Model acquisition and intelligence platform construction method based on artificial intelligence

Abstract

The invention provides a model acquisition and intelligence platform construction method based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining the input demand data of a user; determining a target model tag from a model tag library of the artificial intelligence multi-dimensional model intelligent service platform based on the input demand data; calling at least one candidate model from a model library of the artificial intelligence multi-dimensional model intelligent service platform based on the target model label; and assembling the candidate models to generate a target model, and providing the target model to a user. Therefore, the target model required by the user is acquired from the artificial intelligence multi-dimensional model intelligent service platform by acquiring the demand information of the user and matching the model label, comprehensive and effective model selection can be provided for the user, and the use experience of the user is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and in particular to a method for model acquisition and intelligence platform construction based on artificial intelligence. Background Art

[0002] With the rise of AI big model technology, there will be a boom in research and application based on massive data and big models, generative data analysis, intelligent question and answer, and intelligent services in various scenarios. However, there are still deficiencies in service models and activating the knowledge vitality of the whole society. Users have no way to obtain the required models through effective channels, which greatly reduces the user experience. Summary of the invention

[0003] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0004] To this end, one purpose of the present disclosure is to propose a model acquisition method based on artificial intelligence.

[0005] The second objective of the present disclosure is to propose a method for building an intelligence platform based on artificial intelligence.

[0006] The third objective of the present disclosure is to propose a model acquisition device based on artificial intelligence.

[0007] The fourth objective of the present disclosure is to propose an intelligence platform construction device based on artificial intelligence.

[0008] A fifth objective of the present disclosure is to provide an electronic device.

[0009] A sixth object of the present disclosure is to provide a non-transitory computer-readable storage medium.

[0010] A seventh objective of the present disclosure is to provide a computer program product.

[0011] To achieve the above-mentioned purpose, the first aspect of the present disclosure proposes an artificial intelligence-based model acquisition method, including: obtaining user input demand data; determining a target model label from a model label library of the artificial intelligence multidimensional model intelligent service platform based on the input demand data; retrieving at least one candidate model from the model library of the artificial intelligence multidimensional model intelligent service platform based on the target model label; assembling the candidate models to generate a target model, and providing the target model to the user.

[0012] According to one embodiment of the present disclosure, determining the target model tag from the model tag library of the artificial intelligence multi-dimensional model intelligent service platform based on the input demand data includes: parsing the input request data to determine at least one demand tag; matching the demand tag with the candidate model tags in the model tag library to determine at least one candidate sub-model tag that successfully matches; and generating the target model tag based on the candidate sub-model tags.

[0013] According to one embodiment of the present disclosure, the method of retrieving at least one candidate model from the model library of the artificial intelligence multi-dimensional model intelligent service platform based on the target model label includes: taking the model whose model label in the model library matches the target model label as the candidate model, and retrieving the candidate model.

[0014] According to one embodiment of the present disclosure, before providing the target model to the user, it also includes: sending usage verification information to the user; in response to receiving confirmation of use from the user based on the usage verification information feedback, providing the target model to the user.

[0015] To achieve the above-mentioned purpose, the second aspect embodiment of the present disclosure proposes a method for constructing an intelligence platform based on artificial intelligence, including: obtaining a candidate model to be updated; generating a model label based on the candidate model, and updating it to the model label library of the artificial intelligence multidimensional model intelligent service platform, and storing the candidate model in the model library of the artificial intelligence multidimensional model intelligent service platform.

[0016] According to one embodiment of the present disclosure, before generating a model label based on the candidate model and updating it to the model label library of the artificial intelligence multi-dimensional model intelligent service platform, and storing the candidate model in the model library of the artificial intelligence multi-dimensional model intelligent service platform, it also includes: performing functional verification on the candidate model; in response to passing the functional verification, generating a model label based on the candidate model and updating it to the model label library of the artificial intelligence multi-dimensional model intelligent service platform, and storing the candidate model in the model library of the artificial intelligence multi-dimensional model intelligent service platform.

[0017] To achieve the above-mentioned purpose, the third aspect embodiment of the present disclosure proposes an artificial intelligence-based model acquisition device, including: an acquisition module, used to obtain the user's input demand data; a determination module, used to determine the target model label from the model label library of the artificial intelligence multi-dimensional model intelligent service platform based on the input demand data; a calling module, used to call at least one candidate model from the model library of the artificial intelligence multi-dimensional model intelligent service platform based on the target model label; a providing module, used to assemble the candidate models to generate a target model, and provide the target model to the user.

[0018] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present disclosure proposes an artificial intelligence-based intelligence platform construction device, including: a receiving module, used to obtain a candidate model to be updated; an updating module, used to generate a model label based on the candidate model, and update it to the model label library of the artificial intelligence multidimensional model intelligent service platform, and store the candidate model in the model library of the artificial intelligence multidimensional model intelligent service platform.

[0019] To achieve the above-mentioned purpose, the fifth aspect embodiment of the present disclosure proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the artificial intelligence-based model acquisition method as described in the first aspect embodiment of the present disclosure.

[0020] To achieve the above-mentioned purpose, the sixth aspect embodiment of the present disclosure proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the artificial intelligence-based model acquisition method as described in the first aspect embodiment of the present disclosure.

[0021] To achieve the above-mentioned purpose, the fifth aspect embodiment of the present disclosure proposes a computer program product, including a computer program, which, when executed by a processor, is used to implement the artificial intelligence-based model acquisition method as described in the first aspect embodiment of the present disclosure.

[0022] Therefore, by obtaining user demand information and matching model labels, the target model of user needs can be obtained from the artificial intelligence multi-dimensional model intelligent service platform, which can provide users with comprehensive and effective model selection and improve the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a schematic diagram of a model acquisition method based on artificial intelligence according to an embodiment of the present disclosure;

[0024] Figure 2is a schematic diagram of another artificial intelligence-based model acquisition method according to an embodiment of the present disclosure;

[0025] Figure 3 is a schematic diagram of a method for constructing an intelligence platform based on artificial intelligence according to an embodiment of the present disclosure;

[0026] Figure 4 is a schematic diagram of an artificial intelligence-based model acquisition device according to an embodiment of the present disclosure;

[0027] Figure 5 is a schematic diagram of an artificial intelligence-based intelligence platform construction device according to an embodiment of the present disclosure;

[0028] Figure 6 is a schematic diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0030] The acquisition, storage, use, and processing of data in the technical solution disclosed in this disclosure are in compliance with the relevant provisions of national laws and regulations.

[0031] Figure 1 A schematic diagram of an exemplary implementation of an artificial intelligence-based model acquisition method proposed in the present disclosure is shown in FIG. Figure 1 As shown, the artificial intelligence-based model acquisition method includes the following steps:

[0032] S101, obtaining user input requirement data.

[0033] The artificial intelligence-based model acquisition method of the embodiment of the present application can be applied to the scenario in which users search for demand models. The executor of the artificial intelligence-based model acquisition of the embodiment of the present application can be the artificial intelligence-based model acquisition device of the embodiment of the present application, and the artificial intelligence-based model acquisition device can be set on an electronic device.

[0034] In the embodiment of the present disclosure, the input requirement data may include multiple types, which are not limited here. For example, the input requirement data may include the functions that the user currently needs to implement, the preset cost that the user invests in the model, etc.

[0035] S102, determining a target model label from a model label library of an artificial intelligence multi-dimensional model intelligent service platform based on input demand data.

[0036] The present disclosure proposes an artificial intelligence multi-dimensional model intelligent service platform, which is an intelligent service platform that integrates artificial intelligence multi-dimensional models and has clear property rights, can be traded and commented. It should be noted that the artificial intelligence multi-dimensional model intelligent service platform in the present disclosure can provide a unified comprehensive entrance, connect to the exclusive models with their own property rights trained by different subjects (people / organizations / institutions, etc.) for users to call, provide knowledge services to users while obtaining corresponding fees, and innovate the knowledge payment model based on artificial intelligence and other technologies, which can improve the vitality of knowledge creation and use in society.

[0037] In the disclosed embodiment, the model label library is a database specifically used to store various models in the artificial intelligence multi-dimensional model intelligent service platform. It should be noted that the model labels in the model label library are different and correspond one-to-one to the models, so that the corresponding model can be quickly determined through the model labels in the model label library.

[0038] It should be noted that there are many methods for determining the target model label, which are not limited here. Optionally, the input demand data may be parsed to determine multiple selection conditions, and then the target model label may be determined from the model label library based on the selection conditions.

[0039] Optionally, the input requirement data may be matched with the model tags in the model tag library to generate the target model tags.

[0040] It is understandable that there are multiple ways to achieve user needs, that is, different model combinations can be used to achieve the needs. Therefore, the target model labels generated based on the input demand data can also be multiple, without any limitation here, and need to be determined based on the actual user input demand data.

[0041] S103, based on the target model label, retrieve at least one candidate model from the model library of the artificial intelligence multi-dimensional model intelligent service platform.

[0042] It should be noted that the model library is the storage space used by the artificial intelligence multi-dimensional model intelligent service platform to store various models.

[0043] There are many ways to retrieve the candidate model, which are not limited here. For example, after determining the candidate model, the candidate model can be copied to the target space to retrieve the candidate model. Alternatively, the candidate model can be retrieved in real time by obtaining the storage address of the candidate model.

[0044] S104, assembling the candidate models to generate a target model, and providing the target model to the user.

[0045] It is understandable that in order to meet the needs of users, multiple candidate models can be used to cooperate with each other. In the embodiments of the present disclosure, there are many ways to assemble candidate models, which are not limited here. In one possible implementation method, candidate models can be assembled according to the user's input demand data to generate a target model.

[0046] In the embodiment of the present disclosure, there may be multiple target model labels, so there may be multiple target models generated in the end. Users can select a more suitable model from the multiple target models according to their own needs.

[0047] In the disclosed embodiment, the user's input demand data is first obtained, and then the target model label is determined from the model label library of the artificial intelligence multidimensional model intelligent service platform based on the input demand data, and then at least one candidate model is retrieved from the model library of the artificial intelligence multidimensional model intelligent service platform based on the target model label, and finally the candidate models are assembled to generate the target model, and the target model is provided to the user. Thus, by obtaining the user's demand information and obtaining the target model of the user's demand from the artificial intelligence multidimensional model intelligent service platform in the form of matching model labels, a comprehensive and effective model selection can be provided to the user, thereby improving the user's experience.

[0048] It should be noted that the artificial intelligence multi-dimensional model intelligent service platform in the embodiment of the present disclosure can also train the target model, and train the target model through the training conditions set by the user to ultimately generate the target model required by the user.

[0049] It should be noted that the training conditions can be various, and the specific requirements are limited according to the actual needs of the user, and no limitation is made here. For example, the training conditions can be reaching the training time, the output loss of the model reaching the loss threshold, etc.

[0050] In the above embodiment, the target model label is determined from the model label library of the artificial intelligence multi-dimensional model intelligent service platform based on the input demand data, and the target model label can also be determined by Figure 2 Explaining further, the method includes:

[0051] S201, parse input request data to determine at least one requirement tag.

[0052] It should be noted that the requirement tag is the smallest requirement unit that can be executed by the model.

[0053] In the embodiments of the present disclosure, there may be multiple methods for obtaining the demand tag, and no limitation is made here.

[0054] In one possible implementation, the input request data may be parsed using a natural language algorithm to generate a requirement tag.

[0055] Optionally, at least one requirement label may be generated by inputting the request data into a requirement label generation model.

[0056] S202, matching the requirement tag with the candidate model tags in the model tag library to determine at least one successfully matched candidate sub-model tag.

[0057] It should be noted that the relationship between the requirement label and the candidate model label can be one-to-one. In one possible implementation, the relationship between the requirement label and the candidate model label can also be many-to-one, that is, multiple requirement labels can be executed through one candidate model.

[0058] S203: Generate a target model label based on the candidate sub-model labels.

[0059] In the disclosed embodiment, the input request data is first parsed to determine at least one requirement tag, then the requirement tag is matched with the candidate model tags in the model tag library to determine at least one successfully matched candidate sub-model tag, and finally the target model tag is generated based on the candidate sub-model tags. Thus, by decomposing the input request data into requirement tags, the candidate sub-model tags can be accurately selected, thereby improving the accuracy of the target model tag finally generated.

[0060] After obtaining the target model label, the candidate model can be retrieved by taking the model whose model label in the model library matches the target model label as the candidate model.

[0061] It should be noted that before providing the target model to the user, usage verification information may be sent to the user, and in response to receiving a confirmation of use from the user based on the usage verification information feedback, the target model is provided to the user.

[0062] It should be noted that the verification information may include a variety of contents, which are not limited here. For example, in the case of multiple target models, the verification information may include the target model that the user needs to finally select.

[0063] In one possible implementation, the models in the artificial intelligence multi-dimensional model intelligent service platform can also be used for a fee, thereby protecting the intellectual property rights of the model developers. Therefore, the verification information can also include information reminding the user to pay.

[0064] In the disclosed embodiment, after using the target model provided by the artificial intelligence multidimensional model intelligent service platform, the user can also score, evaluate, and perform other operations on the target model through the artificial intelligence multidimensional model intelligent service platform, thereby providing corrective suggestions to the model developer and improving the practicality of the artificial intelligence multidimensional model intelligent service platform.

[0065] Figure 3 A schematic diagram of an exemplary implementation of a method for building an intelligence platform based on artificial intelligence proposed in the present disclosure is shown in FIG. Figure 1 As shown, the method for building an intelligence platform based on artificial intelligence includes the following steps:

[0066] S301, obtaining a candidate model to be updated.

[0067] It should be noted that the candidate model to be updated is uploaded to the artificial intelligence multi-dimensional model intelligent service platform by the developer of the candidate model or the owner of the candidate model.

[0068] S302, generating a model label based on the candidate model, updating it to the model label library of the artificial intelligence multi-dimensional model intelligent service platform, and storing the candidate model in the model library of the artificial intelligence multi-dimensional model intelligent service platform.

[0069] In the disclosed embodiment, before performing this operation, the candidate model may also be functionally verified. In response to the functional verification being passed, a model label is generated based on the candidate model and updated to the model label library of the artificial intelligence multi-dimensional model intelligent service platform, and the candidate model is stored in the model library of the artificial intelligence multi-dimensional model intelligent service platform. In this way, the quality of the model provided by the artificial intelligence multi-dimensional model intelligent service platform can be guaranteed, and the user experience can be improved.

[0070] In the disclosed embodiment, the candidate model to be updated is first obtained, and then a model label is generated based on the candidate model, and the label is updated to the model label library of the artificial intelligence multi-dimensional model intelligent service platform, and the candidate model is stored in the model library of the artificial intelligence multi-dimensional model intelligent service platform. Thus, by generating a model label for the candidate model and updating it to the artificial intelligence multi-dimensional model intelligent service platform, it is convenient for subsequent users to select and use it.

[0071] Corresponding to the artificial intelligence-based model acquisition methods provided in the above-mentioned embodiments, an embodiment of the present disclosure further provides an artificial intelligence-based model acquisition device. Since the artificial intelligence-based model acquisition device provided in the embodiment of the present disclosure corresponds to the artificial intelligence-based model acquisition methods provided in the above-mentioned embodiments, the implementation methods of the above-mentioned artificial intelligence-based model acquisition methods are also applicable to the artificial intelligence-based model acquisition device provided in the embodiment of the present disclosure, and will not be described in detail in the following embodiments.

[0072] Figure 4 A schematic diagram of an artificial intelligence-based model acquisition device proposed in the present disclosure, such as Figure 4As shown, the artificial intelligence-based model acquisition device 400 includes: an acquisition module 410, a determination module 420, a retrieval module 430 and a providing module 440.

[0073] The acquisition module 410 is used to acquire the user's input demand data.

[0074] A determination module 420 is used to determine a target model label from a model label library of an artificial intelligence multi-dimensional model intelligent service platform based on input demand data;

[0075] A retrieval module 430 is used to retrieve at least one candidate model from a model library of the artificial intelligence multi-dimensional model intelligent service platform based on the target model label;

[0076] A module 440 is provided for assembling the candidate models to generate a target model, and providing the target model to the user.

[0077] In one possible implementation, the determination module 420 is further used to: parse the input request data to determine at least one requirement tag; match the requirement tag with the candidate model tags in the model tag library to determine at least one successfully matched candidate sub-model tag; and generate a target model tag based on the candidate sub-model tags.

[0078] In a possible implementation, the retrieval module 430 is further used to: use the model whose model label in the model library matches the target model label as a candidate model, and retrieve the candidate model.

[0079] In a possible implementation, module 440 is provided, and is further used to: send usage verification information to the user; in response to receiving a confirmation of usage from the user based on feedback of the usage verification information, provide the target model to the user.

[0080] Therefore, by obtaining user demand information and matching model labels, the target model of user needs can be obtained from the artificial intelligence multi-dimensional model intelligent service platform, which can provide users with comprehensive and effective model selection and improve the user experience.

[0081] Corresponding to the artificial intelligence-based intelligence platform construction methods provided in the above-mentioned embodiments, an embodiment of the present disclosure further provides an artificial intelligence-based intelligence platform construction device. Since the artificial intelligence-based intelligence platform construction device provided in the embodiment of the present disclosure corresponds to the artificial intelligence-based intelligence platform construction methods provided in the above-mentioned embodiments, the implementation methods of the above-mentioned artificial intelligence-based intelligence platform construction methods are also applicable to the artificial intelligence-based intelligence platform construction device provided in the embodiment of the present disclosure, and will not be described in detail in the following embodiments.

[0082] Figure 5A schematic diagram of an artificial intelligence-based intelligence platform construction device proposed in the present disclosure, such as Figure 5 As shown, the artificial intelligence-based intelligence platform construction device 500 includes: a receiving module 510 and an updating module 520.

[0083] The receiving module 510 is used to obtain the candidate model to be updated.

[0084] The update module 520 is used to generate a model label based on the candidate model, update it to the model label library of the artificial intelligence multi-dimensional model intelligent service platform, and store the candidate model in the model library of the artificial intelligence multi-dimensional model intelligent service platform.

[0085] In one possible implementation, the update module 520 is also used to: perform functional verification on the candidate model; in response to passing the functional verification, generate a model label based on the candidate model, and update it to the model label library of the artificial intelligence multidimensional model intelligent service platform, and store the candidate model in the model library of the artificial intelligence multidimensional model intelligent service platform.

[0086] Therefore, by generating model labels for candidate models and updating them to the artificial intelligence multi-dimensional model intelligent service platform, it is convenient for subsequent users to select and use them.

[0087] In order to implement the above embodiment, the present disclosure also provides an electronic device 600, such as Figure 6 As shown, the electronic device 600 includes: a processor 601 and a memory 602 connected to the processor in communication, the memory 602 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 601 to implement the present disclosure. Figure 1 and Figure 2 The artificial intelligence-based model acquisition method of the embodiment, or Figure 3 An artificial intelligence-based intelligence platform construction method of an embodiment.

[0088] In order to implement the above embodiments, the embodiments of the present disclosure also propose a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to implement an artificial intelligence-based model acquisition method as in the first aspect of the embodiments of the present disclosure.

[0089] In order to implement the above embodiments, the embodiments of the present disclosure also propose a computer program product, including a computer program, which, when executed by a processor, implements the artificial intelligence-based model acquisition method as in the first aspect of the embodiments of the present disclosure.

[0090] In the description of the present disclosure, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present disclosure and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present disclosure.

[0091] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present disclosure, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0092] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0093] Although the embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present disclosure. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present disclosure.

Claims

1. A model acquisition method based on artificial intelligence, characterized in that: include: Obtain user input requirement data; Determine a target model label from a model label library of the artificial intelligence multi-dimensional model intelligent service platform based on the input demand data; Retrieving at least one candidate model from the model library of the artificial intelligence multi-dimensional model intelligent service platform based on the target model label; The candidate models are assembled to generate a target model, and the target model is provided to the user.

2. The method according to claim 1, characterized in that The step of determining a target model label from a model label library of the artificial intelligence multi-dimensional model intelligent service platform based on the input demand data includes: Parsing the input request data to determine at least one requirement tag; Based on the matching of the requirement tag with the candidate model tags in the model tag library, at least one candidate sub-model tag that matches successfully is determined; The target model label is generated based on the candidate sub-model labels.

3. The method according to claim 1, characterized in that The retrieving at least one candidate model from the model library of the artificial intelligence multi-dimensional model intelligent service platform based on the target model label includes: The model whose model label in the model library matches the target model label is used as the candidate model, and the candidate model is retrieved.

4. The method according to claim 1, characterized in that: Before providing the target model to the user, the method further includes: Sending usage verification information to the user; In response to receiving a confirmation of use from the user based on the use verification information feedback, the target model is provided to the user.

5. A method for constructing an intelligence platform based on artificial intelligence, characterized in that: include: Get the candidate model to be updated; Generate a model label based on the candidate model and update it to the model label library of the artificial intelligence multi-dimensional model intelligent service platform, and store the candidate model in the model library of the artificial intelligence multi-dimensional model intelligent service platform.

6. The method according to claim 5, characterized in that The method further includes generating a model label based on the candidate model and updating the model label library of the artificial intelligence multi-dimensional model intelligent service platform, and storing the candidate model in the model library of the artificial intelligence multi-dimensional model intelligent service platform: Performing functional verification on the candidate model; In response to the functional verification being passed, a model label is generated based on the candidate model and updated to the model label library of the artificial intelligence multi-dimensional model intelligent service platform, and the candidate model is stored in the model library of the artificial intelligence multi-dimensional model intelligent service platform.

7. A model acquisition device based on artificial intelligence, characterized in that: include: The acquisition module is used to obtain the user's input requirement data; A determination module, configured to determine a target model label from a model label library of the artificial intelligence multi-dimensional model intelligent service platform based on the input demand data; A retrieval module, used to retrieve at least one candidate model from the model library of the artificial intelligence multi-dimensional model intelligent service platform based on the target model label; A module is provided for assembling the candidate models to generate a target model, and providing the target model to the user.

8. An artificial intelligence-based intelligence platform construction device, characterized in that: include: A receiving module, used to obtain a candidate model to be updated; An updating module is used to generate a model label based on the candidate model, update it to the model label library of the artificial intelligence multi-dimensional model intelligent service platform, and store the candidate model in the model library of the artificial intelligence multi-dimensional model intelligent service platform.

9. An electronic device, characterized in that: Including memory and processor; In which, the processor runs the program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the artificial intelligence-based model acquisition method as described in any one of claims 1 to 4 or the artificial intelligence-based intelligence platform construction method as described in claim 5 or 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer execution instructions, which, when executed by a processor, are used to implement the artificial intelligence-based model acquisition method as described in any one of claims 1 to 4 or the artificial intelligence-based intelligence platform construction method as described in claim 5 or 6.

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