A control method and system for quenching metal products

Abstract

The present invention belongs to the field of factory control technology. A control method and system for quenching metal products are provided. The method includes: collecting training data, using the training data to train the constructed first deep prediction model and second deep prediction model; determining the target hardness of the metal part after quenching, using the first deep prediction model and the second deep prediction model to perform a deep analysis of the target hardness to obtain the target insulation time, cooling medium type and cooling medium temperature; performing insulation operation on the metal part heated to form austenite according to the target insulation time, and selecting the target cooling medium according to the cooling medium type and cooling medium temperature, and using the target cooling medium to perform rapid cooling treatment on the austenite metal part. The present invention can determine the optimal insulation parameters and rapid cooling parameters according to the customer's target hardness of the metal part after quenching, thereby meeting the customer's personalized needs.

Description

Technical Field

[0001] The present invention relates to the field of factory control technology, and in particular to a control method and system for quenching metal products. Background Art

[0002] Quenching is a key step in the heat treatment of metals, primarily used to increase their hardness and strength. Quenching involves three main process steps: heating, holding, and rapid cooling. These steps are described below: Heating: The metal workpiece is heated to a specific temperature, known as the austenitizing temperature, which is typically above the material's critical temperature. At this temperature, the metal's internal microstructure changes, forming austenite.

[0003] Holding: After reaching the austenitizing temperature, the workpiece needs to be held for a period of time to ensure uniform heating throughout the workpiece and complete transformation of the microstructure to austenite.

[0004] Rapid Cooling: After holding, the workpiece needs to be cooled rapidly to form martensite or other hardening phases. The cooling rate must be fast enough to avoid the formation of undesirable microstructures such as pearlite or bainite. The cooling medium can be water, oil, air, or other specialized quenching fluids.

[0005] It can be seen that heat preservation and rapid cooling are the key processes of quenching, which directly affect the quality of metal parts after quenching, such as whether there are cracks on the surface of the metal parts and whether there are undesirable microstructures such as pearlite or bainite inside the metal parts.

[0006] A technical problem with existing technologies is that they cannot tailor the holding and rapid cooling parameters in the quenching process to the customer's desired hardness of the metal part after quenching. This makes it difficult to meet the customer's personalized needs for quenching metal parts. The present invention aims to address this technical problem. Summary of the Invention

[0007] In response to the above technical problems, the present invention provides a control method, system, electronic equipment, computer storage medium and computer program product for quenching metal products.

[0008] The present invention discloses a control method for quenching a metal product, the method comprising the following steps: collecting training data, and using the training data to train a first deep prediction model and a second deep prediction model; determining a target hardness of the metal part after quenching, and using the first deep prediction model and the second deep prediction model to perform a deep analysis of the target hardness to obtain a target insulation time, a cooling medium type, and a cooling medium temperature; performing insulation operations on the metal part heated to form austenite according to the target insulation time, and selecting a target cooling medium according to the cooling medium type and the cooling medium temperature, and using the target cooling medium to perform rapid cooling treatment on the austenitic metal part.

[0009] Optionally, the collecting of training data and the use of the training data to train the constructed first depth prediction model and the second depth prediction model include: collecting a group of historical quenching data, and separating from the group of historical quenching data the first historical quenching data corresponding to the holding operation and the second historical quenching data corresponding to the rapid cooling operation; constructing a first training data set and a first test data set, a second training data set and a second test data set based on the first historical quenching data and the second historical quenching data; wherein the first training data set and the first test data set, the second training data set and the second test data set all include label one and label two, the label one is the hardness of the metal part after quenching, and the label two is the quality parameter of the metal part after quenching; using the first training data set and the first test data set to train and test the first depth prediction model, and using the second training data set and the second test data set to train and test the second depth prediction model, until the first depth prediction model and the second depth prediction model are trained to meet the standards.

[0010] Optionally, after the first depth prediction model and the second depth prediction model are trained to meet the standards, the method further includes: using the first test data set to test the first depth prediction model that has been trained to meet the standards to obtain a set of predicted insulation times; screening out second training data from the second training data set that belong to the same historical quenching data as each first test data, using each second training data screened out as the data body of the third training data, using the corresponding predicted insulation time as the label three of the third training data, and using the original label of each second training data, that is, the hardness and quality parameters of the metal part after quenching, as the label one and label two of the third training data, respectively, and each third training data constitutes a third training data set; using the third training data set to perform secondary training on the second depth prediction model that has been trained to meet the standards, and using the second test data set to test the second depth prediction model after the secondary training, until the second depth prediction model is trained to meet the standards, that is, the final first depth prediction model and second depth prediction model are obtained.

[0011] Optionally, the first depth prediction model that has been trained to meet the standards is tested using the first test data set to obtain a set of predicted insulation times, including: determining the number of types of metal plates in the first test data set, determining a screening number based on the number of types, randomly screening some first test data from the first test data set according to the screening number, and the screened first test data constitute a third test data set; and the first depth prediction model that has been trained to meet the standards is tested using the third test data set to obtain a set of predicted insulation times.

[0012] Optionally, the screening quantity is calculated based on the number of types and the following formula, including: Where, is the screening quantity; is the total amount of first test data in the first test data set; is the number of roughly classified types of metal plates in the first test dataset, is the number of subdivision types of metal plates in the first test dataset, is the total number of subdivision types of known metal plates; a is a constant value.

[0013] Optionally, the first depth prediction model is constructed based on CNN, and the second depth prediction model is constructed based on Transformer.

[0014] The present invention also discloses a control system for quenching metal products, the system including a processing device and a storage device, the computer code stored in the storage device is called and executed by the processing device to implement the following steps: The present invention also discloses an electronic device including: at least one processor, a memory, and a computer program stored in the memory and capable of running on the at least one processor, the processor executing the computer program to implement any of the methods described above.

[0015] The present invention further discloses a computer storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement any of the above methods.

[0016] The present invention further discloses a computer program product, which includes computer code. When the computer code is executed by a processor of an electronic device, any of the above methods is implemented.

[0017] The beneficial effect of the present invention is at least that: the solution of the present invention can determine the optimal insulation parameters and rapid cooling parameters according to the customer's personalized needs for the target hardness of the metal parts after quenching, and quench the metal parts separately according to the optimal insulation parameters and rapid cooling parameters, thereby meeting the customer's personalized needs and helping to improve the performance of the quenching company. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] 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 embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 The present invention is a flowchart of a method for controlling quenching of a metal product disclosed in an embodiment of the present invention.

[0020] Figure 2 This is a flow chart of a method for generating a predicted insulation time disclosed in an embodiment of the present invention.

[0021] Figure 3 It is a structural schematic diagram of a control system for quenching metal products disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following specific embodiments illustrate the implementation of this application. Those familiar with the art can easily understand the other advantages and functions of this application from the content disclosed in this specification. Obviously, the described embodiments are part of the embodiments of this application, but not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0023] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0024] like Figure 1 As shown, an embodiment of the present invention discloses a control method for quenching a metal product, the method comprising the following steps: collecting training data, and using the training data to train a first deep prediction model and a second deep prediction model; determining a target hardness of the metal part after quenching, and using the first deep prediction model and the second deep prediction model to perform a deep analysis on the target hardness to obtain a target insulation time, a cooling medium type, and a cooling medium temperature; performing insulation operation on the metal part heated to form austenite according to the target insulation time, and selecting a target cooling medium according to the cooling medium type and the cooling medium temperature, and using the target cooling medium to perform rapid cooling treatment on the austenitic metal part.

[0025] The above scheme of the present invention supports the selection of the optimal insulation parameters and rapid cooling parameters according to the target hardness of the quenched metal part required by the customer. Specifically, the pre-built first depth prediction model and the second depth prediction model are trained using training data. Then, the target hardness of the metal part after quenching treatment is received according to the customer's requirements, and the first depth prediction model is used to deeply analyze the target hardness to obtain the optimal insulation parameters, that is, the target insulation time, and the second depth prediction model is used to deeply analyze the target hardness to obtain the optimal rapid cooling parameters, that is, the cooling medium type and the cooling medium temperature. Finally, the insulation operation of the metal part heated to form austenite is controlled according to the target insulation time, and the target cooling medium is selected according to the cooling medium type and the cooling medium temperature, and the austenitic metal part is rapidly cooled using the target cooling medium, so that a metal part that meets the target hardness and meets the quality standards can be obtained. Wherein, meeting the quality standards means that there are no cracks on the surface of the metal part and there are no undesirable microstructures such as pearlite or bainite inside the metal part.

[0026] Therefore, the solution of the present invention can determine the optimal insulation parameters and rapid cooling parameters based on the customer's personalized needs for the target hardness of the metal parts after quenching, and quench the metal parts separately according to the optimal insulation parameters and rapid cooling parameters, thereby meeting the customer's personalized needs and helping to improve the performance of the quenching company.

[0027] Optionally, the collecting of training data and the use of the training data to train the constructed first depth prediction model and the second depth prediction model include: collecting a group of historical quenching data, and separating from the group of historical quenching data the first historical quenching data corresponding to the holding operation and the second historical quenching data corresponding to the rapid cooling operation; constructing a first training data set and a first test data set, a second training data set and a second test data set based on the first historical quenching data and the second historical quenching data; wherein the first training data set and the first test data set, the second training data set and the second test data set all include label one and label two, the label one is the hardness of the metal part after quenching, and the label two is the quality parameter of the metal part after quenching; using the first training data set and the first test data set to train and test the first depth prediction model, and using the second training data set and the second test data set to train and test the second depth prediction model, until the first depth prediction model and the second depth prediction model are trained to meet the standards.

[0028] In this embodiment, the present invention collects a set of historical quenching data recorded during an actual quenching operation. Each piece of historical quenching data includes quenching data during a holding operation (i.e., holding duration) and quenching data during a rapid cooling operation (i.e., cooling medium type and cooling medium temperature), as well as the corresponding hardness and quality parameters of the metal part after quenching (whether cracks are present on the metal part's surface and whether undesirable microstructures such as pearlite or bainite are present within the metal part). The historical quenching data is separated one by one according to the holding operation and the rapid cooling operation, thereby obtaining the aforementioned first and second historical quenching data. The first and second historical quenching data are used as the training data bodies, and the hardness and quality parameters of the metal part after quenching are used as labels one and two, respectively, to form two training datasets. These two training datasets are divided into a first training dataset and a first test dataset, and a second training dataset and a second test dataset, respectively.

[0029] Then, the first training data set and the first test data set constructed above are used to train and test the first depth prediction model, and the second training data set and the second test data set are used to train and test the second depth prediction model, until both the first depth prediction model and the second depth prediction model are trained to meet the standards, that is, the convergence conditions are met.

[0030] Optionally, after the first depth prediction model and the second depth prediction model are trained to meet the standards, the method further includes: using the first test data set to test the first depth prediction model that has been trained to meet the standards to obtain a set of predicted insulation times; screening out second training data from the second training data set that belong to the same historical quenching data as each first test data, using each second training data screened out as the data body of the third training data, using the corresponding predicted insulation time as the label three of the third training data, and using the original label of each second training data, that is, the hardness and quality parameters of the metal part after quenching, as the label one and label two of the third training data, respectively, and each third training data constitutes a third training data set; using the third training data set to perform secondary training on the second depth prediction model that has been trained to meet the standards, and using the second test data set to test the second depth prediction model after the secondary training, until the second depth prediction model is trained to meet the standards, that is, the final first depth prediction model and second depth prediction model are obtained.

[0031] In this embodiment, the first training data set and the first test data set, the second training data set and the second test data set constructed above correspond to the holding operation and the rapid cooling operation, respectively. For the second depth prediction model, the austenite transformation of the metal parts in the previous process, i.e., the holding operation (i.e., whether it is completely converted to austenite) in the previous process is not considered during training. Therefore, the above-mentioned one-time training of the second depth prediction model is essentially insufficient, and a second training is required.

[0032] In response to the above technical problems, the present invention further uses a first test data set (including multiple first test data) to test the first deep prediction model that has been trained to meet the standards, and then collects a set of predicted holding times predicted by the first deep prediction model; and from the second training data set (including multiple second training data), screens out second training data that belong to the same historical quenching data as each first test data, uses each second training data obtained through screening as the data body of the third training data, uses the corresponding predicted holding time as the label three of the third training data, and uses the original label of each second training data, i.e., the hardness and quality parameters of the metal part after quenching, as the label one and label two of the third training data, respectively, to obtain a new third training data set. It can be seen that the new third training data set contains the label of the predicted holding time output by the first deep prediction model. Using it to perform a second training on the second deep prediction model that has been trained to meet the standards once, the second deep prediction model can gradually master the optimal holding parameters predicted by the first deep prediction model and analyze and obtain the optimal rapid cooling parameters. At the same time, the original second test data set is used to train and test the second depth prediction model that has undergone secondary training. If the test result meets the standard, it will be used as the final second depth prediction model, and the first depth prediction model that meets the standard after one training is the final first depth prediction model.

[0033] Therefore, the solution of the present invention uses training data corresponding to the heat preservation operation and training data corresponding to the rapid cooling operation to independently train the first depth prediction model and the second depth prediction model respectively, and then uses the test output data of the first depth prediction model to fuse new training data to perform another fusion training on the second depth prediction model, so that the second depth prediction model can be coordinated with the first depth prediction model, so that the heat preservation parameters and rapid cooling parameters predicted by the first depth prediction model and the second depth prediction model respectively are matched, ensuring that the actual hardness of the metal plate obtained after quenching treatment is closest to the target hardness required by the customer, and the quality parameters of the metal plate are still up to standard.

[0034] Alternatively, as Figure 2 As shown, the first depth prediction model that has been trained to meet the standards is tested using the first test data set to obtain a set of predicted insulation times, including: determining the number of types of metal plates in the first test data set, determining a screening number based on the number of types, randomly screening some first test data from the first test data set according to the screening number, and the screened first test data constitute a third test data set; the first depth prediction model that has been trained to meet the standards is tested using the third test data set to obtain a set of predicted insulation times.

[0035] In this embodiment, metal plates are classified into various types, such as carbon steel, low-alloy steel (e.g., 45#, T7, T8), alloy structural steel (e.g., 40Cr, 40MnB, 35CrMo), and high-alloy tool steel (e.g., 9SiCr, CrWMn, Cr12MoV, W6, W8). Different types of metal plates have different requirements for heat preservation and rapid cooling parameters. Accordingly, the previously collected historical quenching data also includes historical quenching data for different types of metal plates, and the first test dataset corresponds to historical quenching data for multiple different types of metal plates.

[0036] Based on the above situation, the present invention first determines the number of metal plate types involved in the first test dataset. Based on this number of types, it determines a screening number. Based on this screening number, a portion of first test data is randomly screened from the first test dataset to obtain the selected first test data. The selected first test data constitute a third test dataset. The third test dataset is then used to test the first deep prediction model that has been trained to meet the requirements, obtaining a set of predicted holding times.

[0037] Among them, the screening number and the number of metal plate types are negatively correlated, that is, the more types of metal plates there are, the greater the probability that the first depth prediction model has been fully trained in one training process, and the higher the confidence of the predicted insulation time output by the first depth prediction model, the smaller the number of first test data is screened from the first test data set to form the third test data set; the fewer types of metal plates there are, the smaller the probability that the first depth prediction model has been fully trained in one training process, and the lower the confidence of the predicted insulation time output by the first depth prediction model, the larger the number of first test data is screened from the first test data set to form the third test data set to ensure the effect of the secondary training of the second depth prediction model.

[0038] Optionally, the screening quantity is calculated based on the number of types and the following formula, including: Where, is the screening quantity; is the total amount of first test data in the first test data set; is the number of roughly classified types of metal plates in the first test dataset, is the number of subdivision types of metal plates in the first test dataset, is the total number of subdivision types of known metal plates; a is a constant value.

[0039] In this embodiment, the present invention classifies the types of metal plates into coarse and fine categories. The coarse classification refers to classifying metal plates into carbon steel, low alloy steel, alloy structural steel, high alloy tool steel, etc., while the fine classification refers to classifying metal plates into carbon steel, low alloy steel (45#), low alloy steel (T7), low alloy steel (T8), alloy structural steel (40Cr), alloy structural steel (40MnB), alloy structural steel (35CrMo), high alloy tool steel (9SiCr), high alloy tool steel (CrWMn), high alloy tool steel (Cr12MoV), high alloy tool steel (W6), high alloy tool steel (W8), etc. The number of coarse classification types of metal plates in the first test data set can then be calculated. , the number of subdivision types of metal plates in the first test dataset ; and, the total number of known metal sheet sub-types is a constant. Substituting the above parameters into the above calculation formula, the screening quantity can be calculated. According to the screening quantity, a portion of the first test data is screened from the first test data set to form a third test data set.

[0040] It should be noted that the constant value a in the above formula is used to ensure A value less than 1.

[0041] Optionally, the first depth prediction model is constructed based on CNN, and the second depth prediction model is constructed based on Transformer.

[0042] In this embodiment, both the first and second depth prediction models can be constructed based on CNN. However, since the first depth prediction model does not need to consider the rapid cooling parameters of the subsequent quenching process when predicting the optimal insulation parameters, the prediction difficulty of the first depth prediction model is relatively low, so it can be constructed using a conventional CNN (convolutional neural network). In contrast, the second depth prediction model also needs to consider the insulation parameters predicted by the first depth prediction model of the previous quenching process when predicting the optimal rapid cooling parameters, and its prediction difficulty is greater, so it is preferred to set the second depth prediction model to be constructed based on Transformer.

[0043] like Figure 3As shown, an embodiment of the present invention also discloses a control system for quenching metal products, the system including a processing device and a storage device, the computer code stored in the storage device is called and executed by the processing device to implement the following steps: collecting training data, using the training data to train the constructed first depth prediction model and the second depth prediction model; determining the target hardness of the metal part after quenching treatment, using the first depth prediction model and the second depth prediction model to perform a deep analysis of the target hardness to obtain the target insulation time, cooling medium type and cooling medium temperature; performing insulation operation on the metal part heated to form austenite according to the target insulation time, and selecting the target cooling medium according to the cooling medium type and the cooling medium temperature, and using the target cooling medium to perform rapid cooling treatment on the austenitic metal part.

[0044] An embodiment of the present invention further discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method described in the aforementioned embodiment.

[0045] An embodiment of the present invention further discloses a computer storage medium, wherein the computer storage medium stores a computer program, and the computer program is executed by a processor to implement the method described in the above embodiment.

[0046] An embodiment of the present invention further discloses a computer program product, which includes computer code. When the computer code is executed by a processor of an electronic device, the method described in the above embodiment is implemented.

[0047] The computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or apparatuses, or any suitable combination thereof. Alternatively, the computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media may include electrical connections based on 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 (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0048] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0049] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0050] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for controlling quenching of a metal product, characterized in that: The method includes the following steps: collecting training data, and using the training data to train a first deep prediction model and a second deep prediction model; determining a target hardness of a metal part after quenching, and using the first deep prediction model and the second deep prediction model to perform a deep analysis on the target hardness to obtain a target holding time, a cooling medium type, and a cooling medium temperature; holding the metal part heated to austenite according to the target holding time, selecting a target cooling medium according to the cooling medium type and the cooling medium temperature, and using the target cooling medium to rapidly cool the austenite metal part; The collecting of training data and the use of the training data to train the constructed first depth prediction model and the second depth prediction model include: collecting a group of historical quenching data, separating from the group of historical quenching data first historical quenching data corresponding to a holding operation and second historical quenching data corresponding to a rapid cooling operation; constructing a first training data set and a first test data set, a second training data set and a second test data set based on the first historical quenching data and the second historical quenching data; wherein the first training data set and the first test data set, the second training data set and the second test data set all include a label one and a label two, the label one being the hardness of the metal part after quenching, and the label two being the quality parameter of the metal part after quenching; using the first training data set and the first test data set to train and test the first depth prediction model, and using the second training data set and the second test data set to train and test the second depth prediction model, until the first depth prediction model and the second depth prediction model are trained to meet the standards; After the first depth prediction model and the second depth prediction model are trained to meet the standards, the method also includes: using the first test data set to test the first depth prediction model that has been trained to meet the standards to obtain a set of predicted insulation times; screening out second training data from the second training data set that belong to the same historical quenching data as each first test data, using each second training data screened out as the data body of the third training data, using the corresponding predicted insulation time as the label three of the third training data, and using the original label of each second training data, that is, the hardness and quality parameters of the metal part after quenching, as the label one and label two of the third training data, respectively, and each third training data constitutes a third training data set; using the third training data set to perform secondary training on the second depth prediction model that has been trained to meet the standards, and using the second test data set to test the second depth prediction model after the secondary training, until the second depth prediction model is trained to meet the standards, that is, the final first depth prediction model and second depth prediction model are obtained.

2. The method for controlling quenching of a metal product according to claim 1, wherein: The first depth prediction model that has been trained to meet the standards is tested using the first test data set to obtain a set of predicted insulation times, including: determining the number of types of metal parts in the first test data set, determining a screening number based on the number of types, randomly screening some first test data from the first test data set according to the screening number, and the screened first test data constitute a third test data set; using the third test data set to test the first depth prediction model that has been trained to meet the standards to obtain a set of predicted insulation times.

3. The method for controlling quenching of a metal product according to claim 2, wherein: The number of screenings is calculated based on the number of types and the following formula, including: Where, is the screening quantity; is the total amount of first test data in the first test data set; is the number of roughly classified types of metal parts in the first test data set, is the number of subdivision types of metal parts in the first test data set, is the total number of subdivision types of known metal parts; a is a constant value.

4. The method for controlling quenching of a metal product according to claim 3, wherein: The first depth prediction model is constructed based on CNN, and the second depth prediction model is constructed based on Transformer.

5. A control system for quenching a metal product, the system being used to implement the method according to any one of claims 1 to 4, comprising a processing device and a storage device, characterized in that: The computer code stored in the storage device is called and executed by the processing device to implement the following steps: collecting training data, and using the training data to train the constructed first depth prediction model and second depth prediction model; determining the target hardness of the metal part after quenching treatment, and using the first depth prediction model and the second depth prediction model to perform a deep analysis of the target hardness to obtain the target insulation time, cooling medium type and cooling medium temperature; performing insulation operation on the metal part heated to form austenite according to the target insulation time, and selecting the target cooling medium according to the cooling medium type and the cooling medium temperature, and using the target cooling medium to perform rapid cooling treatment on the austenitic metal part.

6. An electronic device comprising: At least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 4.

7. A computer storage medium storing a computer program, wherein: The computer program is executed by a 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 code, and when the computer code is executed by a processor of an electronic device, the method according to any one of claims 1 to 4 is implemented.

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