A grid code driven power grid data resource multi-dimensional management method and system

Abstract

The application discloses a kind of grid code driven power grid data resource multidimensional management method and system, it is related to power grid data management related technical field, method includes: for the partition planning of grid division granularity to preset power grid area, generate grid code encoding result;Obtain the generation characteristics of multidimensional power grid data type in preset power grid area;Execute time dimension, spatial dimension and equipment dimension on hierarchical relationship analysis, establish multidimensional data hierarchical relationship;Combined with grid code encoding result and multidimensional data hierarchical relationship constructs multidimensional hierarchical index large model;With multidimensional hierarchical index large model executes the storage and call management of the power grid data resource of preset power grid area.It solves the technical problems that power grid data multidimensional characteristics is difficult to be efficiently managed, time, spatial, equipment dimension data processing is dispersed, query management efficiency and accuracy is low in the prior art, achieves the technical effect of improving the accuracy and efficiency of power grid data resource integration management.

Description

A Grid Code-Driven Multidimensional Management Method and System for Power Grid Data Resources Technical Field

[0001] This application relates to the technical field of power grid data management, specifically to a grid code-driven multidimensional management method and system for power grid data resources. Background Technology

[0002] With the widespread application of numerous smart devices, sensors, and IoT technologies in the power grid, the data generated during power grid operation is experiencing explosive growth. This data not only covers real-time monitoring data from all aspects of transmission, substation, and distribution, but also includes equipment lifecycle information and user electricity consumption behavior data. Traditional power grid data management methods often process multi-dimensional data such as time, space, and equipment in isolation, making it difficult to efficiently handle the correlation analysis between high-frequency real-time data and historical data. They also lack precise location and management of geographically distributed data, resulting in an inability to effectively establish data correlations between different dimensions. This makes it difficult to meet the requirements of modern power grids for efficient utilization and precise control of data resources, impacting the accuracy and efficiency of multi-dimensional integration and management of power grid data.

[0003] Therefore, current technologies face technical challenges such as the difficulty in efficiently managing the multi-dimensional characteristics of power grid data, the fragmented processing of data across time, space, and equipment dimensions, and low efficiency and accuracy in query management. Summary of the Invention

[0004] This application provides a grid code-driven multidimensional management method and system for power grid data resources, which solves the technical problems in the prior art of inefficiently managing the multidimensional characteristics of power grid data, the scattered processing of time, space and equipment dimensions of data, and the low efficiency and accuracy of query management. It achieves the technical effect of improving the accuracy and efficiency of power grid data resource integration and management.

[0005] This application provides a grid code-driven multi-dimensional management method for power grid data resources. The method includes: performing grid-level partitioning planning for a preset power grid area to generate grid code encoding results; obtaining multi-dimensional power grid data type generation features within the preset power grid area, including time, space, and equipment dimensions; performing hierarchical relationship analysis on the time, space, and equipment dimensions based on the power grid data type generation features to establish multi-dimensional data hierarchical relationships; constructing a multi-dimensional hierarchical index model by combining the grid code encoding results and the multi-dimensional data hierarchical relationships; and using the multi-dimensional hierarchical index model to perform storage and retrieval management of power grid data resources in the preset power grid area.

[0006] In a possible implementation, the grid code-driven multidimensional management method for power grid data resources further performs the following processing: dividing the preset power grid area into regions based on load density to generate load density partitioning results; dividing the preset power grid area into regions based on power grid equipment distribution density to generate equipment density partitioning results; performing grid granularity partitioning of the preset power grid area using the load density partitioning results and the equipment density partitioning results to generate the grid code encoding results, wherein the partitioned grid granularity is inversely proportional to the load density and the power grid equipment distribution density.

[0007] In a possible implementation, the grid code-driven multi-dimensional management method for power grid data resources further performs the following processing: after aligning the load density partitioning results and the equipment density partitioning results, extracting the first load density and the first equipment density within the first partition; obtaining preset grid unit processor constraints; adapting and partitioning the first load density and the first equipment density into grid unit processing resources based on the preset grid unit processor constraints to generate a first partition grid partitioning result; performing grid unique identifier encoding based on the first partition grid partitioning result to generate a first grid code encoding result, and adding it to the grid code encoding result.

[0008] In a possible implementation, the grid code-driven multi-dimensional management method for power grid data resources further performs the following processing: monitoring the power grid topology for the preset power grid area to determine whether the power grid topology has changed; if so, extracting topology change features to perform a linkage impact analysis of load density and equipment distribution density to determine the target impact area; and performing adaptive dynamic optimization of the grid granularity for the target impact area, using the optimization results to update the grid code encoding results.

[0009] In a possible implementation, the grid code-driven multidimensional management method for power grid data resources further performs the following processing: determining a first intelligent business processing module configured for the preset power grid area; reading the power grid data input requirements of the first intelligent business processing module; analyzing the collaborative relationships of various power grid data types in the time, space, and equipment dimensions based on the power grid data input requirements, generating a first-layer multidimensional relationship corresponding to the first intelligent business processing module; and adding the first-layer multidimensional relationship to the multidimensional data hierarchy relationship.

[0010] In a possible implementation, the grid code-driven multidimensional management method for power grid data resources further performs the following processes: parsing the periodic coordination relationships of various power grid data types in the time dimension based on the power grid data input requirements; parsing the location coordination relationships of various power grid data types in the spatial dimension based on the power grid data input requirements; parsing the device coordination relationships of various power grid data types in the device dimension based on the power grid data input requirements; and constructing a multi-level data relationship network using the periodic coordination relationships, the location coordination relationships, and the device coordination relationships to generate the multidimensional data hierarchy relationship.

[0011] In a possible implementation, the grid code-driven multidimensional management method for power grid data resources further performs the following processing: performing hierarchical association of each coded grid based on the multidimensional data hierarchy to generate multi-level association relationships; and performing index model construction based on the multi-level association relationships to generate the multi-dimensional hierarchical index large model.

[0012] In a possible implementation, the grid code-driven multidimensional management method for power grid data resources further performs the following processing: the multidimensional hierarchical index model is constructed based on the multidimensional data hierarchy relationship, and each level corresponds to a data index interface that connects to the outside.

[0013] In a possible implementation, the grid code-driven multidimensional management method for power grid data resources further performs the following processing: identifying the encirclement relationship of power grid subsystems for power grid equipment within the preset power grid area; constructing an encirclement relationship index layer for the power grid subsystems based on the encirclement relationship; and optimizing the multidimensional hierarchical index model.

[0014] This application also provides a grid code-driven multi-dimensional power grid data resource management system, the system comprising: a partitioning planning module, used for performing grid-level partitioning planning for a preset power grid area and generating grid code encoding results; a power grid data feature acquisition module, used for acquiring multi-dimensional power grid data type generation features within the preset power grid area, the multi-dimensional features including time dimension, spatial dimension, and equipment dimension; a hierarchical relationship analysis module, used for performing hierarchical relationship analysis on the time dimension, spatial dimension, and equipment dimension based on the power grid data type generation features, and establishing multi-dimensional data hierarchical relationships; a hierarchical index large model construction module, used for constructing a multi-dimensional hierarchical index large model by combining the grid code encoding results and the multi-dimensional data hierarchical relationships; and a power grid data resource management module, used for performing storage and retrieval management of power grid data resources in the preset power grid area using the multi-dimensional hierarchical index large model.

[0015] This application proposes a grid-code-driven multi-dimensional management method and system for power grid data resources. The method involves: partitioning a pre-defined power grid area using grid granularity to generate grid code encoding results; acquiring multi-dimensional power grid data type generation characteristics within the pre-defined power grid area; performing hierarchical relationship analysis across time, space, and equipment dimensions to establish multi-dimensional data hierarchical relationships; constructing a large-scale multi-dimensional hierarchical index model based on the grid code encoding results and the multi-dimensional data hierarchical relationships; and using this large-scale multi-dimensional hierarchical index model to manage the storage and retrieval of power grid data resources within the pre-defined power grid area. This addresses the technical problems in existing technologies, such as the difficulty in efficiently managing the multi-dimensional characteristics of power grid data, the fragmented processing of time, space, and equipment-dimensional data, and the low efficiency and accuracy of query management. The goal is to improve the accuracy and efficiency of integrated management of power grid data resources. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 is a schematic flowchart of a grid code-driven multidimensional management method for power grid data resources provided in an embodiment of this application.

[0018] Figure 2 is a schematic diagram of the structure of a grid code-driven multidimensional management system for power grid data resources provided in an embodiment of this application.

[0019] Figure labeling: 10 for zoning planning module, 20 for power grid data feature acquisition module, 30 for hierarchical relationship analysis module, 40 for hierarchical index large model construction module, and 50 for power grid data resource management module. Detailed Implementation

[0020] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below.

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0023] This application provides a grid code-driven multi-dimensional management method for power grid data resources, as shown in Figure 1. The method includes:

[0024] Step S100: Perform grid division granularity partitioning planning for the preset power grid area and generate grid code encoding results.

[0025] Preferably, the preset power grid area may be an urban power supply area or an industrial park power supply area, so as to centrally manage and analyze the power grid data within this area. Then, the preset power grid area is divided into grid-based partitioning. Specifically, the preset power grid area is divided into multiple smaller grid units, each with a unique grid code to accurately identify and distinguish different grid units or partitions, similar to dividing a map into multiple small squares, each of which is a grid. This allows for more detailed management and analysis of power grid data, improving the accuracy and efficiency of data processing. Granularity refers to the coarseness of the grid division. If the granularity is fine, the grid units are smaller, which can more accurately locate and manage various elements in the power grid, but may also increase the complexity and cost of data processing. If the granularity is coarse, the grid units are larger. Although data processing is relatively simple, some detailed information may be lost. For example, for an urban power supply network, if the grid is divided by street, the granularity is relatively coarse; if it is divided by each building or each distribution substation, the granularity is fine.

[0026] Preferably, the entire preset power grid area is then rationally partitioned, including combining and dividing various grid units into different regions for better data management. For example, based on the power grid topology, power supply range, and equipment type, the preset power grid area is divided into multiple partitions, each containing several grid units. The partitioned grid units are then encoded to generate grid code results. For instance, a combination of numbers and letters can be used to assign a unique code to each grid unit, such as "01A001" representing the first grid unit in the first partition. This facilitates the location, querying, and management of various elements within the power grid, improving the efficiency and accuracy of power grid data management.

[0027] Furthermore, step S100 also includes step S110, dividing the preset power grid area into regions based on load density to generate load density partitioning results; step S120, dividing the preset power grid area into regions based on power grid equipment distribution density to generate equipment density partitioning results; and step S130, performing grid granularity partitioning of the preset power grid area using the load density partitioning results and the equipment density partitioning results to generate the grid code encoding results, wherein the partitioned grid granularity is inversely proportional to the load density and the power grid equipment distribution density.

[0028] Preferably, by calculating and analyzing the load density at various locations within a preset power grid area, areas with similar load densities are grouped into one region. Load density refers to the amount of electricity load per unit area, usually measured in units such as kilowatts per square kilometer or megawatts per square kilometer, reflecting the degree of electricity demand at different locations within the preset power grid area. For example, areas with high load density, such as urban central business districts, are grouped into one region, while areas with relatively low load density, such as urban suburbs, are grouped into another region, and so on, generating load density zoning results.

[0029] Preferably, the distribution of power grid equipment within a preset power grid area is statistically analyzed, and the area is divided according to the different equipment distribution densities. Here, the power grid equipment distribution density refers to the number of power grid equipment (such as transformers, distribution boxes, poles, etc.) per unit area, indicating the distribution of power grid equipment within the preset power grid area. For example, areas with dense substations have high equipment distribution density and are divided into one area; while remote areas have relatively few power grid equipment and low equipment distribution density and are divided into another area. This results in equipment density zoning, which helps to effectively manage the operation and maintenance of power grid equipment and allocate resources.

[0030] Preferably, the grid granularity is determined by comprehensively considering the load density zoning results and the equipment density zoning results. The grid granularity is inversely proportional to the load density and the distribution density of power grid equipment. This means that in areas with high load density and high power grid equipment distribution density, the grid division will be more detailed and the grid granularity will be smaller. For example, in urban commercial areas, where the load density is high and the power grid equipment is relatively dense, each block or even each large building may be divided into a grid unit with a smaller granularity. In areas with low load density and equipment distribution density, such as rural areas on the outskirts of cities, the grid division will be relatively coarse and the grid granularity will be larger. After completing the grid granularity zoning, each grid unit is assigned a unique code, i.e., a grid code, which can contain information such as the grid's location and the zoning it belongs to. This enables the quick and accurate location and identification of each grid unit, facilitating the management and analysis of power grid data.

[0031] Furthermore, step S130 also includes step S131, after aligning the load density partitioning result and the equipment density partitioning result, extracting the first load density and the first equipment density within the first partition; step S132, obtaining preset grid cell processor constraints; step S133, based on the preset grid cell processor constraints, performing grid cell processing resource adaptation partitioning on the first load density and the first equipment density to generate the first partition grid partitioning result; step S134, based on the first partition grid partitioning result, performing grid unique identifier encoding to generate the first grid code encoding result, and adding it to the grid code encoding result.

[0032] Preferably, since load density zoning and equipment density zoning are regional divisions based on different indicators (load density and equipment distribution density), the boundaries and ranges of the divided regions may be inconsistent. Therefore, location alignment is performed, that is, the two zoning results are matched geographically to make them correspond in location. Then, one of the zoning regions is selected as the first zoning region, and the load density and equipment density data within this region are extracted, including the first load density and the first equipment density. Here, the first load density refers to the average load density value within the first zoning region, and the first equipment density refers to the number of grid devices per unit area within the zoning region, reflecting the power demand and equipment distribution of the region. Preset grid unit processor constraints are obtained. Preset grid unit processor constraints refer to the pre-set limitations on grid unit processing resources (such as computing power, storage capacity, etc.) based on actual hardware device performance, management strategies, etc. For example, it is stipulated that the processor computing power of each grid unit cannot exceed a threshold, or that each grid unit has a threshold upper limit on storage capacity, etc., used to guide the adaptation and partitioning of grid unit processing resources.

[0033] Preferably, based on the extracted first load density and first equipment density data, combined with preset grid unit processor constraints, the first partition is further divided into grid units, and the processing resource allocation for each grid unit is determined. Specifically, areas with high load density and high equipment density usually require more processing resources to process related data (such as real-time monitoring data, equipment operating status data, etc.). Therefore, based on data characteristics and processor constraints, the first partition is reasonably divided into multiple grid units, and appropriate processing resources are allocated to each grid unit. For example, in areas with both high load density and high equipment density, smaller grid units may be divided, and more computing and storage resources may be allocated to them to meet the data processing needs, generating the first partition grid division result, clarifying the boundaries, locations, and allocated processing resources of each grid unit within the first partition; finally, each grid unit is assigned a unique identifier (grid code), and the grid unit's location, partition, processing resources, and other information are encoded into the grid code to generate the first grid code encoding result, which is then added to the grid code encoding result, thereby improving the efficiency and accuracy of power grid data management.

[0034] Furthermore, step S100 also includes step S140, performing grid topology monitoring on the preset grid area to determine whether the grid topology has changed; step S150, if so, extracting topology change features to perform linkage impact analysis of load density and equipment distribution density to determine the target impact area; step S160, performing adaptive dynamic optimization of the grid granularity on the target impact area, and updating the grid code encoding result with the optimization result.

[0035] Preferably, the power grid topology is used to describe the connection relationships and layout between various devices in the power grid (such as generators, transformers, transmission lines, switches, etc.). Through various sensors (such as current sensors, voltage sensors, position sensors, etc.) installed on the power grid equipment and communication equipment, the operating status information and connection status information of the devices in the power grid are collected in real time to construct a real-time topology model of the power grid, which intuitively displays the connection status between various devices in the power grid. Then, the real-time acquired power grid topology model is compared with the stable topology model to check whether there are any additions, removals, or changes in connection relationships of devices. For example, when a new transmission line is put into operation or a transformer is disconnected, the topology of the power grid changes. If a difference in the topology is found, it is determined that the power grid topology has changed.

[0036] Preferably, if a change in the power grid topology is determined, a detailed analysis of the specific characteristics of the topology change is conducted, such as changes in the type, location, and connection method of the equipment. Changes in the power grid topology may lead to a redistribution of power flow, thereby affecting the load density in different areas. At the same time, the connection of new equipment or the removal of old equipment will also change the equipment distribution density. This leads to a linked analysis of the impact on load density and equipment distribution density. For example, when a new transmission line is put into operation, it may disperse the load in areas that were originally heavily loaded, thereby reducing the load density in those areas. Simultaneously, the related equipment on the new line will also change the equipment distribution density in those areas. Finally, based on the results of the linked impact analysis, the areas most affected by the topology change are identified, i.e., the target impact areas.

[0037] Preferably, the grid granularity of the target influence area is adaptively adjusted and optimized based on changes in load density and equipment distribution density within the target influence area. Specifically, if load density and equipment distribution density increase, the grid is divided into finer sections, i.e., the grid granularity is reduced, to manage and analyze data more accurately; conversely, if load density and equipment distribution density decrease, the grid granularity is appropriately increased to reduce the complexity of data processing, thereby achieving the optimal processing effect. The optimized results are then used to update the grid code encoding results, i.e., the grid of the target influence area is re-encoded. This includes assigning a new unique identifier (grid code) to each grid unit based on the new grid division, and updating the original grid code encoding results with the new grid code information. This ensures that the latest situation of the target influence area after changes in the power grid topology is accurately reflected, guaranteeing the accuracy and effectiveness of power grid data management.

[0038] Step S200: Obtain multi-dimensional power grid data type generation features within the preset power grid area. The multi-dimensional features include time dimension, spatial dimension, and device dimension.

[0039] Preferably, the generation characteristics of power grid data types within a preset power grid area are obtained from the time, space, and equipment dimensions. Specifically, smart meters, Supervisory Control and Data Acquisition (SCADA) systems, and other devices in the power grid are used to collect power grid operation data, including voltage, current, and power, at certain time intervals (such as minutes or hours). The power grid operation data has a precise timestamp. Time series analysis is used to process the collected time series data. For example, by calculating statistical quantities such as mean, variance, and peak value, the changing trends of the data at different time scales can be analyzed, such as intraday fluctuations, intraweekly changes, and seasonal changes. Fourier transform, wavelet analysis, and other methods can also be used to decompose the time series and extract the characteristics of different frequency components to reveal the periodic and non-periodic characteristics in the data.

[0040] Preferably, GIS technology is used to combine the geographical location information of power grid equipment with power grid data. First, a geospatial model of the preset power grid area is performed, and the location coordinates and line directions of the power grid equipment are entered into the GIS system. Then, through the spatial analysis functions of GIS, such as buffer analysis and overlay analysis, the spatial distribution characteristics of power grid equipment, as well as the spatial characteristics of power grid load density and power supply radius in different areas, are analyzed. According to the geographical area and power grid structure, the preset power grid area is divided into different sub-regions, and the power grid data in each sub-region is statistically analyzed, such as calculating the average load, number of equipment, and line length in the sub-region, in order to understand the distribution differences of power grid data in different spatial regions.

[0041] Preferably, various sensors and monitoring devices are installed on the power grid equipment to collect real-time operational status data, such as transformer oil temperature, winding temperature, and oil chromatography data; circuit breaker opening and closing status, operating time, and contact wear, directly reflecting the equipment's operating condition and health level. Then, the power grid equipment is classified according to its type, function, and specifications. Specifically, key characteristic parameters are extracted for different types of equipment, such as transformer rated capacity, short-circuit impedance, and no-load loss; and transmission line voltage level, line length, and conductor type. Through the analysis and comparison of these characteristic parameters, the data generation characteristics and operating features of different types of equipment can be understood.

[0042] Step S300: Based on the power grid data type, perform hierarchical relationship analysis on the time dimension, spatial dimension and equipment dimension of the feature execution to establish a multi-dimensional data hierarchical relationship.

[0043] Preferably, by conducting in-depth analysis of the type characteristics of power grid data in the time, spatial, and equipment dimensions, the hierarchical structure and relationships of data within and between each dimension are obtained. Specifically, the changes in power grid data at shorter time scales such as minutes and hours are analyzed, such as instantaneous load fluctuations and short-term voltage fluctuations, which are usually related to factors such as users' instantaneous electricity consumption behavior and temporary equipment failures. Then, the changing trends of power grid data at longer time scales such as days, months, and years are analyzed, such as seasonal changes in load and aging trends of power grid equipment. At the same time, the operation of the power grid has different time cycles, such as daily, weekly, and annual cycles. Analyzing the nesting relationships between different time cycles, such as the changing pattern of the daily load curve within a week and the changing pattern of the weekly load curve within a year, helps to deeply understand the periodic characteristics of power grid load and the mutual influence between different cycles.

[0044] Preferably, the system analyzes the power grid data characteristics of the specific locations of substations, transmission lines, distribution substations, and other equipment in the power grid, as well as their surrounding areas. This includes data such as the load distribution of a single substation and the loss status of a transmission line. Then, it analyzes the overall spatial layout and operation of the pre-defined power grid area, including the total load distribution and power supply reliability within the area. Through this analysis from the local to the overall perspective, a comprehensive understanding of the power grid's spatial operation is achieved. Next, based on geographical regions and power grid structural characteristics, the pre-defined power grid area is divided into different sub-regions. The hierarchical relationships between these sub-regions are analyzed, such as the power supply relationship from higher-level regions to lower-level regions and the power exchange relationship between different sub-regions. This forms a hierarchical spatial structure system, enabling better power grid planning and operation management.

[0045] Preferably, the analysis focuses on the individual components of the power grid equipment, such as the windings, cores, and cooling systems of transformers, and the towers, insulators, and conductors of transmission lines. This analysis examines the operational status data and performance characteristics of these components to understand the internal workings of the equipment. A comprehensive analysis of the entire equipment's operating parameters, functional characteristics, and connections with other equipment is also conducted. This includes assessing the transformer's rated capacity, short-circuit impedance, and its location and role within the power grid. This allows for a holistic understanding of the equipment's operational status and its impact on the grid. Furthermore, the analysis considers the various types of equipment within the power grid, such as power generation equipment, transmission equipment, transformer equipment, and distribution equipment. It analyzes the hierarchical relationships between different types of equipment. For example, power generation equipment provides power to transmission equipment, which then transmits the power to transformer equipment for voltage transformation. The transformer equipment then distributes the power to distribution equipment for user use, clarifying the interrelationships between these devices. Finally, the hierarchical relationship analysis results of the three dimensions of time, space and equipment are integrated to establish a multi-dimensional data hierarchy. At the same time, the load changes of the region at different time scales (time dimension), the spatial distribution characteristics of different sub-regions (spatial dimension) and the operating status of various power grid equipment (equipment dimension) are considered to ensure the refined management and management efficiency of power grid data resources.

[0046] Furthermore, step S300 also includes step S310, determining the first intelligent service processing module configured for the preset power grid area; step S320, reading the power grid data input requirements of the first intelligent service processing module; step S330, analyzing the collaborative relationships of various power grid data types in the time dimension, spatial dimension, and equipment dimension based on the power grid data input requirements, and generating a first-layer multi-dimensional relationship corresponding to the first intelligent service processing module; and step S340, adding the first-layer multi-dimensional relationship to the multi-dimensional data hierarchy relationship.

[0047] Preferably, a first intelligent business processing module for a preset power grid area is configured, integrating functions such as inspection, forecasting, and power grid planning. During operation, it collects and calls various types of power grid data. Specifically, the inspection module needs to acquire real-time equipment operating status data, geographical location information, and timestamps to promptly detect equipment faults and anomalies; the forecasting module needs information on changes in historical load data, meteorological data, and other time-series data, as well as spatial information such as load distribution in different regions, to predict future electricity demand; the power grid planning module needs equipment-dimensional information such as equipment layout and capacity, as well as spatial-dimensional information such as regional development plans and time-dimensional information such as long-term electricity growth trends.

[0048] Preferably, for the identified first intelligent business processing module, its specific input requirements for power grid data are obtained, including determining what types of power grid data are needed, and the specific requirements of these data in dimensions such as time, space, and equipment. For example, the inspection module requires real-time acquisition of the current operating status data of the equipment (immediacy in the time dimension), accurate location of each piece of equipment (precise positioning in the spatial dimension), and various specific equipment parameters (detailed information in the equipment dimension); the prediction module needs historical load data from the past few years (long-term data in the time dimension), load data divided by region (regional classification in the spatial dimension), and data related to power generation, transmission, and distribution of different types of equipment (classified data in the equipment dimension).

[0049] Preferably, based on the power grid data input requirements, the collaborative relationships of various power grid data types in the time, spatial, and equipment dimensions are analyzed, and a first-level multi-dimensional relationship corresponding to the first intelligent business processing module is generated. For the prediction module, the combination of historical load data in the time dimension and regional load distribution data in the spatial dimension can analyze the changing trends of load in different regions over time, thereby predicting the future load demand in different regions. The collaborative relationship between power generation equipment capacity data in the equipment dimension, power generation plan data in the time dimension, and power distribution data in the spatial dimension can predict the power supply capacity in different time periods and regions. Finally, the first-level multi-dimensional relationship corresponding to the first intelligent business processing module is integrated into the multi-dimensional data hierarchy relationship, that is, the data relationship required by the intelligent business processing module is integrated with the multi-dimensional data hierarchy relationship of the entire preset power grid area, making the multi-dimensional data hierarchy relationship richer and more complete, thereby helping to better understand and manage power grid data as a whole.

[0050] Furthermore, step S300 also includes step S350, analyzing the periodic coordination relationship of various power grid data types in the time dimension based on the power grid data input requirements; step S360, analyzing the location coordination relationship of various power grid data types in the spatial dimension based on the power grid data input requirements; step S370, analyzing the equipment coordination relationship of various power grid data types in the equipment dimension based on the power grid data input requirements; and step S380, performing multi-level data relationship network construction based on the periodic coordination relationship, the location coordination relationship, and the equipment coordination relationship to generate the multi-dimensional data hierarchy relationship.

[0051] Preferably, based on the power grid data input requirements, a multi-level data relationship network is constructed by analyzing the collaborative relationships of power grid data types across different dimensions such as time, space, and equipment. This generates multi-dimensional data hierarchical relationships. Specifically, different power grid data may have different periodic characteristics in the time dimension. Based on the power grid data input requirements, the interrelationships of various power grid data types in the time cycle are analyzed. For example, the changing cycle of meteorological data may affect the periodic changes of power grid load data, clarifying the periodic collaborative relationship between meteorological data and power grid load data in the time dimension. Power grid data is spatially related to geographical location. Power grid equipment and power grid operation data in different locations have specific spatial distribution characteristics. Based on the power grid data input requirements, the collaborative relationships of various power grid data types in spatial location are analyzed. For example, the transmission capacity of transmission lines is related to the terrain and landforms along the line, reflecting the locational collaborative relationship between transmission line data and geospatial data.

[0052] Preferably, the power grid consists of numerous different types of equipment, such as generators, transformers, switches, and transmission lines. Each type of equipment has its specific operating parameters and functions. Based on the power grid data input requirements, the collaborative relationships of various power grid data types at the equipment level are analyzed. For example, the output power of a generator needs to be matched with the transformer's voltage transformation capacity and the transmission capacity of a transmission line to ensure that electricity can be effectively transmitted from the generation end to the consumption end, reflecting the collaborative relationships of related data from different equipment at the equipment level. Based on the analyzed periodic collaborative relationships, location collaborative relationships, and equipment collaborative relationships, a multi-level data relationship network is constructed. This involves integrating data relationships at different dimensions and then organizing and dividing them according to different dimensions, granularities, and business needs, generating multi-dimensional data hierarchical relationships. This better supports the management and analysis of power grid data and ensures the efficiency and accuracy of power grid data management.

[0053] Step S400: Construct a multi-dimensional hierarchical index model by combining the grid code encoding results and the multi-dimensional data hierarchy relationship.

[0054] Step S400 further includes step S410, performing hierarchical association of each coding grid based on the multi-dimensional data hierarchy relationship to generate a multi-level association relationship; step S420, performing index model construction based on the multi-level association relationship to generate the multi-dimensional hierarchical index large model.

[0055] Preferably, by combining the grid code encoding results with multi-dimensional data hierarchical relationships, a large-scale multi-dimensional hierarchical index model capable of comprehensively and efficiently managing and utilizing power grid data is established. Specifically, the multi-dimensional data hierarchical relationships include data relationships in terms of time dimension, spatial dimension, and equipment dimension. In the time dimension, there is power grid operation data at different time scales, such as minute-level, hour-level, and day-level data. In the spatial dimension, the power grid area is divided through grid codes, and different grids have different geographic spatial attributes and power grid characteristics. In the equipment dimension, various types of power grid equipment and their interrelationships are covered. Then, each coded grid is associated with the corresponding data in the multi-dimensional data hierarchical relationship. For example, for a certain coded grid, the corresponding power grid operation data at different time points (time dimension), the power grid equipment information within the grid (equipment dimension), and its geographic location and surrounding environment information (spatial dimension) are found. This forms a multi-level association relationship, where each coded grid is a node in this network, interconnected through multi-dimensional data relationships.

[0056] Preferably, a suitable index structure is designed based on multi-level relationships to reflect the relationships between different dimensions and between coded grids. For example, a composite index can be designed, including multiple parts such as grid code index, time index, and device index. The grid code index is used to quickly locate a specific grid; the time index can organize data according to time order or time interval, facilitating the querying of data in different time periods; and the device index targets different types and numbers of devices, enabling the quick retrieval of data related to specific devices. Using the designed index structure, data in the multi-level relationships is organized and stored to form an index model. Grid data is arranged according to rules to enable efficient data retrieval and querying. For example, a tree structure or graph structure can be used to represent the index model, where nodes represent different coded grids or data elements, and edges represent the relationships between them. This transforms the complex multi-level relationships into an operable index model. Finally, optimization and expansion are performed, including reducing data redundancy, improving index query efficiency, and enhancing the stability and scalability of the model. Ultimately, a large-scale multi-dimensional hierarchical index model is formed, capable of adapting to the storage and management needs of large-scale grid data and responding quickly and accurately to various query requests.

[0057] Furthermore, step S420 also includes that the multi-dimensional hierarchical index model is constructed based on the multi-dimensional data hierarchy relationship, and each level corresponds to a data index interface that connects to the outside.

[0058] Preferably, the multi-dimensional hierarchical index model organizes and stores data according to multi-dimensional data hierarchy relationships during construction. For example, time data at different levels is arranged and indexed in chronological order, enabling rapid location when querying data within a specific time range. In the spatial dimension, spatially related data is organized into corresponding levels based on grid code encoding results and regional divisions. For the device dimension, various device attributes and operational data are integrated according to device hierarchy relationships. The data index interface serves as the channel for data interaction between the multi-dimensional hierarchical index model and external applications. It is primarily used to quickly access and retrieve power grid data at corresponding levels within the model based on requirements. Each level corresponds to a data index interface, making data access more flexible and efficient. Different external applications may require data at different dimensions and levels, and can directly connect to the relevant level's interface to quickly obtain the required power grid data. Furthermore, when adding new external applications or expanding the functionality of existing applications, only the corresponding level's data index interface needs to be configured according to their requirements, without requiring large-scale modifications to the entire model, thus providing a convenient, efficient, and secure data access mode.

[0059] Furthermore, step S420 also includes step S421, identifying the encirclement relationship of the power grid subsystem for the power grid equipment in the preset power grid area; and step S422, constructing an encirclement relationship index layer of the power grid subsystem based on the encirclement relationship, and optimizing the multi-dimensional hierarchical index model.

[0060] Preferably, the power grid system can be divided into multiple subsystems based on function, equipment type, or geographical region. Then, the power grid equipment within the preset power grid area is analyzed to identify protection relationships, i.e., to determine their affiliation and the inclusion or being-included relationships between different power grid subsystems. For example, equipment within a substation (such as transformers and circuit breakers) belongs to the transmission system of that substation, while multiple substations may be included in a larger regional transmission system. By identifying the enclosure relationships between equipment and subsystems, the hierarchical structure and composition of the power grid can be clearly identified. Then, based on the identified enclosure relationships of the power grid subsystems, an enclosure relationship index layer is constructed, which can be represented using a data structure (such as a tree structure or graph structure). In a tree structure, the root node represents the entire preset power grid area, and the child nodes sequentially represent different levels of power grid subsystems, progressively subdividing from higher-level subsystems to lower-level subsystems. The connections between nodes represent enclosure relationships. The enclosing relationship index layer is mainly used to manage and query the relationships between power grid subsystems and the ownership of related equipment. In other words, through the enclosing relationship index layer, it is possible to quickly locate which subsystem a certain device belongs to, and which devices and subordinate subsystems a certain subsystem contains.

[0061] Preferably, the enclosing relationship index layer of the power grid subsystems is integrated into the multi-dimensional hierarchical index model. This allows the multi-dimensional hierarchical index model to not only include data hierarchical relationships in dimensions such as time, space, and equipment, but also incorporate enclosing relationship information between power grid subsystems. This enables a more comprehensive reflection of the power grid's structure and data relationships. When processing queries involving the affiliation of power grid subsystems and equipment, the optimized multi-dimensional hierarchical index model can more efficiently obtain relevant information. For example, when querying all equipment belonging to a certain power grid subsystem within a certain area, the model can quickly filter out the equipment that meets the conditions using the enclosing relationship index layer, without having to traverse the entire dataset. This allows for better management and analysis of power grid data, improving the efficiency and reliability of power grid data management.

[0062] Step S500: Utilize the multi-dimensional hierarchical index model to perform storage and retrieval management of power grid data resources for the preset power grid region.

[0063] Preferably, the multi-dimensional hierarchical index model has a hierarchical structure encompassing time, space, and equipment. When storing power grid data, the data is classified and organized according to these dimensions. Specifically, in the time dimension, power grid operation data is stored in layers according to different time scales (such as second, minute, hour, day, month, and year). Real-time monitoring data can be stored at the top level of the time dimension (such as second or minute level), while long-term statistical analysis data is stored at lower levels (such as month or year level). In the spatial dimension, power grid data in different grid areas are stored in corresponding spatial levels based on the grid code encoding results. In terms of equipment, relevant data of various power grid equipment (such as generators, transformers, and transmission lines) are stored according to equipment type and hierarchical relationship. Furthermore, the relationships between the dimensions in the multi-dimensional hierarchical index model can be utilized to integrate and store related data. For example, equipment operation data within a certain grid area can be spatially associated with load data and geographic information data for that area; simultaneously, these data are arranged in the time sequence of their generation. Finally, based on factors such as the importance and access frequency of the data, the storage method is optimized. For frequently accessed real-time data or data from critical devices, more efficient storage media and storage structures can be used to improve data reading speed. For some historical data or data with low access frequency, compression storage and other methods can be used to save storage space.

[0064] Preferably, when power grid data needs to be accessed, users can quickly retrieve data from multiple dimensions such as time, space, and equipment based on the index structure provided by the multi-dimensional hierarchical index model. For example, to query the operating data of a certain type of equipment (equipment dimension) within a specific time period (time dimension) and a certain grid area (spatial dimension), users can input the corresponding query conditions through the model's index interface. The model can quickly locate the data storage location that meets the conditions and extract the required data, greatly improving data query efficiency and reducing the time cost of data acquisition. The multi-dimensional hierarchical index model can set different data access permissions for different users or applications, restricting their access to specific dimensions or levels of data according to their roles and responsibilities, ensuring data security and confidentiality, and preventing data from being illegally accessed or misused. During the data retrieval process, the multi-dimensional hierarchical index model can also provide more in-depth data analysis based on the relationships between data. For example, when retrieving load data for a certain region, the model can automatically associate it with equipment operation data, geographic information data, etc. in that region, providing users with a more comprehensive data analysis perspective. Users can use this associated data for load forecasting, equipment fault diagnosis, power grid planning, etc., thereby fully leveraging the model's multi-dimensional and hierarchical advantages to achieve efficient management of power grid data.

[0065] In the preceding text, a grid code-driven multidimensional management method for power grid data resources according to an embodiment of the present invention was described in detail with reference to FIG1. ​​Next, a grid code-driven multidimensional management system for power grid data resources according to an embodiment of the present invention will be described with reference to FIG2.

[0066] According to an embodiment of the present invention, a grid code-driven multi-dimensional power grid data resource management system is used to solve the technical problems existing in the prior art, such as the difficulty in efficiently managing the multi-dimensional characteristics of power grid data, the scattered data processing of time, space, and equipment dimensions, and the low efficiency and accuracy of query management. This system achieves the technical effect of improving the accuracy and efficiency of integrated management of power grid data resources. As shown in Figure 2, the grid code-driven multi-dimensional power grid data resource management system includes: a partitioning planning module 10, a power grid data feature acquisition module 20, a hierarchical relationship analysis module 30, a hierarchical index large model construction module 40, and a power grid data resource management module 50.

[0067] The zoning planning module 10 is used to perform zoning planning at the grid division granularity for a preset power grid area and generate grid code encoding results; the power grid data feature acquisition module 20 is used to acquire multi-dimensional power grid data type generation features within the preset power grid area, including time, space, and equipment dimensions; the hierarchical relationship analysis module 30 is used to perform hierarchical relationship analysis on the time, space, and equipment dimensions based on the power grid data type generation features and establish multi-dimensional data hierarchical relationships; the hierarchical index large model construction module 40 is used to construct a multi-dimensional hierarchical index large model by combining the grid code encoding results and the multi-dimensional data hierarchical relationships; and the power grid data resource management module 50 is used to perform storage and retrieval management of power grid data resources in the preset power grid area using the multi-dimensional hierarchical index large model.

[0068] The specific configuration of the zoning planning module 10 will be described in detail below. The zoning planning module 10 further includes: dividing the preset power grid area into regions based on load density to generate load density zoning results; dividing the preset power grid area into regions based on power grid equipment distribution density to generate equipment density zoning results; performing grid-granularity zoning of the preset power grid area using the load density zoning results and the equipment density zoning results to generate the grid code encoding results, wherein the divided grid granularity is inversely proportional to the load density and the power grid equipment distribution density.

[0069] The specific configuration of the zoning planning module 10 will be described in detail below. The zoning planning module 10 further includes: aligning the load density zoning result and the equipment density zoning result in terms of position; extracting the first load density and the first equipment density within the first zoning zone; obtaining preset grid cell processor constraints; adapting and dividing the first load density and the first equipment density into grid cell processing resources based on the preset grid cell processor constraints to generate a first zoning zone grid division result; performing grid unique identifier encoding based on the first zoning zone grid division result to generate a first grid code encoding result, and adding it to the grid code encoding result.

[0070] The specific configuration of the zoning planning module 10 will be described in detail below. The zoning planning module 10 further includes: performing grid topology monitoring for the preset grid area to determine whether the grid topology has changed; if so, extracting topology change features to perform a linkage impact analysis of load density and equipment distribution density to determine the target impact area; and performing adaptive dynamic optimization of the grid granularity for the target impact area, using the optimization results to update the grid code encoding results.

[0071] The specific configuration of the hierarchical relationship analysis module 30 will be described in detail below. The hierarchical relationship analysis module 30 further includes: determining a first intelligent service processing module configured for the preset power grid area; reading the power grid data input requirements of the first intelligent service processing module; analyzing the collaborative relationships of various power grid data types in the time, spatial, and equipment dimensions based on the power grid data input requirements, generating a first-layer multi-dimensional relationship corresponding to the first intelligent service processing module; and adding the first-layer multi-dimensional relationship to the multi-dimensional data hierarchy.

[0072] The following will describe the specific configuration of the hierarchical relationship analysis module 30 in detail. The hierarchical relationship analysis module 30 further includes: parsing the periodic coordination relationships of various power grid data types in the time dimension based on the power grid data input requirements; parsing the locational coordination relationships of various power grid data types in the spatial dimension based on the power grid data input requirements; parsing the equipment coordination relationships of various power grid data types in the equipment dimension based on the power grid data input requirements; and constructing a multi-level data relationship network using the periodic coordination relationships, the locational coordination relationships, and the equipment coordination relationships to generate the multi-dimensional data hierarchical relationship.

[0073] The specific configuration of the hierarchical index large model construction module 40 will be described in detail below. The hierarchical index large model construction module 40 further includes: performing hierarchical association of each coding grid based on the multi-dimensional data hierarchical relationship to generate multi-level association relationships; and performing index model construction based on the multi-level association relationships to generate the multi-dimensional hierarchical index large model.

[0074] The following will describe in detail the specific configuration of the hierarchical index large model construction module 40. The hierarchical index large model construction module 40 further includes: the multi-dimensional hierarchical index large model is constructed based on the multi-dimensional data hierarchy relationship, and each level corresponds to a data index interface that connects to the outside.

[0075] The following will describe in detail the specific configuration of the hierarchical index large model construction module 40. The hierarchical index large model construction module 40 further includes: identifying the enclosing relationship of the power grid subsystem for the power grid equipment within the preset power grid area; constructing an enclosing relationship index layer of the power grid subsystem based on the enclosing relationship; and optimizing the multi-dimensional hierarchical index large model.

[0076] The grid code-driven multidimensional management system for power grid data resources provided in this embodiment of the invention can execute the grid code-driven multidimensional management method for power grid data resources provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0077] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0078] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A grid code-driven multi-dimensional management method for power grid data resources, characterized in that, include: For a preset power grid area, perform zoning planning with grid granularity and generate grid code encoding results; Obtain multi-dimensional power grid data type generation features within the preset power grid area, including time dimension, spatial dimension and device dimension; Based on the power grid data type, hierarchical relationship analysis is performed on the time, space and equipment dimensions to establish a multi-dimensional data hierarchy; A large multi-dimensional hierarchical index model is constructed by combining the grid code encoding results and the multi-dimensional data hierarchy relationship; The storage and retrieval management of power grid data resources in the preset power grid area are performed using the multi-dimensional hierarchical index model. The process includes: performing zoning planning at a grid granularity for a preset power grid area and generating grid code encoding results; dividing the preset power grid area into regions based on load density to generate load density zoning results; dividing the preset power grid area into regions based on power grid equipment distribution density to generate equipment density zoning results; performing grid granularity zoning of the preset power grid area using the load density zoning results and the equipment density zoning results to generate the grid code encoding results, wherein the divided grid granularity is inversely proportional to the load density and power grid equipment distribution density; after generating the grid code encoding results, the process further includes: performing power grid topology monitoring for the preset power grid area to determine whether the power grid topology has changed; if so, extracting topology change features for load density and equipment distribution. Density linkage impact analysis is performed to determine the target impact area; adaptive dynamic optimization of the grid granularity is applied to the target impact area, and the grid code encoding result is updated based on the optimization result; hierarchical relationship analysis is performed on the time, space, and equipment dimensions based on the power grid data type to establish a multi-dimensional data hierarchy, including: determining the first intelligent business processing module configured for the preset power grid area; reading the power grid data input requirements of the first intelligent business processing module; analyzing the collaborative relationship of various power grid data types on the time, space, and equipment dimensions based on the power grid data input requirements to generate a first-layer multi-dimensional relationship corresponding to the first intelligent business processing module; and adding the first-layer multi-dimensional relationship to the multi-dimensional data hierarchy.

2. The grid code-driven multi-dimensional management method for power grid data resources as described in claim 1, characterized in that, The process of performing grid-granular partitioning of the preset power grid area based on the load density partitioning results and the equipment density partitioning results, and generating the grid code encoding results, includes: aligning the load density partitioning results and the equipment density partitioning results in terms of position, extracting the first load density and the first equipment density within the first partition; obtaining preset grid unit processor constraints; adapting and partitioning the first load density and the first equipment density into grid unit processing resources based on the preset grid unit processor constraints, generating the first partition grid partitioning result; performing grid unique identifier encoding based on the first partition grid partitioning result, generating the first grid code encoding result, and adding it to the grid code encoding result.

3. The grid code-driven multi-dimensional management method for power grid data resources as described in claim 1, characterized in that, Based on the power grid data input requirements, the analysis of the collaborative relationships of various power grid data types in the time, spatial, and equipment dimensions generates the multi-dimensional data hierarchy, including: analyzing the periodic collaborative relationships of various power grid data types in the time dimension based on the power grid data input requirements; analyzing the locational collaborative relationships of various power grid data types in the spatial dimension based on the power grid data input requirements; analyzing the equipment collaborative relationships of various power grid data types in the equipment dimension based on the power grid data input requirements; and constructing a multi-level data relationship network using the periodic collaborative relationships, the locational collaborative relationships, and the equipment collaborative relationships to generate the multi-dimensional data hierarchy.

4. The grid code-driven multi-dimensional management method for power grid data resources as described in claim 1, characterized in that, The construction of a multi-dimensional hierarchical index model by combining the grid code encoding results and the multi-dimensional data hierarchy relationship includes: performing hierarchical association of each encoded grid based on the multi-dimensional data hierarchy relationship to generate multi-level association relationships; and performing index model construction based on the multi-level association relationships to generate the multi-dimensional hierarchical index model.

5. The grid code-driven multi-dimensional management method for power grid data resources as described in claim 4, characterized in that, The multi-dimensional hierarchical index model is constructed based on the multi-dimensional data hierarchy relationship, and each level corresponds to a data index interface that connects to the outside world.

6. The grid code-driven multi-dimensional management method for power grid data resources as described in claim 1, characterized in that, The construction of a multi-dimensional hierarchical index model also includes: identifying the encirclement relationship of power grid subsystems for power grid equipment within the preset power grid area; constructing an encirclement relationship index layer for the power grid subsystems based on the encirclement relationship; and optimizing the multi-dimensional hierarchical index model.

7. A grid code-driven multi-dimensional management system for power grid data resources, characterized in that, The system is used to implement a grid code-driven multi-dimensional management method for power grid data resources as described in any one of claims 1 to 6. The system includes: a partitioning planning module for performing grid-level partitioning planning on a preset power grid area and generating grid code encoding results; a power grid data feature acquisition module for acquiring multi-dimensional power grid data type generation features within the preset power grid area, including time, space, and equipment dimensions; a hierarchical relationship analysis module for performing hierarchical relationship analysis on the time, space, and equipment dimensions based on the power grid data type generation features, and establishing multi-dimensional data hierarchical relationships; a hierarchical index large model construction module for constructing a multi-dimensional hierarchical index large model by combining the grid code encoding results and the multi-dimensional data hierarchical relationships; and a power grid data resource management module for performing storage and retrieval management of power grid data resources in the preset power grid area using the multi-dimensional hierarchical index large model.

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