groups. Continue to select the power generation groups that have not been used according to the similarity value sequence. Add the second year data axis of these power generation groups to the top of the previous second year data axis. This process is similar to stacking the data of different power generation groups to accumulate enough data information.

[0083] Based on the initial dividing line, the cutoff dividing line, and the moving dividing line, the pixel points and the second sub-information corresponding to the newly added second-year data are obtained until the number of second sub-information is greater than or equal to the missing prediction number or there are no more unselected power generation groups in the similarity value sequence. The newly added second-year data are processed using the initial dividing line, the cutoff dividing line, and the moving dividing line. The pixel points and the second sub-information corresponding to the new data are extracted. Such operations may involve complex image processing or signal processing techniques to locate and extract in a multidimensional data space. The process continues until one of the following conditions is met: the number of extracted second sub-information is greater than or equal to the missing prediction number. There are no unselected power generation groups in the similarity value sequence.

[0084] S33, generating a display slot and a second annual data axis corresponding to the second power generation group, and completing the first prediction sample set based on the second historical synchronous data of the second power generation group to obtain a second prediction sample set.

[0085] This solution will generate a corresponding display slot and a second annual data axis for each second power generation group. Based on the historical second synchronous data of the second power generation group, the first forecast sample set is supplemented with data. Data supplementation refers to filling in missing values ​​or incomplete information in the forecast sample set with historical data to form a second forecast sample set. This supplementation method helps to improve the comprehensiveness and accuracy of the forecast results, especially when the data is missing or incomplete. After the above steps, the second forecast sample set can provide a more complete and accurate data basis for subsequent analysis and forecasting. This method is particularly suitable for complex power generation system data analysis, and can achieve efficient data sharing and utilization among different power generation groups.

[0086] S4, performing power generation prediction on the first power generation group based on the first prediction sample set or the second prediction sample set to obtain a new energy power generation prediction result.

[0087] The first prediction sample set or the second prediction sample set is selected based on the specific situation. The selection may be based on the completeness, accuracy and relevance of the data. The first prediction sample set may be based on the initial data, while the second prediction sample set may be more complete after further processing of data completion and historical data.

[0088] In some embodiments, the step of performing power generation prediction on the first power generation group based on the first prediction sample set or the second prediction sample set to obtain a new energy power generation prediction result includes: S41, if it is determined that only the first prediction sample set exists, the prediction interval is not generated, and the prediction result of new energy power generation at the prediction target time is calculated based on the first prediction sample set by the following formula: ; ; ; in, To calculate the new energy power generation prediction result at the prediction target time based on the first prediction sample set, is the average power generation result of all first sub-information in the first prediction sample set, The first prediction sample set affects the power generation results. F The weight value of the dimension, The predicted time for the first generating unit is F The value of the dimension, The first prediction sample set affects the power generation results. F The average value of the dimension, B is a constant value, G is the upper limit of the dimension that affects the power generation results, The first prediction sample set The generation result of the first sub-information, is the upper limit value of the first prediction sample, is the number of the first prediction sample, The first prediction sample set The first sub-information is in F The value of the dimension, D is the upper limit value of the first sub-information in the first prediction sample set, X is the quantity value of the first sub-information in the first prediction sample set;

[0089] First, it can be understood that if there is only the first prediction sample set, the data does not need to be adjusted, and the power generation prediction result at the prediction target time can be directly calculated by the formula. In the above formula, is the average power generation result of all first sub-information in the first prediction sample set, The power generation results of all the first sub-information in the first prediction sample set are averaged to obtain the average power generation result. The first sub-information is in F The sum of the values ​​of the dimensions is averaged to obtain the first factor affecting the power generation result in the first prediction sample set. F Finally, the weighted deviation of each dimension is calculated to evaluate the impact of different factors on the power generation results. Combined with the historical average results of the sample The weighted deviation under the current conditions provides a comprehensive forecast result. The formula is designed to estimate the renewable energy power generation more accurately based on historical data and current forecast conditions. and constant B, the model can flexibly adapt to the prediction needs under different scenarios and conditions.

[0090] S42: If it is determined that the second prediction sample set exists, the second prediction sample set is divided into a first prediction sample subset of the first power generation group and a second prediction sample subset of the second power generation group.

[0091] If there is a second prediction sample set, this solution requires sample set segmentation, and then the second prediction sample set is segmented into a first prediction sample subset of the first power generation group and a second prediction sample subset of the second power generation group, and then calculated separately.

[0092] S43, calculating and processing the first prediction sample subset in accordance with the calculation method of only the first prediction sample set to obtain the first sub-power generation result, calculating the second sub-power generation result of the second prediction sample subset, and obtaining the new energy power generation prediction result based on the fusion of the first sub-power generation result, the second sub-power generation result, and the prediction offset label.

[0093] For the processing of the first prediction sample subset, this solution will process it in the same way as the calculation method of the first prediction sample set to obtain the first sub-power generation result. For the processing of the second prediction sample subset, the second sub-power generation result is calculated. Result fusion: The first sub-power generation result and the second sub-power generation result are combined with the prediction offset label to obtain the final new energy power generation prediction result. This prediction method enhances the flexibility and accuracy of the prediction through the reasonable division and calculation of different sample sets: by segmenting and subset calculating the second prediction sample set, the system can integrate multiple types of data for more comprehensive prediction, not only relying on a single data source, thereby improving the reliability of the prediction results. Result fusion mechanism: By combining the prediction offset label, the prediction results can be further adjusted and optimized, which is especially important for new energy power generation scenarios that change in real time.

[0094] The first prediction sample subset is calculated and processed according to the calculation method of only the first prediction sample set to obtain the first sub-generation result, the second sub-generation result of the second prediction sample subset is calculated, and the new energy generation prediction result is obtained based on the first sub-generation result, the second sub-generation result, and the prediction offset label fusion, including: The second sub-generation result is calculated by the following formula: ; ; in, The result of generating electricity for the second child is, Based on I The predicted result of the second electronic information of the second power generation group, h is the upper limit of the second power generation group, indicating the total number of second power generation groups. is the quantity value of the second power generation group, used to calculate the average value, For the I The average power generation value of the second power generation group, For the I The average value of the second generation group in the Tth dimension, is the average value of the first power generation group in the Tth dimension, is the weight value of the Tth dimension, N is the upper limit of the dimension when comparing the first power generation group and the second power generation group, M It is the quantitative value of the dimension when comparing the first power generation group and the second power generation group.

[0095] The average of the first sub-generation result and the second sub-generation result is calculated to obtain a predicted mean value, and the offset coefficients corresponding to each predicted offset label are determined to be added and weighted to obtain the sum of the offset coefficients, and each offset label has a preset offset coefficient.

[0096] This solution will obtain the predicted mean by calculating the mean of the first sub-generation result and the second sub-generation result. Determine the offset coefficient corresponding to each predicted offset label, add the coefficients of all offset labels, and calculate the sum of the offset coefficients. Each offset label has a preset offset coefficient for adjusting the predicted result. It can be understood that the smaller the fusion similarity value, the larger the offset coefficient of the corresponding predicted offset label needs to be, and the larger the amplitude will be when adjusting.

[0097] Based on the sum of the offset coefficients, the predicted mean is offset up and down to obtain the prediction result interval as the new energy power generation prediction result.

[0098] Finally, based on the sum of the offset coefficients, the predicted mean is offset up and down. The resulting range is the final new energy power generation forecast result. The process first calculates the first and second sub-generation results separately, then combines these results and adjusts them through the offset label to finally obtain a more accurate power generation forecast. The calculation of the second sub-generation result quantifies the impact of various factors on the forecast by considering the differences between the second generation group and the first generation group in different dimensions, as well as the weights of different dimensions. A more refined forecasting method is provided, which enhances the accuracy of the forecast through grouping and dimensional comparison.

[0099] The present invention also provides a new energy power generation prediction platform based on collected data, comprising: A time module, used for the server to obtain a first power generation time point when the first power generation group performs initial power generation, and to obtain first historical synchronization data of the first power generation group based on the first power generation time point, wherein the first synchronization data is the collected data of the first power generation group at a historical moment; A decomposition module, configured to decompose the data volume of the first synchronization data according to the period to obtain first sub-information under different periods, determine a target period based on a current time point, and determine the first sub-information corresponding to the target period to obtain a first prediction sample set; A completion module, for traversing a second power generation group in the database corresponding to the attribute of the first power generation group, and completing the first prediction sample set based on the historical second synchronization data of the second power generation group to obtain a second prediction sample set if it is determined that the prediction sample set does not meet the power generation prediction requirement; The prediction module is used to perform power generation prediction on the first power generation group based on the first prediction sample set or the second prediction sample set to obtain a new energy power generation prediction result.

[0100] The present invention also provides a storage medium, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the methods provided by the various embodiments described above.

[0101] Among them, the storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, the storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application-specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). In addition, the ASIC can be located in a user device. Of course, the processor and the storage medium can also exist in a communication device as discrete components. The storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0102] The present invention also provides a program product, which includes an execution instruction, which is stored in a storage medium. At least one processor of a device can read the execution instruction from the storage medium, and at least one processor executes the execution instruction so that the device implements the methods provided in the above various embodiments.

[0103] In the above-mentioned terminal or server embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A new energy power generation prediction method based on collected data, characterized in that: include: The server obtains a first power generation time point when the first power generation group performs initial power generation, and obtains first historical synchronization data of the first power generation group based on the first power generation time point, where the first synchronization data is the collected data of the first power generation group at a historical moment; Decomposing the data volume of the first synchronization data according to the period to obtain first sub-information under different periods, determining a target period based on the current time point, and determining the first sub-information corresponding to the target period to obtain a first prediction sample set; If it is determined that the prediction sample set does not meet the power generation prediction requirements, traverse the second power generation group corresponding to the attributes of the first power generation group in the database, and complete the first prediction sample set based on the historical second synchronization data of the second power generation group to obtain a second prediction sample set; Based on the first prediction sample set or the second prediction sample set, power generation prediction is performed on the first power generation group to obtain a new energy power generation prediction result.

2. The new energy power generation prediction method based on collected data according to claim 1 is characterized in that: The step of decomposing the data volume of the first synchronization data according to the period to obtain first sub-information in different periods, determining a target period based on a current time point, and determining the first sub-information corresponding to the target period to obtain a first prediction sample set includes: Extracting the earliest time of the first synchronization data, determining the annual time period between the earliest time and the current time based on the earliest time, and establishing a first annual data axis corresponding to each annual time period; Establish corresponding display slots based on the first-year data axis, place each first-year data axis in the display slot, establish a division slot at the bottom of the last display slot, and establish a division data axis corresponding to the first-year data axis in the division slot; All first-year data axes are divided based on the divided data axes to obtain a target period and retrieve the corresponding first sub-information to obtain a first prediction sample set.

3. The new energy power generation prediction method based on collected data according to claim 2 is characterized in that: The extracting the earliest time of the first synchronization data, determining the annual time period between the earliest time and the current time based on the earliest time, and establishing the first annual data axis corresponding to each annual time period, includes: Determine a first number of pixel points corresponding to the first annual data axis; Determine the number of days corresponding to each first-year data axis, normalize the number of days of all first-year data axes to obtain a unified number of days, obtain the day time information corresponding to each group of pixel points based on the first number and the unified number of days, and establish a storage format for the collected data based on the day time information, where each day time information corresponds to a storage unit; According to the order of each group of pixels, corresponding time of day information is added to each group of pixels, and the information in the collected data is stored in correspondence with the corresponding group of pixels according to the time of day information, so that each group of pixels has a corresponding storage unit.

4. The new energy power generation prediction method based on collected data according to claim 3 is characterized in that: The step of dividing all first-year data axes based on the divided data axes to obtain a target period and adjusting the corresponding first sub-information to obtain a first prediction sample set includes: Determine a target period based on the current time point, each current time point has a preset target period, and extract an initial time point and a deadline time point of the target period; Determine the time information and pixel points corresponding to the initial time point and the end time point in the divided data axis, and obtain the initial pixel point and the end pixel point; All first-year data axes are divided based on the initial pixel point and the cutoff pixel point, and the first sub-information is retrieved based on the storage format of the collected data to obtain a first prediction sample set.

5. The new energy power generation prediction method based on collected data according to claim 4 is characterized in that: The step of dividing all first-year data axes based on the initial pixel point and the cutoff pixel point, and retrieving the first sub-information based on the storage format of the collected data to obtain the first prediction sample set includes: An initial dividing line perpendicular to the dividing data axis is established upward based on the initial pixel point as a starting point, and a cutoff dividing line perpendicular to the dividing data axis is established upward based on the cutoff pixel point as a starting point; All pixel points of each first-year data axis located within the initial dividing line and the cutoff dividing line are obtained, and the first sub-information of the corresponding storage unit in the storage format is retrieved according to the order of the pixel points to obtain the first prediction sample set.

6. The new energy power generation prediction method based on collected data according to claim 5 is characterized in that: The step of obtaining all pixel points of each first-year data axis located within the initial dividing line and the cutoff dividing line, and retrieving the first sub-information of the corresponding storage unit in the storage format according to the order of the pixel points to obtain the first prediction sample set includes: If it is determined that the user selects the initial dividing line and / or the cut-off dividing line based on the interactive terminal and moves, the initial dividing line and / or the cut-off dividing line are synchronously moved in response to the user's movement; After determining that the movement of the initial dividing line and / or the cut-off dividing line has terminated, counting the pixel points of the initial dividing line and / or the cut-off dividing line; If it is determined that the user double-clicks at any position of the initial dividing line and / or the cut-off dividing line selected by the interactive terminal, the initial dividing line and / or the cut-off dividing line is segmented at the double-clicked position to obtain multiple moving segmenting lines; If it is determined that the user selects and moves any moving dividing line based on the interactive terminal, the moving dividing line is moved synchronously in response to the user's movement, and the pixel points between the moving dividing line and the moving dividing line, the moving dividing line and the cutoff dividing line, or the initial dividing line and the moving dividing line are counted.

7. The method for predicting new energy power generation based on collected data according to claim 4, characterized in that: If it is determined that the prediction sample set does not meet the power generation prediction requirements, traverse the second power generation group in the database corresponding to the attributes of the first power generation group, and complete the first prediction sample set based on the historical second synchronization data of the second power generation group to obtain the second prediction sample set, including: If the current prediction quantity of the first sub-information in the prediction sample set is less than the prediction threshold quantity, it is determined that the prediction sample set does not meet the power generation prediction requirement; Calculate the difference between the current predicted quantity and the predicted threshold quantity to obtain the missing predicted quantity, and traverse the second power generation group in the database corresponding to the attribute of the first power generation group; A display slot and a second annual data axis corresponding to the second power generation group are generated, and the first prediction sample set is supplemented based on the second historical synchronous data of the second power generation group to obtain a second prediction sample set.

8. The method for predicting new energy power generation based on collected data according to claim 7, characterized in that: The step of calculating the difference between the current predicted quantity and the predicted threshold quantity to obtain the missing predicted quantity and traversing the second power generation group in the database corresponding to the attribute of the first power generation group includes: Acquire attributes of the first power generation group, wherein the attributes include at least a power generation type attribute, a power generation specification attribute, a power generation region attribute, and a power generation environment attribute; Based on the power generation type attribute, determine to filter other power generation groups in the database to determine other power generation groups with the same power generation type; Calculate the fusion similarity values ​​of the other power generation groups and the first power generation group in terms of power generation specification attributes, power generation region attributes, and power generation environment attributes; A second power generation group is determined from other power generation groups based on the fused similarity values ​​and the number of missing predictions, and a corresponding prediction offset label is added to the second power generation group based on each fused similarity value.

9. The method for predicting new energy power generation based on collected data according to claim 8, characterized in that: The calculating of the fusion similarity values ​​of the other power generation groups and the first power generation group in terms of power generation specification attributes, power generation region attributes, and power generation environment attributes includes: Differentiating and processing the power generation specification attributes of the power generation group to obtain power generation specification coefficients of different dimensions, wherein the power generation specification coefficients at least include rated power generation, area information and / or volume specification information; Quantifying the power generation area attributes of the power generation group to obtain a regional quantification coefficient, wherein the regional quantification coefficient at least includes plains, hills, mountains, and cities; Differentiating and processing the power generation specification attributes of the power generation group to obtain power generation environment coefficients of different dimensions, wherein the power generation environment coefficients at least include a temperature coefficient and a humidity coefficient; Based on the power generation specification coefficient, the regional quantification coefficient and the power generation environment coefficient, a fusion calculation is performed to obtain the fusion similarity value of other power generation groups and the first power generation group.

10. The method for predicting new energy power generation based on collected data according to claim 9, characterized in that: The fusion calculation based on the power generation specification coefficient, the regional quantification coefficient and the power generation environment coefficient is performed to obtain the fusion similarity value of other power generation groups and the first power generation group, including: The fusion similarity value is calculated by the following formula: ； in, For the c The fusion similarity value of the first power generation group and the first power generation group, is the power generation specification weight, For the i The power generation specification coefficient of each dimension, For the i The specification coefficient weights of the dimensions, n is the upper limit of the dimension of power generation specification, is the regional specification weight, For the m The regional specification coefficient of the dimension, For the m The specification coefficient weights of the dimensions, q is the upper limit of the dimension of the region specification. is the environmental specification weight, For the r Environmental specification coefficients for each dimension, For the r The specification coefficient weights of the dimensions, e The upper limit value of the dimension for the environment specification.

11. The method for predicting new energy power generation based on collected data according to claim 9, characterized in that: The method of determining the second power generation group from other power generation groups based on the fused similarity value and the missing prediction quantity, and adding a corresponding prediction offset label to the second power generation group based on each fused similarity value, includes: Sort all other power generation groups based on the fused similarity value to obtain a similarity value sequence; Sequentially obtain the second year data axis of the first power generation group in the similar value sequence and add it above the first year data axis, and obtain the pixel points and second sub-information corresponding to the second year data based on the initial dividing line, the cutoff dividing line, and the moving dividing line; If the number of the second sub-information is greater than or equal to the missing prediction number, it is determined that the currently acquired second sub-information meets the requirement, and the second power generation group corresponding to the currently acquired second sub-information is selected, and the second sub-information belongs to the second synchronization data of the corresponding second power generation group; A corresponding prediction offset label is determined based on the fused similarity value of the second power generation group, and each similarity value interval has a preset prediction offset label.

12. The method for predicting new energy power generation based on collected data according to claim 11, characterized in that: Also includes: If the amount of the second sub-information is less than the missing prediction amount, selecting another power generation group again according to the similarity value sequence, and adding the second year data axis of the other power generation group to the top of the previous second year data axis; Based on the initial dividing line, the cutoff dividing line and the moving dividing line, the pixel points and the second sub-information corresponding to the newly added second year data are obtained until the number of second sub-information is greater than or equal to the missing prediction number or there are no more unselected power generation groups in the similar value sequence.

13. The method for predicting new energy power generation based on collected data according to claim 11, characterized in that: The step of performing power generation prediction on the first power generation group based on the first prediction sample set or the second prediction sample set to obtain a new energy power generation prediction result includes: If it is determined that only the first prediction sample set exists, the prediction interval is not generated, and the prediction result of new energy power generation at the prediction target time is calculated based on the first prediction sample set by the following formula: ； ； ； in, To calculate the new energy power generation prediction result at the prediction target time based on the first prediction sample set, is the average power generation result of all first sub-information in the first prediction sample set, The first prediction sample set affects the power generation results. F The weight value of the dimension, The predicted time for the first generating unit is F The value of the dimension, The first prediction sample set affects the power generation results. F The average value of the dimension, B is a constant value, G is the upper limit of the dimension that affects the power generation results, The first prediction sample set The generation result of the first sub-information, is the upper limit value of the first prediction sample, is the number of the first prediction sample, The first prediction sample set The first sub-information is in F The value of the dimension, D is the upper limit value of the first sub-information in the first prediction sample set, X is the quantity value of the first sub-information in the first prediction sample set; If it is determined that the second prediction sample set exists, the second prediction sample set is divided to obtain a first prediction sample subset of the first power generation group and a second prediction sample subset of the second power generation group; The first prediction sample subset is calculated and processed according to the calculation method of only the first prediction sample set to obtain the first sub-power generation result, and the second sub-power generation result of the second prediction sample subset is calculated. The new energy power generation prediction result is obtained based on the fusion of the first sub-power generation result, the second sub-power generation result and the prediction offset label.

14. The method for predicting new energy power generation based on collected data according to claim 13, characterized in that: The first prediction sample subset is calculated and processed according to the calculation method of only the first prediction sample set to obtain a first sub-generation result, a second sub-generation result of the second prediction sample subset is calculated, and a new energy generation prediction result is obtained based on the first sub-generation result, the second sub-generation result, and the prediction offset label fusion, including: The second sub-generation result is calculated by the following formula: ； ； in, The result of generating electricity for the second child is, Based on I The predicted result of the second electronic information of the second power generation group, h is the upper limit value of the second generating set, is the quantity value of the second power generation group, For the I The average power generation value of the second power generation group, For the I The average value of the second generation group in the Tth dimension, is the average value of the first power generation group in the Tth dimension, is the weight value of the Tth dimension, N is the upper limit value of the dimension when comparing the first power generation group and the second power generation group, M is the quantitative value of the dimension of the first power generation group and the second power generation group when compared; Calculate the average of the first sub-generation result and the second sub-generation result to obtain a predicted mean, determine the offset coefficients corresponding to each predicted offset label, add them together and weight them to obtain the sum of the offset coefficients, and each offset label has a preset offset coefficient; Based on the sum of the offset coefficients, the predicted mean is offset up and down to obtain the prediction result interval as the new energy power generation prediction result.

15. A new energy generation prediction platform based on collected data, characterized in that: include: A time module, used for the server to obtain a first power generation time point when the first power generation group performs initial power generation, and to obtain first historical synchronization data of the first power generation group based on the first power generation time point, wherein the first synchronization data is the collected data of the first power generation group at a historical moment; A decomposition module, configured to decompose the data volume of the first synchronization data according to the period to obtain first sub-information under different periods, determine a target period based on a current time point, and determine the first sub-information corresponding to the target period to obtain a first prediction sample set; A completion module, for traversing a second power generation group in the database corresponding to the attribute of the first power generation group, and completing the first prediction sample set based on the historical second synchronization data of the second power generation group to obtain a second prediction sample set if it is determined that the prediction sample set does not meet the power generation prediction requirement; The prediction module is used to perform power generation prediction on the first power generation group based on the first prediction sample set or the second prediction sample set to obtain a new energy power generation prediction result.

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