A power grid dispatching business management and control system based on big data analysis

Abstract

This invention discloses a power grid dispatching and management system based on big data analysis. The system includes a data acquisition module, a data transmission module, a data aggregation module, a big data analysis module, an access control module, a dispatching and adjustment module, a control module, a power supply module, a network module, and a data storage module. This invention features a reasonable design and simplified structure, combining big data analysis with power grid dispatching and management, reducing the workload of staff and the risk of staff errors. It can automatically issue warnings when safety hazards exist, demonstrating good performance, high work efficiency, practicality, and ease of implementation.

Description

Technical Field

[0001] This invention relates to the field of power grid dispatching and control technology, and specifically to a power grid dispatching and control system based on big data analysis. Background Technology

[0002] In the field of power grid dispatching, many information systems have provided technical support for power grid operation and management. For example, CN105281912A, "Power Grid Operation and Dispatch System Based on Mobile Network," discloses "a power grid operation and dispatching system based on mobile network, including a server and a mobile device. After the server obtains power grid operation data, it feeds it back to the application on the mobile device to realize unified management of the mobile application. The mobile device uses a cross-platform human-machine interface to display power grid operation information." This invention proposes a power grid operation and dispatching system to help managers and operators at all levels grasp the power grid operation status anytime and anywhere, thereby improving work efficiency and dispatching management level, and adapting to the ever-evolving needs of power grid operation and management. However, with the rapid development of information technology, power dispatching business has placed higher demands on the network in terms of security, reliability, and real-time performance. Moreover, manual monitoring of the power grid operation status and manual control operations are prone to errors. In this context, big data, with its massive data volume that cannot be captured, managed, processed, and organized into information that helps enterprises make more proactive business decisions within a reasonable timeframe using mainstream software tools, is a data mining method. How to enable the power dispatching business management system to perform intelligent dispatching based on big data analysis has become a new research direction for power grid dispatching and management. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a power grid dispatching and management system based on big data analytics. The system includes a data acquisition module, a data transmission module, a data aggregation module, a big data analytics module, a control module, and a management module, wherein: The data acquisition module is used to collect grid operation and maintenance data in real time and transmit the grid operation and maintenance data to the data aggregation module through the data transmission module. The grid operation and maintenance data includes power generation data, power supply data and power consumption data. The data aggregation module is used to aggregate power grid operation and maintenance data and transmit the aggregated data to the big data analysis module; The big data analysis module is used to perform data analysis on the received aggregated data, obtain analysis results, and transmit them to the control module; The control module is used to issue operation instructions to the business management module based on the analysis results, and the business management module manages and adjusts the power grid dispatching business according to the operation instructions.

[0004] Preferably, the power generation data are parameters of each node power station, the power supply data are electrical line parameters, and the power consumption data are transformer parameters.

[0005] Preferably, the big data analysis module is used to perform data analysis on the received aggregated data to obtain analysis results, specifically as follows: Historical operation and maintenance data and corresponding influencing factors of the power grid area are obtained in advance. The historical operation and maintenance data includes historical power generation data, historical power supply data, and historical power consumption data. The power consumption time periods of the power grid are determined based on the historical operation and maintenance data, including peak power consumption time periods, off-peak power consumption time periods, and equal peak power consumption time periods. Historical operation and maintenance data during electricity consumption periods are divided into time-series datasets, which include peak power generation datasets, peak power supply datasets, peak electricity consumption datasets, off-peak power generation datasets, off-peak power supply datasets, off-peak electricity consumption datasets, off-peak power generation datasets, off-peak power supply datasets, and off-peak electricity consumption datasets. Establish an operation and maintenance data prediction model, which includes a power generation prediction model, a power supply prediction model, and a power consumption prediction model, and use the operation and maintenance data prediction model to obtain predicted power grid operation and maintenance data; The real-time collected power grid operation and maintenance data is compared with the predicted power grid operation and maintenance data to obtain the comparison results, and the real-time power grid adequacy is calculated. Based on the adequacy, it is determined whether the power grid needs to be dispatched.

[0006] Preferably, the operation and maintenance data prediction model is a coupled model of gray GM(1,1) model and multiple linear regression model. Specifically, the operation and maintenance data prediction model is used to obtain the predicted power grid operation and maintenance data. The gray model prediction result is obtained by using the gray GM(1,1) model to make predictions based on the time series dataset. The gray model prediction result and the corresponding influencing factors of the operation and maintenance data are used as the input data of the multiple linear regression model to make predictions and obtain the predicted power grid operation and maintenance data.

[0007] Preferably, the grey model prediction result is obtained by using the grey GM(1,1) model, which is expressed by the following formula:

[0008] In the formula, The result is the prediction result of the grey model, where a is the development grey level, u is the internal generation control grey level, and k is the number of time series data sets.

[0009] Preferably, the grey model prediction results are used as input data for a multiple linear regression model to obtain predicted power grid operation and maintenance data, expressed by the formula: + +…+ ; In the formula, To predict power grid operation and maintenance data, These are the non-standardized coefficients estimated using the least squares method. For the influencing factors corresponding to operation and maintenance data, t is the time point.

[0010] Preferably, the real-time power grid adequacy is calculated using the following formula: Power generation adequacy = (Maximum power generation - Real-time power generation) / Maximum power generation; Power supply adequacy = (Maximum power supply - Real-time power supply) / Maximum power supply; Power adequacy ratio = (maximum power consumption - real-time power supply) / maximum power consumption.

[0011] Preferably, the power grid dispatching and management system further includes a power supply module, an early warning module, a result output module, and a cloud subsystem, wherein: The power supply module is any one or a combination of two or more of the following: voltage conversion circuit, power conversion chip, and battery. The voltage conversion circuit and voltage conversion chip are used to convert the externally input voltage into a stable on-board voltage. The early warning module is equipped with a comparison threshold and a parameter threshold. When the comparison result between the real-time collected power grid operation and maintenance data and the predicted power grid operation and maintenance data exceeds the comparison threshold, or when the parameter that the business control module is going to adjust exceeds the parameter threshold, an early warning is issued. The result output module is used to print the output results of the business management module; The cloud subsystem includes a network module and a database module. The database module is used to back up and store the power grid operation and maintenance data transmitted by the control module through the network module.

[0012] Preferably, when the database module has insufficient memory, the control module will issue a deletion command to the database module, and the database module will periodically delete the data with the earliest storage time according to the storage time sequence.

[0013] Preferably, the power grid dispatching business management and control system further includes an access control module and a dispatching adjustment module, wherein: The permission judgment module is used to determine the permissions of the user who initiates the service request. If the user meets the permission requirements, the scheduling and adjustment module reads the service request parameter characteristics of the user and transmits them to the control module. The control module issues operation instructions to the service management module according to the request parameter characteristics, and the service management module manages and adjusts the power grid scheduling service according to the operation instructions.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This invention provides a power grid dispatching and management system based on big data analysis, including a data acquisition module, a data transmission module, a data aggregation module, a big data analysis module, an access control module, a dispatching and adjustment module, a control module, a power supply module, a network module, and a database module. This invention features a reasonable design and simplified structure, combining big data analysis with power grid dispatching and management, reducing the workload of staff and the risk of staff errors. It can automatically issue warnings when safety hazards exist, demonstrating good performance, high work efficiency, practicality, and ease of implementation. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the system structure in an embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1 This invention discloses a power grid dispatching and management system based on big data analysis, such as... Figure 1 As shown, the power grid dispatching business management and control system includes a data acquisition module, a data transmission module, a data aggregation module, a big data analysis module, a control module, and a business management and control module, wherein: The output terminal of the data acquisition module is electrically connected to the input terminal of the data transmission module, and the output terminal of the data transmission module is electrically connected to the data aggregation module. Preferably, in this embodiment, the data transmission module is a transmission component, including a first interface and a wireless communication module. The first interface is connected to the output terminal of the data acquisition module. After receiving the operation and maintenance data collected by the data acquisition module, it transmits the data to the data aggregation module through the wireless communication module. The data acquisition module is used to collect grid operation and maintenance data in real time and transmit the grid operation and maintenance data to the data aggregation module through the data transmission module. The grid operation and maintenance data includes power generation data, power supply data, and power consumption data. The output of the data aggregation module is electrically connected to the big data analysis module, and is used to aggregate power grid operation and maintenance data and transmit the aggregated data to the big data analysis module; the big data analysis module is used to perform data analysis on the received aggregated data, obtain analysis results, and transmit them to the control module, wherein the data analysis process specifically includes: S1. Pre-acquire historical operation and maintenance data of the power grid area and the corresponding influencing factors of the operation and maintenance data; S11. The historical operation and maintenance data includes historical power generation data, historical power supply data, and historical power consumption data. The power consumption time periods of the power grid are determined based on the historical operation and maintenance data, including peak power consumption time periods, off-peak power consumption time periods, and neutral power consumption time periods. S12. Electricity consumption, power supply, and power generation are affected by various factors. For example, the GDP, total population, and per capita GDP of the region where the power grid is located affect electricity consumption. The corresponding influencing factors of power generation include the GDP, total population, and per capita GDP of the region where the power grid is located. The coal consumption of power plants affects power supply. The corresponding influencing factors of power supply include the coal consumption of power plants. The corresponding influencing factors of operation and maintenance data are selected according to the different regions where the power grid is located, and the factors with the greatest impact on operation and maintenance data are selected.

[0018] S2. Allocate historical maintenance data that falls within the electricity consumption period to the corresponding time-series dataset; S21, The time-series dataset includes peak power generation dataset, peak power supply dataset, peak power consumption dataset, off-peak power generation dataset, off-peak power supply dataset, off-peak power consumption dataset, off-peak power generation dataset, off-peak power supply dataset, and off-peak power consumption dataset; S3. Establish an operation and maintenance data prediction model, which includes a power generation prediction model, a power supply prediction model, and a power consumption prediction model. Use the operation and maintenance data prediction model to obtain predicted power grid operation and maintenance data. S31. The operation and maintenance data prediction model is a coupled model of gray GM(1,1) model and multiple linear regression model. The specific method of obtaining the predicted power grid operation and maintenance data by using the operation and maintenance data prediction model is to use the gray GM(1,1) model to make predictions based on the time series dataset to obtain the gray model prediction results, and use the gray model prediction results and the corresponding influencing factors of operation and maintenance data as input data of the multiple linear regression model to make predictions to obtain the predicted power grid operation and maintenance data. S311. Grey prediction is a method for predicting systems containing uncertain factors. Grey prediction identifies the degree of difference in development trends among system factors (i.e., performs correlation analysis) and processes the original data to find patterns in system changes, generating data sequences with strong regularity. Then, a corresponding differential equation model is established to predict the future development trend of things. It constructs a grey prediction model using a series of quantitative values ​​reflecting the characteristics of the object to be predicted, observed at equal time intervals, to predict the characteristic quantity at a certain future moment, or the time to reach a certain characteristic quantity. The grey GM(1,1) model is used to obtain the grey model prediction result, expressed by the formula:

[0019] In the formula, The result is the prediction result of the grey model, where a is the development grey level, u is the internal generation control grey level, and k is the number of time series data sets.

[0020] S312. Using the grey model prediction results as input data for a multiple linear regression model, predictive power grid operation and maintenance data is obtained, expressed by the formula: + +…+ ; In the formula, To predict power grid operation and maintenance data These are the non-standardized coefficients estimated using the least squares method. For the influencing factors corresponding to operation and maintenance data, t is the time point.

[0021] S4. Compare the real-time collected power grid operation and maintenance data with the predicted power grid operation and maintenance data to obtain the comparison results, calculate the real-time power grid adequacy, and determine whether the power grid needs to be dispatched based on the adequacy. S41. The real-time power grid adequacy is calculated using the following formula: Power generation adequacy = (Maximum power generation - Real-time power generation) / Maximum power generation; Power supply adequacy = (Maximum power supply - Real-time power supply) / Maximum power supply; Power adequacy ratio = (maximum power consumption - real-time power supply) / maximum power consumption.

[0022] Preferably, the power grid dispatching business management system further includes a power supply module, an early warning module, a result output module, and a cloud subsystem. In this embodiment, the control module is a PLC controller, model NX7-28ADT. A PLC controller is a digital computing electronic device specifically designed for industrial applications. It uses a programmable memory to store instructions for performing logical operations, sequential operations, timing, counting, and arithmetic operations, and can control various types of machinery or production processes through digital or analog inputs and outputs. In this embodiment, the output terminal of the control module is connected to the input terminals of the business management system, the early warning module, the result output module, and the cloud subsystem, and is used to issue corresponding operation instructions to the corresponding modules based on the received data, wherein: The business management module controls and adjusts the power grid dispatching business according to the operation instructions; The power supply module is any one or a combination of two or more of the following: voltage conversion circuit, power conversion chip, and battery. The voltage conversion circuit and voltage conversion chip are used to convert the externally input voltage into a stable on-board voltage. The input terminal of the early warning module is electrically connected to the control module. It has a built-in comparison threshold and parameter threshold. When the comparison result between the real-time collected power grid operation and maintenance data and the predicted power grid operation and maintenance data exceeds the comparison threshold, or when the parameter that the business management module is going to adjust exceeds the parameter threshold, an early warning is issued. The result output module is used to print the output results of the business management module; The cloud subsystem includes a network module and a database module. The database module is used to back up and store the power grid operation and maintenance data transmitted by the control module through the network module. When the database module is short of memory, the control module will issue a deletion command to the database module. The database module will periodically delete the oldest data according to the storage time.

[0023] Preferably, the power grid dispatching business management and control system further includes an access control module and a dispatching adjustment module, wherein: The permission judgment module is used to determine the permissions of the user who initiates the service request. If the user meets the permission requirements, the scheduling and adjustment module reads the service request parameter characteristics of the user and transmits them to the control module. The control module issues operation instructions to the service management module according to the request parameter characteristics, and the service management module manages and adjusts the power grid scheduling service according to the operation instructions.

[0024] Example 2 This embodiment discloses a power grid dispatching and management system based on big data analysis, including the data acquisition module, data transmission module, data aggregation module, control module, business management module, power supply module, early warning module, result output module, cloud subsystem, permission judgment module, and dispatching and adjustment module described in Embodiment 1. This embodiment also includes a big data analysis module, wherein the input terminal of the big data analysis module is electrically connected to the data aggregation module, and its output terminal is electrically connected to the control module, used to perform data analysis on the received aggregated data, obtain analysis results, and transmit them to the control module. The data analysis process specifically includes: S1. Obtain historical power generation data, historical power supply data, and historical electricity consumption data for the same period in the current month; S2. Based on historical power generation data, historical power supply data, and historical electricity consumption data, determine the peak electricity consumption period, off-peak electricity consumption period, and peak-consumption period. Simultaneously, determine the power generation, power supply, and electricity consumption during the peak electricity consumption period; the power generation, power supply, and electricity consumption during the off-peak electricity consumption period; and the power generation, power supply, and electricity consumption during the peak-consumption period. Predict the current power generation, power supply, and electricity consumption for the above three periods (peak electricity consumption period, off-peak electricity consumption period, and peak-consumption period). S21. The big data analysis module reads at least 3 years of historical power generation data, historical power supply data, and historical electricity consumption data to determine peak electricity consumption periods, off-peak electricity consumption periods, and peak electricity consumption periods, and calculates the power generation, power supply, and electricity consumption within these three periods. Based on historical power generation, power supply, and electricity consumption over three time periods, the power generation, power supply, and electricity consumption for the current three time periods are predicted. In this embodiment, the power generation, power supply, and electricity consumption for the peak electricity consumption periods over three consecutive years are obtained by averaging. Taking power generation as an example, the power generation over the three years is averaged to predict the power generation during the current peak electricity consumption period; the power supply over the three years is averaged to predict the power supply during the current peak electricity consumption period; and the electricity consumption over the three years is averaged to predict the electricity consumption during the current peak electricity consumption period. S3, The data acquisition module obtains the power generation data, power supply data, and power consumption data for the current time period; S4. Compare the power generation, power supply, and power consumption data for the current time period with the predicted power generation, power supply, and power consumption for the current time period, and calculate the current power grid adequacy. S5. Determine whether grid dispatch is needed based on the sufficiency level.

[0025] Example 3 This embodiment discloses a power grid dispatching and management system based on big data analysis, including the data acquisition module, data transmission module, data aggregation module, control module, business management module, power supply module, early warning module, result output module, cloud subsystem, permission judgment module, and dispatching and adjustment module described in Embodiment 1. This embodiment also includes a big data analysis module, wherein the input terminal of the big data analysis module is electrically connected to the data aggregation module, and its output terminal is electrically connected to the control module, used to perform data analysis on the received aggregated data, obtain analysis results, and transmit them to the control module. The data analysis process specifically includes: S1. Obtain historical power generation data, historical power supply data, and historical electricity consumption data for the same period in the current month; S2. Based on historical power generation data, historical power supply data, and historical electricity consumption data, determine the peak electricity consumption period, off-peak electricity consumption period, and peak-consumption period. Simultaneously, determine the power generation, power supply, and electricity consumption during the peak electricity consumption period; the power generation, power supply, and electricity consumption during the off-peak electricity consumption period; and the power generation, power supply, and electricity consumption during the peak-consumption period. Predict the current power generation, power supply, and electricity consumption for the above three periods (peak electricity consumption period, off-peak electricity consumption period, and peak-consumption period). S21. The big data analysis module reads at least 3 years of historical power generation data, historical power supply data, and historical electricity consumption data to determine peak electricity consumption periods, off-peak electricity consumption periods, and peak electricity consumption periods, and calculates the power generation, power supply, and electricity consumption within these three periods. Based on the historical power generation, power supply, and power consumption in three time periods, the power generation, power supply, and power consumption in the current three time periods are predicted. In this embodiment, the growth rate is calculated to predict the power generation, power supply, and power consumption in the current three time periods. Specifically, the generation, supply, and consumption of electricity during peak electricity consumption periods over five consecutive years are obtained. The growth rates of generation, supply, and consumption in adjacent years are calculated. The calculated growth rates of generation are averaged to predict the growth rate of generation during the current peak electricity consumption period. The calculated growth rates of supply are averaged to predict the growth rate of supply during the current peak electricity consumption period. The calculated growth rates of consumption are averaged to predict the growth rate of consumption during the current peak electricity consumption period. Based on the growth rates of generation, supply, and consumption, the electricity consumption during the previous peak electricity consumption period is predicted. S3, The data acquisition module obtains the power generation data, power supply data, and power consumption data for the current time period; S4. Compare the power generation, power supply, and power consumption data for the current time period with the predicted power generation, power supply, and power consumption for the current time period, and calculate the current power grid adequacy. S5. Determine whether grid dispatch is needed based on the sufficiency level.

[0026] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0027] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0028] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

[0029] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A power grid dispatching business management and control system based on big data analysis, characterized in that, The power grid dispatching business management and control system includes a data acquisition module, a data transmission module, a data aggregation module, a big data analysis module, a control module, a business management and control module, an access control module, and a dispatching adjustment module, wherein: The data acquisition module is used to collect grid operation and maintenance data in real time and transmit the grid operation and maintenance data to the data aggregation module through the data transmission module. The grid operation and maintenance data includes power generation data, power supply data and power consumption data. The power generation data is the parameters of each power station node, the power supply data is the electrical line parameters, and the power consumption data is the transformer parameters. The data aggregation module is used to aggregate power grid operation and maintenance data and transmit the aggregated data to the big data analysis module; The big data analysis module is used to analyze the received aggregated data, obtain the analysis results, and transmit them to the control module. Specifically: Historical operation and maintenance data and corresponding influencing factors of the power grid area are obtained in advance. The historical operation and maintenance data includes historical power generation data, historical power supply data, and historical power consumption data. Based on the historical operation and maintenance data, the power consumption time periods of the power grid are determined, including peak power consumption time periods, off-peak power consumption time periods, and equal peak power consumption time periods. Historical operation and maintenance data during electricity consumption periods are divided into time-series datasets, which include peak power generation datasets, peak power supply datasets, peak electricity consumption datasets, off-peak power generation datasets, off-peak power supply datasets, off-peak electricity consumption datasets, off-peak power generation datasets, off-peak power supply datasets, and off-peak electricity consumption datasets. A maintenance data prediction model is established, which is a coupled model of a grey GM(1,1) model and a multiple linear regression model. The model includes a power generation prediction model, a power supply prediction model, and a power consumption prediction model. Predicted grid maintenance data is obtained using this model. Specifically, the grey GM(1,1) model is used to predict data based on a time-series dataset, resulting in the grey model prediction results, expressed by the following formula: In the formula, is the prediction result of the gray model, a is the development gray, u is the internal generation control gray, and k is the number of time series set data. The predicted power grid operation and maintenance data are obtained by using the gray model prediction results and the corresponding influencing factors of operation and maintenance data as input data of a multiple linear regression model. The real-time collected power grid operation and maintenance data is compared with the predicted power grid operation and maintenance data to obtain the comparison results, and the real-time power grid adequacy is calculated. Based on the adequacy, it is determined whether the power grid needs to be dispatched. The control module is used to issue operation instructions to the business management module based on the analysis results, and the business management module manages and adjusts the power grid dispatching business according to the operation instructions. The permission judgment module is used to determine the permissions of the user who initiates the service request. If the user meets the permission requirements, the scheduling and adjustment module reads the service request parameter characteristics of the user and transmits them to the control module. The control module issues operation instructions to the service management module according to the request parameter characteristics, and the service management module manages and adjusts the power grid scheduling service according to the operation instructions.

2. The power grid dispatching business management and control system based on big data analysis according to claim 1, characterized in that, The prediction results of the grey model are used as input data for a multiple linear regression model to obtain predicted power grid operation and maintenance data, expressed by the formula: + +…+ ； In the formula, For predicting power grid operation and maintenance data, is a non-standardized coefficient for estimation by the least square method, is an impact factor corresponding to the operation and maintenance data, and t is a time point.

3. The power grid dispatching business management and control system based on big data analysis according to claim 2, characterized in that, The real-time power grid adequacy can be calculated using the following formula: Power generation adequacy = (Maximum power generation - Real-time power generation) / Maximum power generation; Power supply adequacy = (Maximum power supply - Real-time power supply) / Maximum power supply; Power adequacy ratio = (maximum power consumption - real-time power supply) / maximum power consumption.

4. The power grid dispatching business management and control system based on big data analysis according to claim 3, characterized in that, The power grid dispatching and management system also includes a power supply module, an early warning module, a result output module, and a cloud subsystem, wherein: The power supply module is any one or a combination of two or more of the following: voltage conversion circuit, power conversion chip, and battery. The voltage conversion circuit and voltage conversion chip are used to convert the externally input voltage into a stable on-board voltage. The early warning module is equipped with a comparison threshold and a parameter threshold. When the comparison result between the real-time collected power grid operation and maintenance data and the predicted power grid operation and maintenance data exceeds the comparison threshold, or when the parameter that the business control module is going to adjust exceeds the parameter threshold, an early warning is issued. The result output module is used to print the output results of the business management module; The cloud subsystem includes a network module and a database module. The database module is used to back up and store the power grid operation and maintenance data transmitted by the control module through the network module.

5. The power grid dispatching business management and control system based on big data analysis according to claim 4, characterized in that, When the database module runs out of memory, the control module will issue a deletion command to the database module, which will periodically delete the oldest data according to the storage time.

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