Power grid boundary data processing method and device, power grid boundary data processing system, readable storage medium and program product

Abstract

The invention relates to the technical field of power grid data management, and provides a power grid boundary data processing method and device, a power grid boundary data processing system, a readable storage medium and a program product. The method comprises the following steps: mapping each piece of power grid boundary data into standard boundary data; determining a multi-dimensional identifier of each standard boundary data according to a regulation and control object and a scheduling version of the power grid boundary data to obtain compressed boundary data corresponding to the same multi-dimensional identifier; determining the compressed boundary data of the same scheduling version according to the multi-dimensional identifier of the compressed boundary data to obtain a boundary data snapshot of the corresponding scheduling version, and storing the boundary data snapshot in a snapshot database; when a query request is received, a first boundary data snapshot set is determined in the snapshot database, and then a second boundary data snapshot set is obtained to form a query result. By adopting the method, complex multi-condition screening and multi-granularity time window analysis are supported.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid data management, in particular to a power grid boundary data processing method and device, a power grid boundary data processing system, a computer readable storage medium and a computer program product. BACKGROUND

[0002] With large-scale access of new energy and increasingly complex load characteristics, the change of power grid operation state on the time axis presents obvious high-frequency characteristics. Especially in the power spot market environment, the power grid boundary state is frequently disturbed by factors such as market clearing, prediction correction, dispatching strategy adjustment, and related boundary data often needs to be updated at a second level or even a millisecond level. This puts extremely high requirements on data storage efficiency, update mechanism, historical traceability, etc.

[0003] However, the traditional power grid boundary data management method has many limitations and cannot meet the actual needs of full-grid simulation and playback tasks. Specifically, there are significant deficiencies in multi-dimensional search capability, and often only linear queries by time are available. For example, in playback analysis, users often want to quickly locate the wind power prediction deviation data of a certain region in a certain dispatching period or the power flow change of a certain section in a certain clearing period, but the traditional power grid boundary data management method cannot meet such precise query needs, which seriously limits the strategy tracing and simulation efficiency. SUMMARY

[0004] Therefore, it is necessary to provide a power grid boundary data processing method, device, system, computer readable storage medium and computer program product to solve the above technical problems.

[0005] In a first aspect, the present application provides a power grid boundary data processing method, comprising:

[0006] obtaining multi-time multi-source heterogeneous power grid boundary data, and mapping each power grid boundary data to standard boundary data;

[0007] determining the multi-dimensional identifier of each standard boundary data according to the regulation and control object and dispatch version of the power grid boundary data;

[0008] According to the compression algorithm matched with the data characteristics of the standard boundary data, the multi-time standard boundary data corresponding to the same multi-dimensional identifier is compressed to obtain the compressed boundary data corresponding to the same multi-dimensional identifier;

[0009] According to the multi-dimensional identifier of the compressed boundary data, the compressed boundary data of the same dispatch version is determined to obtain the boundary data snapshot of the corresponding dispatch version and store it to the snapshot database; the metadata information of the boundary data snapshot includes snapshot identifier code, snapshot generation timestamp and dispatch version;

[0010] When receiving the query request, according to the correspondence between the region identification code and the snapshot identification code, the region identification code information carried by the search request and the snapshot identification code included in the metadata information of the boundary data snapshot, a first boundary data snapshot set is determined in the snapshot database;

[0011] According to the time information and the scheduling version information carried by the search request, and the snapshot generation timestamp and the scheduling version included in the metadata information of the boundary data snapshot, a second boundary data snapshot set is determined in the first boundary data snapshot set;

[0012] According to the correspondence between the region identification code and the control object, target standard boundary data is determined in the second boundary data snapshot set to form a query result.

[0013] In a second aspect, the application further provides a power grid boundary data processing device, comprising:

[0014] A standard boundary data acquisition module is configured to acquire multi-time multi-source heterogeneous power grid boundary data, and map each power grid boundary data into standard boundary data;

[0015] A multi-dimensional identification determination module is configured to determine the multi-dimensional identification of each standard boundary data according to the control object and the scheduling version of the power grid boundary data;

[0016] A compressed boundary data acquisition module is configured to compress the standard boundary data of the same multi-dimensional identification at different times according to a compression algorithm matched with the data characteristics of the standard boundary data, to obtain compressed boundary data corresponding to the same multi-dimensional identification;

[0017] A boundary data snapshot acquisition module is configured to determine the compressed boundary data of the same scheduling version according to the multi-dimensional identification of the compressed boundary data, to obtain a boundary data snapshot of the corresponding scheduling version and store it into a snapshot database; the metadata information of the boundary data snapshot includes a snapshot identification code, a snapshot generation timestamp and a scheduling version;

[0018] A search module is configured to, when receiving a query request, determine a first boundary data snapshot set in the snapshot database according to the correspondence between the region identification code and the snapshot identification code, the region identification code information carried by the search request and the snapshot identification code included in the metadata information of the boundary data snapshot; determine a second boundary data snapshot set in the first boundary data snapshot set according to the time information and the scheduling version information carried by the search request, and the snapshot generation timestamp and the scheduling version included in the metadata information of the boundary data snapshot; and determine target standard boundary data in the second boundary data snapshot set according to the correspondence between the region identification code and the control object, to form a query result.

[0019] In a third aspect, the present application also provides a power grid boundary data processing. The power grid boundary data processing comprises a memory and a processor, the memory stores a computer program, and the processor executes the above method.

[0020] In a fourth aspect, the present application also provides a computer readable storage medium. The computer readable storage medium stores a computer program, and the computer program is executed by a processor to execute the above method.

[0021] In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program, and the computer program is executed by a processor to execute the above method.

[0022] The power grid boundary data processing method, device, system, computer readable storage medium and computer program product described above, the present application maps each power grid boundary data to standard boundary data, determines the multi-dimensional identifier of each standard boundary data according to the control object and scheduling version of the power grid boundary data, realizes the standardized management of multi-source heterogeneous power grid boundary data, greatly improves the consistency and fusion of the power grid boundary data, and facilitates cross-system coordination and joint analysis; according to the compression algorithm matched with the data characteristics of the standard boundary data, the standard boundary data of multiple time points corresponding to the same multi-dimensional identifier is compressed to obtain compressed boundary data corresponding to the same multi-dimensional identifier, which significantly reduces the storage capacity and network bandwidth overhead while ensuring the key accuracy; according to the multi-dimensional identifier of the compressed boundary data, the compressed boundary data of the same scheduling version is determined to obtain the boundary data snapshot of the corresponding scheduling version and store it to the snapshot database, which has a clear scheduling version control system and supports fast switching and simulation reconstruction of key historical states; when receiving a query request, according to the corresponding relationship between the region identifier code and the snapshot identifier code, the region identifier code information carried by the search request, and the snapshot identifier code included in the metadata information of the boundary data snapshot, a first boundary data snapshot set is determined in the snapshot database; according to the time information and scheduling version information carried by the search request, and the snapshot generation timestamp and scheduling version included in the metadata information of the boundary data snapshot, a second boundary data snapshot set is determined in the first boundary data snapshot set; according to the corresponding relationship between the region identifier code and the control object, the target standard boundary data is determined in the second boundary data snapshot set to form the query result, which significantly improves the search speed and query flexibility, supports complex multi-condition filtering and multi-granularity time window analysis, and meets the diversified data access requirements in power grid scheduling and market operation. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 It is a flowchart of the power grid boundary data processing method in one embodiment;

[0024] Figure 2 An architecture diagram of a power grid boundary data processing system in an embodiment;

[0025] Figure 3 A flow diagram of a boundary data adaptive compression strategy in an embodiment;

[0026] Figure 4 A flow diagram of a sliding window rolling update mechanism in an embodiment;

[0027] Figure 5 A hierarchical storage architecture diagram of power grid boundary data processing in an embodiment;

[0028] Figure 6 A flow diagram of an event-driven mechanism in an embodiment;

[0029] Figure 7 A flow diagram of a multi-dimensional index structure and historical data fast query in an embodiment;

[0030] Figure 8 A structure block diagram of a power grid boundary data processing apparatus in an embodiment. DETAILED DESCRIPTION

[0031] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not intended to limit the present application. The terms "comprise" and "have" and any variations thereof used in the present application are intended to cover non-exclusive inclusion. The term "multiple" used in the present application refers to two or more. The term "and / or" used in the present application refers to one of the solutions or any combination of multiple solutions. Mapping to standard boundary data query request query result

[0032] The power grid boundary data processing method of the present application comprises Figure 1 The steps shown can be applied to a power grid boundary data processing system.

[0033] In step S101, multiple-time and multiple-source heterogeneous power grid boundary data are acquired, and each power grid boundary data is mapped to standard boundary data.

[0034] The multi-time multi-source heterogeneous grid boundary data can be a common data source in dispatching business, including unit output plan in an operating control system (OCS), real-time measurement values in an energy management system (EMS), and short-term load and new energy prediction results in a prediction system. The multi-time multi-source heterogeneous grid boundary data can be extracted and the core fields thereof, such as node output, section power flow, load level, and new energy output prediction value, can be abstracted, and the core fields can be uniformly mapped to standard boundary data (which can be referred to as standard boundary variables) that can be identified and analyzed in a multi-dispatching business model.

[0035] In step S102, multi-dimensional identifiers of the standard boundary data are determined according to the regulation objects and dispatch versions of the grid boundary data.

[0036] The multi-dimensional identifiers of the standard boundary data can be determined according to the regulation objects and dispatch versions of the grid boundary data, so that accurate identification and version control of each type of standard boundary data can be achieved.

[0037] Specifically, a four-dimensional identification system of “regulation object-time stamp-data type-version number” can be established for the standard boundary data, so that accurate identification and version control of each type of standard boundary data can be achieved. The regulation object can be a power generation unit, a substation, or a line section; the time stamp can be accurate to the dispatch period or the data refresh granularity; the data type can indicate whether the data is a planned value, a measured value, or a predicted value; and the version number can be used to distinguish multiple iterative versions of the data at the same time point due to strategy adjustment or algorithm correction.

[0038] In step S103, the standard boundary data at different times corresponding to the same multi-dimensional identifier are compressed according to a compression algorithm matched with the data characteristics of the standard boundary data, to obtain compressed boundary data corresponding to the same multi-dimensional identifier.

[0039] In order to improve the storage efficiency of the standard boundary data in the full-grid model while ensuring the reconstruction accuracy of the key time sequence information, a self-adaptive data compression algorithm framework can be used to dynamically select a matched compression algorithm according to the data characteristics of the standard boundary data, such as the time-varying characteristics and business sensitivity of the standard boundary data, so as to achieve the best balance between data integrity and real-time performance in a limited storage space.

[0040] In step S104, the compressed boundary data of the same dispatch version is determined according to the multi-dimensional identifier of the compressed boundary data, to obtain a boundary data snapshot of the corresponding dispatch version and store the boundary data snapshot in a snapshot database. The metadata information of the boundary data snapshot includes a snapshot identifier code, a snapshot generation time stamp, and a dispatch version.

[0041] The compressed boundary data of the same scheduling version can be determined according to the multi-dimensional identifier of the compressed boundary data, and the boundary data snapshot of the corresponding scheduling version can be obtained according to the compressed boundary data of the same scheduling version and stored in the snapshot database.

[0042] The full-amount boundary data snapshot is generated according to the preset large period, a plurality of preset small periods are set in each preset large period, and the incremental boundary data snapshot is generated by extracting the differential boundary data between adjacent boundary data snapshots in each preset small period, so that only the differential boundary data is saved to save the storage space.

[0043] In step S105, when receiving the query request, the first boundary data snapshot set is determined in the snapshot database according to the corresponding relationship between the region identifier code and the snapshot identifier code, the region identifier code information carried by the retrieval request, and the snapshot identifier code included in the metadata information of the boundary data snapshot.

[0044] The query request can specify the query conditions, including four-dimensional information such as time range, region, regulation object and scheduling version number.

[0045] When receiving the query request, the snapshot identifier code associated with the region identifier code information carried by the retrieval request is determined in the snapshot identifier code included in the metadata information of each boundary data snapshot in the snapshot database according to the corresponding relationship between the region identifier code and the snapshot identifier code and the region identifier code information carried by the retrieval request. The boundary data snapshot corresponding to the above associated snapshot identifier code is taken as the first boundary data snapshot, so as to determine the first boundary data snapshot set.

[0046] In step S106, the second boundary data snapshot set is determined according to the time information and the scheduling version information carried by the retrieval request, and the snapshot generation time stamp and the scheduling version included in the metadata information of the boundary data snapshot.

[0047] The second boundary data snapshot set can be determined according to the time information and the scheduling version information carried by the retrieval request, and the snapshot generation time stamp and the scheduling version included in the metadata information of the boundary data snapshot, and the second boundary data snapshot matching the time information and the scheduling version information carried by the retrieval request is determined in the first boundary data snapshot set, so as to determine the second boundary data snapshot set.

[0048] In step S107, the target standard boundary data is determined in the second boundary data snapshot set according to the corresponding relationship between the region identifier code and the regulation object, so as to form the query result.

[0049] The target standard boundary data of the regulation object matching the region identifier code can be determined in the second boundary data snapshot set according to the corresponding relationship between the region identifier code and the regulation object, so as to form the query result.

[0050] In the method for processing the power grid boundary data, when the query request is received, the first boundary data snapshot set is determined in the snapshot database according to the correspondence between the region identification code and the snapshot identification code, the region identification code information carried by the search request, and the snapshot identification code included in the metadata information of the boundary data snapshot; the second boundary data snapshot set is determined in the first boundary data snapshot set according to the time information and the scheduling version information carried by the search request, and the snapshot generation time stamp and the scheduling version included in the metadata information of the boundary data snapshot; the target standard boundary data is determined in the second boundary data snapshot set according to the correspondence between the region identification code and the control object, so as to form the query result, which significantly improves the search speed and the query flexibility, supports complex multi-condition screening and multi-granularity time window analysis, and meets the diversified data access requirements in power grid scheduling and market operation.

[0051] In one embodiment, according to the compression algorithm matching the data characteristics of the standard boundary data, the standard boundary data of multiple time points corresponding to the same multi-dimensional identification is compressed to obtain the compressed boundary data corresponding to the same multi-dimensional identification. The specific steps are as follows: when the data characteristics of the standard boundary data are that the data redundancy is higher than the redundancy threshold and the error tolerance is lower than the tolerance threshold, the standard boundary data of multiple time points corresponding to the same multi-dimensional identification is compressed according to the lossless compression algorithm to obtain the compressed boundary data corresponding to the same multi-dimensional identification; when the data characteristics of the standard boundary data are that the data volatility is higher than the volatility threshold and the error tolerance is higher than the tolerance threshold, the standard boundary data of multiple time points corresponding to the same multi-dimensional identification is compressed according to the lossy compression algorithm to obtain the compressed boundary data corresponding to the same multi-dimensional identification; when the data characteristics of the standard boundary data are that the data volatility is higher than the volatility threshold, the uncertainty characteristic is higher than the uncertainty characteristic threshold, and the error tolerance is higher than the tolerance threshold, the standard boundary data of multiple time points corresponding to the same multi-dimensional identification is compressed according to the lossy quantization compression algorithm to obtain the compressed boundary data corresponding to the same multi-dimensional identification.

[0052] When the data characteristics of the standard boundary data are that the data redundancy is higher than the redundancy threshold and the error tolerance is lower than the tolerance threshold, the standard boundary data of multiple time points corresponding to the same multi-dimensional identification is compressed according to the lossless compression algorithm to obtain the compressed boundary data corresponding to the same multi-dimensional identification.

[0053] When the data characteristics of the standard boundary data are that the data volatility is higher than the volatility threshold and the error tolerance is higher than the tolerance threshold, the standard boundary data of multiple time points corresponding to the same multi-dimensional identification is compressed according to the lossy compression algorithm to obtain the compressed boundary data corresponding to the same multi-dimensional identification.

[0054] When the data characteristics of the standard boundary data are that the data fluctuation is higher than the fluctuation threshold, the uncertainty characteristic is higher than the uncertainty characteristic threshold, and the error tolerance is higher than the tolerance threshold, the standard boundary data corresponding to the same multi-dimensional identifier at multiple time points is compressed according to the lossy quantization compression algorithm to obtain compressed boundary data corresponding to the same multi-dimensional identifier.

[0055] In this embodiment, according to the data characteristics of the standard boundary data, a compression algorithm matched with the data characteristics of the standard boundary data is determined to compress the standard boundary data corresponding to the same multi-dimensional identifier at multiple time points to obtain compressed boundary data corresponding to the same multi-dimensional identifier, which can significantly reduce the storage capacity and network bandwidth overhead while ensuring the key accuracy.

[0056] In one embodiment, the standard boundary data corresponding to the same multi-dimensional identifier at multiple time points is compressed according to the lossy quantization compression algorithm to obtain compressed boundary data corresponding to the same multi-dimensional identifier, and the specific steps are as follows: a multi-level error feedback vector is obtained according to error distribution of different scales; the feedback error of each layer in the multi-level error feedback vector is superimposed according to the weight to obtain a multi-level error feedback superposition vector; the standard boundary data corresponding to the same multi-dimensional identifier at multiple time points is corrected according to the multi-level error feedback superposition vector to obtain standard boundary correction data; the quantization step is dynamically adjusted according to the noise level of each time point of the standard boundary data corresponding to the same multi-dimensional identifier at multiple time points; the standard boundary correction data is quantized according to the quantization step to obtain a standard boundary quantization sequence; and the compressed boundary data corresponding to the same multi-dimensional identifier is obtained according to the standard boundary quantization sequence.

[0057] The error distribution of different scales can be captured to obtain a multi-level error feedback vector as shown in formula (1).

[0058]

[0059] wherein, e t denotes the multi-level error feedback vector at time t, denotes the error feedback vector of the Mth level at time t, and M denotes the number of error feedback levels, each level being responsible for capturing error distribution of different scales and improving error correction capability.

[0060] A time scale weighted error control mechanism is adopted to allocate different weights at multiple time levels such as minute level and hour level, and the feedback error of each layer in the multi-level error feedback vector is superimposed according to the weight to obtain a multi-level error feedback superposition vector.

[0061] The standard boundary data corresponding to the same multi-dimensional identifier at multiple time points is corrected according to the multi-level error feedback superposition vector to obtain standard boundary correction data, as shown in formula (2).

[0062]

[0063] wherein, denotes the standard boundary correction data at time t, x t denotes the standard boundary data at time t, denotes the error feedback vector of the mth stage at time t-1, M denotes the number of error feedback stages, and the weight coefficient w m satisfies is adaptively adjusted, and an exponential decay weight method as shown in equation (3) is adopted.

[0064]

[0065] wherein, β denotes a decay coefficient.

[0066] According to the noise level of the standard boundary data at each time corresponding to the same multi-dimensional identifier at multiple times, the quantization step is dynamically adjusted, as shown in equation (4).

[0067]

[0068] wherein, Δ t denotes the adjusted quantization step, Δ base denotes the basic quantization step, and α, γ denote adjustment parameters, α, γ>0, x t denotes the standard boundary data at time t, and σ denotes the standard deviation of historical data, SNR t denotes the signal-to-noise ratio at time t, which is used to dynamically adjust the quantization precision according to the noise level; and ∈ denotes a small positive number to avoid division by zero.

[0069] wherein, the calculation of the signal-to-noise ratio is based on the estimation of the signal power and the noise power of the standard boundary data in the sliding window, as shown in equation (5).

[0070]

[0071] wherein, P signal,t denotes the signal power at time t, and P noise,t denotes the noise power at time t.

[0072] The standard boundary correction data can be quantized according to the quantization step to obtain a standard boundary quantization sequence, as shown in equation (6).

[0073]

[0074] wherein, q t denotes the standard boundary quantization sequence at time t, denotes the standard boundary correction data, Δ t denotes the quantization step at time t, and round(·) denotes the rounding operation, and the standard boundary quantization sequence q tis an integer coding, which is convenient for subsequent difference and entropy coding processing, and the corresponding reconstructed value is as shown in equation (7).

[0075]

[0076] The same multi-dimensional identifier corresponding compressed boundary data can be obtained according to the standard boundary quantization sequence.

[0077] Specifically, the standard boundary quantization sequence q t The first-order difference is applied to obtain the incremental data sequence, as shown in equation (8).

[0078] d t = q t -q t-1 (8)

[0079] Wherein, d t represents the incremental data sequence (also can be called difference sequence) at t time, q t represents the standard boundary quantization sequence at t time, q t-1 represents the standard boundary quantization sequence at t-1 time.

[0080] The incremental data sequence can be converted into binary compressed code stream by using Huffman coding algorithm, as shown in equation (9).

[0081] C = EntropyEncoder ({d t}) (9)

[0082] Wherein, C represents the binary compressed code stream.

[0083] Wherein, the entropy coding compression is carried out on the incremental data sequence d t , and Huffman coding is used for entropy coding compression.

[0084] In this embodiment, through the multi-stage error feedback mechanism, the gradual accumulation of quantization error in the standard boundary data compression and the local deviation caused by the sudden abnormality are effectively alleviated, and the numerical fidelity in the long-term data compression process is ensured. On this basis, the dynamic quantization step adjustment strategy based on signal-to-noise ratio (SNR) is introduced, and the coding precision can be adaptively adjusted according to the noise level of the standard boundary data.

[0085] In one embodiment, according to error distribution of different scales, a multi-level error feedback vector is obtained, and the specific steps are as follows: when performing first quantization, a set feedback error is obtained as the multi-level error feedback vector; when performing non-first quantization, a feedback error of a first level is obtained according to a difference between target time series data to be compressed and corresponding reconstructed data at last quantization; for each level except the first level, a feedback error of the level is obtained according to error change of a previous level of the level; and a multi-level error feedback vector is obtained according to feedback errors of each level.

[0086] When performing first quantization, the set feedback error is zero, as shown in formula (10).

[0087]

[0088] The set feedback error is obtained as the multi-level error feedback vector.

[0089] When performing non-first quantization, a feedback error of a first level is obtained according to a difference between target time series data to be compressed and corresponding reconstructed data at last quantization, as shown in formula (11).

[0090]

[0091] For each level except the first level, a feedback error of a higher level captures error change of a previous level, a feedback error of the level is obtained according to error change of a previous level of the level, as shown in formula (12).

[0092]

[0093] A multi-level error feedback vector is obtained according to feedback errors of each level.

[0094] In this embodiment, a multi-level error feedback vector is obtained according to feedback errors of each level, which can effectively track and correct error accumulation at different time scales, thereby significantly improving data quantization accuracy.

[0095] In one embodiment, the method provided by the application further includes: capturing a boundary data change event through an event-driven mechanism to obtain target power grid boundary data; performing change identification on the target power grid boundary data according to the valid boundary data snapshot to obtain a set of changed boundary data; and obtaining an updated boundary data snapshot according to the set of changed boundary data.

[0096] To ensure the timeliness and accuracy of the boundary data snapshot in the full power grid model, the event-driven mechanism can be used to efficiently capture power grid boundary data changes from multiple source heterogeneous external data sources, that is, to capture boundary data change events, to quickly complete data collection, verification, processing and dynamic updating, and to ensure agile response to high-frequency real-time data and stable operation.

[0097] In the data acquisition link, through the event-driven architecture, combined with modern data middleware technologies such as Kafka message queue, real-time active monitoring and data pushing of multiple heterogeneous data sources such as dispatching control system, energy management system and new energy prediction system can be realized.

[0098] After capturing the boundary data change event, the boundary data corresponding to the boundary data change event can be obtained as the target grid boundary data. Through the intelligent change identification module, the target grid boundary data can be changed according to the effective boundary data snapshot, and the changed boundary data set is obtained; the changed boundary data set identified is dynamically written into the rolling cache structure based on the sliding time window, and the cache mechanism can automatically cover the expired boundary data snapshot and eliminate invalid information, so as to maintain the latest and compactness of the boundary data snapshot.

[0099] In this embodiment, the boundary data change event is captured through the event-driven mechanism, and the target grid boundary data is obtained to obtain the updated boundary data snapshot, which can realize the boundary data snapshot update in seconds or even shorter time scale, and meet the strict requirements of real-time review and dispatching optimization of the power spot market.

[0100] In one embodiment, according to the effective boundary data snapshot, the target grid boundary data is changed to obtain the changed boundary data set, and the specific steps are as follows: according to the boundary data full snapshot closest to the boundary data change event occurrence time and the boundary data incremental snapshot between the boundary data full snapshot generation time and the boundary data change event occurrence time, the effective boundary data snapshot is obtained; the effective boundary data snapshot and the target grid boundary data are compared at the field level to obtain the changed boundary data set.

[0101] According to the boundary data full snapshot closest to the boundary data change event occurrence time and the boundary data incremental snapshot between the boundary data full snapshot generation time and the boundary data change event occurrence time, the effective boundary data snapshot is obtained.

[0102] The effective boundary data snapshot and the target grid boundary data are compared at the field level to perform field-level accurate comparison. Through event type classification (such as planned modification, measurement data update, prediction adjustment) and timestamp and version number verification, the actual change range of the boundary data is accurately determined, redundant information is filtered, and subsequent processing procedures are only executed for real changed boundary data, effectively reducing system load and improving overall response efficiency.

[0103] The effective boundary data snapshot can be defined as C(τ t-1 ), and the target grid boundary data is d t , and the field-level change judgment is as shown in formula (13).

[0104] Ad t = d t -C(τ t-1 ) (13)

[0105] wherein Ad t represents the change boundary data.

[0106] For each field i, let the change threshold be ∈ i , then the change boundary data set is as shown in equation (14).

[0107]

[0108] Only the fields in are subsequently processed, reducing invalid updates.

[0109] In this embodiment, according to the full-quantity snapshot of the boundary data closest to the time when the boundary data change event occurs, and the incremental snapshot of the boundary data between the time when the full-quantity snapshot of the boundary data is generated and the time when the boundary data change event occurs, an effective boundary data snapshot is obtained; a field-level comparison is performed on the effective boundary data snapshot and the target power grid boundary data, and a relatively accurate change boundary data set can be obtained.

[0110] As shown in Figure 2 , the present application provides a power grid boundary data processing system, which comprises a unified data access layer, a compression processing module, a version snapshot management module, a multi-dimensional index and query module, and an event-driven update module. The unified data access layer supports multi-protocol access and standardized analysis from various dispatch boundary data sources of the power grid (such as actual output, prediction data, device parameters, planning instructions, etc.), serving as the data entrance of the system. The compression processing module dynamically selects algorithms such as difference encoding, sliding average, and quantization compression according to data characteristics and business scenarios, and performs adaptive compression on the accessed data to reduce storage load. The compressed data is synchronously transmitted to the version snapshot management module, which generates multi-granularity time snapshots using a sliding window and incremental storage strategy, and realizes traceable management of historical states through a version control mechanism. The index and query module constructs a multi-dimensional index system of time, device, region, etc., and supports users to quickly query historical boundary data or trace back to any point snapshot on demand. The event-driven update module listens to key dispatch events (such as load prediction update, device switching, etc.) in real time, and automatically triggers the refresh, anomaly detection, and cleaning process of related boundary data. The modules work collaboratively through standard interfaces, forming a unified closed-loop data management system, ensuring high compression rate, low latency, and strong consistency of the system.

[0111] As shown in Figure 3As shown, the present application adopts a boundary data adaptive compression strategy when compressing standard boundary data. This strategy is an intelligent decision-making mechanism designed to flexibly select the optimal compression algorithm to balance storage efficiency and data accuracy in response to the diversity and dynamic changes of standard boundary data. First, the input standard boundary data is classified by type, which is divided into two categories: measured data and predicted data. For measured data, the rate of change within the sliding time window is calculated to assess the stability and volatility of the data. When the rate of change is low, lossless compression algorithms such as difference encoding or Zstandard are preferred to ensure data integrity and high-precision storage. If the rate of change is high, the strictness of data accuracy based on current business is judged. For strong consistency requirements, lossless compression is continued, while for scenarios that allow some loss of accuracy, lossy compression strategies such as lossy encoding or adaptive quantization encoding are selected to effectively reduce storage space. For predicted data, the error tolerance and actual business requirements are combined to intelligently decide whether to use high-precision lossless compression or adopt lossy methods such as segmented compression and quantization compression to adapt to the uncertainty characteristics of new energy prediction data. The entire process supports dynamic adjustment of compression strategies, and the final decision is fed back to the compression processing module to achieve closed-loop optimization and continuous improvement, effectively improving the overall performance and application adaptability of power grid boundary data management.

[0112] As shown, Figure 4 The sliding window rolling update mechanism details the key steps and collaboration process between modules in the process of receiving, processing, and storing power grid boundary data. First, external data sources continuously push boundary data change events to the event-driven module, which serves as the entry point of the entire update process and is responsible for capturing and delivering the latest data change signals in real time. After receiving the data, the event-driven module transfers the target power grid boundary data to the data verification module for multi-level data integrity and validity verification, including format verification, range detection, and timestamp consistency confirmation. During the verification process, the exception handling module intervenes to automatically correct abnormal data (such as interpolation, filtering) or mark it as abnormal. If the exception is serious, it triggers a manual review process to ensure data quality and system stability.

[0113] The target grid boundary data that passes the check is notified to the version control module by the event-driven module, which is responsible for maintaining the version number system and incremental dependency relationship, ensuring that each boundary data snapshot update forms a unique and ordered version chain. Subsequently, the updated boundary data snapshot that has passed version confirmation is written to the sliding window cache structure. The sliding window is bounded by a fixed length, automatically covering the oldest boundary data snapshot and eliminating expired data, ensuring that the latest and continuous time series data is always retained in the cache. While ensuring high-speed read and write, the cache data is synchronized in real time to the underlying persistent storage system, achieving safe storage and long-term retention of data. The system supports multi-thread concurrent writing, and the sliding window and version control module work closely together to achieve synchronous updating and version consistency in high-frequency data flow environments. At the same time, the query module quickly indexes the required boundary data snapshot through the version control module, requests historical boundary data snapshots from the storage system, and supports second-level historical version query and operation review. The overall mechanism ensures an end-to-end closed loop from data generation, verification, version management, cache maintenance to storage query, efficiently handling the high-frequency updating and multi-version management needs of grid boundary data, and achieving real-time, stability and reliability of the system.

[0114] The hierarchical storage architecture for processing of grid boundary data is shown in Figure 5 The organization and flow of boundary data in different storage levels in the system are depicted to meet the diverse needs of grid dispatch for data timeliness and historical traceability. The system first receives and caches grid boundary data change events from multiple heterogeneous data sources in the real-time layer. The real-time layer uses high-speed memory database or memory caching technology to support millisecond-level write and query response, ensuring that the latest boundary data can be quickly captured and processed in real time. Data in the real-time layer is continuously updated and maintained by a sliding window mechanism with a fixed length of time series, ensuring the time series integrity and continuity of data. Over time, part of the data in the real-time layer is gradually sunk to the short-term layer according to the preset time window rules.

[0115] The short-term layer is mainly responsible for storing grid boundary data in the last few days to weeks, using high-performance time series databases or fast-access solid state drives (SSD) for storage, which not only ensures fast retrieval of data, but also achieves a certain degree of storage optimization. The short-term layer not only saves complete version snapshots and differential increments, but also supports multi-dimensional index structures for dispatch review and short-term analysis. The data in the short-term layer is received from the real-time layer through periodic batch or incremental writing, and according to the data access frequency and historical value, some data is filtered and migrated to the long-term layer.

[0116] The long-term tier handles the archiving of massive amounts of historical boundary data, typically employing large-capacity, low-cost hard disk drives or cloud storage solutions. The long-term tier archives data in monthly, quarterly, or even annual units, with compressed and optimized storage formats to reduce space consumption, and efficient indexed metadata supports cross-period historical queries. Access to long-term tier data has relatively high latency, primarily serving non-real-time scenarios such as in-depth analysis, scheduling review, and market research. The overall tiered storage architecture, through the organic collaboration of the real-time, short-term, and long-term tiers, achieves a balance between high-speed response and massive storage, effectively supporting the full lifecycle management and multi-scenario application needs of power grid boundary data.

[0117] Event-driven mechanisms (also known as event-driven dynamic data update and cleansing mechanisms) such as Figure 6 The diagram illustrates in detail how the system efficiently updates and ensures the quality of power grid boundary data after receiving key events such as adjustments to the power grid market plan and equipment switching. The process begins with the multi-source heterogeneous data interface capturing boundary data change events via message middleware. These events are then pushed to the system's event processing module, which categorizes and identifies them, determining the scope and priority of data to be updated based on the event type. Subsequently, the system pulls or receives the latest boundary data from the corresponding data source, entering the intelligent change identification phase. This phase accurately compares the old and new data, filters redundant updates, and ensures that subsequent processing is only performed on genuine changes, reducing system load. Next, the updated data is written to a sliding window-based rolling cache. The caching mechanism automatically overwrites expired information, ensuring data timeliness and continuity. During this process, an anomaly detection module is activated in parallel, using statistical thresholds, business rules, and machine learning models to monitor data quality and identify anomalous data such as mutations, missing values, and logical conflicts. For minor anomalies, the system automatically triggers a data cleaning process, including interpolation correction and smoothing filtering, to ensure data continuity and accuracy. For severe anomalies, alarms are generated and the system initiates a manual review process. After data cleaning is completed, the updated boundary data snapshot is synchronously pushed to the version snapshot management and multi-dimensional indexing modules, enabling version control and efficient retrieval of boundary data snapshots, supporting subsequent scheduling simulation and market analysis. This mechanism achieves closed-loop automated management from event triggering to data cleaning and updating, significantly improving the real-time performance, accuracy, and intelligent level of power grid boundary data and system operation.

[0118] Multidimensional index structure and fast historical data query process, as follows Figure 7As shown, the data index system based on key dimensions such as time, region, and equipment is displayed, as well as its corresponding retrieval path. This structure takes the time dimension as the main line, covering multiple time granularities such as scheduling period, clearing period, and running period, forming a multi-layer time index to support fast positioning of data within any time range. The region dimension index classifies boundary data by region according to the geographical division or administrative region of the power grid, facilitating cross-regional data comparison and selection. The equipment dimension index classifies data into specific equipment levels according to the control objects in the power grid, such as generator units, AC / DC lines, and new energy prediction units, to meet the needs of special analysis. Through the joint use of these three-dimensional indexes, query requests can accurately match the corresponding time period, geographical region, and equipment, significantly improving data retrieval efficiency and accuracy, meeting the needs of second-level response and historical review. The overall structure adopts a tag-based storage mode combined with time series database technology to ensure that the index is closely coupled with the actual data, enabling efficient and flexible multi-condition filtering and aggregation queries to support various application scenarios such as dispatch simulation, market analysis, and abnormality tracking.

[0119] In order to better understand the above method, the following detailed application embodiment of a power grid boundary data processing method of the present application can be applied to a power grid boundary data processing system.

[0120] With the rapid development of the electricity spot market, the system dispatching mode is undergoing a profound transformation. From the past plan-driven to the data-driven based dynamic regulation and strategy optimization. Under this trend, the power grid dispatching operation highly depends on the accurate review of historical operating status, high-frequency simulation analysis based on actual boundary conditions, and intelligent model calculation supporting predictive decision-making. The prerequisite for achieving the above functions is to build a unified boundary data management system that covers the entire power grid range and can be dynamically updated and traced back. Among them, the power grid boundary data refers to the data set used to define the input and output boundary conditions of the control model, covering key control parameters such as unit output plan and dispatching instructions of the dispatching / provincial dispatching, real-time power flow across the AC / DC section, renewable energy power prediction and actual output, market clearing results, bidding curves, and reserve plans. These data not only come from a wide range of sources and have heterogeneous formats, but also have high update frequency and fast state changes, and are the basic data set supporting operation simulation and market behavior review.

[0121] With the large-scale access of new energy and the increasing complexity of load characteristics, the power grid operating status on the time axis shows obvious high-frequency characteristics. Especially in the spot market environment, the power grid boundary state is frequently disturbed by market clearing, prediction correction, dispatching strategy adjustment, etc., and related boundary data often needs to be updated at a second or even millisecond level. This puts high demands on data storage efficiency, update mechanism, and historical traceability.

[0122] However, the traditional power grid boundary data processing system has many limitations and has been difficult to meet the actual needs of full-grid simulation and replay tasks. First, in terms of storage mechanism, the traditional system mostly uses a fixed-cycle full-amount storage strategy, lacks a sliding window or incremental compression mechanism, resulting in high redundancy and high storage cost of high-frequency data (such as new energy output and real-time power flow), and low efficiency of reading long-term historical data, which seriously restricts the model's ability to quickly build and reconstruct scenarios. Second, in terms of dynamic update mechanism, the traditional system generally relies on periodic collection and cannot respond to sudden events such as plan modification and power flow mutation in real time, resulting in data version lag and inconsistent state, which affects the simulation accuracy of the operation scenario based on the data.

[0123] In addition, there are significant deficiencies in multi-dimensional retrieval capabilities. The traditional system can only query linearly by time and cannot support multi-dimensional combined indexing by region + object + time + type. For example, in the replay analysis, users often want to quickly locate "wind power prediction deviation data in a certain region during a certain dispatching period" or "power flow changes at a certain section during a certain clearing period", but the traditional system cannot meet such precise query needs, severely limiting strategy tracing and simulation efficiency.

[0124] More critically, there is currently a lack of systematic version management mechanism. Dispatching decisions often involve multiple version stages (such as original manual plans, market clearing adjustments, and real-time dispatch corrections), and the boundary data corresponding to each stage differs. If the version information cannot be recorded and managed completely, it is difficult to accurately restore the real boundary conditions at that time during the replay, which affects the judgment of the rationality of the dispatching behavior and the evaluation of the optimization strategy. In addition, in terms of data quality assurance, the traditional system lacks the ability to actively identify and correct abnormal data, and problems such as new energy prediction deviation exceeding the limit and real-time power flow mutation are often mistakenly treated as real inputs, causing simulation results to be distorted.

[0125] Therefore, based on traditional technology, it is necessary to propose a power grid boundary data processing method suitable for full-grid simulation scenarios, which has the following capabilities: first, it supports the unified access and abstract modeling of multi-source heterogeneous boundary data; second, it has efficient rolling storage and incremental snapshot functions to improve storage efficiency; third, it has a clear data version control system to support accurate backtracking of the operation state at any time point; fourth, it establishes a multi-dimensional efficient indexing mechanism to achieve second-level response data retrieval; fifth, it integrates data compression and abnormality repair modules to ensure the timeliness, accuracy, and integrity of boundary data. The establishment of such a method will become a core basic capability to support intelligent dispatching, operation simulation, and strategy optimization in the spot market environment.

[0126] The specific steps of the power grid boundary data processing method are as follows:

[0127] Step 1, Unified modeling of grid boundary data:

[0128] The unified boundary data structure is based on the common data sources in dispatching services, including unit output plans in operational control planning systems, real-time measurement values in energy management systems, short-term load and new energy prediction results in prediction systems, etc. The core fields such as node output, section power flow, load level, and new energy output prediction value are extracted and abstracted, and are uniformly mapped to standard boundary data that can be identified and analyzed in the model. To effectively support data backtracking and multi-version management required for dispatch scenario reconstruction and result review, a four-dimensional identification system of "control object-time stamp-data type-version number" is further introduced to achieve accurate identification and version control of each type of standard boundary data. The control object can be a power generation unit, a substation, or a line section; the time stamp can be accurate to the dispatching period or data refresh granularity; the data type can indicate whether the data is a planned value, a measured value, or a predicted value; and the version number is used to distinguish multiple iterative versions of the data at the same time point due to strategy adjustment or algorithm modification. Through this four-dimensional identification system, the system can quickly and accurately locate the required boundary data when performing operation review and real-time control scenario switching, and provide consistent data interfaces for different algorithm modules, thereby improving the reconfigurability and data tracing ability of the overall control process.

[0129] Step 2, Rolling storage mechanism and snapshot construction:

[0130] In this step, boundary data snapshots are periodically constructed according to the set time granularity (e.g., every 1 minute or 5 minutes), and a sliding window structure is used for rolling storage to achieve the coverage of new snapshot data and the automatic elimination of old data. Each snapshot record embeds complete metadata information, including time stamp, data source system identification, dispatch version number, etc., ensuring that historical data is traceable and reproducible, providing a foundation for subsequent simulation calculations or fault backtracking.

[0131] To meet the real-time access requirements of large-scale boundary data such as high-frequency updates, accurate tracing, and low-latency access of time-series grid boundary data in the grid control process, a rolling storage mechanism and snapshot construction method based on a sliding window are proposed. Combined with dynamic data writing, multi-thread concurrent control, and version management, efficient storage and fast response of data are achieved. This mechanism aims to periodically collect and package the state of the entire grid boundary data at a set time granularity (e.g., 1 minute or 5 minutes), construct multi-version and multi-type boundary data snapshots, and ensure stable and controllable system resource occupation in the case of continuous data growth.

[0132] The specific steps of this mechanism are as follows:

[0133] Specifically, the sequence of boundary data snapshots on the time axis can be defined as For:

[0134]

[0135] wherein, denotes the boundary data snapshot at time point t i A complete boundary data snapshot is generated at a certain time interval (e.g. every 1 minute or 5 minutes), containing the boundary data state of all regulated objects at that moment.

[0136] Each boundary data snapshot is defined as an ordered quadruple: denotes the boundary data matrix, recording the multi-type boundary values of each regulated object at that moment; denotes the data source identifier (e.g. running control plan system, energy management system and prediction system); denotes the dispatch version number, used to identify the business scenario corresponding to the boundary data snapshot; denotes the metadata information set, including collection timestamp, data precision level, data label, etc.

[0137] According to actual needs, set the time window length W of the cache (e.g. 24 hours, 7 days), and initialize the sliding window data structure. Pre-allocate cache space and establish a timestamp-based index to support fast positioning and updating. Configure the upper limit of the cache capacity to avoid memory overflow, and set the threshold of the elimination strategy. Capture boundary data change events through an event-driven mechanism, and the system performs validity check and change identification on the received new data items, filtering out the actual changed boundary data set D update . Only perform cache write operations on this set to reduce invalid data writing.

[0138] In order to avoid the continuous accumulation of storage burden over time, the system uses a sliding window structure to maintain the data snapshot queue. Set the sliding window length to W, i.e. the system only retains the last W snapshots, and the excess part is automatically eliminated. This process can be expressed as follows:

[0139] This process can be expressed as follows:

[0140]

[0141] wherein, denotes the active boundary data snapshot, denotes the boundary data snapshot at time t N-W+1 .

[0142] When a new boundary data snapshot is generated, the window slides one position forward:

[0143]

[0144] The above mechanism can ensure that the boundary data snapshots of the latest W versions are always maintained, meeting the data integrity required by real-time data analysis, regulation deduction and review, and ensuring that the storage resources are in a controlled state.

[0145] The effective change data D update Write to cache by timestamp t. If the write time exceeds the current window start time t start , start the sliding window to slide forward, and update the window range to

[0146]

[0147] Meanwhile, old data d old outside the window is automatically eliminated.

[0148]

[0149] This method cleans up expired data to release cache space and ensures that the cache always contains the latest data.

[0150] In addition, to support efficient retrieval and multi-source comparison, the boundary data entries in each snapshot are structured and organized through a unified four-dimensional index (regulation object ID, timestamp, data type, version number), forming the following index function:

[0151]

[0152] Where o represents the regulation object, type represents the data type (such as planned value, measured value, predicted value), v represents the scheduling version number, represents the specific boundary data value obtained under this index.

[0153] In this way, when new version data is written, if the version number is updated, the old version is automatically replaced, while the historical version copy is retained for backtracking queries and multi-version comparison, supporting the system to quickly recover to any historical state during review analysis. This snapshot construction and rolling storage mechanism realizes the time series compressed storage, version traceability and consistent access of boundary data, and is an important foundation module for subsequent model invocation, algorithm training and simulation reproduction.

[0154] Multi-thread concurrent write control is adopted, and multi-thread processing is used for cache writing to handle high-frequency data inflow. The Read-Write Lock mechanism is designed to ensure thread safety: read operations are executed concurrently to ensure fast access response; write operations are locked to ensure mutual exclusion of write operations at the same time; and the optimistic lock mechanism is used to reduce lock waiting time and improve concurrency efficiency.

[0155] Further, in order to improve the storage efficiency of boundary data in the full grid model, while ensuring the reconstruction accuracy of key timing information, the system introduces an adaptive data compression algorithm framework, dynamically selects the appropriate compression strategy according to the time-varying characteristics and business sensitivity of the boundary data, and thus realizes the best balance between data integrity and real-time performance under limited storage space.

[0156] For different types of boundary data, dynamically match the compression strategy to realize intelligent selection and adjustment of the compression mode. Based on the volatility, trend and error tolerance characteristics of the data itself, this method flexibly adopts multiple compression methods including time moving average, difference encoding and segmented compression to achieve the optimal balance between data accuracy and storage cost.

[0157] Specifically, for scheduling instructions, control boundaries, actual output and other data, although the data redundancy is high, but due to its high data accuracy requirements in simulation backtracking, control boundary consistency checking, fine optimization scheduling and other applications, in addition to being used for metering backtracking and fine scheduling control, lossless compression algorithm can be selected first to ensure the accuracy without loss and improve the compression rate, and to avoid the deletion or abnormal judgment of key period details. For wind power / photovoltaic prediction data and high-frequency real-time power flow measurement, lossy compression algorithm is used. Real-time power flow measurement data fluctuates violently, has high sampling frequency, and contains a large amount of short-term disturbance and redundant information. Using lossy compression algorithm can remove invalid high-frequency fluctuations within the allowed accuracy error range, significantly improve compression efficiency and storage performance, while still retaining the main trend characteristics to meet the engineering accuracy requirements of simulation analysis and scheduling review, and significantly reduce the storage and transmission burden on the premise of ensuring the retention of key fluctuation characteristics. New energy prediction data fluctuates violently and has high uncertainty, and its main use is trend identification and power evaluation, which allows some information loss within the control error range. For the high uncertainty characteristics of new energy prediction data, a lossy quantization compression algorithm based on dynamic error feedback mechanism can be selected to achieve efficient compression by using non-uniform quantization and adaptive step adjustment, taking into account data compression rate and accuracy retention.

[0158] The adaptive data compression algorithm framework adopts the following segmented compression strategy:

[0159]

[0160] Where X represents the standard boundary data to be compressed, X i represents one segment of the standard boundary data to be compressed, represents the standard boundary data at time t i For each segment of standard boundary data X iAccording to the trend of the changes, a compression strategy (lossy compression, lossless compression or no compression) is selected, and the segment header information and compression type metadata are recorded to realize flexible decoding.

[0161] In addition, meta information is added to each piece of compressed data record, including time range, data source system, data version number (Version ID), and compression strategy type (such as delta compression, quantized compression, and raw compression).

[0162] The adaptive data compression algorithm framework can be formally represented as:

[0163]

[0164] where x t represents the data to be compressed, C represents the selected compression algorithm, represents the set of candidate compression algorithms, E represents the compression error, R represents the storage rate, and λ represents the compression accuracy weight coefficient, which can be automatically adjusted based on the data characteristics to dynamically select the optimal compression strategy C * .

[0165] Considering the high uncertainty of new energy prediction data, an improved adaptive quantization compression algorithm is designed, which takes into account the data compression rate and accuracy preservation. Based on the dynamic error feedback mechanism, the algorithm uses non-uniform quantization and adaptive step adjustment to achieve efficient compression.

[0166] Due to the strong volatility of new energy prediction data and the relatively high tolerance to errors, lossy quantization compression strategy can be introduced to reduce the data storage and transmission burden. To improve the compression effect, an improved method is proposed: first, combined with the non-uniform quantization strategy, the distribution characteristics of the prediction data are better adapted, the accuracy of the small fluctuation part is preferentially preserved, and the accuracy requirement of the large fluctuation part is moderately relaxed; second, the error feedback mechanism is introduced to accumulate and correct the error generated in the quantization process, thereby effectively suppressing error propagation and distortion expansion; finally, by dynamically adjusting the quantization step Δt, the accuracy and compression rate in the compression process can be adaptively optimized according to the data characteristics. The specific steps are shown in the above embodiments.

[0167] The algorithm effectively alleviates the gradual accumulation of quantization errors in time series compression and the local deviation caused by sudden anomalies through a multi-level error feedback mechanism, ensuring numerical fidelity during long-term data compression. On this basis, a dynamic quantization step adjustment strategy based on signal-to-noise ratio (SNR) is introduced, enabling the system to adaptively adjust the encoding precision according to the noise level of real-time data, particularly suitable for the high uncertainty and high frequency fluctuation characteristics commonly found in new energy prediction data. At the same time, a time scale weighted error control mechanism is adopted to allocate different weights at multiple time levels such as minutes and hours, and to differentiate and adjust long-term and short-term errors, improving overall stability and prediction availability.

[0168] In terms of compression methods, the system combines difference encoding and entropy encoding to significantly improve data compression rate. Difference processing can remove trend components, and entropy encoding can further approach the theoretical optimal encoding length, meeting the storage resource constraints of new energy data at a second-level update frequency. The overall algorithm structure is clear, with low computational complexity, making it easy to deploy and implement in existing boundary data collection and control systems. It is a new energy boundary data management solution that balances real-time performance, compression rate, and decoding accuracy.

[0169] Step 3, snapshot version management and differential storage:

[0170] This step introduces a version-oriented boundary data storage strategy. The differences between consecutive snapshots of boundary data are extracted and stored, only recording the differences between the current snapshot and the previous snapshot, significantly saving storage space. At the same time, for key scheduling nodes (such as day-ahead clearing, real-time scheduling, etc.), complete snapshots are retained to support scenario restoration. By integrating version control mechanisms, the change trajectory of each clearing result, plan adjustment, etc. is recorded, enabling controllable snapshot versions and traceable modifications.

[0171] To ensure that multiple scheduling business models in the power grid dispatching system can achieve complete backtracking and stable recovery of power grid boundary data at critical moments, a key scheduling node snapshot mechanism is set up to generate full-amount boundary data snapshots during the running core phase. This mechanism is designed for scheduling business scenarios with high frequency changes and high reliability requirements, particularly for the following key nodes:

[0172] Important scheduling period start and end points: such as day-ahead plan switching to real-time scheduling, whole-hour plan rolling start, etc.; significant scheduling instruction change moments: such as running mode changes, large unit start-stop, interconnection scheme adjustment, etc.; market clearing result publishing moments: such as day-ahead clearing, real-time clearing or adjustment plan publishing nodes.

[0173] At the above key nodes, a complete boundary data snapshot S k is generated, which is defined as the full-amount copy of the boundary data set at the scheduling node t k moment, i.e.:

[0174] S k ={s k,1 ,s k,2 ,…,s k,n} (23)

[0175] Each boundary data snapshot contains not only all boundary data items, but also metadata information, including: snapshot generation timestamp T k , snapshot version number V k , source system identification Source k , snapshot generation trigger type TriggerType k (periodic / event-driven).

[0176] The overall boundary data snapshot structure Snapshot k can be formally represented as:

[0177] Snapshot k =(S k ,T k ,V k ,Source k ,TriggerType k ) (24)

[0178] In order to balance data integrity and storage efficiency, the snapshot mechanism combines the composite strategy of periodic trigger and event-driven trigger:

[0179] The specific event trigger function Trigger(t) can be formally defined as:

[0180]

[0181] Where ΔD(t) represents the scheduling instruction change amplitude, θ D represents the set threshold, and ΔP learning (t) represents whether the market clearing power has changed.

[0182] Once the boundary data snapshot is generated, it is immediately written to a high-reliability storage structure and hung in a version control chain in a chain structure as an anchor point for all incremental version recovery after this time. If it is necessary to roll back to any version V j in the future, the efficient restoration can be achieved through the following process:

[0183]

[0184] Where S j represents the boundary data snapshot at time j, and ΔS i represents the incremental boundary data snapshot at time j.

[0185] To meet the high requirements of the power grid dispatching system for power grid boundary data, such as traceability, rollback and auditability, a version control mechanism is provided in the application to systematically manage all key data changes. The mechanism is developed around four core functions, namely version identification, change tracking, dependency maintenance and historical reproduction, to ensure high consistency and controllability of data management under the background of frequent updates of power grid boundary data and highly dynamic scenarios.

[0186] Unified version identification management: Each update of power grid boundary data triggers automatic generation of a version number, forming a unique version identification. The version number adopts a multi-level coding format (such as year-dispatching period-change sequence number), facilitating version organization and quick positioning across time and scenarios. The identification runs through the entire data processing process, ensuring version consistency in each processing link.

[0187] Change record log mechanism: A detailed operation log is generated for each update of power grid boundary data, recording information such as timestamp, operation user identification (ID) or system source, modified data item identification, old and new value summaries, modification reason or dispatch trigger event label. The change log is stored in a chain structure and strongly associated with the corresponding version number, forming a complete data evolution chain to provide a reliable foundation for abnormal tracing and dispatch analysis.

[0188] Version dependency and incremental recovery mechanism: The dependency graph between boundary data snapshot versions and incremental data is maintained, and each target version can be represented as a boundary data snapshot plus several increments: this structure ensures that when the historical running state needs to be restored, the boundary data can be efficiently and accurately reconstructed through "snapshot + incremental playback".

[0189] Version switching and data restoration interface: To facilitate the use of dispatchers, market analysis personnel and system self-diagnosis modules, a standardized version switching interface is provided, supporting one-key switching to any historical version, automatically loading the corresponding snapshot and incremental sequence, and allowing the data state at a specific time point to be restored, improving the flexibility and accuracy of dispatch scenario review.

[0190] In summary, this version control mechanism improves the timeliness, integrity, and traceability of data management through structured modeling and procedural management, significantly enhancing the robustness and operation transparency of new energy dispatching systems in a changing operating environment. The differential storage strategy combining incremental storage and snapshots has significant advantages over traditional full storage methods. This strategy not only significantly reduces storage capacity requirements, especially in scenarios with high-frequency updates of boundary data, but also significantly improves data write and read efficiency, supporting real-time updates and historical rollback operations at the second level. In addition, this scheme supports flexible historical version management, meeting the diverse needs of dispatch review and optimization analysis, while being compatible with multiple heterogeneous data structures, facilitating the integration of boundary data from different systems, and greatly enhancing the applicability and expansion capability of the system.

[0191] Step 4, Multi-dimensional Indexing and Efficient Retrieval Mechanism:

[0192] A multi-dimensional joint indexing system is constructed, including time, region, control object, and version number as four core dimensions. The time dimension covers different dispatching periods, clearing periods, and operating periods, supporting multi-granularity time range queries; the region dimension is based on geography and administrative division, enabling cross-regional data positioning; the control object dimension is subdivided into units, lines, and new energy units, facilitating specialized analysis of specific objects; and the version number dimension ensures the accuracy of data review. The flexible combination of multi-dimensional indexes meets the needs of precise data positioning and rapid response in complex query scenarios.

[0193] In terms of underlying data management, an advanced time series database is used as the storage engine, taking full advantage of its optimized design for time series data. The time series database has high-throughput batch write capability, fast retrieval based on time, automatic compression and multi-level aggregation functions, and rich support for tag queries. The time, region, and control object information in the multi-dimensional index are mapped to the tags and fields of the time series database, enabling efficient filtering and aggregation queries in multiple dimensions, significantly improving overall read-write performance and system scalability.

[0194] In addition, considering the multi-time scale characteristics of power grid dispatching and market operation data, the system supports a multi-period indexing mechanism, including dispatching period (minute level), clearing period (market node), and operating period (hour, day level) indexing, allowing flexible switching between different time granularity perspectives. Combined with parallel query execution, incremental index dynamic updating, and caching mechanisms, the system achieves second-level response to complex joint queries. This retrieval framework is widely applicable to real-time operation review, market strategy optimization, and new energy fluctuation analysis, greatly improving the intelligence and refinement level of power grid boundary data management.

[0195] Step 5, Real-time Dynamic Update Mechanism:

[0196] This step realizes data linkage update with external systems through the introduction of an event-driven data refresh mechanism. The system can actively listen to boundary data update events (such as plan modification, measurement refresh, prediction adjustment, etc.), and trigger a rolling cache update process after identifying data changes. At the same time, abnormal detection is performed on the accessed data, and once a mutation value or missing item is identified, data cleaning and quality verification processes are automatically triggered to ensure that the boundary data input into the system is complete, accurate, and real-time.

[0197] To ensure the timeliness and accuracy of boundary data snapshots in the full grid model, an event-driven mechanism can be used to efficiently capture changes in grid boundary data from multiple heterogeneous external data sources, i.e., capture boundary data change events, quickly complete data collection, verification, processing, and dynamic update, and ensure agile response and stable operation for high-frequency real-time data.

[0198] In the data collection link, an event-driven architecture can be used in combination with modern data middleware technologies such as Kafka message queues to achieve real-time active listening and data pushing for multiple heterogeneous data sources such as dispatching control systems, energy management systems, and new energy prediction systems.

[0199] After capturing the boundary data change event, the corresponding grid boundary data of the boundary data change event can be obtained as the target grid boundary data. Through an intelligent change identification module, the target grid boundary data can be identified based on valid boundary data snapshots, and a set of changed boundary data can be obtained. The identified set of changed boundary data is dynamically written into a rolling cache structure based on a sliding time window, and the cache mechanism can automatically overwrite expired boundary data snapshots and eliminate invalid information, maintaining the latest and compactness of the boundary data snapshots.

[0200] To ensure the high quality of boundary data snapshots, an automatic anomaly detection and cleaning module is set up, which uses statistical thresholds, machine learning anomaly detection models, and power system business rules to automatically identify anomalies such as data mutations, missing values, and logical conflicts (such as over-limit power flow and negative output). For minor anomalies, the module uses interpolation, smoothing filtering, and other algorithms for automatic correction, significantly reducing the impact of anomalies on subsequent model simulation and dispatching decisions. For serious abnormal data, a manual review process is triggered to ensure the reliability and accuracy of the overall data chain. At the same time, the module continuously detects data integrity and time series continuity to ensure seamless connection and overall consistency of the boundary data stream.

[0201] Statistical anomaly detection is based on the distribution of data in a sliding window. The mean value μ i and the standard deviation σ i of field i in time window W are defined as:

[0202]

[0203] wherein d k,i represents target grid boundary data.

[0204] Abnormality determination condition:

[0205] |d t,i -μ i |>ασ i (28)

[0206] wherein α represents an abnormality threshold coefficient.

[0207] Automatic cleaning using interpolation or smoothing function Correcting abnormal values:

[0208]

[0209] wherein d′ t,i represents corrected target grid boundary data.

[0210] In summary, the event-driven mechanism has the advantages of low delay, high reliability, intelligence, and efficient use of resources, and can realize second-level or even shorter time scale boundary data updating, meeting the strict requirements of real-time review and dispatch optimization of the power spot market. This mechanism is compatible with multi-source heterogeneous data, flexibly adapts to various data interfaces and formats, effectively guarantees the timeliness and data quality of the whole grid boundary data, and is the key technical support and core guarantee for the digital transformation, intelligent dispatch and accurate market operation review of the power system.

[0211] The above-mentioned grid boundary data processing method has the following innovations:

[0212] (1) Unified modeling mechanism for boundary data of multi-source heterogeneous systems: A four-element identification system of 'control object-time stamp-data type-version number' is introduced to realize semantic fusion and structural unification of multi-source high-frequency boundary data in the dispatching system. (2) Rolling snapshot mechanism controlled by sliding window: The sliding window structure is used to generate data snapshots periodically, and the snapshot length is controlled by time granularity, effectively balancing data real-time and storage resource overhead. (3) Differentiated storage + snapshot version chain mechanism: To solve the content redundancy problem between snapshot data, incremental differential storage and key point complete snapshot retention strategy are introduced to improve snapshot storage efficiency and data tracing ability. (4) Fast historical data retrieval mechanism based on multi-dimensional index structure: High-performance index structure is constructed according to time, region, control object and other dimensions, combined with time series database optimization, to realize second-level query and accurate traceability of historical boundary data. (5) Event-driven data updating and abnormality checking mechanism: Kafka / Webhook and other methods are used to access heterogeneous system event streams, and abnormality detection and cleaning are performed at the same time of data updating, to ensure the accuracy and reliability of boundary data.

[0213] The processing method of the power grid boundary data realizes the standardized management of multi-source heterogeneous data by constructing a unified boundary data model and a four-dimensional identification system, greatly improves the consistency and fusibility of the data, and facilitates cross-system coordination and joint analysis. Secondly, the rolling storage mechanism and the sliding window snapshot construction can effectively control the storage size, avoid data redundancy, ensure that the system always maintains the latest boundary data set, and meet the second-level data update demand. At the same time, the combination of snapshot version management and differential storage strategy realizes efficient data storage and access, supports fast historical version backtracking and scene recovery, and improves the accuracy and efficiency of dispatching simulation and operation review. In addition, the fusion of multi-dimensional index and time series database technology significantly improves the data retrieval speed and query flexibility, supports complex multi-condition filtering and multi-granularity time window analysis, and meets the diversified data access demand in power grid dispatching and market operation. Finally, the real-time dynamic update mechanism is designed based on event-driven, realizes agile response and efficient processing of external boundary data changes, and cooperates with automatic cleaning and checking of abnormal data, to ensure the accuracy and stability of the system data. The present application effectively solves the delay, redundancy and backtracking problems of boundary data management in the current dispatching system, and provides a solid technical support for intelligent dispatching, accurate review and spot market analysis.

[0214] It should be understood that, although each step in the flowchart involved in each embodiment as described above is shown in sequence according to the direction of the arrow, these steps are not necessarily executed in sequence according to the direction of the arrow. Unless otherwise specified herein, there is no strict order limitation for the execution of these steps, and these steps can be executed in other orders. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least part of other steps or steps or stages in other steps. The steps in different embodiments can be freely combined as needed, and various non-contradictory schemes formed by the combination are within the scope of protection of the present application.

[0215] In an exemplary embodiment, as shown in Figure 8 The present application embodiment provides a power grid boundary data processing device, wherein:

[0216] The standard boundary data acquisition module 801 is configured to acquire multi-time multi-source heterogeneous power grid boundary data, and map each power grid boundary data into standard boundary data.

[0217] The multi-dimensional identifier determination module 802 is configured to determine a multi-dimensional identifier of each standard boundary data according to the regulation object and the scheduling version of the power grid boundary data.

[0218] The compressed boundary data acquisition module 803 is configured to compress the standard boundary data corresponding to the same multi-dimensional identifier at different time points according to a compression algorithm matched with the data characteristics of the standard boundary data, to obtain compressed boundary data corresponding to the same multi-dimensional identifier.

[0219] The boundary data snapshot acquisition module 804 is configured to determine the compressed boundary data of the same scheduling version according to the multi-dimensional identifier of the compressed boundary data, to obtain a boundary data snapshot of a corresponding scheduling version and store the boundary data snapshot into a snapshot database. The metadata information of the boundary data snapshot includes a snapshot identifier code, a snapshot generation timestamp and a scheduling version.

[0220] The retrieval module 805 is configured to, when receiving a query request, determine a first boundary data snapshot set in the snapshot database according to the corresponding relationship between the region identifier code and the snapshot identifier code, the region identifier code information carried by the query request and the snapshot identifier code included in the metadata information of the boundary data snapshot; determine a second boundary data snapshot set in the first boundary data snapshot set according to the time information and the scheduling version information carried by the query request and the snapshot generation timestamp and the scheduling version included in the metadata information of the boundary data snapshot; and determine target standard boundary data in the second boundary data snapshot set according to the corresponding relationship between the region identifier code and the regulation object, to form a query result.

[0221] In one of the embodiments, the compressed boundary data acquisition module 803 is further configured to: obtain a multi-level error feedback vector according to error distributions of different scales; superimpose feedback errors of each layer in the multi-level error feedback vector according to weights, to obtain a multi-level error feedback superimposition vector; correct the standard boundary data corresponding to the same multi-dimensional identifier at different time points according to the multi-level error feedback superimposition vector, to obtain standard boundary correction data; dynamically adjust a quantization step according to the noise level of each time point of the standard boundary data corresponding to the same multi-dimensional identifier at different time points; quantize the standard boundary correction data according to the quantization step, to obtain a standard boundary quantization sequence; and obtain the compressed boundary data corresponding to the same multi-dimensional identifier according to the standard boundary quantization sequence.

[0222] In one of the embodiments, the compressed boundary data obtaining module 803 is further configured to: when the data characteristic of the standard boundary data is that the data redundancy is higher than the redundancy threshold and the error tolerance is lower than the tolerance threshold, compress the standard boundary data corresponding to the same multi-dimensional identifier at different time points according to a lossless compression algorithm to obtain the compressed boundary data corresponding to the same multi-dimensional identifier; when the data characteristic of the standard boundary data is that the data volatility is higher than the volatility threshold and the error tolerance is higher than the tolerance threshold, compress the standard boundary data corresponding to the same multi-dimensional identifier at different time points according to a lossy compression algorithm to obtain the compressed boundary data corresponding to the same multi-dimensional identifier; and when the data characteristic of the standard boundary data is that the data volatility is higher than the volatility threshold, the uncertainty characteristic is higher than the uncertainty characteristic threshold, and the error tolerance is higher than the tolerance threshold, compress the standard boundary data corresponding to the same multi-dimensional identifier at different time points according to a lossy quantization compression algorithm to obtain the compressed boundary data corresponding to the same multi-dimensional identifier.

[0223] In one of the embodiments, the compressed boundary data obtaining module 803 is further configured to: when performing the first quantization, obtain a set feedback error as the multi-level error feedback vector; when performing the non-first quantization, obtain a feedback error of a first level according to a difference between the target time sequence data to be compressed and the corresponding reconstructed data at the last quantization; for each level except the first level, obtain a feedback error of the level according to an error change of a previous level of the level; and obtain the multi-level error feedback vector according to the feedback errors of the levels.

[0224] In one of the embodiments, the device further comprises a boundary data snapshot updating module configured to: capture a boundary data change event through an event-driven mechanism to obtain target power grid boundary data; perform change identification on the target power grid boundary data according to an effective boundary data snapshot to obtain a changed boundary data set; and obtain an updated boundary data snapshot according to the changed boundary data set.

[0225] In one of the embodiments, the boundary data snapshot updating module is further configured to: obtain an effective boundary data snapshot according to a boundary data full snapshot closest to a time point at which the boundary data change event occurs and a boundary data incremental snapshot between a time point at which the boundary data full snapshot is generated and the time point at which the boundary data change event occurs; and perform field-level comparison between the effective boundary data snapshot and the target power grid boundary data to obtain the changed boundary data set.

[0226] The above-mentioned modules in the power grid boundary data processing device can be realized by software, hardware, or a combination thereof. The above-mentioned modules can be embedded in or independent of a processor in a power grid boundary data processing system in hardware form, or stored in a memory in the power grid boundary data processing system in software form, so as to be called and executed by a processor to perform the operations corresponding to the above-mentioned modules.

[0227] In an embodiment, a processing system of grid boundary data is also provided, comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above method embodiments when executing the computer program.

[0228] In an embodiment, a computer readable storage medium is provided, storing a computer program, which, when executed by a processor, implements the steps in the above method embodiments.

[0229] In an embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps in the above method embodiments.

[0230] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0231] A person of ordinary skill in the art can understand that all or part of the processes in the above method embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above method embodiments. Each technical feature of the above embodiments can be combined arbitrarily. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application. The above embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be considered as a limitation on the scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, some modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of the present application should be subject to the appended claims.

Claims

1. A method of processing grid boundary data, characterized by, The method comprises: acquiring multi-time multi-source heterogeneous power grid boundary data, and mapping each power grid boundary data to standard boundary data; determining multi-dimensional identifiers of each standard boundary data according to a regulation object and a dispatch version of the power grid boundary data; compressing the standard boundary data corresponding to the same multi-dimensional identifier at different times according to a compression algorithm matched with a data characteristic of the standard boundary data, to obtain compressed boundary data corresponding to the same multi-dimensional identifier; determining compressed boundary data of the same dispatch version according to the multi-dimensional identifiers of the compressed boundary data, to obtain a boundary data snapshot of a corresponding dispatch version and store the boundary data snapshot in a snapshot database; metadata information of the boundary data snapshot comprises a snapshot identifier code, a snapshot generation timestamp and a dispatch version; when a query request is received, determining a first boundary data snapshot set in the snapshot database according to a corresponding relationship between a region identifier code and a snapshot identifier code, region identifier code information carried by the query request and the snapshot identifier code included in the metadata information of the boundary data snapshot; determining a second boundary data snapshot set in the first boundary data snapshot set according to time information and dispatch version information carried by the query request and the snapshot generation timestamp and the dispatch version included in the metadata information of the boundary data snapshot; determining target standard boundary data in the second boundary data snapshot set according to a corresponding relationship between the region identifier code and the regulation object, to form a query result.

2. The method of claim 1, wherein, The method comprises: when the data characteristic of the standard boundary data is that data redundancy is higher than a redundancy threshold and error tolerance is lower than a tolerance threshold, compressing the standard boundary data corresponding to the same multi-dimensional identifier at different times according to a lossless compression algorithm, to obtain compressed boundary data corresponding to the same multi-dimensional identifier; when the data characteristic of the standard boundary data is that data volatility is higher than a volatility threshold and error tolerance is higher than a tolerance threshold, compressing the standard boundary data corresponding to the same multi-dimensional identifier at different times according to a lossy compression algorithm, to obtain compressed boundary data corresponding to the same multi-dimensional identifier; when the data characteristic of the standard boundary data is that data volatility is higher than a volatility threshold, uncertainty characteristic is higher than an uncertainty characteristic threshold and error tolerance is higher than a tolerance threshold, compressing the standard boundary data corresponding to the same multi-dimensional identifier at different times according to a lossy quantization compression algorithm, to obtain compressed boundary data corresponding to the same multi-dimensional identifier.

3. The method of claim 2, wherein, The method comprises: obtaining a multi-level error feedback vector according to error distribution of different scales; superimposing feedback errors of each layer in the multi-level error feedback vector according to weights, to obtain a multi-level error feedback superimposition vector; correcting the standard boundary data corresponding to the same multi-dimensional identifier at different times according to the multi-level error feedback superimposition vector, to obtain standard boundary correction data; According to the noise level of each time of the standard boundary data corresponding to the same multi-dimensional identifier at different times, a quantization step is dynamically adjusted; According to the quantization step, the standard boundary correction data is quantized to obtain a standard boundary quantization sequence; According to the standard boundary quantization sequence, the compressed boundary data corresponding to the same multi-dimensional identifier is obtained.

4. The method of claim 3, wherein, According to the error distribution of different scales, a multi-level error feedback vector is obtained, including: When performing the first quantization, a set feedback error is obtained as the multi-level error feedback vector; When performing the non-first quantization, a first level feedback error is obtained according to the difference between the target time sequence data to be compressed and the corresponding reconstructed data at the last quantization; For each level except the first level, a feedback error of the level is obtained according to the error change of the previous level of the level; According to the feedback error of each level, a multi-level error feedback vector is obtained.

5. The method of claim 1, wherein, The method further includes: A boundary data change event is captured through an event-driven mechanism to obtain target power grid boundary data; According to the effective boundary data snapshot, the target power grid boundary data is changed to obtain a set of changed boundary data; According to the set of changed boundary data, an updated boundary data snapshot is obtained.

6. The method of claim 1, wherein, According to the effective boundary data snapshot, the target power grid boundary data is changed to obtain a set of changed boundary data, including: According to the boundary data full snapshot closest to the time when the boundary data change event occurs and the boundary data incremental snapshot between the time when the boundary data full snapshot is generated and the time when the boundary data change event occurs, an effective boundary data snapshot is obtained; The effective boundary data snapshot and the target power grid boundary data are compared at the field level to obtain a set of changed boundary data.

7. An apparatus for processing grid boundary data, characterized by The device includes: A standard boundary data acquisition module for acquiring multi-time multi-source heterogeneous power grid boundary data, and mapping each power grid boundary data to standard boundary data; A multi-dimensional identifier determination module for determining the multi-dimensional identifier of each standard boundary data according to the control object and the dispatching version of the power grid boundary data; A compressed boundary data acquisition module for compressing the standard boundary data corresponding to the same multi-dimensional identifier at different times according to a compression algorithm matching the data characteristics of the standard boundary data to obtain compressed boundary data corresponding to the same multi-dimensional identifier; A boundary data snapshot acquisition module for determining the compressed boundary data of the same dispatching version according to the multi-dimensional identifier of the compressed boundary data to obtain the boundary data snapshot of the corresponding dispatching version and store it in the snapshot database; The metadata information of the boundary data snapshot includes a snapshot identifier code, a snapshot generation timestamp and a dispatching version; The retrieval module is configured to, when receiving a query request, determine a first boundary data snapshot set in the snapshot database according to a correspondence between a region identifier and a snapshot identifier, region identifier information carried by the query request, and snapshot identifier included in metadata information of the boundary data snapshot; determine a second boundary data snapshot set from the first boundary data snapshot set according to time information and scheduling version information carried by the query request, and snapshot generation time stamp and scheduling version included in the metadata information of the boundary data snapshot; and determine target standard boundary data in the second boundary data snapshot set according to a correspondence between a region identifier and a regulation and control object, so as to form a query result.

8. A system for processing grid boundary data, comprising a memory and a processor, the memory storing a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 6. The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 6.

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