Microgrid measurement and control method and system based on Internet of Things

Abstract

The invention discloses a micro-grid measurement and control method and system based on the Internet of Things, and the method comprises the steps: obtaining a power grid distribution diagram in an industrial park, collecting the operation data of an operation device in each factory in real time, and obtaining the production plan and environment data of each factory, dividing the interior of the industrial park into a plurality of micro-grids based on the power grid distribution diagram, analyzing the influence of the production plan and the environmental data on the power demand of each factory, obtaining a power demand prediction model of each operation device, putting the production plan into the corresponding power demand prediction model, and predicting the power demand of each operation device according to the power demand prediction model. According to the method, the power consumption demand of each factory in each time period in the future is obtained, the power consumption demand is compared with the electric quantity output in the corresponding micro-grid, and an energy supply and demand balance graph among the micro-grids is formulated, and the method has the advantages of reducing dependence on fossil fuel and improving the energy utilization efficiency.

Description

Technical Field

[0001] The present invention relates to the field of microgrid measurement and control technology, and in particular to a microgrid measurement and control method and system based on the Internet of Things. Background Art

[0002] Microgrid is a small, flexible and efficient power system that integrates multiple energy sources (such as solar energy, wind energy, energy storage equipment, etc.) and can work in a mode connected to the main grid or independently. Through intelligent measurement, control and management systems, microgrids can monitor and adjust energy production, storage and distribution in real time to ensure the stability and reliability of power supply. It not only improves energy utilization efficiency and reduces dependence on traditional fossil fuels, but also enhances the disaster resistance and emergency response capabilities of the power system. However, the energy utilization efficiency of existing industrial parks still needs to be improved, especially the lack of attention to the utilization of energy such as waste heat and waste cooling. The utilization rate of industrial waste heat and waste cooling is low, and energy has not been fully and comprehensively utilized. There is still a great dependence on traditional fossil fuels. In addition, the electricity demand forecast of each factory in the existing industrial park is insufficient, resulting in a certain lag in the power distribution and management of each microgrid, which cannot accurately meet the actual electricity demand of each factory, thereby affecting the energy utilization efficiency and economic benefits of the entire industrial park. Therefore, it is necessary to design a microgrid measurement and control method and system based on the Internet of Things to reduce dependence on fossil fuels and improve energy utilization efficiency. Summary of the invention

[0003] The purpose of the present invention is to provide a microgrid measurement and control method and system based on the Internet of Things to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a microgrid measurement and control method based on the Internet of Things, the operation steps of the method include: Step S1: Obtain a power grid distribution map within the industrial park, collect the operating data of the operating devices in each factory in real time, and obtain the production plan and environmental data of each factory, wherein the operating devices include: surplus energy recovery devices, production capacity devices, and other devices, and the other devices are other power-consuming devices in the factory operation except the production devices; Step S2: dividing the industrial park into multiple microgrids based on the power grid distribution map, analyzing the impact of the production plan and the environmental data on the power demand of each factory, and obtaining a power demand prediction model for each operating device; Step S3: Put the production plan into the corresponding power demand forecasting model to obtain the power demand of each factory in each future time period, compare the power demand with the power output in the corresponding microgrid, and formulate an energy supply and demand balance diagram between the microgrids.

[0005] Furthermore, the step S2 further comprises the following steps: Step S21: According to the power grid distribution map of the industrial park, combined with the geographical location, production scale and energy consumption characteristics of the factory, the industrial park is divided into multiple microgrids, and each operating device in the industrial park is matched to a corresponding factory; Step S22: Associating the production device with the corresponding production plan, analyzing the power demand of each production device in different production plans and building a power demand prediction model, analyzing the deviation between the power demand prediction model and the actual power demand, and adjusting the power demand prediction model, wherein the production plan includes: a historical production plan and a future production plan; Step S23: obtaining the factory's shift plan according to the historical production plan, classifying the other devices in combination with the environmental data, and predicting the power demand of each other device according to the classification.

[0006] Furthermore, the step S22 further includes the following steps: Step S221: Classifying the energy generating devices into recoverable energy generating devices and common energy generating devices, and associating the surplus energy recovery devices with corresponding recoverable devices, wherein the recoverable energy generating devices refer to the surplus energy that can be generated during operation and recovered by the surplus energy recovery devices for power generation; Step S222: sorting out the production capacity devices and power consumption patterns required to be operated in different production plans according to the historical production plans, and obtaining the power consumption curves of each production capacity device in each time period within the production cycle in different production plans according to the order quantity and production cycle of the production plans; Step S223: Smoothing the curve to obtain the power consumption curves of different production plans under the same environment, and establishing a power demand prediction model of the power consumption curve, the order quantity and the production cycle through support vector regression: , where Indicates The weight coefficients of the support vectors, Indicates A support vector containing order quantity and production cycle, represents the input vector, represents the kernel function, Represents the bias term of the model.

[0007] Furthermore, the step S223 further includes the following steps: Step S2231: extracting production plans under different environments, putting corresponding order quantities and production cycles into the power demand forecasting model to simulate the power demand curve, and calculating the deviation value between the power demand curve and the actual power demand curve. When the deviation value is within the deviation threshold range, it indicates that the power demand of the production capacity device is not affected by the environmental data, and the power demand forecasting model is not modified. Step S2232: When the deviation value exceeds the deviation threshold interval, it indicates that the power demand of the energy generation device is affected by the environmental data, and training is performed based on the characteristic difference between the deviation value and the environmental data to obtain the environmental energy efficiency factor of the environmental data on the power consumption curve, and the environmental energy efficiency factor is integrated into the original power demand forecasting model to obtain a new power demand forecasting model; Step S2231: When the power generation device is a recyclable power generation device, obtain the power generation curve of the surplus energy recovery device under the corresponding power consumption curve, obtain the surplus energy power generation prediction model according to the power demand of the associated recyclable power generation device and the corresponding environmental data, subtract the corresponding power generation prediction model from the power demand prediction model of the recyclable power generation device, and the recyclable power generation device obtains a new power demand prediction model.

[0008] Furthermore, the step S23 further includes the following steps: Step S231: Classifying the other devices according to the shift schedule and the environmental data, and dividing the other devices into stable other devices and unstable other devices, wherein the unstable other devices refer to devices used under the influence of the environmental data, and vice versa, the unstable other devices are the stable other devices; Step S232: forecasting the daily electricity demand of the other stable devices, obtaining the electricity demand curve of each other stable device one month before the forecast time, and taking the daily similar electricity demand curve as the electricity demand forecast model; Step S233: Obtain the environmental data characteristic influencing factor for the unstable other device, formulate the environmental data impact usage value for the historical operation data of the unstable other device, and when the environmental data reaches the usage value, predict the unstable other device and build a power demand prediction model based on the historical operation data.

[0009] Furthermore, the step S3 further comprises the following steps: Step S31: putting the future production plan into the power demand prediction model of each operating device, obtaining the power demand of each operating device in each future time period, and obtaining the power generation of each new energy power generation device in the industrial park in each future time period according to the environmental data; Step S32: marking the prediction results of the power generation or the power demand in the form of a bar graph of uniform height at the corresponding position of the power grid distribution map, and dynamically updating according to the real-time measurement and control data, wherein the closer to the bottom of the bar graph, the later the predicted time period; Step S33: Calculate the ratio A of the power output and consumption of each microgrid according to the power generation or power demand predicted in the bar graph. When A>1, mark the corresponding time period of the bar graph at each end of the microgrid in green. When A=1, mark the corresponding time period of the bar graph at each end of the microgrid in yellow. When A<1, mark the corresponding time period of the bar graph at each end of the microgrid in red. Develop an energy supply and demand balance diagram between the microgrids. Step S34: Calculate the total power generation and total power demand in the industrial park, compare the total power generation with the total power demand, and when the total power generation is greater than the total power demand, provide power to the first power generation end and the second power consumption end of the microgrid marked in red from the first power generation end of the microgrid marked in green during the corresponding time period; when the total power generation is less than the total power demand, request power supply from the energy storage system or the main power grid in the industrial park.

[0010] Furthermore, the system includes a data acquisition module and a power demand model building module: The data acquisition module is used to obtain the power grid distribution map in the industrial park, collect the operating data of the operating devices in each factory in real time, and obtain the production plan and environmental data of each factory, wherein the operating devices include: surplus energy recovery devices, production capacity devices, and other devices; The electricity demand model building module is used to divide the industrial park into multiple microgrids based on the power grid distribution map, analyze the impact of the production plan and the environmental data on the electricity demand of each factory, and obtain the electricity demand prediction model of each operating device.

[0011] Furthermore, the system also includes an energy supply and demand balance scheduling module: The energy supply and demand balance scheduling module is used to put the production plan into the corresponding electricity demand prediction model, obtain the electricity demand of each factory in each future time period, compare the electricity demand with the corresponding power output in the microgrid, and formulate an energy supply and demand balance diagram between the microgrids.

[0012] The third aspect of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the electronic device implements the method described in the first aspect of the present application.

[0013] The fourth aspect of the present application provides a computer-readable storage medium, which is used to store a computer program. When the computer program runs on a computer, the computer executes the method described in the first aspect of the present application.

[0014] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention can more effectively balance the energy supply and demand between the microgrids by dividing the industrial park into multiple microgrids and predicting the electricity demand of each factory based on the production plan and environmental data, and proposes a variety of methods to optimize the electricity demand prediction model, including adjusting model parameters according to historical data, considering the influence of environmental data, and special processing methods for new factories or new equipment. According to the predicted power generation and electricity demand, the present invention can formulate an energy supply and demand balance diagram, and perform energy scheduling and optimal configuration according to actual conditions, thereby reducing dependence on fossil fuels and improving system stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 A schematic diagram of a flow chart of a microgrid measurement and control method based on the Internet of Things provided in Example 1 of the present invention.

[0016] Figure 2 A schematic diagram of the module composition of a microgrid measurement and control system based on the Internet of Things provided in Example 2 of the present invention.

[0017] Figure 3 is a schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0019] This embodiment can be applied to the scenario of distributed energy management in industrial parks. The method can be executed by a microgrid measurement and control system based on the Internet of Things provided by this embodiment. Figure 1 A flowchart diagram of a microgrid measurement and control method based on the Internet of Things provided in Embodiment 1 of the present invention, the method specifically comprises the following steps: Step S1: Obtain a power grid distribution map within the industrial park, collect the operating data of the operating devices in each factory in real time, and obtain the production plan and environmental data of each factory, wherein the operating devices include: surplus energy recovery devices, production capacity devices, and other devices, and the other devices are other power-consuming devices in the factory operation except the production devices; Step S2: dividing the industrial park into multiple microgrids based on the power grid distribution map, analyzing the impact of the production plan and the environmental data on the power demand of each factory, and obtaining a power demand prediction model for each operating device; Step S3: Put the production plan into the corresponding power demand forecasting model to obtain the power demand of each factory in each future time period, compare the power demand with the power output in the corresponding microgrid, and formulate an energy supply and demand balance diagram between the microgrids.

[0020] In a specific embodiment, by obtaining the power grid distribution map in the industrial park and collecting the operation data, production plan and environmental data of the operating devices in each factory in real time, a comprehensive grasp of the energy status of the industrial park is achieved. This provides a detailed data foundation for subsequent energy management and optimization, ensuring the accuracy and effectiveness of energy management.

[0021] In addition, the industrial park was divided into microgrids based on the power grid distribution map, and the impact of production plans and environmental data on the electricity demand of each factory was deeply analyzed, and the electricity demand forecasting model of each operating device was successfully obtained. This step not only improves the accuracy of energy forecasting, but also can flexibly adjust the forecasting model according to the production characteristics and environmental changes of different factories, providing strong support for the formulation of scientific energy management strategies. The production plan is placed in the corresponding electricity demand forecasting model, and the electricity demand of each factory in the future time period is accurately obtained. It is compared with the power output in the corresponding microgrid, and the energy supply and demand balance diagram between each microgrid is formulated. Real-time monitoring and dynamic adjustment of the energy supply and demand situation in the industrial park are realized, which helps to timely discover and solve the problem of imbalance in energy supply and demand, improve energy utilization efficiency, and reduce energy costs.

[0022] In some preferred embodiments, step S2 further comprises the following steps: Step S21: According to the power grid distribution map of the industrial park, combined with the geographical location, production scale and energy consumption characteristics of the factory, the industrial park is divided into multiple microgrids, and each operating device in the industrial park is matched to a corresponding factory; Step S22: Associating the production device with the corresponding production plan, analyzing the power demand of each production device in different production plans and building a power demand prediction model, analyzing the deviation between the power demand prediction model and the actual power demand, and adjusting the power demand prediction model, wherein the production plan includes: a historical production plan and a future production plan; Step S23: obtaining the factory's shift plan according to the historical production plan, classifying the other devices in combination with the environmental data, and predicting the power demand of each other device according to the classification.

[0023] Specifically, by dividing the industrial park into multiple microgrids according to the power grid distribution map of the industrial park, the geographical location of the factory, the production scale and energy consumption characteristics, and matching each operating device, the refined division and optimal configuration of the industrial park energy management are realized, and the energy utilization efficiency is improved. By associating the production equipment with the corresponding production plan, building a power demand prediction model, and analyzing the deviation value between the prediction model and the actual power demand for adjustment, the power demand of each production device under different production plans is accurately predicted, which helps the industrial park to better plan and dispatch power resources and reduce energy waste. By combining the scheduling plan of the historical production plan and environmental data to classify other devices and predict the power demand of each type of device, the flexibility and adaptability of the energy management of the industrial park are further improved, and the energy allocation strategy can be adjusted in time according to different situations to ensure the stable operation of each device and the reliability of energy supply.

[0024] In some optional embodiments, the step S2 further includes: when the historical data of the new factory in the industrial park is insufficient, similarity matching is performed on each operating device in the new factory to obtain similar operating devices, and the power demand prediction model of the similar operating devices is used to predict the power demand: , where Represents the scaling factor for adjusting the electricity demand of similarly operating plants to accommodate the new plant.

[0025] In some preferred embodiments, the step S22 further comprises the following steps: Step S221: Classifying the energy generating devices into recoverable energy generating devices and common energy generating devices, and associating the surplus energy recovery devices with corresponding recoverable devices, wherein the recoverable energy generating devices refer to the surplus energy that can be generated during operation and recovered by the surplus energy recovery devices for power generation; Step S222: sorting out the production capacity devices and power consumption patterns required to be operated in different production plans according to the historical production plans, and obtaining the power consumption curves of each production capacity device in each time period within the production cycle in different production plans according to the order quantity and production cycle of the production plans; Step S223: Smoothing the curve to obtain the power consumption curves of different production plans under the same environment, and establishing a power demand prediction model of the power consumption curve, the order quantity and the production cycle through support vector regression: , where Indicates The weight coefficients of the support vectors, Indicates A support vector containing order quantity and production cycle, represents the input vector, represents the kernel function, Represents the bias term of the model.

[0026] Specifically, through the precise association between the classification of production capacity devices and the residual energy recovery devices, efficient recovery and utilization of residual energy is achieved. At the same time, based on the historical production plans, the electricity consumption patterns of production capacity devices in different production plans are sorted out, and combined with the order volume and production cycle, a power demand forecasting model is successfully constructed. The model uses the support vector regression method to smooth the power consumption curve and obtain the power demand forecast for different production plans under the same environment. This not only improves the level of refinement of energy management, but also effectively predicts future power demand, providing strong support for the reasonable scheduling of production capacity devices and the optimal allocation of energy, thereby achieving multiple benefits of energy conservation and emission reduction, improving energy utilization efficiency and controlling production costs.

[0027] In some preferred embodiments, the step S223 further comprises the following steps: Step S2231: extracting production plans under different environments, putting corresponding order quantities and production cycles into the power demand forecasting model to simulate the power demand curve, and calculating the deviation value between the power demand curve and the actual power demand curve. When the deviation value is within the deviation threshold range, it indicates that the power demand of the production capacity device is not affected by the environmental data, and the power demand forecasting model is not modified. Step S2232: When the deviation value exceeds the deviation threshold interval, it indicates that the power demand of the energy generation device is affected by the environmental data, and training is performed based on the characteristic difference between the deviation value and the environmental data to obtain the environmental energy efficiency factor of the environmental data on the power consumption curve, and the environmental energy efficiency factor is integrated into the original power demand forecasting model to obtain a new power demand forecasting model; Step S2231: When the power generation device is a recyclable power generation device, obtain the power generation curve of the surplus energy recovery device under the corresponding power consumption curve, obtain the surplus energy power generation prediction model according to the power demand of the associated recyclable power generation device and the corresponding environmental data, subtract the corresponding power generation prediction model from the power demand prediction model of the recyclable power generation device, and the recyclable power generation device obtains a new power demand prediction model.

[0028] Specifically, by extracting production plans under different environments and simulating power consumption curves, calculating the deviation value from the actual power consumption curve, an accurate judgment of the impact of environmental data on the power demand of the production capacity device is achieved. When the deviation value is within the set deviation threshold range, the effectiveness of the power demand prediction model is confirmed, unnecessary model modification is avoided, and the prediction efficiency is improved. When the deviation value exceeds the deviation threshold range, the model is trained according to the characteristic difference between the deviation value and the environmental data, and the environmental energy efficiency factor is obtained and integrated into the original power demand prediction model, thereby improving the model's adaptability to environmental changes and making the prediction results more accurate and reliable. In addition, for recyclable production capacity devices, a surplus energy power generation prediction model is constructed by obtaining the power generation curve of the surplus energy recovery device and combining the power demand and corresponding environmental data of the associated recyclable production capacity device. The power demand prediction model of the recyclable production capacity device is subtracted from the corresponding power generation prediction model to obtain a new power demand prediction model, which realizes the refined prediction of the power demand of the recyclable production capacity device, improves energy utilization efficiency, and helps to achieve the goal of energy conservation and emission reduction.

[0029] In some preferred embodiments, step S23 further comprises the following steps: Step S231: Classifying the other devices according to the shift schedule and the environmental data, and dividing the other devices into stable other devices and unstable other devices, wherein the unstable other devices refer to devices used under the influence of the environmental data, and vice versa, the unstable other devices are the stable other devices; Step S232: forecasting the daily electricity demand of the other stable devices, obtaining the electricity demand curve of each other stable device one month before the forecast time, and taking the daily similar electricity demand curve as the electricity demand forecast model; Step S233: Obtain the environmental data characteristic influencing factor for the unstable other device, formulate the environmental data impact usage value for the historical operation data of the unstable other device, and when the environmental data reaches the usage value, predict the unstable other device and build a power demand prediction model based on the historical operation data.

[0030] Specifically, by incorporating future production plans into the electricity demand forecasting model of each operating device, and combining environmental data to predict the power generation of each new energy power generation device in the industrial park in each future time period, an accurate forecast of the power supply and demand situation in the industrial park is achieved. By marking the forecast results of power generation or power demand in the form of a bar chart of uniform height on the power grid distribution map, and dynamically updating it according to real-time measurement and control data, an intuitive visualization of the power supply and demand situation is achieved. This visualization method enables managers to quickly grasp the power supply and demand situation of each microgrid in the industrial park, making it easier to discover and respond to potential energy problems in a timely manner. According to the predicted power generation or power demand in the bar chart, the power output and consumption ratio of each microgrid is calculated, and the corresponding time period is marked with different colors according to the different ratios, and an energy supply and demand balance diagram between each microgrid is developed. An accurate assessment of the energy supply and demand balance in the industrial park is achieved, which helps managers to formulate targeted energy scheduling strategies and optimize energy allocation. Finally, by calculating and comparing the total power generation and total power demand in the industrial park, the energy supply strategy is flexibly adjusted according to the comparison results. For example, when the total power generation is greater than the total power demand, internal resources are prioritized to achieve energy balance; when the total power generation is less than the total power demand, power is requested from the energy storage system or the main grid in a timely manner. The flexibility and reliability of the energy supply in the industrial park are achieved, ensuring the stable operation of production.

[0031] In some preferred embodiments, step S3 further comprises the following steps: Step S31: putting the future production plan into the power demand prediction model of each operating device, obtaining the power demand of each operating device in each future time period, and obtaining the power generation of each new energy power generation device in the industrial park in each future time period according to the environmental data; Step S32: marking the prediction results of the power generation or the power demand in the form of a bar graph of uniform height at the corresponding position of the power grid distribution map, and dynamically updating according to the real-time measurement and control data, wherein the closer to the bottom of the bar graph, the later the predicted time period; Step S33: Calculate the ratio A of the power output and consumption of each microgrid according to the power generation or power demand predicted in the bar graph. When A>1, mark the corresponding time period of the bar graph at each end of the microgrid in green. When A=1, mark the corresponding time period of the bar graph at each end of the microgrid in yellow. When A<1, mark the corresponding time period of the bar graph at each end of the microgrid in red. Develop an energy supply and demand balance diagram between the microgrids. Step S34: Calculate the total power generation and total power demand in the industrial park, compare the total power generation with the total power demand, and when the total power generation is greater than the total power demand, provide power to the first power generation end and the second power consumption end of the microgrid marked in red from the first power generation end of the microgrid marked in green during the corresponding time period; when the total power generation is less than the total power demand, request power supply from the energy storage system or the main power grid in the industrial park.

[0032] Specifically, by classifying other devices according to the shift schedule and environmental data, and subdividing the devices into stable other devices and unstable other devices, accurate prediction of the power demand of different types of devices is achieved. For stable other devices, the accuracy and reliability of the prediction are improved by obtaining the power consumption curve of the month before the prediction time, and selecting the daily similar power consumption curve as the power demand prediction model. For unstable other devices, by obtaining the influencing factors of environmental data characteristics, and formulating the use value of environmental data influence based on historical operation data, a prediction is made when the environmental data reaches the use value, and a power demand prediction model is constructed based on historical operation data, thereby achieving flexible prediction of the power demand of devices affected by environmental data. This method comprehensively considers the impact of shift schedules and environmental data, improves the comprehensiveness and accuracy of power demand prediction, and helps optimize energy distribution and management.

[0033] Based on the same inventive concept as the above method embodiment, the embodiment of the present invention also provides a microgrid measurement and control system based on the Internet of Things. Figure 2 A schematic diagram of the module composition of a microgrid measurement and control system based on the Internet of Things provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the system includes a data acquisition module, a power demand model building module, and an energy supply and demand balance scheduling module: The data acquisition module is used to obtain the power grid distribution map in the industrial park, collect the operating data of the operating devices in each factory in real time, and obtain the production plan and environmental data of each factory, wherein the operating devices include: surplus energy recovery devices, production capacity devices, and other devices; The power demand model building module is used to divide the industrial park into multiple microgrids based on the power grid distribution map, analyze the impact of the production plan and the environmental data on the power demand of each factory, and obtain a power demand prediction model for each operating device; The energy supply and demand balance scheduling module is used to put the production plan into the corresponding electricity demand prediction model, obtain the electricity demand of each factory in each future time period, compare the electricity demand with the corresponding power output in the microgrid, and formulate an energy supply and demand balance diagram between the microgrids.

[0034] It should be noted that, although several units or subunits of the device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided into multiple units to be embodied.

[0035] Based on the same inventive concept as the above-mentioned method embodiment, an electronic device is also provided in an embodiment of the present application, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the electronic device implements the control method in the above-mentioned embodiment.

[0036] In one embodiment, the electronic device may be a server. In this embodiment, the structure of the electronic device may be as follows: Figure 3 As shown, it includes a memory 2001 , a communication module 2003 and one or more processors 2002 .

[0037] The memory 2001 is used to store computer programs executed by the processor 2002. The memory 2001 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and programs required for running the instant messaging function, etc.; the data storage area may store various instant messaging information and operation instruction sets, etc.

[0038] The memory 2001 may be a volatile memory, such as a random-access memory (RAM); the memory 2001 may also be a non-volatile memory, such as a read-only memory, a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD); or the memory 2001 may be any other medium that can be used to carry or store a desired computer program in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2001 may be a combination of the above memories.

[0039] The processor 2002 may include one or more central processing units (CPU) or a digital processing unit, etc. The processor 2002 is configured to implement the above-mentioned audio data processing method when calling the computer program stored in the memory 2001 .

[0040] The communication module 2003 is used to communicate with terminal devices and other servers.

[0041] The specific connection medium between the memory 2001, the communication module 2003 and the processor 2002 is not limited in the embodiment of the present application. Figure 3 In the embodiment, the memory 2001 and the processor 2002 are connected via a bus 2004. The bus 2004 is Figure 3 The arrows in the figure are used to describe the connection between other components, which is only for illustrative purposes and is not intended to be limiting. Bus 2004 can be divided into address bus, data bus, control bus, etc. For ease of description, Figure 3 The diagram only uses one arrow, but does not describe only one bus or one type of bus.

[0042] Based on the same inventive concept as the above method embodiment, an embodiment of the present invention also provides a computer-readable storage medium, which is used to store a computer program. When the computer program is run on a computer, the electronic device implements the control method in the above embodiment. The computer-readable storage medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0043] Based on the same inventive concept as the above-mentioned method embodiment, an embodiment of the present invention further provides a computer program product, the computer program product including a computer program. When the program product is run on an electronic device, the computer program is used to enable the electronic device to execute the steps of the control method according to various exemplary embodiments of the present application described above in this specification. The program product may adopt any combination of one or more readable media. These computer program commands may be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the commands executed by the processor of the computer or other programmable data processing device generate commands for implementing the steps in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0044] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

Claims

1. A microgrid measurement and control method based on the Internet of Things, characterized in that: Obtain the power grid distribution map within the industrial park, collect the operating data of the operating devices in each factory in real time, and obtain the production plan and environmental data of each factory, where the operating devices include: surplus energy recovery devices, production capacity devices, and other devices, and the other devices are other power-consuming devices in the factory operation except the production devices; Based on the power grid distribution map, the industrial park is divided into multiple microgrids, the impact of the production plan and the environmental data on the power demand of each factory is analyzed, and a power demand prediction model for each operating device is obtained; Extracting production plans under different environments, putting corresponding order quantities and production cycles into the power demand forecasting model to simulate the power consumption curve, and calculating the deviation value between the power consumption curve and the actual power consumption curve. When the deviation value is within the deviation threshold range, it indicates that the power demand of the production capacity device is not affected by the environmental data, and the power demand forecasting model is not modified. When the deviation value exceeds the deviation threshold interval, it indicates that the power demand of the energy generation device is affected by the environmental data, and training is performed based on the characteristic difference between the deviation value and the environmental data to obtain the environmental energy efficiency factor of the environmental data on the power consumption curve, and the environmental energy efficiency factor is integrated into the original power demand forecasting model to obtain a new power demand forecasting model; When the energy generation device is a recyclable energy generation device, the power generation curve of the surplus energy recovery device under the corresponding power consumption curve is obtained, and the surplus energy power generation prediction model is obtained according to the power demand of the associated recyclable energy generation device and the corresponding environmental data, and the corresponding power generation prediction model is subtracted from the power demand prediction model of the recyclable energy generation device, and the recyclable energy generation device obtains a new power demand prediction model; The production plan is placed into the corresponding electricity demand forecasting model to obtain the electricity demand of each factory in each future time period. The electricity demand is compared with the power output in the corresponding microgrid to develop an energy supply and demand balance diagram between the microgrids.

2. According to a microgrid measurement and control method based on the Internet of Things according to claim 1, it is characterized in that: The method of dividing the industrial park into a plurality of microgrids based on the power grid distribution map, analyzing the impact of the production plan and the environmental data on the power demand of each factory, and obtaining a power demand prediction model for each operating device includes: According to the power grid distribution map of the industrial park, combined with the geographical location, production scale and energy consumption characteristics of the factory, the industrial park is divided into multiple microgrids, and each operating device in the industrial park is matched to the corresponding factory; Associating the production device with the corresponding production plan, analyzing the power demand of each production device in different production plans and building a power demand prediction model, analyzing the deviation between the power demand prediction model and the actual power demand, and adjusting the power demand prediction model, wherein the production plan includes: a historical production plan and a future production plan; The factory's shift plan is obtained according to the historical production plan, the other devices are classified in combination with the environmental data, and the power demand of each other device is predicted according to the classification.

3. The microgrid measurement and control method based on the Internet of Things according to claim 2 is characterized in that: The production device is associated with the corresponding production plan, the power demand of each production device in different production plans is analyzed and a power demand prediction model is constructed, the deviation value between the power demand prediction model and the actual power demand is analyzed, and the power demand prediction model is adjusted. The production plan includes: a historical production plan and a future production plan, including: Classifying the energy generating devices into recoverable energy generating devices and common energy generating devices, and associating the surplus energy recovery devices with corresponding recoverable devices, wherein the recoverable energy generating devices refer to the surplus energy that can be generated during operation and recovered by the surplus energy recovery devices for power generation; According to the historical production plan, the production capacity devices that need to be operated in different production plans and the power consumption rules are sorted out, and according to the order quantity and production cycle of the production plan, the power consumption curve of each production capacity device in each time period within the production cycle in different production plans is obtained; The curve is smoothed to obtain the power consumption curves of different production plans under the same environment, and a power demand prediction model of the power consumption curve, the order volume and the production cycle is established through support vector regression: , where Indicates The weight coefficients of the support vectors, Indicates A support vector containing order quantity and production cycle, represents the input vector, represents the kernel function, Represents the bias term of the model.

4. The microgrid measurement and control method based on the Internet of Things according to claim 3 is characterized in that: The obtaining of the factory shift plan according to the historical production plan, classifying the other devices in combination with the environmental data, and predicting the power demand of each other device according to the classification includes: Classifying the other devices according to the shift schedule and the environmental data, and dividing the other devices into stable other devices and unstable other devices, wherein the unstable other devices refer to devices used under the influence of the environmental data, and vice versa, the unstable other devices are the stable other devices; Forecasting the daily electricity demand of the other stable devices, obtaining the electricity consumption curve of each other stable device one month before the forecast time, and taking the daily similar electricity consumption curve as the electricity demand forecast model; The environmental data characteristic influencing factor is obtained for the unstable other devices, and the environmental data impact usage value is formulated for the historical operation data of the unstable other devices. When the environmental data reaches the usage value, the unstable other devices are predicted, and a power demand prediction model is constructed based on the historical operation data.

5. The microgrid measurement and control method based on the Internet of Things according to claim 4 is characterized in that: The production plan is put into the corresponding power demand forecasting model to obtain the power demand of each factory in each future time period, and the power demand is compared with the power output in the corresponding microgrid to formulate an energy supply and demand balance diagram between the microgrids, including: The future production plan is put into the power demand prediction model of each operating device to obtain the power demand of each operating device in each future time period, and the power generation of each new energy power generation device in the industrial park in each future time period is obtained according to the environmental data; The prediction results of the power generation or the power demand are marked in the form of a bar graph of uniform height at the corresponding position of the power grid distribution map, and dynamically updated according to the real-time measurement and control data, wherein the closer to the bottom of the bar graph, the later the predicted time period; According to the predicted power generation or power demand in the bar graph, the ratio A of the power output and consumption of each microgrid is calculated. When A>1, the corresponding time period of the bar graph at each end of the microgrid is marked green. When A=1, the corresponding time period of the bar graph at each end of the microgrid is marked yellow. When A<1, the corresponding time period of the bar graph at each end of the microgrid is marked red, and an energy supply and demand balance diagram between each microgrid is formulated; The total power generation and total power demand in the industrial park are calculated, and the total power generation is compared with the total power demand. When the total power generation is greater than the total power demand, the first power generation end of the microgrid marked in green provides power to the first consumption end and the second consumption end of the microgrid marked in red during the corresponding time period. When the total power generation is less than the total power demand, power supply is requested from the energy storage system or the main power grid in the industrial park.

6. A microgrid measurement and control system based on the Internet of Things, characterized by: The system includes data acquisition module and electricity demand model building module: The data acquisition module is used to obtain the power grid distribution map in the industrial park, collect the operating data of the operating devices in each factory in real time, and obtain the production plan and environmental data of each factory, wherein the operating devices include: surplus energy recovery devices, production capacity devices, and other devices; The electricity demand model building module is used to divide the industrial park into multiple microgrids based on the power grid distribution map, analyze the impact of the production plan and the environmental data on the electricity demand of each factory, and obtain the electricity demand prediction model of each operating device.

7. The microgrid measurement and control system based on the Internet of Things according to claim 6 is characterized in that: The system also includes an energy supply and demand balancing scheduling module: The energy supply and demand balance scheduling module is used to put the production plan into the corresponding electricity demand prediction model, obtain the electricity demand of each factory in each future time period, compare the electricity demand with the corresponding power output in the microgrid, and formulate an energy supply and demand balance diagram between the microgrids.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the electronic device implements the method according to any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store a computer program, and when the computer program is run on a computer, the computer is caused to execute the method according to any one of claims 1 to 5.

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