New energy data space-time alignment and standardization processing method and system
By acquiring spatiotemporal data sources from new energy sources, setting weighted cross-correlation functions, and implementing gridded processing, the problems of high computational complexity and distorted prediction results in existing technologies have been solved. This has enabled the synchronization and mapping of spatiotemporal data, improving the prediction accuracy of new energy clusters and the accuracy of power grid dispatch.
Patent Information
- Application Number
- CN202511347622.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-19
- Publication Date
- 2026-01-02
AI Technical Summary
Existing technologies have high computational complexity in large-scale gridding and interpolation operations, making it difficult to balance accuracy and efficiency. Furthermore, they lack comprehensive modeling of differences in observation types and spatial attenuation characteristics, leading to distorted prediction results for new energy cluster areas and affecting power grid dispatch and operational safety.
By acquiring spatiotemporal data sources of new energy, setting weighted cross-correlation functions to calculate time offset and time drift, projecting spatiotemporal data onto a unified time scale, and performing gridding and interpolation processing, the synchronization and mapping of spatiotemporal data are achieved, and missing values are filled in.
It achieves timestamp synchronization and geographic grid mapping of spatiotemporal data, solves the problem of spatiotemporal consistency of cross-source data, improves computational efficiency and prediction accuracy, and ensures the accuracy of power grid dispatch.
Smart Images
Figure CN121256180A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the technical field of power system data processing, and more particularly to a new energy data space-time alignment and standardization processing method and system. BACKGROUND
[0002] The prior art often needs to calculate the geometric relationship of station grid distance, neighborhood range in real time in large-scale gridding and interpolation operation, which has high computational complexity, resulting in insufficient operation efficiency in high-resolution, long-time sequence calculation tasks. Although some improved schemes pre-construct spatial indexes to speed up neighborhood queries, they lack comprehensive modeling of observation type differences, spatial decay characteristics and weight distribution mechanism, and still cannot effectively balance accuracy and efficiency. Another outstanding problem is that existing methods rarely introduce physical constraints in the prior stage, but rely on statistical correction in the post-processing stage, which makes the spatial distribution deviate in the initial construction stage and is difficult to completely correct through subsequent assimilation. Especially in the application scenario where meteorological and power data are highly coupled, such deviations may cause systematic distortion of prediction results in key areas (such as high-capacity new energy cluster areas), affecting power grid scheduling and operation safety. SUMMARY
[0003] To solve the above technical problems, the application provides a new energy data space-time alignment and standardization processing method, which comprises:
[0004] Obtaining the space-time data source of the new energy, converting the space-time data in the space-time data source into space-time change rate data, setting a weighted cross-correlation function, calculating the time offset and time drift of the space-time change rate data, and projecting the time stamp of the space-time data to a unified time scale according to the time offset and time drift;
[0005] Grid processing is performed on the area where the new energy is located, the new energy is mapped to the corresponding grid according to the area of the new energy, and the space-time data of the unified time scale is mapped to the corresponding grid according to the grid where the new energy is located;
[0006] Traverse each grid, if it is found that there are missing values in the space-time data in a certain grid, interpolation processing is performed to complete the missing values in all grids.
[0007] Further, converting the space-time data in the space-time data source into space-time change rate data comprises: taking two-by-two space-time data with correlation in the space-time data source as a space-time data pair, and converting the two-by-two space-time data as the space-time data pair into space-time change rate data.
[0008] Further, setting a weighted cross-correlation function and calculating the time offset and time drift of the space-time change rate data comprises:
[0009] The weighted cross-correlation function is:
[0010]
[0011] The time offset and the time drift amount of the spatio-temporal change rate data are calculated as follows:
[0012]
[0013] wherein C(δ,ε) is a weighted cross-correlation value, δ is a candidate time offset, ε is a candidate time drift amount, w k is a weight of the kth timestamp t k , x and y are spatio-temporal change rate data of two spatio-temporal data of the spatio-temporal data pair, is a first time offset, is a first time drift amount.
[0014] Further, the time of a day is divided into multiple time windows, the first time offset and the first time drift amount of each time window are obtained, all the first time offsets are weighted and averaged to obtain a final time offset and all the first time drift amounts are weighted and averaged to obtain a final time drift amount
[0015] Further, the projection of the timestamps of the spatio-temporal change rate data to the unified time scale according to the time offset and the time drift amount comprises:
[0016]
[0017] wherein τ is the unified time scale, and t is the timestamp.
[0018] Further, the region where the new energy is located is subjected to grid processing: the region where the new energy is located is subjected to grid processing through UTM zoning, and each grid is assigned an ID and a center coordinate;
[0019] Mapping the spatio-temporal data of the unified time scale to the corresponding grid according to the grid where the new energy is located comprises:
[0020]
[0021] wherein α g is a grid allocation coefficient of the grid g, A g is an intersection area of the new energy and the grid g, A g′ is an intersection area of the new energy and the grid g', and plant is all the grids where the new energy is located;
[0022] Multiplying the spatio-temporal data of the unified time scale by the grid allocation coefficient α gAs the space-time data of the grid g, the space-time data of the unified time scale is mapped into the corresponding grid according to the grid where the new energy source is located.
[0023] Further, the interpolation processing is performed on the corresponding grid by the Kriging interpolation method.
[0024] Further, the prior weight of each grid is calculated, the prior weight is sorted in descending order, each grid is traversed according to the sorting order, and the interpolation processing is performed.
[0025] Further, the prior weight of each grid is calculated, the prior weight is sorted in descending order, each grid is traversed according to the sorting order, and the interpolation processing is performed.
[0026]
[0027] Wherein, a ig is the prior weight of the grid i relative to the grid g, d i，g is the Euclidean distance between the center point of the grid i and the center point of the grid g, p s is a space adjustment factor, h i is the altitude of the grid i, h g is the altitude of the grid g, H s is the altitude reference value.
[0028] The application also provides a new energy data space-time alignment and standardization processing system, comprising:
[0029] The time unification module is used for acquiring the space-time data source of the new energy source, converting the space-time data in the space-time data source into space-time change rate data, setting a weighted cross-correlation function, calculating the time offset and time drift of the space-time change rate data, and projecting the time stamp of the space-time data to a unified time scale according to the time offset and time drift.
[0030] The space unification module is used for grid processing on the area where the new energy source is located, mapping the new energy source into the corresponding grid according to the area of the new energy source, and mapping the space-time data of the unified time scale into the corresponding grid according to the grid where the new energy source is located.
[0031] The interpolation module is used for traversing each grid, and if it is found that there is a missing value in the space-time data in a certain grid, interpolation processing is performed to complete the missing values in all grids.
[0032] Overall, compared with the prior art, the above technical solutions conceived by the application have the following beneficial effects:
[0033] The technical scheme of the present application realizes timestamp synchronization by projecting the timestamp of the space-time data to a unified time scale, and realizes the mapping of the space-time data and the geographic grid through grid mapping, and therefore solves the problem of cross-source data space-time consistency. BRIEF DESCRIPTION OF DRAWINGS
[0034] Figure 1 is a method flowchart of embodiment 1 of the present application.
[0035] Figure 2 is a system structure diagram of embodiment 2 of the present application. DETAILED DESCRIPTION
[0036] In order to better understand the above technical solutions, the above technical solutions will be described in detail below in combination with the drawings in the specification and specific embodiments.
[0037] The method provided by the present application can be implemented in a terminal environment, which can include one or more of the following components: a processor, a storage medium and a display screen. Among them, the storage medium stores at least one instruction, which is loaded and executed by the processor to realize the method described in the following embodiments.
[0038] The processor can include one or more processing cores. The processor connects various parts in the entire terminal through various interfaces and lines, executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.
[0039] The storage medium can include random access memory (RAM) and read-only memory (ROM). The storage medium can be used to store instructions, programs, codes, code sets or instructions.
[0040] The display screen is used to display the user interface of each application program.
[0041] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal can include more or fewer components, or combine certain components, or different component arrangements. For example, the terminal also includes radio frequency circuit, input unit, sensor, audio circuit, power supply and other components, which will not be described here.
[0042] Embodiment 1
[0043] As shown in Figure 1 , the present embodiment proposes a new energy data space-time alignment and standardization processing method, which includes:
[0044] At step 101, a spatio-temporal data source of new energy is acquired (the spatio-temporal data source includes not only a spatio-temporal data source of new energy itself, such as new energy output, new energy rated power and the like, but also meteorological spatio-temporal data corresponding to the spatio-temporal data source of new energy itself), the spatio-temporal data in the spatio-temporal data source is converted into spatio-temporal change rate data, a weighted cross-correlation function is set, a time offset and a time drift of the spatio-temporal change rate data are calculated, and a time stamp of the spatio-temporal data is projected to a unified time scale according to the time offset and the time drift;
[0045] Preferably, the spatio-temporal data source is preprocessed before step 101, and the preprocessing specifically includes:
[0046] 1. Unified column name and unit: for each record, ensure that the UTC time, source type (meteorology / photovoltaic / wind power and the like), variable name (GHI (global horizontal irradiance) / DNI (normal direct irradiance) / WS (wind speed) / T (temperature) / P (air pressure) / L (location (geographical coordinates)) and the like), numerical value, longitude, latitude, altitude, power station identification, and original message digest (for tracing) are included, and all powers are unified to kW or MW;
[0047] 2. Boundary judgment: for each variable, set a physical upper and lower limit (for example, GHI is 0-1400 W / m 2 , WS is 0-60 m / s, T is -20℃-60℃ and the like), and a variable exceeding the limit is marked as OUT_OF_RANGE and the original value is recorded;
[0048] 3. Stuck / flat-top detection: identify a segment of consecutive N (N≥3) same or almost same numerical values, and mark as STUCK;
[0049] 4. Missing and invalid code: a null value or a special invalid code is uniformly marked as MISSING.
[0050] Specifically, converting the spatio-temporal data in the spatio-temporal data source into spatio-temporal change rate data includes: taking two-by-two spatio-temporal data with correlation in the spatio-temporal data source as a spatio-temporal data pair, and converting the two-by-two spatio-temporal data as the spatio-temporal data pair into spatio-temporal change rate data.
[0051] Preferably, the present embodiment gives the following examples to describe the spatio-temporal data pair:
[0052] When the new energy is photovoltaic, the two-by-two spatio-temporal data with correlation can be photovoltaic output at a certain time and a certain location and irradiation at a certain time and a certain location, and the two data have correlation and can form a spatio-temporal data pair, and for example, wind power output at a certain time and a certain location and wind speed at a certain time and a certain location, and the two data have correlation and can form a spatio-temporal data pair, in short, the spatio-temporal data of new energy itself and the corresponding meteorological spatio-temporal data form a spatio-temporal data pair.
[0053] Specifically, the weighted cross-correlation function is set, and the time offset and time drift of the spatiotemporal change rate data are calculated.
[0054] The weighted cross-correlation function is:
[0055]
[0056] The time offset and time drift of the spatiotemporal change rate data are calculated as:
[0057]
[0058] Wherein, C(δ,ε) is the weighted cross-correlation value, δ is the candidate time offset, ε is the candidate time drift, w k is the weight of the kth timestamp t k , x and y are the spatiotemporal change rate data of the two spatiotemporal data of the spatiotemporal data pair, is the first time offset, is the first time drift.
[0059] Specifically, the time of a day is divided into multiple time windows, the first time offset and the first time drift of each time window are obtained The final time offset is obtained by weightedly averaging all the first time offsets , and the final time drift is obtained by weightedly averaging all the first time drifts
[0060] Specifically, the time stamp of the spatiotemporal change rate data is projected to the unified time scale according to the time offset and the time drift, including:
[0061]
[0062] Wherein, τ is the unified time scale, and t is the time stamp.
[0063] Step 102, grid processing is performed on the region where the new energy source is located, the new energy source is mapped to the corresponding grid according to the area of the new energy source, and the spatiotemporal data of the unified time scale is mapped to the corresponding grid according to the grid where the new energy source is located;
[0064] Specifically, the region where the new energy source is located is grid processed: the region where the new energy source is located is grid processed through UTM zoning, and each grid is assigned an ID and a center coordinate;
[0065] Mapping the spatiotemporal data of the unified time scale to the corresponding grid according to the grid where the new energy source is located includes:
[0066]
[0067] wherein, a g is the grid assignment coefficient of the grid g, A g is the intersection area of the new energy and the grid g, A g′ is the intersection area of the new energy and the grid g', plant is all grids where the new energy is located;
[0068] multiply the space-time data of the unified time scale by the grid assignment coefficient a g of the grid g to complete the mapping of the space-time data of the unified time scale to the corresponding grid according to the grid where the new energy is located. Preferably, the space-time data of the new energy itself is mapped in the corresponding grid together with the corresponding meteorological space-time data.
[0069] Step 103, traverse each grid, if it is found that the space-time data in a certain grid has missing values, then interpolation processing is performed to complete the missing values in all grids.
[0070] Specifically, traversing each grid, if it is found that the space-time data in a certain grid has missing values, then interpolation processing includes: performing interpolation processing on the corresponding grid by Kriging interpolation method.
[0071] Specifically, the prior weight of each grid is calculated, the prior weight is sorted in descending order, each grid is traversed according to the sorting order, and interpolation processing is performed.
[0072] Specifically, the prior weight of each grid is calculated, the prior weight is sorted in descending order, each grid is traversed according to the sorting order, and interpolation processing is performed.
[0073]
[0074] wherein, a ig is the prior weight of the grid i relative to the grid g, d i，g is the Euclidean distance between the center point of the grid i and the center point of the grid g, p s is a spatial adjustment factor, h i is the altitude of the grid i, h g is the altitude of the grid g, H s is the altitude reference value.
[0075] Preferably, the spatial adjustment factor p s is used to control the influence degree of the Euclidean distance d i，g on the weight, and the commonly used experience value can be set to 1.0-2.0.
[0076] Embodiment 2
[0077] As Figure 2 shown, the present embodiment proposes a new energy data space-time alignment and standardization processing system, which comprises:
[0078] a time unification module configured to obtain a spatio-temporal data source of a new energy, convert spatio-temporal data in the spatio-temporal data source into spatio-temporal change rate data, set a weighted cross-correlation function, calculate a time offset and a time drift of the spatio-temporal change rate data, and project timestamps of the spatio-temporal data to a unified time scale according to the time offset and the time drift;
[0079] Specifically, converting the spatio-temporal data in the spatio-temporal data source into the spatio-temporal change rate data includes: taking two-by-two spatio-temporal data with correlation in the spatio-temporal data source as a spatio-temporal data pair, and converting the two-by-two spatio-temporal data as the spatio-temporal data pair into the spatio-temporal change rate data.
[0080] Specifically, setting the weighted cross-correlation function and calculating the time offset and the time drift of the spatio-temporal change rate data includes:
[0081] The weighted cross-correlation function is:
[0082]
[0083] The calculation of the time offset and the time drift of the spatio-temporal change rate data is:
[0084]
[0085] wherein C(δ, ε) is a weighted cross-correlation value, δ is a candidate time offset, ε is a candidate time drift, w k is a weight of a kth timestamp t k , x and y are spatio-temporal change rate data of two-by-two spatio-temporal data of the spatio-temporal data pair, is a first time offset, is a first time drift.
[0086] Specifically, a day is divided into a plurality of time windows, a first time offset and a first time drift of each time window are obtained, all the first time offsets are weightedly averaged to obtain a final time offset , and all the first time drifts are weightedly averaged to obtain a final time drift
[0087] Specifically, projecting the timestamps of the spatio-temporal change rate data to the unified time scale according to the time offset and the time drift includes:
[0088]
[0089] wherein τ is the unified time scale, and t is the timestamp.
[0090] The space unification module is configured to perform grid processing on the region where the new energy source is located, map the new energy source into a corresponding grid according to the area of the new energy source, and map the space-time data of the unified time scale into the corresponding grid according to the grid where the new energy source is located.
[0091] Specifically, the region where the new energy source is located is subjected to grid processing: the region where the new energy source is located is subjected to grid processing by UTM zoning, and each grid is assigned an ID and a center coordinate.
[0092] The space-time data of the unified time scale is mapped into the corresponding grid according to the grid where the new energy source is located, including:
[0093]
[0094] wherein, α g is the grid assignment coefficient of the grid g, A g is the intersection area of the new energy source and the grid g, A g′ is the intersection area of the new energy source and the grid g', and plant is all the grids where the new energy source is located.
[0095] The space-time data of the unified time scale is multiplied by the grid assignment coefficient α g of the grid g to obtain the space-time data of the grid g, so as to map the space-time data of the unified time scale into the corresponding grid according to the grid where the new energy source is located.
[0096] The interpolation module is configured to traverse each grid, and if it is found that the space-time data in a certain grid has missing values, interpolation processing is performed to complete the missing values in all grids.
[0097] Specifically, the traversal of each grid and the interpolation processing if it is found that the space-time data in a certain grid has missing values include: performing interpolation processing on the corresponding grid by the Kriging interpolation method.
[0098] Specifically, the prior weight of each grid is calculated, the prior weights are sorted in descending order, each grid is traversed in the order of sorting, and interpolation processing is performed.
[0099] Specifically, the prior weight of each grid is calculated, including:
[0100]
[0101] wherein, a ig is the prior weight of the grid i relative to the grid g, d i，g is the Euclidean distance between the center point of the grid i and the center point of the grid g, p s is a space adjustment factor, h i is the altitude of the grid i, h g is the altitude of the grid g, and H sAltitude reference value.
[0102] Embodiment 3
[0103] The embodiment of the present application also provides a storage medium storing a plurality of instructions for implementing the new energy data space-time alignment and standardization processing method.
[0104] Optionally, in the embodiment, the storage medium can be located in any one of computer terminals in a computer terminal group in a computer network, or in any one of mobile terminals in a mobile terminal group.
[0105] Optionally, in the embodiment, the storage medium is configured to store program codes for executing the following method steps: step 101, obtaining a space-time data source of new energy, converting space-time data in the space-time data source into space-time change rate data, setting a weighted cross-correlation function, calculating a time offset and a time drift of the space-time change rate data, and projecting a time stamp of the space-time data to a unified time scale according to the time offset and the time drift;
[0106] Specifically, converting the space-time data in the space-time data source into the space-time change rate data comprises: taking two pieces of space-time data with correlation in the space-time data source as a space-time data pair, and converting the two pieces of space-time data as the space-time data pair into space-time change rate data.
[0107] Specifically, setting the weighted cross-correlation function and calculating the time offset and the time drift of the space-time change rate data comprises:
[0108] The weighted cross-correlation function is:
[0109]
[0110] The time offset and the time drift of the space-time change rate data are calculated as:
[0111]
[0112] Wherein, C(δ,ε) is a weighted cross-correlation value, δ is a candidate time offset, ε is a candidate time drift, w k is a weight of the kth time stamp t k , x and y are space-time change rate data of two pieces of space-time data of the space-time data pair, is the first time offset, is the first time drift.
[0113] Specifically, the time of a day is divided into a plurality of time windows, the first time offset and the first time drift of each time window are obtained, The weighted average is performed to obtain a final time offset and all first time drifts The weighted average is performed to obtain a final time drift
[0114] Specifically, the projection of the time stamp of the space-time change rate data to the unified time scale according to the time offset and the time drift includes:
[0115]
[0116] Wherein, τ is the unified time scale, and t is the time stamp.
[0117] In step 102, the region where the new energy is located is grid processed, the new energy is mapped to the corresponding grid according to the area of the new energy, and the space-time data of the unified time scale is mapped to the corresponding grid according to the grid where the new energy is located;
[0118] Specifically, the region where the new energy is located is grid processed: the region where the new energy is located is grid processed through UTM zoning, and each grid is assigned an ID and a center coordinate;
[0119] The space-time data of the unified time scale is mapped to the corresponding grid according to the grid where the new energy is located, including:
[0120]
[0121] Wherein, α g is the grid allocation coefficient of the grid g, A g is the intersection area of the new energy and the grid g, A g′ is the intersection area of the new energy and the grid g', and plant is all the grids where the new energy is located.
[0122] The space-time data of the unified time scale is multiplied by the grid allocation coefficient α g of the grid g as the space-time data of the grid g, so as to complete the mapping of the space-time data of the unified time scale to the corresponding grid according to the grid where the new energy is located.
[0123] In step 103, each grid is traversed, and if it is found that the space-time data in a certain grid has missing values, interpolation processing is performed to complete the missing values in all grids.
[0124] Specifically, if it is found that the space-time data in a certain grid has missing values, the interpolation processing includes: the corresponding grid is interpolated by the Kriging interpolation method.
[0125] Specifically, the prior weight of each grid is calculated, the prior weight is sorted in descending order, each grid is traversed according to the sorting order, and interpolation processing is performed.
[0126] Specifically, calculating the prior weight of each grid comprises:
[0127]
[0128] wherein a ig is the prior weight of grid i relative to grid g, d i，g is the Euclidean distance between the center point of grid i and the center point of grid g, p s is a space adjustment factor, h i is the altitude of grid i, h g is the altitude of grid g, and H s is an altitude reference value.
[0129] Embodiment 4
[0130] The embodiment of the present application also provides an electronic device, which comprises a processor and a storage medium connected to the processor, and the storage medium stores a plurality of instructions which can be loaded and executed by the processor to enable the processor to perform the new energy data space-time alignment and standardization processing method.
[0131] Specifically, the electronic device of the embodiment can be a computer terminal, which can comprise one or more processors and a storage medium.
[0132] The storage medium can be used to store software programs and modules, such as the new energy data space-time alignment and standardization processing method of the embodiment of the present application, and the corresponding program instructions / modules. The processor performs various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, implements the new energy data space-time alignment and standardization processing method. The storage medium can comprise a high-speed random storage medium, and can also comprise a non-volatile storage medium, such as one or more magnetic storage systems, flash memories, or other non-volatile solid-state storage media. In some examples, the storage medium can further comprise storage media remotely arranged relative to the processor, which can be connected to the terminal through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.
[0133] The processor can call the information and application programs stored in the storage medium through a transmission system to perform the following method steps: step 101, acquiring a space-time data source of new energy, converting space-time data in the space-time data source into space-time change rate data, setting a weighted cross-correlation function, calculating a time offset and a time drift amount of the space-time change rate data, projecting a time stamp of the space-time data to a unified time scale according to the time offset and the time drift amount;
[0134] Specifically, the converting the spatio-temporal data in the spatio-temporal data source into the spatio-temporal change rate data comprises: taking two two spatio-temporal data with correlation in the spatio-temporal data source as a spatio-temporal data pair, and converting the two two spatio-temporal data as the spatio-temporal data pair into the spatio-temporal change rate data.
[0135] Specifically, the setting the weighted cross-correlation function and calculating the time offset and the time drift of the spatio-temporal change rate data comprises:
[0136] The weighted cross-correlation function is:
[0137]
[0138] The calculating the time offset and the time drift of the spatio-temporal change rate data is:
[0139]
[0140] Wherein, C(δ,ε) is the weighted cross-correlation value, δ is the candidate time offset, ε is the candidate time drift, w k is the weight of the kth timestamp t k , x and y are the spatio-temporal change rate data of the two two spatio-temporal data of the spatio-temporal data pair, is the first time offset, is the first time drift.
[0141] Specifically, the time of a day is divided into a plurality of time windows, the first time offset and the first time drift of each time window are obtained, all the first time offsets are weightedly averaged to obtain the final time offset and all the first time drifts are weightedly averaged to obtain the final time drift
[0142] Specifically, the projecting the timestamps of the spatio-temporal change rate data to the unified time scale according to the time offset and the time drift comprises:
[0143]
[0144] Wherein, τ is the unified time scale, and t is the timestamp.
[0145] In step 102, the region where the new energy is located is grid processed, the new energy is mapped to the corresponding grid according to the area of the new energy, and the spatio-temporal data of the unified time scale is mapped to the corresponding grid according to the grid where the new energy is located;
[0146] Specifically, the region where the new energy is located is grid processed: the region where the new energy is located is grid processed through UTM zoning, and each grid is allocated with an ID and a center coordinate.
[0147] mapping the space-time data of the unified time scale to the corresponding grid according to the grid where the new energy source is located comprises:
[0148]
[0149] wherein, α g is the grid distribution coefficient of the grid g, A g is the intersection area of the new energy source and the grid g, A g′ is the intersection area of the new energy source and the grid g', and plant is all the grids where the new energy source is located;
[0150] multiplying the space-time data of the unified time scale by the grid distribution coefficient α g of the grid g as the space-time data of the grid g, so as to complete the mapping of the space-time data of the unified time scale to the corresponding grid according to the grid where the new energy source is located.
[0151] In step 103, each grid is traversed, and if it is found that the space-time data in a certain grid has missing values, interpolation processing is performed to complete the missing values in all grids.
[0152] Specifically, if it is found that the space-time data in a certain grid has missing values, interpolation processing includes: performing interpolation processing on the corresponding grid by the Kriging interpolation method.
[0153] Specifically, the prior weight of each grid is calculated, the prior weights are sorted in descending order, each grid is traversed according to the sorting order, and interpolation processing is performed.
[0154] Specifically, the prior weight of each grid is calculated, the prior weights are sorted in descending order, each grid is traversed according to the sorting order, and interpolation processing is performed.
[0155]
[0156] wherein, a ig is the prior weight of the grid i relative to the grid g, d i，g is the Euclidean distance between the center point of the grid i and the center point of the grid g, p s is a space adjustment factor, h i is the altitude of the grid i, h g is the altitude of the grid g, and H s is the altitude reference value.
[0157] The above embodiment numbers of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.
[0158] In the above embodiments of the present application, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.
[0159] In several embodiments provided by the present application, it should be understood that the disclosed technology can be implemented in other manners. In the embodiments described above, the division of the system is merely illustrative, and the division of the units can be different. For example, the units or components can be combined or integrated into another system, or some features can be ignored or not implemented. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, and can be in electrical or other forms.
[0160] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place or distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.
[0161] In addition, the functional units in each embodiment of the present application can be integrated into a processing unit, or each unit can be physically present separately, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.
[0162] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the present application, the essential part or the whole or part of the technical solutions of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.
[0163] Obviously, the above embodiments are merely examples for clear illustration, and are not limitations on the implementation. For those skilled in the art, based on the above description, other different forms of changes or modifications can also be made. Here, all the implementations are not required or possible to be exhausted. The obvious changes or modifications derived therefrom are still within the protection scope of the present application.
Claims
1. A method for spatiotemporal alignment and standardization of new energy data, characterized in that, The method comprises the following steps: Obtaining a spatio-temporal data source of new energy, converting spatio-temporal data in the spatio-temporal data source into spatio-temporal change rate data, setting a weighted cross-correlation function, calculating the time offset and time drift of the spatio-temporal change rate data, and projecting the time stamp of the spatio-temporal data to a unified time scale according to the time offset and time drift; Grid processing is performed on the region where the new energy is located, the new energy is mapped to the corresponding grid according to the area of the new energy, and the spatio-temporal data of the unified time scale is mapped to the corresponding grid according to the grid where the new energy is located; If missing values are found in the spatio-temporal data in a certain grid, interpolation processing is performed to complete the missing values in all grids.
2. The method of claim 1, wherein, Converting the spatio-temporal data in the spatio-temporal data source into spatio-temporal change rate data comprises: taking two spatio-temporal data in the spatio-temporal data source as a spatio-temporal data pair, and converting the two spatio-temporal data as the spatio-temporal data pair into spatio-temporal change rate data.
3. The method of claim 2, wherein the method further comprises: Setting the weighted cross-correlation function and calculating the time offset and time drift of the spatio-temporal change rate data comprises: The weighted cross-correlation function is: The time offset and time drift of the spatio-temporal change rate data are calculated as: wherein C(δ,ε) is a weighted cross-correlation value, δ is a candidate time offset, ε is a candidate time drift, w k is a weight of the kth timestamp t k , x and y are the temporal change rate data of the two two-way spatio-temporal data of the spatio-temporal data pair, is a first time offset, is a first time drift.
4. The method of claim 3, wherein the method further comprises: dividing a day into multiple time windows, obtaining a first time offset for each time window and a first time drift amount weighting and averaging all first time offsets to obtain a final time offset and weighting and averaging all first time drift amounts to obtain a final time drift amount 5. The method of claim 4, wherein, The time stamp of the spatio-temporal change rate data is projected to a unified time scale according to the time offset and time drift, which comprises: Wherein, τ is the unified time scale, and t is the time stamp.
6. The method of claim 1, wherein the method is characterized by, Grid processing is performed on the region where the new energy is located: the region where the new energy is located is grid processed through UTM zoning, and each grid is assigned an ID and a center coordinate; Mapping the spatio-temporal data of the unified time scale to the corresponding grid according to the grid where the new energy is located comprises: wherein, α g is the lattice assignment coefficient of the grid g, A g is the intersection area of the new energy and the grid g, A g′ is the intersection area of the new energy and the grid g', plant is all grids where the new energy is located; Multiply the space-time data of the unified time scale by the grid distribution coefficient α of the grid g g As the space-time data of the grid g, complete the mapping of the space-time data of the unified time scale to the corresponding grid according to the grid where the new energy is located.
7. The method of claim 1, wherein the method further comprises: determining a time interval of the new energy data; and determining a time interval of the reference data. If missing values are found in the spatio-temporal data in a certain grid, interpolation processing is performed, which comprises: performing interpolation processing on the corresponding grid by Kriging interpolation method.
8. The method of claim 7, wherein the method further comprises: The prior weight of each grid is calculated, the prior weight is sorted in descending order, each grid is traversed in the order of sorting, and interpolation processing is performed.
9. The new energy data space-time alignment and standardization processing method of claim 8, wherein, The prior weight of each grid is calculated, which comprises: wherein a ig is the prior weight of grid i relative to grid g, d i，g is the Euclidean distance between the center point of grid i and the center point of grid g, p s is the spatial adjustment factor, h i is the altitude of grid i, h g is the altitude of grid g, H s is the altitude reference value.
10. A new energy data space-time alignment and standardization processing system, characterized in that, The method comprises the following steps: The time unification module is used to obtain a spatio-temporal data source of new energy, convert spatio-temporal data in the spatio-temporal data source into spatio-temporal change rate data, set a weighted cross-correlation function, calculate the time offset and time drift of the spatio-temporal change rate data, and project the time stamp of the spatio-temporal data to a unified time scale according to the time offset and time drift; The space unification module is used to perform grid processing on the region where the new energy is located, map the new energy to the corresponding grid according to the area of the new energy, and map the spatio-temporal data of the unified time scale to the corresponding grid according to the grid where the new energy is located; The interpolation module is used to traverse each grid, and if missing values are found in the spatio-temporal data in a certain grid, interpolation processing is performed to complete the missing values in all grids.