Power production capacity and energy storage scheduling method, device and equipment based on dynamic planning

Abstract

The invention discloses a dynamic planning-based power production and energy storage scheduling method, device and equipment. The method comprises the following steps of: constructing a standby energy storage generating capacity vector equation according to a daily average time-sharing total power consumption vector, a user power consumption oscillation parameter and a user power supply demand response parameter; constructing an energy supply demand inequality according to the daily time-sharing generating capacity vector, the standby energy storage generating capacity vector and the daily average time-sharing total electricity consumption vector; calculating the daily total energy storage consumption cost according to the daily time-phased generating capacity vector, the initial energy storage capacity and the energy storage upper limit; and taking the minimum sum of the daily total energy storage consumption cost and the daily total power generation cost as a target, and taking an energy supply demand inequality as a constraint condition to construct an optimal scheduling model, and solving to obtain a real-time power capacity and energy storage planning scheme. According to the invention, on the premise of meeting the basic power demand of a user, the loss caused by high electric quantity in short-time production and energy storage is considered to the greatest extent, so that the purposes of balancing production and controlling cost are achieved.

Description

Technical Field

[0001] The present invention relates to the field of power capacity planning, and in particular to a method, device and equipment for scheduling power capacity and energy storage based on dynamic planning. Background Art

[0002] The electricity demand of users is unstable, random and intermittent in time and space, and the electricity consumption varies significantly in different periods. By connecting users to the large power grid and relying on its huge energy storage system for power supply, the continuous and stable supply of electricity to users can be effectively guaranteed. In order to maintain the stability of the power grid's energy storage level, the large power grid can supplement electricity by adjusting production capacity in real time while supplying power to users. For the power grid, by planning an efficient production capacity plan, it can not only guarantee the power supply needs of users, but also effectively stabilize the power grid's energy storage level.

[0003] However, both the power production and energy storage processes will generate non-negligible natural losses. The efficiency of power production is limited by hardware conditions. When power shortages occur and lead to higher energy demand, excessive production efficiency may cause equipment overload, thereby exacerbating the natural loss of production capacity and increasing the risk of hardware damage. On the other hand, excessive power storage will not only increase storage losses, but also increase the risk of high-voltage leakage, thereby increasing equipment maintenance costs. Therefore, how to reasonably plan the power production capacity plan while minimizing the natural loss of power production capacity and energy storage has become the key and challenge. Summary of the invention

[0004] The purpose of the present invention is to overcome the above-mentioned defects and problems existing in the prior art, and to provide a method, device and equipment for scheduling power production capacity and energy storage based on dynamic programming. On the premise of meeting the basic electricity demand of users, the losses caused by high-amount electricity in short-term production and energy storage are taken into consideration to the greatest extent, so as to achieve the purpose of balancing production and controlling costs.

[0005] To achieve the above objectives, the technical solution of the present invention is: a method for dispatching power production capacity and energy storage based on dynamic programming, comprising:

[0006] Calculate the daily average time-period total power consumption vector based on the daily average user time-period power consumption matrix; construct the backup energy storage power generation vector equation based on the daily average time-period total power consumption vector, user power consumption fluctuation parameters, and user power supply demand response parameters;

[0007] Define a daily time-slot power generation vector according to the number of daily time slots, and calculate the daily total power generation cost according to the daily time-slot power generation vector;

[0008] The energy demand inequality is constructed based on the daily time-divided power generation vector, the backup energy storage power generation vector, and the daily average time-divided total power consumption vector;

[0009] Calculate the daily time-based energy storage consumption based on the daily time-based power generation vector, initial storage capacity, and energy storage upper limit, and calculate the daily total energy storage consumption cost based on the daily time-based energy storage consumption;

[0010] With the goal of minimizing the sum of the daily total energy storage consumption cost and the daily total power generation cost, an optimization scheduling model is constructed with the energy supply and demand inequality as a constraint, and the real-time power production capacity and energy storage planning scheme is solved.

[0011] The daily average total power consumption vector is D, whose vector length is the number of daily time periods A, and its ath element is:

[0012]

[0013] Where D a is the total power consumption of all users in the ath period; B is the number of users; C a，b is the average daily electricity consumption of the bth user in the ath period.

[0014] The standby energy storage power generation vector equation is:

[0015]

[0016] Where, L a is the backup energy storage power generation in the ath period; J is the user power demand response parameter; D a is the total power consumption of all users in the ath period; I is the user power consumption oscillation parameter.

[0017] The energy demand inequality is:

[0018] E a -L a -D a ≥0; a∈[1,A];

[0019] In the formula, E a is the power generation in the ath period; L a is the reserve energy storage power generation in the ath period; D a is the total electricity consumption of all users in the ath period; A is the number of daily periods.

[0020] The calculation method of the total daily energy storage consumption cost is:

[0021] Calculate the original energy storage N for the a=1 period a for:

[0022] N a =G;

[0023] Where G is the initial energy storage;

[0024] Calculate the energy consumption J in the ath period a for:

[0025] J a =βN a +χ;

[0026] In the formula, β is the energy consumption coefficient parameter; χ is the energy consumption constant parameter;

[0027] Calculate the energy storage N in the a+1th period a+1 for:

[0028] N a+1 =max{min{N a +E a -J a ,H},0};

[0029] In the formula, E a is the power generation in the ath period; H is the upper limit of energy storage;

[0030] Iterate according to the above steps, stop when the number of time periods reaches the upper limit A of the daily time period, and calculate the energy storage consumption J in the Ath time period. A for:

[0031] J A =βN A +χ;

[0032] Where N A is the energy storage in the Ath period;

[0033] Calculate the total daily energy storage consumption cost K as:

[0034]

[0035] Where A is the number of daily time periods.

[0036] The total daily electricity generation cost F is:

[0037]

[0038] In the formula, E a is the power generation in the ath period; A is the number of daily periods; α is the power generation cost parameter.

[0039] The optimization scheduling model is:

[0040]

[0041] Where K is the total daily energy storage consumption cost; F is the total daily power generation cost; E is the daily time-divided power generation vector, A is the number of daily time periods; E ais the power generation in the ath period; L a is the reserve energy storage power generation in the ath period; D a is the total electricity consumption of all users in the ath period.

[0042] A power production capacity and energy storage scheduling device based on dynamic programming, which is applied to the above-mentioned method, and comprises:

[0043] The backup energy storage power generation calculation module is used to calculate the daily average time-period total power consumption vector based on the daily average user time-period power consumption matrix; construct the backup energy storage power generation vector equation based on the daily average time-period total power consumption vector, user power consumption oscillation parameters, and user power supply demand response parameters;

[0044] A daily total power generation cost calculation module is used to define a daily time-segment power generation vector according to the number of daily time periods, and calculate the daily total power generation cost according to the daily time-segment power generation vector;

[0045] An energy supply and demand inequality construction module is used to construct an energy supply and demand inequality based on the daily time-divided power generation vector, the backup energy storage power generation vector, and the daily average time-divided total power consumption vector;

[0046] The daily total energy storage consumption cost calculation module is used to calculate the daily time-divided energy storage consumption according to the daily time-divided power generation vector, the initial storage capacity, and the energy storage upper limit, and calculate the daily total energy storage consumption cost according to the daily time-divided energy storage consumption;

[0047] The power production capacity and energy storage planning scheme acquisition module is used to build an optimization scheduling model with the goal of minimizing the sum of the daily total energy storage consumption cost and the daily total power generation cost, using the energy supply and demand inequality as a constraint condition, and solve to obtain the real-time power production capacity and energy storage planning scheme.

[0048] A power production capacity and energy storage scheduling device based on dynamic programming, comprising a memory and a processor;

[0049] The memory is used to store computer program code and transmit the computer program code to the processor;

[0050] The processor is used to execute the method according to the instructions in the computer program code.

[0051] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] In the power production capacity and energy storage scheduling method, device and equipment based on dynamic programming of the present invention, on the one hand, based on the real-time power consumption historical data of users, combined with the mathematical distribution of user power consumption, the user's power consumption expectation is obtained, and the power generation expectation of the system is further considered to obtain the power generation expectation of the system by considering the power generation response demand limit, thereby obtaining the power generation limit condition of the system. On the other hand, an iterative relationship between the real-time power generation, the real-time power storage and the real-time consumption is constructed, and the real-time power generation cost and loss are calculated in combination with the initial power storage, so as to output the daily power generation cost and loss synthesis, and finally construct an optimization problem. The optimization problem takes the guarantee of the user's response power generation as the constraint condition, takes the minimum daily total energy storage consumption cost and the daily total power generation cost as the goal, and solves the problem to obtain the optimal system real-time capacity and energy storage planning scheme. The real-time capacity and energy storage planning scheme, under the premise of meeting the basic power demand of users, takes into account the loss caused by high-amount power in short-term production and energy storage to the greatest extent, thereby achieving the purpose of balancing production and controlling costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flow chart of a method for dispatching electric power production capacity and energy storage based on dynamic programming of the present invention.

[0055] Figure 2 It is a structural block diagram of a power production capacity and energy storage scheduling device based on dynamic programming of the present invention.

[0056] Figure 3 It is a structural block diagram of a power production capacity and energy storage scheduling device based on dynamic programming of the present invention. DETAILED DESCRIPTION

[0057] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0058] See also Figure 1 , a method for dispatching power production capacity and energy storage based on dynamic programming, comprising:

[0059] S1. Calculate the daily average time-period total power consumption vector based on the daily average user time-period power consumption matrix; construct the standby energy storage power generation vector equation based on the daily average time-period total power consumption vector, user power consumption oscillation parameters, and user power supply demand response parameters;

[0060] S2. Define the daily time-slot power generation vector according to the number of daily time slots, and calculate the daily total power generation cost according to the daily time-slot power generation vector;

[0061] S3, constructing an energy demand inequality based on the daily time-divided power generation vector, the backup energy storage power generation vector, and the daily average time-divided total power consumption vector;

[0062] S4. Calculate the daily time-divided energy storage consumption according to the daily time-divided power generation vector, the initial storage capacity, and the energy storage upper limit, and calculate the daily total energy storage consumption cost according to the daily time-divided energy storage consumption;

[0063] S5. Taking the minimum sum of the daily total energy storage consumption cost and the daily total power generation cost as the goal, an optimization scheduling model is constructed with the energy supply and demand inequality as the constraint condition, and the real-time power production capacity and energy storage planning scheme is solved.

[0064] In view of the deficiencies in the existing large-scale power grid energy storage and capacity balancing strategies, facing the uncertainty and randomness of users' real-time power supply needs, and taking into full account the temporal and spatial differences between power demand and power supply system, the present invention provides a power production capacity and energy storage scheduling method based on dynamic programming, aiming to reduce the consumption in the power production and storage process as much as possible. Under the premise of meeting the random centralized power supply needs of users, the power loss caused by high-value power storage and short-time production is minimized.

[0065] Furthermore, the daily average total power consumption vector is D, the vector length is the number of daily time periods A, and the ath element is:

[0066]

[0067] Where D a is the total power consumption of all users in the ath period; B is the number of users; C is the daily average power consumption matrix of users in different periods, and its matrix size is A*B, C a，b It is the element in the ath row and bth column of the daily average user time period electricity consumption matrix C, which means the daily average electricity consumption of the bth user in the ath time period.

[0068] Furthermore, the standby energy storage power generation vector equation is:

[0069]

[0070] The above equation is solved after normalization, and the normalized equation is:

[0071]

[0072] Where L is the backup energy storage power generation vector, and its vector length is A, L a is the ath element of the backup energy storage power generation vector L, which means the backup energy storage power generation in the ath period; J is the user power supply demand response parameter, whose value satisfies J∈(0,1), which means that the power demand of at least J percentage of users at any time is met; D ais the total power consumption of all users in the ath period; I is the user power consumption oscillation parameter, which means the standard deviation of the normal distribution of user power consumption. The normalization equation can be solved by directly querying the standard normal distribution table.

[0073] Furthermore, the daily time-divided power generation vector E is:

[0074] E=(E1,E2,...,E a , ..., E A );

[0075] In the formula, E is the independent variable, and its vector length is A, E a It is the ath element of the daily time-divided power generation vector E, which means the power generation in the ath time period.

[0076] Furthermore, the energy supply demand inequality is:

[0077] E a -L a -D a ≥0; a∈[1,A];

[0078] In the formula, E a is the power generation in the ath period; L a is the reserve energy storage power generation in the ath period; D a is the total electricity consumption of all users in the ath period; A is the number of daily periods.

[0079] Furthermore, the calculation method of the total daily energy storage consumption cost is:

[0080] Calculate the original energy storage N for the a=1 period a for:

[0081] N a =G;

[0082] Where G is the initial energy storage;

[0083] Calculate the energy consumption J in the ath period a for:

[0084] J a =βN a +χ;

[0085] In the formula, β is the energy consumption coefficient parameter; χ is the energy consumption constant parameter; the existence of these two parameters is due to: the self-discharge phenomenon of the power storage device, that is, the phenomenon that the power stored in the device is consumed spontaneously; the self-discharge phenomenon of different performance and materials needs to be analyzed in a specific context, such as the equipment ambient temperature, equipment materials, voltage, etc. (only materials, lithium battery 1%, lead-acid battery 12%~14%). Generally speaking, the self-discharge function under a steady-state environment is close to linear, and the energy consumption coefficient parameter β and the energy consumption constant parameter χ can be found on the power storage device or can be measured by professional instruments. For some power storage devices with better performance or devices with lower storage capacity, the energy consumption constant parameter χ is close to 0.

[0086] Calculate the energy storage N in the a+1th period a+1 for:

[0087] N a+1 =max{min{N a +E a -J a ,H},0};

[0088] In the formula, E a is the power generation in the ath period; H is the upper limit of energy storage; min{N a +E a -J a , H} is a small value operation, and the output is N a +E a -J a and H; max{min{N a +E a -J a , H}, 0} is the maximum value operation, and the output is min{N a +E a -J a , H} and 0;

[0089] Iterate according to the above steps, stop when the number of time periods reaches the upper limit A of the daily time period, and calculate the energy storage consumption J in the Ath time period. A for:

[0090] J A =βN A +χ;

[0091] Where N A is the energy storage in the Ath period;

[0092] Calculate the total daily energy storage consumption cost K as:

[0093]

[0094] Where A is the number of daily time periods.

[0095] Furthermore, the total daily electricity generation cost F is:

[0096]

[0097] In the formula, E a is the power generation in the ath period; A is the number of daily periods; α is the power generation cost parameter, which is obtained based on the historical data of the power grid. The power generation and power generation cost are not a simple linear relationship; when the power generation is too large, it will be affected by factors such as equipment hardware, coal prices, temperature loss, etc., and its basic power generation cost parameters can be obtained based on the historical data of the power grid.

[0098] Furthermore, the optimization scheduling model is:

[0099]

[0100] Where K is the total daily energy storage consumption cost; F is the total daily power generation cost; E is the daily time-divided power generation vector, A is the number of daily time periods; E a is the power generation in the ath period; L a is the reserve energy storage power generation in the ath period; D a is the total electricity consumption of all users in the ath period.

[0101] The power grid has multiple high power demand periods in a day, but the average daily total power supply is basically cyclical, and a significant increase in power generation in a short period of time will lead to a significant exponential increase in costs. Therefore, the power supply pressure can be reduced by generating power in different time periods throughout the day. The total power demand is always limited, and the corresponding power generation demand is also limited. At the same time, early power generation will also lead to a linear increase in energy storage consumption. The purpose of this invention is to find a balance between the two, so that under the basic power supply demand, the short-term increase in power generation cost (power generation price) and the early power generation energy storage cost (self-discharge) are balanced, and both are as low as possible. The optimization scheduling model can be efficiently solved by the cvx module in the matlab program.

[0102] See also Figure 2 The present invention also provides a power production capacity and energy storage scheduling device based on dynamic programming, which is applied to the above-mentioned power production capacity and energy storage scheduling method based on dynamic programming, and the device includes:

[0103] The backup energy storage power generation calculation module is used to calculate the daily average time-period total power consumption vector based on the daily average user time-period power consumption matrix; construct the backup energy storage power generation vector equation based on the daily average time-period total power consumption vector, user power consumption oscillation parameters, and user power supply demand response parameters;

[0104] A daily total power generation cost calculation module is used to define a daily time-segment power generation vector according to the number of daily time periods, and calculate the daily total power generation cost according to the daily time-segment power generation vector;

[0105] An energy supply and demand inequality construction module is used to construct an energy supply and demand inequality based on the daily time-divided power generation vector, the backup energy storage power generation vector, and the daily average time-divided total power consumption vector;

[0106] The daily total energy storage consumption cost calculation module is used to calculate the daily time-divided energy storage consumption according to the daily time-divided power generation vector, the initial storage capacity, and the energy storage upper limit, and calculate the daily total energy storage consumption cost according to the daily time-divided energy storage consumption;

[0107] The power production capacity and energy storage planning scheme acquisition module is used to build an optimization scheduling model with the goal of minimizing the sum of the daily total energy storage consumption cost and the daily total power generation cost, using the energy supply and demand inequality as a constraint condition, and solve to obtain the real-time power production capacity and energy storage planning scheme.

[0108] See also Figure 3 , the present invention also provides a power production capacity and energy storage scheduling device based on dynamic programming, including a memory and a processor;

[0109] The memory is used to store computer program code and transmit the computer program code to the processor;

[0110] The processor is used to execute the above-mentioned power production capacity and energy storage scheduling method based on dynamic programming according to the instructions in the computer program code.

[0111] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned power production capacity and energy storage scheduling method based on dynamic programming is implemented.

[0112] Generally speaking, the computer instructions for implementing the method of the present invention may be carried by any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media may include any computer-readable media, except for the signal itself that is temporarily propagating.

[0113] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EKROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0114] Computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages, in particular, Python suitable for neural network computing and platform frameworks based on TensorFlow, PyTorch, etc. can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer or to an external computer (for example, using an Internet service provider to connect via the Internet) through any type of network, including a local area network (LAN) or a wide area network (WAN).

[0115] The above-mentioned device and non-temporary computer-readable storage medium can be found in the specific description of a power production capacity and energy storage scheduling method based on dynamic programming and its beneficial effects, which will not be repeated here.

[0116] Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for dispatching power production capacity and energy storage based on dynamic programming, characterized in that: include: Calculate the daily average time-based total power consumption vector based on the daily average user time-based power consumption matrix; Construct a vector equation for standby energy storage power generation based on the daily average total power consumption vector, user power consumption fluctuation parameters, and user power supply demand response parameters; Define a daily time-slot power generation vector according to the number of daily time slots, and calculate the daily total power generation cost according to the daily time-slot power generation vector; The energy supply demand inequality is constructed based on the daily time-divided power generation vector, the backup energy storage power generation vector, and the daily average time-divided total power consumption vector; Calculate the daily time-based energy storage consumption based on the daily time-based power generation vector, initial storage capacity, and energy storage upper limit, and calculate the daily total energy storage consumption cost based on the daily time-based energy storage consumption; With the goal of minimizing the sum of the daily total energy storage consumption cost and the daily total power generation cost, an optimization scheduling model is constructed with the energy supply and demand inequality as a constraint, and the real-time power production capacity and energy storage planning scheme is solved.

2. The method for dispatching power production capacity and energy storage based on dynamic programming according to claim 1, characterized in that: The daily average total power consumption vector is D, whose vector length is the number of daily time periods A, and its ath element is: Where D a is the total power consumption of all users in the ath period; B is the number of users; C a，b is the average daily electricity consumption of the bth user in the ath period.

3. The method for dispatching power production capacity and energy storage based on dynamic programming according to claim 1, characterized in that: The standby energy storage power generation vector equation is: Where, L a is the backup energy storage power generation in the ath period; J is the user power demand response parameter; D a is the total power consumption of all users in the ath period; I is the user power consumption oscillation parameter.

4. The method for dispatching power production capacity and energy storage based on dynamic programming according to claim 1, characterized in that: The energy demand inequality is: AND a -L a -D a ≥0;a∈[1,A]; In the formula, E a is the power generation in the ath period; L a is the reserve energy storage power generation in the ath period; D a is the total electricity consumption of all users in the ath period; A is the number of daily periods.

5. The method for dispatching power production capacity and energy storage based on dynamic programming according to claim 1, characterized in that: The calculation method of the total daily energy storage consumption cost is: Calculate the original energy storage N for the a=1 period a for: N a =G; Where G is the initial energy storage; Calculate the energy consumption J in the ath period a for: J a =βN a +x; In the formula, β is the energy consumption coefficient parameter; X is the energy consumption constant parameter; Calculate the energy storage N in the a+1th period α+1 for: N a+1 =max{min{N a +E a -J a ,H},0}; In the formula, E a is the power generation in the ath period; H is the upper limit of energy storage; Iterate according to the above steps, stop when the number of time periods reaches the upper limit A of the daily time period, and calculate the energy storage consumption J in the Ath time period. A for: J A =βN A +x; Where N A is the energy storage in the Ath period; Calculate the total daily energy storage consumption cost K as: Where A is the number of daily time periods.

6. The method for dispatching power production capacity and energy storage based on dynamic programming according to claim 1, characterized in that: The total daily electricity generation cost F is: In the formula, E a is the power generation in the ath period; A is the number of daily periods; α is the power generation cost parameter.

7. The method for dispatching power production capacity and energy storage based on dynamic programming according to claim 1, characterized in that: The optimization scheduling model is: Where K is the total daily energy storage consumption cost; F is the total daily power generation cost; E is the daily time-divided power generation vector, A is the number of daily time periods; E a is the power generation in the ath period; L a is the reserve energy storage power generation in the ath period; D a is the total electricity consumption of all users in the ath period.

8. A power production capacity and energy storage scheduling device based on dynamic programming, characterized in that: The device is applied to the method described in any one of claims 1 to 7, and the device comprises: The backup energy storage power generation calculation module is used to calculate the daily average time-period total power consumption vector based on the daily average user time-period power consumption matrix; construct the backup energy storage power generation vector equation based on the daily average time-period total power consumption vector, user power consumption oscillation parameters, and user power supply demand response parameters; A daily total power generation cost calculation module is used to define a daily time-segment power generation vector according to the number of daily time periods, and calculate the daily total power generation cost according to the daily time-segment power generation vector; An energy supply and demand inequality construction module is used to construct an energy supply and demand inequality based on the daily time-divided power generation vector, the backup energy storage power generation vector, and the daily average time-divided total power consumption vector; The daily total energy storage consumption cost calculation module is used to calculate the daily time-divided energy storage consumption according to the daily time-divided power generation vector, the initial storage capacity, and the energy storage upper limit, and calculate the daily total energy storage consumption cost according to the daily time-divided energy storage consumption; The power production capacity and energy storage planning scheme acquisition module is used to build an optimization scheduling model with the goal of minimizing the sum of the daily total energy storage consumption cost and the daily total power generation cost, using the energy supply and demand inequality as a constraint condition, and solve to obtain the real-time power production capacity and energy storage planning scheme.

9. A power production capacity and energy storage scheduling device based on dynamic programming, characterized in that: including memory and processor; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is configured to execute the method according to any one of claims 1 to 7 according to instructions in the computer program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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