CN111797282A - Product label weight determination method and device, electronic equipment and readable storage medium - Google Patents

Product label weight determination method and device, electronic equipment and readable storage medium Download PDF

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CN111797282A
CN111797282A CN202010496967.9A CN202010496967A CN111797282A CN 111797282 A CN111797282 A CN 111797282A CN 202010496967 A CN202010496967 A CN 202010496967A CN 111797282 A CN111797282 A CN 111797282A
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CN111797282B (en
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王招辉
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China Construction Bank Corp
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CCB Finetech Co Ltd
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Abstract

The embodiment of the application provides a method and a device for determining product label weight, electronic equipment and a readable storage medium. The method comprises the following steps: acquiring a user label of a target user and the operation behavior of the target user on a product in a preset time period; determining a product label for the product based on the user label; and determining the label weight of the product label based on the importance degree of the product label to the product, the preset operation behavior weight and the time attenuation factor. Based on this scheme, can confirm the product label of product through user's user label, compare in the product label of artifical affirmation, the product label description that determines in this scheme is comprehensive, accurate to can determine the credibility of each product label, be favorable to the use of product label.

Description

Product label weight determination method and device, electronic equipment and readable storage medium
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for determining product label weight, an electronic device, and a readable storage medium.
Background
In the financial field, the product label of a product is mostly determined by business personnel according to information such as the attribute and the category of the product, and operations such as determining the product association degree and performing association recommendation on the product can be performed based on the product label.
The existing product labels have high dependence on service personnel, and because the service personnel can know the products with certain subjectivity, the product labels determined by the service personnel can have the problems of incomplete and inaccurate description of the products, and the credibility of each product label can not be known, so that the product labels are not beneficial to use.
Disclosure of Invention
The present application aims to solve at least one of the above technical drawbacks. The technical scheme adopted by the application is as follows:
in a first aspect, an embodiment of the present application provides a method for determining a product tag weight, where the method includes:
acquiring a user label of a target user and the operation behavior of the target user on a product in a preset time period;
determining a product label for the product based on the user label;
and determining the label weight of the product label based on the importance degree of the product label to the product, the preset operation behavior weight and the time attenuation factor.
Optionally, determining a product label for the product based on the user label comprises:
and determining the user label as a product label of a product for which the corresponding target user performs the operation behavior.
Optionally, determining the importance of the product label to the product comprises:
and determining the importance degree of the product label to the product based on the appearance frequency of the product label in all product labels of the product and the reverse file frequency of the product label.
Optionally, determining the label weight of the product label based on the importance degree of the product label to the product, a preset operation behavior weight and a time decay factor, includes:
determining a label weight for the product label by:
Figure BDA0002523257770000021
wherein i is any one of the products, T is any one of the product labels of the product i, T is the time period corresponding to the label weight, w(i，t，T)Label weight, TF, of product label T for product i during time T(i，t，T)IDF is the frequency of occurrence of product label T in all product labels of product i in period T(t，T)The reverse file frequency of a product label T in a T time period, bw is a behavior weight corresponding to an operation behavior of a target user on a product i, and w(i，t，T-1)The weight of the tag in the last time segment of the T time segment, dTIs the time decay factor within the T period,
Figure BDA0002523257770000022
is a time decay weight calculated from the time decay factor.
Optionally, obtaining the user tag of the target user includes:
and acquiring the user label of the target user from a preset user image library.
Optionally, the obtaining of the operation behavior of the target user on the product within the preset time period includes:
and acquiring the operation behavior of the target user on the product in a preset time period from a preset user behavior information base.
Optionally, the method further includes:
constructing a product label matrix based on the product labels and the label weights;
and determining the similarity of the products based on the product label matrix.
Optionally, determining similarity of the product based on the product label matrix includes:
performing singular value decomposition on the product label matrix to determine a singular value matrix of the product label matrix;
converting the singular value matrix into a dense matrix;
and determining the similarity of the products based on the dense matrix.
In a second aspect, an embodiment of the present application provides an apparatus for determining product label weight, where the apparatus includes:
the basic data acquisition module is used for acquiring a user label of a target user and the operation behavior of the target user on a product in a preset time period;
a product label determination module for determining a product label for the product based on the user label;
and the label weight determining module is used for determining the label weight of the product label based on the importance degree of the product label to the product, the preset operation behavior weight and the time attenuation factor.
Optionally, the product tag determination module is specifically configured to:
and determining the user label as a product label of a product for which the corresponding target user performs the operation behavior.
Optionally, the tag weight determining module, when determining the importance degree of the product tag to the product, is specifically configured to:
and determining the importance degree of the product label to the product based on the appearance frequency of the product label in all product labels of the product and the reverse file frequency of the product label.
Optionally, the tag weight determining module is specifically configured to:
determining a label weight for the product label by:
Figure BDA0002523257770000031
wherein i is any one of the products, T is any one of the product labels of the product i, T is the time period corresponding to the label weight, w(i，t，T)Label weight, TF, of product label T for product i during time T(i，t，T)IDF is the frequency of occurrence of product label T in all product labels of product i in period T(t，T)The reverse file frequency of a product label T in a T time period, bw is a behavior weight corresponding to an operation behavior of a target user on a product i, and w(i，t，T-1)The weight of the tag in the last time segment of the T time segment, dTFor time decay within T periodThe factor(s) is (are),
Figure BDA0002523257770000032
is a time decay weight calculated from the time decay factor.
Optionally, when the basic data obtaining module obtains the user tag of the target user, the basic data obtaining module is specifically configured to:
and acquiring the user label of the target user from a preset user image library.
Optionally, the obtaining, by the basic data obtaining module, an operation behavior of the target user on the product in a preset time period includes:
and acquiring the operation behavior of the target user on the product in a preset time period from a preset user behavior information base.
Optionally, the apparatus further includes a similarity determination module, where the similarity determination module is specifically configured to:
constructing a product label matrix based on the product labels and the label weights;
and determining the similarity of the products based on the product label matrix.
Optionally, when determining the similarity of the product based on the product label matrix, the similarity determination module is specifically configured to:
performing singular value decomposition on the product label matrix to determine a singular value matrix of the product label matrix;
converting the singular value matrix into a dense matrix;
and determining the similarity of the products based on the dense matrix.
In a third aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory;
a memory for storing operating instructions;
a processor, configured to execute the product tag weight determination method as shown in any implementation manner of the first aspect of the present application by calling an operation instruction.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the product tag weight determining method shown in any implementation manner of the first aspect of the present application.
The technical scheme provided by the embodiment of the application has the following beneficial effects:
according to the scheme, the user label is determined as the product label of the product for which the corresponding target user performs the operation behavior, and the label weight of the product label is determined based on the importance degree of the product label to the product, the preset operation behavior weight and the time attenuation factor. Based on this scheme, can confirm the product label of product through user's user label, compare in the product label of artifical affirmation, the product label description that determines in this scheme is comprehensive, accurate to can determine the credibility of each product label, be favorable to the use of product label.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments of the present application will be briefly described below.
Fig. 1 is a schematic flowchart of a method for determining a weight of a product label according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a product label weight determining apparatus according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1 shows a schematic flowchart of a method for determining a product label weight according to an embodiment of the present application, and as shown in fig. 1, the method mainly includes:
step S110, acquiring a user label of a target user and the operation behavior of the target user on a product in a preset time period;
step S120, determining a product label of the product based on the user label;
and S130, determining the label weight of the product label based on the importance degree of the product label to the product, the preset operation behavior weight and the time attenuation factor.
In the embodiment of the application, the target user can be a user who operates a product to be labeled, and the operation behavior can be purchasing, searching, clicking and the like. Since the target user performs an operation action on the product, the user tag of the target user can be used to characterize the characteristics of the product to a certain extent, and therefore, the product tag of the product can be determined based on the user tag.
Specifically, the user tag of the target user may be determined as a product tag of a product for which the target user performs the operation behavior.
In the embodiment of the application, a large number of user groups can be determined as target users, and the user labels are wide in source, so that the determined product labels can comprehensively and accurately represent product characteristics.
As an example, user a's label is a high-risk investment, user a purchases a financial product B, and then financial product B may be a high-risk investment product, and the product label of financial product B may be determined as a high-risk investment, and the credibility of the high-risk investment product label may be characterized by the label weight of the product label.
In the embodiment of the application, the label weight of the product label can be calculated, and the credibility of each product label is represented by the label weight. Specifically, the label weight of the product label may be determined based on the importance degree of the product label to the product, a preset operation behavior weight, and a time decay factor.
In the embodiment of the application, the importance degree of the product label to the product can be determined through the occurrence condition of the product label of the target product.
In the embodiment of the application, since the user may perform different operation behaviors on the product, and the product preference degrees for the different operation behaviors are different, the operation behavior weights can be respectively preset for the different operation behaviors.
In the implementation of the application, as time goes by, the historical operation behavior of the user and the current correlation are weakened continuously, so that a time attenuation factor can be set, and the time attenuation factor is considered when calculating the label weight.
According to the method, the user label is determined as the product label of the product for which the corresponding target user performs the operation behavior, and the label weight of the product label is determined based on the importance degree of the product label to the product, the preset operation behavior weight and the time attenuation factor. Based on this scheme, can confirm the product label of product through user's user label, compare in the product label of artifical affirmation, the product label description that determines in this scheme is comprehensive, accurate to can determine the credibility of each product label, be favorable to the use of product label.
In an optional mode of the embodiment of the present application, determining the importance degree of the product label to the product includes:
and determining the importance degree of the product label to the product based on the appearance frequency of the product label in all product labels of the product and the reverse file frequency of the product label.
In the embodiment of the application, the TF-IDF algorithm can be used for evaluating the importance degree of each product label on the product. Specifically, the frequency of occurrence of the product label in all product labels of the product and the reverse file frequency of the product label may be used.
In actual use, the product label Y of the product X is taken as an example. The frequency of occurrence of the product label Y in all product labels of the product X may be obtained by dividing the number of times the product label Y marks the product X by the number of all product labels of the product X. The reverse file frequency of the product label Y in all products can be obtained by dividing the total number of products by the number of products including the product label.
In an optional mode of the embodiment of the present application, determining a label weight of a product label based on an importance degree of the product label to a product, a preset operation behavior weight, and a time attenuation factor includes:
determining a label weight for the product label by:
Figure BDA0002523257770000071
wherein i is any one of the products, T is any one of the product labels of the product i, T is the time period corresponding to the label weight, w(i，t，T)Label weight, TF, of product label T for product i during time T(i，t，T)For the appearance of the product label T in all product labels of all products within the period TFrequency, IDF(t，T)The reverse file frequency of a product label T in a T time period, bw is a behavior weight corresponding to an operation behavior of a target user on a product i, and w(i，t，T-1)The weight of the tag in the last time segment of the T time segment, dTIs the time decay factor within the T period,
Figure BDA0002523257770000072
is a time decay weight calculated from the time decay factor.
According to the embodiment of the application, the label weight of each product label of a product can be determined by combining the preset operation behavior weight and the time attenuation factor on the basis of calculating the importance degree of the product label to the product through the TF-IDF algorithm.
In an optional manner of the embodiment of the present application, obtaining a user tag of a target user includes:
and acquiring the user label of the target user from a preset user image library.
According to the user portrait library creating method and device, the user portrait library can be created based on the user behaviors, and the user tags of the target users can be obtained from the user portrait library.
According to the embodiment of the application, the user behavior information base can be preset, and the user behavior information base stores operation behavior data such as behavior logs of the target user, so that the operation behaviors of the user can be acquired in time.
In an optional manner of the embodiment of the present application, the method further includes:
constructing a product label matrix based on the product labels and the label weights;
and determining the similarity of the products based on the product label matrix.
In the embodiment of the application, when the product label and the label weight are determined, the similarity of each product can be determined based on the product label and the label weight, so that a product with higher similarity of the product is recommended as a related product.
In the embodiment of the application, a product label matrix can be constructed based on the item labels and the label weights, so that the similarity of the products can be determined based on the product label matrix.
Specifically, after the product label matrix is constructed, singular value decomposition may be performed on the product label matrix to determine the singular value matrix of the product label matrix, and the noise may be eliminated by decomposing the product label matrix into the singular value matrix. And converting the singular value matrix into a dense matrix so as to determine the similarity of the products based on the dense matrix. In actual use, the similarity measurement methods such as cosine similarity and Euclidean distance can be used for calculation, and the similarity is ranked from high to low, so that the product with the highest similarity of each product is obtained and used as the associated product for recommendation.
Based on the same principle as the method shown in fig. 1, fig. 2 shows a schematic structural diagram of a product label weight determining apparatus provided by an embodiment of the present application, and as shown in fig. 2, the product label weight determining apparatus 20 may include:
a basic data obtaining module 210, configured to obtain a user tag of a target user and an operation behavior of the target user on a product within a preset time period;
a product tag determination module 220 for determining a product tag of a product based on the user tag;
the tag weight determining module 230 is configured to determine a tag weight of the product tag based on the importance degree of the product tag to the product, a preset operation behavior weight, and a time decay factor.
The device provided by the application determines the user label as a product label of a product for which the operation behavior of a corresponding target user is carried out, and determines the label weight of the product label based on the importance degree of the product label to the product, the preset operation behavior weight and the time attenuation factor. Based on this scheme, can confirm the product label of product through user's user label, compare in the product label of artifical affirmation, the product label description that determines in this scheme is comprehensive, accurate to can determine the credibility of each product label, be favorable to the use of product label.
Optionally, the product tag determination module is specifically configured to:
and determining the user label as a product label of a product for which the corresponding target user performs the operation behavior.
Optionally, the tag weight determining module, when determining the importance degree of the product tag to the product, is specifically configured to:
and determining the importance degree of the product label to the product based on the appearance frequency of the product label in all product labels of the product and the reverse file frequency of the product label.
Optionally, the tag weight determining module is specifically configured to:
determining a label weight for the product label by:
Figure BDA0002523257770000091
wherein i is any one of the products, T is any one of the product labels of the product i, T is the time period corresponding to the label weight, w(i，t，T)Label weight, TF, of product label T for product i during time T(i，t，T)IDF is the frequency of occurrence of product label T in all product labels of product i in period T(t，T)The reverse file frequency of a product label T in a T time period, bw is a behavior weight corresponding to an operation behavior of a target user on a product i, and w(i，t，T-1)The weight of the tag in the last time segment of the T time segment, dTIs the time decay factor within the T period,
Figure BDA0002523257770000092
is a time decay weight calculated from the time decay factor.
Optionally, when the basic data obtaining module obtains the user tag of the target user, the basic data obtaining module is specifically configured to:
and acquiring the user label of the target user from a preset user image library.
Optionally, the obtaining, by the basic data obtaining module, an operation behavior of the target user on the product in a preset time period includes:
and acquiring the operation behavior of the target user on the product in a preset time period from a preset user behavior information base.
Optionally, the apparatus further includes a similarity determination module, where the similarity determination module is specifically configured to:
constructing a product label matrix based on the product labels and the label weights;
and determining the similarity of the products based on the product label matrix.
Optionally, when determining the similarity of the product based on the product label matrix, the similarity determination module is specifically configured to:
performing singular value decomposition on the product label matrix to determine a singular value matrix of the product label matrix;
converting the singular value matrix into a dense matrix;
and determining the similarity of the products based on the dense matrix.
It is to be understood that the above modules of the product label weight determining apparatus in the present embodiment have functions of implementing the corresponding steps of the product label weight determining method in the embodiment shown in fig. 1. The function can be realized by hardware, and can also be realized by executing corresponding software by hardware. The hardware or software includes one or more modules corresponding to the functions described above. The modules can be software and/or hardware, and each module can be implemented independently or by integrating a plurality of modules. For the functional description of each module of the product label weight determining apparatus, reference may be specifically made to the corresponding description of the product label weight determining method in the embodiment shown in fig. 1, and details are not repeated here.
The embodiment of the application provides an electronic device, which comprises a processor and a memory;
a memory for storing operating instructions;
and the processor is used for executing the product label weight determining method provided by any embodiment of the application by calling the operation instruction.
As an example, fig. 3 shows a schematic structural diagram of an electronic device to which an embodiment of the present application is applicable, and as shown in fig. 3, the electronic device 2000 includes: a processor 2001 and a memory 2003. Wherein the processor 2001 is coupled to a memory 2003, such as via a bus 2002. Optionally, the electronic device 2000 may also include a transceiver 2004. It should be noted that the transceiver 2004 is not limited to one in practical applications, and the structure of the electronic device 2000 is not limited to the embodiment of the present application.
The processor 2001 is applied to the embodiment of the present application to implement the method shown in the above method embodiment. The transceiver 2004 may include a receiver and a transmitter, and the transceiver 2004 is applied to the embodiments of the present application to implement the functions of the electronic device of the embodiments of the present application to communicate with other devices when executed.
The Processor 2001 may be a CPU (Central Processing Unit), general Processor, DSP (Digital Signal Processor), ASIC (Application specific integrated Circuit), FPGA (Field Programmable Gate Array) or other Programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor 2001 may also be a combination of computing functions, e.g., comprising one or more microprocessors, DSPs and microprocessors, and the like.
Bus 2002 may include a path that conveys information between the aforementioned components. The bus 2002 may be a PCI (Peripheral Component Interconnect) bus, an EISA (extended industry Standard Architecture) bus, or the like. The bus 2002 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 3, but this does not mean only one bus or one type of bus.
The Memory 2003 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, an EEPROM (Electrically erasable programmable Read Only Memory), a CD-ROM (Compact disk Read Only Memory) or other optical disk storage, optical disk storage (including Compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to these.
Optionally, the memory 2003 is used for storing application program code for performing the disclosed aspects, and is controlled in execution by the processor 2001. The processor 2001 is configured to execute application program codes stored in the memory 2003 to implement the product tag weight determination method provided in any of the embodiments of the present application.
The electronic device provided by the embodiment of the application is applicable to any embodiment of the method, and is not described herein again.
Compared with the prior art, the embodiment of the application provides the electronic equipment, the user label is determined as the product label of the product for which the corresponding target user performs the operation behavior, and the label weight of the product label is determined based on the importance degree of the product label to the product, the preset operation behavior weight and the time attenuation factor. Based on this scheme, can confirm the product label of product through user's user label, compare in the product label of artifical affirmation, the product label description that determines in this scheme is comprehensive, accurate to can determine the credibility of each product label, be favorable to the use of product label.
The embodiment of the present application provides a computer-readable storage medium, which stores a computer program thereon, and when the program is executed by a processor, the computer program implements the method for determining the product tag weight shown in the above method embodiment.
The computer-readable storage medium provided in the embodiments of the present application is applicable to any of the embodiments of the foregoing method, and is not described herein again.
Compared with the prior art, the embodiment of the application provides a computer-readable storage medium, which determines a user label as a product label of a product for which a corresponding target user performs an operation behavior, and determines the label weight of the product label based on the importance degree of the product label to the product, a preset operation behavior weight and a time attenuation factor. Based on this scheme, can confirm the product label of product through user's user label, compare in the product label of artifical affirmation, the product label description that determines in this scheme is comprehensive, accurate to can determine the credibility of each product label, be favorable to the use of product label.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The foregoing is only a partial embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (11)

1. A method for determining product label weight, comprising:
acquiring a user label of a target user and an operation behavior of the target user on a product within a preset time period;
determining a product label for the product based on the user label;
and determining the label weight of the product label based on the importance degree of the product label to the product, the preset operation behavior weight and the time attenuation factor.
2. The method of claim 1, wherein the determining the product label for the product based on the user label comprises:
and determining the user label as a product label of a product for which the corresponding target user performs the operation behavior.
3. The method of claim 1, wherein determining the importance of the product label to the product comprises:
determining the importance degree of the product label to the product based on the appearance frequency of the product label in all product labels of the product and the reverse file frequency of the product label.
4. The method of claim 3, wherein determining the label weight of the product label based on the importance of the product label to the product, a preset operational behavior weight, and a time decay factor comprises:
determining a label weight for the product label by:
Figure FDA0002523257760000011
wherein i is any one of the products, T is any one of the product labels of the product i, T is a time period corresponding to the label weight, w(i，t，T)Label weight, TF, of product label T for product i during time T(i，t，T)IDF is the frequency of occurrence of product label T in all product labels of product i in period T(t，T)The reverse file frequency of a product label T in a T time period, bw is a behavior weight corresponding to the operation behavior of the target user on the product i, and w(i，t，T-1)The weight of the tag in the last time segment of the T time segment, dTIs the time decay factor within the T period,
Figure FDA0002523257760000012
is a time decay weight calculated from the time decay factor.
5. The method of claim 1, wherein the obtaining the user tag of the target user comprises:
and acquiring the user label of the target user from a preset user image library.
6. The method of claim 1, wherein obtaining the operation behavior of the target user on the product within a preset time period comprises:
and acquiring the operation behavior of the target user on the product in a preset time period from a preset user behavior information base.
7. The method according to any one of claims 1-6, further comprising:
constructing a product label matrix based on the product labels and the label weights;
determining similarity of the product based on the product label matrix.
8. The method of claim 7, wherein determining the similarity of the product based on the product tag matrix comprises:
performing singular value decomposition on the product label matrix to determine a singular value matrix of the product label matrix;
converting the singular value matrix into a dense matrix;
determining a similarity of the product based on the dense matrix.
9. A product tag weight determining apparatus, comprising:
the basic data acquisition module is used for acquiring a user label of a target user and the operation behavior of the target user on a product in a preset time period;
a product tag determination module to determine a product tag of the product based on the user tag;
and the label weight determining module is used for determining the label weight of the product label based on the importance degree of the product label to the product, the preset operation behavior weight and the time attenuation factor.
10. An electronic device comprising a processor and a memory;
the memory is used for storing operation instructions;
the processor is used for executing the method of any one of claims 1-8 by calling the operation instruction.
11. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the method of any one of claims 1-8.
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