Power distribution method and system for integrated energy electrical system

Abstract

The invention discloses a power distribution method and system for an integrated energy electrical system, and the method comprises the steps: building an integrated energy electrical system mathematical model according to collected operation data, predicting the load demand in a period of time in the future based on the integrated energy electrical system mathematical model, and predicting the power generation amount of renewable energy, and determining the minimization of the total cost of the system as an optimization target, constructing a constraint condition for the operation of the electrical system, and solving an optimal power distribution scheme by using a linear programming method. Efficient power distribution can be realized, so that the operation efficiency and reliability of the system are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power distribution, and in particular relates to a power distribution method and system for an integrated energy electrical system. Background Art

[0002] In modern energy systems, the allocation and management of power resources become particularly critical, especially in scenarios involving multiple sources of power supply and multiple load demands, such as industrial facilities, data centers, and smart grids.

[0003] At the same time, with the widespread application of renewable energy and the continuous changes in load demand, traditional static power allocation methods can no longer meet the needs of modern energy systems. These traditional methods usually adopt fixed allocation strategies and cannot flexibly respond to the dynamic changes of different loads, resulting in waste and uneven distribution of power resources, which in turn affects the overall efficiency and reliability of the system.

[0004] Specifically, the traditional static allocation method has the following problems: 1. Lack of flexibility: The static allocation method cannot be adjusted according to the real-time load demand and power supply situation, resulting in power shortages during peak hours and power surpluses during off-peak hours; 2. Waste of resources: Static allocation methods may lead to waste of power resources due to the inability to respond to load changes in a timely manner, especially in the case of multi-source power supply, failing to fully utilize the advantages of various energy sources; 3. Inefficient system: The fixed allocation strategy cannot optimize the utilization of power resources, thus reducing the operating efficiency of the entire system and increasing operating costs; 4. Reduced reliability: Due to the inability to adjust dynamically, the system is prone to instability when faced with emergencies (such as equipment failure, weather changes, etc.), affecting the reliability and safety of power supply. Summary of the invention

[0005] The present invention provides a power distribution method and system for an integrated energy electrical system, which are used to solve any of the above-mentioned technical problems.

[0006] In a first aspect, the present invention provides a power distribution method for an integrated energy electrical system, comprising: Acquire the operation data of each node in the integrated energy electrical system, and construct a mathematical model of the integrated energy electrical system according to the operation data, wherein the operation data includes real-time load data, power generation data and environmental data, and the mathematical model includes node voltage equations, branch power equations and power balance equations; Based on the mathematical model, time series analysis combined with machine learning methods are used to predict load demand in the future period, and the power generation of renewable energy is predicted in combination with operating data; Determine the objective function of minimizing the total system cost, build constraints on the operation of the electrical system, and then use the linear programming method to solve the optimal power allocation solution; The power allocation plan is sent to each device, and the power output of the device is controlled through the SCADA system to dynamically adjust the power.

[0007] In a second aspect, the present invention provides a power distribution system for an integrated energy electrical system, comprising: A construction module is configured to obtain operation data of each node in the integrated energy electrical system, and construct a mathematical model of the integrated energy electrical system according to the operation data, wherein the operation data includes real-time load data, power generation data and environmental data, and the mathematical model includes a node voltage equation, a branch power equation and a power balance equation; A prediction module is configured to predict the load demand in the future period of time based on the mathematical model by using time series analysis combined with machine learning methods, and to predict the power generation of renewable energy in combination with operation data; A solution module is configured to determine minimizing the total system cost as an objective function, construct constraints for the operation of the electrical system, and then use a linear programming method to solve an optimal power allocation solution; The adjustment module is configured to send the power allocation plan to each device, control the power output of the device through the SCADA system, and dynamically adjust the power.

[0008] According to a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the power distribution method for an integrated energy electrical system according to any embodiment of the present invention.

[0009] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the steps of a power distribution method for an integrated energy electrical system according to any embodiment of the present invention.

[0010] The power distribution method and system for the integrated energy electrical system of the present application have the following beneficial effects: 1. Data-driven accurate prediction: (1) Real-time data collection: By collecting real-time load data, power generation data, and environmental data from each node in the electrical system, the accuracy and timeliness of the data are ensured. (2) Time series analysis and machine learning: Using time series analysis and machine learning methods, the load demand and renewable energy power generation in the future can be more accurately predicted, which not only improves the prediction accuracy but also adapts to complex and changing environmental conditions. 2. Accuracy of mathematical models: (1) Mathematical model of integrated energy electrical system: including node voltage equations, branch current equations and power balance equations, which can fully describe the operating status of the electrical system and provide a solid foundation for the optimization algorithm; (2) Multi-factor consideration: the model not only considers the internal factors of the power system, but also combines environmental data, making the model more comprehensive and accurate; 3. Efficiency of optimization algorithm: (1) Minimizing the total system cost: Taking minimizing the total system cost as the optimization goal ensures the economy of the system. (2) Linear programming method: Using the linear programming method to solve the optimal power allocation scheme has high computational efficiency and can find the global optimal solution in a short time. (3) Constraint conditions: The constraints of the electrical system operation are constructed to ensure the feasibility of the optimization scheme. 4. Dynamic adjustment and real-time control: The power output of the equipment is controlled by the SCADA system (Supervisory Control and Data Acquisition), which realizes the dynamic adjustment of the power distribution scheme, can quickly respond to system changes, maintain the stable operation of the system, and improve the flexibility and adaptability of the system; 5. Safety backup and fault handling: (1) Fault detection: Detect fault information of the electrical system based on support vector machines, with high accuracy and robustness; (2) Backup power allocation plan: Customize the backup power allocation plan based on the fault information to ensure that the system can quickly resume operation when a fault occurs, reducing downtime and losses; 6. Integration and scalability: (1) Multi-system integration: not only applicable to a single power system, but also can be extended to a comprehensive energy system, including the coordinated optimization of multiple energy forms such as electricity, heat, and gas. (2) Modular design: each step is relatively independent, which facilitates system maintenance and upgrades. 7. Utilization of renewable energy: By predicting the power generation of renewable energy and optimizing the utilization of renewable energy, the consumption of fossil energy is reduced, carbon emissions are reduced, and the requirements of sustainable development are met; In summary, the present invention comprehensively improves the operating efficiency and reliability of the system through data-driven prediction, accurate mathematical models, efficient optimization algorithms, dynamic real-time control, safe fault handling, and good integration and environmental friendliness. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A flow chart of a power distribution method for an integrated energy electrical system provided by an embodiment of the present invention; Figure 2 A structural block diagram of a power distribution system for an integrated energy electrical system provided by an embodiment of the present invention; Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] See also Figure 1 , which shows a flow chart of a power distribution method for an integrated energy electrical system of the present application.

[0015] like Figure 1 As shown, the power distribution method for the integrated energy electrical system specifically includes the following steps: Step S101, obtain the operating data of each node in the integrated energy electrical system, and construct a mathematical model of the integrated energy electrical system based on the operating data, the operating data includes real-time load data, power generation data and environmental data, and the mathematical model includes node voltage equations, branch power equations and power balance equations.

[0016] In this step, load data is obtained from power grid companies or historical records; environmental data is obtained from meteorological stations or online APIs to obtain historical and future weather data, including temperature, humidity, wind speed, light intensity, etc.; renewable energy power generation data is obtained from renewable energy power stations to obtain historical power generation data. The collected operating data is preprocessed as follows: 1. Missing value processing: missing values ​​are processed using interpolation, mean filling and other methods; 2. Outlier processing: outliers are detected and processed using statistical methods (such as Z-score, IQR); 3. Feature extraction: useful features are extracted, such as time features (hours, days, weeks), weather features, etc.

[0017] The node voltage equation is expressed as: , , In the formula, is the node voltage vector, is the node admittance matrix, For Node and nodes The admittance between is the number of nodes in the integrated energy and electrical system, Inject current vector into the node; The expression of the branch power equation is: , , In the formula, is the branch current, is the branch impedance matrix, For Node and nodes The admittance between is the number of branches in the integrated energy electrical system, and are the voltages at the nodes at both ends of the branch respectively; The expression of the power balance equation is: , In the formula, , Node The active power and reactive power of is an imaginary unit, For Node The voltage, For Node The voltage, For Node and nodes The conductance between For Node and nodes The electrical susceptance between represents the complex conjugate.

[0018] Step S102, based on the mathematical model, using time series analysis combined with machine learning methods to predict load demand in the future, and combining operation data to predict the power generation of renewable energy.

[0019] In this step, the decomposition method is used to decompose the time series into trend, seasonality and residual parts, expressed as: , In the formula, For time series The observed value at time, is the trend component, For seasonal ingredients, is the residual component; The ADF test method is used to check the stationarity of the time series. If the stationarity meets the set requirements, it is output, otherwise it is subjected to differential processing. The expression of differential processing is: , In the formula, is the time series after difference processing, For time series The observed value at time; Use correlation analysis or principal component analysis to select features whose correlation is greater than a preset threshold; Construct an ARIMA model and use it to output load demand. The expression of load demand is: , In the formula, is the load demand output by the ARIMA model, is a constant term, is the first autoregressive coefficient, For time series The observed value at time, is the second autoregressive coefficient, For time series The observed value at time, For the The autoregressive coefficients, For time series The observed value at time, is the first sliding average coefficient, For the White noise, is the second sliding mean coefficient, For the White noise, For the The sliding average coefficient, For the White noise, For the White noise; The random forest is used to predict the power generation of renewable energy. The expression is: , In the formula, is the amount of electricity generated from renewable energy sources exported, is the number of trees, For the The prediction function of the tree.

[0020] Step S103, determine minimizing the total system cost as the objective function, construct constraints for the operation of the electrical system, and then use a linear programming method to solve the optimal power allocation solution.

[0021] In this step, an objective function is constructed, and the expression of the objective function is: , In the formula, is the objective function, For Node The unit cost, is the number of nodes in the integrated energy and electrical system, Assign to the node Power; Constructing constraints for the operation of the total energy electrical system, including power summation limits , minimum power limit , Maximum power limit , Node voltage range , branch current limitation And equipment capacity ,in, is the total available power, , Node The minimum power and maximum power, , are the minimum and maximum node voltages, respectively. is the maximum value of the branch current, is the maximum value of the equipment capacity; The objective function and the constraint conditions are combined to form a linear programming model, which is expressed as follows: , Solve the linear programming model according to MATLAB or Python to obtain the optimal power allocation solution.

[0022] Step S104, the power allocation plan is sent to each device, and the power output of the device is controlled by the SCADA system to dynamically adjust the power.

[0023] In a specific embodiment, after the power allocation plan is sent to each device, the power output of the device is controlled by the SCADA system, and the power is dynamically adjusted, the fault information of the electrical system is detected based on the support vector machine, and the backup power allocation plan is customized based on the fault information. Specifically: Collect the operating data of the integrated energy electrical system in normal state and in fault state respectively; label the corresponding fault type for each piece of collected operating data to form the labels required for supervised learning, obtain the data set, and divide the data set into training set and test set; select the radial basis function kernel function , the expression is: , where is a positive parameter, , are all input vectors; use the training set data to train the support vector machine model until convergence, and use the test set data to evaluate the performance of the trained support vector machine model; perform feature extraction on the collected real-time operation data to extract the same real-time features as those used in the training support vector machine model; input the extracted real-time features into the support vector machine model to predict whether the current integrated energy and electrical system is in a fault state; if in a fault state, identify the specific fault type based on the prediction results of the support vector machine model, and assign corresponding backup power allocation plans to different fault types; if not in a fault state, continue to collect the operation data at the next moment.

[0024] It should be noted that the impact of the fault type on the operation of the electrical system is determined based on historical data, and the impact is divided into levels, that is, it is divided into level one impact, level two impact and level three impact from light to heavy; 10% of the backup power is allocated to level one impact, 20% to level two impact and 50% to level three impact.

[0025] In summary, the method of the present application establishes a mathematical model of an integrated energy and electrical system based on the collected operating data, and based on the mathematical model of the integrated energy and electrical system, predicts the load demand in the future and the power generation of renewable energy, determines minimizing the total system cost as the optimization goal, and constructs the constraints for the operation of the electrical system. Then, the linear programming method is used to solve the optimal power allocation plan, which can achieve efficient power allocation and improve the operating efficiency and reliability of the system.

[0026] See also Figure 2 , which shows a structural block diagram of a power distribution system for an integrated energy electrical system of the present application.

[0027] like Figure 2 As shown, the power distribution system 200 includes a construction module 210 , a prediction module 220 , a solution module 230 and an adjustment module 240 .

[0028] Among them, the construction module 210 is configured to obtain the operating data of each node in the integrated energy electrical system, and construct a mathematical model of the integrated energy electrical system based on the operating data, the operating data includes real-time load data, power generation data and environmental data, and the mathematical model includes node voltage equations, branch power equations and power balance equations; the prediction module 220 is configured to use time series analysis combined with machine learning methods according to the mathematical model to predict the load demand in the future period, and predict the power generation of renewable energy in combination with the operating data; the solution module 230 is configured to determine the minimization of the total system cost as the objective function, and construct the constraints of the electrical system operation, and then use the linear programming method to solve the optimal power allocation plan; the adjustment module 240 is configured to send the power allocation plan to each device, control the power output of the device through the SCADA system, and dynamically adjust the power.

[0029] It should be understood that Figure 2 Modules and references documented in Figure 1 Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to Figure 2 The modules in it will not be described in detail here.

[0030] In other embodiments, embodiments of the present invention further provide a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor is caused to execute the power allocation method for an integrated energy electrical system in any of the above method embodiments; As an implementation mode, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows: Acquire the operation data of each node in the integrated energy electrical system, and construct a mathematical model of the integrated energy electrical system according to the operation data, wherein the operation data includes real-time load data, power generation data and environmental data, and the mathematical model includes node voltage equations, branch power equations and power balance equations; Based on the mathematical model, time series analysis combined with machine learning methods are used to predict load demand in the future period, and the power generation of renewable energy is predicted in combination with operating data; Determine the objective function of minimizing the total system cost, build constraints on the operation of the electrical system, and then use the linear programming method to solve the optimal power allocation solution; The power allocation plan is sent to each device, and the power output of the device is controlled through the SCADA system to dynamically adjust the power.

[0031] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the power distribution system for the integrated energy electrical system, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the power distribution system for the integrated energy electrical system via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0032] Figure 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 3 As shown, the device includes: a processor 310 and a memory 320. The electronic device may also include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means. Figure 3 In the example, the connection through the bus is taken as an example. The memory 320 is the above-mentioned computer-readable storage medium. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, the power distribution method for the integrated energy electrical system of the above-mentioned method embodiment is implemented. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the power distribution system for the integrated energy electrical system. The output device 340 may include display devices such as display screens.

[0033] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0034] As an implementation mode, the electronic device is applied to a power distribution system for an integrated energy electrical system, and is used for a client, and includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: Acquire the operation data of each node in the integrated energy electrical system, and construct a mathematical model of the integrated energy electrical system according to the operation data, wherein the operation data includes real-time load data, power generation data and environmental data, and the mathematical model includes node voltage equations, branch power equations and power balance equations; Based on the mathematical model, time series analysis combined with machine learning methods are used to predict load demand in the future period, and the power generation of renewable energy is predicted in combination with operating data; Determine the objective function of minimizing the total system cost, build constraints on the operation of the electrical system, and then use the linear programming method to solve the optimal power allocation solution; The power allocation plan is sent to each device, and the power output of the device is controlled through the SCADA system to dynamically adjust the power.

[0035] Through the description of the above implementation modes, those skilled in the art can clearly understand that each implementation mode can be implemented by means of software plus a necessary general hardware platform, or of course by hardware. Based on such an understanding, the above technical solution can essentially or in other words be embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0036] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A power distribution method for an integrated energy electrical system, characterized in that: include: Acquire the operation data of each node in the integrated energy electrical system, and construct a mathematical model of the integrated energy electrical system according to the operation data, wherein the operation data includes real-time load data, power generation data and environmental data, and the mathematical model includes node voltage equations, branch power equations and power balance equations; Based on the mathematical model, time series analysis combined with machine learning methods are used to predict load demand in the future period, and the power generation of renewable energy is predicted in combination with operating data; Determine the objective function of minimizing the total system cost, build constraints on the operation of the electrical system, and then use the linear programming method to solve the optimal power allocation solution; The power allocation plan is sent to each device, and the power output of the device is controlled through the SCADA system to dynamically adjust the power.

2. A power distribution method for an integrated energy electrical system according to claim 1, characterized in that: The expression of the node voltage equation is: ， ， In the formula, is the node voltage vector, is the node admittance matrix, For Node and nodes The admittance between is the number of nodes in the integrated energy and electrical system, Inject current vector into the node; The expression of the branch power equation is: ， ， In the formula, is the branch current, is the branch impedance matrix, For Node and nodes The admittance between is the number of branches in the integrated energy electrical system, and are the voltages at the nodes at both ends of the branch respectively; The expression of the power balance equation is: ， In the formula, , Node The active power and reactive power of is an imaginary unit, For Node The voltage, For Node The voltage, For Node and nodes The conductance between For Node and nodes The electrical susceptance between represents the complex conjugate.

3. A power distribution method for an integrated energy electrical system according to claim 1, characterized in that: The method of using time series analysis combined with machine learning methods based on the mathematical model to predict load demand in the future and combining operation data to predict the power generation of renewable energy includes: Use the decomposition method to decompose the time series into trend, seasonality and residual parts, expressed as: ， In the formula, For time series The observed value at time, For the trend component, For seasonal ingredients, is the residual component; Use the ADF test method to check the stationarity of the time series. If the stationarity meets the set requirements, it will be output. Otherwise, differential processing will be performed. The expression for differential processing is: ， In the formula, is the time series after difference processing, For time series The observed value at time; Use correlation analysis or principal component analysis to select features whose correlation is greater than a preset threshold; Construct an ARIMA model and use it to output load demand. The expression of load demand is: ， In the formula, is the load demand output by the ARIMA model, is a constant term, is the first autoregressive coefficient, For time series The observed value at time, is the second autoregressive coefficient, For time series The observed value at time, For the The autoregressive coefficients, For time series The observed value at time, is the first sliding average coefficient, For the White noise, is the second sliding mean coefficient, For the White noise, For the The sliding average coefficient, For the White noise, For the White noise; The random forest is used to predict the power generation of renewable energy. The expression is: ， In the formula, is the amount of electricity generated from renewable energy sources exported, is the number of trees, For the The prediction function of the tree.

4. A power distribution method for an integrated energy electrical system according to claim 1, characterized in that: The objective function is determined to minimize the total system cost, and the constraints of the electrical system operation are constructed, and then the linear programming method is used to solve the optimal power allocation solution, which includes: Construct an objective function, the expression of which is: ， In the formula, is the objective function, For Node The unit cost, is the number of nodes in the integrated energy and electrical system, Assign to the node Power; Constructing constraints for the operation of the total energy electrical system, including power summation limits , minimum power limit , Maximum power limit , Node voltage range , Branch current limitation And equipment capacity ,in, is the total available power, , Node The minimum power and maximum power, , are the minimum and maximum node voltages, respectively. is the maximum value of the branch current, is the maximum value of the equipment capacity; The objective function and the constraint conditions are combined to form a linear programming model, which is expressed as follows: ， Solve the linear programming model according to MATLAB or Python to obtain the optimal power allocation solution.

5. A power distribution method for an integrated energy electrical system according to claim 1, characterized in that: After the power allocation scheme is sent to each device, the power output of the device is controlled by the SCADA system, and the power is dynamically adjusted, the method further includes: Collect the operating data of the integrated energy and electrical system in normal state and in fault state respectively; Label each piece of collected operating data with the corresponding fault type to form the labels required for supervised learning, obtain the data set, and divide the data set into a training set and a test set; Select the radial basis function kernel , the expression is: ， In the formula, is a positive parameter, , are all input vectors; Use the training set data to train the support vector machine model until convergence, and use the test set data to evaluate the performance of the trained support vector machine model; Perform feature extraction on the collected real-time operation data to extract the same real-time features as those used to train the support vector machine model; The extracted real-time features are input into the support vector machine model to predict whether the current integrated energy and electrical system is in a fault state; If it is in a fault state, the specific fault type is identified according to the prediction results of the support vector machine model, and the corresponding backup power allocation scheme is allocated to different fault types. If it is not in a fault state, the operation data of the next moment will continue to be collected.

6. A power distribution system for an integrated energy electrical system, characterized in that: include: A construction module is configured to obtain operation data of each node in the integrated energy electrical system, and construct a mathematical model of the integrated energy electrical system according to the operation data, wherein the operation data includes real-time load data, power generation data and environmental data, and the mathematical model includes a node voltage equation, a branch power equation and a power balance equation; A prediction module is configured to predict the load demand in the future period of time based on the mathematical model by using time series analysis combined with machine learning methods, and to predict the power generation of renewable energy in combination with operation data; A solution module is configured to determine minimizing the total system cost as an objective function, construct constraints for the operation of the electrical system, and then use a linear programming method to solve an optimal power allocation solution; The adjustment module is configured to send the power allocation plan to each device, control the power output of the device through the SCADA system, and dynamically adjust the power.

7. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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