Storage resource allocation method, device, equipment and medium

Abstract

The present invention discloses a storage resource allocation method, device, equipment and medium, which relates to the field of computer technology, including: obtaining the embedding vector of the current prompt word, using the first target pre-training model to search for the corresponding target text in the preset knowledge base; the preset knowledge base stores the original text and embedding vector in the preset operation related manual for persistent volume declaration; splicing the current prompt word, target text, and preset executable operation list to obtain a pending request, using the first target pre-training model to identify the pending request, if the obtained recognition result indicates that the pending request is used to trigger the call of the target persistent volume declaration management function, then executing the target persistent volume declaration management function based on the target parameter to obtain each current persistent volume declaration; binding each persistent volume to the corresponding current persistent volume declaration. Through the above scheme, when performing storage resource allocation, the difficulty of users completing PVC management operations is reduced.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a storage resource allocation method, device, equipment and medium. Background Art

[0002] Container Cluster Orchestration Management System (COS) is an open-source container orchestration management platform for automating the deployment, scaling, and management of containerized applications. For example, Kubernetes manages container clusters across multiple hosts and provides a mechanism for deploying, maintaining, and scaling applications. Its robust scalability and flexibility make it a preferred platform for modern microservices architectures and cloud-native applications. In artificial intelligence development platforms, COS is a fundamental component, used to schedule and allocate inference services across multiple hosts of varying specifications to fully utilize cluster resources. In COS, PVs (Persistent Volumes) are cluster-wide storage resources. These are allocated to PVCs by binding them to PVs based on storage classes.

[0003] To manage storage resources in a multi-tenant environment, PVC management operations on the AIStation platform, such as querying, deleting, and creating PVCs, typically require professional training or self-reading of the PVC operation manual. This requires a high level of expertise and is not user-friendly for users who are new to the platform.

[0004] It can be seen that how to reduce the difficulty for users to complete PVC management operations when allocating storage resources is a problem that those skilled in the art need to solve. Summary of the Invention

[0005] The purpose of the embodiments of the present invention is to provide a storage resource allocation method, apparatus, device, and medium that reduces the difficulty for users to complete PVC management operations when performing storage resource allocation. The specific solution is as follows:

[0006] In a first aspect, the present invention discloses a storage resource allocation method, comprising:

[0007] Obtaining an embedding vector of a current prompt word related to persistent volume declaration, and using a first target pre-trained model to search a preset knowledge base for target text corresponding to the embedding vector of the current prompt word; wherein the preset knowledge base stores original text and the embedding vector of the original text, and the original text is each text in a preset operation manual related to persistent volume declaration;

[0008] Concatenate the current prompt word, the target text, and a preset executable operation list to obtain a pending request, and use the first target pre-trained model to identify the pending request. If the obtained recognition result indicates that the pending request is used to trigger the call of a target persistent volume claim management function, then obtain target parameters required by the target persistent volume claim management function;

[0009] Executing the target persistent volume claim management function based on the target parameter to complete the persistent volume claim management operation corresponding to the current prompt word to obtain each current persistent volume claim;

[0010] Bind each persistent volume in the container cluster orchestration management system to the corresponding current persistent volume claim to allocate the persistent volume for the current persistent volume claim.

[0011] Optionally, before obtaining the embedding vector of the current prompt word related to the persistent volume claim, the method further includes:

[0012] Segmenting each text in the manual related to the preset operation of the persistent volume claim according to the preset segmentation requirements to obtain each original text, and encoding each original text using the second target pre-trained model to obtain an embedding vector of the original text;

[0013] Saving the original text and the embedding vector of the original text to a preset knowledge base;

[0014] Accordingly, obtaining the embedding vector of the current prompt word related to the persistent volume declaration includes:

[0015] The second target pre-trained model is used to encode a current prompt word related to the persistent volume declaration to obtain an embedding vector of the current prompt word.

[0016] Optionally, the preset knowledge base also stores source information of each original text;

[0017] The acquiring of the embedding vector of the current prompt word related to the persistent volume declaration, and searching for a target text corresponding to the embedding vector of the current prompt word in a preset knowledge base using the first target pre-trained model, includes:

[0018] Get the embedding vector of the current hint word associated with the persistent volume claim and the source lookup requirement;

[0019] Based on the source information of each of the original texts, and using a first target pre-trained model, filter out the initial text corresponding to the source search requirement and the embedding vector of the initial text from a preset knowledge base;

[0020] Searching for each target embedding vector corresponding to the embedding vector of the current prompt word in the embedding vector of the initial text, so as to determine the initial text corresponding to each target embedding vector as the target text corresponding to the embedding vector of the current prompt word.

[0021] Optionally, the target persistent volume declaration management function is a preset persistent volume declaration creation function and / or a preset persistent volume declaration deletion function, and the target parameters required by the target persistent volume declaration management function include any one or more of the namespace, storage class name, storage class mode, access mode, and persistent volume declaration name, and the persistent volume declaration management operation is a persistent volume declaration creation operation and / or a persistent volume declaration deletion operation.

[0022] Optionally, executing the target persistent volume declaration management function based on the target parameter includes:

[0023] Generate pending confirmation information corresponding to the target persistent volume declaration management function, and receive confirmation information returned by the user based on the pending confirmation information; wherein the pending confirmation information indicates whether to execute the target persistent volume declaration management function;

[0024] If the confirmation information indicates that the target persistent volume claim management function is confirmed not to be executed, the process of executing the target persistent volume claim management function is terminated, and a prompt message of termination of execution is generated;

[0025] If the confirmation information indicates confirmation of executing the target persistent volume declaration management function, verifying the target parameters;

[0026] If the target parameters pass the verification, executing the target persistent volume declaration management function based on the target parameters;

[0027] If the target parameter fails to pass the verification and the current number of attempts to obtain the parameter is greater than a preset threshold, an abnormal prompt message is generated;

[0028] If the target parameter fails to pass the verification and the current number of attempts to obtain the parameter is not greater than the preset threshold, a re-enter parameter prompt message is generated to receive the new target parameter returned by the user based on the re-enter parameter prompt message, and jump again to the step of verifying the target parameter.

[0029] Optionally, searching a preset knowledge base for a target text corresponding to the embedding vector of the current prompt word using the first target pre-trained model includes:

[0030] The first target pre-training model is used to determine the similarity between the embedding vector of each original text in a preset knowledge base and the embedding vector of the current prompt word, and the embedding vectors of a preset number of original texts with the highest similarity are determined as target embedding vectors, so as to determine the original text corresponding to each target embedding vector as the target text corresponding to the embedding vector of the current prompt word.

[0031] Optionally, before binding each persistent volume in the container cluster orchestration management system with the corresponding current persistent volume declaration, the method further includes:

[0032] Creating a storage class based on preset storage definition parameters; wherein the preset storage definition parameters include network file system service IP, network file system service path, recycling mechanism, volume binding mode and total capacity;

[0033] The storage resource configuration information corresponding to each current persistent volume declaration is used, and each persistent volume is created in the container cluster orchestration management system using the storage class.

[0034] In a second aspect, the present invention discloses a storage resource allocation device, comprising:

[0035] A text acquisition module is configured to obtain an embedding vector of a current prompt word related to persistent volume declaration, and to search a preset knowledge base for target text corresponding to the embedding vector of the current prompt word using a first target pre-trained model; wherein the preset knowledge base stores original text and the embedding vector of the original text, and the original text is each text in a preset operation manual related to persistent volume declaration;

[0036] a parameter acquisition module, configured to concatenate the current prompt word, the target text, and a preset executable operation list to obtain a pending request, identify the pending request using the first target pre-trained model, and obtain target parameters required by the target persistent volume claim management function if the obtained recognition result indicates that the pending request is used to trigger the call of a target persistent volume claim management function;

[0037] a request management module, configured to execute the target persistent volume claim management function based on the target parameter to complete the persistent volume claim management operation corresponding to the current prompt word and obtain each current persistent volume claim;

[0038] A resource allocation module is used to bind each persistent volume in the container cluster orchestration management system to the corresponding current persistent volume declaration to allocate the persistent volume according to the current persistent volume declaration.

[0039] In a third aspect, the present invention discloses an electronic device, comprising:

[0040] Memory, used to store computer programs;

[0041] The processor is configured to execute the computer program to implement the steps of the aforementioned disclosed storage resource allocation method.

[0042] In a fourth aspect, the present invention discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the steps of the aforementioned storage resource allocation method are implemented.

[0043] Thus, the present invention obtains an embedding vector of a current prompt word related to a persistent volume declaration, and uses a first target pre-trained model to search a preset knowledge base for a target text corresponding to the embedding vector of the current prompt word; wherein the preset knowledge base stores original text and its embedding vector, and the original text is each text in a preset operation-related manual for persistent volume declaration; the current prompt word, the target text, and a preset executable operation list are concatenated to obtain a pending request, and the pending request is identified using the first target pre-trained model. If the obtained recognition result indicates that the pending request is used to trigger the call of a target persistent volume declaration management function, target parameters required for the target persistent volume declaration management function are obtained; the target persistent volume declaration management function is executed based on the target parameters to complete the persistent volume declaration management operation corresponding to the current prompt word, thereby obtaining each current persistent volume declaration; and each persistent volume in the container cluster orchestration management system is bound to the corresponding current persistent volume declaration to allocate the persistent volume for the current persistent volume declaration.

[0044] The beneficial effect is that the preset knowledge base of the present invention stores the original text and the embedding vector of the original text, that is, it stores the various texts in the preset operation-related manual for persistent volume declaration and their corresponding embedding vectors. When the embedding vector of the current prompt word related to the persistent volume declaration issued by the user is obtained, the first target pre-trained model can search for the corresponding target text in the preset knowledge base. That is, the first target pre-trained model can supplement the content of the PVC operation-related manual corresponding to the current prompt word. Then, when the first target pre-trained model identifies the pending request obtained by splicing the current prompt word, the target text, and the preset executable operation list, it can replace the understanding of the persistent volume declaration management operation to be performed by the user. That is, when the recognition result indicates that the pending request is used to trigger the call of the target persistent volume declaration management function, the target persistent volume declaration management function is executed based on the target parameters required by the obtained target persistent volume declaration management function. In this way, the user does not need to fully master the content of the preset operation-related manual for persistent volume declaration, reducing the difficulty of the user completing the PVC management operation. The subsequent persistent volume is bound to the current persistent volume declaration to complete the allocation of storage resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0046] Figure 1 A flow chart of a storage resource allocation method provided by an embodiment of the present invention;

[0047] Figure 2 A specific storage resource allocation principle diagram provided by an embodiment of the present invention;

[0048] Figure 3 A specific sequence diagram of the persistent volume declaration operation execution provided by an embodiment of the present invention;

[0049] Figure 4 A flow chart of a specific storage resource allocation method provided by an embodiment of the present invention;

[0050] Figure 5 A specific schematic diagram of the execution of persistent volume declaration management operations provided by an embodiment of the present invention;

[0051] Figure 6 A specific persistent volume declaration management function execution flow chart provided by an embodiment of the present invention;

[0052] Figure 7 A schematic diagram of the structure of a storage resource allocation device provided by an embodiment of the present invention;

[0053] Figure 8 A structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0055] The Container Cluster Orchestration Management System is an open-source container orchestration management platform used to automate the deployment, scaling, and management of containerized applications. For example, the Kubernetes system manages container clusters across multiple hosts and provides a mechanism for deploying, maintaining, and scaling applications. The Container Cluster Orchestration Management System's powerful scalability and flexibility make it the platform of choice for modern microservice architectures and cloud-native applications. In artificial intelligence development platforms, the Container Cluster Orchestration Management System is a foundational component used to schedule and allocate inference services across multiple hosts of varying specifications to fully utilize cluster resources. In the Container Cluster Orchestration Management System, PVs are cluster-wide storage resources. PVs are bound to PVCs based on storage classes, enabling PV allocation to PVCs.

[0056] To manage storage resources in a multi-tenant environment, PVC management operations on the AIStation platform, such as querying, deleting, and creating PVCs, typically require professional training or self-reading of the PVC operation manual. This requires a high level of expertise and is not user-friendly for users who are new to the platform.

[0057] The terms "including" and "having," as used in the present description and accompanying drawings, and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements and may include steps or elements that are not listed.

[0058] In order to enable those skilled in the art to better understand the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0059] Next, a storage resource allocation solution provided by an embodiment of the present invention is introduced in detail. Figure 1 A storage resource allocation method provided in an embodiment of the present invention includes:

[0060] Step S11: Obtain the embedding vector of the current prompt word related to the persistent volume declaration, and use the first target pre-trained model to search for the target text corresponding to the embedding vector of the current prompt word in the preset knowledge base; wherein, the preset knowledge base stores the original text and the embedding vector of the original text, and the original text is each text in the preset operation-related manual for persistent volume declaration.

[0061] In this embodiment, before obtaining the embedding vector of the current prompt word related to the persistent volume declaration, the method further includes: segmenting each text in the preset operation-related manual for the persistent volume declaration according to the preset segmentation requirements to obtain each original text, and encoding each original text using the second target pre-trained model to obtain the embedding vector of the original text; saving the original text and the embedding vector of the original text to the preset knowledge base. The preset operation-related manual for the persistent volume declaration can specifically be a user manual, product description, installation and deployment manual, interface document, outline design and other manuals of the artificial intelligence development platform. First, the each text in the preset operation-related manual for the persistent volume declaration is segmented according to the preset segmentation requirements to obtain each original text (general_text), wherein the preset segmentation requirements include paragraph word count requirements, punctuation segmentation requirements, and paragraph requirements. The paragraph word count requirement refers to the word count requirement for each paragraph of the original text. The punctuation segmentation requirement refers to segmenting based on the preset punctuation marks to obtain each original text, for example, each period is a paragraph, each semicolon is a paragraph, and the paragraph requirement refers to different The content is segmented based on the topic; secondly, the original text is encoded using the second target pre-training model to obtain the embedding vector (general_vector) of the original text, that is, the second target pre-training model is a word embedding model; then the original text and the embedding vector of the original text are saved to the preset knowledge base. Therefore, the preset knowledge base contains at least two fields, the "general_text" field of text type, which is used to save the original text of each document, and the "general_vector" field of dense_vector type, which is used to save the embedding vector corresponding to each original text.

[0062] In this embodiment, obtaining the embedding vector of the current prompt word related to the persistent volume declaration includes: encoding the current prompt word related to the persistent volume declaration using the second target pre-trained model to obtain the embedding vector of the current prompt word. Because the preset knowledge base stores the embedding vectors for each original text, after obtaining the current prompt word related to the persistent volume declaration, it is also necessary to encode the current prompt word related to the persistent volume declaration using the second target pre-trained model to obtain the embedding vector of the current prompt word, so that the first target pre-trained model can be used to subsequently search the preset knowledge base for the target text corresponding to the embedding vector of the current prompt word.

[0063] Furthermore, the method of using the first target pre-training model to search for the target text corresponding to the embedding vector of the current prompt word in the preset knowledge base includes: using the first target pre-training model to determine the similarity between the embedding vector of each original text in the preset knowledge base and the embedding vector of the current prompt word, and determining the embedding vectors of a preset number of original texts with the highest similarity as the target embedding vector, so as to determine the original text corresponding to each target embedding vector as the target text corresponding to the embedding vector of the current prompt word. The specific process of searching for the target text in the preset knowledge base is: first, using the first target pre-training model to determine the similarity between the embedding vector of each original text in the preset knowledge base and the embedding vector of the current prompt word, that is, calculating the similarity S1 between general_vector1 and prompt_vector, the similarity S2 between general_vector2 and prompt_vector, ..., general_vector n Similarity S with prompt_vector n , then the embedding vectors of the preset number of original texts with the highest similarity are determined as the target embedding vectors, for example, the preset number k is 3, where the similarities S2, S7, S 11 are the three similarities with the highest values, then general_vector2, general_vector7, general_vector 11 Is the target embedding vector, then the corresponding general_text2, general_text7, general_text 11 It is the target text corresponding to the embedding vector of the current prompt word; because in natural language processing, the same word may have different meanings in different sentences, by considering multiple similar but not identical embedding vectors, this context sensitivity can be captured more accurately. Therefore, in this embodiment, multiple target texts can be screened out, thereby improving the accuracy of the subsequent target pre-training model in identifying pending requests.

[0064] In this embodiment, the preset knowledge base also stores the source information of each of the original texts; the acquiring of the embedding vector of the current prompt word related to the persistent volume declaration, and the use of the first target pre-trained model to search for the target text corresponding to the embedding vector of the current prompt word in the preset knowledge base, includes: acquiring the embedding vector of the current prompt word related to the persistent volume declaration and the source search requirement; based on the source information of each of the original texts, and using the first target pre-trained model to filter out the initial text corresponding to the source search requirement and the embedding vector of the initial text from the preset knowledge base; searching for each target embedding vector corresponding to the embedding vector of the current prompt word in the embedding vector of the initial text, so as to determine the initial text corresponding to each target embedding vector as the target text corresponding to the embedding vector of the current prompt word. The original texts come from different preset operation manuals for persistent volume declarations. There are a large number of original texts. If the corresponding target text is directly searched based on the embedding vector of the current prompt word, it will take a long time. Therefore, the preset knowledge base also stores the source information of each original text. Then, a preliminary screening can be performed according to the source search requirements, that is, based on the source information of each original text, and using the first target pre-trained model, the initial text and the embedding vector of the initial text corresponding to the source search requirements are filtered out from the preset knowledge base. The operation of preliminary screening according to the source search requirements is very simple and does not require special processing. The source information and the source search requirements are directly matched to eliminate a large number of original texts that do not meet the conditions. That is, the target embedding vectors corresponding to the embedding vector of the current prompt word are searched in the embedding vector of the initial text to determine the initial text corresponding to each target embedding vector as the target text corresponding to the embedding vector of the current prompt word. Only more accurate embedding vector similarity calculations are performed within a smaller range, which greatly reduces the workload.

[0065] like Figure 2The figure shows a specific storage resource allocation principle diagram. The artificial intelligence development platform includes LLM (Large Language Model)-services, internal-rag-service (internal retrieval enhancement generation service), ElasticSearch (search server), storage management service, and LLM-agen (agent) service. The preset knowledge base is saved in ElasticSearch. The first target pre-training model is used for natural language processing. Different first target pre-training models can be pre-integrated in LLM-services. The first target pre-training model is, for example, Qwen-Chat (chat model), ChatGLM3 (Generative Language Model), etc. The second target pre-training model is used to encode text into embedding vectors. Different second target pre-training models are pre-integrated in internal-rag-service. The second target pre-training model is, for example, BGE (Bilingual General Embedding), GloVe (Global Vectors for Word Representation), ELMo (Embeddings from Language Models), etc. The LLM-agen service is used to process the current prompt word returned by the front end, and after calling different back-end services for processing, the result is returned to the UI interface (UserInterface) for display to the user. The storage management service is used to handle normal persistent volume declarations, storage class (StorageClass) creation, deletion, update, query and other operations and other business logic. It is also responsible for receiving requests and interacting with the container cluster orchestration management system to implement operations on container cluster orchestration management resources such as persistent volume declarations and storage classes.

[0066] Step S12: Concatenate the current prompt word, the target text, and the preset executable operation list to obtain a pending request, and use the first target pre-trained model to identify the pending request. If the obtained recognition result indicates that the pending request is used to trigger the call of the target persistent volume declaration management function, then obtain the target parameters required by the target persistent volume declaration management function.

[0067] The first target pre-trained model is used to identify pending requests. Pending requests can be divided into two categories: business consultation requests and business operation requests. Business consultation requests refer to users asking the first target pre-trained model about business-related processes. For example, a pending request might represent "How do I enable the inference service to use the model weight file on the host machine?" The first target pre-trained model will provide users with professional operation process instructions based on the document content in the knowledge base and its own capabilities, so that users can understand the specific business process and subsequently complete related operations according to the business process. Business operation requests refer to users proposing specific operations to the first target pre-trained model, triggering a function call. Through interaction with the first target pre-trained model, business logic is executed. For example, a pending request represents "Create PVC." In other words, the recognition result indicates that the pending request is used to trigger the call of the target persistent volume claim management function, that is, triggering a function call. If the target persistent volume claim management function lacks the target parameters, the user needs to enter the corresponding target parameters. If the target persistent volume claim management function does not lack the required target parameters, the target persistent volume claim management function can be executed directly.

[0068] In this embodiment, the target persistent volume declaration management function is a preset persistent volume declaration creation function and / or a preset persistent volume declaration deletion function, and the target parameters required by the target persistent volume declaration management function include any one or more of the namespace, storage class name, storage class mode, access mode, and persistent volume declaration name. In the business operation request, the target persistent volume claim management function is the preset persistent volume claim creation function and / or the preset persistent volume claim deletion function. The target parameters required by the target persistent volume claim management function include the namespace (namespace), storage class name, storage class mode, access mode (access mode), and may also include the PVC name (pvc_name), volume capacity (capacity), and description (description). Among them, if the business operation request is specifically a PVC creation request, then the namespace can specifically represent the specific namespace corresponding to the PVC created by the user, the storage class name is the storage class used, the PVC name is the name of the created PVC, the access mode is selected by the user from ReadWriteOnce (read and write once), ReadOnlyMany (read only multiple copies), and ReadWriteMany (read and write multiple copies), the volume capacity is the storage space size allocated to the created PVC, and the description indicates that the user can further describe the purpose of the PVC to be created or other related information.

[0069] Step S13: executing the target persistent volume claim management function based on the target parameter to complete the persistent volume claim management operation corresponding to the current prompt word and obtain each current persistent volume claim.

[0070] In this embodiment, the persistent volume claim management operation is a persistent volume claim creation operation and / or a persistent volume claim deletion operation. It is understandable that if the target persistent volume claim management function is a preset persistent volume claim creation function, then the persistent volume claim management operation is a persistent volume claim creation operation; if the target persistent volume claim management function is a preset persistent volume claim deletion function, then the persistent volume claim management operation is a persistent volume claim deletion operation, for example Figure 3 A specific persistent volume declaration operation execution sequence diagram is shown, which executes the target persistent volume declaration management function based on the target parameters to complete the persistent volume declaration management operation corresponding to the current prompt word, obtains each current persistent volume declaration, and returns each current persistent volume declaration to the LLM-agen service. The LLM-agen service returns the result to the UI interface to display it to the user.

[0071] Step S14: Bind each persistent volume in the container cluster orchestration management system to the corresponding current persistent volume claim, so as to allocate the persistent volume for the current persistent volume claim.

[0072] Common storage scenarios for container cluster orchestration management: Database applications typically require persistent storage. Using PVCs ensures that database data remains available after a Pod (container) restart. Some applications need to store logs in persistent volumes for log analysis or backup. Multiple Pods may need to access the same shared file volume. In this case, shared storage volumes can be used to meet the needs, such as NFS (Network File System). In a cloud environment, the dynamic storage scheduling mechanism of container cluster orchestration management can be used to store applications in the cloud provider's persistent storage service. The storage management of container cluster orchestration management makes handling persistent storage in a containerized environment more flexible and efficient, supporting various application scenarios and simplifying complex storage configuration. Specifically in artificial intelligence development platforms, it is usually used to store pre-trained model weight files, configuration files, training data, and other content. The specific storage content depends on the service deployed by the user. If non-inference services are deployed, such as ordinary web (network) services, it may be used to store business data, databases, or user files.

[0073] In this embodiment, before binding each persistent volume in the container cluster orchestration management system with the corresponding current persistent volume declaration, it also includes: creating a storage class based on preset storage definition parameters; wherein the preset storage definition parameters include the network file system service IP, the network file system service path, the recycling mechanism, the volume binding mode and the total capacity; according to the storage resource configuration information corresponding to each of the current persistent volume declarations, and using the storage class, each persistent volume is created in the container cluster orchestration management system. Obtain the preset storage definition parameters specified by the user, specifically the NFS service IP (Internet Protocol), NFS service path, storage class name, access mode, recycling mechanism, volume binding mode and total capacity to create a storage class, and create a storage class (StorageClass) based on the preset storage definition parameters; allocate the created storage class to the user group to which the user belongs according to a certain capacity on the system administrator management page, so that ordinary users or group administrator users can log in to the ordinary user interface and allocate the storage class to the scenario where the user joins according to a certain capacity; next, according to the storage resource configuration information corresponding to each current persistent volume declaration, and using the storage class to create each persistent volume in the container cluster orchestration management system, the storage class in the container cluster orchestration management automatically creates PV and binds it to PVC. The storage resource configuration information can specifically be volume type, capacity, performance and other information.

[0074] Thus, the present invention obtains an embedding vector of a current prompt word related to a persistent volume declaration, and uses a first target pre-trained model to search a preset knowledge base for a target text corresponding to the embedding vector of the current prompt word; wherein the preset knowledge base stores original text and its embedding vector, and the original text is each text in a preset operation-related manual for persistent volume declaration; the current prompt word, the target text, and a preset executable operation list are concatenated to obtain a pending request, and the pending request is identified using the first target pre-trained model. If the obtained recognition result indicates that the pending request is used to trigger the call of a target persistent volume declaration management function, target parameters required for the target persistent volume declaration management function are obtained; the target persistent volume declaration management function is executed based on the target parameters to complete the persistent volume declaration management operation corresponding to the current prompt word, thereby obtaining each current persistent volume declaration; and each persistent volume in the container cluster orchestration management system is bound to the corresponding current persistent volume declaration to allocate the persistent volume for the current persistent volume declaration.

[0075] The beneficial effect is that the preset knowledge base of the present invention stores the original text and the embedding vector of the original text, that is, it stores the various texts in the preset operation-related manual for persistent volume declaration and their corresponding embedding vectors. When the embedding vector of the current prompt word related to the persistent volume declaration issued by the user is obtained, the first target pre-trained model can search for the corresponding target text in the preset knowledge base. That is, the first target pre-trained model can supplement the content of the PVC operation-related manual corresponding to the current prompt word. Then, when the first target pre-trained model identifies the pending request obtained by splicing the current prompt word, the target text, and the preset executable operation list, it can replace the understanding of the persistent volume declaration management operation to be performed by the user. That is, when the recognition result indicates that the pending request is used to trigger the call of the target persistent volume declaration management function, the target persistent volume declaration management function is executed based on the target parameters required by the obtained target persistent volume declaration management function. In this way, the user does not need to fully master the content of the preset operation-related manual for persistent volume declaration, reducing the difficulty of the user completing the PVC management operation. The subsequent persistent volume is bound to the current persistent volume declaration to complete the allocation of storage resources.

[0076] See also Figure 4 The embodiment of the present invention discloses a specific storage resource allocation method. Compared with the previous embodiment, this embodiment further illustrates and optimizes the technical solution. It includes:

[0077] Step S21: Obtain the embedding vector of the current prompt word related to the persistent volume declaration, and use the first target pre-trained model to search for the target text corresponding to the embedding vector of the current prompt word in the preset knowledge base; wherein, the preset knowledge base stores the original text and the embedding vector of the original text, and the original text is each text in the preset operation-related manual for persistent volume declaration.

[0078] In the process of obtaining the embedding vector of the current prompt word related to the persistent volume declaration, it is necessary to first obtain the current prompt word related to the persistent volume declaration input by the user. The user can enter the current prompt word in text form in the text input box, or in voice form in the voice input box. Voice input provides an interactive method that does not rely on vision, and text input can express the current prompt word more accurately. Supporting multiple input methods helps to improve the inclusiveness of the system, allowing users with different abilities and different needs to easily use the system, thereby expanding the user base.

[0079] Step S22: Concatenate the current prompt word, the target text, and the preset executable operation list to obtain a pending request, and use the first target pre-trained model to identify the pending request. If the obtained recognition result indicates that the pending request is used to trigger the call of the target persistent volume declaration management function, then obtain the target parameters required for the target persistent volume declaration management function.

[0080] For example Figure 5 The diagram shows a specific persistent volume claim management operation execution diagram. The first target pre-trained model supports users entering a prompt word and selecting a tools workflow. This concatenates the current prompt word, target text, and a preset list of executable operations to obtain a pending request. The first target pre-trained model then identifies the concatenated pending request. Users can also independently call the target persistent volume claim management function without entering a prompt word or using the tools workflow. However, this method requires a high level of user expertise. The tools parameter (i.e., the preset list of executable operations) is a list, where each tool represents an executable operation. When the persistent volume claim management operation is "Create PVC," the preset list of executable operations can be as follows: [

[0082] {

[0083] "type": "function",

[0084] "function": {

[0085] "name": "create_pvc",

[0086] "description": "To create a persistent volume claim (PVC), you need to specify the scenario (namespace), storage class (storage class), PVC name, access mode, volume capacity, and description.",

[0087] "parameters": {

[0088] "type": "object",

[0089] "properties": {

[0090] "namespace": {

[0091] "type": "string",

[0092] "description": "The namespace where the persistent volume claim is located"

[0093] },

[0094] "storageClass": {

[0095] "type": "string",

[0096] "description": "Specified storage class"

[0097] },

[0098] "pvcName": {

[0099] "type": "string",

[0100] "description": "The name of the persistent volume claim"

[0101] },

[0102] "accessMode": {

[0103] "type": "string",

[0104] "enum": ["ReadWriteOnce", "ReadOnlyMany", "ReadWriteMany"],

[0105] "description": "Specified access mode"

[0106] },

[0107] "capacity": {

[0108] "type": "string",

[0109] "description": "Specified volume capacity, for example '10Gi'"

[0110] },

[0111] "description": {

[0112] "type": "string",

[0113] "description": "Description of the persistent volume claim"

[0114] }

[0115] }

[0116] }

[0117] }

[0118] } ]

[0120] The target persistent volume declaration management function corresponding to the "Create PVC" operation is named "create_pvc" and has a "function" type, indicating that it is an executable function. "parameters" are the parameters required by the function, and the parameter type is "object", that is, an object. The member variables of this parameter object are in "properties". In this example, they include namespace, StorageClass, pvcName, accessMode, capacity, and description.

[0121] Step S23: Generate pending confirmation information corresponding to the target persistent volume declaration management function, and receive confirmation information returned by the user based on the pending confirmation information; wherein the pending confirmation information indicates whether to execute the target persistent volume declaration management function.

[0122] After obtaining the target parameters required by the target persistent volume declaration management function, the pending confirmation information corresponding to the target persistent volume declaration management function is generated. The pending confirmation information includes the name of the target persistent volume declaration management function and its corresponding operation, so that the user can confirm whether to start executing the target persistent volume declaration management function to prevent the first target pre-trained model from recognizing errors and then starting to execute the wrong function, resulting in wrong results. Whether to execute the function needs to be determined based on the confirmation information returned by the receiving user based on the pending confirmation information.

[0123] Step S24: If the confirmation information indicates that the target persistent volume declaration management function is not to be executed, the process of executing the target persistent volume declaration management function is terminated, and a termination prompt message is generated; if the confirmation information indicates that the target persistent volume declaration management function is to be executed, the target parameters are verified.

[0124] It is understandable that if the confirmation information indicates that a specific persistent volume claim management function is not to be executed, the execution process of the function will be interrupted and a prompt message will be displayed to indicate that the execution has terminated; on the contrary, if the confirmation information indicates that the persistent volume claim management function needs to be executed, the target parameters required by the function will be verified next.

[0125] S25: If the target parameters pass the verification, the target persistent volume declaration management function is executed based on the target parameters to complete the persistent volume declaration management operation corresponding to the current prompt word and obtain each current persistent volume declaration.

[0126] For example Figure 6A specific persistent volume claim management function execution flowchart is shown. If the target parameters pass the verification process, the target persistent volume claim management function will be executed according to these parameters to complete the persistent volume claim management task associated with the current prompt word and generate all related current persistent volume claims.

[0127] Step S26: If the target parameter fails to pass the verification and the current number of attempts to obtain the parameter is greater than the preset threshold, an abnormal prompt message is generated; if the target parameter fails to pass the verification and the current number of attempts to obtain the parameter is not greater than the preset threshold, a re-enter parameter prompt message is generated to receive the new target parameter returned by the user based on the re-enter parameter prompt message, and jump back to the step of verifying the target parameter.

[0128] like Figure 6 As shown, if the target parameter fails to pass the verification and the user has tried many times, that is, the current number of attempts to obtain the parameter is greater than the preset threshold, an abnormal prompt message will be generated; however, if the parameter fails to pass the verification but the number of user attempts is still within the preset threshold, the user will be prompted to re-enter the parameter, and the user will be prompted with the wrong parameter and the reason for the error, and the new parameters returned by the user based on this prompt will be received, and the system will then perform the verification process for these new parameters again.

[0129] Verifying the target parameters can ensure data accuracy. That is, the verification process can verify whether the target parameters meet the expected format, range, and logical requirements. This helps avoid operation failures or data corruption due to parameter errors, thereby ensuring that the configuration information of storage resources is accurate and improving stability. By verifying the target parameters, potential errors can be discovered and corrected in advance, reducing system anomalies or crashes caused by parameter errors, and ensuring the smooth progress of persistent volume declaration management operations.

[0130] When the user re-enters the target parameters based on the prompt for re-entering parameters, computing resources need to be used to re-verify the new target parameters. However, in order to prevent excessive consumption of system resources, it is prohibited to allow users to re-enter the target parameters without restriction. This can avoid affecting the overall performance and stability, and ensure that system resources are used reasonably. In some cases, frequent incorrect attempts may be regarded as potential security threats. Setting a threshold can be used as a simple security measure to prevent malicious users from attacking the system through brute force cracking and other methods by limiting the number of attempts.

[0131] Step S27: Bind each persistent volume in the container cluster orchestration management system to the corresponding current persistent volume claim, so as to allocate the persistent volume for the current persistent volume claim.

[0132] It can be seen that when the user needs to execute the persistent volume declaration management function, he only needs to enter the prompt word. The first target pre-trained model can supplement the prompt word according to the various texts in the preset operation-related manual for persistent volume declaration in the preset knowledge base, thereby obtaining a pending request and judging whether the pending request is used to trigger the call of the target persistent volume declaration management function. If so, the target parameters are obtained to generate the pending confirmation information corresponding to the target persistent volume declaration management function. When the user confirms to execute the target persistent volume declaration management function, the persistent volume declaration management function can be executed to complete the persistent volume declaration management operation. The professional requirements for users are greatly reduced, the learning burden of users is reduced, and they can quickly start using the platform without guidance. They can understand the operation and resource concepts and usage methods of the artificial intelligence development platform during the interaction, and also reduce the burden on technical support personnel.

[0133] Figure 7 A schematic structural diagram of a storage resource allocation device provided in an embodiment of the present invention includes:

[0134] A text acquisition module 11 is configured to acquire an embedding vector of a current prompt word related to a persistent volume declaration, and to search a preset knowledge base for a target text corresponding to the embedding vector of the current prompt word using a first target pre-trained model; wherein the preset knowledge base stores original text and the embedding vector of the original text, and the original text is each text in a preset operation manual related to persistent volume declaration;

[0135] A parameter acquisition module 12 is configured to concatenate the current prompt word, the target text, and a preset executable operation list to obtain a pending request, and identify the pending request using the first target pre-trained model. If the obtained recognition result indicates that the pending request is used to trigger the call of a target persistent volume claim management function, then target parameters required by the target persistent volume claim management function are obtained.

[0136] A request management module 13 is configured to execute the target persistent volume claim management function based on the target parameter to complete the persistent volume claim management operation corresponding to the current prompt word and obtain each current persistent volume claim;

[0137] The resource allocation module 14 is configured to bind each persistent volume in the container cluster orchestration management system to the corresponding current persistent volume claim, so as to allocate the persistent volume according to the current persistent volume claim.

[0138] Thus, the present invention obtains an embedding vector of a current prompt word related to a persistent volume declaration, and uses a first target pre-trained model to search a preset knowledge base for a target text corresponding to the embedding vector of the current prompt word; wherein the preset knowledge base stores original text and its embedding vector, and the original text is each text in a preset operation-related manual for persistent volume declaration; the current prompt word, the target text, and a preset executable operation list are concatenated to obtain a pending request, and the pending request is identified using the first target pre-trained model. If the obtained recognition result indicates that the pending request is used to trigger the call of a target persistent volume declaration management function, target parameters required for the target persistent volume declaration management function are obtained; the target persistent volume declaration management function is executed based on the target parameters to complete the persistent volume declaration management operation corresponding to the current prompt word, thereby obtaining each current persistent volume declaration; and each persistent volume in the container cluster orchestration management system is bound to the corresponding current persistent volume declaration to allocate the persistent volume for the current persistent volume declaration.

[0139] The beneficial effect is that the preset knowledge base of the present invention stores the original text and the embedding vector of the original text, that is, it stores the various texts in the preset operation-related manual for persistent volume declaration and their corresponding embedding vectors. When the embedding vector of the current prompt word related to the persistent volume declaration issued by the user is obtained, the first target pre-trained model can search for the corresponding target text in the preset knowledge base. That is, the first target pre-trained model can supplement the content of the PVC operation-related manual corresponding to the current prompt word. Then, when the first target pre-trained model identifies the pending request obtained by splicing the current prompt word, the target text, and the preset executable operation list, it can replace the understanding of the persistent volume declaration management operation to be performed by the user. That is, when the recognition result indicates that the pending request is used to trigger the call of the target persistent volume declaration management function, the target persistent volume declaration management function is executed based on the target parameters required by the obtained target persistent volume declaration management function. In this way, the user does not need to fully master the content of the preset operation-related manual for persistent volume declaration, reducing the difficulty of the user completing the PVC management operation. The subsequent persistent volume is bound to the current persistent volume declaration to complete the allocation of storage resources.

[0140] Furthermore, the embodiment of the present application also discloses an electronic device, Figure 8This is a structural diagram of an electronic device according to an exemplary embodiment. The content in the diagram should not be considered as limiting the scope of use of this application. The electronic device may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps of the storage resource allocation method disclosed in any of the aforementioned embodiments. In addition, the electronic device in this embodiment may specifically be an electronic computer.

[0141] In this embodiment, the power supply 23 is used to provide operating voltage for various hardware devices on the electronic device; the communication interface 24 can create a data transmission channel between the electronic device and external devices. The communication protocol it follows is any communication protocol that can be applied to the technical solution of this application and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world. Its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0142] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or CD, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0143] The operating system 221 is used to manage and control the hardware devices on the electronic device and the computer program 222, which can be Windows Server, NetWare, Unix, Linux, etc. In addition to including a computer program capable of implementing the storage resource allocation method performed by the electronic device disclosed in any of the aforementioned embodiments, the computer program 222 can further include a computer program capable of implementing other specific tasks.

[0144] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when executed by a processor, the computer program implements the aforementioned storage resource allocation method. The specific steps of this method can be referred to the corresponding contents disclosed in the aforementioned embodiments and will not be repeated here.

[0145] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. Reference can be made to the descriptions of the identical or similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and the relevant parts can be referred to the descriptions of the methods.

[0146] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0147] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0148] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0149] The above is a detailed introduction to the technical solution provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for those skilled in the art, according to the ideas of the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A storage resource allocation method, characterized in that: include: Obtaining an embedding vector of a current prompt word related to persistent volume declaration, and using a first target pre-trained model to search a preset knowledge base for target text corresponding to the embedding vector of the current prompt word; wherein the preset knowledge base stores original text and the embedding vector of the original text, and the original text is each text in a preset operation manual related to persistent volume declaration; Concatenate the current prompt word, the target text, and a preset executable operation list to obtain a pending request, and use the first target pre-trained model to identify the pending request. If the obtained recognition result indicates that the pending request is used to trigger the call of a target persistent volume claim management function, then obtain target parameters required by the target persistent volume claim management function; Executing the target persistent volume claim management function based on the target parameter to complete the persistent volume claim management operation corresponding to the current prompt word to obtain each current persistent volume claim; Bind each persistent volume in the container cluster orchestration management system to the corresponding current persistent volume claim to allocate the persistent volume for the current persistent volume claim.

2. The storage resource allocation method according to claim 1, wherein: Before obtaining the embedding vector of the current prompt word related to the persistent volume claim, the method further includes: Segmenting each text in the manual related to the preset operation of the persistent volume claim according to the preset segmentation requirements to obtain each original text, and encoding each original text using the second target pre-trained model to obtain an embedding vector of the original text; Saving the original text and the embedding vector of the original text to a preset knowledge base; Accordingly, obtaining the embedding vector of the current prompt word related to the persistent volume declaration includes: The second target pre-trained model is used to encode a current prompt word related to the persistent volume declaration to obtain an embedding vector of the current prompt word.

3. The storage resource allocation method according to claim 1, wherein: The preset knowledge base also stores the source information of each original text; The acquiring of the embedding vector of the current prompt word related to the persistent volume declaration, and searching for a target text corresponding to the embedding vector of the current prompt word in a preset knowledge base using the first target pre-trained model, includes: Get the embedding vector of the current hint word associated with the persistent volume claim and the source lookup requirement; Based on the source information of each of the original texts, and using a first target pre-trained model, filter out the initial text corresponding to the source search requirement and the embedding vector of the initial text from a preset knowledge base; Searching for each target embedding vector corresponding to the embedding vector of the current prompt word in the embedding vector of the initial text, so as to determine the initial text corresponding to each target embedding vector as the target text corresponding to the embedding vector of the current prompt word.

4. The storage resource allocation method according to claim 1, wherein: The target persistent volume declaration management function is a preset persistent volume declaration creation function and / or a preset persistent volume declaration deletion function. The target parameters required by the target persistent volume declaration management function include any one or more of the namespace, storage class name, storage class mode, access mode, and persistent volume declaration name. The persistent volume declaration management operation is a persistent volume declaration creation operation and / or a persistent volume declaration deletion operation.

5. The storage resource allocation method according to claim 1, wherein: The executing the target persistent volume declaration management function based on the target parameter includes: Generate pending confirmation information corresponding to the target persistent volume declaration management function, and receive confirmation information returned by the user based on the pending confirmation information; wherein the pending confirmation information indicates whether to execute the target persistent volume declaration management function; If the confirmation information indicates that the target persistent volume claim management function is confirmed not to be executed, the process of executing the target persistent volume claim management function is terminated, and a prompt message for termination of execution is generated; If the confirmation information indicates that the target persistent volume declaration management function is confirmed to be executed, then verifying the target parameters; If the target parameters pass the verification, executing the target persistent volume declaration management function based on the target parameters; If the target parameter fails to pass the verification and the current number of attempts to obtain the parameter is greater than a preset threshold, an abnormal prompt message is generated; If the target parameter fails to pass the verification and the current number of attempts to obtain the parameter is not greater than the preset threshold, a re-enter parameter prompt message is generated to receive the new target parameter returned by the user based on the re-enter parameter prompt message, and jump again to the step of verifying the target parameter.

6. The storage resource allocation method according to claim 1, wherein: The step of searching a preset knowledge base for a target text corresponding to the embedding vector of the current prompt word using the first target pre-trained model includes: The first target pre-training model is used to determine the similarity between the embedding vector of each original text in a preset knowledge base and the embedding vector of the current prompt word, and the embedding vectors of a preset number of original texts with the highest similarity are determined as target embedding vectors, so as to determine the original text corresponding to each target embedding vector as the target text corresponding to the embedding vector of the current prompt word.

7. The storage resource allocation method according to any one of claims 1 to 6, characterized in that: Before binding each persistent volume in the container cluster orchestration management system with the corresponding current persistent volume declaration, the method further includes: Creating a storage class based on preset storage definition parameters; wherein the preset storage definition parameters include network file system service IP, network file system service path, recycling mechanism, volume binding mode and total capacity; The storage resource configuration information corresponding to each current persistent volume declaration is used, and each persistent volume is created in the container cluster orchestration management system using the storage class.

8. A storage resource allocation device, characterized in that: include: A text acquisition module is configured to obtain an embedding vector of a current prompt word related to persistent volume declaration, and to search a preset knowledge base for target text corresponding to the embedding vector of the current prompt word using a first target pre-trained model; wherein the preset knowledge base stores original text and the embedding vector of the original text, and the original text is each text in a preset operation manual related to persistent volume declaration; a parameter acquisition module, configured to concatenate the current prompt word, the target text, and a preset executable operation list to obtain a pending request, identify the pending request using the first target pre-trained model, and obtain target parameters required by the target persistent volume claim management function if the obtained recognition result indicates that the pending request is used to trigger the call of a target persistent volume claim management function; a request management module, configured to execute the target persistent volume claim management function based on the target parameter to complete the persistent volume claim management operation corresponding to the current prompt word and obtain each current persistent volume claim; A resource allocation module is used to bind each persistent volume in the container cluster orchestration management system to the corresponding current persistent volume declaration to allocate the persistent volume according to the current persistent volume declaration.

9. An electronic device, characterized in that: include: memory for storing computer programs; A processor, configured to execute the computer program to implement the steps of the storage resource allocation method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the storage resource allocation method according to any one of claims 1 to 7 are implemented.

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