Power supply optimization methods, devices, and systems based on power grid monitoring data

Abstract

This invention discloses a power supply optimization method, apparatus, and system based on power grid monitoring data. The method includes: receiving monitoring data of the power system; inputting the monitoring data into a risk assessment model to obtain a risk level; determining the power consumption priority of electrical equipment based on the demand-side risk level; obtaining the current total power demand of each electrical device and the current power supply from the power grid, and calculating the difference; when the difference is less than zero and / or the power grid risk level is higher than a preset threshold, controlling the power grid to stop supplying power, and determining the power supply priority of each power supply device on the virtual power plant side based on the risk level on the virtual power plant side; using the virtual power plant to control each power supply device to supply power sequentially according to its power supply priority, so that each electrical device receives power sequentially according to its power consumption priority. This invention solves the technical problem in related technologies where it is difficult to combine power grid power consumption risk for power dispatching, resulting in unreasonable power supply to the demand side.

Description

Technical Field

[0001] This invention relates to the field of power grid environmental monitoring technology, and more specifically, to a power supply optimization method and device, and a power supply optimization system based on power grid monitoring data. Background Technology

[0002] Existing methods for monitoring the power grid environment have the following drawbacks: 1) The current power dispatch priority is generally set in advance, making it difficult to automatically update the priority; it is impossible to analyze power consumption risks based on the power grid environment monitoring results; and the dispatch control of virtual power plants according to priority results in the power dispatch process being unable to automatically prioritize according to the current power consumption risk level, affecting the reasonable power supply response to the demand side; 2) The power grid environment monitoring results can be superimposed on the power grid monitoring video for compressed transmission, but the data decoding time is long and the storage space is large, making it difficult to achieve targeted transmission and storage, resulting in a large viewing and storage pressure on the management platform.

[0003] There is currently no effective solution to the problem that the aforementioned technologies make it difficult to combine power grid power consumption risks for power dispatching, resulting in unreasonable power supply to the demand side. Summary of the Invention

[0004] This invention provides a power supply optimization method and device, and a power supply optimization system based on power grid monitoring data, to at least solve the technical problem in related technologies that it is difficult to combine power grid power consumption risks for power dispatching, resulting in unreasonable power supply to the demand side.

[0005] According to one aspect of the present invention, a power supply optimization method based on power grid monitoring data is provided, comprising: receiving monitoring data obtained by monitoring a power system, wherein the monitoring data includes: first monitoring data obtained by monitoring the power grid side of the power system, second monitoring data obtained by monitoring the virtual power plant side of the power system, and third monitoring data obtained by monitoring the electricity demand side of the power system; inputting the monitoring data into a risk assessment model to process the monitoring data using the risk assessment model to obtain a risk level corresponding to the monitoring data, wherein the risk assessment model is trained using multiple sets of training data through machine learning, each set of multiple sets of training data including: sample monitoring data and a sample risk level corresponding to the sample monitoring data; determining the electricity consumption priority of each electrical device on the electricity demand side according to the demand-side risk level, wherein the demand-side risk level is the risk level corresponding to the second monitoring data. The data corresponds to a risk level; the total current power demand of each electrical device on the demand side and the current power supply on the grid side are obtained, and the demand difference between the total power demand and the current power supply is calculated, wherein the total power demand is the sum of the current power demand of each electrical device; if the demand difference is less than zero and / or the risk level on the grid side is higher than a preset level threshold, the power grid side is controlled to stop supplying power, and the power supply priority of each power supply device on the virtual power plant side is determined according to the risk level on the virtual power plant side, wherein the risk level on the grid side is the risk level corresponding to the first monitoring data, and the risk level on the virtual power plant side is the risk level corresponding to the third monitoring data; the power consumption priority and the power supply priority are sent to the virtual power plant, so that the virtual power plant controls each power supply device to supply power to each electrical device in sequence according to the power supply priority, so that each electrical device receives power in sequence according to the power consumption priority.

[0006] Optionally, before inputting the monitoring data into the risk assessment model to process the monitoring data and obtain the risk level corresponding to the monitoring data, the power supply optimization method based on power grid monitoring data further includes: acquiring multiple sets of historical monitoring data within a historical time period, wherein each set of the multiple sets of historical monitoring data includes multiple sets of historical monitoring data; performing data cleaning on the multiple sets of historical monitoring data to remove invalid and duplicate data; converting the multiple sets of historical monitoring data into a format to obtain multiple sets of historical monitoring data in the same format; determining the historical risk level corresponding to each set of the multiple sets of historical monitoring data; and training the risk assessment model using a support vector machine classification algorithm with the multiple sets of historical monitoring data and the historical risk levels corresponding to the historical monitoring data to obtain the risk assessment model.

[0007] Optionally, determining the historical risk level of each group of historical monitoring data in the plurality of groups of historical monitoring data includes: determining that a combination of multiple historical monitoring data in each group of historical monitoring data forms a historical monitoring data group; comparing each historical monitoring data in the historical monitoring data group with a predetermined range to obtain a comparison result; and determining the historical risk level corresponding to the historical monitoring data group based on the comparison result.

[0008] Optionally, determining the historical risk level corresponding to the historical monitoring data group based on the comparison result includes: determining the historical risk level corresponding to the historical monitoring data group as a safe level when the comparison result indicates that each historical monitoring data item in the historical monitoring data group is within the predetermined range; determining the deviation degree of the historical monitoring data exceeding the predetermined range when the comparison result indicates that there is historical monitoring data in the historical monitoring data group that is not within the predetermined range; and determining the historical risk level corresponding to the historical monitoring data group as a low-risk level when the deviation degree of at least two historical monitoring data items in the historical monitoring data group is not greater than a first deviation threshold or the deviation degree of at least one historical monitoring data item is greater than a second deviation threshold but not greater than a third deviation threshold, wherein the second deviation threshold... If the deviation of at least two historical monitoring data points in the historical monitoring data set is greater than the first deviation threshold and not greater than the fourth deviation threshold, or if the deviation of at least one historical monitoring data point is greater than the third deviation threshold and not greater than the fifth deviation threshold, the historical risk level corresponding to the historical monitoring data set is determined to be a medium risk level, wherein the fourth deviation threshold is greater than the third deviation threshold and the fifth deviation threshold is greater than the fourth deviation threshold; if the deviation of at least two historical monitoring data points in the historical monitoring data set is greater than the fourth deviation threshold, or if the deviation of at least one historical monitoring data point is greater than the fifth deviation threshold, the historical risk level corresponding to the historical monitoring data set is determined to be a high risk level.

[0009] Optionally, determining the electricity priority of each electrical device on the electricity demand side based on the demand difference and the demand-side risk level includes: obtaining the device type and the required power supply of each electrical device on the electricity demand side; calculating the degree of electricity demand for each electrical device using a first formula based on the device type, the required power supply, and the demand-side risk level, wherein the first formula is: Y represents the degree of demand, D represents the equipment type, E represents the first risk value of the demand-side risk level, and C represents the required power supply. 总 The demand difference is represented; the electrical devices are sorted in descending order according to the degree of electricity demand to obtain a first sorting result; the electricity priority of each electrical device is determined according to the first sorting result, wherein the electrical device ranked earlier in the first sorting result has a higher electricity priority.

[0010] Optionally, determining the power supply priority of each power supply device on the virtual power plant side based on the risk level of the virtual power plant side includes: obtaining the current electricity price and the stored capacity of each power supply device; calculating the power supply capacity index of each power supply device using a second formula based on the electricity price, the stored capacity, and the risk level of the virtual power plant side, wherein the second formula is: Z = (H*10% + G*40%) / (F*50%), where Z represents the power supply capacity index, H represents the second risk value of the risk level of the virtual power plant side, G represents the stored capacity, and F represents the electricity price; sorting each power supply device in descending order according to the power supply capacity index to obtain a second sorting result; and determining the power supply priority of each power supply device based on the second sorting result, wherein the power supply device ranked higher in the second sorting result has a higher power supply priority.

[0011] Optionally, the power supply optimization method based on power grid monitoring data further includes: dividing the monitoring data into high-risk monitoring data, medium-risk monitoring data, low-risk monitoring data, and safety monitoring data according to the risk level; removing the safety monitoring data from the monitoring data to obtain risk monitoring data, wherein the risk monitoring data includes at least one of the following: the high-risk monitoring data, the medium-risk monitoring data, and the low-risk monitoring data; transmitting the risk monitoring data to a management platform, wherein the management platform issues an early warning signal when the received risk monitoring data includes the high-risk monitoring data and / or the medium-risk monitoring data.

[0012] According to another aspect of the present invention, a power supply optimization device based on power grid monitoring data is also provided, comprising: a receiving unit, configured to receive monitoring data obtained by monitoring a power system, wherein the monitoring data includes: first monitoring data obtained by monitoring the power grid side of the power system, second monitoring data obtained by monitoring the virtual power plant side of the power system, and third monitoring data obtained by monitoring the electricity demand side of the power system; a first acquisition unit, configured to input the monitoring data into a risk assessment model, so as to process the monitoring data using the risk assessment model to obtain a risk level corresponding to the monitoring data, wherein the risk assessment model is trained using multiple sets of training data through machine learning, each set of multiple sets of training data including: sample monitoring data and a sample risk level corresponding to the sample monitoring data; and a first determination unit, configured to determine the electricity priority of each electrical device on the electricity demand side according to the demand-side risk level, wherein the demand-side risk level is the risk level that corresponds to the second monitoring data. The system comprises: a first monitoring unit and a second determining unit; a second determining unit; a third determining unit; and a fourth determining unit. The first determining unit is configured to control the grid to stop supplying power when the demand difference is less than zero and / or the grid-side risk level is higher than a preset threshold. The second determining unit is configured to determine the power supply priority of each power supply device on the virtual power plant side based on the virtual power plant's risk level, where the grid-side risk level corresponds to the first monitoring data and the virtual power plant's risk level corresponds to the third monitoring data. The fifth determining unit is configured to send the power consumption priority and the power supply priority to the virtual power plant, so that the virtual power plant controls each power supply device to supply power to each of the power consumption devices sequentially according to the power supply priority, ensuring that each of the power consumption devices receives power sequentially according to the power consumption priority.

[0013] Optionally, the power supply optimization device based on power grid monitoring data further includes: a third acquisition unit, used to acquire multiple sets of historical monitoring data within a historical time period before inputting the monitoring data into a risk assessment model to process the monitoring data using the risk assessment model and obtain the risk level corresponding to the monitoring data, wherein each set of the multiple sets of historical monitoring data includes multiple sets of historical monitoring data; a removal unit, used to perform data cleaning on the multiple sets of historical monitoring data to remove invalid and duplicate data from the multiple sets of historical monitoring data; a fourth acquisition unit, used to perform format conversion on the multiple sets of historical monitoring data to obtain multiple sets of historical monitoring data in the same format; a third determination unit, used to determine the historical risk level corresponding to each set of the multiple sets of historical monitoring data; and a fifth acquisition unit, used to train the risk assessment model using a support vector machine classification algorithm with the multiple sets of historical monitoring data and the historical risk level corresponding to the historical monitoring data.

[0014] Optionally, the third determining unit includes: a first determining module, configured to determine that a combination of multiple historical monitoring data in each group of historical monitoring data constitutes a historical monitoring data group; a first obtaining module, configured to compare each historical monitoring data in the historical monitoring data group with a predetermined range to obtain a comparison result; and a second determining module, configured to determine the historical risk level corresponding to the historical monitoring data group based on the comparison result.

[0015] Optionally, the second determining module includes: a first determining submodule, configured to determine the historical risk level corresponding to the historical monitoring data group as a safe level when the comparison result indicates that each historical monitoring data item in the historical monitoring data group is within the predetermined range; a second determining submodule, configured to determine the deviation degree of the historical monitoring data exceeding the predetermined range when the comparison result indicates that there is historical monitoring data in the historical monitoring data group that is not within the predetermined range; and a third determining submodule, configured to determine the historical risk level corresponding to the historical monitoring data group as a low-risk level when the deviation degree of at least two historical monitoring data items in the historical monitoring data group is not greater than a first deviation threshold or the deviation degree of at least one historical monitoring data item is greater than a second deviation threshold but not greater than a third deviation threshold, wherein the second deviation threshold is less than the first deviation threshold. A first deviation threshold, wherein the first deviation threshold is less than the third deviation threshold; a fourth determining submodule, configured to determine the historical risk level corresponding to the historical monitoring data group as a medium risk level when the deviation of at least two historical monitoring data in the historical monitoring data group is greater than the first deviation threshold and not greater than the fourth deviation threshold, or the deviation of at least one historical monitoring data is greater than the third deviation threshold and not greater than the fifth deviation threshold, wherein the fourth deviation threshold is greater than the third deviation threshold and the fifth deviation threshold is greater than the fourth deviation threshold; a fifth determining submodule, configured to determine the historical risk level corresponding to the historical monitoring data group as a high risk level when the deviation of at least two historical monitoring data in the historical monitoring data group is greater than the fourth deviation threshold, or the deviation of at least one historical monitoring data is greater than the fifth deviation threshold.

[0016] Optionally, the first determining unit includes: a second acquiring module, configured to acquire the equipment type and power demand of each electrical device on the power demand side; and a first calculating module, configured to calculate the power demand level of each electrical device based on the equipment type, the power demand, and the demand-side risk level using a first formula, wherein the first formula is: Y represents the degree of demand, D represents the equipment type, E represents the first risk value of the demand-side risk level, and C represents the required power supply. 总 The first module represents the demand difference; the second module is used to sort each of the electrical devices in descending order according to the degree of electricity demand to obtain a first sorting result; the third module is used to determine the electricity priority of each of the electrical devices according to the first sorting result, wherein the electrical device that ranks higher in the first sorting result has a higher electricity priority.

[0017] Optionally, the second determining unit includes: a fourth obtaining module, used to obtain the current electricity price and the stored power of each of the power supply devices; a second calculating module, used to calculate the power supply capacity index of each of the power supply devices based on the electricity price, the stored power, and the virtual power plant side risk level using a second formula, wherein the second formula is: Z = (H*10% + G*40%) / (F*50%), where Z represents the power supply capacity index, H represents the second risk value of the virtual power plant side risk level, G represents the stored power, and F represents the electricity price; a fifth obtaining module, used to sort each of the power supply devices in descending order according to the power supply capacity index to obtain a second sorting result; and a fourth determining module, used to determine the power supply priority of each of the power supply devices according to the second sorting result, wherein the power supply device ranked higher in the second sorting result has a higher power supply priority.

[0018] Optionally, the power supply optimization device based on power grid monitoring data further includes: a division unit, used to divide the monitoring data into high-risk monitoring data, medium-risk monitoring data, low-risk monitoring data, and safety monitoring data according to the risk level; a fifth acquisition unit, used to remove the safety monitoring data from the monitoring data to obtain risk monitoring data, wherein the risk monitoring data includes at least one of the following: the high-risk monitoring data, the medium-risk monitoring data, and the low-risk monitoring data; and a transmission unit, used to transmit the risk monitoring data to a management platform, wherein the management platform issues an early warning signal when the received risk monitoring data includes the high-risk monitoring data and / or the medium-risk monitoring data.

[0019] According to another aspect of the present invention, a power supply optimization system based on power grid monitoring data is also provided. The power supply optimization system based on power grid monitoring data uses any of the above-described power supply optimization methods based on power grid monitoring data, and includes: an environmental monitoring and acquisition module for collecting monitoring data from the power grid side, the electricity demand side, and the virtual power plant side of the power system; an edge intelligent gateway for receiving the monitoring data collected by the environmental monitoring and acquisition module and processing the monitoring data to obtain the electricity consumption priority of each electrical device on the electricity demand side, the power supply priority of each power supply device on the virtual power plant side, and the risk levels corresponding to the power grid side, the electricity demand side, and the virtual power plant side, respectively; and a virtual power plant for adjusting the power supply based on the edge intelligent gateway. The power consumption priority and power supply priority obtained by processing the monitoring data control the power supply equipment on the virtual power plant side to supply power to the power consumption equipment on the power demand side; the 5G base station is used to transmit the risk level, power consumption priority, power supply priority and risk monitoring data corresponding to the monitoring data, wherein the risk monitoring data is the data obtained after removing data with the risk level of safety level from the monitoring data; the management platform is used to receive the risk level, power consumption priority, power supply priority and risk monitoring data corresponding to the monitoring data transmitted by the 5G base station, and to issue a warning signal when the risk monitoring data contains monitoring data with high risk level and medium risk level.

[0020] Optionally, the environmental monitoring and acquisition module includes: a video monitor for acquiring video data from the power grid side, the electricity demand side, and the virtual power plant side of the power system; a temperature and humidity sensor for acquiring temperature and humidity data from the power grid side, the electricity demand side, and the virtual power plant side of the power system; a smoke sensor for acquiring smoke data from the power grid side, the electricity demand side, and the virtual power plant side of the power system; a harmful gas sensor for acquiring harmful gas data from the power grid side, the electricity demand side, and the virtual power plant side of the power system; and a power sensor for acquiring power operation data from the power grid side, the electricity demand side, and the virtual power plant side of the power system.

[0021] Optionally, the edge intelligent gateway includes: a risk assessment module, used to assess the risk levels of the power grid side, the electricity demand side, and the virtual power plant side based on the monitoring data; an electricity priority update module, used to update the electricity priority of each electrical device on the electricity demand side based on the risk level of the electricity demand side; and a power supply priority update module, used to update the power supply priority of each power supply device on the virtual power plant side based on the risk levels of the power grid side and the virtual power plant side.

[0022] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein the program executes any of the above-described power supply optimization methods based on power grid monitoring data.

[0023] According to another aspect of the present invention, a processor is also provided, the processor being used to run a program, wherein the program, when running, executes any of the above-described power supply optimization methods based on power grid monitoring data.

[0024] According to another aspect of the present invention, a computer program product is also provided, including computer instructions, which, when executed by a processor, perform any of the above-described power supply optimization methods based on power grid monitoring data.

[0025] In this embodiment of the invention, monitoring data obtained from monitoring the power system is received. This monitoring data includes: first monitoring data obtained from monitoring the grid side of the power system, second monitoring data obtained from monitoring the virtual power plant side of the power system, and third monitoring data obtained from monitoring the electricity demand side of the power system. The monitoring data is input into a risk assessment model to process the data and obtain the risk level corresponding to the monitoring data. The risk assessment model is trained using multiple sets of training data through machine learning. Each set of training data includes: sample monitoring data and a sample risk level corresponding to the sample monitoring data. The electricity consumption priority of each electrical device on the demand side is determined based on the demand-side risk level. The demand-side risk level is the risk level corresponding to the second monitoring data. Risk level; Obtain the current total power demand of each electrical device on the demand side and the current power supply on the grid side, and calculate the demand difference between the total power demand and the current power supply, where the total power demand is the sum of the current power demand of each electrical device; If the demand difference is less than zero and / or the risk level on the grid side is higher than a preset risk level threshold, control the grid side to stop supplying power, and determine the power supply priority of each power supply device on the virtual power plant side according to the risk level on the virtual power plant side, where the risk level on the grid side is the risk level corresponding to the first monitoring data, and the risk level on the virtual power plant side is the risk level corresponding to the third monitoring data; Send the power consumption priority and the power supply priority to the virtual power plant, so that the virtual power plant can control each power supply device to supply power to each electrical device in sequence according to the power supply priority, so that each electrical device receives power supply in sequence according to the power consumption priority. The above technical solutions achieve the goal of analyzing power grid risks based on power grid monitoring data and updating the priority of power dispatch based on the risk levels obtained from the analysis. This controls the power supply equipment to supply power to the power consumption equipment in order of power supply priority, and ensures that the power consumption equipment also receives power in order of power consumption priority. This achieves the technical effect of automatically updating the priority of power dispatch based on the power grid's power consumption risks, so as to provide more rational power supply to the demand side. In this way, it solves the technical problem in related technologies that it is difficult to combine the power grid's power consumption risks for power dispatch, resulting in unreasonable power supply to the demand side. Attached Figure Description

[0026] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0027] Figure 1 This is a hardware structure block diagram of a mobile terminal for a power supply optimization method based on power grid monitoring data, according to an embodiment of the present invention.

[0028] Figure 2 This is a flowchart of a power supply optimization method based on power grid monitoring data according to an embodiment of the present invention;

[0029] Figure 3 This is a flowchart illustrating the determination of electricity consumption priority according to an embodiment of the present invention;

[0030] Figure 4 This is a flowchart for determining power supply priority according to an embodiment of the present invention;

[0031] Figure 5 This is a schematic diagram of a power supply optimization device based on power grid monitoring data according to an embodiment of the present invention;

[0032] Figure 6 This is a schematic diagram of a power supply optimization system based on power grid monitoring data according to an embodiment of the present invention. Detailed Implementation

[0033] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0035] As described in the background section, related technologies struggle to incorporate power grid usage risks into power dispatching, leading to unreasonable power supply to the demand side. To address these shortcomings, embodiments of this invention provide a power supply optimization method and apparatus, and a power supply optimization system based on power grid monitoring data.

[0036] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0037] The methods and embodiments provided in this invention can be executed on a mobile terminal, a computer terminal, or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a power supply optimization method based on power grid monitoring data, according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0038] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the power supply optimization method based on power grid monitoring data in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0039] According to an embodiment of the present invention, a method embodiment of a power supply optimization method based on power grid monitoring data is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0040] Figure 2 This is a flowchart of a power supply optimization method based on power grid monitoring data according to an embodiment of the present invention, such as... Figure 2 As shown, the method includes the following steps:

[0041] Step S202: Receive monitoring data obtained from monitoring the power system, wherein the monitoring data includes: first monitoring data obtained from monitoring the grid side of the power system, second monitoring data obtained from monitoring the virtual power plant side of the power system, and third monitoring data obtained from monitoring the electricity demand side of the power system.

[0042] In this embodiment, an environmental monitoring and acquisition system can be used to monitor the power system in order to obtain monitoring data on the grid side, the electricity demand side, and the virtual power plant side.

[0043] For example, an environmental monitoring and acquisition system may include video monitors and sensor components, such as temperature and humidity sensors, smoke sensors, harmful gas sensors, and power sensors. The environmental monitoring and acquisition system can acquire various detection information / data from the power grid, the demand side of electricity consumption, and the virtual power plant side. The acquired data can then be transmitted to an edge intelligent gateway. After receiving the monitoring data obtained by the environmental monitoring and acquisition system from monitoring the power system, the edge intelligent gateway can further analyze and process the monitoring data.

[0044] It should be noted that the monitoring data here includes: the first monitoring data obtained from monitoring the power grid side of the power system, the second monitoring data obtained from monitoring the virtual power plant side, and the third monitoring data obtained from monitoring the electricity demand side.

[0045] Specifically, video monitors can record video from the grid side, the electricity demand side, and the virtual power plant side to obtain video information; temperature and humidity sensors detect temperature and humidity data from the grid side, the electricity demand side, and the virtual power plant side; smoke sensors detect smoke data from the grid side, the electricity demand side, and the virtual power plant side; harmful gas sensors detect harmful gas data from the grid side, the electricity demand side, and the virtual power plant side; power sensors detect power operation status data from the grid side, the electricity demand side, and the virtual power plant side; all detected information / data can be transmitted to the edge intelligent gateway.

[0046] Step S204: Input the monitoring data into the risk assessment model to process the monitoring data and obtain the risk level corresponding to the monitoring data. The risk assessment model is trained using multiple sets of training data through machine learning. Each set of training data includes: sample monitoring data and the sample risk level corresponding to the sample monitoring data.

[0047] In this embodiment, the received monitoring data can be processed using a risk assessment model to obtain the risk level of potential risks to the power system.

[0048] It should be noted that the data here actually consists of three separate risk assessment models: the first monitoring data obtained from monitoring the power grid side, the second monitoring data obtained from monitoring the virtual power plant side, and the third monitoring data obtained from monitoring the electricity demand side. Specifically, the first monitoring data is processed using the power grid side risk assessment model to obtain the risk level of the power grid side; the third monitoring data is processed using the electricity demand side risk assessment model to obtain the risk level of the electricity demand side; and the second monitoring data is processed using the virtual power plant side risk assessment model to obtain the risk level of the virtual power plant side.

[0049] In essence, all three models are risk assessment models, and their establishment and training methods are the same. Below, we will collectively refer to them as risk assessment models and elaborate on their establishment and training methods.

[0050] In an optional embodiment of the present invention, before inputting the monitoring data into the risk assessment model to process the monitoring data and obtain the risk level corresponding to the monitoring data, the power supply optimization method based on power grid monitoring data further includes: acquiring multiple sets of historical monitoring data within a historical time period, wherein each set of the multiple sets of historical monitoring data includes multiple historical monitoring data; performing data cleaning on the multiple sets of historical monitoring data to remove invalid and duplicate data; converting the multiple sets of historical monitoring data to obtain multiple sets of historical monitoring data in the same format; determining the historical risk level of each set of historical monitoring data corresponding to the historical monitoring data; and training the risk assessment model using a support vector machine classification algorithm with the multiple sets of historical monitoring data and the historical risk levels corresponding to the historical monitoring data to obtain the risk assessment model.

[0051] Specifically, historical monitoring data can be preprocessed to remove invalid values ​​and duplicate data; then, the historical monitoring data can be mapped to a unified attribute through a mapping module (for example, unifying "voltage 3 Ford" and "U 3V" into "voltage 3V"); then, the risk level corresponding to each group of historical monitoring data can be determined based on the number of items exceeding a predetermined range in each group of historical monitoring data; finally, the historical monitoring data can be classified according to the risk level (or power state type) obtained above, and used as training samples respectively. Various training samples are used to train the SVM classifier support vector machine classification algorithm to obtain the power state SVM classification algorithm model (i.e., risk assessment model) corresponding to each power state type.

[0052] Each set of historical monitoring data here can include video data, temperature and humidity data, smoke data, harmful gas data, and power operation status data from the power grid side, the electricity demand side, and the virtual power plant side within the same historical monitoring time period. Of course, it can also include other data that may be collected during the monitoring of the power grid, without specific restrictions.

[0053] The risk levels can be specifically divided into: high risk level, medium risk level, low risk level and safe level, and their priority is: high risk level > medium risk level > low risk level > safe level. The higher the priority, the higher the risk.

[0054] In the above embodiments of the present invention, determining the historical risk level of each set of historical monitoring data in multiple sets of historical monitoring data includes: determining that a combination of multiple historical monitoring data in each set of historical monitoring data constitutes a historical monitoring data group; comparing each historical monitoring data in the historical monitoring data group with a predetermined range to obtain a comparison result; and determining the historical risk level corresponding to the historical monitoring data group based on the comparison result.

[0055] Specifically, each historical monitoring data item in each set of historical monitoring data can be compared with the corresponding conventional standard range (predetermined range, the same below). The risk level corresponding to the set of historical monitoring data can be specifically determined by judging the number and extent of historical monitoring data items that exceed the conventional standard range.

[0056] In one specific embodiment of the present invention, determining the historical risk level corresponding to a historical monitoring data set based on the comparison result includes: if the comparison result indicates that every historical monitoring data item in the historical monitoring data set is within a predetermined range, determining the historical risk level corresponding to the historical monitoring data set as a safe level; if the comparison result indicates that some historical monitoring data items in the historical monitoring data set are outside the predetermined range, determining the deviation degree of the historical monitoring data items exceeding the predetermined range; and if the deviation degree of at least two historical monitoring data items in the historical monitoring data set is not greater than a first deviation threshold, or the deviation degree of at least one historical monitoring data item is greater than a second deviation threshold but not greater than a third deviation threshold, determining the historical risk level corresponding to the historical monitoring data set as a low-risk level. Wherein, the second deviation threshold is less than the first deviation threshold, and the first deviation threshold is less than the third deviation threshold; if the deviation of at least two historical monitoring data in the historical monitoring data group is greater than the first deviation threshold and not greater than the fourth deviation threshold, or the deviation of at least one historical monitoring data is greater than the third deviation threshold and not greater than the fifth deviation threshold, the historical risk level corresponding to the historical monitoring data group is determined to be a medium risk level, wherein the fourth deviation threshold is greater than the third deviation threshold, and the fifth deviation threshold is greater than the fourth deviation threshold; if the deviation of at least two historical monitoring data in the historical monitoring data group is greater than the fourth deviation threshold, or the deviation of at least one historical monitoring data is greater than the fifth deviation threshold, the historical risk level corresponding to the historical monitoring data group is determined to be a high risk level.

[0057] For example, if at least one piece of monitoring data exceeds the standard range by 10%, or at least two pieces exceed the standard range by 6%, it indicates a high-risk level; if at least one piece of monitoring data exceeds the standard range by 4% but less than or equal to 10%, or at least two pieces exceed the standard range by 3% but less than or equal to 6%, it indicates a medium-risk level; if at least one piece of monitoring data exceeds the standard range by 2% but less than or equal to 4%, or at least two pieces exceed the standard range by 3% but less than or equal to 3%, it indicates a low-risk level; and if the monitoring data is within the standard range, it indicates a safe level.

[0058] It should be noted that when the features of the monitoring data correspond to both low-risk and medium-risk SVM classifiers, that is, when the data meets both low-risk and medium-risk criteria, the medium-risk SVM classifier should be selected for identification to determine the power status type, and the medium-risk level should be used as the power status type. This approach can also be used for other monitoring data features that correspond to one or two types.

[0059] Step S206: Determine the power consumption priority of each electrical device on the demand side according to the demand-side risk level, wherein the demand-side risk level is the risk level corresponding to the second monitoring data in the risk level.

[0060] In this embodiment, the power consumption priority of each electrical device on the power demand side can be updated based on the power status type (i.e., demand-side risk level) obtained from the above steps.

[0061] According to the above embodiments of the present invention, in step S206, determining the power consumption priority of each electrical device on the power demand side based on the demand difference and the demand-side risk level includes: obtaining the equipment type and power demand of each electrical device on the power demand side; calculating the power demand level of each electrical device using a first formula based on the equipment type, power demand, and demand-side risk level, wherein the first formula is: Y represents the level of demand, D represents the type of equipment, E represents the first risk value of the demand-side risk level, and C represents the required power supply. 总 This represents the demand difference; the electrical equipment is sorted in descending order according to the degree of electricity demand to obtain the first sorting result; the electricity priority of each electrical equipment is determined based on the first sorting result, wherein the electrical equipment ranked earlier in the first sorting result has a higher electricity priority.

[0062] The following is combined with Figure 3 The embodiments of the present invention will be described in detail below. Figure 3 This is a flowchart for determining electricity consumption priority according to an embodiment of the present invention.

[0063] like Figure 3 As shown, the specific steps for updating the power priority of each electrical device on the demand side are as follows: 1) Based on the current power consumption and the expected power consumption time, estimate the total power demand A on the demand side in real time. The expected power consumption time is based on historical usage time; 2) Calculate the demand difference C between the power supply B on the grid side and the total power demand A on the demand side during the expected power consumption time. 总 When the demand difference C 总 When <0, it indicates that the power supply on the grid side is expected to be insufficient; 3) Combining the power status type, power supply demand, and object type on the demand side, according to the formula: Calculate the importance coefficient Y of each device on the electricity demand side. The object type includes four categories: residential, commercial, industrial, and others. Assign values ​​D to them: residential = 10, commercial = 7, industrial = 5, others = 1. Assign values ​​E to the power status type on the electricity demand side: high risk level = 0, medium risk level = 3, low risk level = 5, safe power = 10. C represents the power demand of a single electrical device on the electricity demand side during the expected power consumption time. 4) After calculating the importance coefficient Y of each device on the electricity demand side, sort them from smallest to largest. According to this sorting, set the power priority of each electrical device from high to low so that power can be supplied to the devices on the electricity demand side with high power priority first.

[0064] For example, if the power supply can only meet the needs of the top 100 devices with the highest power demand, then power should be supplied to these 100 devices first; the remaining devices should not be supplied with power temporarily, and power should only be supplied when it is determined that the power demand of the subsequent devices can be met.

[0065] Step S208: Obtain the current total power demand of each electrical device on the demand side and the current power supply on the grid side, and calculate the demand difference between the total power demand and the power supply. The total power demand is the sum of the current power demand of each electrical device.

[0066] In this embodiment, the current total power demand A of each electrical device on the demand side and the current power supply B on the grid side can be obtained, and the demand difference C between them can be calculated. 总 By analyzing the demand difference C 总 To determine whether the current power supply on the grid side is sufficient.

[0067] Step S210: When the demand difference is less than zero and / or the grid-side risk level is higher than the preset level threshold, control the grid side to stop supplying power, and determine the power supply priority of each power supply device on the virtual power plant side according to the virtual power plant side risk level. The grid-side risk level is the risk level corresponding to the first monitoring data, and the virtual power plant side risk level is the risk level corresponding to the third monitoring data.

[0068] In this embodiment, at the demand difference C 总If the power supply is less than zero (meaning the power supply from the grid cannot meet the total power demand of all electrical devices on the demand side), or if the power status of the grid is at a high or medium risk level (meaning it is higher than a preset threshold, where the preset threshold can be low risk or high or medium risk), or if both of these conditions occur simultaneously, it indicates that the grid is no longer suitable to continue supplying power. The grid needs to be stopped, and a warning report needs to be sent to the 5G base station to transmit the warning report to the management platform. Simultaneously, the power supply equipment on the virtual power plant side needs to continue supplying power. Further updates to the power supply priority of each power supply device on the virtual power plant side are required based on the power status type (i.e., the risk level) obtained from the above steps.

[0069] According to the above embodiments of the present invention, in step S210, determining the power supply priority of each power supply device on the virtual power plant side based on the risk level on the virtual power plant side includes: obtaining the current electricity price and the stored capacity of each power supply device; calculating the power supply capacity index of each power supply device using a second formula based on the electricity price, stored capacity, and risk level on the virtual power plant side, wherein the second formula is: Z = (H*10% + G*40%) / (F*50%), where Z represents the power supply capacity index, H represents the second risk value of the risk level on the virtual power plant side, G represents the stored capacity, and F represents the electricity price; sorting each power supply device in descending order according to the power supply capacity index to obtain a second sorting result; determining the power supply priority of each power supply device based on the second sorting result, wherein the power supply device ranked higher in the second sorting result has a higher power supply priority.

[0070] The following is combined with Figure 4 The embodiments of the present invention will be described in detail below. Figure 4 This is a flowchart for determining power supply priority according to an embodiment of the present invention.

[0071] like Figure 4 As shown, the specific steps for updating the power supply priority of each power supply device on the virtual power plant side are as follows: 1) Combine the power status type, stored power G, and price F on the virtual power plant side, calculate the priority coefficient Z of each power supply device on the virtual power plant side according to the formula: Z = (H * 10% + G * 40%) / (F | 50%), where the risk level corresponding to the power status type on the virtual power plant side is assigned a value H: high risk level = 0, medium risk level = 0, low risk level = 5, safe power = 10; 2) After calculating the priority coefficient Z of each device on the virtual power plant side, sort them from largest to smallest. According to this sorting, the power supply priority is set from high to low; so as to give priority to the power supply devices on the virtual power plant side with high power supply priority.

[0072] Here, you can first roughly determine how many virtual power plant-side power supply devices are needed to provide power based on the demand difference, and then select the corresponding number of power supply devices according to the power supply priority; for example, based on the demand difference, it is determined that the top 10 virtual power plant-side power supply devices with the highest power supply priority need to be used to provide power; it may also be necessary to use all virtual power plant-side power supply devices to provide power.

[0073] Step S212: Send the power consumption priority and power supply priority to the virtual power plant so that the virtual power plant can control each power supply device to supply power to each power consumption device in sequence according to the power supply priority, so that each power consumption device receives power in sequence according to the power consumption priority.

[0074] In this embodiment, the virtual power plant and the edge smart gateway can communicate with each other. The edge smart gateway can send the power consumption priority of each power consumption device on the demand side and the power supply priority of each power supply device on the virtual power plant side to the virtual power plant. The virtual power plant can then control the power supply devices on the virtual power plant side to supply power to the power consumption devices on the demand side in order of power supply priority, and the power consumption devices on the demand side can receive power in order of power consumption priority. In addition, the virtual power plant can also charge and store electricity from the grid during off-peak hours.

[0075] In an optional embodiment of the present invention, the power supply optimization method based on power grid monitoring data further includes: dividing the monitoring data into high-risk monitoring data, medium-risk monitoring data, low-risk monitoring data, and safety monitoring data according to risk level; removing safety monitoring data from the monitoring data to obtain risk monitoring data, wherein the risk monitoring data includes at least one of the following: high-risk monitoring data, medium-risk monitoring data, and low-risk monitoring data; transmitting the risk monitoring data to a management platform, wherein the management platform issues an early warning signal when the received risk monitoring data includes high-risk monitoring data and / or medium-risk monitoring data.

[0076] In this embodiment, the edge smart gateway can extract video information recorded by video monitors on the grid side, the demand side, and the virtual power plant side according to the time parameters corresponding to the power status type, obtaining high-risk segments, medium-risk segments, low-risk segments, and safe segments respectively. Safe segments are removed, and the remaining segments are combined with the corresponding power status type and time, compressed, and sent to the 5G base station. The 5G base station then transmits the data to the management platform. After receiving the data transmitted from the 5G base station, the management platform can monitor the power grid in real time. Furthermore, if the data received by the management platform includes high-risk or medium-risk segments from the grid side, an early warning signal is issued to prompt maintenance personnel to perform maintenance. Additionally, by ensuring the management platform only receives high-risk, medium-risk, and low-risk segments and can quickly determine the corresponding power status type, maintenance personnel can easily obtain monitoring results, which is beneficial for subsequent maintenance. By removing a large number of safe segments, data decoding time and storage space are reduced, achieving targeted transmission and storage, and reducing the viewing and storage pressure on the management platform.

[0077] As can be seen from the above steps, the technical solution provided by the above embodiments of the present invention can receive monitoring data obtained from monitoring the power system. The monitoring data includes: first monitoring data obtained from monitoring the power grid side of the power system, second monitoring data obtained from monitoring the virtual power plant side of the power system, and third monitoring data obtained from monitoring the electricity demand side of the power system. The monitoring data is input into a risk assessment model to process the monitoring data and obtain the risk level corresponding to the monitoring data. The risk assessment model is trained using multiple sets of training data through machine learning. Each set of training data includes: sample monitoring data and the sample risk level corresponding to the sample monitoring data. The electricity priority of each electrical device on the demand side is determined based on the demand-side risk level, where the demand-side risk level is the risk level corresponding to the second monitoring data. The current total demand for electricity and the current power supply on the power grid side are obtained for each electrical device on the demand side, and the demand difference between the total demand and the power supply is calculated. The power consumption is the sum of the current power demand of each electrical device. When the demand difference is less than zero and / or the risk level on the grid side is higher than the preset threshold, the power grid side is controlled to stop supplying power. The power supply priority of each power supply device on the virtual power plant side is determined according to the risk level on the virtual power plant side. The risk level on the grid side is the risk level corresponding to the first monitoring data, and the risk level on the virtual power plant side is the risk level corresponding to the third monitoring data. The power consumption priority and the power supply priority are sent to the virtual power plant so that the virtual power plant can control each power supply device to supply power to each electrical device in sequence according to the power supply priority. This achieves the technical effect of automatically updating the power consumption dispatch priority based on the power grid monitoring data and the risk level obtained from the analysis, so as to control the power supply device to supply power to the electrical device in sequence according to the power supply priority, and ensure that the electrical device also receives power in sequence according to the power consumption priority. This achieves the technical effect of automatically updating the power consumption dispatch priority based on the power grid's power consumption risk, so as to provide more reasonable power supply to the demand side.

[0078] Therefore, the technical solution provided by the above embodiments of the present invention solves the technical problem in the related art that it is difficult to combine the power grid's power consumption risk for power dispatching, resulting in unreasonable power supply to the demand side.

[0079] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0080] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0081] According to embodiments of the present invention, a power supply optimization device based on power grid monitoring data is also provided for implementing the above-described power supply optimization method based on power grid monitoring data. Figure 5 This is a schematic diagram of a power supply optimization device based on power grid monitoring data according to an embodiment of the present invention, such as... Figure 5 As shown, the device includes: a receiving unit 501, a first acquisition unit 503, a first determination unit 505, a second acquisition unit 507, a second determination unit 509, and a transmitting unit 511. The power supply optimization device based on power grid monitoring data will be described in detail below.

[0082] The receiving unit 501 is used to receive monitoring data obtained from monitoring the power system. The monitoring data includes: first monitoring data obtained from monitoring the grid side of the power system, second monitoring data obtained from monitoring the virtual power plant side of the power system, and third monitoring data obtained from monitoring the electricity demand side of the power system.

[0083] The first acquisition unit 503 is used to input monitoring data into the risk assessment model so as to process the monitoring data using the risk assessment model and obtain the risk level corresponding to the monitoring data. The risk assessment model is trained using multiple sets of training data through machine learning. Each set of training data includes: sample monitoring data and the sample risk level corresponding to the sample monitoring data.

[0084] The first determining unit 505 is used to determine the power consumption priority of each electrical device on the power demand side according to the demand-side risk level, wherein the demand-side risk level is the risk level corresponding to the second monitoring data in the risk level.

[0085] The second acquisition unit 507 is used to acquire the current total power demand of each electrical device on the power demand side and the current power supply on the grid side, and to calculate the demand difference between the total power demand and the power supply, wherein the total power demand is the sum of the current power demand of each electrical device.

[0086] The second determining unit 509 is used to control the grid side to stop supplying power when the demand difference is less than zero and / or the grid side risk level is higher than a preset level threshold, and to determine the power supply priority of each power supply device on the virtual power plant side according to the virtual power plant side risk level, wherein the grid side risk level is the risk level corresponding to the first monitoring data in the risk level, and the virtual power plant side risk level is the risk level corresponding to the third monitoring data in the risk level.

[0087] The sending unit 511 is used to send the power consumption priority and the power supply priority to the virtual power plant, so that the virtual power plant can control each power supply device to supply power to each power consumption device in sequence according to the power supply priority, so that each power consumption device can receive power in sequence according to the power consumption priority.

[0088] It should be noted that the above-mentioned receiving unit 501, first acquisition unit 503, first determination unit 505, second acquisition unit 507, second determination unit 509 and sending unit 511 correspond to steps S202 to S212 in the above embodiments. The six units and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiments.

[0089] As can be seen from the above, in the scheme described in the above embodiments of the present invention, a receiving unit can be used to receive monitoring data obtained from monitoring the power system. The monitoring data includes: first monitoring data obtained from monitoring the power grid side of the power system, second monitoring data obtained from monitoring the virtual power plant side of the power system, and third monitoring data obtained from monitoring the electricity demand side of the power system. Then, a first acquisition unit inputs the monitoring data into a risk assessment model to process the monitoring data and obtain the risk level corresponding to the monitoring data. The risk assessment model is trained using multiple sets of training data through machine learning. Each set of training data includes: sample monitoring data and the sample risk level corresponding to the sample monitoring data. Next, a first determination unit determines the electricity priority of each electrical device on the electricity demand side based on the demand-side risk level. The demand-side risk level is the risk level corresponding to the second monitoring data. Then, a second acquisition unit obtains the current total demand for electricity from each electrical device on the electricity demand side and the current power supply from the power grid side, and calculates the demand difference between the total demand for electricity and the current power supply. The total power demand is the sum of the current power demand of each electrical device. Then, the second determining unit controls the grid to stop supplying power when the demand difference is less than zero and / or the grid-side risk level is higher than a preset threshold. The power supply priority of each power supply device on the virtual power plant side is determined based on the risk level on the virtual power plant side. The grid-side risk level is the risk level corresponding to the first monitoring data, and the virtual power plant-side risk level is the risk level corresponding to the third monitoring data. Finally, the sending unit sends the power consumption priority and power supply priority to the virtual power plant, allowing the virtual power plant to control each power supply device to supply power to each electrical device sequentially according to its power supply priority. This achieves the technical effect of automatically updating the power consumption dispatch priority based on grid monitoring data and the resulting risk level, thus controlling the power supply devices to supply power to the electrical devices sequentially according to their power supply priority, and ensuring that the electrical devices also receive power sequentially according to their power consumption priority.

[0090] Therefore, the technical solution provided by the above embodiments of the present invention solves the technical problem in the related art that it is difficult to combine the power grid's power consumption risk for power dispatching, resulting in unreasonable power supply to the demand side.

[0091] Optionally, the power supply optimization device based on power grid monitoring data further includes: a third acquisition unit, used to acquire multiple sets of historical monitoring data within a historical time period before inputting the monitoring data into a risk assessment model to process the monitoring data using the risk assessment model and obtain the risk level corresponding to the monitoring data; each set of the multiple sets of historical monitoring data includes multiple sets of historical monitoring data; a removal unit, used to perform data cleaning on the multiple sets of historical monitoring data to remove invalid and duplicate data; a fourth acquisition unit, used to convert the format of the multiple sets of historical monitoring data to obtain multiple sets of historical monitoring data in the same format; a third determination unit, used to determine the historical risk level of each set of historical monitoring data corresponding to the historical monitoring data; and a fifth acquisition unit, used to train a risk assessment model using a support vector machine classification algorithm with the multiple sets of historical monitoring data and the historical risk levels corresponding to the historical monitoring data.

[0092] Optionally, the third determining unit includes: a first determining module, used to determine that a combination of multiple historical monitoring data in each group of historical monitoring data forms a historical monitoring data group; a first acquiring module, used to compare each historical monitoring data in the historical monitoring data group with a predetermined range to obtain a comparison result; and a second determining module, used to determine the historical risk level corresponding to the historical monitoring data group based on the comparison result.

[0093] Optionally, the second determining module includes: a first determining submodule, configured to determine the historical risk level corresponding to the historical monitoring data group as a safe level when the comparison result indicates that each historical monitoring data in the historical monitoring data group is within a predetermined range; a second determining submodule, configured to determine the deviation degree of the historical monitoring data exceeding the predetermined range when the comparison result indicates that there is historical monitoring data in the historical monitoring data group that is not within the predetermined range; and a third determining submodule, configured to determine the historical risk level corresponding to the historical monitoring data group as a low-risk level when the deviation degree of at least two historical monitoring data in the historical monitoring data group is not greater than a first deviation threshold or the deviation degree of at least one historical monitoring data is greater than a second deviation threshold but not greater than a third deviation threshold, wherein the second deviation threshold is less than a third deviation threshold. The first deviation threshold is less than the third deviation threshold; the fourth determining submodule is used to determine the historical risk level corresponding to the historical monitoring data group as medium risk level when the deviation of at least two historical monitoring data in the historical monitoring data group is greater than the first deviation threshold and not greater than the fourth deviation threshold, or the deviation of at least one historical monitoring data is greater than the third deviation threshold and not greater than the fifth deviation threshold, wherein the fourth deviation threshold is greater than the third deviation threshold and the fifth deviation threshold is greater than the fourth deviation threshold; the fifth determining submodule is used to determine the historical risk level corresponding to the historical monitoring data group as high risk level when the deviation of at least two historical monitoring data in the historical monitoring data group is greater than the fourth deviation threshold or the deviation of at least one historical monitoring data is greater than the fifth deviation threshold.

[0094] Optionally, the first determining unit includes: a second acquiring module, used to acquire the equipment type and power demand of each electrical device on the demand side; and a first calculating module, used to calculate the degree of electricity demand of each electrical device based on the equipment type, power demand, and demand-side risk level using a first formula, wherein the first formula is: Y represents the level of demand, D represents the type of equipment, E represents the first risk value of the demand-side risk level, and C represents the required power supply. 总 The first module represents the demand difference; the second module is used to sort each electrical device in descending order according to the degree of electricity demand to obtain the first sorting result; the third module is used to determine the electricity priority of each electrical device based on the first sorting result, wherein the electrical device ranked earlier in the first sorting result has a higher electricity priority.

[0095] Optionally, the second determining unit includes: a fourth acquisition module for acquiring the current electricity price and the stored power of each power supply device; a second calculation module for calculating the power supply capacity index of each power supply device based on the electricity price, stored power, and the risk level of the virtual power plant using a second formula, wherein the second formula is: Z = (H*10% + G*40%) / (F*50%), where Z represents the power supply capacity index, H represents the second risk value of the risk level of the virtual power plant, G represents the stored power, and F represents the electricity price; a fifth acquisition module for sorting each power supply device in descending order according to the power supply capacity index to obtain a second sorting result; and a fourth determining module for determining the power supply priority of each power supply device based on the second sorting result, wherein the power supply device ranked higher in the second sorting result has a higher power supply priority.

[0096] Optionally, the power supply optimization device based on power grid monitoring data further includes: a division unit, used to divide the monitoring data into high-risk monitoring data, medium-risk monitoring data, low-risk monitoring data, and safety monitoring data according to the risk level; a fifth acquisition unit, used to remove safety monitoring data from the monitoring data to obtain risk monitoring data, wherein the risk monitoring data includes at least one of the following: high-risk monitoring data, medium-risk monitoring data, and low-risk monitoring data; and a transmission unit, used to transmit the risk monitoring data to the management platform, wherein the management platform issues an early warning signal when the received risk monitoring data includes high-risk monitoring data and / or medium-risk monitoring data.

[0097] According to another aspect of the present invention, a power supply optimization system based on power grid monitoring data is also provided. This power supply optimization system uses any of the above-described power supply optimization methods based on power grid monitoring data. Figure 6 This is a schematic diagram of a power supply optimization system based on power grid monitoring data according to an embodiment of the present invention, such as... Figure 6 As shown, the system includes: an environmental monitoring and acquisition module, an edge intelligent gateway, a virtual power plant, a 5G base station, and a management platform. The following is a detailed description of this power supply optimization system based on power grid monitoring data. This power supply optimization system based on power grid monitoring data includes:

[0098] The environmental monitoring and acquisition module is used to collect monitoring data from the power grid side, the electricity demand side, and the virtual power plant side of the power system.

[0099] The edge intelligent gateway is used to receive monitoring data collected by the environmental monitoring acquisition module, process the monitoring data, and obtain the power consumption priority of each power consumption device on the power demand side, the power supply priority of each power supply device on the virtual power plant side, and the corresponding risk levels on the grid side, the power demand side, and the virtual power plant side.

[0100] A virtual power plant is used to control the power supply equipment on the virtual power plant side to supply power to the power demand side equipment based on the power consumption priority and power supply priority obtained by processing monitoring data by the edge intelligent gateway.

[0101] 5G base stations are used to transmit monitoring data corresponding to risk levels, power consumption priorities, power supply priorities, and risk monitoring data. Among them, the risk monitoring data is the data obtained after removing the data whose risk level is the safety level from the monitoring data.

[0102] The management platform is used to receive the risk level, power consumption priority, power supply priority and risk monitoring data corresponding to the monitoring data transmitted by 5G base stations, and to issue early warning signals when the risk monitoring data includes monitoring data of high risk level and medium risk level.

[0103] As described above, the solution in the above embodiments of the present invention can achieve optimized power dispatching by combining analysis of power grid power consumption risks through a power supply optimization system composed of an environmental monitoring and acquisition module, an edge intelligent gateway, a virtual power plant, a 5G base station, and a management platform. Specifically, the environmental monitoring and acquisition module can collect monitoring data from the power grid side, the power demand side, and the virtual power plant side of the power system; then, the edge intelligent gateway receives the monitoring data collected by the environmental monitoring and acquisition module, processes the monitoring data to obtain the power consumption priority of each power-consuming device on the power demand side, the power supply priority of each power supply device on the virtual power plant side, and the corresponding risk levels for the power grid side, the power demand side, and the virtual power plant side; then, the virtual power plant uses the power consumption priority and power supply priority obtained from the edge intelligent gateway's processing of the monitoring data to control the power supply devices on the virtual power plant side to supply power to the power-consuming devices on the power demand side; and then... The system uses 5G base stations to transmit monitoring data, including risk levels, power consumption priorities, power supply priorities, and risk monitoring data. The risk monitoring data is obtained by removing data with a risk level of "safety." A management platform receives this data from the 5G base stations and issues warning signals when high-risk and medium-risk data are included. This achieves the goal of analyzing grid risks based on grid monitoring data and updating power dispatch priorities according to the analyzed risk levels. The aim is to control power supply equipment to supply power to devices according to their power supply priorities, ensuring that devices receive power according to their priorities. This achieves the technical effect of automatically updating power dispatch priorities based on grid power consumption risks, resulting in more rational power supply to the demand side.

[0104] Therefore, the technical solution provided by the above embodiments of the present invention solves the technical problem in the related art that it is difficult to combine the power grid's power consumption risk for power dispatching, resulting in unreasonable power supply to the demand side.

[0105] Specifically, according to the above embodiments of the present invention, the environmental monitoring and acquisition module includes: a video monitor for acquiring video data from the power grid side, the electricity demand side, and the virtual power plant side of the power system; a temperature and humidity sensor for acquiring temperature and humidity data from the power grid side, the electricity demand side, and the virtual power plant side of the power system; a smoke sensor for acquiring smoke data from the power grid side, the electricity demand side, and the virtual power plant side of the power system; a harmful gas sensor for acquiring harmful gas data from the power grid side, the electricity demand side, and the virtual power plant side of the power system; and a power sensor for acquiring power operation data from the power grid side, the electricity demand side, and the virtual power plant side of the power system.

[0106] In practice, the video monitor and various sensor components in the environmental monitoring and acquisition module can be used to monitor and collect data in real time on the power grid side, virtual power plant side, and electricity demand side of the power system.

[0107] Specifically, according to the above embodiments of the present invention, the edge intelligent gateway includes: a risk assessment module, used to assess the risk levels of the power grid side, the electricity demand side, and the virtual power plant side based on monitoring data; an electricity priority update module, used to update the electricity priority of each electrical device on the electricity demand side according to the risk level of the electricity demand side; and a power supply priority update module, used to update the power supply priority of each power supply device on the virtual power plant side according to the risk levels of the power grid side and the virtual power plant side.

[0108] In the specific implementation process, the edge intelligent gateway can be used to perform risk analysis on the monitoring data collected by the environmental monitoring and acquisition module, so as to update the power consumption priority of each power consumption device on the power demand side, and control the power grid to stop power supply when the power grid is insufficient to supply power to the power consumption device on the power demand side, and update the power supply priority of each power supply device on the virtual power plant side, so as to continue to supply power to the power consumption device on the power demand side using the power supply device on the virtual power plant side.

[0109] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein the program executes any of the above-described power supply optimization methods based on power grid monitoring data.

[0110] Optionally, in this embodiment, the computer-readable storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any communication device in a group of communication devices.

[0111] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: receiving monitoring data obtained from monitoring the power system, wherein the monitoring data includes: first monitoring data obtained from monitoring the grid side of the power system, second monitoring data obtained from monitoring the virtual power plant side of the power system, and third monitoring data obtained from monitoring the electricity demand side of the power system; inputting the monitoring data into a risk assessment model to process the monitoring data using the risk assessment model to obtain the risk level corresponding to the monitoring data, wherein the risk assessment model is trained using multiple sets of training data through machine learning, and each set of multiple sets of training data includes: sample monitoring data and sample risk level corresponding to the sample monitoring data; determining the electricity priority of each electrical device on the electricity demand side according to the demand-side risk level, wherein the demand-side risk level is a risk level. The system identifies the risk level corresponding to the second monitoring data in the risk classification; it obtains the current total power demand of each electrical device on the demand side and the current power supply on the grid side, and calculates the demand difference between the total power demand and the current power supply, where the total power demand is the sum of the current power demand of each electrical device; when the demand difference is less than zero and / or the risk level on the grid side is higher than a preset threshold, it controls the grid side to stop supplying power, and determines the power supply priority of each power supply device on the virtual power plant side according to the risk level on the virtual power plant side, where the risk level on the grid side is the risk level corresponding to the first monitoring data in the risk classification, and the risk level on the virtual power plant side is the risk level corresponding to the third monitoring data in the risk classification; it sends the power consumption priority and the power supply priority to the virtual power plant, so that the virtual power plant controls each power supply device to supply power to each electrical device in sequence according to the power supply priority, so that each electrical device receives power in sequence according to the power consumption priority.

[0112] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: acquiring multiple sets of historical monitoring data within a historical time period, wherein each set of the multiple sets of historical monitoring data includes multiple historical monitoring data; performing data cleaning on the multiple sets of historical monitoring data to remove invalid and duplicate data; converting the multiple sets of historical monitoring data into the same format to obtain multiple sets of historical monitoring data; determining the historical risk level of each set of historical monitoring data corresponding to the historical monitoring data; and training a risk assessment model using a support vector machine classification algorithm based on the multiple sets of historical monitoring data and the historical risk levels corresponding to the historical monitoring data to obtain a risk assessment model.

[0113] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: determining a combination of multiple historical monitoring data in each group of historical monitoring data into a historical monitoring data group; comparing each historical monitoring data in the historical monitoring data group with a predetermined range to obtain a comparison result; and determining the historical risk level corresponding to the historical monitoring data group based on the comparison result.

[0114] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: if the comparison result indicates that each historical monitoring data item in the historical monitoring data group is within a predetermined range, determine the historical risk level corresponding to the historical monitoring data group as a safe level; if the comparison result indicates that some historical monitoring data in the historical monitoring data group is outside the predetermined range, determine the deviation degree of the historical monitoring data exceeding the predetermined range; if the deviation degree of at least two historical monitoring data items in the historical monitoring data group is not greater than a first deviation threshold or the deviation degree of at least one historical monitoring data item is greater than a second deviation threshold but not greater than a third deviation threshold, determine the historical risk level corresponding to the historical monitoring data group as a low-risk level, wherein... If the second deviation threshold is less than the first deviation threshold, and the first deviation threshold is less than the third deviation threshold; if the deviation of at least two historical monitoring data points in the historical monitoring data set is greater than the first deviation threshold but not greater than the fourth deviation threshold, or if the deviation of at least one historical monitoring data point is greater than the third deviation threshold but not greater than the fifth deviation threshold, the historical risk level corresponding to the historical monitoring data set is determined to be a medium risk level, wherein the fourth deviation threshold is greater than the third deviation threshold, and the fifth deviation threshold is greater than the fourth deviation threshold; if the deviation of at least two historical monitoring data points in the historical monitoring data set is greater than the fourth deviation threshold, or if the deviation of at least one historical monitoring data point is greater than the fifth deviation threshold, the historical risk level corresponding to the historical monitoring data set is determined to be a high risk level.

[0115] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: obtaining the equipment type and power demand of each electrical device on the demand side; calculating the degree of power demand of each electrical device using a first formula based on the equipment type, power demand, and demand-side risk level, wherein the first formula is: Y represents the level of demand, D represents the type of equipment, E represents the first risk value of the demand-side risk level, and C represents the required power supply. 总 This represents the demand difference; the electrical equipment is sorted in descending order according to the degree of electricity demand to obtain the first sorting result; the electricity priority of each electrical equipment is determined based on the first sorting result, wherein the electrical equipment ranked earlier in the first sorting result has a higher electricity priority.

[0116] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: obtaining the current electricity price and the stored power of each power supply device; calculating the power supply capacity index of each power supply device using a second formula based on the electricity price, stored power, and the risk level of the virtual power plant, wherein the second formula is: Z = (H*10% + G*40%) / (F*50%), where Z represents the power supply capacity index, H represents the second risk value of the risk level of the virtual power plant, G represents the stored power, and F represents the electricity price; sorting each power supply device in descending order according to the power supply capacity index to obtain a second sorting result; determining the power supply priority of each power supply device according to the second sorting result, wherein the power supply device ranked higher in the second sorting result has a higher power supply priority.

[0117] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: dividing the monitoring data into high-risk monitoring data, medium-risk monitoring data, low-risk monitoring data, and safe monitoring data according to the risk level; removing safe monitoring data from the monitoring data to obtain risk monitoring data, wherein the risk monitoring data includes at least one of the following: high-risk monitoring data, medium-risk monitoring data, and low-risk monitoring data; transmitting the risk monitoring data to the management platform, wherein the management platform issues an early warning signal when the received risk monitoring data includes high-risk monitoring data and / or medium-risk monitoring data.

[0118] According to another aspect of the present invention, a processor is also provided, which is used to run a program, wherein the program executes any of the above-described power supply optimization methods based on power grid monitoring data.

[0119] According to another aspect of the present invention, a computer program product is also provided, including computer instructions, which, when executed by a processor, perform any of the above-described power supply optimization methods based on power grid monitoring data.

[0120] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0121] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0123] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0124] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0125] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0126] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A power supply optimization method based on power grid monitoring data, characterized in that, include: The system receives monitoring data obtained from monitoring the power system, wherein the monitoring data includes: first monitoring data obtained from monitoring the grid side of the power system, second monitoring data obtained from monitoring the virtual power plant side of the power system, and third monitoring data obtained from monitoring the electricity demand side of the power system. The monitoring data is input into the risk assessment model to process the monitoring data and obtain the risk level corresponding to the monitoring data. The risk assessment model is trained using multiple sets of training data through machine learning. Each set of training data includes: sample monitoring data and the sample risk level corresponding to the sample monitoring data. The power consumption priority of each electrical device on the demand side is determined according to the demand-side risk level, wherein the demand-side risk level is the risk level corresponding to the second monitoring data in the risk level; Obtain the current total power demand of each electrical device on the power demand side and the current power supply on the power grid side, and calculate the demand difference between the total power demand and the power supply, wherein the total power demand is the sum of the current power demand of each electrical device; If the demand difference is less than zero and / or the grid-side risk level is higher than a preset level threshold, control the grid side to stop supplying power, and determine the power supply priority of each power supply device on the virtual power plant side according to the virtual power plant side risk level, wherein the grid-side risk level is the risk level corresponding to the first monitoring data, and the virtual power plant side risk level is the risk level corresponding to the third monitoring data. The power consumption priority and the power supply priority are sent to the virtual power plant so that the virtual power plant can control each power supply device to supply power to each power consumption device in sequence according to the power supply priority, so that each power consumption device receives power in sequence according to the power consumption priority.

2. The power supply optimization method based on power grid monitoring data according to claim 1, characterized in that, Before inputting the monitoring data into the risk assessment model to process the monitoring data and obtain the risk level corresponding to the monitoring data, the method further includes: Acquire multiple sets of historical monitoring data within a historical time period, wherein each set of the multiple sets of historical monitoring data includes multiple sets of historical monitoring data; Data cleaning is performed on multiple sets of historical monitoring data to remove invalid and duplicate data. The format of multiple sets of historical monitoring data is converted to obtain multiple sets of historical monitoring data with the same format. Determine the historical risk level for each group of historical monitoring data in the multiple groups of historical monitoring data; The risk assessment model is obtained by training multiple sets of historical monitoring data and the corresponding historical risk levels using a support vector machine classification algorithm.

3. The power supply optimization method based on power grid monitoring data according to claim 2, characterized in that, Determine the historical risk level of each group of historical monitoring data in the multiple groups, including: Each set of historical monitoring data is defined as a combination of multiple historical monitoring data points within each set of historical monitoring data. Each item of historical monitoring data in the historical monitoring data group is compared with a predetermined range to obtain the comparison result; The historical risk level corresponding to the historical monitoring data group is determined based on the comparison results.

4. The power supply optimization method based on power grid monitoring data according to claim 3, characterized in that, Determining the historical risk level corresponding to the historical monitoring data group based on the comparison results includes: If the comparison result indicates that each item of historical monitoring data in the historical monitoring data group is within the predetermined range, the historical risk level corresponding to the historical monitoring data group is determined to be a safety level. If the comparison result indicates that there is historical monitoring data in the historical monitoring data group that is not within the predetermined range, the deviation degree of the historical monitoring data exceeding the predetermined range is determined. If the deviation of at least two historical monitoring data in the historical monitoring data group is not greater than a first deviation threshold, or the deviation of at least one historical monitoring data is greater than a second deviation threshold but not greater than a third deviation threshold, the historical risk level corresponding to the historical monitoring data group is determined to be a low risk level, wherein the second deviation threshold is less than the first deviation threshold, and the first deviation threshold is less than the third deviation threshold. If the deviation of at least two historical monitoring data in the historical monitoring data group is greater than the first deviation threshold and not greater than the fourth deviation threshold, or if the deviation of at least one historical monitoring data is greater than the third deviation threshold and not greater than the fifth deviation threshold, the historical risk level corresponding to the historical monitoring data group is determined to be a medium risk level, wherein the fourth deviation threshold is greater than the third deviation threshold and the fifth deviation threshold is greater than the fourth deviation threshold. If the deviation of at least two historical monitoring data points in the historical monitoring data set is greater than the fourth deviation threshold or the deviation of at least one historical monitoring data point is greater than the fifth deviation threshold, the historical risk level corresponding to the historical monitoring data set is determined to be a high-risk level.

5. The power supply optimization method based on power grid monitoring data according to claim 1, characterized in that, Based on the demand difference and the demand-side risk level, the electricity consumption priority of each electrical device on the electricity demand side is determined, including: Obtain the equipment type and the required power supply for each electrical device on the electricity demand side; Based on the equipment type, the required power supply, and the demand-side risk level, the power demand level of each of the electrical devices is calculated using a first formula, wherein the first formula is: Y represents the degree of demand, D represents the equipment type, E represents the first risk value of the demand-side risk level, and C represents the required power supply. 总 This represents the difference in demand; The electrical equipment is sorted in descending order according to the degree of electricity demand to obtain the first sorting result; The power consumption priority of each electrical device is determined according to the first sorting result, wherein the electrical device ranked higher in the first sorting result has a higher power consumption priority.

6. The power supply optimization method based on power grid monitoring data according to claim 1, characterized in that, The power supply priority of each power supply device on the virtual power plant side is determined based on the risk level on the virtual power plant side, including: Obtain the current electricity price and the stored power of each of the aforementioned power supply devices; Based on the electricity price, the stored electricity volume, and the risk level of the virtual power plant, the power supply capacity index of each power supply device is calculated using the second formula, wherein the second formula is: Z = (H*10% + G*40%) / (F*50%), where Z represents the power supply capacity index, H represents the second risk value of the risk level of the virtual power plant, G represents the stored electricity volume, and F represents the electricity price; The power supply equipment is sorted in descending order according to the power supply capacity index to obtain a second sorting result; The power supply priority of each power supply device is determined according to the second sorting result, wherein the power supply device ranked earlier in the second sorting result has a higher power supply priority.

7. The power supply optimization method based on power grid monitoring data according to claim 1, characterized in that, Also includes: Based on the risk level, the monitoring data is divided into high-risk monitoring data, medium-risk monitoring data, low-risk monitoring data, and safety monitoring data; Remove the safety monitoring data from the monitoring data to obtain risk monitoring data, wherein the risk monitoring data includes at least one of the following: high-risk monitoring data, medium-risk monitoring data, and low-risk monitoring data; The risk monitoring data is transmitted to the management platform, wherein the management platform issues an early warning signal when the received risk monitoring data includes the high-risk monitoring data and / or the medium-risk monitoring data.

8. A power supply optimization device based on power grid monitoring data, characterized in that, include: The receiving unit is used to receive monitoring data obtained from monitoring the power system, wherein the monitoring data includes: first monitoring data obtained from monitoring the grid side of the power system, second monitoring data obtained from monitoring the virtual power plant side of the power system, and third monitoring data obtained from monitoring the electricity demand side of the power system. The first acquisition unit is used to input the monitoring data into the risk assessment model, so as to process the monitoring data using the risk assessment model to obtain the risk level corresponding to the monitoring data. The risk assessment model is trained using multiple sets of training data through machine learning. Each set of multiple sets of training data includes: sample monitoring data and the sample risk level corresponding to the sample monitoring data. The first determining unit is used to determine the power consumption priority of each electrical device on the power demand side according to the demand-side risk level, wherein the demand-side risk level is the risk level corresponding to the second monitoring data in the risk level; The second acquisition unit is used to acquire the current total power demand of each electrical device on the power demand side and the current power supply on the power grid side, and to calculate the demand difference between the total power demand and the power supply, wherein the total power demand is the sum of the current power demand of each electrical device; The second determining unit is used to control the power grid side to stop supplying power when the demand difference is less than zero and / or the risk level on the power grid side is higher than a preset level threshold, and to determine the power supply priority of each power supply device on the virtual power plant side according to the risk level on the virtual power plant side, wherein the risk level on the power grid side is the risk level corresponding to the first monitoring data in the risk level, and the risk level on the virtual power plant side is the risk level corresponding to the third monitoring data in the risk level; The sending unit is used to send the power consumption priority and the power supply priority to the virtual power plant, so as to use the virtual power plant to control each of the power supply devices to supply power to each of the power consumption devices in sequence according to the power supply priority, so that each of the power consumption devices receives power in sequence according to the power consumption priority.

9. A power supply optimization system based on power grid monitoring data, characterized in that, The power supply optimization system based on power grid monitoring data uses the power supply optimization method based on power grid monitoring data as described in any one of claims 1 to 7, comprising: The environmental monitoring and acquisition module is used to collect monitoring data from the power grid side, the electricity demand side, and the virtual power plant side in the power system. An edge intelligent gateway is used to receive the monitoring data collected by the environmental monitoring and acquisition module, and process the monitoring data to obtain the power consumption priority of each power consumption device on the power demand side, the power supply priority of each power supply device on the virtual power plant side, and the corresponding risk levels of the power grid side, the power demand side, and the virtual power plant side, respectively. A virtual power plant is used to control the power supply equipment on the virtual power plant side to supply power to the power consumption equipment on the power demand side based on the power consumption priority and the power supply priority obtained by the edge smart gateway through processing the monitoring data. A 5G base station is used to transmit the risk level, power consumption priority, power supply priority, and risk monitoring data corresponding to the monitoring data, wherein the risk monitoring data is the data obtained after removing data in the monitoring data whose risk level is a safety level; The management platform is used to receive the risk level, power consumption priority, power supply priority, and risk monitoring data corresponding to the monitoring data transmitted by the 5G base station, and to issue an early warning signal when the risk monitoring data includes monitoring data of high risk level and medium risk level.

10. The power supply optimization system based on power grid monitoring data according to claim 9, characterized in that, The environmental monitoring and data acquisition module includes: A video monitor is used to collect video data from the power grid side, the electricity demand side, and the virtual power plant side of the power system. Temperature and humidity sensors are used to collect temperature and humidity data from the power grid side, the electricity demand side, and the virtual power plant side of the power system. A smoke sensor is used to collect smoke data from the power grid side, the electricity demand side, and the virtual power plant side of the power system. A hazardous gas sensor is used to collect hazardous gas data from the power grid side, the electricity demand side, and the virtual power plant side of the power system. Power sensors are used to collect power operation data from the power grid side, the power demand side, and the virtual power plant side of the power system.

11. The power supply optimization system based on power grid monitoring data according to claim 9, characterized in that, Edge intelligent gateway, including: The risk assessment module is used to assess the risk levels of the power grid side, the electricity demand side, and the virtual power plant side based on the monitoring data. The power consumption priority update module updates the power consumption priority of each electrical device on the power consumption demand side according to the risk level on the power consumption demand side. The power supply priority update module is used to update the power supply priority of each power supply device on the virtual power plant side according to the risk level of the grid side and the virtual power plant side.

12. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, the power supply optimization method based on power grid monitoring data as described in any one of claims 1 to 7 is performed.

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