Electric vehicle battery pack thermal management control method and related equipment

Abstract

The invention discloses an electric vehicle battery pack thermal management control method and related equipment, and the method comprises the steps: driving style recognition: collecting the driving data of a driver, extracting the key features of the driving data, classifying the driving styles according to the extracted features through employing a fuzzy C-means clustering algorithm, constructing a driving style recognition model, and carrying out the recognition of the driving styles; the recognition module recognizes driving styles of drivers; designing a thermal management system control model based on DDPG: constructing action variables by using a depth deterministic strategy gradient algorithm and taking battery temperature, current, current change rate and driving style membership as state variables, designing a reward function considering temperature deviation, and training the thermal management system control model; and performing thermal management control on the battery pack of the electric vehicle by adopting the trained model. According to the invention, driving style identification and battery thermal management system control are combined, and a depth deterministic strategy gradient algorithm is introduced to optimize a thermal management control strategy, so that the control precision is effectively improved, and the energy consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the field of new energy vehicles, and particularly to a thermal management control method and related equipment for an electric vehicle battery pack. Background Art

[0002] With the widespread application of electric vehicles, the thermal management system of the power battery pack has become a key part to ensure the performance, safety and battery life of the whole vehicle. Most of the existing thermal management control methods adopt traditional PID control, whose control strategy is fixed and it is difficult to adjust in real time according to the actual driving conditions and driver behavior, resulting in problems such as inaccurate battery temperature control and high energy consumption.

[0003] At the same time, the actual operation process of electric vehicles is affected by multiple factors such as driving style, road condition changes, and traffic environment, resulting in significant changes in battery heat load. Therefore, developing a thermal management control method that can perceive driving style and has an adaptive learning ability has become an important research direction.

[0004] Currently, there is no publicly available technology that combines driving style recognition with a deep reinforcement learning control strategy and applies it to the electric vehicle battery thermal management system, which has the value of popularization. Summary of the Invention

[0005] To solve at least one of the technical problems existing in the prior art to a certain extent, the purpose of the present invention is to provide a thermal management control method and related equipment for an electric vehicle battery pack based on driving style recognition and deep reinforcement learning.

[0006] The first technical solution adopted by the present invention is:

[0007] A thermal management control method for an electric vehicle battery pack, comprising the following steps:

[0008] Driving style recognition: Collect the driving data of the driver, extract the key features of the driving data, classify the driving style according to the extracted features using the fuzzy C-means (FCM) clustering algorithm, and construct a driving style recognition model for identifying the driving style of the driver;

[0009] Design of a thermal management system control model based on DDPG: Use the deep deterministic policy gradient (DDPG) algorithm, take battery temperature, current, current change rate, and driving style membership as state variables, construct an action variable (compression control), design a reward function considering temperature deviation, and train the thermal management system control model; Use the trained model to control the thermal management of the electric vehicle battery pack.

[0010] Further, the driving style recognition includes:

[0011] Collect data of pure electric vehicles under various road conditions, conduct actual driving based on drivers with different styles, and establish a complete data set;

[0012] Extract 6 characteristic parameters from the data set that can reflect driving styles: acceleration, jerk, torque request change rate, accelerator pedal opening change rate, brake pedal opening change rate, and steering wheel angle. Then, train a driving style recognition model through the fuzzy C-means clustering algorithm, and divide the recognition results into two types of styles: aggressive and conservative. Use the trained driving style recognition model to identify the driving style of the driver.

[0013] Furthermore, the input of the thermal management system control model includes battery temperature, output current and its change rate, and the current driving style. The output is a continuous action variable used to control the refrigeration or heating of the thermal management system; the reward function is the difference between the current temperature of the battery pack and the target temperature.

[0014] Furthermore, under high-temperature conditions, that is, when the battery temperature is higher than the target temperature, the thermal management system needs to start the compressor for refrigeration;

[0015] Under low-temperature conditions, that is, when the initial temperature of the battery is lower than the target temperature, the thermal management system needs to start the WPTC for heating.

[0016] The second technical solution adopted by the present invention is:

[0017] An electric vehicle battery pack thermal management control device, comprising:

[0018] A driving style recognition module, configured to collect the driving data of the driver, extract the key features of the driving data, classify the driving style according to the extracted features by using the fuzzy C-means clustering algorithm, and construct a driving style recognition model for identifying the driving style of the driver;

[0019] A thermal management system control module, configured to use the deep deterministic policy gradient algorithm, with battery temperature, current, current change rate, and driving style membership as state variables, construct an action variable, design a reward function considering temperature deviation, and train the thermal management system control model; use the trained model to control the thermal management of the battery pack of the electric vehicle.

[0020] The third technical solution adopted by the present invention is:

[0021] An electronic device, the electronic device includes a processor and a memory, and at least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the above-mentioned method for controlling the thermal management of an electric vehicle battery pack.

[0022] The fourth technical solution adopted by the present invention is as follows:

[0023] A computer-readable storage medium stores at least one instruction, at least one program, a code set, or an instruction set. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the method for controlling the thermal management of an electric vehicle battery pack as described above.

[0024] The fifth technical solution adopted by the present invention is as follows:

[0025] A computer program product or a computer program includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to execute the above-mentioned method for controlling the thermal management of an electric vehicle battery pack.

[0026] The beneficial effects of the present invention are as follows: The present invention combines driving style recognition with the control of the battery thermal management system, recognizes the driving style of the driver through a clustering algorithm, realizes thermal management control based on the driving style of the driver, and improves the adaptability and intelligence level of the system. In addition, the present invention introduces the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize the thermal management control strategy, dynamically adjusts the operating parameters of the thermal management system according to the real-time recognized driving style, thereby realizing the optimization of energy consumption while ensuring the safety and performance of the battery. The present invention breaks through the traditional rule-based control strategy, can realize the adaptive optimization of the battery pack thermal management control strategy, significantly improves the response speed and control accuracy of the thermal management system, and reduces energy consumption. Description of the Drawings

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following introduces the drawings of the related technical solutions in the embodiments of the present invention or the prior art. It should be understood that the drawings introduced below only facilitate the clear expression of some embodiments of the technical solutions in the present invention. For those skilled in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0028] Figure 1 It is a schematic diagram of the result after training the driving style recognition model in the embodiment of the present invention;

[0029] Figure 2 It is a schematic diagram of the structure of the thermal management system using WPTC for heating in the embodiment of the present invention;

[0030] Figure 3 It is a flowchart of the operation of the thermal management system control model in the embodiment of the present invention;

[0031] Figure 4 It is a comparison chart of the effects of the DDPG control strategy considering driving style and PID control at high temperature in the embodiments of the present invention;

[0032] Figure 5 It is a comparison chart of the effects of the DDPG control strategy considering driving style and PID control at low temperature in the embodiments of the present invention;

[0033] Figure 6 It is a comparison chart of the energy consumption of the DDPG control strategy considering driving style and PID control at high and low temperatures in the embodiments of the present invention;

[0034] Figure 7 It is a step flow chart of a thermal management control method for an electric vehicle battery pack in the embodiments of the present invention. Detailed implementation manners

[0035] The embodiments of the present application are described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present application, and should not be construed as a limitation to the present application. For the step numbers in the following embodiments, they are only set for the convenience of description and illustration, and no limitation is imposed on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0036] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the embodiments of the present application. The singular forms of "a", "the", and "said" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. In addition, unless otherwise clearly defined, words such as "set", "installed", and "connected" should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above words in the present invention in combination with the specific content of the technical solution.

[0037] In the description of the present application, it should be understood that the orientation or positional relationship indicated by terms such as up, down, front, back, left, right, etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present application.

[0038] In the description of this application, "several" means one or more, "multiple" means more than two, "greater than", "less than", "exceeding", etc. are understood not to include the base number, and "above", "below", "within", etc. are understood to include the base number. If "first" and "second" are described, it is only for the purpose of distinguishing technical features and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence relationship of the indicated technical features.

[0039] In the description of this application, "and / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0040] Term Explanation:

[0041] WPTC: Refers to a water heating-driven positive temperature coefficient thermistor heating system. The principle is that when current passes through the positive temperature coefficient thermistor element, the element will generate heat and increase in temperature, and transfer the heat to the coolant; its function is to generate heat through electrothermal conversion to provide heating for the thermal management system.

[0042] Embodiment 1

[0043] As Figure 7 shown, this embodiment provides an electric vehicle battery pack thermal management control method based on driving style recognition and deep reinforcement learning, which can realize the adaptive optimization of the battery pack thermal management control strategy, improve the control accuracy and reduce the energy consumption. The method specifically includes the following steps:

[0044] S1. Driving style recognition: Collect the driving data of the driver, extract the key features of the driving data, and classify the driving style according to the extracted features using the fuzzy C-means (FCM) clustering algorithm, and construct a driving style recognition model for identifying the driving style of the driver;

[0045] S2. Design of the thermal management system control model based on DDPG: Use the deep deterministic policy gradient (DDPG) algorithm, take the battery temperature, current, current change rate, and driving style membership as state variables, construct an action variable (compression control), design a reward function considering the temperature deviation, and train the thermal management system control model; use the trained model to control the thermal management of the battery pack of the electric vehicle.

[0046] In this embodiment, the driving style recognition is combined with the battery thermal management system control. Most of the existing battery thermal management systems adopt a predetermined control strategy, ignoring the influence of the driver's operation behavior on the battery thermal load. In this embodiment, the driving style of the driver is recognized through a clustering algorithm, realizing the thermal management control based on the driver's driving style, and improving the adaptability and intelligence level of the system.

[0047] In addition, the Deep Deterministic Policy Gradient (DDPG) algorithm is introduced to optimize the thermal management control strategy. The decision-making ability of the DDPG algorithm in complex environments enables it to dynamically adjust the operating parameters of the thermal management system according to the real-time recognized driving style, so as to optimize the energy consumption while ensuring the safety and performance of the battery. This method breaks through the traditional rule-based control strategy and significantly improves the response speed and control accuracy of the thermal management system.

[0048] The method of this embodiment will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0049] This embodiment takes a pure electric vehicle as the object and implements the control strategy of the present invention by the following steps:

[0050] (1) Driving style recognition

[0051] Collect data of the vehicle under various road conditions, invite drivers with different styles to drive actually, and establish a complete data set. Extract 6 characteristic parameters that can reflect the driving style from the data set: acceleration, jerk, torque request change rate, accelerator pedal opening change rate, brake pedal opening change rate, and steering wheel angle, and train the driving style recognition model through FCM clustering to divide into two styles: aggressive and conservative. The structure and training results are as Figure 1 shown.

[0052] (2) Training of the thermal management system control model

[0053] 2.1) Build a simulation platform for the battery thermal management system

[0054] Refer to Figure 2 , Figure 2 which is a schematic diagram of the structure of the thermal management system using WPTC for heating. Construct a thermal management system control strategy based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The input of this algorithm is the battery temperature, output current and its change rate, and the current driving style. The output is a continuous action variable to control whether the thermal management system cools or heats. The reward function is the difference between the current temperature of the battery pack and the target temperature. Then use the simulation platform as the environment and use the collected data to train the DDPG control strategy to obtain the optimal control strategy through training, as Figure 3 shown.

[0055] 2.2) Verification of control effect

[0056] Under high-temperature working conditions, that is, when the battery temperature is higher than the target temperature, the thermal management system needs to start the compressor for refrigeration, and the DDPG control strategy considering driving style is compared and verified with the PID control. The external environmental temperature and the initial temperature are both set to 30 °C, and the target temperature of the battery is 25 °C. The battery temperature changes, the difference from the target temperature, and the compressor speed changes of the DDPG control strategy considering driving style and the PID control under high temperature are as Figure 4 shown.

[0057] From Figure 4 (a) in it, it can be found that both control strategies can quickly reduce the temperature of the battery pack to the target temperature. However, compared with the DDPG control strategy, since the PID control mainly depends on the deviation between the current battery temperature and the target temperature, the response speed in the initial stage is slower, and it takes 170.5 s to reach the target temperature. While the DDPG control strategy considering driving style reaches the target temperature in 155.9 s, which takes less time than the PID control. After the battery pack temperature reaches the target temperature for the first time, the PID control fails to quickly stabilize the battery temperature, showing a large overshoot. The maximum overshoot reaches 0.39 °C, and the subsequent oscillation amplitude is large. The battery temperature fluctuates around the target temperature, and the maximum deviation reaches 0.47 °C, indicating that the PID control adjustment is lagging and it is difficult to achieve precise control. While the maximum overshoot of the DDPG control strategy in this embodiment is only 0.22 °C, and the later temperature curve is also smoother, with a maximum deviation of only 0.21 °C. From Figure 4 (b) and (c) in it, it can be found that compared with the PID control, the DDPG control strategy controls the compressor more precisely and can change the compressor speed in advance according to the change of battery heat generation. For example, around 1000 s, when changing from high-speed driving to low-speed driving, compared with the PID control, the DDPG control strategy makes a reaction in advance and reduces the speed of the compressor, and the temperature fluctuation here is also small.

[0058] Under low-temperature working conditions, that is, when the initial temperature of the battery is lower than the target temperature, the thermal management system needs to start the WPTC for heating, and the DDPG control strategy considering driving style is also compared and verified with the PID control. The external environmental temperature is set to -10 °C, the initial temperatures of the battery and the coolant are 0 °C, and the target temperature of the battery is also 25 °C. The battery temperature change curve, the compressor speed change, and the system energy consumption of the DDPG and PID control strategies considering driving style in the heating mode are as Figure 5 shown, and it can be found that the advantages and disadvantages of the two control strategies are basically the same as those in the refrigeration mode. From Figure 5 (a) in it, it can be found that compared with the PID control, the DDPG control strategy makes the temperature of the battery pack rise faster in the initial stage, with a smaller overshoot and a smaller oscillation amplitude after reaching the target temperature.Figure 5 It can be found from (b) that the DDPG control strategy is more refined for the control of WPTC and can change the power of WPTC in advance according to the change of battery heat generation.

[0059] Under the two working conditions, the energy consumption of the compressor and WPTC in the thermal management system is as Figure 6 shown. Since the DDPG control strategy considering driving style can dynamically adjust the control strategy and reduce unnecessary energy consumption, under the two working conditions of high temperature and low temperature, its energy consumption is reduced by 10.55% and 5.36% respectively compared with the PID control.

[0060] In summary, compared with the prior art, the present invention has at least the following advantages and beneficial effects: 1) It can automatically adjust the thermal management intensity according to the driving style; 2) It improves the accuracy of battery temperature control and avoids overcooling / overheating; 3) It significantly reduces the energy consumption of the thermal management system.

[0061] Embodiment 2

[0062] The present embodiment provides a thermal management control device for an electric vehicle battery pack, including:

[0063] A driving style recognition module, configured to collect the driving data of the driver, extract the key features of the driving data, classify the driving style according to the extracted features by using the fuzzy C-means clustering algorithm, and construct a driving style recognition model for recognizing the driving style of the driver;

[0064] A thermal management system control module, configured to use the deep deterministic policy gradient algorithm, take the battery temperature, current, current change rate, and driving style membership as state variables, construct an action variable, design a reward function considering the temperature deviation, and train the thermal management system control model; use the trained model to control the thermal management of the battery pack of the electric vehicle.

[0065] Since this device is a thermal management control device for an electric vehicle battery pack in an embodiment of the present invention, and the principle of solving problems by this device is similar to that of this method, the implementation of this device can refer to the implementation process of the above method embodiment, and the repeated parts will not be described again.

[0066] Embodiment 3

[0067] The embodiment of the present invention further provides an electronic device, the electronic device includes a processor and a memory, and at least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement as Figure 7 shown a thermal management control method for an electric vehicle battery pack.

[0068] It can be understood that the memory may include a Random Access Memory (RAM), or may also include a Read-Only Memory. Optionally, the memory includes a non-transitory computer-readable storage medium. The memory can be used to store instructions, programs, codes, code sets or instruction sets. The memory may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function, instructions for implementing the above-mentioned method embodiments, etc.; the data storage area can store data created according to the use of the server, etc.

[0069] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts within the entire server, and by running or executing instructions, programs, code sets or instruction sets stored in the memory, and by calling data stored in the memory, it executes various functions of the server and processes data. Optionally, the processor may be implemented in at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor may integrate a combination of one or several of a Central Processing Unit (CPU) and a modem, etc. Among them, the CPU mainly processes the operating system and application programs, etc.; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor and may be implemented separately by a single chip.

[0070] Since this electronic device is the electronic device corresponding to a method for controlling the thermal management of an electric vehicle battery pack according to an embodiment of the present invention, and the principle by which this electronic device solves problems is similar to that of this method, the implementation of this electronic device can refer to the implementation process of the above method embodiment, and the repeated parts will not be elaborated.

[0071] Embodiment 4

[0072] An embodiment of the present invention also provides a computer-readable storage medium, in which at least one instruction, at least one segment of program, code set or instruction set is stored, and the at least one instruction, the at least one segment of program, the code set or instruction set is loaded and executed by a processor to implement Figure 7 a method for controlling the thermal management of an electric vehicle battery pack as shown.

[0073] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program, and this program can be stored in a computer-readable storage medium. The storage medium includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memories, magnetic disc memories, tape memories, or any other computer-readable medium that can be used to carry or store data.

[0074] Since this storage medium is the storage medium corresponding to a method for thermal management control of an electric vehicle battery pack in an embodiment of the present invention, and the principle of solving problems by this storage medium is similar to that of this method, the implementation of this storage medium can refer to the implementation process of the above method embodiment, and the repeated parts will not be described again.

[0075] Embodiment 5

[0076] In some possible implementation manners, various aspects of the method in an embodiment of the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a computer device, the program code is used to cause the computer device to execute the steps of a method for thermal management control of an electric vehicle battery pack according to various exemplary implementation manners described above in this specification. Among them, the executable computer program code or "code" for executing each embodiment can be written in a high-level programming language such as C, C++, Python, Smalltalk, Java, JavaScript, Visual Basic, structured query language (for example, Transact-SQL), Perl, or written in various other programming languages.

[0077] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0078] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0079] The above embodiments are only for illustrating the technical concept and features of the present invention, and the purpose is to enable those of ordinary skill in the art to understand the content of the present invention and implement it accordingly, and it cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the essence of the content of the present invention should be covered within the protection scope of the present invention.

Claims

1. A thermal management control method for an electric vehicle battery pack, characterized in that, It includes the following steps: Driving style recognition: Collect the driving data of the driver, extract the key features of the driving data, classify the driving style using the fuzzy C-means clustering algorithm based on the extracted features, and construct a driving style recognition model for identifying the driving style of the driver; Design of the thermal management system control model based on DDPG: Use the deep deterministic policy gradient algorithm, take the battery temperature, current, current change rate, and driving style membership as state variables, construct action variables, design a reward function considering the temperature deviation, and train the thermal management system control model; Use the trained model to control the thermal management of the battery pack of the electric vehicle.

2. The thermal management control method for an electric vehicle battery pack according to claim 1, characterized in that, The driving style recognition includes: Collect data of the pure electric vehicle under various road conditions, and conduct actual driving based on drivers with different styles to establish a complete data set; Extract 6 characteristic parameters that can reflect the driving style from the data set: acceleration, jerk, torque request change rate, accelerator pedal opening change rate, brake pedal opening change rate, and steering wheel angle, and train the driving style recognition model using the fuzzy C-means clustering algorithm. The recognition results are divided into two types of styles: aggressive and conservative; Use the trained driving style recognition model to identify the driving style of the driver.

3. A thermal management control method for an electric vehicle battery pack according to claim 1, characterized in that The input of the thermal management system control model includes the battery temperature, output current and its change rate, and the current driving style. The output is a continuous action variable for controlling the cooling or heating of the thermal management system; The reward function is the difference between the current temperature of the battery pack and the target temperature.

4. A thermal management control method for an electric vehicle battery pack according to claim 3, characterized in that, Under high-temperature working conditions, that is, when the battery temperature is higher than the target temperature, the thermal management system needs to start the compressor for cooling; Under low-temperature working conditions, that is, when the initial temperature of the battery is lower than the target temperature, the thermal management system needs to start the WPTC for heating.

5. A thermal management control device for an electric vehicle battery pack, characterized in that, It includes: A driving style recognition module for collecting the driving data of the driver, extracting the key features of the driving data, classifying the driving style using the fuzzy C-means clustering algorithm based on the extracted features, and constructing a driving style recognition model for identifying the driving style of the driver; A thermal management system control module for using the deep deterministic policy gradient algorithm, taking the battery temperature, current, current change rate, and driving style membership as state variables, constructing action variables, designing a reward function considering the temperature deviation, and training the thermal management system control model; Use the trained model to control the thermal management of the battery pack of the electric vehicle.

6. An electronic device, characterized in that, The electronic device includes a processor and a memory. At least one instruction, at least one program, a code set, or an instruction set is stored in the memory. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, At least one instruction, at least one program, a code set, or an instruction set is stored in the storage medium. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the method according to any one of claims 1 to 4.

8. A computer program product, characterized in that, The computer program product includes computer instructions which, when executed by a processor, are used to perform the method according to any one of claims 1-4.

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