AI processing method for NAS device and NAS system

Abstract

The invention relates to an AI processing method for NAS equipment and an NAS system, the method is used for an NAS control unit in the NAS equipment, the NAS control unit realizes butt joint with an AI model in a mode of communication connection with an AI processing module, and the method comprises the following steps: the NAS control unit calls a user operation layer in a first form, generates a first AI demand, and sends the first AI demand to the AI model; the AI model receives a first AI demand, converts the first AI demand into an AI instruction, and feeds back the AI instruction to the user operation layer in the first form; the user operation layer in the first form is converted into a user operation layer in a second form based on an AI instruction; and the NAS control unit calls the user operation layer in the second form as an AI processing result. According to the scheme, a super-large-scale model or data set can be processed without being limited by local hardware configuration, and the functions of data security and privacy protection can be achieved in a multi-user access environment at low cost.

Description

Technical Field

[0001] The present invention relates to the field of network storage, and particularly to an AI processing method for NAS devices and a NAS system. Background Art

[0002] With the development of artificial intelligence (AI) technology, the dependence of enterprises and individuals on AI is also growing continuously. The popularization of AI technology makes it possible to process massive data and realize intelligent applications, especially in the field of distributed network storage (hereinafter referred to as NAS). The NAS system not only has efficient data management capabilities, but also can be combined with the AI deployed locally.

[0003] However, in the prior art, there are also some disadvantages in the AI deployed locally in NAS: First, its computing power is limited by the local hardware configuration and it is difficult to process ultra-large models or datasets; Second, additional resources need to be invested in data security and privacy protection, especially in an environment with multi-user access. Therefore, the existing technology of integrating local AI in NAS still needs to be improved and developed, and an AI processing method for NAS devices and a NAS system need to be designed to solve the above technical problems. Summary of the Invention

[0004] The technical problem to be solved by the present invention is: how to design a technical solution for NAS devices that can process ultra-large models or datasets without being limited by local hardware configuration, and can achieve the functions of data security and privacy protection at low cost in an environment with multi-user access.

[0005] In a first aspect, an embodiment of the present invention provides an AI processing method for a NAS device. The method is used for a NAS control unit in the NAS device. The NAS control unit realizes docking with an AI model by means of communication connection with an AI processing module. The method includes: S1, the NAS control unit calls a user operation layer in a first form, generates a first AI requirement, and sends the first AI requirement to the AI model; S2, the AI model receives the first AI requirement, converts the first AI requirement into an AI instruction, and feeds it back to the user operation layer in the first form; S3, the user operation layer in the first form is transformed into a user operation layer in a second form based on the AI instruction; S4, the NAS control unit calls the user operation layer in the second form as the AI processing result.

[0006] In the above solution, the user operation layer in the first form is transformed into the user operation layer in the second form based on AI instructions, which can be automatically adjusting the file position or automatically adjusting the picture information; the user operation layer can issue a series of instructions, such as AI-generated content, AI conversations, AI knowledge bases, AI document management, AI audio-visual entertainment, etc. Further, the AI processing module has AI capabilities, and the definition of the AI capabilities is that it can receive a certain degree of intelligent responses, automatic operations, and generative content capabilities by module installation, software local deployment, or requesting externally through a network interface.

[0007] A further technical solution thereof is that the NAS control unit internally sets a first module group, including a NAS manufacturer intelligent module A, a third-party intelligent module B, and a self-developed intelligent module C; the NAS control unit also internally sets a second module group, including a container virtual intelligent module D, a manufacturer integrated intelligent module E, and a non-manufacturer intelligent module F; in S1, the NAS control unit calls the user operation layer in the first form to generate a first AI requirement and sends the first AI requirement to the AI model, specifically including: the NAS control unit calls the user operation layer in the first form to generate a first AI requirement corresponding to the user operation layer, encapsulates the first AI requirement into a standardized data packet through a preset interface and protocol, and the data packet contains requirement parameters, application scenario identifiers, and user permission information; sends the requirement parameters, application scenario identifiers, and user permission information to the AI model.

[0008] A further technical solution thereof is that sending the requirement parameters, application scenario identifiers, and user permission information to the AI model specifically includes: the NAS control unit generates a module startup response according to the requirement parameters, application scenario identifiers, and user permission information, and starts the first module group and / or the second module group.

[0009] A further technical solution of the method further includes: the NAS control unit generates a module startup response and starts the first module group. If the module startup response is associated with the NAS manufacturer intelligent module A, the NAS manufacturer intelligent module A directly calls the user operation layer through the API interface integrated in the NAS system and sends the requirement to the AI model; if the module startup response is associated with the third-party intelligent module B, the third-party intelligent module B encrypts the first AI requirement through a third-party interface compatible with the NAS system and sends it to the AI model; if the module startup response is associated with the self-developed intelligent module C, the self-developed intelligent module C sends the first AI requirement to the AI model in a specific format through a custom script and interface.

[0010] A further technical solution thereof is that the method further includes: the NAS control unit generation module starts a response and starts a second module group. If the module start response is associated with the container virtual intelligent module D, the container virtual intelligent module D runs in the container environment and sends the first AI requirement to the AI model through the network interface inside the container; if the module start response is associated with the manufacturer integrated intelligent module E, the manufacturer integrated intelligent module E sends the first AI requirement to the AI model in the form of a script call through the tool class script developed by the manufacturer; if the module start response is associated with the non-manufacturer intelligent module F, the non-manufacturer intelligent module F sends the first AI requirement to the AI model through a third-party developed tool class script via a preset protocol.

[0011] A further technical solution thereof is that in S2, the AI model receives the first AI requirement, converts the first AI requirement into an AI instruction, and feeds it back to the user operation layer in the first form, which specifically includes: the AI model docks with the first AI requirement sent by the NAS control unit, parses the first AI requirement through a preset parsing module, and extracts the requirement parameters, application scenario identifiers, and user permission information therein; the AI model matches the corresponding AI algorithm according to the extracted requirement parameters in combination with a preset AI algorithm library and generates a corresponding AI instruction; the AI model feeds the generated AI instruction back to the user operation layer in the first form in a standardized instruction format through a preset feedback channel, and the feedback channel includes a network communication interface and a local communication interface.

[0012] A further technical solution thereof is that in S3, the user operation layer in the first form is transformed into the user operation layer in the second form, which specifically includes: after the user operation layer in the first form receives the AI instruction fed back by the AI model, it parses the AI instruction through a preset instruction parsing module and extracts the operation parameters and target status in the instruction; the user operation layer in the first form calls a preset scenario conversion module according to the extracted operation parameters and gradually adjusts the configuration and status of the user operation layer according to the preset conversion rules; during the conversion process, the user operation layer in the first form monitors the conversion status in real time, and when it monitors that the conversion is completed and meets the preset second form conditions, it confirms the transformation into the user operation layer in the second form, and the second form conditions include preset configuration parameters and status identifiers.

[0013] A further technical solution thereof is that in step S4, the NAS control unit calls the user operation layer in the second form as the AI processing result, which specifically includes: the NAS control unit detects through a preset scenario detection module that the user operation layer in the second form has been successfully converted and is in a stable state; the NAS control unit calls the user operation layer in the second form and obtains the current status information and processing result data of the user operation layer through a preset interface; the NAS control unit encapsulates the obtained processing result data to generate an AI processing result, and sends the AI processing result to a preset user terminal or storage system through a preset output interface, and the output interface includes a network interface and a local storage interface.

[0014] In a second aspect, the present invention proposes an AI processing method for a NAS device and a NAS system. The NAS system uses the NAS device as the core of data storage and combines an AI processing module externally or internally installed in the NAS. The AI processing module has AI capabilities, and the definition of the AI capabilities is that it can obtain a certain degree of intelligent response, automatic operation, and generative content capabilities through module installation, software local deployment, or requesting externally through a network interface. This architecture is designed modularly and includes a NAS storage management module, a data preprocessing module, and an AI processing module. Each module achieves seamless cooperation through a standardized interface. The system supports efficient model training and inference, and has the characteristics of scalability, low latency, and intelligence, and is applicable to a variety of application scenarios.

[0015] The functions of each module of the present invention include: the NAS storage management module is responsible for storing raw data and intermediate results, providing multi-user data access permission management, version control, and backup functions, and supporting connection to the AI processing module through a protocol; the data preprocessing module is used for preprocessing stored data such as format conversion, denoising, and data enhancement; the AI processing module is deployed on a local server or in the cloud and is responsible for model training and inference, communicating with the NAS device in a wireless or wired form through a high-speed network to ensure efficient data transmission; the functions of each module support distributed computing and edge computing architectures to adapt to task requirements of different scales.

[0016] The NAS system described in the present invention includes a NAS device and a computer device connected to each other, and its characteristics are as follows:

[0017] First, efficient separation of storage and computing: By decoupling the storage and computing modules, independent expansion of data and computing resources is achieved.

[0018] Second, modular design: System components are interconnected through standardized interfaces, and function modules can be flexibly replaced or expanded.

[0019] Third, low-latency communication: Use network communication protocols to ensure the data transfer efficiency between storage and computing.

[0020] Fourth, intelligent task scheduling: Through the AI scheduling system, dynamically allocate computing resources and file locations according to file types and resource utilization.

[0021] Fifth, cross-platform compatibility: Support multiple operating systems and development frameworks, and users can deploy in multiple environments.

[0022] The main objective of the present invention is that the NAS with an online or external AI architecture can efficiently integrate the NAS system with AI computing. This architecture can not only make full use of the storage capacity of the NAS, but also provide users with certain data analysis and intelligent decision-making support; at the same time, it avoids the risk of inconsistent outputs caused by multiple users operating the same AI; in addition, it can also enable existing NAS systems (without AI) to have a certain ability to output AI analysis.

[0023] In summary, the solution of the present invention combines the high-efficiency storage capacity of the NAS and the intelligent computing ability of the AI module, providing a flexible, scalable, and efficient intelligent service solution, providing users with personalized and diverse AI-side services, NAS data-side services (such as automatically adjusting file locations or automatically adjusting picture information), having the ability of AI dialogue, being able to conveniently answer users' questions, and helping users better manage the NAS. Therefore, it has broad market prospects and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings herein are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present invention, and are used together with the specification to explain the principles of the present invention.

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0026] Figure 1 It is the main flowchart of the AI processing method for NAS devices provided by the embodiments of the present invention.

[0027] Figure 2 It is the sub-flowchart of the AI processing method for NAS devices provided by the embodiments of the present invention.

[0028] Figure 3 It is the schematic framework diagram of the NAS system provided by the embodiments of the present invention.

[0029] Figure 4 Schematic diagram of the NAS system provided by the embodiment of the present invention.

[0030] Figure 5 Simplified framework diagram of the electronic device provided by the embodiment of the present invention. Detailed implementation manners

[0031] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0032] It should be understood that when used in this specification and the appended claims, the terms "comprises" and "comprising" indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or other features, wholes, steps, operations, elements, components, and / or their combinations.

[0033] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0034] It should be further understood that the term "and / or" used in the specification of the present invention and the appended claims refers to any one or any combination of the related listed items and all possible combinations, and includes these combinations.

[0035] As used in this specification and the appended claims, the term "if" can be interpreted as "when...", "once", "in response to determining" or "in response to detecting" according to the context. Similarly, the phrase "if determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once determined", "in response to determining", "once [the described condition or event] is detected" or "in response to detecting [the described condition or event]" according to the context.

[0036] In this specification and the appended claims, there may be multiple ways of expressing the same technical feature or technical term, such as using upper-level generalization, lower-level limitation, or synonymous substitution and other different forms of expression; those skilled in the art can clearly understand the substantially same technical meaning pointed to by different forms of expression based on their professional knowledge and in combination with the overall content of the specification and the drawings; the differences in different forms of expression only lie in the diversity at the literal level, do not constitute a substantial modification or limitation to the technical solution, and do not affect the certainty of the protection scope of the patent claims and the full disclosure of the technical content of the specification.

[0037] Embodiment 1

[0038] Please refer to Figures 1 to 4 , in which Figure 1 is a flowchart of an AI processing method for a NAS device provided by an embodiment of the present invention. An embodiment of the present invention proposes an AI processing method for a NAS device. The method is used for the NAS control unit in the NAS device. The NAS control unit realizes docking with the AI model by means of communication connection with the AI processing module. The NAS device belongs to the original NAS device, without a built-in AI module and without its own AI capabilities; the method includes: S1, the NAS control unit calls the user operation layer in the first form, generates a first AI requirement, and sends the first AI requirement to the AI model; S2, the AI model receives the first AI requirement, converts the first AI requirement into an AI instruction, and feeds it back to the user operation layer in the first form; S3, the user operation layer in the first form is transformed into the user operation layer in the second form based on the AI instruction; S4, the NAS control unit calls the user operation layer in the second form as the AI processing result. In the above solution, the user operation layer in the first form is transformed into the user operation layer in the second form based on the AI instruction, which can be to automatically adjust the file position or automatically adjust the picture information; the user operation layer can issue a series of instructions, such as AI-generated content, AI dialogue, AI knowledge base, AI document management, AI audio-visual entertainment, etc. Further, the AI processing module has AI capabilities. The definition of the AI capabilities is the ability to receive a certain degree of intelligent response, automatic operation, and generative content by means of module installation, software local deployment, or requesting externally through a network interface.

[0039] In one embodiment, a first module group is provided inside the NAS control unit, including a NAS manufacturer intelligent module A, a third-party intelligent module B, and a self-developed intelligent module C; a second module group is also provided inside the NAS control unit, including a container virtual intelligent module D, a manufacturer integrated intelligent module E, and a non-manufacturer intelligent module F; for S1, the NAS control unit calls the user operation layer in the first form, generates a first AI requirement, and sends the first AI requirement to the AI model, specifically including: the NAS control unit calls the user operation layer in the first form, generates a first AI requirement corresponding to the user operation layer, encapsulates the first AI requirement into a standardized data packet through a preset interface and protocol, and the data packet contains requirement parameters, application scenario identifiers, and user permission information; sends the requirement parameters, application scenario identifiers, and user permission information to the AI model.

[0040] In one embodiment, sending the requirement parameters, application scenario identifiers, and user permission information to the AI model specifically includes: the NAS control unit generates a module startup response according to the requirement parameters, application scenario identifiers, and user permission information, and starts the first module group and / or the second module group.

[0041] In one embodiment, the method further includes: the NAS control unit generates a module startup response and starts the first module group. If the module startup response is associated with the NAS manufacturer intelligent module A, the NAS manufacturer intelligent module A directly calls the user operation layer through the API interface integrated in the NAS system and sends the requirement to the AI model; if the module startup response is associated with the third-party intelligent module B, the third-party intelligent module B encrypts the first AI requirement through a third-party interface compatible with the NAS system and then sends it to the AI model; if the module startup response is associated with the self-developed intelligent module C, the self-developed intelligent module C sends the first AI requirement to the AI model in a specific format through a custom script and interface.

[0042] In one embodiment, the method further includes: the NAS control unit generates a module startup response and starts the second module group. If the module startup response is associated with the container virtual intelligent module D, the container virtual intelligent module D runs in the container environment and sends the first AI requirement to the AI model through the network interface inside the container; if the module startup response is associated with the manufacturer integrated intelligent module E, the manufacturer integrated intelligent module E sends the first AI requirement to the AI model in the form of a script call through a tool class script developed by the manufacturer; if the module startup response is associated with the non-manufacturer intelligent module F, the non-manufacturer intelligent module F sends the first AI requirement to the AI model through a preset protocol through a tool class script developed by a third party.

[0043] In one embodiment, for S2, the AI model receives a first AI requirement, converts the first AI requirement into an AI instruction, and feeds it back to the user operation layer in the first form. Specifically, it includes: the AI model docks with the first AI requirement sent by the NAS control unit, parses the first AI requirement through a preset parsing module, and extracts the requirement parameters, application scenario identifier, and user permission information therein; the AI model combines the extracted requirement parameters with a preset AI algorithm library, matches the corresponding AI algorithm, and generates a corresponding AI instruction; the AI model feeds the generated AI instruction back to the user operation layer in the first form through a preset feedback channel in a standardized instruction format, and the feedback channel includes a network communication interface and a local communication interface.

[0044] In one embodiment, for S3, the user operation layer in the first form is transformed into the user operation layer in the second form based on the AI instruction. Specifically, it includes: after the user operation layer in the first form receives the AI instruction fed back by the AI model, it parses the AI instruction through a preset instruction parsing module and extracts the operation parameters and target state in the instruction; the user operation layer in the first form calls a preset scenario conversion module according to the extracted operation parameters and gradually adjusts the configuration and state of the user operation layer according to the preset conversion rules; during the conversion process, the user operation layer in the first form monitors the conversion state in real time, and when it detects that the conversion is completed and meets the preset conditions for the second form, it confirms the transformation into the user operation layer in the second form, and the conditions for the second form include preset configuration parameters and status identifiers.

[0045] In one embodiment, for S4, the NAS control unit calls the user operation layer in the second form as the AI processing result. Specifically, it includes: the NAS control unit detects through a preset scenario detection module that the user operation layer in the second form has been successfully converted and is in a stable state; the NAS control unit calls the user operation layer in the second form and obtains the current state information and processing result data of the user operation layer through a preset interface; the NAS control unit encapsulates the obtained processing result data to generate an AI processing result, and sends the AI processing result to a preset user terminal or storage system through a preset output interface, and the output interface includes a network interface and a local storage interface.

[0046] In the above solution, the NAS control unit internally sets a first module group, including NAS manufacturer intelligent module A, third-party intelligent module B, and self-developed intelligent module C; the NAS control unit also internally sets a second module group, including container virtual intelligent module D, manufacturer integrated intelligent module E, and non-manufacturer intelligent module F.

[0047] The A-class application programs corresponding to the NAS manufacturer's intelligent module A can be: file manager, video center, photo album, virtual machine, synchronization backup, music, app store, etc. For example, a file manager application or program or script with AI artificial intelligence function provided by the NAS manufacturer.

[0048] The B-class application programs corresponding to the third-party intelligent module B can be: Thunderbolt, Zhihu, WeChat, Douyin, etc., which are application programs or scripts with AI artificial intelligence function that can run on the NAS and are developed by non-NAS official.

[0049] The C-class application programs corresponding to the self-developed intelligent module C can be: application programs or scripts with AI artificial intelligence function independently developed, used or called by the NAS holder (including NAS purchaser) in the NAS.

[0050] The D-class application programs corresponding to the container virtual intelligent module D can be: application programs or scripts with AI artificial intelligence function developed or used in containers or virtual machines such as Docker, virtual machine, VMWare, etc. of the NAS. For example, those with AI artificial intelligence function attached in container projects such as ALi st, Emby, Fi refox, Chrome, etc.

[0051] The E-class application programs corresponding to the manufacturer-integrated intelligent module E can be: scripts with AI artificial intelligence function developed or purchased and integrated by the NAS manufacturer in the NAS. For example, the NAS manufacturer uses AI in the NAS for automatic device switching on / off, password reset, alarm clock, fault archiving, fault handling, problem troubleshooting, file archiving / categorization, etc. with AI capabilities.

[0052] The F-class application programs corresponding to the non-manufacturer intelligent module F can be: scripts with AI artificial intelligence function developed or purchased and integrated by non-NAS manufacturers in the NAS. For example, those with AI capabilities for local or remote data synchronization, information archiving / retrieval, generative Q&A, etc. in the NAS.

[0053] In one embodiment, the present invention provides an AI processing method and a NAS system for a NAS device. The NAS system uses the NAS device as the core of data storage and combines an external or internal AI processing module in the NAS. The AI processing module has AI capabilities, which are defined as the ability to receive a certain degree of intelligent responses, perform automatic operations, and generate content through module installation, local software deployment, or by requesting externally through a network interface. This architecture is modularly designed and includes a NAS storage management module, a data preprocessing module, and an AI processing module. These modules achieve seamless collaboration through standardized interfaces. The system supports efficient model training and inference, and has the characteristics of scalability, low latency, and intelligence, making it suitable for a variety of application scenarios.

[0054] The functions of each module of the present invention include: The NAS storage management module is responsible for storing raw data and intermediate results. It provides multi-user data access permission management, version control, and backup functions. It supports connecting to the AI processing module through a protocol; The data preprocessing module is used to perform preprocessing on the stored data, such as format conversion, denoising, and data augmentation; The AI processing module is deployed on a local server or in the cloud and is responsible for model training and inference. It communicates with the NAS device in a wireless or wired form through a high-speed network to ensure efficient data transmission; The functions of each module support distributed computing and edge computing architectures to adapt to task requirements of different scales.

[0055] In summary, the solution of the present invention combines the efficient storage capabilities of the NAS with the intelligent computing capabilities of the AI module, providing a flexible, scalable, and efficient intelligent service solution. It provides personalized and diverse AI-side services and NAS data-side services (such as automatically adjusting file locations or automatically adjusting picture information) for users, has the ability to conduct AI conversations, can conveniently answer users' questions, and helps users better manage the NAS. Therefore, it has broad market prospects and application value.

[0056] Embodiment 2

[0057] Please refer to Figure 5 , Figure 5 , which is a block diagram of an electronic device provided by the present invention. This electronic device can be a terminal or a server. Among them, the terminal can be an electronic device with communication functions such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a personal digital assistant, and a wearable device. It includes a processor 111, a communication interface 112, a memory 113, and a communication bus 114. Among them, the processor 111, the communication interface 112, and the memory 113 communicate with each other through the communication bus 114.

[0058] The memory 113 is used to store computer programs.

[0059] In one embodiment of the present invention, when the processor 111 executes the program stored in the memory 113, the method provided in any of the foregoing method embodiments is implemented.

[0060] It should be understood that in the embodiments of the present application, the processor 111 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0061] Those of ordinary skill in the art can understand that all or part of the processes of the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a storage medium, and the storage medium is a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the process steps of the method embodiments.

[0062] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0063] In several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0064] The steps in the method of the embodiments of the present invention can be adjusted, combined, and deleted according to actual needs. The units in the device of the embodiments of the present invention can be combined, divided, and deleted according to actual needs. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0065] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0066] In the above embodiments, the descriptions of the various embodiments have their own focuses. For the parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0067] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, provided that these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

[0068] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. An AI processing method for a NAS device, characterized in that: The method is used for a NAS control unit in a NAS device, and the NAS control unit is connected to an AI processing module by communication to achieve docking with an AI model. The method includes: S1, the NAS control unit calls the first form of the user operation layer, generates a first AI requirement, and sends the first AI requirement to the AI ​​model; S2, the AI ​​model receives the first AI demand, converts the first AI demand into an AI instruction, and feeds back to the first form of user operation layer; S3, the first form of user operation layer is transformed into the second form of user operation layer based on AI instructions; S4, the NAS control unit calls the second form of the user operation layer as the AI ​​processing result.

2. The AI ​​processing method for NAS device according to claim 1, characterized in that: The NAS control unit includes a first module group, including a NAS manufacturer intelligent module A, a third-party intelligent module B, and a self-developed intelligent module C; the NAS control unit also includes a second module group, including a container virtual intelligent module D, a manufacturer integrated intelligent module E, and a non-manufacturer intelligent module F; S1, the NAS control unit calls the first form of the user operation layer, generates a first AI demand, and sends the first AI demand to the AI ​​model, specifically including: The NAS control unit calls the first form of the user operation layer, generates a first AI requirement corresponding to the user operation layer, and encapsulates the first AI requirement into a standardized data packet through a preset interface and protocol, wherein the data packet includes a requirement parameter, an application scenario identifier, and user authority information; Send the demand parameters, application scenario identifiers, and user permission information to the AI ​​model.

3. The AI ​​processing method for NAS device according to claim 2, characterized in that: The sending of the demand parameters, application scenario identifier, and user authority information to the AI ​​model specifically includes: The NAS control unit generates a module startup response according to the demand parameters, the application scenario identifier, and the user authority information, and starts the first module group and / or the second module group.

4. The AI ​​processing method for NAS device according to claim 3, characterized in that: The method further comprises: The NAS control unit generates a module startup response and starts the first module group. If the module startup response is associated with the NAS vendor intelligent module A, the NAS vendor intelligent module A directly calls the user operation layer through the API interface integrated in the NAS system and sends the demand to the AI ​​model. If the module startup response associates with the third-party intelligent module B, the third-party intelligent module B encrypts the first AI requirement and sends it to the AI ​​model through a third-party interface compatible with the NAS system; If the module startup response is associated with the self-developed intelligent module C, the self-developed intelligent module C sends the first AI requirement to the AI ​​model in a specific format through a customized script and interface.

5. The AI ​​processing method for NAS device according to claim 4, characterized in that: The method further comprises: The NAS control unit generates a module startup response, starts the second module group, and if the module startup response is associated with the container virtual intelligent module D, the container virtual intelligent module D runs in the container environment and sends the first AI requirement to the AI ​​model through the network interface inside the container; If the module startup response is associated with the manufacturer integrated intelligent module E, the manufacturer integrated intelligent module E sends the first AI requirement to the AI ​​model in the form of a script call through a tool script developed by the manufacturer; If the module startup response is associated with the non-manufacturer intelligent module F, the non-manufacturer intelligent module F sends the first AI requirement to the AI ​​model through a preset protocol through a tool script developed by a third party.

6. The AI ​​processing method for NAS device according to claim 5, characterized in that: S2, the AI ​​model receives the first AI demand, converts the first AI demand into an AI instruction, and feeds back to the user operation layer of the first form, specifically including: The AI ​​model is connected to the first AI requirement sent by the NAS control unit, and the first AI requirement is parsed by a preset parsing module to extract the requirement parameters, application scenario identifier, and user authority information therein; The AI ​​model matches the corresponding AI algorithm according to the extracted demand parameters and combines with the preset AI algorithm library, and generates corresponding AI instructions; The AI ​​model feeds back the generated AI instructions to the first form of user operation layer in a standardized instruction format through a preset feedback channel, and the feedback channel includes a network communication interface and a local communication interface.

7. The AI ​​processing method for NAS device according to claim 6, characterized in that: The step S3, in which the user operation layer of the first form is transformed into the user operation layer of the second form based on the AI ​​instruction, specifically includes: After receiving the AI ​​instruction fed back by the AI ​​model, the user operation layer of the first form parses the AI ​​instruction through a preset instruction parsing module to extract the operation parameters and target state in the instruction; The user operation layer of the first form calls a preset scene conversion module according to the extracted operation parameters, and gradually adjusts the configuration and state of the user operation layer according to the preset conversion rules; During the conversion process, the user operation layer of the first form monitors the conversion status in real time. When it is detected that the conversion is completed and the preset second form conditions are met, it confirms the transition to the user operation layer of the second form. The second form conditions include preset configuration parameters and status identifiers.

8. The AI ​​processing method for NAS device according to claim 7, characterized in that: In S4, the NAS control unit calls the second form of the user operation layer as the AI ​​processing result, specifically including: The NAS control unit detects, through a preset scene detection module, that the user operation layer of the second form has been successfully converted and is in a stable state; The NAS control unit calls the second form of the user operation layer, and obtains the current state information and processing result data of the user operation layer through a preset interface; The NAS control unit encapsulates the acquired processing result data, generates an AI processing result, and sends the AI ​​processing result to a preset user terminal or storage system through a preset output interface, wherein the output interface includes a network interface and a local storage interface.

9. A NAS system, characterized in that: The NAS system includes mutually connected NAS devices and computer devices, a NAS control unit in the NAS device is respectively connected to a NAS storage management module, a data preprocessing module, and an AI processing module, the NAS storage management module is connected to the data preprocessing module, the data preprocessing module is connected to the AI ​​processing module, and the NAS control unit is used to execute the AI ​​processing method according to any one of claims 1 to 8.

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