Intelligent asset management method, device and system and storage medium

Abstract

The invention provides an intelligent asset management method, device and system and a storage medium. The intelligent asset management method comprises the following steps: extracting intelligent asset information from a registration request; judging whether intelligent assets corresponding to the intelligent asset information pass a feasibility test or not; under the condition that the intelligent assets pass the feasibility test, the intelligent assets are managed according to a preset AI capability management strategy; and storing the intelligent assets in the intelligent asset warehouse.

Description

Technical Field

[0001] The present disclosure relates to the field of network technology, and in particular to an intelligent asset management method, device and system, and storage medium. Background Art

[0002] The automation and intelligence of cloud network operations are important means for telecom operators to implement the strategy of building a strong cyber power, build a high-speed, ubiquitous, space-ground integrated, cloud-network integrated, smart and agile, green and low-carbon, safe and controllable intelligent comprehensive digital information infrastructure, and practice the digital transformation of cloud network operations.

[0003] The Willing Self-Intelligence Center is an AI (Artificial Intelligence) capability base for cloud network operations. It supports the management, sedimentation and sharing of AI capabilities, and supports the construction of a full-professional, full-scenario cloud network AI big model, covering networks, businesses, and customers, to achieve a multi-dimensional intention-driven self-intelligence closed loop. Summary of the invention

[0004] The inventors noticed that in the related technologies, since the relevant strategies, algorithms, models, etc. are constructed in a decentralized manner in multiple systems, the reusability rate is low, the operation and maintenance costs are high, and there is a lack of a unified AI capability view and unified evaluation standards.

[0005] Based on this, the present disclosure provides an intelligent asset management solution, which effectively solves the problem of AI capability fragmentation by uniformly managing the intelligent assets scattered in various systems, and effectively realizes the sharing and reuse of cloud network AI capabilities.

[0006] In a first aspect of the present disclosure, a smart asset management method is provided, including: extracting smart asset information from a registration request; determining whether a smart asset corresponding to the smart asset information has passed a feasibility test; if the smart asset has passed the feasibility test, managing the smart asset according to a preset artificial intelligence (AI) capability management strategy; and storing the smart asset in a smart asset warehouse.

[0007] In some embodiments, the smart assets include AI capabilities, wherein the AI ​​capabilities include AI models and knowledge graphs.

[0008] In some embodiments, the AI ​​capability management strategy includes at least one of an indicator access strategy, a mirror access strategy, and a model access strategy.

[0009] In some embodiments, the indicator access strategy is used to register the AI ​​capability and periodically provide at least one of status information and value assessment indicator information of the AI ​​capability.

[0010] In some embodiments, the image access policy is used to encapsulate the AI ​​capability and the corresponding operating environment into a reasoning image and a training image, and register the reasoning image and the training image.

[0011] In some embodiments, the model access strategy is used to manage the code model of the AI ​​model.

[0012] In some embodiments, the code model includes at least one of a model parameter file and a data specification requirement.

[0013] In some embodiments, storing the smart asset in the smart asset warehouse includes: determining whether there is a replaceable asset in the smart asset warehouse that is replaced by the smart asset; if the replaceable asset exists in the smart asset warehouse, storing the smart asset in the smart asset warehouse and canceling the replaceable asset.

[0014] In some embodiments, the smart asset is published.

[0015] In some embodiments, after receiving a smart asset sharing request, the type of smart asset requested to be shared is determined; and the smart asset sharing is performed according to a sharing policy associated with the smart asset type.

[0016] In some embodiments, the sharing strategy includes at least one of a standardized sharing strategy, a retraining sharing strategy, an inference call strategy, a model embedded reference strategy, and a mirror download strategy.

[0017] In some embodiments, the standardized sharing strategy is used to share specified standardized AI capabilities.

[0018] In some embodiments, the retraining sharing strategy is used to retrain specified managed AI capabilities and share the retrained AI capabilities.

[0019] In some embodiments, the inference calling strategy is used to call a specified AI capability for model inference on a cloud server through an application programming interface (API).

[0020] In some embodiments, the model embedded reference strategy is used to encapsulate a specified AI model for embedding in a specified system.

[0021] In some embodiments, the image download strategy is used to package a specified AI reasoning model and a corresponding operating environment for downloading into a specified system.

[0022] In a second aspect of the present disclosure, there is provided an intelligent asset management device, comprising: a first processing module, configured to extract intelligent asset information from a registration request; a second processing module, configured to determine whether the intelligent asset corresponding to the intelligent asset information has passed a feasibility test; and a third processing module, configured to manage the intelligent asset according to a preset artificial intelligence (AI) capability management strategy if the intelligent asset has passed the feasibility test, and store the intelligent asset in an intelligent asset warehouse.

[0023] In a third aspect of the present disclosure, there is provided an intelligent asset management device, comprising: a memory; and a processor, coupled to the memory, the processor being configured to execute a method as described in any of the above embodiments based on instructions stored in the memory.

[0024] In a fourth aspect of the present disclosure, there is provided an intelligent asset management system, comprising: an intelligent asset management device as described in any of the above embodiments.

[0025] According to a fifth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and when the instructions are executed by a processor, the method described in any of the above embodiments is implemented.

[0026] Other features and advantages of the present disclosure will become apparent from the following detailed description of exemplary embodiments of the present disclosure with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0028] Figure 1 A schematic diagram of a cloud network operating system according to an embodiment of the present disclosure;

[0029] Figure 2 This is a functional architecture diagram of a voluntary self-intelligence center according to an embodiment of the present disclosure;

[0030] Figure 3 This is a functional block diagram of intelligent asset management according to an embodiment of the present disclosure;

[0031] Figure 4 A flowchart of a smart asset management method according to an embodiment of the present disclosure;

[0032] Figure 5 A schematic diagram of the structure of an intelligent asset management device according to an embodiment of the present disclosure;

[0033] Figure 6 A schematic diagram of the structure of a smart asset management device according to another embodiment of the present disclosure;

[0034] Figure 7 A schematic diagram of the structure of an intelligent asset management system according to an embodiment of the present disclosure;

[0035] Figure 8 A schematic diagram of the structure of an intelligent asset management system according to another embodiment of the present disclosure;

[0036] Fig. 9 A schematic diagram of the intelligent asset management process collaboration of an embodiment of the present disclosure;

[0037] Fig.10 A collaborative schematic diagram of the intelligent asset management process according to another embodiment of the present disclosure. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present disclosure and its application or use. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0039] Unless specifically stated otherwise, the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure.

[0040] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0041] Technologies, methods, and apparatus known to ordinary technicians in the relevant field may not be discussed in detail, but where appropriate, such technologies, methods, and apparatus should be considered part of the authorization specification.

[0042] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0043] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0044] Figure 1FIG. 1 is a schematic diagram of a cloud network operating system according to an embodiment of the present disclosure. Figure 1 As shown in the figure, the Willing Self-Intelligence Center is an AI capability base for cloud network operations. It supports the management, sedimentation and sharing of AI capabilities, and supports the construction of a full-professional, full-scenario cloud network AI big model, covering networks, businesses, and customers, to achieve a multi-dimensional intention-driven self-intelligence closed loop.

[0045] Figure 2 FIG. 1 is a functional architecture diagram of a voluntary self-intelligence center according to an embodiment of the present disclosure. Figure 2 As shown, the intelligent asset management module has the capabilities of standardized management of cloud network intelligent assets, classification management of intelligent assets, and standardized sharing.

[0046] Because related strategies, algorithms, models, etc. are constructed in multiple systems in a decentralized manner, resulting in low reusability, high operation and maintenance costs, and a lack of a unified AI capability view and unified evaluation standards.

[0047] Based on this, the present disclosure provides an intelligent asset management solution, which effectively solves the problem of AI capability fragmentation by uniformly managing the intelligent assets scattered in various systems, and effectively realizes the sharing and reuse of cloud network AI capabilities.

[0048] Figure 3 FIG. 1 is a functional block diagram of intelligent asset management according to an embodiment of the present disclosure. Figure 3 As shown in the figure, through the constructed cloud network intelligent asset management model, various forms of intelligent assets can be managed, and the management and control processes and technical implementations of offline / online subscription sharing / new construction / retraining (knowledge fusion) of intelligent assets can be explored to solve the problems of difficult classification, management, sharing and administration of intelligent assets, and complete the unified management and control of intelligent assets across the entire group.

[0049] Figure 4 The following is a flow chart of a smart asset management method according to an embodiment of the present disclosure. In some embodiments, the following smart asset management method is executed by a smart asset management device.

[0050] In step 401, smart asset information is extracted from the registration request.

[0051] In step 402, it is determined whether the smart asset corresponding to the smart asset information passes the feasibility test.

[0052] In some embodiments, the smart assets include AI capabilities. For example, AI capabilities include AI models and knowledge graphs.

[0053] In step 403, when the smart asset passes the feasibility test, the smart asset is managed according to the preset AI capability management strategy.

[0054] In some embodiments, the AI ​​capability management strategy includes at least one of an indicator access strategy, a mirror access strategy, and a model access strategy.

[0055] The indicator access strategy is used to register AI capabilities and periodically provide at least one of the status information and value assessment indicator information of the AI ​​capabilities.

[0056] For example, registration can be performed by filling in a page or importing an electronic file such as Excel.

[0057] The image access policy is used to encapsulate AI capabilities and the corresponding operating environment into inference images and training images, and to register inference images and training images.

[0058] The model access strategy is used to manage the code model of the AI ​​model.

[0059] For example, the code model includes at least one of a model parameter file and a data specification requirement.

[0060] At step 404, the smart asset is stored in the smart asset repository.

[0061] In some embodiments, it is determined whether there is a replaceable asset in the smart asset warehouse that is replaced by the smart asset. If there is a replaceable asset in the smart asset warehouse, the smart asset is stored in the smart asset warehouse and the replaceable asset is cancelled. This can effectively avoid the existence of redundant smart assets in the smart asset warehouse.

[0062] In some embodiments, after the smart asset is stored in the smart asset repository, the smart asset is released.

[0063] In the smart asset management method provided in the above embodiment of the present disclosure, when the smart asset passes the feasibility test, the smart asset is managed according to the preset AI capability management strategy, and the smart asset is stored in the smart asset warehouse. In this way, the smart assets scattered in various systems are uniformly managed, thereby effectively solving the problem of AI capability fragmentation and effectively realizing the sharing and reuse of cloud network AI capabilities.

[0064] In some embodiments, after receiving a smart asset sharing request, the type of smart asset requested to be shared is determined, and the smart asset sharing is performed according to a sharing policy associated with the smart asset type.

[0065] For example, the sharing strategy includes at least one of a standardized sharing strategy, a retraining sharing strategy, an inference call strategy, a model embedded reference strategy, and an image download strategy.

[0066] The standardized sharing strategy is used to share specified standardized AI capabilities.

[0067] The retraining sharing strategy is used to retrain specified managed AI capabilities and share the retrained AI capabilities.

[0068] The inference call strategy is used to call the specified AI capabilities for model inference on the cloud server through the API (Application Programming Interface).

[0069] The model embedded reference strategy is used to encapsulate the specified AI model and embed it into the specified system, thereby providing computing resources for the specified system.

[0070] The image download strategy is used to encapsulate the specified AI inference model and the corresponding operating environment for downloading to the specified system, thereby providing computing resources for the specified system.

[0071] Figure 5 FIG. 1 is a schematic diagram of the structure of an intelligent asset management device according to an embodiment of the present disclosure. Figure 5 As shown, the intelligent asset management device includes a first processing module 51, a second processing module 52 and a third processing module 53.

[0072] The first processing module 51 is configured to extract the smart asset information from the registration request.

[0073] The second processing module 52 is configured to determine whether the smart asset corresponding to the smart asset information passes the feasibility test.

[0074] In some embodiments, the smart assets include AI capabilities. For example, AI capabilities include AI models and knowledge graphs.

[0075] The third processing module 53 is configured to manage the smart assets according to the preset artificial intelligence AI capability management strategy when the smart assets pass the feasibility test, and store the smart assets in the smart asset warehouse.

[0076] In some embodiments, the AI ​​capability management strategy includes at least one of an indicator access strategy, a mirror access strategy, and a model access strategy.

[0077] The indicator access strategy is used to register AI capabilities and periodically provide at least one of the status information and value assessment indicator information of the AI ​​capabilities.

[0078] For example, registration can be performed by filling in a page or importing an electronic file such as Excel.

[0079] The image access policy is used to encapsulate AI capabilities and the corresponding operating environment into inference images and training images, and to register inference images and training images.

[0080] The model access strategy is used to manage the code model of the AI ​​model.

[0081] For example, the code model includes at least one of a model parameter file and a data specification requirement.

[0082] In some embodiments, the third processing module 53 determines whether there is a replaceable asset in the smart asset warehouse that is replaced by the smart asset. If there is a replaceable asset in the smart asset warehouse, the smart asset is stored in the smart asset warehouse and the replaceable asset is cancelled. This can effectively avoid the existence of redundant smart assets in the smart asset warehouse.

[0083] In some embodiments, the third processing module 53 releases the smart asset after storing the smart asset in the smart asset warehouse.

[0084] In the smart asset management device provided in the above embodiment of the present disclosure, when the smart asset passes the feasibility test, the smart asset is managed according to the preset AI capability management strategy, and the smart asset is stored in the smart asset warehouse. In this way, the smart assets scattered in various systems are uniformly managed, thereby effectively solving the problem of AI capability fragmentation and effectively realizing the sharing and reuse of cloud network AI capabilities.

[0085] In some embodiments, after receiving the smart asset sharing request, the third processing module 53 determines the type of smart asset requested to be shared, and performs smart asset sharing according to a sharing policy associated with the smart asset type.

[0086] For example, the sharing strategy includes at least one of a standardized sharing strategy, a retraining sharing strategy, an inference call strategy, a model embedded reference strategy, and an image download strategy.

[0087] The standardized sharing strategy is used to share specified standardized AI capabilities.

[0088] The retraining sharing strategy is used to retrain specified managed AI capabilities and share the retrained AI capabilities.

[0089] The inference call strategy is used to call the specified AI capabilities for model inference on the cloud server through API calls.

[0090] The model embedded reference strategy is used to encapsulate the specified AI model and embed it into the specified system, thereby providing computing resources for the specified system.

[0091] The image download strategy is used to encapsulate the specified AI inference model and the corresponding operating environment for downloading to the specified system, thereby providing computing resources for the specified system.

[0092] Figure 6FIG. 1 is a schematic diagram of the structure of an intelligent asset management device according to another embodiment of the present disclosure. Figure 6 As shown, the intelligent asset management device includes a memory 61 and a processor 62.

[0093] The memory 61 is used to store instructions. The processor 62 is coupled to the memory 61. The processor 62 is configured to execute the instructions stored in the memory to implement the following. Figure 4 The method of any one of the embodiments.

[0094] like Figure 6 As shown, the intelligent asset management device also includes a communication interface 63 for information exchange with other devices. At the same time, the intelligent asset management device also includes a bus 64, through which the processor 62, the communication interface 63, and the memory 61 communicate with each other.

[0095] The memory 61 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory. The memory 61 may also be a memory array. The memory 61 may also be divided into blocks, and the blocks may be combined into virtual volumes according to certain rules.

[0096] In addition, the processor 62 may be a central processing unit (CPU), or may be an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present disclosure.

[0097] The present disclosure also relates to a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, which are executed by a processor to implement the following Figure 4 The method of any one of the embodiments.

[0098] Figure 7 FIG. 1 is a schematic diagram of the structure of an intelligent asset management system according to an embodiment of the present disclosure. Figure 7 As shown, the intelligent asset management system 70 includes an intelligent asset management device 71. The intelligent asset management device 71 is Figure 5 or Figure 6 An intelligent asset management device is involved in any of the embodiments.

[0099] It should be noted that the intelligent asset management system described here is the voluntary self-intelligence center described above.

[0100] Figure 8 FIG. 1 is a schematic diagram of the structure of an intelligent asset management system according to another embodiment of the present disclosure. Figure 8As shown, the AI ​​general platform can also be called the group big data center. The business system may include the Zhixing Cloud Network Brain, 5GC (5G Core) capability scheduling subsystem, wireless network capability scheduling subsystem, group transmission capability scheduling subsystem, PON (Passive Optical Network) capability scheduling subsystem, IP (Internet Protocol) capability scheduling subsystem, etc.

[0101] Process 1: The Zhixing Cloud Network Brain, capability scheduling subsystem, AI general platform (Group AI Big Data Center), and provincial company-related systems encapsulate AI capabilities and knowledge graphs, register them to the intelligent asset management device, and manage them in a unified manner through indicator access.

[0102] Process 2: For AI capabilities and knowledge graphs that have the ability to be shared, you need to further submit image / inference code, training data, verification data, knowledge graphs and other materials to complete the registration of smart assets.

[0103] Process 3: The smart asset management device pushes the registered smart assets to the AI ​​capability and knowledge center base platform to complete the smart asset AI capability verification and knowledge graph update and integration.

[0104] Process 4: Use the cloud network AI development and operation and knowledge center technology base to complete AI capability construction and retraining, knowledge graph construction and integration. The newly realized AI capability / knowledge graph is packaged as a new intelligent asset, registered to the intelligent asset management device, and managed uniformly.

[0105] Process 5: The Zhixing Cloud Network Brain, related capability scheduling subsystems and provincial company-related systems can subscribe to AI capabilities and knowledge graphs in smart assets.

[0106] Process 6: For smart assets reused in local reasoning mode, download relevant images / knowledge graphs from the smart asset warehouse to local applications. Shared in the form of online OpenAPI calls, smart assets dispatch related capabilities, complete the deployment of AI capability images and knowledge graph capabilities, and submit cloud OpenAPI capabilities.

[0107] Process 7: The intelligent asset management device and the self-intelligence level assessment module identify the shortcomings of self-intelligence capabilities based on the self-intelligence capability blueprint knowledge graph, make suggestions for improving self-intelligence capabilities, and recommend matching intelligent assets.

[0108] Fig. 9 A collaborative schematic diagram of the intelligent asset management process according to an embodiment of the present disclosure.

[0109] like Fig. 9 As shown in the figure, the intelligent asset management process of a certain operator group is as follows:

[0110] 1. The group / provincial company-related intelligent cloud network brain, group / provincial company capability scheduling subsystem, AI and big data center (AI general platform) encapsulate AI capabilities and register them in the form of intelligent assets to the intelligent asset management device of the willing self-intelligence center. For the intelligent assets that can be shared / retrained, submit the image / inference code and related ancillary information to complete the intelligent asset registration.

[0111] 2. The smart asset management device collaborates with the AI ​​capability base of the Willing Self-Intelligence Center to complete the verification of registered smart assets, store those that meet the conditions into the smart asset warehouse, and publish them.

[0112] 3. The group / provincial company-related intelligent cloud network brain and group / provincial company capability scheduling subsystem can initiate intelligent asset retraining / fusion, or capability call requests, complete the reconstruction of intelligent assets, offline mirror calls of AI capabilities, and online calls of OpenAPI of AI capabilities.

[0113] Fig.10 A collaborative schematic diagram of the intelligent asset management process according to another embodiment of the present disclosure.

[0114] like Fig.10 As shown in the figure, the intelligent asset management process of provincial companies of a certain operator group is as follows:

[0115] 1. Provincial company users log in to the Smart Center to complete smart asset registration, enrollment, retraining and sharing.

[0116] 2. After the smart asset request is reviewed and approved, the provincial company's capability demand system completes the mirror call or OpenAPI call of the smart asset in the willing self-intelligent center.

[0117] In some embodiments, the functional unit described above can be implemented as a general-purpose processor, a programmable logic controller (PLC), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component or any appropriate combination thereof for performing the functions described in the present disclosure.

[0118] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0119] The description of the present disclosure is given for the purpose of illustration and description, and is not intended to be exhaustive or to limit the present disclosure to the disclosed form. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present disclosure, and to enable those of ordinary skill in the art to understand the present disclosure and thereby design various embodiments with various modifications suitable for specific uses.

Claims

1. An intelligent asset management method, comprising: Extracting smart asset information from the registration request; Determining whether the smart asset corresponding to the smart asset information passes the feasibility test; If the smart asset passes the feasibility test, the smart asset is managed according to the preset artificial intelligence (AI) capability management strategy; The smart assets are stored in a smart asset warehouse.

2. The method according to claim 1, wherein: The intelligent assets include AI capabilities, wherein the AI ​​capabilities include AI models and knowledge graphs.

3. The method according to claim 2, wherein: The AI ​​capability management strategy includes at least one of an indicator access strategy, a mirror access strategy, and a model access strategy.

4. The method according to claim 3, wherein: The indicator access strategy is used to register the AI ​​capability and periodically provide at least one of the status information and value assessment indicator information of the AI ​​capability.

5. The method according to claim 3, wherein: The image access strategy is used to encapsulate the AI ​​capability and the corresponding operating environment into an inference image and a training image, and register the inference image and the training image.

6. The method according to claim 3, wherein: The model access strategy is used to manage the code model of the AI ​​model.

7. The method according to claim 6, wherein: The code model includes at least one of a model parameter file and a data specification requirement.

8. The method according to claim 1, wherein: Storing the smart asset in the smart asset warehouse includes: Determining whether there is a replaceable asset in the smart asset warehouse that can be replaced by the smart asset; If the replaceable asset exists in the smart asset warehouse, the smart asset is stored in the smart asset warehouse and the replaceable asset is cancelled.

9. The method according to claim 1, further comprising: The smart asset is released.

10. The method according to any one of claims 1 to 9, further comprising: After receiving the smart asset sharing request, determine the type of smart asset requested to be shared; Smart asset sharing is performed according to a sharing policy associated with the smart asset type.

11. The method according to claim 10, wherein: The sharing strategy includes at least one of a standardized sharing strategy, a retraining sharing strategy, an inference call strategy, a model embedded reference strategy, and a mirror download strategy.

12. The method according to claim 11, wherein: The standardized sharing strategy is used to share specified standardized AI capabilities.

13. The method according to claim 11, wherein: The retraining sharing strategy is used to retrain the specified managed AI capabilities and share the retrained AI capabilities.

14. The method according to claim 11, wherein: The inference calling strategy is used to call the specified AI capability of model inference on the cloud server through the application program interface API.

15. The method according to claim 11, wherein: The model embedded reference strategy is used to encapsulate the specified AI model so as to embed it into the specified system.

16. The method according to claim 11, wherein: The image download strategy is used to encapsulate the specified AI reasoning model and the corresponding operating environment for downloading to the specified system.

17. An intelligent asset management device, comprising: A first processing module is configured to extract smart asset information from a registration request; A second processing module is configured to determine whether the smart asset corresponding to the smart asset information passes the feasibility test; The third processing module is configured to manage the smart assets according to a preset artificial intelligence (AI) capability management strategy when the smart assets pass the feasibility test, and store the smart assets in a smart asset warehouse.

18. An intelligent asset management device, comprising: Memory; A processor is coupled to the memory, and the processor is configured to execute the method according to any one of claims 1 to 16 based on instructions stored in the memory.

19. An intelligent asset management system, comprising: An intelligent asset management device as described in any one of claims 17 or 18.

20. A computer-readable storage medium, wherein: The computer-readable storage medium stores computer instructions, and when the instructions are executed by a processor, the method according to any one of claims 1 to 16 is implemented.

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