Target data generation method and device, electronic equipment and storage medium
The raw data is extracted through configuration files written in high-performance languages and automatically generate target data, solving the high cost and inefficiency problems caused by manual operations in traditional methods, and achieving more efficient data processing and more flexible configuration management.
Patent Information
- Application Number
- CN202411061624.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-02
- Publication Date
- 2025-01-07
AI Technical Summary
In traditional methods, generating target data requires more manual operations, resulting in high labor and time costs and low processing efficiency.
The configuration file written in a high-performance language extracts the original data and automatically generates the target data, including obtaining the original data to be processed and its corresponding configuration files, and extracting and organizing the original data by parsing the deregulation rules in the configuration file. .
It reduces labor and time costs, improves processing efficiency and configuration flexibility and expression capabilities, and is suitable for fields such as intelligent search, big data processing and code programming.
Smart Images
Figure CN119271185A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of artificial intelligence technology, and in particular to target data generation methods, devices, electronic devices and storage media in the fields of intelligent search, big data processing and code programming. Background Art
[0002] In many scenarios of actual applications, there is a need to generate target data, such as generating corresponding streamlined target data for the original data to be processed. In traditional methods, target data is generated by manual operations, which increases manpower and time costs and reduces processing efficiency. Summary of the invention
[0003] The present disclosure provides a target data generation method, device, electronic device and storage medium.
[0004] A method for generating target data, comprising:
[0005] Obtaining raw data to be processed;
[0006] Obtaining a configuration file corresponding to the original data, where the configuration file is a configuration file written in a high-performance language;
[0007] The original data is extracted according to the configuration file, and the target data is generated according to the extracted data.
[0008] A target data generating device comprises: a first acquiring module, a second acquiring module and a data generating module;
[0009] The first acquisition module is used to acquire the raw data to be processed;
[0010] The second acquisition module is used to acquire a configuration file corresponding to the original data, wherein the configuration file is a configuration file written in a high-performance language;
[0011] The data generation module is used to extract the original data according to the configuration file, and generate the target data according to the extracted data.
[0012] An electronic device, comprising:
[0013] at least one processor; and
[0014] a memory communicatively connected to the at least one processor; wherein,
[0015] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the method described above.
[0016] A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the method as described above.
[0017] A computer program product comprises a computer program / instruction, wherein the computer program / instruction implements the method described above when executed by a processor.
[0018] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.
[0020] Figure 1 This is a flowchart of a first embodiment of the target data generating method disclosed in the present invention;
[0021] Figure 2 It is a schematic diagram of the overall implementation process of the target data generation method disclosed in the present invention;
[0022] Figure 3 This is a flow chart of a second embodiment of the target data generating method disclosed in the present invention;
[0023] Figure 4 It is a schematic diagram of the composition structure of the target data generating device embodiment 400 of the present disclosure;
[0024] Figure 5 A schematic block diagram of an electronic device 500 that can be used to implement an embodiment of the present disclosure is shown. DETAILED DESCRIPTION
[0025] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.
[0026] In addition, it should be understood that the term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.
[0027] Figure 1Flow chart of the first embodiment of the target data generation method disclosed in the present invention. Figure 1 As shown, the following specific implementation methods are included.
[0028] In step 101, original data to be processed is obtained.
[0029] In step 102, a configuration file corresponding to the original data is obtained, where the configuration file is a configuration file written in a high-performance language.
[0030] In step 103, data extraction is performed on the original data according to the configuration file, and target data is generated according to the extracted data.
[0031] By adopting the scheme described in the above method embodiment, data can be extracted from the original data to be processed according to the corresponding configuration file, and target data can be generated according to the extracted data. The whole process can be completed automatically, thereby reducing manpower and time costs and improving processing efficiency. Moreover, the configuration file can be a configuration file written in a high-performance language, thereby improving the flexibility and expression ability of the configuration. In addition, with the help of the powerful capabilities of the high-performance language, the processing efficiency can be further improved.
[0032] In some embodiments of the present disclosure, the original data may be: original data in a lightweight data exchange (JSON, JavaScript Object Notation) format.
[0033] The JSON format uses a text format that is completely independent of programming languages to store and represent data. Its hierarchical structure is concise and clear, making it easy to read and understand, and easy for machines to parse and generate.
[0034] In addition, in some embodiments of the present disclosure, obtaining the original data to be processed may include: obtaining the corresponding processing result for any search stage in the search process, and using the processing result as the original data, and the search process includes different search stages.
[0035] For example, the search process may include different search stages such as recall, coarse ranking, fine-grained ranking, and re-ranking. Assuming that the current search stage is the recall stage, the corresponding raw data may be the search results of the recall. For example, after obtaining the search request issued by the user, the search results matching the search request may be determined by searching in the search engine, and the obtained search results may be used as the raw data, i.e., the raw JSON data.
[0036] It can be seen that the solution disclosed in the present invention can be applied to search scenarios, and can be applied to various different search scenarios, such as e-commerce platform search, content recommendation system, etc., and can be applied to different search stages, and has wide applicability.
[0037] Further, a configuration file corresponding to the raw data to be processed may be obtained. In some embodiments of the present disclosure, a configuration file corresponding to the current search may be determined according to the search type and search phase corresponding to the current search as the configuration file corresponding to the raw data.
[0038] Search types can refer to different search scenarios. Different or the same configuration files can be set for different search stages of the same search type. In addition, different or the same configuration files can be set for the same search stages of different search types, which is very flexible and convenient.
[0039] According to the determined configuration file, data extraction can be performed on the original data. In some embodiments of the present disclosure, the configuration file can be parsed to obtain the decomposition rules in the configuration file, and then data extraction can be performed on the original data according to the decomposition rules.
[0040] The configuration file can be manually written and generated, and the generated configuration file can be saved to a predetermined storage location. The specific location is not limited and can be determined according to actual needs. In addition, the configuration file can be updated at any time according to actual needs, and the updated configuration file can be used to replace the original stored configuration file.
[0041] When the configuration file is needed, the configuration file can be obtained and loaded from the storage location, and the configuration file can be parsed using a parser to obtain a parsing result, that is, the decomposition rules in the configuration file. There is no restriction on the specific type of parser, as long as the corresponding function can be realized.
[0042] Through the above processing, the required decomposition rules can be easily and accurately obtained, thus laying a good foundation for subsequent processing.
[0043] Furthermore, data extraction may be performed on the original data according to the decomposition rules. In some embodiments of the present disclosure, the decomposition rules may include: defined data extraction rules, and accordingly, data extraction on the original data according to the decomposition rules may include: extracting data that meets the data extraction rules from the original data by traversing the original data.
[0044] The specific content included in the data extraction rules can be determined according to actual needs. For example, the data extraction rules can define which fields need to be extracted, etc., then these fields are the fields to be extracted. Accordingly, the field data of the fields to be extracted can be extracted from the original data, that is, the necessary field data can be extracted.
[0045] Through the above processing, optimized data processing can be achieved. For example, based on the user's search request, a large amount of raw data in JSON format can be obtained. Before providing these raw data to the next search stage for processing, the method described in the present invention can be used to extract the raw data, thereby reducing the amount of data transmitted in the search link and optimizing the performance of the search link.
[0046] Furthermore, the required target data may be generated according to the extracted data. In some embodiments of the present disclosure, the extracted data may be organized in a predetermined manner to obtain the required target data.
[0047] The specific method of the predetermined method is not limited and can be determined according to actual needs. It is very flexible and convenient. For example, it can be a predetermined data combination method, and the extracted data can be combined according to the predetermined data combination method to obtain the target data.
[0048] Based on the above introduction, Figure 2 Schematic diagram of the overall implementation process of the target data generation method described in the present disclosure. Figure 2 As shown, for the raw data to be processed, after determining its corresponding configuration file, the configuration file can be loaded, and the configuration file can be parsed by a parser to obtain a parsing result, and then the target data can be generated based on the parsing result and the raw data. The specific implementation can refer to the above-mentioned related description, which will not be repeated here.
[0049] In addition, in some embodiments of the present disclosure, the high-performance language may include: a C (GO)-like language.
[0050] GO is a statically strongly typed, compiled, concurrent, and garbage-collected programming language. Its syntax is similar to that of C, so it can be called a C-like language. GO inherits many ideas from C, such as similar expression syntax, control flow structure, basic data types, and parameter value passing. However, GO abandons the flexible but dangerous pointer operations in C, redesigns the priority of some unreasonable operators in C, and makes necessary changes in many other aspects.
[0051] In the solution disclosed in the present invention, the execution efficiency of the decomposition rules can be improved based on the strong type system and efficient concurrency model of the GO language. Moreover, the use of the GO language to write configuration files can improve the flexibility of strategy (decomposition rule) adjustment.
[0052] Figure 3 Flowchart of the second embodiment of the target data generation method disclosed in the present invention. In this embodiment, it is assumed that the high-performance language is GO language. Figure 3As shown, the following specific implementation methods are included.
[0053] In step 301, original data to be processed is obtained.
[0054] For example, for any search stage in the search process, the corresponding processing result can be obtained, and the processing result can be used as the original data.
[0055] In addition, the original data may be original data in JSON format.
[0056] In step 302, a configuration file corresponding to the original data is obtained, where the configuration file is a configuration file written in GO language.
[0057] For example, the configuration file corresponding to the original data can be determined according to the search type and search stage corresponding to the original data.
[0058] In step 303, the configuration file is parsed to obtain the decomposition rules in the configuration file.
[0059] In step 304, the original data is traversed to extract data that meets the data extraction rules from the original data, and the extraction rules include: the defined data extraction rules.
[0060] In step 305, the extracted data is organized in a predetermined manner to obtain the desired target data.
[0061] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the described order of actions, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present disclosure. In addition, for parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.
[0062] The above is an introduction to the method embodiment. The following is a further explanation of the scheme disclosed in the present invention through an apparatus embodiment.
[0063] Figure 4 FIG. 4 is a schematic diagram of the structure of the target data generating device embodiment 400 of the present disclosure. Figure 4 As shown, it includes: a first acquisition module 401, a second acquisition module 402 and a data generation module 403.
[0064] The first acquisition module 401 is used to acquire the original data to be processed.
[0065] The second acquisition module 402 is used to acquire a configuration file corresponding to the original data, where the configuration file is a configuration file written in a high-performance language.
[0066] The data generation module 403 is used to extract the original data according to the configuration file and generate target data according to the extracted data.
[0067] By adopting the scheme described in the above-mentioned device embodiment, data can be extracted from the original data to be processed according to the corresponding configuration file, and target data can be generated according to the extracted data. The whole process can be completed automatically, thereby reducing manpower and time costs and improving processing efficiency. Moreover, the configuration file can be a configuration file written in a high-performance language, thereby improving the flexibility and expression ability of the configuration. In addition, with the help of the powerful capabilities of the high-performance language, the processing efficiency can be further improved.
[0068] In some embodiments of the present disclosure, the original data acquired by the first acquisition module 401 may be original data in JSON format.
[0069] In addition, in some embodiments of the present disclosure, the first acquisition module 401 can acquire corresponding processing results for any search stage in the search process, and can use the processing results as original data. The search process includes different search stages.
[0070] Further, the second acquisition module 402 may acquire a configuration file corresponding to the raw data to be processed. In some embodiments of the present disclosure, the second acquisition module 402 may determine the configuration file corresponding to the current search as the configuration file corresponding to the raw data according to the search type and search phase corresponding to the current search.
[0071] According to the determined configuration file, the data generation module 403 can extract data from the original data. In some embodiments of the present disclosure, the data generation module 403 can parse the configuration file to obtain the decomposition rules in the configuration file, and then extract data from the original data according to the decomposition rules.
[0072] The configuration file can be manually written and generated, and the generated configuration file can be saved to a predetermined storage location. The specific location is not limited and can be determined according to actual needs. In addition, the configuration file can be updated at any time according to actual needs, and the updated configuration file can be used to replace the original stored configuration file.
[0073] When the configuration file needs to be used, the data generation module 403 can obtain and load the configuration file from the storage location, and can parse the configuration file using a parser to obtain a parsing result, that is, obtain the decomposition rules in the configuration file.
[0074] In addition, the data generation module 403 can extract data from the original data according to the decomposition rules. In some embodiments of the present disclosure, the decomposition rules may include: defined data extraction rules, and accordingly, the data generation module 403 can extract data that meets the data extraction rules from the original data by traversing the original data.
[0075] The data generation module 403 may also generate target data according to the extracted data. In some embodiments of the present disclosure, the data generation module 403 may organize the extracted data in a predetermined manner to obtain the required target data.
[0076] In addition, in some embodiments of the present disclosure, the high-performance language may include: GO language.
[0077] Figure 4 The specific working process of the illustrated device embodiment can refer to the relevant description in the aforementioned method embodiment and will not be described in detail.
[0078] In summary, the solution described in the present disclosure can reduce manpower and time costs, improve processing efficiency and the accuracy of processing results, and has wide applicability.
[0079] The scheme disclosed in the present invention can be applied to the field of artificial intelligence, especially to the fields of intelligent search, big data processing and code programming. Artificial intelligence is a discipline that studies how to use computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.). It has both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing. Artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.
[0080] In addition, the configuration files, original data, and target data in the embodiments described in this disclosure are not for a specific user and do not reflect the personal information of a specific user. In the technical solution of this disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.
[0081] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
[0082] Figure 5A schematic block diagram of an electronic device 500 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.
[0083] like Figure 5 As shown, the device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 to a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.
[0084] A number of components in the device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.
[0085] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI, Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSP, Digital Signal Processing), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods and processes described above, such as the methods described in the present disclosure. For example, in some embodiments, the methods described in the present disclosure may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the methods described in the present disclosure may be executed. Alternatively, in other embodiments, the computing unit 501 may be configured to execute the method described in the present disclosure in any other appropriate manner (for example, by means of firmware).
[0086] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard parts (ASSPs), system on chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.
[0087] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.
[0088] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory (EPROM, Electronically Programmable Read-Only Memory), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM, Compact Disc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
[0089] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a cathode ray tube (CRT) or a liquid crystal display (LCD) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the computer. 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).
[0090] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.
[0091] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.
[0092] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.
[0093] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.
Claims
1. A method for generating target data, comprising: Obtaining raw data to be processed; Obtaining a configuration file corresponding to the original data, where the configuration file is a configuration file written in a high-performance language; The original data is extracted according to the configuration file, and the target data is generated according to the extracted data.
2. The method according to claim 1, wherein: The original data includes: original data in a lightweight data exchange format.
3. The method according to claim 1, wherein: The obtaining of the original data to be processed includes: obtaining a corresponding processing result for any search stage in a search process, and using the processing result as the original data, wherein the search process includes different search stages.
4. The method according to claim 3, wherein: The obtaining of the configuration file corresponding to the original data comprises: According to the search type and search phase corresponding to the original data, a configuration file corresponding to the original data is determined.
5. The method according to claim 1, wherein: The extracting the original data according to the configuration file comprises: Parsing the configuration file to obtain the decomposition rules in the configuration file; Data extraction is performed on the original data according to the decomposition rule.
6. The method according to claim 5, wherein: The decomposition rules include: defined data extraction rules; The extracting data from the original data according to the decomposition rule includes: extracting data that meets the data extraction rule from the original data by traversing the original data.
7. The method according to claim 1, wherein: The generating the target data according to the extracted data includes: organizing the extracted data in a predetermined manner to obtain the target data.
8. The method according to any one of claims 1 to 7, wherein: The high-performance language includes: C-like language.
9. A target data generating device, comprising: A first acquisition module, a second acquisition module and a data generation module; The first acquisition module is used to acquire the raw data to be processed; The second acquisition module is used to acquire a configuration file corresponding to the original data, wherein the configuration file is a configuration file written in a high-performance language; The data generation module is used to extract the original data according to the configuration file, and generate the target data according to the extracted data.
10. The device according to claim 9, wherein: The original data includes: original data in a lightweight data exchange format.
11. The device according to claim 9, wherein: The first acquisition module acquires the corresponding processing result for any search stage in the search process, and uses the processing result as the original data. The search process includes different search stages.
12. The device according to claim 11, wherein The second acquisition module determines the configuration file corresponding to the original data according to the search type and the search phase corresponding to the original data.
13. The device according to claim 9, wherein: The data generation module parses the configuration file to obtain a decomposition rule in the configuration file, and extracts data from the original data according to the decomposition rule.
14. The device according to claim 13, wherein: The decomposition rules include: defined data extraction rules; The data generation module extracts data that meets the data extraction rules from the original data by traversing the original data.
15. The device according to claim 9, wherein: The data generation module organizes the extracted data in a predetermined manner to obtain the target data.
16. The device according to any one of claims 9 to 15, wherein: The high-performance language includes: C-like language.
17. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.
18. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to make a computer execute the method according to any one of claims 1 to 8.
19. A computer program product, comprising a computer program / instruction, wherein when the computer program / instruction is executed by a processor, the method according to any one of claims 1 to 8 is implemented.