Intelligent IoT power distribution gateway control method and system based on IoT platform

Abstract

The present invention discloses a control method and system for an intelligent IoT distribution gateway based on an IoT platform, and relates to the field of IoT technology, including: obtaining the historical operation data of the power grid of the intelligent IoT distribution gateway of the IoT platform, evaluating the operation status of the power grid under the IoT distribution gateway based on the historical operation data of the power grid of the intelligent IoT distribution gateway of the IoT platform, obtaining the historical load data of the power grid, evaluating the load status of the power grid under the IoT distribution gateway based on the historical load data of the power grid, monitoring the overload moment of the power grid based on the load status of the power grid under the IoT distribution gateway, establishing an intelligent dispatching model for the power grid, and generating a control scheme for the intelligent IoT distribution gateway. The advantages of the present invention are: formulating personalized dispatching strategies according to the characteristics and requirements of different power grids, and improving the operation efficiency and intelligent management level of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a control method and system for an intelligent Internet of Things power distribution gateway based on an Internet of Things platform. Background Art

[0002] The IoT distribution gateway is an intelligent device that integrates multiple functions such as data acquisition, communication, data processing, protocol conversion, edge computing, equipment management, security and remote control. As a key node in the distribution network under the IoT technology framework, it is responsible for connecting and managing various intelligent devices and systems in the power grid, realizing the effective collection and transmission of power grid data, and is a core component to promote the development of smart grids, improve grid operation efficiency and intelligent management level.

[0003] The problem with the existing IoT platform's intelligent IoT distribution gateway control method is that it lacks integrity and intelligence. It can usually only achieve simple remote monitoring and basic control functions, and cannot achieve intelligent management and optimized scheduling of complex distribution networks. At the same time, it also has deficiencies in safety and reliability and cannot meet the growing needs of the power system. Summary of the invention

[0004] In order to solve the above technical problems, an intelligent IoT distribution gateway control method and system based on the Internet of Things platform are provided. The technical solution solves the problem that the intelligent IoT distribution gateway control method of the above-mentioned existing Internet of Things platform lacks integrity and intelligence, and usually can only realize simple remote monitoring and basic control functions, and cannot realize intelligent management and optimized scheduling of complex distribution networks. At the same time, there are also deficiencies in safety and reliability, and it cannot meet the growing needs of the power system.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] The intelligent IoT power distribution gateway control method based on the IoT platform includes:

[0007] Obtain historical power grid operation data from the smart IoT distribution gateway of the IoT platform;

[0008] Based on the historical operation data of the power grid of the intelligent IoT power distribution gateway on the IoT platform, the operation status of the power grid under the IoT power distribution gateway is evaluated;

[0009] Obtain historical load data of the power grid;

[0010] Based on the historical load data of the power grid, evaluate the load status of the power grid under the IoT distribution gateway;

[0011] Based on the load status of the power grid under the IoT distribution gateway, the overload moment of the power grid is monitored, and a smart grid dispatching model is established to generate a control solution for the smart IoT distribution gateway.

[0012] Preferably, based on the historical operation data of the power grid of the intelligent IoT power distribution gateway of the IoT platform, evaluating the power grid operation status under the IoT power distribution gateway specifically includes:

[0013] Based on the historical maintenance data of the power grid of the IoT power distribution gateway, the abnormal events of the power grid under the IoT power distribution gateway are screened out as the characteristic data of the abnormal events;

[0014] Based on logistic regression, a grid failure risk probability prediction model for IoT power distribution gateways is established;

[0015] The power grid fault risk probability prediction model based on the IoT power distribution gateway takes the power grid abnormal event under each IoT power distribution gateway in the abnormal event feature data as input, and takes the power grid abnormal risk probability affected by the power grid abnormal event under the IoT power distribution gateway as output;

[0016] Among them, the grid failure risk probability prediction model expression of the IoT power distribution gateway is:

[0017]

[0018] Where D(Y=1|X) is the probability of abnormal risk of Y occurring in the power grid under the condition of historical maintenance log X, and X i is the ith abnormal power grid event in the historical maintenance log, e is an exponential function, V represents the total number of abnormal events, α0, α1, α i , α v are the coefficients of the model.

[0019] Preferably, based on the historical operation data of the power grid of the intelligent IoT power distribution gateway of the IoT platform, evaluating the load status of the power grid under the IoT power distribution gateway specifically includes:

[0020] Obtain the historical power demand of target users of power supply of the power grid under the IoT platform and evaluate the basic power demand;

[0021] Determine the power demand for fluctuating capacity based on the future capacity plan of the target users of power supply;

[0022] The basic power demand and the fluctuating power demand are combined into the power demand characteristic data;

[0023] Based on multi-factor linear regression, a load forecasting model is established;

[0024] Based on the load forecasting model, the power demand characteristic data per unit time in the power demand characteristic data is used as the independent variable input, and the power grid load change trend in the future time is used as the dependent variable output. The predicted power grid load change trend exceeds the maximum carrying capacity of the power grid and is recorded as the overload moment;

[0025] The load forecasting model expression is:

[0026] P(t)=β0+β1·G basc (t)+β2·G var (t)+β i ·G i (t)+…+β n ·G n (t)+∈

[0027] Where P(t) is the overload moment, G basc (t) is the basic power demand per unit time, G var (t) is the fluctuating power demand per unit time, G i (t) is the electricity demand under the influence of the ith independent variable in t unit time, n is the total number of independent variables, β0 is the intercept term, ∈ is the error term, β1, β2, β i , β n are the regression coefficients of the model.

[0028] Preferably, based on the load state of the power grid under the IoT power distribution gateway, the overload moment of the power grid is monitored, and a power grid intelligent dispatching model is established. The generated intelligent IoT power distribution gateway control scheme specifically includes:

[0029] Determine the abnormal risk probability of the power grid operation status under the IoT distribution gateway of the IoT platform;

[0030] Determine the power dispatching cost based on the connected substations and connecting lines of the power grid;

[0031] Obtain the real-time power redundancy value of the power grid under the IoT platform, select the power redundancy value that meets the power demand of the power grid when it is overloaded, and record it as the auxiliary power supply network;

[0032] The abnormal risk probability of the power grid operation status under the IoT distribution gateway of the IoT platform is used as the first allocation condition with the minimum abnormal risk probability of the power grid;

[0033] The minimum power dispatching cost of the power grid power redundancy value under the Internet of Things platform that meets the power demand at the time of power grid overload is used as the second allocation condition;

[0034] Construct a smart grid dispatching model;

[0035] Based on the intelligent dispatching model of the power grid, the power demand at the time of power grid overload is used as characteristic data as the model input, and the power supply path label data of the power grid that meets the power demand at the time of power grid overload under the constraints of the first allocation condition and the second allocation condition is used as the output;

[0036] Among them, the expression of the power grid intelligent dispatching model is:

[0037]

[0038] Where Z is the objective function of the power grid intelligent dispatching model, W r The weights of the r routes are assigned according to the cost of the dispatching route, h T is the overload moment, H k is the power redundancy value of the kth power grid that meets the power demand at the moment of power grid overload, D is the abnormal risk probability of the power grid, and r is the power dispatch route cost.

[0039] Furthermore, the same inventive conception of the intelligent IoT power distribution gateway control method based on the IoT platform proposes an intelligent IoT power distribution gateway control system based on the IoT platform, including:

[0040] The power gateway data acquisition module is used to obtain the historical operation data of the power grid of the intelligent IoT distribution gateway of the IoT platform;

[0041] The power grid status assessment module is connected to the power gateway data acquisition module by wires. The power grid status assessment module uses the historical power grid operation data of the intelligent IoT power distribution gateway based on the IoT platform to assess the power grid operation status under the IoT power distribution gateway.

[0042] A power grid load data acquisition module, which is used to obtain historical load data of the power grid;

[0043] A power grid load state evaluation module, the power grid load state evaluation module is connected to the power grid load data acquisition module by wires, and the power grid load state evaluation module is used to evaluate the power grid load state under the Internet of Things power distribution gateway based on the historical load data of the power grid;

[0044] The intelligent power distribution gateway dispatching module is connected to the power grid state assessment module and the power grid load state assessment module by wires. The intelligent power distribution gateway dispatching module is used to monitor the overload moment of the power grid based on the power grid load state under the Internet of Things distribution gateway, establish a power grid intelligent dispatching model, and generate a control plan for the intelligent Internet of Things distribution gateway.

[0045] Optionally, the power grid status assessment module includes:

[0046] The abnormal event unit, based on the historical maintenance data of the power grid of the IoT power distribution gateway, screens out abnormal power grid events under the IoT power distribution gateway as abnormal event feature data;

[0047] The first model unit, based on logistic regression, establishes a grid failure risk probability prediction model for the IoT power distribution gateway;

[0048] The analysis and prediction unit, based on the power grid failure risk probability prediction model of the Internet of Things distribution gateway, takes the power grid abnormal event input under each Internet of Things distribution gateway in the abnormal event feature data, and takes the probability of abnormal risk of the power grid affected by the power grid abnormal event under the Internet of Things distribution gateway as the output.

[0049] Optionally, the power grid load status assessment module includes:

[0050] The basic power demand unit obtains the historical power demand of the target users of power supply of the power grid under the IoT platform and evaluates the basic power demand;

[0051] The fluctuating demand unit determines the fluctuating capacity power demand based on the future capacity plan of the target power supply users;

[0052] The feature annotation unit combines the basic power demand and the fluctuating capacity power demand into power demand feature data;

[0053] The second model unit establishes a load forecasting model based on multi-factor linear regression;

[0054] The load forecasting unit, based on the load forecasting model, takes the power demand characteristic data per unit time in the power demand characteristic data as the independent variable input, takes the power grid load change trend in the future time as the dependent variable output, and records the predicted power grid load change trend that exceeds the maximum carrying capacity of the power grid as the overload moment.

[0055] Optionally, the intelligent power distribution gateway scheduling module includes:

[0056] Risk probability confirmation unit, which determines the abnormal risk probability of the power grid operation status under the IoT distribution gateway of the IoT platform

[0057] The cost confirmation unit determines the power dispatching cost based on the connected substations and connecting lines of the power grid;

[0058] The auxiliary power supply unit obtains the real-time power redundancy value of the power grid under the IoT platform, and selects the power redundancy value that meets the power demand of the power grid when it is overloaded, which is recorded as the auxiliary power supply network;

[0059] The first condition unit uses the abnormal risk probability of the power grid operation state under the Internet of Things distribution gateway of the Internet of Things platform as the first allocation condition with the minimum abnormal risk probability of the power grid;

[0060] The second condition unit uses the minimum power dispatching cost of the power grid power redundancy value under the Internet of Things platform that meets the power demand at the time of power grid overload as the second allocation condition;

[0061] The third model unit builds a power grid intelligent dispatching model;

[0062] The dispatching scheme generating unit is based on the intelligent dispatching model of the power grid, takes the power demand at the time of power grid overload as the characteristic data as the model input, and takes the power grid power supply path label data that meets the power demand at the time of power grid overload under the constraints of the first allocation condition and the second allocation condition as the output.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] The present invention proposes an intelligent IoT distribution gateway control scheme based on the IoT platform. This scheme establishes an efficient intelligent grid dispatching model. The model can predict the load change trend of the grid, and can automatically generate the optimal intelligent IoT distribution gateway control scheme when the overload risk occurs, thereby ensuring the stable operation of the grid and the reliability of power supply. It can formulate personalized dispatching strategies according to the characteristics and needs of different grids, thereby improving the operation efficiency and intelligent management capabilities of the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a flow chart of the control method of the intelligent IoT power distribution gateway based on the IoT platform;

[0066] Figure 2 A flow chart of a method for evaluating the operation status of a power grid under an IoT power distribution gateway;

[0067] Figure 3 A flow chart of a method for evaluating the load status of a power grid under an IoT power distribution gateway;

[0068] Figure 4 A flow chart of the generated intelligent IoT power distribution gateway control solution method;

[0069] Figure 5 This is a framework diagram of the control system of the intelligent IoT distribution gateway based on the IoT platform. DETAILED DESCRIPTION

[0070] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0071] Reference Figure 1 As shown, the intelligent IoT power distribution gateway control method based on the IoT platform includes:

[0072] Obtain historical power grid operation data from the smart IoT distribution gateway of the IoT platform;

[0073] Based on the historical operation data of the power grid of the intelligent IoT power distribution gateway on the IoT platform, the operation status of the power grid under the IoT power distribution gateway is evaluated;

[0074] Obtain historical load data of the power grid;

[0075] Based on the historical load data of the power grid, evaluate the load status of the power grid under the IoT distribution gateway;

[0076] Based on the load status of the power grid under the IoT distribution gateway, the overload moment of the power grid is monitored, and a smart grid dispatching model is established to generate a control solution for the smart IoT distribution gateway.

[0077] This solution is based on the smart grid management strategy of the Internet of Things platform. It collects and analyzes the historical operation data and grid load data of the smart IoT distribution gateway, evaluates the operation status and load status of the grid, monitors the overload risk in real time, and uses the established intelligent scheduling model to automatically generate the optimal control solution to achieve efficient, stable and reliable operation of the grid. It can formulate personalized scheduling strategies according to the specific conditions of the grid and enhance the intelligent management capabilities of the grid.

[0078] Reference Figure 2 As shown in the figure, based on the historical operation data of the power grid of the intelligent IoT distribution gateway on the IoT platform, the evaluation of the power grid operation status under the IoT distribution gateway specifically includes:

[0079] Based on the historical maintenance data of the power grid of the IoT power distribution gateway, the abnormal events of the power grid under the IoT power distribution gateway are screened out as the characteristic data of the abnormal events;

[0080] Based on logistic regression, a grid failure risk probability prediction model for IoT power distribution gateways is established;

[0081] The power grid fault risk probability prediction model based on the IoT power distribution gateway takes the power grid abnormal event under each IoT power distribution gateway in the abnormal event feature data as input, and takes the power grid abnormal risk probability affected by the power grid abnormal event under the IoT power distribution gateway as output;

[0082] Among them, the grid failure risk probability prediction model expression of the IoT power distribution gateway is:

[0083]

[0084] Where D(Y=1|X) is the probability of abnormal risk of Y occurring in the power grid under the condition of historical maintenance log X, and X iis the ith abnormal power grid event in the historical maintenance log, e is an exponential function, V represents the total number of abnormal events, α0, α1, α i , α v are the coefficients of the model.

[0085] It is understandable that since the surrounding environment of power grid deployment is usually complex, it is necessary to predict the probability of abnormal risks in each power grid to reduce the fluctuation of the overall solution stability due to abnormalities in the power grid in the selected power route during subsequent power dispatch.

[0086] Reference Figure 3 As shown in the figure, based on the historical operation data of the power grid of the intelligent IoT distribution gateway on the IoT platform, the evaluation of the power grid load status under the IoT distribution gateway specifically includes:

[0087] Obtain the historical power demand of target users of power supply of the power grid under the IoT platform and evaluate the basic power demand;

[0088] Determine the power demand for fluctuating capacity based on the future capacity plan of the target users of power supply;

[0089] The basic power demand and the fluctuating power demand are combined into the power demand characteristic data;

[0090] Based on multi-factor linear regression, a load forecasting model is established;

[0091] Based on the load forecasting model, the power demand characteristic data per unit time in the power demand characteristic data is used as the independent variable input, and the power grid load change trend in the future time is used as the dependent variable output. The predicted power grid load change trend exceeds the maximum carrying capacity of the power grid and is recorded as the overload moment;

[0092] The load forecasting model expression is:

[0093] P(t)=β0+β1·G basc (t)+β2·G var (t)+β i ·G i (t)+…+β n ·G n (t)+∈

[0094] Where P(t) is the overload moment, G basc (t) is the basic power demand per unit time, G var (t) is the fluctuating power demand per unit time, G i (t) is the electricity demand under the influence of the ith independent variable in t unit time, n is the total number of independent variables, β0 is the intercept term, ∈ is the error term, β1, β2, β i , βn are the regression coefficients of the model.

[0095] It can be understood that the load forecasting model is a multi-factor linear regression model, in which the fluctuating capacity power demand refers to the surge in power demand of the current power grid's target supply users in a short period of time. For example, a power grid's supply target is a metal smelting processing plant. The power demand is usually more balanced during business slow periods, but when a larger order is received, the power demand will surge in a short period of time, causing the power grid's supply to be loaded.

[0096] In some embodiments, factors that lead to a surge in electricity demand include, but are not limited to, holidays, weather factors, and adjustments in electricity prices. When constructing a load forecasting model, multiple influencing factors can be used as feature data to predict future electricity demand, which will not be described in detail here.

[0097] Reference Figure 4 As described above, based on the load state of the power grid under the IoT power distribution gateway, the overload moment of the power grid is monitored, and a power grid intelligent dispatching model is established. The generated intelligent IoT power distribution gateway control scheme specifically includes:

[0098] Determine the abnormal risk probability of the power grid operation status under the IoT distribution gateway of the IoT platform;

[0099] Determine the power dispatching cost based on the connected substations and connecting lines of the power grid;

[0100] Obtain the real-time power redundancy value of the power grid under the IoT platform, select the power redundancy value that meets the power demand of the power grid when it is overloaded, and record it as the auxiliary power supply network;

[0101] The abnormal risk probability of the power grid operation status under the IoT distribution gateway of the IoT platform is used as the first allocation condition with the minimum abnormal risk probability of the power grid;

[0102] The minimum power dispatching cost of the power grid power redundancy value under the Internet of Things platform that meets the power demand at the time of power grid overload is used as the second allocation condition;

[0103] Construct a smart grid dispatching model;

[0104] Based on the intelligent dispatching model of the power grid, the power demand at the time of power grid overload is used as characteristic data as the model input, and the power supply path label data of the power grid that meets the power demand at the time of power grid overload under the constraints of the first allocation condition and the second allocation condition is used as the output;

[0105] Among them, the expression of the power grid intelligent dispatching model is:

[0106]

[0107] Where Z is the objective function of the power grid intelligent dispatching model, W r The weights of the r routes are assigned according to the cost of the dispatching route, h T is the overload moment, H k is the power redundancy value of the kth power grid that meets the power demand at the moment of power grid overload, D is the abnormal risk probability of the power grid, and r is the power dispatch route cost.

[0108] This solution constructs a smart grid dispatching model by analyzing the risk probability and power dispatching cost of the power grid. The model uses the latest risk probability and the minimum power dispatching cost as the basic conditions to generate several dispatching schemes. The scheme with the minimum risk probability and power dispatching cost is taken as the optimal implementation plan to complete the smart IoT distribution gateway control of the IoT platform.

[0109] Reference Figure 5 As shown, the same invention concept as the control method of the intelligent IoT power distribution gateway based on the IoT platform is proposed, and an intelligent IoT power distribution gateway control system based on the IoT platform is proposed, including:

[0110] The power gateway data acquisition module is used to obtain the historical operation data of the power grid of the intelligent IoT distribution gateway of the IoT platform;

[0111] The power grid status assessment module is connected to the power gateway data acquisition module by wires. The power grid status assessment module uses the historical power grid operation data of the intelligent IoT power distribution gateway based on the IoT platform to assess the power grid operation status under the IoT power distribution gateway.

[0112] A power grid load data acquisition module, which is used to obtain historical load data of the power grid;

[0113] A power grid load state evaluation module, the power grid load state evaluation module is connected to the power grid load data acquisition module by wires, and the power grid load state evaluation module is used to evaluate the power grid load state under the Internet of Things power distribution gateway based on the historical load data of the power grid;

[0114] The intelligent power distribution gateway dispatching module is connected to the power grid state assessment module and the power grid load state assessment module by wires. The intelligent power distribution gateway dispatching module is used to monitor the overload moment of the power grid based on the power grid load state under the Internet of Things distribution gateway, establish a power grid intelligent dispatching model, and generate a control plan for the intelligent Internet of Things distribution gateway.

[0115] The grid status assessment module includes:

[0116] The abnormal event unit, based on the historical maintenance data of the power grid of the IoT power distribution gateway, screens out abnormal power grid events under the IoT power distribution gateway as abnormal event feature data;

[0117] The first model unit, based on logistic regression, establishes a grid failure risk probability prediction model for the IoT power distribution gateway;

[0118] The analysis and prediction unit, based on the power grid failure risk probability prediction model of the Internet of Things distribution gateway, takes the power grid abnormal event input under each Internet of Things distribution gateway in the abnormal event feature data, and takes the probability of abnormal risk of the power grid affected by the power grid abnormal event under the Internet of Things distribution gateway as the output.

[0119] The power grid load status assessment module includes:

[0120] The basic power demand unit obtains the historical power demand of the target users of power supply of the power grid under the IoT platform and evaluates the basic power demand;

[0121] The fluctuating demand unit determines the fluctuating capacity power demand based on the future capacity plan of the target power supply users;

[0122] The feature annotation unit combines the basic power demand and the fluctuating capacity power demand into power demand feature data;

[0123] The second model unit establishes a load forecasting model based on multi-factor linear regression;

[0124] The load forecasting unit, based on the load forecasting model, takes the power demand characteristic data per unit time in the power demand characteristic data as the independent variable input, takes the power grid load change trend in the future time as the dependent variable output, and records the predicted power grid load change trend that exceeds the maximum carrying capacity of the power grid as the overload moment.

[0125] The intelligent power distribution gateway dispatching module includes:

[0126] Risk probability confirmation unit, which determines the abnormal risk probability of the power grid operation status under the IoT distribution gateway of the IoT platform

[0127] The cost confirmation unit determines the power dispatching cost based on the connected substations and connecting lines of the power grid;

[0128] The auxiliary power supply unit obtains the real-time power redundancy value of the power grid under the IoT platform, and selects the power redundancy value that meets the power demand of the power grid when it is overloaded, which is recorded as the auxiliary power supply network;

[0129] The first condition unit uses the abnormal risk probability of the power grid operation state under the Internet of Things distribution gateway of the Internet of Things platform as the first allocation condition with the minimum abnormal risk probability of the power grid;

[0130] The second condition unit uses the minimum power dispatching cost of the power grid power redundancy value under the Internet of Things platform that meets the power demand at the time of power grid overload as the second allocation condition;

[0131] The third model unit builds a power grid intelligent dispatching model;

[0132] The dispatching scheme generating unit is based on the intelligent dispatching model of the power grid, takes the power demand at the time of power grid overload as the characteristic data as the model input, and takes the power grid power supply path label data that meets the power demand at the time of power grid overload under the constraints of the first allocation condition and the second allocation condition as the output.

[0133] The use process of the intelligent IoT distribution gateway control system based on the IoT platform is as follows:

[0134] Step 1: Obtain the historical operation data of the power grid of the smart IoT distribution gateway of the IoT platform;

[0135] Step 2: Based on the historical maintenance data of the power grid of the IoT power distribution gateway, the abnormal events of the power grid under the IoT power distribution gateway are screened out as the abnormal event feature data;

[0136] Step 3: Based on logistic regression, a grid failure risk probability prediction model for the IoT power distribution gateway is established;

[0137] Step 4: Based on the power grid failure risk probability prediction model of the IoT power distribution gateway, the abnormal event of each power grid under the IoT power distribution gateway in the abnormal event feature data is input, and the abnormal risk probability of the power grid affected by the abnormal event under the IoT power distribution gateway is output;

[0138] Step 6: Obtain historical load data of the power grid;

[0139] Step 7: Obtain the historical power demand of the target users of the power supply of the power grid under the IoT platform and evaluate the basic power demand;

[0140] Step 8: Determine the fluctuating capacity power demand based on the future capacity plan of the target power supply users;

[0141] Step 9: Combine the basic power demand and the fluctuating power demand into power demand characteristic data;

[0142] Step 10: Establish a load forecasting model based on multi-factor linear regression;

[0143] Step 11: Based on the load forecasting model, the power demand characteristic data per unit time in the power demand characteristic data is used as the independent variable input, and the power grid load change trend in the future time is used as the dependent variable output, and the predicted power grid load change trend exceeds the maximum carrying capacity of the power grid, which is recorded as the overload moment;

[0144] Step 12: Determine the abnormal risk probability of the power grid operation status under the IoT distribution gateway of the IoT platform

[0145] Step 13: Determine the power dispatching cost based on the connected substations and connected lines of the power grid;

[0146] Step 14: Obtain the real-time power redundancy value of the power grid under the IoT platform, select the power redundancy value that meets the power demand of the power grid when it is overloaded, and record it as the auxiliary power supply network;

[0147] Step 15: The abnormal risk probability of the power grid operation status under the IoT distribution gateway of the IoT platform is used as the first allocation condition with the minimum abnormal risk probability of the power grid;

[0148] Step 16: Taking the minimum power dispatching cost of the power grid power redundancy value under the Internet of Things platform that meets the power demand at the time of power grid overload as the second allocation condition;

[0149] Step 17: Construct a power grid intelligent dispatching model;

[0150] Step 18: Based on the intelligent dispatching model of the power grid, the power demand at the time of power grid overload is used as the characteristic data as the model input, and the power supply path label data of the power grid that meets the power demand at the time of power grid overload under the constraints of the first allocation condition and the second allocation condition is used as the output.

[0151] In summary, the advantages of the present invention are: by establishing an efficient intelligent power grid dispatching model, the model can predict the load change trend of the power grid, and can also automatically generate the optimal intelligent IoT distribution gateway control solution when the risk of overload occurs, thereby ensuring the stable operation of the power grid and the reliability of power supply, and can formulate personalized dispatching strategies according to the characteristics and needs of different power grids, thereby improving the operation efficiency and intelligent management capabilities of the power grid.

[0152] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A control method for an intelligent IoT power distribution gateway based on an IoT platform, characterized in that: include: Obtain historical power grid operation data from the smart IoT distribution gateway of the IoT platform; Based on the historical operation data of the power grid of the intelligent IoT power distribution gateway on the IoT platform, the operation status of the power grid under the IoT power distribution gateway is evaluated; Obtain historical load data of the power grid; Based on the historical load data of the power grid, evaluate the load status of the power grid under the IoT distribution gateway; Based on the load status of the power grid under the IoT distribution gateway, monitor the overload moment of the power grid, establish the intelligent dispatching model of the power grid, and generate the control solution of the intelligent IoT distribution gateway; Establish a smart grid dispatching model, and generate a smart IoT distribution gateway control solution that includes: Determine the abnormal risk probability of the power grid operation status under the IoT distribution gateway of the IoT platform; Obtain the power redundancy value of the power grid under the Internet of Things platform; Based on the abnormal risk probability of the power grid operation status under the IoT distribution gateway of the IoT platform, the power grid is classified into low-risk power grid and high-risk power grid; Determine power demand at times of grid overload; The power demand characteristic data at the time of power grid overload and the power grid power redundancy value under the Internet of Things platform are used as characteristic data, and the low-risk power grid and high-risk power grid are used as label data; The abnormal risk probability of the power grid operation status under the IoT distribution gateway of the IoT platform is used as the first allocation condition with the minimum abnormal risk probability of the power grid; The power redundancy value of the power grid under the IoT platform that meets the power demand at the time of power grid overload and the lowest dispatch route cost are set as the second allocation condition; Construct a smart grid dispatching model; Based on the intelligent dispatching model of the power grid, the power demand at the time of power grid overload is used as the characteristic data as the input of the model, and the low-risk power grid label data that meets the power demand at the time of power grid overload under the constraints of the first allocation condition and the second allocation condition is used as the output.

2. The control method of the intelligent IoT power distribution gateway based on the IoT platform according to claim 1 is characterized in that: Based on the historical operation data of the power grid of the intelligent IoT distribution gateway on the IoT platform, the evaluation of the power grid operation status under the IoT distribution gateway specifically includes: Based on the power grid and historical maintenance log of the IoT power distribution gateway, abnormal power grid events under the IoT power distribution gateway are screened out; Based on logistic regression, a grid failure risk probability prediction model for the IoT power distribution gateway is established; The grid fault risk probability prediction model based on the IoT power distribution gateway takes the grid abnormal events under each IoT power distribution gateway in the historical maintenance log as input, and takes the probability of abnormal risk of the grid affected by the grid abnormal events under the IoT power distribution gateway as output; Among them, the grid failure risk probability prediction model expression of the IoT power distribution gateway is: Where D(Y=1|X) is the probability of abnormal risk of Y occurring in the power grid under the condition of historical maintenance log X, and X i is the ith abnormal power grid event in the historical maintenance log, e is an exponential function, V represents the total number of abnormal events, α0, α1, α i , α v are the coefficients of the model.

3. The control method of the intelligent IoT power distribution gateway based on the IoT platform according to claim 2 is characterized in that: Based on the historical operation data of the power grid of the intelligent IoT distribution gateway on the IoT platform, the evaluation of the power grid load status under the IoT distribution gateway specifically includes: Obtain historical load data of the power grid under the IoT platform; Based on the historical load data of the power grid under the Internet of Things platform, feature marking is performed according to the power grid load data per unit time to obtain the time series load characteristics; Establish load forecasting model; Based on the load forecasting model, the time series load characteristics of the power grid load data under unit time are taken as input, and the power grid load change trend in the future time is taken as output. The predicted power grid load change trend exceeds the maximum carrying capacity of the power grid and is recorded as the overload moment; The load forecasting model expression is: h T =β0+β1X T-1 +b i X T-i +…+b n X T-n +∈T In the formula, h T is the grid load parameter for the Tth unit time in the future, X T-i is the grid load parameter of Ti unit time in history, β0, β1, β i , β n are all model coefficients, and ∈T is a random fluctuation term.

4. The control method of the intelligent IoT power distribution gateway based on the IoT platform according to claim 3 is characterized in that: The power grid intelligent dispatching model expression is: Where Z is the objective function of the power grid intelligent dispatching model, W r is the weight of the rth route, which is assigned according to the scheduling route cost, L T To meet the power demand of the power grid at the time of overload, D is the minimum abnormal risk probability of the power grid, S T is the power redundancy value of T power grid in T unit time.

5. Intelligent IoT power distribution gateway control system based on IoT platform, characterized in that: The method for controlling an intelligent IoT power distribution gateway based on an IoT platform according to any one of claims 1 to 4 comprises: The power gateway data acquisition module is used to obtain the historical operation data of the power grid of the intelligent IoT distribution gateway of the IoT platform; A power grid status assessment module, which is connected to the power gateway data acquisition module by wires. The power grid status assessment module is used to evaluate the power grid operation status under the power distribution gateway of the Internet of Things based on the historical operation data of the power grid of the intelligent Internet of Things distribution gateway on the Internet of Things platform; A power grid load data acquisition module is used to obtain historical load data of the power grid; A power grid load state evaluation module, the power grid load state evaluation module is connected to the power grid load data acquisition module by wires, and the power grid load state evaluation module is used to evaluate the power grid load state under the Internet of Things power distribution gateway based on the historical load data of the power grid; The intelligent power distribution gateway dispatching module is connected to the power grid state assessment module and the power grid load state assessment module by wires. The intelligent power distribution gateway dispatching module is used to monitor the overload moment of the power grid based on the power grid load state under the Internet of Things distribution gateway, establish a power grid intelligent dispatching model, and generate a control plan for the intelligent Internet of Things distribution gateway.

6. The intelligent IoT power distribution gateway control system based on the IoT platform according to claim 5 is characterized in that: The grid status assessment module includes: The abnormal event unit, based on the power grid,historical maintenance log of the IoT power distribution gateway,,screens out abnormal power grid events under the IoT power distribution gateway; The first model unit, based on logistic regression, establishes a grid failure risk probability prediction model for the IoT power distribution gateway; The analysis and prediction unit, based on the power grid failure risk probability prediction model of the Internet of Things distribution gateway, takes the power grid abnormal events under each Internet of Things distribution gateway in the historical maintenance log as input, and takes the probability of abnormal risk of the power grid affected by the power grid abnormal events under the Internet of Things distribution gateway as output.

7. The intelligent IoT power distribution gateway control system based on the IoT platform according to claim 5 is characterized in that: The power grid load status assessment module includes: A historical load data unit, which obtains the historical load data of the power grid under the IoT platform; The feature labeling unit performs feature labeling based on the historical load data of the power grid under the Internet of Things platform and the power grid load data under unit time to obtain the time series load characteristics; The second model unit establishes a load forecasting model; The load forecasting unit, based on the load forecasting model, takes the time series load characteristics of the power grid load data under unit time as input, and takes the power grid load change trend in the future as output. The predicted power grid load change trend exceeds the maximum carrying capacity of the power grid and is recorded as the overload moment.

8. The intelligent IoT power distribution gateway control system based on the IoT platform according to claim 5 is characterized in that: The intelligent power distribution gateway dispatching module includes: A risk probability confirmation unit, which determines the abnormal risk probability of the power grid operation status under the IoT distribution gateway of the IoT platform; A power redundancy confirmation unit, which obtains the power redundancy value of the power grid under the Internet of Things platform; A classification unit, which classifies the power grid based on the abnormal risk probability of the power grid operation status under the Internet of Things distribution gateway of the Internet of Things platform, and obtains low-risk power grids and high-risk power grids; Demand unit, which determines the power demand at the moment of grid overload; The feature label unit uses the power demand feature data at the time of power grid overload and the power grid power redundancy value under the Internet of Things platform as feature data, and uses the low-risk power grid and high-risk power grid as label data; The first condition unit uses the abnormal risk probability of the power grid operation state under the Internet of Things distribution gateway of the Internet of Things platform as the first allocation condition with the minimum abnormal risk probability of the power grid; The second condition unit sets the power redundancy value of the power grid under the Internet of Things platform that meets the power demand at the time of power grid overload and the lowest dispatch route cost as the second allocation condition; The third model unit builds a power grid intelligent dispatching model; The dispatching scheme generating unit is based on the intelligent dispatching model of the power grid, takes the power demand at the time of power grid overload as the characteristic data as the model input, and takes the low-risk power grid label data that meets the power demand at the time of power grid overload under the constraints of the first allocation condition and the second allocation condition as the output.

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