Target data acquisition method for limited type, storage medium and electronic equipment

Abstract

The embodiment of the invention provides a method for acquiring target data for limited types, which can comprise the following steps of: determining the types of target elements according to input information, the number of the types of the target elements being limited; determining mapping relation information of the target element according to the input information and the type; and directly obtaining target data in at least one original database according to the mapping relation information. The embodiment of the invention further provides a computer readable storage medium and electronic equipment.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method for acquiring knowledge expression in a high-order artificial intelligence (AI) language, and more specifically to a method, storage medium, and electronic device for acquiring target data of limited types. Background Art

[0002] With the rise of AI, developing a high-level AI language that supports Artificial General Intelligence (AGI) is one of the important topics in AGI engineering. As a form of knowledge expression about the real world or one of its components, ontology has applications in areas including, but not limited to, artificial intelligence, the semantic web, software engineering, biomedical informatics, library science, and information architecture. Ontology representation (i.e., the representation of ontological knowledge), as one of the basic submodules of high-level AI languages, is responsible for opening up channels for interaction between data knowledge, internal cognitive reasoning, and external events. In computer science and information science, ontological knowledge includes the representation, formal naming, and definition of concepts, data, categories, attributes, and relationships between entities. Ontologies often store data and connections between data in the form of knowledge graphs, and are one of the common data storage formats.

[0003] In the process of realizing the concept of the present invention, the inventors found that there are at least the following problems in the related technology: when the related technology searches for relevant data of a finite element, the relevant data of the finite element is copied into a lookup table or database, and then the relevant data of the finite element is searched in the lookup table or database. The user or AI intelligent agent needs to obtain information such as the category, attributes, and parameters of a specific element, and it is difficult to obtain the relevant target data quickly and accurately.

[0004] Those skilled in the art urgently need to develop a method for quickly and accurately obtaining relevant target data of limited types in order to quickly and accurately obtain target information. Summary of the Invention

[0005] In view of this, the technical problem to be solved by the present invention is to provide a method, storage medium and electronic device for obtaining target data of limited types, which solves the problem in related technologies that it is difficult for users or AI agents to obtain target data quickly and accurately.

[0006] In order to solve the above technical problems, a specific embodiment of the present invention provides a method for acquiring target data of limited types, including: determining the type of a target element based on input information, wherein the types of the target element are finite; determining mapping relationship information of the target element based on the input information and the type; and directly acquiring the target data in at least one original database based on the mapping relationship information.

[0007] Optionally, after directly acquiring the target data from at least one original database according to the mapping relationship information, the method for acquiring target data of limited types may further include: feeding back readable information after special characters in the target data to the AI ​​agent.

[0008] Optionally, after feeding back the readable information after the special character in the target data to the AI ​​agent, the method for acquiring target data of limited types may further include: planning the behavior of the AI ​​agent according to the readable information.

[0009] Optionally, the step of determining the mapping relationship information of the target element according to the input information and the type includes: obtaining the mapping direction of the target element according to the type; and determining the mapping relationship information of the target element according to the input information and the mapping direction.

[0010] Optionally, the types include individuals, categories and attributes.

[0011] Optionally, the mapping direction includes mapping from category to individual, mapping from individual to attribute, and mapping from attribute to individual.

[0012] Optionally, the step of directly acquiring the target data in at least one original database according to the mapping relationship information includes: obtaining identification information of all original databases; filtering out at least one identification information according to the input information; determining a specific original database from all original databases according to the filtered identification information; and directly searching for the target data in the specific original database according to the mapping relationship information.

[0013] Optionally, the input information includes at least one of letters, numbers and common symbols.

[0014] Another aspect of an embodiment of the present invention provides an electronic device, including one or more processors and a storage device, wherein the storage device is used to store executable instructions, and when the executable instructions are executed by the processor, the method of the embodiment of the present invention is implemented.

[0015] Another aspect of an embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the method of the embodiment of the present invention when executed by a processor.

[0016] Another aspect of an embodiment of the present invention provides a computer program, which includes computer-executable instructions. When the instructions are executed, they are used to implement the method of the embodiment of the present invention.

[0017] According to the above-mentioned embodiments of the present invention, directly searching for target data in the original database based on the relevant information of a specific element can at least partially solve the problem in related technologies that it is difficult for users or AI agents to quickly and accurately obtain target data, and thus can achieve the technical effect of quickly and accurately obtaining target data without knowing the category, attributes, parameters and other information of the target element in advance.

[0018] It should be understood that the foregoing general description and the following detailed description are merely exemplary and illustrative and are not intended to limit the scope of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are part of the specification of the present invention, illustrate exemplary embodiments of the present invention and, together with the description, serve to explain the principles of the present invention.

[0020] Figure 1 This is a first schematic flow chart of a method for acquiring target data of limited types provided in a specific embodiment of the present invention.

[0021] Figure 2 A schematic diagram of target data composition provided by a specific embodiment of the present invention.

[0022] Figure 3 This is a second schematic flow chart of a method for acquiring target data of limited types provided in a specific embodiment of the present invention.

[0023] Figure 4 A third schematic flow chart of a method for acquiring target data of limited types provided in a specific embodiment of the present invention.

[0024] Figure 5 This is a fourth schematic flow chart of a method for acquiring target data of limited types provided by a specific embodiment of the present invention.

[0025] Figure 6 A schematic diagram of mapping directions between different types provided in a specific embodiment of the present invention.

[0026] Figure 7 A fifth schematic flow chart of a method for acquiring target data of limited types provided in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the objectives, technical solutions and advantages of the embodiments of the present invention more clearly understood, the spirit of the contents disclosed in the present invention will be clearly illustrated with the accompanying drawings and detailed descriptions below. After understanding the embodiments of the contents of the present invention, any technician in the relevant technical field can change and modify the contents of the present invention based on the techniques taught by the contents of the present invention without departing from the spirit and scope of the contents of the present invention.

[0028] The exemplary embodiments of the present invention and their description are used to explain the present invention, but are not intended to limit the present invention. In addition, elements / components with the same or similar reference numerals used in the drawings and embodiments are used to represent the same or similar parts.

[0029] The terms “first,” “second,” etc. used herein do not particularly refer to an order or sequence, nor are they intended to limit the present invention. They are merely used to distinguish elements or operations described with the same technical terms.

[0030] The directional terms used herein, such as up, down, left, right, front, or back, are only used to refer to the directions in the accompanying drawings. Therefore, the directional terms used are used to illustrate and not to limit the present invention.

[0031] The words “include,” “including,” “have,” “contain,” etc. used in this document are open-ended terms, meaning including but not limited to.

[0032] As used herein, "and / or" includes any and all combinations of the items mentioned.

[0033] Regarding "plurality" in this document, "plurality" includes "two" and "more than two"; regarding "plurality groups" in this document, "plurality groups" includes "two groups" and "more than two groups".

[0034] As used herein, the terms "substantially" and "approximately" are used to modify any quantity or error that may vary slightly, but such variation or error does not alter the essence of the quantity. Generally speaking, the range of such variation or error modified by such terms may be 20% in some embodiments, 10% in some embodiments, 5% in some embodiments, or other values. Those skilled in the art will appreciate that the aforementioned values ​​may be adjusted based on actual needs and are not intended to be limiting.

[0035] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0036] When expressions such as “at least one of A, B, and C, etc.” are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (e.g., “a system having at least one of A, B, and C” should include but is not limited to systems having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, and C, etc.). When expressions such as “at least one of A, B, or C, etc.” are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (e.g., “a system having at least one of A, B, or C” should include but is not limited to systems having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, and C, etc.). Those skilled in the art should also understand that any transitional conjunctions and / or phrases that essentially represent two or more optional items, whether in the specification, claims, or drawings, should be understood to provide the possibility of including one, either, or both of these items. For example, the phrase "A or B" should be understood to include the possibilities of "A" or "B", or "A and B".

[0037] In computer science and information science, ontology is used to describe the world as a set of classes (object types or concepts), attributes, and relationship types. Ontologies often store data and their connections in the form of knowledge graphs and are a common data storage format. Ontologies are composed of a finite number of element types, primarily individuals, classes, and properties. Among them, individuals are instances of categories, for example, cats, bears, fish, whales, etc. are all individuals; categories and categories have a relationship of inheritance and subclass, for example, mammals, vertebrates, animals, etc. are all categories, vertebrates and animals have a relationship of inheritance and subclass, vertebrates are subclasses of animals, vertebrates inherit all the characteristics of animals, mammals and vertebrates have a relationship of inheritance and subclass, mammals are subclasses of vertebrates, mammals inherit all the characteristics of vertebrates; attributes represent the attribute characteristics of individuals and categories, for example, having hair on the body, living in water, having a spine on the body, etc. are all attributes, having hair on the body is an attribute characteristic of individual cats and bears, living in water is an attribute characteristic of fish and whales, having a spine on the body is an attribute characteristic of cats, bears, fish and whales, and having a spine on the body is also an attribute characteristic of mammals and vertebrates.

[0038] Figure 1 This is a first schematic flow chart of a method for acquiring target data of limited types provided in a specific embodiment of the present invention. Figure 2 A schematic diagram of target data composition provided by a specific embodiment of the present invention.

[0039] In a specific embodiment of the present invention, Figure 1 As shown, a method for acquiring target data of limited types may include the following operations S101 to S103:

[0040] In operation S101 : determining the type of a target element according to input information, wherein the types of the target element are finite.

[0041] In a specific embodiment of the present invention, the input information may include at least one of letters, numbers, and common symbols. For example, the input information may be English letters, numbers, or common symbols; the input information may also be a combination of English letters and numbers, a combination of English letters and common symbols, a combination of numbers and common symbols; the input information may also be a combination of English letters, numbers, and common symbols. Common symbols may include an equal sign, a plus sign, a question mark, a greater than sign, a less than sign, etc. Uncommon symbols may include "#", "*", "&", "@", etc. The type of target element is finite, and the type of target element may be 2, 3, 4, 5, 6, or more, etc. For example, the type of target element may be 3, and the type of target element may include individual, category, and attribute.

[0042] Then, in operation S102: mapping relationship information of the target element is determined according to the input information and the type.

[0043] In a specific embodiment of the present invention, the mapping relationship information of a target element represents the mapping relationship between the target element and other elements. For example, when the target element is a category, the category mapping relationship information may represent the mapping relationship from the category to an individual; when the target element is an individual, the individual mapping relationship information may represent the mapping relationship from the individual to an attribute; and when the target element is an attribute, the attribute mapping relationship information may represent the mapping relationship from the attribute to the individual. For example, mammal is a category, bear is an individual, and the attribute of bear is fur. The mapping from mammal to bear is from category to individual, the mapping from bear to furry is from individual to attribute, and the mapping from furry to bear is from attribute to individual.

[0044] Next, in operation S103 : target data is directly acquired from at least one original database according to the mapping relationship information.

[0045] In a specific embodiment of the present invention, the original database may be a third-party database, for example, a third-party database that is accessible via a network. The target data may include readable information and unreadable information, and the readable information and the unreadable information are separated by characteristic characters. The readable information is generally located behind the characteristic characters, and the unreadable information is generally located in front of the characteristic characters. The readable information may include at least one of letters, numbers, and common symbols. The letters may include English letters. For example, the readable information may be English letters, numbers, or common symbols; the readable information may also be a combination of English letters and numbers, a combination of English letters and common symbols, a combination of numbers and common symbols; the readable information may also be a combination of English letters, numbers, and common symbols. Common symbols may include equal signs, plus signs, question marks, greater than signs, less than signs, etc. As Figure 2 As shown, the characteristic characters in the target data A, B, C, and D are all "#", and the readable information is behind the "#". For the target data A, the readable information is "bread"; for the target data B, the readable information is "isFront=true"; for the target data C, the readable information is "Drug"; and for the target data D, the readable information is "anti-cold drug".

[0046] In a specific embodiment of the present invention, if the target element types are finite, the target data can be directly retrieved from at least one original database based on the mapping relationship information. Without prior knowledge of the target element's category, attributes, parameters, and other information, the target data can be quickly and accurately acquired, improving the AI ​​agent's response speed and accuracy, and enabling efficient interaction between the user and the agent.

[0047] Figure 3 This is a second schematic flow chart of a method for acquiring target data of limited types provided in a specific embodiment of the present invention.

[0048] In a specific embodiment of the present invention, Figure 3 As shown, after operation S103 directly acquires the target data from at least one original database according to the mapping relationship information, the method for acquiring target data of limited types may further include the following operation S104:

[0049] In operation S104: the readable information after the special character in the target data is fed back to the AI ​​agent.

[0050] In specific embodiments of the present invention, AI agents may include AI intelligent avatars, intelligent robots, virtual avatars, and the like. The target data may include readable and unreadable information, separated by characteristic characters. The readable information is generally located after the characteristic characters, while the unreadable information is generally located before the characteristic characters. The readable information may include at least one of letters, numbers, and common symbols. The letters may include English letters. The readable information is fed back to the AI ​​agent, which may display the readable information on the AI ​​agent for the user to view. The AI ​​agent may also perform related operations based on the readable information.

[0051] In a specific embodiment of the present invention, only the readable information following a special character in the target data is fed back to the AI ​​agent, making it easier for the user to view and for the AI ​​agent to perform related operations based on the readable information. This further improves the AI ​​agent's response speed and accuracy, allowing for efficient interaction between the user and the agent.

[0052] Figure 4 A third schematic flow chart of a method for acquiring target data of limited types provided in a specific embodiment of the present invention.

[0053] In a specific embodiment of the present invention, Figure 4 As shown, after operation S104 feeds back the readable information after the special character in the target data to the AI ​​agent, the method for obtaining target data of limited types may further include the following operation S105:

[0054] In operation S105: the behavior of the AI ​​agent is planned according to the readable information.

[0055] In a specific embodiment of the present invention, the readable information may include at least one of letters, numbers, and common symbols. For example, the readable information may be English letters, numbers, or common symbols; the readable information may also be a combination of English letters and numbers, a combination of English letters and common symbols, or a combination of numbers and common symbols; or a combination of English letters, numbers, and common symbols. Common symbols may include an equal sign, a plus sign, a question mark, a greater than sign, a less than sign, and the like.

[0056] In a specific embodiment of the present invention, the behavior of the AI ​​agent is planned directly based on the readable information, the reaction speed and reaction accuracy of the AI ​​agent are improved, and the user and the intelligent force can interact efficiently.

[0057] Figure 5 This is a fourth schematic flow chart of a method for acquiring target data of limited types provided by a specific embodiment of the present invention.

[0058] In a specific embodiment of the present invention, Figure 5As shown, operation S102 determines the mapping relationship information of the target element according to the input information and the type, and may include the following operations S1021 to S1022:

[0059] In operation S1021 : obtaining a mapping direction of the target element according to the type.

[0060] In a specific embodiment of the present invention, the target element has a finite number of types. For example, there may be three target element types, including individuals, categories, and attributes. Target element mapping directions include, for example, mapping categories to individuals, mapping individuals to attributes, and mapping attributes to individuals. For example, individuals may include cats, bears, fish, whales, etc.; categories may include mammals, vertebrates, animals, etc.; and attributes may include having hair, living in water, having a spine, etc. The mapping from categories to individuals includes the mapping from mammals to cats, the mapping from mammals to bears, the mapping from mammals to whales, etc.; the mapping from individuals to attributes includes the mapping from cats to hairy bodies, the mapping from bears to hairy bodies, the mapping from fish to animals living in water, the mapping from whales to animals living in water, the mapping from cats to animals with a spine, the mapping from bears to animals with a spine, the mapping from fish to animals with a spine, the mapping from whales to animals with a spine, etc.; the mapping from attributes to individuals includes the mapping from hairy bodies to cats, the mapping from hairy bodies to bears, the mapping from animals living in water to fish, the mapping from animals living in water to whales, the mapping from animals with a spine to cats, the mapping from animals with a spine to bears, the mapping from animals with a spine to fish, the mapping from animals with a spine to whales, etc.

[0061] Then, in operation S1022: mapping relationship information of the target element is determined according to the input information and the mapping direction.

[0062] In a specific embodiment of the present invention, the input information may include at least one of letters, numbers, and common symbols. The mapping relationship information of the target element represents the mapping relationship between the target element and other elements.

[0063] In a specific embodiment of the present invention, target data can be acquired quickly and accurately, thereby improving the reaction speed and reaction accuracy of the AI ​​agent.

[0064] Figure 6 A schematic diagram of mapping directions between different types provided in a specific embodiment of the present invention.

[0065] In a specific embodiment of the present invention, Figure 6 As shown, the types may include individuals, categories, and attributes. The mapping directions may include mapping from categories to individuals, mapping from individuals to attributes, and mapping from attributes to individuals.

[0066] In a specific embodiment of the present invention, the target element types are limited, and target data can be directly retrieved from at least one original database based on the mapping relationship information. Without prior knowledge of the target element's category, attributes, parameters, and other information, target data can be quickly and accurately acquired, improving the AI ​​agent's response speed and accuracy, and enabling efficient interaction between users and the agent.

[0067] Figure 7 A fifth schematic flow chart of a method for acquiring target data of limited types provided in a specific embodiment of the present invention.

[0068] In a specific embodiment of the present invention, operation S103 directly obtains target data from at least one original database according to the mapping relationship information, and may include the following operations S1031 to S1033:

[0069] In operation S1031 : identification information of all original databases is obtained.

[0070] In a specific embodiment of the present invention, the original database may be a third-party database, for example, a third-party database accessible via a network. The identification information is used to identify the original database, and the identification information of different original databases is different, that is, the identification information corresponds to the original database in a one-to-one manner.

[0071] Next, in operation S1032 : at least one piece of identification information is filtered out according to the input information.

[0072] In a specific embodiment of the present invention, at least one piece of identification information is screened out from all the identification information corresponding to the original database according to the input information.

[0073] Then, in operation S1033 : a specific original database is determined from all original databases according to the filtered identification information.

[0074] In a specific embodiment of the present invention, since the identification information corresponds to the original database in a one-to-one manner, a specific original database can be selected from all original databases by using the filtered identification information.

[0075] Next, in operation S1034 : the target data is directly searched in the specific original database according to the mapping relationship information.

[0076] In a specific embodiment of the present invention, mapping relationship information represents a mapping relationship between a target element and other elements. For example, when the target element is a category, the category mapping relationship information may represent a mapping relationship from the category to an individual, i.e., directly searching for individuals belonging to the category in a specific original database based on the category. When the target element is an individual, the individual mapping relationship information may represent a mapping relationship from the individual to an attribute, i.e., directly searching for the attribute corresponding to the individual in a specific original database based on the individual. When the target element is an attribute, the attribute mapping relationship information may represent a mapping relationship from the attribute to the individual, i.e., directly searching for individuals with the attribute in a specific original database based on the attribute.

[0077] In a specific embodiment of the present invention, target data can be directly obtained from at least one original database based on mapping relationship information without knowing the category, attributes, parameters and other information of the target element in advance. The target data can be obtained quickly and accurately, thereby improving the reaction speed and reaction accuracy of the AI ​​agent, and enabling efficient interaction between users and intelligent forces.

[0078] According to an embodiment of the present invention, the method flow according to an embodiment of the present invention can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program includes program code for executing the method shown in the flowchart. According to an embodiment of the present invention, the electronic devices, devices, apparatuses, modules, units, etc. described above can be implemented by computer program modules.

[0079] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the method according to the embodiment of the present invention is implemented. The method may include the following operations:

[0080] In operation S101 : determining the type of a target element according to input information, wherein the types of the target element are finite.

[0081] In operation S102: determining mapping relationship information of the target element according to the input information and the type.

[0082] In operation S103 : directly acquiring target data from at least one original database according to the mapping relationship information.

[0083] In operation S104: the readable information after the special character in the target data is fed back to the AI ​​agent.

[0084] In operation S105: the behavior of the AI ​​agent is planned according to the readable information.

[0085] According to an embodiment of the present invention, a computer-readable storage medium may be a non-volatile computer-readable storage medium, such as, but not limited to, 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), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0086] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0087] Those skilled in the art will appreciate that various combinations and / or combinations of features described in the various embodiments and / or claims of the present invention may be made, even if such combinations and / or combinations are not explicitly described in the present invention. In particular, various combinations and / or combinations of features described in the various embodiments and / or claims of the present invention may be made, without departing from the spirit and teachings of the present invention. All such combinations and / or combinations fall within the scope of the present invention.

[0088] The above describes embodiments of the present invention. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. The scope of the present invention is defined by the appended claims and their equivalents. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which are intended to fall within the scope of the present invention.

Claims

1. A method for acquiring target data of limited types, characterized in that: The method includes: Determining a type of a target element according to input information, wherein the types of the target element are finite; Determining mapping relationship information of the target element according to the input information and the type; and Target data is directly acquired from at least one original database according to the mapping relationship information.

2. The method for acquiring target data of limited types according to claim 1, characterized in that: After directly acquiring target data from at least one original database according to the mapping relationship information, the method further includes: The readable information after the special characters in the target data is fed back to the AI ​​agent.

3. The method for acquiring target data of limited types according to claim 2, characterized in that: After feeding back the readable information after the special character in the target data to the AI ​​agent, the method further includes: The behavior of the AI ​​agent is planned according to the readable information.

4. The method for acquiring target data of limited types according to claim 1, characterized in that: The step of determining the mapping relationship information of the target element according to the input information and the type includes: Obtaining a mapping direction of the target element according to the type; and Determine mapping relationship information of the target element according to the input information and the mapping direction.

5. The method for acquiring target data of limited types according to claim 4, characterized in that: The types include individuals, categories, and attributes.

6. The method for acquiring target data of limited types according to claim 5, characterized in that: The mapping directions include mapping from category to individual, mapping from individual to attribute, and mapping from attribute to individual.

7. The method for acquiring target data of limited types according to claim 1, characterized in that: The step of directly acquiring target data from at least one original database according to the mapping relationship information includes: Obtain identification information of all original databases; Filtering out at least one piece of identification information according to the input information; determining a specific original database from all original databases based on the filtered identification information; and The target data is directly searched for in the specific original database according to the mapping relationship information.

8. The method for acquiring target data of limited types according to claim 1, characterized in that: The input information includes at least one of letters, numbers and common symbols.

9. A computer-readable storage medium having executable instructions stored thereon, wherein the instructions are executed by a processor to implement the method according to any one of claims 1 to 8.

10. An electronic device comprising: one or more processors; A storage device is used to store executable instructions, and when the executable instructions are executed by the processor, the method according to any one of claims 1 to 8 is implemented.

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