Intelligent asset management method, device and system, and storage medium

Abstract

The present disclosure provides a smart asset management method, device and system, and a storage medium. The smart asset management method comprises: extracting smart asset information from a registration request; determining whether a smart asset corresponding to the smart asset information passes a feasibility test; in the case where the smart asset passes the feasibility test, managing the smart asset according to a preset AI capability management strategy; and storing the smart asset into a smart asset warehouse.

Description

TECHNICAL FIELD

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

[0002] Cloud network operation automation and intelligentization is an important starting point for telecom operators to implement the strategy of building a powerful network, to build a high-speed, all-in-one, cloud network integrated, intelligent and agile, green and low-carbon, safe and controllable intelligent comprehensive digital information infrastructure, and to practice the digital transformation of cloud network operation.

[0003] The will self-intelligent center is an AI(Artificial Intelligence, artificial intelligence) capability base for cloud network operation, which supports AI capability management, deposition and sharing, and supports the construction of a full-professional and full-scene cloud network AI large model covering network, business and customers, and realizes a multi-dimensional intention-driven self-intelligent closed loop. SUMMARY

[0004] The inventors have noticed that in the related art, due to the construction of related strategies, algorithms, models and the like in multiple systems, the reusability is low, the operation and maintenance cost is high, and there is a lack of unified AI capability view and unified evaluation standard.

[0005] Accordingly, the present disclosure provides an intelligent asset management scheme, which uniformly manages the intelligent assets scattered in each system, thereby effectively solving the AI capability fragmentation problem and effectively realizing the sharing and reuse of cloud network AI capabilities.

[0006] In a first aspect of the present disclosure, an intelligent asset management method is provided, comprising: extracting intelligent asset information from a registration request; determining whether an intelligent asset corresponding to the intelligent asset information passes a feasibility test; in the case where the intelligent asset passes the feasibility test, managing the intelligent asset according to a preset artificial intelligence AI capability management strategy; and storing the intelligent asset in an intelligent asset warehouse.

[0007] In some embodiments, the intelligent asset includes an AI capability, wherein the AI capability includes an AI model and a knowledge graph.

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

[0009] In some embodiments, the index access strategy is used to register the AI capability, and to periodically provide at least one of state information and value evaluation index information of the AI capability.

[0010] In some embodiments, the mirror access strategy is used to encapsulate the AI capability and the corresponding running environment as an inference mirror and a training mirror, and register the inference mirror and the training mirror.

[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 into the smart asset warehouse includes: determining whether there is a replaceable asset replaced by the smart asset in the smart asset warehouse; if the replaceable asset exists in the smart asset warehouse, storing the smart asset into 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 the smart asset requested to be shared is determined; and the smart asset sharing is performed according to a sharing strategy associated with the type of the smart asset.

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

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

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

[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 to embed in a specified system.

[0021] In some embodiments, the mirror download strategy is used to encapsulate a specified AI inference model and a corresponding running environment to download to a specified system.

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

[0023] In a third aspect of the present disclosure, an intelligent asset management apparatus is provided, comprising: a memory; and a processor coupled to the memory, the processor being configured to implement 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, an intelligent asset management system is provided, comprising: an intelligent asset management apparatus as described in any of the above embodiments.

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

[0026] Other features and advantages of the present disclosure will be apparent from the following detailed description of exemplary embodiments of the present disclosure, taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and for those skilled in the art, other drawings can also be obtained without creative labor.

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

[0029] Figure 2 a schematic diagram of a will self-intelligent center function architecture of an embodiment of the present disclosure;

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

[0031] Figure 4 a schematic diagram of the flow of an intelligent asset management method of an embodiment of the present disclosure;

[0032] Figure 5 a schematic diagram of the structure of an intelligent asset management apparatus of an embodiment of the present disclosure;

[0033] Figure 6 Structure diagram of an intelligent asset management device according to another embodiment of the present disclosure;

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

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

[0036] Figure 9 Structure diagram of an intelligent asset management process collaboration according to an embodiment of the present disclosure;

[0037] Figure 10 Structure diagram of an intelligent asset management process collaboration according to another embodiment of the present disclosure. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and not intended to limit the present disclosure and its applications or uses in any way. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present disclosure.

[0039] The relative arrangement, numerical expressions, and numerical values of the components and steps set forth in the embodiments are not intended to limit the scope of the present disclosure, unless otherwise specifically stated.

[0040] Meanwhile, it should be understood that the sizes of the various parts shown in the drawings are not drawn in accordance with the actual proportional relationship for the convenience of description.

[0041] The techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail, but should be considered as part of the authorized description where appropriate.

[0042] In all examples shown and discussed herein, any specific values should be interpreted as merely exemplary and not as a limitation. Thus, other examples of the exemplary embodiments can have different values.

[0043] It should be noted that similar reference numbers and letters represent similar items in the following drawings, and thus, once an item is defined in one drawing, it need not be discussed further in subsequent drawings.

[0044] Figure 1A schematic diagram of a cloud network operating system according to an embodiment of the present disclosure. As shown in Figure 1 The will self-intelligent center is an AI capability base for cloud network operation, supports AI capability management, deposition and sharing, supports the construction of full-professional and full-scene cloud network AI large models, covers networks, businesses and customers, and realizes multi-dimensional intention-driven self-intelligent closed loop.

[0045] Figure 2 A functional architecture schematic diagram of the will self-intelligent center according to an embodiment of the present disclosure. As shown in Figure 2 The intelligent asset management module has the capabilities of cloud network intelligent asset standardized management, intelligent asset classification management, and standardized sharing.

[0046] Because related strategies, algorithms, models, etc. are scattered in multiple systems, leading to low reusability, high operation and maintenance cost, and lack of unified AI capability view and unified evaluation standard.

[0047] Accordingly, the present disclosure provides an intelligent asset management scheme, which effectively solves the AI capability fragmentation problem and effectively realizes the sharing and reuse of cloud network AI capabilities by uniformly managing the intelligent assets scattered in each system.

[0048] Figure 3 A functional block diagram of the intelligent asset management according to an embodiment of the present disclosure. As shown in Figure 3 The cloud network intelligent asset management model is used to realize the management of intelligent assets in multiple forms, explore the offline / on-line subscription sharing / new creation / retraining (knowledge fusion) management process and technical implementation, solve the problems of intelligent asset classification, management, sharing and management, and complete the unified management of intelligent assets in the whole group.

[0049] Figure 4 A flowchart of the intelligent asset management method according to an embodiment of the present disclosure. In some embodiments, the following intelligent asset management method is executed by an intelligent asset management device.

[0050] In step 401, intelligent asset information is extracted from a registration request.

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

[0052] In some embodiments, the intelligent asset includes AI capabilities. For example, the AI capabilities include AI models and knowledge graphs.

[0053] In step 403, in the case that the intelligent asset passes the feasibility test, the intelligent asset is managed according to a preset AI capability management strategy.

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

[0055] The index access strategy is used to register the AI capability and periodically provide at least one of state information and value evaluation index information of the AI capability.

[0056] For example, the registration can be performed by page filling or electronic file import such as Excel.

[0057] The image access strategy is used to encapsulate the AI capability and the corresponding running environment as inference images and training images, and register the inference images and the 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 data specification requirements.

[0060] In step 404, the smart asset is stored in the smart asset warehouse.

[0061] In some embodiments, it is determined whether there is a replaceable asset replaced by the smart asset in the smart asset warehouse. 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. In this way, redundant smart assets in the smart asset warehouse can be effectively avoided.

[0062] In some embodiments, after the smart asset is stored in the smart asset warehouse, the smart asset is published.

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

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

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

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

[0067] The retraining sharing policy 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 a specified AI model and embed it into a 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 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, smart assets include AI capabilities, such as 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 policy is used to register AI capabilities and periodically provide at least one of AI capability status information and value assessment indicator information.

[0078] For example, registration can be performed by filling in a form 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 replaced by the smart asset in the smart asset warehouse, and if there is a replaceable asset in the smart asset warehouse, stores the smart asset in the smart asset warehouse and cancels the replaceable asset. In this way, redundant smart assets in the smart asset warehouse can be effectively avoided.

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

[0084] In the smart asset management device provided by the above embodiments of the present disclosure, in the case that the smart asset passes the feasibility test, the smart asset is managed according to the preset AI capability management strategy and stored in the smart asset warehouse. In this way, by uniformly managing the smart assets scattered in various systems, the AI capability fragmentation problem is effectively solved, and the sharing and reuse of cloud network AI capabilities are effectively realized.

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

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

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

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

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

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

[0091] The mirror download strategy is used to encapsulate the specified AI inference model and the corresponding running environment to download to a specified system, thereby providing computing power resources for the specified system.

[0092] Figure 6A structural schematic diagram of a smart asset management apparatus according to another embodiment of the present disclosure. As shown in Figure 6 The smart asset management apparatus includes a memory 61 and a processor 62.

[0093] The memory 61 is configured to store instructions, and the processor 62 is coupled to the memory 61 and configured to execute the method according to any one of the embodiments of the present disclosure based on the instructions stored in the memory. Figure 4

[0094] As shown in Figure 6 The smart asset management apparatus further includes a communication interface 63 configured to interact with other devices. Meanwhile, the smart asset management apparatus further includes a bus 64, and the processor 62, the communication interface 63, and the memory 61 communicate with each other through the bus 64.

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

[0096] In addition, the processor 62 can be a central processing unit (CPU), or can be an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement 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, and the instructions are executed by a processor to implement the method according to any one of the embodiments of the present disclosure. Figure 4

[0098] Figure 7 A structural schematic diagram of a smart asset management system according to an embodiment of the present disclosure. As shown in Figure 7 The smart asset management system 70 includes a smart asset management apparatus 71, and the smart asset management apparatus 71 is the smart asset management apparatus according to any one of the embodiments of the present disclosure. Figure 5 Figure 6

[0099] It should be noted that the smart asset management system described herein is the will self-intelligent center described above.

[0100] Figure 8 A structural schematic diagram of a smart asset management system according to another embodiment of the present disclosure. As shown in Figure 8 ​​​​As shown, the AI general platform can also be referred to as a group big data center, and the business system can include a smart cloud network brain, a 5GC (5G Core, 5G core network) capability scheduling subsystem, a wireless network capability scheduling subsystem, a group transmission capability scheduling subsystem, a PON (Passive Optical Network) capability scheduling subsystem, an IP (Internet Protocol) capability scheduling subsystem, and the like.

[0101] Flow 1: The smart cloud network brain, the capability scheduling subsystem, the AI general platform (group AI big data center), and the provincial company related system encapsulate AI capabilities and knowledge graphs, and register to the intelligent asset management device for unified management in an index access manner.

[0102] Flow 2: For AI capabilities and knowledge graphs with sharing capabilities, mirror / inference code, training data, verification data, knowledge graphs, and the like need to be further submitted to complete the registration of intelligent assets.

[0103] Flow 3: The intelligent asset management device pushes the registered intelligent assets to the AI capability and knowledge hub platform to complete intelligent asset AI capability verification and knowledge graph update fusion.

[0104] Flow 4: The cloud network AI development and operation and the knowledge hub technology base are used to complete AI capability construction and retraining, and knowledge graph construction and fusion. The newly implemented AI capabilities / knowledge graphs are encapsulated as new intelligent assets, registered to the intelligent asset management device, and uniformly managed.

[0105] Flow 5: The smart cloud network brain, the related capability scheduling subsystem, and the provincial company related system can subscribe to AI capabilities and knowledge graphs in intelligent assets.

[0106] Flow 6: The intelligent assets that are reused in a local inference manner download related images / knowledge graphs from the intelligent asset warehouse to the local application. For the shared intelligent assets scheduling related capabilities in an online OpenAPI calling manner, the AI capability mirror and knowledge graph capability are deployed, and the cloud end OpenAPI capability is submitted.

[0107] Flow 7: The intelligent asset management device and the self-intelligent level evaluation module identify the self-intelligent capability short board according to the self-intelligent capability blueprint knowledge graph, propose self-intelligent capability improvement suggestions, and recommend matched intelligent assets.

[0108] Figure 9 The intelligent asset management process coordination diagram for an embodiment of the present disclosure.

[0109] As Figure 9 shown, the intelligent asset management process of a certain operator group is as follows:

[0110] 1. The group / provincial company's relevant intelligent driving cloud network brain, group / provincial company capacity scheduling subsystem, and AI and big data center (AI general platform) encapsulate AI capabilities and register them as smart assets in the intelligent asset management device of the Willing Self-Intelligence Center. For smart assets that can be shared / retrained, submit the image / inference code and related ancillary information to complete the smart 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 in the smart asset warehouse, and publish them.

[0112] 3. The Zhixing Cloud Network Brain and the Group / Provincial Company's capability scheduling subsystem can initiate intelligent asset retraining / fusion, or capability call requests, to complete the reconstruction of intelligent assets, offline mirror calls of AI capabilities, and online calls of AI capability OpenAPIs.

[0113] Figure 10 This is a collaborative diagram of the intelligent asset management process according to another embodiment of the present disclosure.

[0114] like Figure 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 requirement system completes the mirror call or OpenAPI call of the smart asset in the willing self-intelligent center.

[0117] In some embodiments, the functional units described above may be implemented as general-purpose processors, programmable logic controllers (PLC), digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or any appropriate combination thereof, for performing the functions described in the present disclosure.

[0118] Those skilled in the art can understand that all or part of the steps of the above-mentioned embodiments can be completed by hardware, or can be instructed by programs to complete the related hardware, and the programs can be stored in a computer readable storage medium. The storage medium mentioned above can be a read-only memory, a magnetic 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 chosen and described in order to best explain the principles of the present disclosure and its practical application, and to enable others skilled in the art to understand the present disclosure in order to design various embodiments with various modifications for specific use.

Claims

1. An intelligent asset management method, comprising: Extracting smart asset information from the registration request; Determining whether a smart asset corresponding to the smart asset information passes a feasibility test, wherein the smart asset includes an AI capability, and the AI ​​capability includes an AI model and a knowledge graph; If the smart asset passes the feasibility test, the smart asset is managed according to a preset artificial intelligence (AI) capability management policy, wherein the AI ​​capability management policy includes at least one of an indicator access policy, an image access policy, and a model access policy. The indicator access policy 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. The image access policy 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. The model access policy is used to manage the code model of the AI ​​model. The smart asset is stored in a smart asset warehouse.

2. The method according to claim 1, wherein The code model includes at least one of a model parameter file and a data specification requirement.

3. The method according to claim 1, wherein Storing the smart asset in the smart asset warehouse includes: Determining whether there is a fungible 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.

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

5. The method according to any one of claims 1 to 4, further comprising: Upon receiving a request for sharing a smart asset, determining the type of smart asset requested for sharing; Smart asset sharing is performed according to a sharing policy associated with the smart asset type.

6. The method according to claim 5, 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.

7. The method according to claim 6, wherein: The standardized sharing strategy is used to share specified standardized AI capabilities.

8. The method according to claim 6, wherein: The retraining sharing strategy is used to retrain the specified managed AI capabilities and share the retrained AI capabilities.

9. The method according to claim 6, 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.

10. The method according to claim 6, wherein: The model embedded reference strategy is used to encapsulate the specified AI model to embed it into the specified system.

11. The method according to claim 6, 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.

12. An intelligent asset management device, comprising: A first processing module is configured to extract smart asset information from the registration request; a second processing module configured to determine whether a smart asset corresponding to the smart asset information passes a feasibility test, wherein the smart asset includes an AI capability, and the AI ​​capability includes an AI model and a knowledge graph; The third processing module is configured to manage the smart asset according to a preset artificial intelligence (AI) capability management strategy when the smart asset passes the feasibility test, and store the smart asset in the smart asset warehouse, wherein the AI ​​capability management strategy includes at least one of an indicator access strategy, an image access strategy, and a model access strategy, 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, 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, and the model access strategy is used to manage the code model of the AI ​​model.

13. 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 11 based on instructions stored in the memory.

14. An intelligent asset management system, comprising: An intelligent asset management device as claimed in any one of claims 12 or 13.

15. 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 11 is implemented.

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