Equipment parameter configuration method and device, storage medium and computer equipment

Abstract

According to the equipment parameter configuration method and device, the storage medium and the computer equipment provided by the invention, when the equipment configuration requirement corresponding to the to-be-configured equipment input by the target user is received, the configuration intention corresponding to the equipment configuration requirement can be firstly determined; extracting a device configuration grammar rule corresponding to the configuration intention from a pre-constructed knowledge graph; in this way, a preliminary configuration instruction can be generated according to the device configuration grammar rule, then the preliminary configuration instruction can be optimized and verified to improve the accuracy of the configuration instruction, and after verification is passed, the optimized configuration instruction can be sent to the to-be-configured device to improve the accuracy of the configuration instruction. And performing parameter configuration on the to-be-configured equipment according to the optimized configuration instruction. According to the method, the configuration instruction matched with the to-be-configured equipment is quickly generated in a man-machine conversation mode, and quick deployment and configuration of the equipment are realized, so that the equipment configuration time is greatly saved, and the working efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a device parameter configuration method, apparatus, storage medium, and computer equipment. Background Art

[0002] Currently, traditional non-intelligent devices typically use manual configuration. This requires professional technicians to manually write configuration instructions based on the device manual and complex configuration logic. For example, administrators access the device backend to configure the device IP address, configure attendance and access control policies and parameters, and configure or modify default system management parameters.

[0003] The above configuration method requires the configuration personnel to be familiar with the relevant configuration menu paths and configuration methods, and if necessary, to study the user manual in advance. This process is not only time-consuming and labor-intensive, requiring a high level of technical expertise, but also prone to configuration errors due to human negligence. In addition, the configuration format and logic vary significantly between different devices, further increasing the difficulty and complexity of configuration.

[0004] As the types and number of devices continue to increase, the inefficiency of traditional configuration methods has become increasingly prominent. Therefore, there is an urgent need for a more intelligent and efficient device configuration solution. Summary of the Invention

[0005] The purpose of this application is to solve at least one of the above-mentioned technical deficiencies, especially the technical deficiencies in the prior art that the traditional configuration method is time-consuming and labor-intensive, and is prone to configuration errors due to human negligence.

[0006] This application provides a device parameter configuration method, the method comprising:

[0007] Receive device configuration requirements corresponding to the device to be configured, input by the target user;

[0008] Determine a configuration intent corresponding to the device configuration requirement, and extract device configuration grammar rules corresponding to the configuration intent from a pre-built knowledge graph;

[0009] generating preliminary configuration instructions according to the device configuration grammar rules, and optimizing and verifying the preliminary configuration instructions;

[0010] The optimized configuration instruction is sent to the device to be configured, so that the device to be configured performs parameter configuration according to the optimized configuration instruction.

[0011] Optionally, determining a configuration intent corresponding to the device configuration requirement includes:

[0012] Performing lexical analysis on the device configuration requirement and obtaining a first analysis result;

[0013] Performing syntax analysis on the device configuration requirement according to the first analysis result to obtain a second analysis result;

[0014] Semantic annotation is performed on the second analysis result, and the configuration intention is determined according to the annotation result.

[0015] Optionally, extracting the device configuration grammar rules corresponding to the configuration intent from the pre-built knowledge graph includes:

[0016] Matching the configuration intention with nodes in a pre-built knowledge graph, and determining knowledge fragments related to the configuration intention based on the matching results;

[0017] A device configuration grammar rule corresponding to the configuration intent is determined according to a specified attribute in the knowledge fragment.

[0018] Optionally, the optimizing and verifying the preliminary configuration instructions includes:

[0019] Verifying the preliminary configuration instructions according to a preset verification strategy;

[0020] The configuration instructions that have passed the verification are sent to the target user through a user interaction interface, and the configuration instructions that have passed the verification are optimized according to the feedback result of the target user.

[0021] Optionally, verifying the preliminary configuration instruction according to a preset verification strategy includes:

[0022] Performing semantic verification on the preliminary configuration instruction to check whether the preliminary configuration instruction complies with the device configuration logic;

[0023] Performing parameter validity check on the preliminary configuration instruction to verify whether the parameter value in the preliminary configuration instruction is within the range supported by the device;

[0024] Furthermore, a format compliance check is performed on the preliminary configuration instruction to determine whether the instruction format of the preliminary configuration instruction meets the device requirements.

[0025] Optionally, optimizing the configuration instructions that have passed verification according to the feedback result of the target user includes:

[0026] If the target user's feedback result is that the configuration instruction that has passed the verification meets the device configuration requirement, then there is no need to optimize the configuration instruction that has passed the verification;

[0027] If the feedback result of the target user is that the configuration instruction that has passed the verification does not meet the device configuration requirements, the configuration instruction that has passed the verification is adjusted according to the modification suggestions in the feedback result until the feedback result of the target user is that the configuration instruction that has passed the verification meets the device configuration requirements.

[0028] Optionally, the method further includes:

[0029] During the configuration process, if the device configuration grammar rules corresponding to the configuration intent cannot be extracted from the knowledge graph, the knowledge graph is updated.

[0030] This application also provides a device parameter configuration device, including:

[0031] A requirement receiving module is used to receive a device configuration requirement corresponding to the device to be configured, input by a target user;

[0032] A rule generation module is used to determine the configuration intent corresponding to the device configuration requirement and extract the device configuration grammar rules corresponding to the configuration intent from a pre-built knowledge graph;

[0033] an optimization and verification module, configured to generate preliminary configuration instructions according to the device configuration grammar rules, and to optimize and verify the preliminary configuration instructions;

[0034] The parameter configuration module is used to send the optimized configuration instruction to the device to be configured, so that the device to be configured performs parameter configuration according to the optimized configuration instruction.

[0035] The present application also provides a computer-readable storage medium, which stores computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the device parameter configuration method described in any of the above embodiments.

[0036] The present application also provides a computer device, comprising: one or more processors, and a memory;

[0037] The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the device parameter configuration method described in any one of the above embodiments are performed.

[0038] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0039] The device parameter configuration method, apparatus, storage medium and computer equipment provided by the present application, when receiving the device configuration requirements corresponding to the device to be configured input by the target user, can first determine the configuration intent corresponding to the device configuration requirements, and extract the device configuration grammar rules corresponding to the configuration intent from the pre-built knowledge graph; in this way, a preliminary configuration instruction can be generated according to the device configuration grammar rules, and then, the present application can optimize and verify the preliminary configuration instructions to improve the accuracy of the configuration instructions. When the verification is passed, the present application can send the optimized configuration instructions to the device to be configured, so that the device to be configured performs parameter configuration according to the optimized configuration instructions. The present application quickly generates configuration instructions that match the device to be configured through human-computer dialogue, and realizes rapid deployment and configuration of the device. In this process, the administrator does not need to manually write complex configuration instructions, which greatly saves the time of device configuration and improves work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0041] Figure 1 A flow chart of a device parameter configuration method provided in an embodiment of the present application;

[0042] Figure 2 A schematic diagram of the process of extracting grammatical rules for device configuration provided in an embodiment of the present application;

[0043] Figure 3 A schematic diagram of the process of updating the knowledge graph provided in an embodiment of the present application;

[0044] Figure 4 A schematic diagram of the structure of a device parameter configuration apparatus provided in an embodiment of the present application;

[0045] Figure 5 A schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0047] In one embodiment, Figure 1 As shown, Figure 1 A flow chart of a device parameter configuration method provided in an embodiment of the present application; the present application provides a device parameter configuration method, which may include:

[0048] S110: Receive a device configuration requirement corresponding to the device to be configured, input by a target user.

[0049] In this step, when configuring device parameters, the administrator can input the device configuration requirements of the device to be configured into the multimodal large model to generate device configuration instructions through the multimodal large model.

[0050] The multimodal large model of this application mainly includes an AI (Artificial Intelligence) interaction module, an AI agent module, and a knowledge base module. The AI interaction module is responsible for receiving natural language conversation information input by the administrator and passing it to the AI agent module; the AI agent module is responsible for understanding the administrator's conversational intent and using the knowledge in the knowledge base to generate parameter configuration instructions that conform to the device configuration logic and format; the knowledge base module stores the configuration logic, format specifications, and related domain knowledge of various devices, thereby providing data support for the AI agent module.

[0051] The device configuration requirements of this application refer to the configuration functions and configuration parameters corresponding to the device to be configured. The administrator conducts a human-computer dialogue with the AI interaction module and proposes device configuration requirements to the AI interaction module in a natural language. For example, the administrator can input "Configure [specific function] for [device name], the parameter is [parameter value]" to the AI interaction module, and the AI interaction module sends the dialogue information to the AI agent module. After receiving the dialogue information, the AI agent module first performs natural language processing on it, including semantic understanding, intent recognition, etc., and then matches and infers the knowledge in the knowledge base to generate preliminary configuration instructions.

[0052] S120: Determine the configuration intent corresponding to the device configuration requirement, and extract the device configuration grammar rules corresponding to the configuration intent from the pre-built knowledge graph.

[0053] In this step, after receiving the device configuration requirements corresponding to the device to be configured input by the target user through S110, this application can first determine the configuration intention corresponding to the device configuration requirements, and then extract the device configuration grammar rules corresponding to the configuration intention from the pre-built knowledge graph.

[0054] It is understandable that in order to improve the efficiency of generating device configuration instructions, the present application can construct a knowledge graph in the knowledge base in advance and determine the nodes and edges of the knowledge graph. The nodes can be various entities, such as the device configuration grammar rules, various configuration intentions or knowledge fragments corresponding to each device, etc. The edges represent the relationship between entities, such as "new user-new user instruction" and other relationships. According to business needs, the present application can output the required knowledge data into a document in a fixed format, which records the device configuration grammar rules and various configuration intentions or knowledge fragments of various devices. Then, the present application can import the document into the knowledge base through the interface, so that a knowledge graph can be constructed in the knowledge base. After determining the configuration intention corresponding to the device configuration requirements, the device configuration grammar rules corresponding to the configuration intention can be extracted from the knowledge graph.

[0055] Furthermore, when the configuration intent corresponds to the device configuration requirements, the present application can use natural language processing technology to process the device configuration requirements so as to convert the unstructured configuration requirements into structured operations, thereby significantly improving the efficiency and accuracy of the automated configuration. Specifically, the present application can clean and segment the input device configuration requirements, and then determine the entities in the device configuration requirements. Then, the present application can input the identified entities into predefined configuration intent categories (such as "network settings", "authority allocation") and extract the specific parameters of the configuration from the text. For example, when the target user in the present application inputs "Please set the IP of the office printer to 192.168.2.100, subnet mask 255.255.255.0", the intent recognition result can be "network parameter configuration", so that the corresponding device configuration grammar rules can be quickly extracted through the configuration intent.

[0056] S130: Generate preliminary configuration instructions according to the device configuration syntax rules, and optimize and verify the preliminary configuration instructions.

[0057] In this step, after extracting the device configuration grammar rules corresponding to the configuration intent from the pre-built knowledge graph through S120, the present application can also generate preliminary configuration instructions based on the device configuration grammar rules. For example, the present application can use pre-set prompt words to allow the AI intelligent agent module to return preliminary configuration instructions in the corresponding format, and then optimize and verify the preliminary configuration instructions to further improve the accuracy and completeness of the configuration instructions.

[0058] Among them, when optimizing and verifying the preliminary configuration instructions, the present application can perform verification in one or more dimensions according to pre-configured verification rules, such as semantic verification, parameter legality verification, and format standardization verification of the preliminary configuration instructions. The specific verification rules can be set according to the actual situation and are not limited here. Furthermore, the present application can also optimize the configuration instructions that have passed the verification. The optimization process can be performed by the multimodal large model itself, or through the interaction between the target user and the multimodal large model. The specific settings can be made according to the actual situation and are not limited here.

[0059] For example, for the IP (Internet Protocol) address configuration instructions for a specific device, the AI agent module can not only generate the correct IP address setting statement, but also add necessary subnet mask, gateway and other parameters according to the device requirements. The generated configuration instructions can be fed back to the administrator for confirmation through the AI interaction module. If the administrator has questions about the instructions or needs to modify them, the AI agent module can further optimize the configuration instructions based on the administrator's feedback. In this application, the AI agent module generates configuration instructions based on knowledge fragments in the knowledge base and strict configuration logic. This not only avoids configuration errors caused by human negligence, but also the optimized and verified configuration instructions can ensure the accuracy of device configuration and reduce the probability of device failure due to configuration errors.

[0060] S140: Send the optimized configuration instruction to the device to be configured, so that the device to be configured performs parameter configuration according to the optimized configuration instruction.

[0061] In this step, after optimizing and verifying the preliminary configuration instructions through S130, the present application can send the optimized configuration instructions to the device to be configured, so that the device to be configured can perform parameter configuration according to the optimized configuration instructions after receiving the optimized configuration instructions.

[0062] For example, this application can implement the automatic configuration process of device parameters through the following process, as follows:

[0063] 1. The administrator enters: "Hello, Xiaoentropy (the name of the multimodal large model), I am the administrator and I want to enter the backend to set parameters."

[0064] 2. The agent responds: “OK, please verify your administrator identity.”

[0065] 3. The administrator performs biometric verification and enters the background setting mode after successful verification.

[0066] 4. The administrator said: "Hello, Xiaoentropy, I want to set the IP address of this device."

[0067] 5. The AI intelligent body module analyzes the LAN segment, free IP, subnet mask, gateway address, etc., automatically configures it to an IP address that meets the requirements of the on-site LAN, writes it to the device, and tests the network connection at the same time. Finally, it displays the configuration results and network test results.

[0068] Of course, the application scenarios of this application may also include the following:

[0069] 1. The administrator said: "Hello, Xiao Entropy, our company's working hours are 9:00 am and the closing time is 6:00 pm. Please help me set up the attendance clocking policy."

[0070] 2. The intelligent agent sets the attendance clock-in at 9:00 and the off-duty clock-in at 18:00 according to the attendance clock-in setting requirements of the device, and automatically selects the attendance event mode to the timed switching attendance event mode.

[0071] It can be seen that this application can realize the automatic generation of parameter configuration instructions that conform to the device configuration logic and format through the multimodal big model through the dialogue between the administrator and the multimodal big model and combined with the knowledge base module of the multimodal big model.

[0072] In the above embodiment, when the device configuration requirement corresponding to the device to be configured is received from the target user, the configuration intent corresponding to the device configuration requirement can be determined first, and the device configuration grammar rules corresponding to the configuration intent can be extracted from the pre-built knowledge graph; in this way, a preliminary configuration instruction can be generated according to the device configuration grammar rules. Then, the present application can optimize and verify the preliminary configuration instruction to improve the accuracy of the configuration instruction. When the verification is passed, the present application can send the optimized configuration instruction to the device to be configured, so that the device to be configured performs parameter configuration according to the optimized configuration instruction. The present application quickly generates configuration instructions that match the device to be configured through human-computer dialogue and realizes rapid deployment and configuration of the device. In this process, the administrator does not need to manually write complex configuration instructions, which greatly saves the time of device configuration and improves work efficiency.

[0073] In one embodiment, determining the configuration intent corresponding to the device configuration requirement in S120 may include:

[0074] S121: Perform lexical analysis on the device configuration requirement and obtain a first analysis result.

[0075] S122: Performing syntactic analysis on the device configuration requirement according to the first analysis result, and obtaining a second analysis result.

[0076] S123: Perform semantic annotation on the second analysis result, and determine the configuration intention according to the annotation result.

[0077] In this implementation of Hailizi, when determining the configuration intent corresponding to the device configuration requirements, it can first perform lexical analysis to split the input text into vocabulary units; then perform syntactic analysis on the split vocabulary units to construct the grammatical structure of the sentence; finally, complete semantic annotation, identify key entities, attributes and relationships, and determine the configuration intent.

[0078] Specifically, after obtaining the device configuration requirements input by the target user, the present application can first perform a lexical analysis on the device configuration requirements. During the analysis process, the device configuration requirements can be first segmented, and the part of speech of each word can be marked and the basic entity type can be identified. For example, when the target user inputs "Set the DNS of the main router to 8.8.8.8", the part of speech analysis is performed and the segmentation and part of speech are output: [("will", "preposition"), ("main", "adjective"), ("router", "noun / device"), ("DNS", "noun / parameter"), ("set to", "verb"), ("8.8.8.8", "value / IP")].

[0079] Furthermore, after the present application performs lexical analysis on the device configuration requirements and obtains a first analysis result, the present application can also perform syntactic analysis on the device configuration requirements based on the first analysis result. During the analysis, the present application can first mark the subordinate relationships between words, and then divide the sentence components to obtain a second analysis result. Next, the present application can perform semantic annotation on the second analysis result to identify deep semantic entities, attributes, and relationships, and map them to configuration intents. For example, the present application can mark the agent, patient, and target of the action (such as "set to" + "DNS" + "8.8.8.8"), then link common words to device configuration parameters (such as "DNS" → network.dns_server), and extract relationships between entities (such as the "DNS" of the "main router" needs to be modified), and finally synthesize the semantics to output the configuration intent (such as modify_network_config). Through layered processing, the present application's model gradually parses out actionable configuration intents from surface text, achieving both accuracy and interpretability.

[0080] In one embodiment, Figure 2 As shown, Figure 2 Schematic diagram of the process of extracting device configuration grammar rules provided in an embodiment of the present application; extracting device configuration grammar rules corresponding to the configuration intent from the pre-built knowledge graph in S120 may include:

[0081] S124: Match the configuration intention with the nodes in the pre-built knowledge graph, and determine the knowledge fragments related to the configuration intention based on the matching results.

[0082] S125: Determine a device configuration grammar rule corresponding to the configuration intent according to the specified attributes in the knowledge fragment.

[0083] In this embodiment, since the knowledge graph pre-constructed by this application contains nodes and edges, the nodes include various entities, such as the device configuration grammar rules, various configuration intentions or knowledge fragments corresponding to each device, and the edges represent the relationship between entities, such as "new user-new user instruction" and other relationships. This application can output the required knowledge data into a document in a fixed format according to business needs. The document records the device configuration grammar rules and various configuration intentions or knowledge fragments of various devices. Then, this application can import the document into the knowledge base through the interface, so that a knowledge graph can be constructed in the knowledge base. After determining the configuration intention corresponding to the device configuration requirement, the device configuration grammar rules corresponding to the configuration intention can be extracted from the knowledge graph.

[0084] Specifically, after this application constructs the knowledge graph, since the entities corresponding to the nodes in the knowledge graph contain the device configuration grammar rules, various configuration intentions or knowledge fragments corresponding to each device, this application can match the configuration intentions with the nodes in the knowledge graph. After matching the corresponding node, another node can be determined through the edge connected to the node. The other node is the knowledge fragment related to the configuration intention in this application. Since the knowledge fragment contains knowledge such as device configuration logic and parameter constraints, this application can first determine the specified attributes in the knowledge fragment, such as the attributes related to the configuration intention of this application, and then determine the device configuration grammar rules corresponding to the specified attributes. In this way, the AI intelligent agent module can return the preliminary configuration instructions in the corresponding format through the pre-set prompt words.

[0085] In one embodiment, optimizing and verifying the preliminary configuration instructions in S130 may include:

[0086] S131: Verify the preliminary configuration instruction according to a preset verification strategy.

[0087] S132: Sending the configuration instructions that have passed the verification to the target user through the user interaction interface, and optimizing the configuration instructions that have passed the verification according to the feedback result of the target user.

[0088] In this embodiment, when optimizing and verifying the preliminary configuration instructions, the present application can obtain a preset verification strategy and verify the preliminary configuration instructions according to the verification strategy. The verification process includes but is not limited to semantic verification, parameter legality verification, and format standardization verification of the preliminary configuration instructions. The specific verification rules can be set according to actual conditions and are not limited here.

[0089] Furthermore, the present application can also optimize the configuration instructions that have passed the verification. The optimization process can be performed by the multimodal large model itself, or by the interaction between the target user and the multimodal large model. The specific settings can be made according to the actual situation and are not limited here. When the present application chooses to optimize through the interaction between the target user and the multimodal large model, the model can send the configuration instructions that have passed the verification to the target user through the user interaction interface. The target user can view the instruction content, confirm whether it meets the configuration requirements, and input feedback results. At this time, the model can optimize the configuration instructions that have passed the verification based on the feedback results input by the target user. The optimization process includes but is not limited to no optimization, regeneration of instructions, etc. The specific optimization method can be set according to the actual situation and is not limited here.

[0090] In one embodiment, verifying the preliminary configuration instruction according to a preset verification strategy in S131 may include:

[0091] S1311: Perform semantic verification on the preliminary configuration instruction to check whether the preliminary configuration instruction complies with device configuration logic.

[0092] S1312: Perform parameter validity check on the preliminary configuration instruction to verify whether the parameter value in the preliminary configuration instruction is within the device support range.

[0093] S1313: Also, perform format compliance check on the preliminary configuration instruction to determine whether the instruction format of the preliminary configuration instruction meets the device requirements.

[0094] In this embodiment, when the preliminary configuration instructions are verified according to the verification strategy, the verification process includes but is not limited to semantic verification, parameter validity verification, and format standardization verification of the preliminary configuration instructions.

[0095] Specifically, this application can use an optimization algorithm based on rules combined with deep learning to perform semantic verification on the preliminary configuration instructions to check whether the preliminary configuration instructions conform to the device configuration logic. For example, this application can allow the model to return an instruction format of The initial configuration instruction is generated based on the query of the target user. The value of operation can be insert, update, or delete, and the corresponding values are insert, update, and delete, respectively. id corresponds to the ID entered by the target user. The returned data is then used to determine whether it is consistent with the device configuration requirements entered by the target user, thereby performing semantic verification on the initial configuration instruction.

[0096] The present application may also perform parameter validity checks on the preliminary configuration instructions to verify whether the parameter values in the preliminary configuration instructions are within the device support range. For example, the present application may check the parameter values in the preliminary configuration instructions based on the length range and format. For example, if the parameter is age, it is necessary to check whether the parameter is between 0 and 100; if it is price, it is necessary to check whether the parameter is greater than zero and determine whether the parameters in the preliminary configuration instructions are in a pre-defined format.

[0097] Furthermore, the present application can also perform format compliance verification on the preliminary configuration parameters. During verification, it can detect whether the format of the preliminary configuration parameters is a pre-set format type, such as returning data in a json format, so as to determine whether the instruction format of the preliminary configuration instruction meets the device requirements.

[0098] In one embodiment, optimizing the configuration instructions that have passed verification according to the feedback result of the target user in S132 may include:

[0099] S1321: If the feedback result of the target user is that the configuration instruction that has passed the verification meets the device configuration requirement, there is no need to optimize the configuration instruction that has passed the verification.

[0100] S1322: If the feedback result from the target user is that the configuration instruction that has passed the verification does not meet the device configuration requirements, the configuration instruction that has passed the verification is adjusted according to the modification suggestions in the feedback result until the feedback result from the target user is that the configuration instruction that has passed the verification meets the device configuration requirements.

[0101] In this embodiment, when optimizing the configuration instructions that have passed the verification based on the feedback results of the target user, if the feedback result is that the configuration instructions that have passed the verification meet the device configuration requirements, there is no need to optimize the configuration instructions that have passed the verification; if the feedback result is that the configuration instructions that have passed the verification do not meet the device configuration requirements, it means that the target user has questions about the configuration instructions that have passed the verification or needs to modify them. At this time, the target user enters the modification opinions in the interactive interface and submits them. The system can adjust the configuration instructions that have passed the verification based on the modification opinions. For example, this application can re-perform natural language processing, knowledge graph matching, instruction generation and optimization processes based on the modification opinions until the target user is satisfied.

[0102] Once the target user confirms that everything is correct, the system will send the final configuration instructions to the device to be configured through the device communication interface (such as serial port, network port, etc.). After receiving the instructions, the device to be configured will perform the corresponding configuration operations.

[0103] In one embodiment, Figure 3 As shown, Figure 3 A schematic diagram of a process for updating a knowledge graph provided in an embodiment of the present application; the method may further include:

[0104] S150: During the configuration process, if the device configuration grammar rules corresponding to the configuration intent cannot be extracted from the knowledge graph, the knowledge graph is updated.

[0105] In this embodiment, during the configuration process, if the AI agent module encounters knowledge gaps or conflicts, it can automatically trigger a knowledge update process. For complex knowledge updates, administrators or domain experts can review, supplement, and correct them to update the knowledge graph.

[0106] It is understandable that with the emergence of new devices and changes in device configuration logic, the knowledge base requires continuous updating and maintenance. The knowledge base update mechanism proposed in this application allows the system to prompt the administrator to supplement and update knowledge when the AI agent module encounters an unresolved problem during configuration tasks. Administrators can add new device configuration knowledge to the knowledge base so that the AI agent module can use this new knowledge to configure device parameters in subsequent configuration tasks.

[0107] The device parameter configuration apparatus provided in an embodiment of the present application is described below. The device parameter configuration apparatus described below and the device parameter configuration method described above can be referenced to each other.

[0108] In one embodiment, Figure 4 As shown, Figure 4 This is a schematic diagram of the structure of a device parameter configuration device provided in an embodiment of the present application. The present application also provides a device parameter configuration device, which may include a demand receiving module 210, a rule generating module 220, an optimization verification module 230, and a parameter configuration module 240, specifically including the following:

[0109] The requirement receiving module 210 is configured to receive a device configuration requirement input by a target user corresponding to the device to be configured.

[0110] The rule generation module 220 is used to determine the configuration intention corresponding to the device configuration requirement and extract the device configuration grammar rules corresponding to the configuration intention from the pre-built knowledge graph.

[0111] The optimization and verification module 230 is configured to generate preliminary configuration instructions according to the device configuration syntax rules, and optimize and verify the preliminary configuration instructions.

[0112] The parameter configuration module 240 is configured to send the optimized configuration instruction to the device to be configured, so that the device to be configured performs parameter configuration according to the optimized configuration instruction.

[0113] In the above embodiment, when the device configuration requirement corresponding to the device to be configured is received from the target user, the configuration intent corresponding to the device configuration requirement can be determined first, and the device configuration grammar rules corresponding to the configuration intent can be extracted from the pre-built knowledge graph; in this way, a preliminary configuration instruction can be generated according to the device configuration grammar rules. Then, the present application can optimize and verify the preliminary configuration instruction to improve the accuracy of the configuration instruction. When the verification is passed, the present application can send the optimized configuration instruction to the device to be configured, so that the device to be configured performs parameter configuration according to the optimized configuration instruction. The present application quickly generates configuration instructions that match the device to be configured through human-computer dialogue and realizes rapid deployment and configuration of the device. In this process, the administrator does not need to manually write complex configuration instructions, which greatly saves the time of device configuration and improves work efficiency.

[0114] In one embodiment, the present application also provides a computer-readable storage medium, which stores computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the device parameter configuration method described in any of the above embodiments.

[0115] In one embodiment, the present application further provides a computer device, including: one or more processors, and a memory.

[0116] The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the device parameter configuration method described in any one of the above embodiments are performed.

[0117] Schematically, as Figure 5 As shown, Figure 5 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. The computer device 300 can be provided as a server. Figure 5 Computer device 300 includes a processing component 302, which further includes one or more processors, and a memory resource represented by memory 301 for storing instructions executable by processing component 302, such as an application. The application stored in memory 301 may include one or more modules, each corresponding to a set of instructions. In addition, processing component 302 is configured to execute the instructions to perform the device parameter configuration method of any of the above-mentioned embodiments.

[0118] The computer device 300 may further include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate based on an operating system stored in the memory 301, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or the like.

[0119] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0120] 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.

[0121] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referenced to each other.

[0122] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A device parameter configuration method, characterized in that: The method comprises: Receive device configuration requirements corresponding to the device to be configured, input by the target user; Determine a configuration intent corresponding to the device configuration requirement, and extract device configuration grammar rules corresponding to the configuration intent from a pre-built knowledge graph; generating preliminary configuration instructions according to the device configuration grammar rules, and optimizing and verifying the preliminary configuration instructions; The optimized configuration instruction is sent to the device to be configured, so that the device to be configured performs parameter configuration according to the optimized configuration instruction.

2. The device parameter configuration method according to claim 1, characterized in that: The determining of the configuration intent corresponding to the device configuration requirement includes: Performing lexical analysis on the device configuration requirement and obtaining a first analysis result; Performing syntax analysis on the device configuration requirement according to the first analysis result to obtain a second analysis result; Semantic annotation is performed on the second analysis result, and the configuration intention is determined according to the annotation result.

3. The device parameter configuration method according to claim 1, characterized in that: The extracting of the device configuration grammar rules corresponding to the configuration intent from the pre-built knowledge graph includes: Matching the configuration intention with nodes in a pre-built knowledge graph, and determining knowledge fragments related to the configuration intention based on the matching results; A device configuration grammar rule corresponding to the configuration intent is determined according to a specified attribute in the knowledge fragment.

4. The device parameter configuration method according to claim 1, characterized in that: The optimizing and verifying the preliminary configuration instructions includes: Verifying the preliminary configuration instructions according to a preset verification strategy; The configuration instructions that have passed the verification are sent to the target user through a user interaction interface, and the configuration instructions that have passed the verification are optimized according to the feedback result of the target user.

5. The device parameter configuration method according to claim 4, characterized in that: Verifying the preliminary configuration instruction according to a preset verification strategy includes: Performing semantic verification on the preliminary configuration instruction to check whether the preliminary configuration instruction complies with the device configuration logic; Performing parameter validity check on the preliminary configuration instruction to verify whether the parameter value in the preliminary configuration instruction is within the range supported by the device; Furthermore, a format compliance check is performed on the preliminary configuration instruction to determine whether the instruction format of the preliminary configuration instruction meets the device requirements.

6. The device parameter configuration method according to claim 4, characterized in that: Optimizing the configuration instructions that have passed the verification according to the feedback result of the target user includes: If the target user's feedback result is that the configuration instruction that has passed the verification meets the device configuration requirement, then there is no need to optimize the configuration instruction that has passed the verification; If the feedback result of the target user is that the configuration instruction that has passed the verification does not meet the device configuration requirements, the configuration instruction that has passed the verification is adjusted according to the modification suggestions in the feedback result until the feedback result of the target user is that the configuration instruction that has passed the verification meets the device configuration requirements.

7. The device parameter configuration method according to any one of claims 1 to 6, characterized in that: The method further comprises: During the configuration process, if the device configuration grammar rules corresponding to the configuration intent cannot be extracted from the knowledge graph, the knowledge graph is updated.

8. A device parameter configuration device, characterized in that: include: A requirement receiving module is used to receive a device configuration requirement corresponding to the device to be configured, input by a target user; A rule generation module is used to determine the configuration intent corresponding to the device configuration requirement and extract the device configuration grammar rules corresponding to the configuration intent from a pre-built knowledge graph; an optimization and verification module, configured to generate preliminary configuration instructions according to the device configuration grammar rules, and to optimize and verify the preliminary configuration instructions; The parameter configuration module is used to send the optimized configuration instruction to the device to be configured, so that the device to be configured performs parameter configuration according to the optimized configuration instruction.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the device parameter configuration method according to any one of claims 1 to 7.

10. A computer device, characterized in that: include: one or more processors, and memory; The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the device parameter configuration method according to any one of claims 1 to 7 are performed.

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