Live data processing system, method and equipment based on power grid disaster prevention and reduction scene

Abstract

The invention provides a live data processing system, method and device based on a power grid disaster prevention and reduction scene. The system comprises a data monitoring module used for monitoring a meteorological platform and executing data detection in the monitoring process; the data compensation and optimization module is used for supplementing meteorological element grid live data; performing temporal-spatial resolution optimization processing on the meteorological element grid live data according to the disaster type; the meteorological data analysis module is used for performing data analysis on the real-time data of the target meteorological element grid; and the review statistics module is used for performing data statistics on the meteorological data analysis result of the to-be-reviewed meteorological elements in the to-be-reviewed time period by using a statistical model corresponding to the statistical type. According to the method, the problems that meteorological disasters cannot be timely handled due to insufficient forecast data accuracy and delayed live data processing and meteorological data application is not strong can be solved, technical support is provided for disaster prevention and reduction of power grid enterprises, and the method is of great significance to safe operation of a power grid.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a real-time data processing system, method and device based on power grid disaster prevention and mitigation scenarios. Background Art

[0002] The safety of power grids is crucial to social and economic development. Stable power supply is the guarantee for the normal operation of various fields such as industrial production, commercial activities and transportation. Ensuring the safety of power grids is not only the responsibility of the power industry, but also a common concern of the entire society. However, in recent years, global climate change has intensified, and the frequency and intensity of extreme weather events such as El Nino and La Nina have increased. These extreme weather events pose a serious threat to the safe operation of the power grid. For example, storms, high temperatures, cold waves, etc. may cause damage to power grid facilities and power outages, affecting the normal order of social economy.

[0003] To deal with these threats, power grid companies usually rely on traditional disaster prevention and mitigation methods, which include using large-scale weather forecast models to obtain data on future weather, using radar inversion technology to monitor weather changes, and conducting risk assessments based on historical data and experience. These data and models are used to predict the impact that natural disasters may have on the power grid and formulate response strategies accordingly.

[0004] However, traditional methods for disaster prevention and mitigation of power grids have obvious deficiencies in data support capabilities and accuracy. Specifically, due to the limitations of model resolution, the uncertainty of initial data and boundary conditions, and the nonlinear characteristics of complex weather events, weather forecast models may not be able to accurately capture local weather changes that are critical to the safe operation of power grids, resulting in forecast results that are inconsistent with actual conditions; the actual application of meteorological data requires many steps such as data collection, transmission, processing, and display. Due to network delays, bad weather, and other reasons, data transmission may be delayed to a certain extent, and humans cannot process it in time. In addition, data processing also takes a certain amount of time, resulting in power grid companies being unable to obtain the latest weather information in a timely manner. Therefore, it is very necessary to propose a real-time data processing process with faster processing speed and higher applicability. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a real-time data processing system, method and equipment based on the power grid disaster prevention and mitigation scenario, which can overcome the problems of inability to respond to meteorological disasters in a timely manner due to insufficient accuracy of forecast data, delayed processing of real-time data, and weak application of meteorological data, and provide technical support for power grid enterprises in disaster prevention and mitigation, which is of great significance to the safe operation of the power grid.

[0006] In a first aspect, the present invention provides a real-time data processing system based on a power grid disaster prevention and mitigation scenario, including a communication-connected data monitoring module, a data compensation and optimization module, a meteorological data analysis module, and a retrospective statistics module;

[0007] The data monitoring module is used to monitor the designated meteorological platform to pull the real-time data of the meteorological element grid corresponding to the target time point from the designated meteorological platform, and perform data detection during the monitoring process, the data detection includes delay time detection and / or integrity detection;

[0008] The data supplementation and optimization module is used to: in the case of failing data detection, use the site observation data corresponding to the target time point to supplement the meteorological element grid real-time data corresponding to the target time point; and optimize the spatiotemporal resolution of the meteorological element grid real-time data according to the disaster type involved in the power grid disaster prevention and mitigation scenario to obtain the target meteorological element grid real-time data corresponding to the disaster type;

[0009] The meteorological data analysis module is used to: perform data analysis on the actual grid data of the target meteorological element to obtain the meteorological data analysis results of each meteorological element;

[0010] The review statistics module is used to: receive meteorological elements and statistical type selection instructions sent for the time period to be reviewed to determine the meteorological elements and statistical types to be reviewed, use the statistical model corresponding to the statistical type, perform data statistics on the meteorological data analysis results of the meteorological elements to be reviewed within the time period to be reviewed to obtain meteorological data statistical results, and the meteorological data statistical results are applied to power grid disaster prevention and mitigation scenarios.

[0011] In one embodiment, the data monitoring module is specifically used to:

[0012] Taking the target time point as the timing starting point, if the meteorological element grid real-time data corresponding to the target time point is not pulled within the preset time length, it is determined that the delay time detection has not been passed;

[0013] Furthermore, if the size of the received meteorological element grid real-time data is inconsistent with the attribute information carried by the meteorological element grid real-time data, it is determined that the integrity check has not been passed.

[0014] In one embodiment, the data compensation and optimization module includes a data compensation submodule, and the data compensation submodule is used to:

[0015] Get the site observation data of the meteorological stations within the current area;

[0016] Using the target time point and missing meteorological elements as search conditions, the target site observation data is screened out from the site observation data;

[0017] The target site observation data are subjected to quality inspection and grid difference processing, so as to use the processed target site observation data to supplement the actual grid data of meteorological elements corresponding to the target time point.

[0018] In one embodiment, the data compensation and optimization module further includes a data optimization submodule, which is used to:

[0019] For each type of disaster involved in the power grid disaster prevention and mitigation scenario, the temporal interpolation processing is performed on the meteorological element grid real-time data corresponding to the disaster type using the time resolution corresponding to the disaster type, and the spatial interpolation processing is performed on the meteorological element grid real-time data corresponding to the disaster type using the spatial resolution corresponding to the disaster type, so as to achieve the optimization of the temporal and spatial resolution of the meteorological element grid real-time data.

[0020] In one embodiment, the data optimization submodule is specifically used for:

[0021] When the disaster type is a rainstorm disaster or a strong wind disaster, the real-time data of the meteorological element grid corresponding to the rainstorm disaster or the strong wind disaster are interpolated at the minute level;

[0022] When the disaster type is a high temperature disaster or a heavy fog disaster, hourly time interpolation processing is performed on the real-time data of the meteorological element grids corresponding to the high temperature disaster or the heavy fog disaster.

[0023] In one embodiment, the data optimization submodule is further used to:

[0024] When the disaster type is a rainstorm disaster or a strong wind disaster, the real-time grid data of meteorological elements corresponding to the rainstorm disaster or the strong wind disaster are subjected to spatial interpolation processing at the line and tower level;

[0025] When the disaster type is a high temperature disaster or a heavy fog disaster, the real-time grid data of meteorological elements corresponding to the high temperature disaster or the heavy fog disaster are subjected to regional-level spatial interpolation processing.

[0026] In one embodiment, the statistical types include site statistics, regional statistics and time statistics. Regional statistics are divided into one or more of provincial statistics, single-city statistics and multi-city statistics. Time statistics are divided into one or more of hourly statistics, daily statistics, ten-day statistics, monthly statistics and annual statistics.

[0027] In a second aspect, the present invention further provides a method for processing live data based on a power grid disaster prevention and mitigation scenario, the method being applied to the live data processing system based on a power grid disaster prevention and mitigation scenario provided in the first aspect, the method comprising:

[0028] Monitor the designated meteorological platform to pull the real-time data of the meteorological element grid corresponding to the target time point from the designated meteorological platform, and perform data detection during the monitoring process, the data detection including delay time detection and / or integrity detection;

[0029] In the case of data failure, the meteorological element grid real-time data corresponding to the target time point is supplemented with the station observation data corresponding to the target time point; and, according to the disaster types involved in the power grid disaster prevention and mitigation scenario, the meteorological element grid real-time data is optimized in time and space resolution to obtain the target meteorological element grid real-time data corresponding to the disaster type;

[0030] Perform data analysis on the actual grid data of the target meteorological elements to obtain the meteorological data analysis results of each meteorological element;

[0031] Receive meteorological elements and statistical type selection instructions sent for the time period to be reviewed to determine the meteorological elements and statistical types to be reviewed, use the statistical model corresponding to the statistical type to perform data statistics on the meteorological data analysis results of the meteorological elements to be reviewed within the time period to be reviewed to obtain meteorological data statistical results, and apply the meteorological data statistical results to power grid disaster prevention and mitigation scenarios.

[0032] In a third aspect, the present invention further provides an electronic device, comprising a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method provided in the second aspect.

[0033] In a fourth aspect, the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method provided in the second aspect.

[0034] The present invention provides a real-time data processing system, method and device based on a power grid disaster prevention and mitigation scenario, comprising a data monitoring module, a data supplementation and optimization module, a meteorological data analysis module and a retrospective statistics module connected in communication; wherein the data monitoring module is used to: monitor a designated meteorological platform to pull the real-time data of the meteorological element grid corresponding to the target time point from the designated meteorological platform, and perform data detection during the monitoring process, wherein the data detection includes delay time detection and / or integrity detection; the data supplementation and optimization module is used to: in the case of failing the data detection, use the site observation data corresponding to the target time point to supplement the real-time data of the meteorological element grid corresponding to the target time point; and The disaster types involved in the disaster prevention and mitigation scenario are optimized for the spatiotemporal resolution of the meteorological element grid real-time data to obtain the target meteorological element grid real-time data corresponding to the disaster type; the meteorological data analysis module is used to: perform data analysis on the target meteorological element grid real-time data to obtain the meteorological data analysis results of each meteorological element; the review statistics module is used to: receive the meteorological element and statistical type selection instructions sent for the time period to be reviewed to determine the meteorological element and statistical type to be reviewed, and use the statistical model corresponding to the statistical type to perform data statistics on the meteorological data analysis results of the meteorological element to be reviewed in the time period to be reviewed to obtain the meteorological data statistical results, and the meteorological data statistical results are applied to the power grid disaster prevention and mitigation scenario. The above system integrates four core functional modules. Through the collaborative work of the data monitoring module, the data compensation module, the meteorological data analysis module and the review statistics module, a closed-loop data processing process is formed, which can overcome the problems of insufficient accuracy of forecast data, delayed processing of real-time data, and the inability to respond to meteorological disasters in a timely manner, as well as the weak application of meteorological data, and provide technical support for disaster prevention and mitigation of power grid enterprises, which is of great significance to the safe operation of the power grid.

[0035] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0036] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 A schematic structural diagram of a real-time data processing system based on a power grid disaster prevention and mitigation scenario provided by an embodiment of the present invention;

[0039] Figure 2 A detailed structural diagram of a real-time data processing system based on a power grid disaster prevention and mitigation scenario provided by an embodiment of the present invention;

[0040] Figure 3 An example diagram of an interpolation grid provided by an embodiment of the present invention;

[0041] Figure 4 A flowchart of a method for processing real-time data based on a power grid disaster prevention and mitigation scenario provided by an embodiment of the present invention;

[0042] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described in combination with the embodiments below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] At present, traditional power grid disaster prevention and mitigation methods have obvious deficiencies in data support capabilities and accuracy. Based on this, the present invention implements a real-time data processing system, method and equipment based on power grid disaster prevention and mitigation scenarios, which can overcome the problems of inability to respond to meteorological disasters in a timely manner due to insufficient accuracy of forecast data, delayed processing of real-time data, and weak application of meteorological data, and provide technical support for power grid enterprises in disaster prevention and mitigation, which is of great significance to the safe operation of the power grid.

[0045] To facilitate understanding of this embodiment, firstly, a real-time data processing system based on a power grid disaster prevention and mitigation scenario disclosed in an embodiment of the present invention is introduced in detail. Figure 1 The structural diagram of a real-time data processing system based on a power grid disaster prevention and mitigation scenario is shown, including a communication-connected data monitoring module 1, a data compensation and optimization module 2, a meteorological data analysis module 3 and a retrospective statistics module 4.

[0046] In one example, the data monitoring module 1 is used to monitor the designated meteorological platform to pull the meteorological element grid live data corresponding to the target time point from the designated meteorological platform, and perform data detection during the monitoring process. Among them, the target time point can be an hour, such as 1:00, 2:00, 3:00, etc. The meteorological element grid live data can be grid live data corresponding to meteorological elements such as wind, temperature, rainfall, visibility, etc. Data detection includes delay time detection and / or integrity detection, the delay time detection is used to detect whether the meteorological element grid live data corresponding to the target time point is pulled within a preset time length (such as 20 minutes), and the integrity detection is used to detect whether the pulled meteorological element grid live data is complete.

[0047] In one example, the data compensation and optimization module 2 is used to: when the data detection fails, use the site observation data corresponding to the target time point to supplement the meteorological element grid actual data corresponding to the target time point; and, according to the disaster type involved in the power grid disaster prevention and mitigation scenario, optimize the spatiotemporal resolution of the meteorological element grid actual data to obtain the target meteorological element grid actual data corresponding to the disaster type.

[0048] In one embodiment, when the delay time detection and / or integrity detection fails, the current area scope can be determined based on the location of the power grid, and the meteorological station observation data within the current area scope, that is, the site observation data, can be obtained. By screening, quality checking, gridding interpolation and other processing of the site observation data, the processed data can be used to supplement the meteorological element grid real-time data corresponding to the target time point.

[0049] Among them, the disaster types include rainstorm disasters, strong wind disasters, high temperature disasters, fog disasters, etc. In one embodiment, multiple disaster types may have an impact on the safe operation of the power grid, and in the process of evaluating whether the power grid can operate safely, the accuracy requirements for the meteorological element grid real-time data corresponding to different disaster types are different. Therefore, the meteorological element grid real-time data pulled by the data monitoring module or the meteorological element grid real-time data supplemented by the data compensation and optimization module itself can be optimized for spatiotemporal resolution. The spatiotemporal resolution optimization includes time interpolation processing and space interpolation processing to interpolate the time resolution and spatial resolution of the meteorological element grid real-time data to the required accuracy to obtain the target meteorological element grid real-time data.

[0050] In one example, the meteorological data analysis module 3 is used to perform data analysis on the target meteorological element grid real-time data to obtain the meteorological data analysis results of each meteorological element. In one embodiment, the data supplementation and optimization module 2 has obtained the complete target meteorological element grid real-time data at that moment, and performs different statistical analyses according to the different target meteorological element grid real-time data, such as wind, temperature, rainfall and visibility, to obtain the meteorological data analysis results of different meteorological elements.

[0051] In one example, the review statistics module 4 is used to: receive the meteorological elements and statistical type selection instructions sent for the time period to be reviewed, so as to determine the meteorological elements and statistical types to be reviewed, and use the statistical model corresponding to the statistical type to perform data statistics on the meteorological data analysis results of the meteorological elements to be reviewed in the time period to be reviewed to obtain meteorological data statistical results, and the meteorological data statistical results are applied to the power grid disaster prevention and mitigation scenario. In one embodiment, the meteorological element grid real-time data can reflect the weather conditions of the day very realistically, and the event review can provide weather statistics (precipitation, temperature, temperature change, strong wind, visibility) for the time period of interest based on the meteorological element grid real-time data. Based on the meteorological element grid real-time data, meteorological data statistical functions for sites, regions and times are provided, supporting the selection of provinces, single / multiple cities, and statistical periods of hours, days, ten days, months and years, covering a variety of meteorological elements and their sub-elements.

[0052] The live data processing system based on the disaster prevention and reduction scenario of the power grid provided by the embodiment of the present invention includes a data monitoring module, a data compensation module, a meteorological data analysis module and a retrospective statistics module. Based on the cooperation of various algorithm modules, a live data processing flow based on the disaster prevention and reduction scenario of the power grid with a clear structure and capable of accurately and real-time processing of grid live data is provided. The embodiment of the present invention overcomes the problem of untimely processing of grid live data, combines real-time monitoring with intelligent file transfer status judgment logic, and uses basic monitoring of file attributes (such as modification time and size) to dynamically determine whether the file has been completely transmitted, thereby improving data processing data and ensuring timely processing of live data. In view of the possible problem of missing grid live data, the site data filling of the data compensation module is used to greatly improve the integrity of the data set and the stability of the processing flow, and the time and space resolution of the data is adjusted according to the specific needs of the disaster type to enhance its applicability, provide technical support for disaster prevention and reduction of power grid enterprises, and have important significance for the safe operation of the power grid.

[0053] For ease of understanding, the embodiment of the present invention provides a specific structure of a real-time data processing system based on a power grid disaster prevention and mitigation scenario, see Figure 2 A detailed structural diagram of a real-time data processing system based on a power grid disaster prevention and mitigation scenario is shown.

[0054] For the aforementioned data monitoring module 1, in the process of real-time monitoring of the generation of meteorological element grid live data, delay time detection and integrity detection will be performed simultaneously. The delay time detection is as follows: Taking the target time point as the timing starting point, if the meteorological element grid live data corresponding to the target time point is not pulled within the preset time length, it is determined that the delay time detection has not been passed. The integrity detection is as follows: and, if the size of the received meteorological element grid live data is inconsistent with the attribute information carried by the meteorological element grid live data, it is determined that the integrity detection has not been passed.

[0055] In the specific implementation, the meteorological element grid real-time data is hourly data, but there may be a certain time delay. In order to ensure that the data is processed and analyzed in time after generation, the data monitoring module 1 is used for real-time monitoring. Once the file generation is detected, the data optimization processing in the data compensation and optimization module 2 can be performed. While monitoring the generation of the grid real-time data, the delay time is determined. If the delay time exceeds 20 minutes, the data compensation strategy (data compensation and optimization module 2) is adopted to avoid long-term data loss, thereby ensuring the continuity and integrity of the data. The specific method for determining data delay is: comparing the reception time of the meteorological element grid real-time data with the hourly time. If the meteorological element grid real-time data of the corresponding time point is not received within 20 minutes after a certain hourly time, it is determined that the data is delayed. If the meteorological element grid real-time data that was delayed at the previous time is received, it is processed while continuing to monitor the meteorological element grid real-time data of the corresponding time point.

[0056] As for the aforementioned data supplementation and optimization module 2, this module has both functions of data supplementation and data optimization.

[0057] In one example, the data compensation and optimization module 2 includes a data compensation submodule, which is used to: obtain site observation data of meteorological sites located within the current area; filter out target site observation data from the site observation data based on the target time point and missing meteorological elements as retrieval conditions; perform quality inspection and grid difference processing on the target site observation data, so as to use the processed target site observation data to supplement the meteorological element grid actual data corresponding to the target time point.

[0058] In the specific implementation, data compensation mainly starts the data compensation submodule to supplement the missing data when the delay time of the meteorological element grid real-time data exceeds 20 minutes. This function mainly reads the site observation data from the database, first determines the delayed meteorological elements and time points; then, according to the current area and delayed meteorological elements and delayed time points, screens out the meteorological stations located in the area, and performs quality checks on the selected target station observation data to eliminate outliers; then, based on the selected standard station observation data, the relevant interpolation method is used for grid interpolation to replace the delayed meteorological element grid real-time data. In terms of data interpolation methods, the natural neighbor interpolation method is selected, which can retain these extreme values ​​to the greatest extent and accurately reflect the current weather conditions.

[0059] In one example, the data compensation and optimization module 2 also includes a data optimization sub-module, which is used to: for each disaster type involved in the power grid disaster prevention and mitigation scenario, use the time resolution corresponding to the disaster type to perform time interpolation processing on the meteorological element grid real-time data corresponding to the disaster type, and use the spatial resolution corresponding to the disaster type to perform spatial interpolation processing on the meteorological element grid real-time data corresponding to the disaster type, so as to achieve the optimization of the temporal and spatial resolution of the meteorological element grid real-time data.

[0060] In practical applications, data optimization mainly optimizes the temporal and spatial resolution of the real-time data of the meteorological element grid. In power disaster prevention, different types of natural disasters have different impacts on the power system, so the requirements for the temporal and spatial resolution of different disasters are also different. Different data optimizations are needed for different types of disasters to make them more applicable. For example, minute-level interpolation is performed for disasters with drastic instantaneous changes and a small impact range, such as heavy rain and strong winds, as well as meter-level interpolation involving lines and towers, to provide more accurate and practical data.

[0061] In a specific implementation, the time resolution optimization process is as follows:

[0062] (1.1) In the case of a rainstorm or a gale disaster, the meteorological element grid real-time data corresponding to the rainstorm or gale disaster is interpolated at the minute level. Specifically, minute-level interpolation (rainstorm, gale): For disasters with drastic instantaneous changes such as rainstorm and gale, minute-level time resolution interpolation is used. Combined with high-frequency meteorological data, such as radar data and meteorological satellites, a time series interpolation algorithm is used to interpolate the meteorological element grid real-time data to the minute level to generate continuous time series data.

[0063] (1.2) In the case of high temperature disaster or heavy fog disaster, the meteorological element grid real-time data corresponding to the high temperature disaster or heavy fog disaster is subjected to hourly time interpolation. Specifically, hourly interpolation (temperature, visibility): For disasters with a large continuous impact such as high temperature and heavy fog, hourly time resolution is used. The meteorological element grid real-time data can meet the requirements of time resolution, combined with the data compensation function, to ensure the integrity of its time series.

[0064] In a specific implementation, the spatial resolution optimization process is as follows:

[0065] (2.1) In the case of a rainstorm or a strong wind disaster, the meteorological element grid real-time data corresponding to the rainstorm or strong wind disaster is subjected to line and tower level spatial interpolation processing. Specifically, line and tower level interpolation (meter level, rainstorm and strong wind): For disasters with a small spatial impact range such as rainstorm and strong wind, line and tower level spatial resolution interpolation is used for line and tower locations. Through high-resolution meteorological data, such as radar data, meteorological satellite data, etc. and power grid infrastructure data (such as lines, tower locations, etc.), the natural neighbor interpolation method is used to interpolate the meteorological element grid real-time data to the line and tower level to generate high-resolution spatial data. See Figure 3 An example of an interpolation grid is shown in the figure. The black dots represent the locations of the towers. The grid where the lines and towers are located is extracted using the power grid infrastructure data, and spatial interpolation processing is performed on the actual data in the grid to obtain the actual data of the target meteorological element grid at the line and tower levels.

[0066] (2.2) In the case of high temperature disaster or heavy fog disaster, the meteorological element grid real-time data corresponding to the high temperature disaster or heavy fog disaster is subjected to regional level spatial interpolation processing. Specifically, regional level interpolation (kilometer level, temperature, visibility): For disasters with a large spatial impact range such as high temperature and heavy fog, the meteorological element grid real-time data is directly used. The kilometer-level resolution of the meteorological element grid real-time data can meet the needs of disaster monitoring such as high temperature and heavy fog, and combined with the data compensation function, its time series is guaranteed to be complete.

[0067] For the meteorological data analysis module 3, based on the data monitoring module 1 and the data supplementation and optimization module 2, the complete target meteorological element grid real-time data at that moment has been obtained. According to different meteorological elements, such as wind, temperature, rainfall and visibility, different statistical analyses are performed to obtain information such as 6-hour rainfall, 12-hour rainfall, 24-hour rainfall, temperature change, etc., and through threshold judgment, information such as strong wind, high temperature, low temperature, etc. is obtained. If the data recovery is delayed, the statistical analysis will be updated synchronously to ensure the accuracy of the data.

[0068] For the review statistics module 4, it is mainly used for review statistics based on real-time data. The statistical types include site statistics, regional statistics and time statistics. Regional statistics are divided into one or more of provincial statistics, single-city statistics and multi-city statistics. Time statistics are divided into one or more of hourly statistics, daily statistics, ten-day statistics, monthly statistics and annual statistics. Supported elements include precipitation, temperature, strong wind, visibility, etc. Each type of element is further subdivided into sub-elements of related elements. Various elements support free combination. The main statistical logic is as follows:

[0069] (1) Select multiple elements on the page, and each element will find a series of fields corresponding to the element;

[0070] (2) Determine the statistical method, such as site statistics, regional statistics, or time statistics;

[0071] (3) Select the corresponding statistical model according to the statistical method and statistical elements. The statistical model includes:

[0072] a. Conventional element statistics, such as cumulative precipitation and maximum temperature;

[0073] b. Statistics based on factor value analysis, such as the number of high temperature days and low temperature days;

[0074] c. Analysis and statistics of long time series, such as breakthrough of extreme values; etc.

[0075] (4) Determine whether statistics need to be merged or split according to different statistical models;

[0076] (5) The final result is returned to the page.

[0077] In summary, the real-time data processing system based on power grid disaster prevention and mitigation scenarios provided by the embodiments of the present invention has at least the following characteristics:

[0078] (1) The present invention aims to realize real-time monitoring and processing and analysis of live meteorological data. By combining real-time monitoring with intelligent file transfer status judgment logic, it not only improves the data processing speed, but also improves the automation level of the data processing process, filling the gap in the existing system in terms of data immediacy. And it uses basic monitoring of file attributes (such as modification time and size) to dynamically determine whether the file has been completely transmitted, thereby deciding whether to start the subsequent data analysis process. This method reduces the reliance on fixed time thresholds and can more flexibly respond to different transmission rates and network conditions, providing timely meteorological data for power grid disaster prevention and mitigation.

[0079] (2) The present invention can perform detailed optimization on the temporal and spatial resolutions of live grid data. In view of the different degrees of impact of different types of natural disasters on the power system, the temporal and spatial resolutions of the data are adjusted according to the specific needs of the disaster type to enhance its applicability. Temporal resolution optimization includes minute-level interpolation, such as disasters with dramatic instantaneous changes such as heavy rain and strong winds, while spatial resolution optimization involves meter-level interpolation at the line and tower levels, such as disasters with smaller spatial impact ranges such as heavy rain and strong winds. Through such an optimization process, the process of the present invention can provide more accurate and practical data to support power disaster prevention work.

[0080] (3) The present invention is a comprehensive data processing system that integrates four core functional modules: data monitoring module, data compensation module, meteorological data analysis module and retrospective statistics module. These four modules work together to form a closed-loop data processing flow, which can fully cover every link from the processing and judgment of real-time data to statistical analysis, ensuring the consistency and integrity of the data processing flow. The data compensation module is an important supplement and optimization link. It addresses the possible problem of missing grid real-time data by filling in site data, greatly improving the integrity of the data set and the stability of the system.

[0081] On the basis of the above-mentioned embodiments, the embodiments of the present invention provide a method for processing real-time data based on a power grid disaster prevention and mitigation scenario. The method is applied to the above-mentioned real-time data processing system based on a power grid disaster prevention and mitigation scenario. The system includes a data monitoring module, a data compensation and optimization module, a meteorological data analysis module and a retrospective statistics module connected by communication; see Figure 4 The flowchart of a method for processing live data based on a power grid disaster prevention and mitigation scenario is shown, and the method mainly includes the following steps S402 to S408:

[0082] Step S402, monitoring the designated meteorological platform to pull the meteorological element grid real-time data corresponding to the target time point from the designated meteorological platform, and performing data detection during the monitoring process, the data detection including delay time detection and / or integrity detection;

[0083] Step S404: if the data detection fails, the meteorological element grid real-time data corresponding to the target time point is supplemented by using the site observation data corresponding to the target time point; and, according to the disaster type involved in the power grid disaster prevention and mitigation scenario, the meteorological element grid real-time data is optimized in time and space resolution to obtain the target meteorological element grid real-time data corresponding to the disaster type;

[0084] Step S406, performing data analysis on the target meteorological element grid real-time data to obtain meteorological data analysis results of each meteorological element;

[0085] Step S408, receiving the meteorological element and statistical type selection instructions sent for the time period to be reviewed to determine the meteorological elements and statistical types to be reviewed, using the statistical model corresponding to the statistical type, performing data statistics on the meteorological data analysis results of the meteorological elements to be reviewed within the time period to be reviewed to obtain meteorological data statistical results, and the meteorological data statistical results are applied to the power grid disaster prevention and mitigation scenario.

[0086] The real-time data processing method based on the power grid disaster prevention and mitigation scenario provided by the embodiment of the present invention integrates four core functional modules, and forms a closed-loop data processing flow through the collaborative work of the data monitoring module, the data compensation module, the meteorological data analysis module and the retrospective statistical module, thereby overcoming the problems of being unable to respond to meteorological disasters in a timely manner due to insufficient accuracy of forecast data, delayed processing of real-time data, and weak application of meteorological data, providing technical support for disaster prevention and mitigation of power grid enterprises, and having important significance for the safe operation of the power grid.

[0087] The method provided in the embodiment of the present invention has the same implementation principle and technical effects as those in the aforementioned system embodiment. For the sake of brief description, for matters not mentioned in the method embodiment, reference may be made to the corresponding contents in the aforementioned system embodiment.

[0088] An embodiment of the present invention provides an electronic device. Specifically, the electronic device includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above-mentioned implementation methods.

[0089] Figure 5 A structural diagram of an electronic device provided in an embodiment of the present invention, the electronic device 100 includes: a processor 50, a memory 51, a bus 52 and a communication interface 53, wherein the processor 50, the communication interface 53 and the memory 51 are connected via the bus 52; the processor 50 is used to execute an executable module stored in the memory 51, such as a computer program.

[0090] The memory 51 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 53 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0091] The bus 52 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0092] Among them, the memory 51 is used to store programs, and the processor 50 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 50 or implemented by the processor 50.

[0093] The processor 50 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 50. The above processor 50 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to be executed, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 51, and the processor 50 reads the information in the memory 51 and completes the steps of the above method in combination with its hardware.

[0094] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be referred to the previous method embodiments, which will not be repeated here.

[0095] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0096] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A real-time data processing system based on power grid disaster prevention and mitigation scenarios, characterized in that: It includes a communication-connected data monitoring module, a data supplement and optimization module, a meteorological data analysis module, and a retrospective statistics module; among which, The data monitoring module is used to monitor the designated meteorological platform to pull the real-time data of the meteorological element grid corresponding to the target time point from the designated meteorological platform, and perform data detection during the monitoring process, wherein the data detection includes delay time detection and / or integrity detection; The data supplementation and optimization module is used to: in the case of failing the data detection, use the site observation data corresponding to the target time point to supplement the meteorological element grid real-time data corresponding to the target time point; and, according to the disaster type involved in the power grid disaster prevention and mitigation scenario, perform spatiotemporal resolution optimization processing on the meteorological element grid real-time data to obtain the target meteorological element grid real-time data corresponding to the disaster type; The meteorological data analysis module is used to: perform data analysis on the target meteorological element grid real-time data to obtain meteorological data analysis results of each meteorological element; The review statistics module is used to: receive meteorological elements and statistical type selection instructions sent for the time period to be reviewed to determine the meteorological elements and statistical types to be reviewed, and use the statistical model corresponding to the statistical type to perform data statistics on the meteorological data analysis results of the meteorological elements to be reviewed within the time period to be reviewed to obtain meteorological data statistical results, which are applied to the power grid disaster prevention and mitigation scenario.

2. The real-time data processing system based on power grid disaster prevention and mitigation scenarios according to claim 1 is characterized in that: The data monitoring module is specifically used for: Taking the target time point as the timing starting point, if the meteorological element grid real-time data corresponding to the target time point is not pulled within the preset time length, it is determined that the delay time detection has not been passed; And, if the size of the received meteorological element grid real-time data is inconsistent with the attribute information carried by the meteorological element grid real-time data, it is determined that the integrity check has not been passed.

3. The real-time data processing system based on power grid disaster prevention and mitigation scenarios according to claim 1 is characterized in that: The data compensation and optimization module includes a data compensation submodule, and the data compensation submodule is used to: Get the site observation data of the meteorological stations within the current area; Using the target time point and the missing meteorological elements as search conditions, the target site observation data is screened out from the site observation data; The target site observation data is subjected to quality inspection processing and grid difference processing, so as to use the processed target site observation data to supplement the meteorological element grid actual data corresponding to the target time point.

4. The real-time data processing system based on power grid disaster prevention and mitigation scenarios according to claim 1 is characterized in that: The data compensation and optimization module also includes a data optimization submodule, which is used to: For each disaster type involved in the power grid disaster prevention and mitigation scenario, the time resolution corresponding to the disaster type is used to perform time interpolation processing on the meteorological element grid real-time data corresponding to the disaster type, and the spatial resolution corresponding to the disaster type is used to perform spatial interpolation processing on the meteorological element grid real-time data corresponding to the disaster type, so as to achieve optimization of the temporal and spatial resolution of the meteorological element grid real-time data.

5. The real-time data processing system based on power grid disaster prevention and mitigation scenario according to claim 4 is characterized in that: The data optimization submodule is specifically used for: When the disaster type is a rainstorm disaster or a strong wind disaster, performing minute-level time interpolation processing on the meteorological element grid real-time data corresponding to the rainstorm disaster or the strong wind disaster; When the disaster type is a high temperature disaster or a heavy fog disaster, hourly time interpolation processing is performed on the real-time data of the meteorological element grid corresponding to the high temperature disaster or the heavy fog disaster.

6. The real-time data processing system based on power grid disaster prevention and mitigation scenario according to claim 4 is characterized in that: The data optimization submodule is also specifically used for: When the disaster type is a rainstorm disaster or a strong wind disaster, performing line and tower-level spatial interpolation processing on the meteorological element grid real-time data corresponding to the rainstorm disaster or the strong wind disaster; When the disaster type is a high temperature disaster or a heavy fog disaster, regional level spatial interpolation processing is performed on the meteorological element grid real-time data corresponding to the high temperature disaster or the heavy fog disaster.

7. The real-time data processing system based on power grid disaster prevention and mitigation scenarios according to claim 1 is characterized in that: The statistical types include site statistics, regional statistics and time statistics. The regional statistics are divided into one or more of province statistics, single-city statistics and multi-city statistics. The time statistics are divided into one or more of hourly statistics, daily statistics, ten-day statistics, monthly statistics and annual statistics.

8. A real-time data processing method based on power grid disaster prevention and mitigation scenarios, characterized in that: The method is applied to the real-time data processing system based on the power grid disaster prevention and mitigation scenario according to any one of claims 1 to 7, and the method comprises: Monitoring a designated meteorological platform to pull the real-time data of the meteorological element grid corresponding to the target time point from the designated meteorological platform, and performing data detection during the monitoring process, wherein the data detection includes delay time detection and / or integrity detection; In the case where the data detection fails, the meteorological element grid real-time data corresponding to the target time point is supplemented by using the site observation data corresponding to the target time point; and, according to the disaster type involved in the power grid disaster prevention and mitigation scenario, the meteorological element grid real-time data is optimized in time and space resolution to obtain the target meteorological element grid real-time data corresponding to the disaster type; Performing data analysis on the actual grid data of the target meteorological element to obtain meteorological data analysis results of each meteorological element; Receive meteorological elements and statistical type selection instructions sent for the time period to be reviewed to determine the meteorological elements and statistical types to be reviewed, use the statistical model corresponding to the statistical type to perform data statistics on the meteorological data analysis results of the meteorological elements to be reviewed within the time period to be reviewed to obtain meteorological data statistical results, and apply the meteorological data statistical results to the power grid disaster prevention and mitigation scenario.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method of claim 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method of claim 8.

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