Resource management method, device and equipment and computer readable storage medium
By converting resource management-related documents into vector data and using large language models to determine the interface call process, the problems of complexity and inefficiency of existing resource management solutions are solved, and more efficient and accurate resource management is achieved.
Patent Information
- Application Number
- CN202510344959.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-21
- Publication Date
- 2025-06-27
AI Technical Summary
The existing resource management solutions are complex and difficult for those who are not familiar with programming, resulting in low management efficiency; at the same time, the number of CDN resources is huge and the GUI interface is complex. Users need to remember the entry location and operation methods of many functions, which increases the management difficulty.
By converting the semantic documents and business process documents of resource management-related interfaces into vector data and depositing them into vector database, responding to the target business operations input by users, and using a large language model to determine the interface call process, realizing automated management of resources.
It reduces the complexity of resource management operations and improves management efficiency and accuracy. Users can complete resource management tasks without memorizing complex operation processes.
Smart Images
Figure CN120216615A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of computer technologies, and in particular, to a resource management method, apparatus, device, and computer-readable storage medium. Background Art
[0002] Content Delivery Network (CDN) nodes enable users to access required resources nearby, thereby improving the access speed of resources. As a CDN provider, it is necessary to manage resources such as nodes, devices, and IPs.
[0003] There are mainly two existing resource management solutions: one is to manage resources by writing code to call interfaces; the other is to manage resources by allowing users to operate a Graphical User Interface (GUI) and perform management through methods such as table query and form submission.
[0004] However, for those who are not familiar with programming, writing code is difficult and error-prone, resulting in low management efficiency. Moreover, usually, the number of CDN resources is huge, and the GUI will be very complex. Users need to remember the entry positions and operation methods of many functions to perform management, which increases the management difficulty and reduces the resource management efficiency. Summary of the Invention
[0005] In order to solve the above technical problems or at least partially solve the above technical problems, the present disclosure provides a resource management method, apparatus, device, and computer-readable storage medium to reduce the complexity of resource management operations and improve management efficiency and accuracy.
[0006] In a first aspect, an embodiment of the present disclosure provides a resource management method, the method including:
[0007] Converting semantic documents of resource management-related interfaces and resource management business process documents into vector data, and storing the vector data in a vector database;
[0008] In response to a user input of an operation for a target service, converting the text content of the target service into a target vector, retrieving based on the target vector in the vector database to obtain a retrieval result;
[0009] Performing similarity re-ranking processing on the retrieval result to obtain a re-ranked result, and performing fusion processing on the retrieval result and the re-ranked result to obtain a search result;
[0010] Using a preset large language model to determine an interface call process based on the search result;
[0011] Perform an interface call based on the said interface call process to obtain a call result, use the preset large language model to obtain the execution result of the target service based on the call result, and return the execution result of the target service to the user.
[0012] In a second aspect, an embodiment of the present disclosure provides a resource management device, which includes:
[0013] A storage module, configured to convert the semantic annotation document of the resource management related interface and the resource management business process document into vector data, and store the vector data in a vector database;
[0014] A retrieval module, configured to, in response to a user input for an operation of a target service, convert the text content of the target service into a target vector, retrieve in the vector database based on the target vector, and obtain a retrieval result;
[0015] A first obtaining module, configured to perform a similarity re-ranking process on the retrieval result to obtain a re-ranked result, and perform a fusion process on the retrieval result and the re-ranked result to obtain a search result;
[0016] A determination module, configured to use a preset large language model to determine an interface call process based on the search result;
[0017] A second obtaining module, configured to perform an interface call based on the interface call process to obtain a call result, use the preset large language model to obtain the execution result of the target service based on the call result, and return the execution result of the target service to the user.
[0018] In a third aspect, an embodiment of the present disclosure provides an electronic device, including:
[0019] A memory;
[0020] A processor; and
[0021] A computer program;
[0022] Wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the method as described in the first aspect.
[0023] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement the method as described in the first aspect.
[0024] In a fifth aspect, an embodiment of the present disclosure further provides a computer program product, which includes a computer program or instruction, and when the computer program or instruction is executed by a processor, it implements the method as described in the first aspect.
[0025] The resource management method, apparatus, device, and computer-readable storage medium provided by the embodiments of the present disclosure convert the semantic documents of resource management-related interfaces and resource management business process documents into vector data, store the vector data in a vector database, in response to a user input for an operation of a target service, convert the text content of the target service into a target vector, retrieve based on the target vector in the vector database to obtain a retrieval result, perform a similarity re-ranking process on the retrieval result to obtain a re-ranked result, perform a fusion process on the retrieval result and the re-ranked result to obtain a search result, use a preset large language model to determine an interface call process based on the search result, perform an interface call based on the interface call process to obtain a call result, use the preset large language model to obtain an execution result of the target service based on the call result, and return the execution result of the target service to the user. Compared with the prior art, the embodiments of the present disclosure retrieve in the vector database, use a large language model to determine an interface call process, and then execute the target service to achieve resource management, which can reduce the complexity of resource management operations and improve management efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS
[0026] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure.
[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure 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, other drawings can be obtained based on these drawings without creative efforts.
[0028] Figure 1 It is a flowchart of the resource management method provided by the embodiments of the present disclosure;
[0029] Figure 2 It is a flowchart of the resource management method provided by another embodiment of the present disclosure;
[0030] Figure 3 It is a flowchart of the resource management method provided by another embodiment of the present disclosure;
[0031] Figure 4 It is a schematic diagram of the overall process of the resource management method provided by the embodiments of the present disclosure;
[0032] Figure 5 It is a schematic diagram of the structure of the resource management apparatus provided by the embodiments of the present disclosure;
[0033] Figure 6 It is a schematic diagram of the structure of the electronic device provided by the embodiments of the present disclosure. Detailed implementation manners
[0034] In order to more clearly understand the above-mentioned objects, features, and advantages of the present disclosure, the solutions of the present disclosure will be further described below. It should be noted that, without conflict, the embodiments of the present disclosure and the features in the embodiments may be combined with each other.
[0035] In the following description, many specific details are set forth in order to fully understand the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only a part of the embodiments of the present disclosure, rather than all the embodiments.
[0036] A Content Delivery Network (CDN) node enables users to access the required resources nearby, thereby improving the access speed of the resources. As a CDN manufacturer, it is necessary to manage resources such as nodes, devices, and IPs.
[0037] There are mainly two existing resource management solutions: one is to manage resources by writing code to call interfaces; the other is to manage resources by using personnel to operate a Graphical User Interface (GUI) interface and through methods such as table query and form submission.
[0038] However, for those who are not familiar with programming, writing code is difficult and error-prone, resulting in low management efficiency. Moreover, usually, the number of CDN resources is huge, and the GUI interface will be very complex. Users need to remember the entry positions and operation methods of many functions to manage, resulting in increased management difficulty and low resource management efficiency.
[0039] To address this problem, the embodiments of the present disclosure provide a resource management method, which will be introduced below in combination with specific embodiments.
[0040] Figure 1 It is a flowchart of the resource management method provided by the embodiments of the present disclosure. The execution subject of this method is an electronic device. The electronic device may be a server device, specifically a server. Among them, the server may be a single server or a server cluster, and the server cluster may be a distributed cluster or a centralized cluster. This method can be applied to scenarios for resource management or scenarios for CDN node resource management. It can be understood that the resource management method provided by the embodiments of the present disclosure can also be applied in other scenarios.
[0041] The following will describe Figure 1 the resource management method shown, and the specific steps included in this method are as follows:
[0042] S101. Convert the semantic documents of the resource management related interfaces and the resource management business process documents into vector data, and store the vector data in a vector database.
[0043] In this step, the user will pre-write the semantic documents of the resource management related interfaces and the resource management business process documents, and send the semantic documents of the resource management related interfaces and the resource management business process documents to the server. The server will convert the semantic documents of the resource management related interfaces and the resource management business process documents into vector data, and store the vector data in a vector database. As Figure 4 shown, the server will pass the semantic documents of the resource management related interfaces and the resource management business process documents to an Embedding model, and convert the semantic documents of the related interfaces and the business process documents into vector data through the Embedding model, and store them in the vector database.
[0044] Optionally, the semantic content of each interface is characterized in the semantic documents of the resource management related interfaces. Taking interface A as an example, for interface A, the call address is xxx, and it implements the following functions: xxx, the input parameters are a, b, c, where a is the name, b is the type, and the value range of c is 1, 2, 3, etc., and the return result format is: xxx, and d in the return result represents the result description, etc.
[0045] Optionally, the operation process of each business is characterized in the resource management business process documents, such as operations like adding nodes, modifying nodes, closing nodes, viewing nodes, etc. Taking the business of closing nodes as an example, to close a node, it is necessary to call interface A for checking. When the return is successful, then call interface B to close it, and so on.
[0046] S102. In response to the user's input of the operation of the target business, convert the text content of the target business into a target vector, and retrieve based on the target vector in the vector database to obtain a retrieval result.
[0047] In this step, the user performs the operation of inputting the target business on the browser of the terminal. The server responds to the user's input of the operation of the target business, converts the text content of the target business into a target vector, and retrieves based on the target vector in the vector database to obtain a retrieval result. In some embodiments, as Figure 4 shown, the user can call the API service through a Language User Interface (LUI) to perform the input operation. The input content can be voice or text, without limitation.
[0048] In some embodiments, S102 may include but is not limited to S1021, S1022, S1023:
[0049] S1021. In response to a user's input operation for a target service, determine whether the user input is a voice input;
[0050] S1022. If the user input is a voice input, call a speech recognition model to recognize the voice input as text content;
[0051] If the user input is a voice input, the server calls a speech recognition model to recognize the voice input as text content; if the user input is a text input, the text content input by the user is directly obtained.
[0052] S1023. Optimize the text content to obtain optimized text content, and convert the optimized text content into a target vector through a mapping vector model.
[0053] In this step, the server will optimize the text content to obtain optimized text content. The optimization process such as the elimination of pronouns is convenient for the subsequent large language model to understand. As Figure 4 shown, the server can optimize the text content through a prompt service to obtain optimized text content. Further, the optimized text content is used as a retrieval prompt, and the retrieval prompt is converted into a target vector through an Embedding model, so that retrieval can be performed in a vector database.
[0054] S103. Perform a similarity re-ranking process on the retrieval results to obtain a re-ranked result, and perform a fusion process on the retrieval results and the re-ranked result to obtain a search result.
[0055] After obtaining the retrieval results, the electronic device can perform a similarity re-ranking process on the retrieval results to obtain a re-ranked result. As Figure 4 shown, the retrieval results can be re-ranked through a Rerank model to obtain a re-ranked result. Further, the server performs a fusion process based on the retrieval results and the re-ranked result to obtain a search result.
[0056] S104. Use a preset large language model to determine an interface call process based on the search result.
[0057] In this step, the server will use a preset large language model to determine the interface call process corresponding to the execution of the target service based on the search result. Specifically, the search result is input into the preset large language model, and the interface call process can be inferred through the preset large language model.
[0058] S105. Perform an interface call based on the interface call process to obtain a call result, use the preset large language model to obtain an execution result of the target service based on the call result, and return the execution result of the target service to the user.
[0059] Further, the server can perform an interface call based on the interface call process, obtain a call result, use the preset large language model to obtain the execution result of the target service based on the call result, and return the execution result of the target service to the user. By determining the interface call process through the preset large language model, users do not need to remember the entry positions and operation methods of many functions, which can reduce the complexity of resource management operations.
[0060] In the embodiment of the present disclosure, by converting the semantic documents of resource management related interfaces and the resource management business process documents into vector data, storing the vector data in a vector database, in response to a user's input of an operation for a target service, converting the text content of the target service into a target vector, retrieving in the vector database based on the target vector to obtain a retrieval result, performing a similarity re-ranking process on the retrieval result to obtain a re-ranked result, performing a fusion process on the retrieval result and the re-ranked result to obtain a search result, using a preset large language model to determine an interface call process based on the search result, performing an interface call based on the interface call process to obtain a call result, and using the preset large language model to obtain the execution result of the target service based on the call result, and returning the execution result of the target service to the user. Compared with the prior art, in the embodiment of the present disclosure, by retrieving in the vector database and using a large language model to determine the interface call process, and then executing the target service, resource management can be realized, which can reduce the complexity of resource management operations and improve management efficiency and accuracy.
[0061] Figure 2 It is a flowchart of a resource management method provided by another embodiment of the present disclosure. As Figure 2 shown, the method includes the following steps:
[0062] S201. Convert the semantic documents of resource management related interfaces and the resource management business process documents into vector data, and store the vector data in a vector database.
[0063] Specifically, the implementation processes and principles of S201 and S101 are the same, and will not be elaborated here.
[0064] S202. In response to a user's input of an operation for a target service, convert the text content of the target service into a target vector, and retrieve in the vector database based on the target vector to obtain a retrieval result.
[0065] Specifically, the implementation processes and principles of S202 and S102 are the same, and will not be elaborated here.
[0066] S203. Perform a similarity re-ranking process on the retrieval result to obtain a re-ranked result.
[0067] In this step, if Figure 4 As shown in Figure 1, the server can re-rank the retrieval results through the Rerank model to obtain the re-ranked results. The Rerank model takes the query conditions and documents as input and directly returns the similarity score instead of the embedding score, which represents the relevance between the input query and the document.
[0068] S204: Merge the search results and the rearrangement results based on a reciprocal sorting fusion algorithm to obtain search results for a target business.
[0069] In this step, the server may combine the search result and the rearrangement result by using a Reciprocal Rank Fusion (RRF) algorithm to obtain the search result of the target business.
[0070] Optionally, the search results include the text content of the target business, the retrieved document content, the continuation word and the target format, and may also include other information, which is not limited here. For example, the text content of the target business is a closed Z node, the retrieved document content is the relevant content of the closed Z node, the continuation word is a word used for a coherent context, and the target format is a set format such as json format.
[0071] S205: passing the search result to a preset large language model.
[0072] In this step, after obtaining the search results, the server will pass the search results to the preset large language model.
[0073] S206: Identify the user's operation requirements based on the search results using a preset large language model, and determine an interface calling process for executing the target business.
[0074] Furthermore, the server recognizes the user's operation requirement, that is, what the target service is to be executed, based on the search results through a preset large language model, and determines the interface calling process for executing the target service.
[0075] In some embodiments, after determining the interface call process for executing the target business, the method further includes: converting the interface call process into data in the target format, and returning the data in the target format to the interface call service.
[0076] In this embodiment, the service end converts the interface calling process into data in the target format, and returns the data in the target format to the interface calling service, so that the interface calling service can perform the interface calling.
[0077] S207. Perform an interface call based on the interface call process to obtain a call result. Use the preset large language model to obtain the execution result of the target service based on the call result, and return the execution result of the target service to the user.
[0078] Specifically, the implementation processes and principles of S207 and S105 are the same, and will not be elaborated here.
[0079] In the embodiments of the present disclosure, the semantic documents of the resource management related interfaces and the resource management business process documents are converted into vector data, and the vector data is stored in a vector database. In response to a user's input of an operation for a target service, the text content of the target service is converted into a target vector, and the target vector is retrieved in the vector database to obtain a retrieval result. Further, a similarity re-ranking process is performed on the retrieval result to obtain a re-ranked result, and the retrieval result and the re-ranked result are merged based on the reciprocal rank fusion algorithm to obtain a search result for the target service. Then, the search result is passed to a preset large language model, and the preset large language model identifies the user's operation requirements based on the search result and determines the interface call process for executing the target service. Furthermore, an interface call is performed based on the interface call process to obtain a call result, and the preset large language model is used to obtain the execution result of the target service based on the call result, and the execution result of the target service is returned to the user. Through this method, the embodiments of the present disclosure can improve the accuracy of operations, reduce losses caused by operation errors, and improve management efficiency. Users can quickly execute management tasks through natural language text.
[0080] Figure 3 The flowchart of the resource management method provided by another embodiment of the present disclosure is as Figure 3 shown, and the method includes the following steps:
[0081] S301. Convert the semantic documents of the resource management related interfaces and the resource management business process documents into vector data, and store the vector data in a vector database.
[0082] Specifically, the implementation processes and principles of S301 and S101 are the same, and will not be elaborated here.
[0083] S302. In response to a user's input of an operation for a target service, convert the text content of the target service into a target vector, and retrieve the target vector in the vector database to obtain a retrieval result.
[0084] Specifically, the implementation processes and principles of S302 and S102 are the same, and will not be elaborated here.
[0085] S303. Perform a similarity re-ranking process on the retrieved results to obtain re-ranked results, and perform a fusion process on the retrieved results and the re-ranked results to obtain search results.
[0086] Specifically, the implementation processes and principles of S303 and S103 are the same, and will not be elaborated here.
[0087] S304. Use a pre-set large language model to determine an interface call process based on the search results.
[0088] Specifically, the implementation processes and principles of S304 and S104 are the same, and will not be elaborated here.
[0089] S305. Convert the interface call process into data in the target format, and return the data in the target format to the interface call service.
[0090] In this embodiment, the server will convert the interface call process into data in the target format, such as json format, and return the data in the target format to the interface call service.
[0091] S306. Parse the data in the target format returned by the pre-set large language model through the interface call service to obtain call parameters.
[0092] In this step, the server will parse the data in the target format returned by the pre-set large language model through the interface call service to obtain call parameters.
[0093] S307. Perform an interface call based on the call parameters through the interface call service to obtain a call result.
[0094] After obtaining the call parameters, further, perform an interface call based on the call parameters through the interface call service to obtain a call result. The call result includes a successful call or a failed call.
[0095] S308. Pass the call result to the prompt service.
[0096] In this step, as Figure 4 shown, the server will pass the call result to the prompt service.
[0097] S309. Output a call result summary prompt word to the pre-set large language model through the prompt service based on the call result.
[0098] As Figure 4As shown, the server will generate a summary prompt for the call result based on the call result passed by the interface call service through the prompt service, and output the summary prompt for the call result to the preset large language model. The summary prompt for the call result can be the success of the call and the complete process of the interface call, or the failure of the call and the analysis of the reasons for the call failure, without specific limitation.
[0099] S310. Perform induction processing based on the summary prompt for the call result through the preset large language model to generate the execution result of the target service.
[0100] As Figure 4 shown, the server will perform induction processing based on the summary prompt for the call result through the preset large language model to generate the execution result of the target service. Optionally, the execution result is used to characterize the execution situation of the target service. Specifically, it can be the successful execution of the target service and the detailed execution process; it can also be the failure of the target service and the analysis of the reasons for the failure, which is convenient for improvement, without specific limitation. By performing induction processing to generate the execution result of the target service, it is convenient for users to understand and view the completion situation of the resource management service, improving the user experience.
[0101] In the embodiments of the present disclosure, the semantic document of the resource management related interface and the resource management service process document are converted into vector data, and the vector data is stored in the vector database. In response to a user inputting an operation of the target service, the text content of the target service is converted into a target vector, retrieved in the vector database based on the target vector to obtain a retrieval result, the retrieval result is subjected to similarity re-ranking processing to obtain a re-ranked result, and the retrieval result and the re-ranked result are fused to obtain a search result. Further, based on the search result, a preset large language model is used to determine the interface call process, the interface call process is converted into data in the target format, and the data in the target format is returned to the interface call service. Then, the interface call service parses the data in the target format returned by the preset large language model to obtain call parameters, and the interface call service performs an interface call based on the call parameters to obtain a call result, and the call result is passed to the prompt service. Furthermore, the prompt service outputs a summary prompt for the call result to the preset large language model based on the call result, and the preset large language model performs induction processing based on the summary prompt for the call result to generate the execution result of the target service. Compared with the prior art, the embodiments of the present disclosure can improve the accuracy of operations, reduce losses caused by operation errors, improve management efficiency, users can quickly execute management tasks through natural language text, reduce the learning cost of users, and manage CDN resources without programming knowledge.
[0102] Figure 5The schematic diagram of the structure of the resource management device provided in the embodiment of the present disclosure. The resource management device may be the electronic device as described in the above embodiment, or the resource management device may be a component or assembly in the electronic device. The resource management device provided in the embodiment of the present disclosure may execute the processing flow provided in the resource management method embodiment, such as Figure 5 As shown, the resource management device 40 includes: a storage module 41, a retrieval module 42, a first obtaining module 43, a determination module 44, and a second obtaining module 45; wherein the storage module 41 is used to convert the semantic annotation document of the resource management related interface and the resource management business process document into vector data, and store the vector data into the vector database; the retrieval module 42 is used to respond to the user input operation of the target business, convert the text content of the target business into a target vector, search in the vector database based on the target vector, and obtain the retrieval result; the first obtaining module 43 is used to perform similarity rearrangement processing on the retrieval result to obtain the rearrangement result, and fuse the retrieval result and the rearrangement result to obtain the search result; the determination module 44 is used to determine the interface call process based on the search result using a preset large language model; the second obtaining module 45 is used to perform an interface call based on the interface call process to obtain a call result, obtain the execution result of the target business based on the call result using the preset large language model, and return the execution result of the target business to the user.
[0103] Optionally, when the retrieval module 42 converts the text content of the target business into a target vector in response to the user inputting the operation of the target business, it is specifically used to: in response to the user inputting the operation of the target business, determine whether the user input is a voice input; if the user input is a voice input, call the voice recognition model to recognize the voice input as text content; optimize the text content to obtain optimized text content, and convert the optimized text content into a target vector through a mapping vector model.
[0104] Optionally, the first obtaining module 43 performs fusion processing on the retrieval result and the rearrangement result to obtain the search result, and is specifically used to: merge the retrieval result and the rearrangement result based on a reciprocal sorting fusion algorithm to obtain the search result of the target business.
[0105] Optionally, the search results include text content of the target business, retrieved document content, transition words, and target format;
[0106] When the determination module 44 uses the preset large language model to determine the interface call process based on the search results, it is specifically used to: pass the search results to the preset large language model; identify user operation requirements based on the search results through the preset large language model, and determine the interface call process for executing the target business.
[0107] Optionally, after the determining module 44 determines the interface call process for executing the target service, the determining module 44 is further configured to: convert the interface call process into data in the target format, and return the data in the target format to the interface call service.
[0108] Optionally, when the second obtaining module 45 performs an interface call based on the interface call process and obtains a call result, it is specifically configured to: parse the data in the target format returned by the preset large language model through the interface call service to obtain call parameters; perform an interface call based on the call parameters through the interface call service to obtain a call result.
[0109] Optionally, when the second obtaining module 45 uses the preset large language model to obtain the execution result of the target service based on the call result, it is specifically configured to: pass the call result to the prompt word service; output a call result summary prompt word to the preset large language model through the prompt word service based on the call result; perform an induction process based on the call result summary prompt word through the preset large language model to generate the execution result of the target service.
[0110] Figure 5 The resource management device in the illustrated embodiment can be used to execute the technical solutions in the above method embodiments, and its implementation principle and technical effects are similar, which will not be elaborated here.
[0111] Figure 6 The following is a schematic structural diagram of an electronic device in an embodiment of the present disclosure. Specifically refer to Figure 6 , which shows a schematic structural diagram of an electronic device 600 suitable for implementing the present disclosure. Figure 6 The electronic device shown is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present disclosure.
[0112] As Figure 6 shown, the electronic device 600 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 601, which can execute various appropriate actions and processes according to the program stored in the read-only memory (ROM) 602 or the program loaded from the storage device 608 into the random access memory (RAM) 603 to implement the resource management method in the embodiments as described in the present disclosure. In the RAM 603, various programs and data required for the operation of the electronic device 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. The input / output (I / O) interface 605 is also connected to the bus 604.
[0113] Typically, the following devices can be connected to the I / O interface 605: input devices 606 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 608 including, for example, magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 can allow the electronic device 600 to communicate with other devices wirelessly or wiredly to exchange data. Although Figure 6 the electronic device 600 with various devices is shown, it should be understood that it is not required to implement or have all the shown devices. Instead, more or fewer devices can be implemented or had.
[0114] In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present disclosure includes a computer program product that includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes program codes for performing the methods shown in the flowcharts, thereby implementing the resource management method as described above. In such an embodiment, the computer program can be downloaded and installed from a network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above functions defined in the method of the embodiment of the present disclosure are executed.
[0115] It should be noted that the above-mentioned computer-readable medium in the present disclosure can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In the present disclosure, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and this computer-readable signal medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.
[0116] In addition, an embodiment of the present disclosure also provides a vehicle, including: a memory; a processor; and a computer program; wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the resource management method as described above.
[0117] In some embodiments, the client and the server can communicate using any currently known or future-developed network protocol such as HTTP (HyperText Transfer Protocol), and can be interconnected with digital data communication in any form or medium (for example, a communication network). Examples of the communication network include a local area network ("LAN"), a wide area network ("WAN"), the Internet (for example, the Internet), and a peer-to-peer network (for example, an ad hoc peer-to-peer network), as well as any currently known or future-developed network.
[0118] The above-mentioned computer-readable medium can be included in the above-mentioned electronic device; or it can exist separately without being assembled into the electronic device.
[0119] The above computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to:
[0120] Convert the semantic document of the resource management-related interface and the resource management business process document into vector data, and store the vector data in a vector database;
[0121] In response to a user input for an operation of a target service, convert the text content of the target service into a target vector, retrieve in the vector database based on the target vector, and obtain a retrieval result;
[0122] Perform a similarity re-ranking process on the retrieval result to obtain a re-ranked result, and perform a fusion process on the retrieval result and the re-ranked result to obtain a search result;
[0123] Use a preset large language model to determine an interface call process based on the search result;
[0124] Perform an interface call based on the interface call process to obtain a call result, use the preset large language model to obtain an execution result of the target service based on the call result, and return the execution result of the target service to the user.
[0125] Optionally, when the one or more programs are executed by the electronic device, the electronic device may also perform the other steps described in the above embodiments.
[0126] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages or combinations thereof. The programming languages include, but are not limited to, object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., by connecting through an Internet service provider using the Internet).
[0127] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in a different order than noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.
[0128] The units involved in the embodiments described in the present disclosure can be implemented in software or in hardware. In some cases, the name of the unit does not constitute a limitation on the unit itself.
[0129] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. By way of example and not limitation, the types of hardware logic components that may be used include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), and the like.
[0130] In the context of the present disclosure, a machine-readable medium may be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
[0131] The above description is only a preferred embodiment of the present disclosure and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the disclosure involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosure concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions disclosed in the present disclosure.
[0132] In addition, although the operations are depicted in a particular order, this should not be construed as requiring that the operations be performed in the particular order shown or in sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features described in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, the various features described in the context of a single embodiment may also be implemented separately or in any suitable sub-combination in multiple embodiments.
[0133] Although the subject matter has been described in language specific to structural features and / or methodological logical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. On the contrary, the specific features and acts described above are merely example forms of implementing the claims.
Claims
1. A resource management method, characterized in that: The method comprises: Converting semantic documents of resource management related interfaces and resource management business process documents into vector data, and storing the vector data in a vector database; In response to a user inputting an operation of a target business, converting text content of the target business into a target vector, searching the vector database based on the target vector to obtain a search result; Performing similarity rearrangement processing on the search results to obtain rearrangement results, and fusing the search results and the rearrangement results to obtain search results; Determine an interface calling process based on the search results using a preset large language model; An interface call is performed based on the interface call process to obtain a call result, an execution result of a target business is obtained based on the call result using the preset large language model, and the execution result of the target business is returned to the user.
2. The method according to claim 1, characterized in that The step of converting the text content of the target business into a target vector in response to the user inputting the target business operation includes: In response to a user input operation of a target service, determining whether the user input is a voice input; If the user input is voice input, calling the voice recognition model to recognize the voice input as text content; The text content is optimized to obtain optimized text content, and the optimized text content is converted into a target vector through a mapping vector model.
3. The method according to claim 1, characterized in that The fusion processing of the search result and the rearrangement result to obtain the search result includes: The search results and the rearrangement results are merged based on a reciprocal sorting fusion algorithm to obtain search results for the target business.
4. The method according to claim 1, characterized in that: The search results include text content of the target business, retrieved document content, transition words and target format; The step of using a preset large language model to determine an interface calling process based on the search results includes: Passing the search results to a preset large language model; Based on the search results, the user operation requirements are identified through a preset large language model, and the interface calling process for executing the target business is determined.
5. The method according to claim 4, characterized in that After determining the interface call process for executing the target service, the method further includes: The interface calling process is converted into data in the target format, and the data in the target format is returned to the interface calling service.
6. The method according to claim 1, characterized in that The step of performing an interface call based on the interface call process and obtaining a call result includes: Call the service through the interface to parse the data in the target format returned by the preset large language model to obtain the calling parameters; The interface call service performs an interface call based on the call parameters to obtain a call result.
7. The method according to claim 1, characterized in that The using the preset large language model to obtain the execution result of the target business based on the calling result includes: Passing the call result to the prompt word service; Outputting a summary prompt word of the call result to the preset large language model based on the call result through the prompt word service; The preset large language model is used to perform inductive processing based on the summary prompt words of the call result to generate the execution result of the target business.
8. A resource management device, characterized in that: The device comprises: A storage module, used to convert the semantic annotation documents of resource management related interfaces and resource management business process documents into vector data, and store the vector data into a vector database; A retrieval module, configured to convert the text content of the target business into a target vector in response to a user inputting a target business operation, and to search the vector database based on the target vector to obtain a retrieval result; The first obtaining module is used to perform similarity rearrangement processing on the search results to obtain rearrangement results, and to perform fusion processing on the search results and the rearrangement results to obtain search results; A determination module, used to determine an interface call process based on the search results using a preset large language model; The second obtaining module is used to perform an interface call based on the interface calling process, obtain a calling result, use the preset large language model to obtain an execution result of the target business based on the calling result, and return the execution result of the target business to the user.
9. An electronic device, characterized in that: include: Memory; processor; as well as Computer programs; The computer program is stored in the memory and is configured to be executed by the processor to implement the method according to any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.