Data processing method and device for product fault problem and electronic equipment

Abstract

The embodiment of the invention provides a data processing method and device for a product fault problem and electronic equipment, and relates to the technical field of intelligent customer service, the method is applied to an agent server side, and the method comprises the steps that a first big model is called to obtain a first candidate question library based on a first candidate question library; searching a target candidate question matched with a to-be-consulted question input by the customer service staff; in response to the target candidate question found in the first candidate question library, determining a target tree structure associated with the target candidate question; displaying an interactive flow chart used for representing the target tree structure; and in response to a predetermined operation of the customer service staff on any specified node element in the interactive flow chart, displaying a reply verbal skill corresponding to the specified node element. Visibly, through the scheme, the response duration of user consultation can be shortened, and the service quality of customer service and the user experience are improved.

Description

Technical Field

[0001] This application relates to the field of intelligent customer service technology, and in particular to a data processing method, apparatus and electronic device for product malfunction problems. Background Technology

[0002] In troubleshooting scenarios, for product malfunctions that require multiple rounds of troubleshooting, such as a camera not recording video, customer service personnel can use the call center system to respond to the problem.

[0003] In related technologies, customer service systems typically employ a method of displaying all relevant content at once. Specifically, after receiving a user's inquiry about a product malfunction, customer service personnel input the issue into the customer service system. The system then presents the customer service personnel with all possible troubleshooting steps related to the product malfunction, along with the data involved in each step. This allows the customer service personnel to match the problem with the presented data and filter information to respond to the user.

[0004] It is evident that the processing methods provided by the relevant technologies require customer service personnel to filter and match information from a large amount of data, which is time-consuming and consequently results in longer response times to user inquiries, affecting the quality of customer service and user experience. Summary of the Invention

[0005] The purpose of this application is to provide a data processing method, apparatus, and electronic device for product malfunction issues, thereby reducing response time to user inquiries and improving customer service quality and user experience. The specific technical solution is as follows:

[0006] In a first aspect, embodiments of this application provide a data processing method for product malfunction issues, applied to a customer service server, the method comprising:

[0007] The first major model is invoked to search for target candidate questions that match the inquiry questions entered by customer service personnel, based on the first candidate question library; wherein, each candidate question in the first candidate question library is a product failure question that has undergone multiple rounds of troubleshooting.

[0008] In response to finding the target candidate question in the first candidate question library, a target tree structure associated with the target candidate question is determined; wherein, each candidate question in the first candidate question library is associated with a tree structure, the node hierarchy of the tree structure is used to characterize the troubleshooting process required for the candidate question, and each target node is associated with a reply script for replying to the user that matches the content represented by the target node, the content represented by the target node is: node content that has a need for user interaction and question answering;

[0009] Display an interactive flowchart representing the target tree structure; and, in response to a pre-defined operation by the customer service representative on any designated node element in the interactive flowchart, display the corresponding response script for that designated node element; wherein the designated node element is a node element representing a target node and the corresponding response script is the response script associated with the target node represented by the designated node element.

[0010] Secondly, embodiments of this application provide a data processing device for product malfunction issues, applied to a customer service server, the device comprising:

[0011] The search module is used to call the first major model to search for target candidate questions that match the questions entered by customer service personnel based on the first candidate question library; wherein, each candidate question in the first candidate question library is a product fault question that has undergone multiple rounds of investigation.

[0012] The tree structure determination module is used to determine the target tree structure associated with the target candidate question in response to finding the target candidate question in the first candidate question library; wherein, each candidate question in the first candidate question library is associated with a tree structure, the node hierarchy of the tree structure is used to represent the troubleshooting process required for the candidate question, and each target node is associated with a reply script for replying to the user that matches the content represented by the target node, and the content represented by the target node is: node content that requires user interaction and question answering;

[0013] The first display module is used to display an interactive flowchart representing the target tree structure; and, in response to a predetermined operation by the customer service personnel on any designated node element in the interactive flowchart, to display the corresponding response script for that designated node element; wherein, the designated node element is a node element representing a target node and the corresponding response script is the response script associated with the target node represented by the designated node element.

[0014] Thirdly, embodiments of this application provide a seat service system, including a client, a client terminal, and a server;

[0015] The client is used to receive questions input by the user.

[0016] The client terminal is used to obtain the user-inputted inquiry sent by the client, and in response to the operation of the customer service personnel, revise the obtained inquiry and send it to the server or send the obtained inquiry to the server.

[0017] The server is configured to execute the data processing method for product malfunction issues described in the first aspect above, based on the received inquiry.

[0018] Fourthly, embodiments of this application provide an electronic device, including: a memory for storing computer programs; and a processor for executing the program stored in the memory to implement any of the aforementioned data processing methods for product malfunction problems.

[0019] Fifthly, embodiments of this application provide a computer-readable storage medium storing a computing program, which, when executed by a processor, implements any of the aforementioned data processing methods for product failure problems.

[0020] This application also provides a computer program product containing instructions that, when run on a computer, cause the computer to execute any of the data processing methods described above for product malfunction problems.

[0021] Beneficial effects of the embodiments in this application:

[0022] As can be seen from the above, the data processing method for product malfunction issues provided in this application is applied to a customer service agent server. After the customer service personnel input the question to be consulted into the customer service agent server, the customer service agent server calls the first major model to search for target candidate questions that match the question to be consulted based on the first candidate question library. It should be noted that each candidate question in the first candidate question library is a product malfunction issue that requires multiple rounds of troubleshooting. The troubleshooting process required for this type of product malfunction issue is relatively complex. Therefore, in this application, a tree structure is set up to associate with each candidate question in the first candidate question library. The node hierarchy of the tree structure is used to represent the troubleshooting process required for the candidate question, and each target node is associated with a reply script for replying to the user that matches the content represented by the target node. Then, in response to finding the target candidate question in the first candidate question database, the target tree structure associated with the target candidate question is determined. Next, an interactive flowchart representing the target tree structure is displayed. This interactive flowchart contains designated node elements, each representing a target node. The corresponding response script is the response script associated with the target node represented by that designated element. Thus, customer service personnel can display the corresponding response script for any designated node element in the interactive flowchart through a predefined operation. In other words, the interactive flowchart intuitively demonstrates the troubleshooting process involved in the target candidate question matching the inquiry, thereby helping customer service personnel quickly locate the problem raised by the user. Furthermore, the provided response script allows customer service personnel to quickly provide feedback to the user. Therefore, this solution can reduce response time to user inquiries and improve customer service quality and user experience. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other embodiments can be obtained based on these drawings.

[0024] Figure 1 A flowchart illustrating the first data processing method for product failure problems provided in this application embodiment;

[0025] Figure 2 A flowchart illustrating a second data processing method for product failure problems provided in this application embodiment;

[0026] Figure 3A flowchart illustrating the third data processing method for product failure problems provided in this application embodiment;

[0027] Figure 4 A flowchart illustrating the fourth data processing method for product failure problems provided in this application embodiment;

[0028] Figure 5 A flowchart illustrating a specific example provided in this application embodiment;

[0029] Figure 6 A diagram illustrating an interface for creating a tree-like hierarchical relationship in the background, as provided in this application example;

[0030] Figure 7 For Figure 6 A schematic diagram of the interactive flowchart generated by the tree-like hierarchical relationship shown;

[0031] Figure 8 This is a schematic diagram of the structure of a customer service system provided in an embodiment of this application;

[0032] Figure 9 This application provides a schematic diagram of the structure of a data processing device for product failure problems.

[0033] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0034] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art based on this application are within the scope of protection of this application.

[0035] To address the aforementioned technical problems, this application provides a data processing method, apparatus, and electronic device for product malfunction issues. This method is applicable to various application scenarios where customer service personnel are assisting in responding to product malfunction problems. For example, it can be used to respond to product malfunctions in cameras or televisions. Furthermore, this method is applied to a customer service agent server, which can run on a terminal device or a server (hereinafter referred to as an electronic device). Additionally, as an example, in one implementation, the agent server can run on a server within an agent service system. The agent service system may also include a client and a customer service client. The client receives user-inputted questions and sends them to the customer service client. The customer service client obtains the questions input by the user through the client, allowing customer service personnel to view the questions and process them using the agent server on the server. It should be noted that this application does not specifically limit the application scenarios or the executing entity of the method.

[0036] This application provides a data processing method for product malfunction issues, applied to a customer service server. The method may include the following steps:

[0037] The first major model is invoked to search for target candidate questions that match the inquiry questions entered by customer service personnel, based on the first candidate question library; wherein, each candidate question in the first candidate question library is a product failure question that has undergone multiple rounds of troubleshooting.

[0038] In response to finding the target candidate question in the first candidate question library, a target tree structure associated with the target candidate question is determined; wherein, each candidate question in the first candidate question library is associated with a tree structure, the node hierarchy of the tree structure is used to characterize the troubleshooting process required for the candidate question, and each target node is associated with a reply script for replying to the user that matches the content represented by the target node, the content represented by the target node is: node content that has a need for user interaction and question answering;

[0039] Display an interactive flowchart representing the target tree structure; and, in response to a pre-defined operation by the customer service representative on any designated node element in the interactive flowchart, display the corresponding response script for that designated node element; wherein the designated node element is a node element representing a target node and the corresponding response script is the response script associated with the target node represented by the designated node element.

[0040] As can be seen from the above, the data processing method for product malfunction issues provided in this application embodiment is applied to a customer service agent server. Customer service personnel input the question to be consulted into the customer service agent server. The customer service agent server calls the first major model to search for target candidate questions that match the question to be consulted based on the first candidate question library. It should be noted that each candidate question in the first candidate question library is a product malfunction issue that requires multiple rounds of troubleshooting. The troubleshooting process required for this type of product malfunction issue is relatively complex. Therefore, in this application, a tree structure is set up to associate with each candidate question in the first candidate question library. The node hierarchy of the tree structure is used to represent the troubleshooting process required for the candidate question, and each target node is associated with a reply script for replying to the user that matches the content represented by the target node. Then, in response to finding the target candidate question in the first candidate question database, the target tree structure associated with the target candidate question is determined. Next, an interactive flowchart representing the target tree structure is displayed. This interactive flowchart contains designated node elements, each representing a target node. The corresponding response script is the response script associated with the target node represented by that designated element. Thus, customer service personnel can display the corresponding response script for any designated node element in the interactive flowchart through a predefined operation. In other words, the interactive flowchart intuitively demonstrates the troubleshooting process involved in the target candidate question matching the inquiry, thereby helping customer service personnel quickly locate the problem raised by the user. Furthermore, the provided response script helps customer service personnel quickly provide feedback to the user. Therefore, this solution can reduce response time to user inquiries and improve customer service quality and user experience.

[0041] The following description, in conjunction with the accompanying drawings, details a data processing method for product malfunctions provided in an embodiment of this application. This method is applied to a call center server, such as... Figure 1 As shown, the method may include the following steps S101-S103:

[0042] S101: Invoke the first major model to find target candidate questions that match the questions entered by customer service personnel, based on the first candidate question library;

[0043] Among them, each candidate question in the first candidate question pool is a product failure question that has undergone multiple rounds of troubleshooting.

[0044] In this application, after receiving a product malfunction issue input by a customer service representative, the agent server can treat the received product malfunction issue as a question to be consulted and execute the subsequent processing procedure.

[0045] It should be noted that the product malfunction questions input into the agent server can be questions directly copied by the customer service representative from the client terminal, or questions that the customer service representative has edited in the client terminal based on questions provided by the user. Therefore, optionally, the client terminal may revise the acquired questions in response to the customer service representative's actions before sending them to the server, or the client terminal may send the acquired questions to the server directly.

[0046] The agent server has a primary model built-in or can call an externally deployed primary model. This primary model analyzes the semantics of the question to be consulted. Then, by comparing the semantics of each candidate question in the primary candidate question library with the semantics of the question to be consulted, it finds a candidate question that matches the semantics of the question to be consulted, and uses this as the target candidate question. It should be noted that the aforementioned matching candidate question can be understood as: the candidate question with the highest semantic similarity to the question to be consulted and exceeding a predetermined threshold.

[0047] In this way, after receiving the question to be consulted, the agent server can call the first major model to analyze the semantics of the question to be consulted, and search for target candidate questions that match the question to be consulted based on the first candidate question library.

[0048] S102: In response to finding a target candidate question in the first candidate question library, determine the target tree structure associated with the target candidate question.

[0049] In this system, each candidate question in the first candidate question library is associated with a tree structure. The hierarchical relationship of the nodes in the tree structure is used to represent the troubleshooting process required for the candidate question. Each target node is associated with a reply script that matches the content represented by the target node and is used to reply to the user. The content represented by the target node is: node content that requires user interaction and Q&A.

[0050] Considering that the causes of the same product malfunction are usually not unique—for example, a camera's inability to record video might be due to a faulty image sensor or a faulty video storage module—the troubleshooting process for such non-unique causes is quite complex. Therefore, this application pre-classifies product malfunctions requiring multiple rounds of investigation into a first candidate problem library, and associates each candidate problem in the first candidate problem library with a tree structure.

[0051] It should be noted that the same processing operation performed to resolve the same candidate problem may have different effects. As shown in the previous example, when the camera cannot record normally, the processing operation to resolve the candidate problem is to restart. However, the color of the indicator light on the camera after restarting may be different, indicating that the reason why the camera cannot record normally may be different. For example, if the indicator light is blue, the reason why the camera cannot record normally may be that the image sensor of the camera is faulty; if the indicator light is red, the reason why the camera cannot record normally may be that the video storage module of the camera is faulty.

[0052] In other words, for the same candidate problem, there is at least one solution direction. Different solution directions correspond to a troubleshooting branch in the troubleshooting process of the candidate problem. If the entire troubleshooting process is associated with a tree structure related to the candidate problem, then the troubleshooting branch corresponds to a path in the tree structure. Each node in the path can be understood as a step in the corresponding troubleshooting branch. Each step is a processing operation, or it can be the execution effect obtained after the processing operation corresponding to the previous step is executed, which is reasonable.

[0053] In this way, by using at least one branch of the tree structure and the hierarchical relationship between the nodes on each branch, the troubleshooting process required for the candidate problem corresponding to the tree structure can be characterized. By using the tree structure associated with the candidate problem, the troubleshooting process for the candidate problem can be broken down into multiple nodes (steps) to facilitate the rapid location of the problem to be consulted.

[0054] Furthermore, considering that the purpose of this application is to respond to user inquiries promptly, when constructing the tree structure associated with each candidate question in the first candidate question database, at least one target node can be identified among the aforementioned nodes. A target node can be understood as a node with a need for user interaction and Q&A, and the content represented by this target node is the content of the node with a need for user interaction and Q&A. Subsequently, a response script for replying to users is associated with each target node, matching the content represented by that target node. This helps customer service personnel directly obtain the response script associated with the node after locating the question to be inquired about, enabling timely responses to user inquiries.

[0055] In other words, the tree structure constructed for each candidate question in the first candidate question library can represent the troubleshooting process required to solve the candidate question. Furthermore, the target node in the tree structure is also equipped with a reply script for responding to users that matches the content represented by the target node.

[0056] For example, when the candidate question is "camera not recording", the response message set for the target node in the corresponding tree structure could be: "Hello, it seems that your device's memory card is not inserted properly. We suggest you power off the device and reinsert the card. If it's convenient, please take a picture after inserting it so we can show you if it's inserted properly."

[0057] Therefore, when the target candidate problem found belongs to the first candidate problem library, the target tree structure associated with the target candidate problem is directly determined.

[0058] S103: Display an interactive flowchart representing the target tree structure; and, in response to a customer service representative's pre-defined action on any specified node element in the interactive flowchart, display the corresponding response script for that specified node element.

[0059] The specified node element is a node element that represents a target node, and the corresponding response message is the response message associated with the target node represented by the specified node element.

[0060] In this application, after determining the target tree structure, an interactive flowchart of the target tree structure can be rendered based on the node hierarchy represented by the target tree structure and the content represented by each node, according to a top-down or left-to-right layout rule. Each node in the resulting interactive flowchart corresponds to a node in the target tree structure, and the node hierarchy represented in the interactive flowchart is the same as that represented by the target tree structure. The process of rendering the target tree structure into an interactive flowchart is prior art and will not be elaborated upon here.

[0061] As an example, when rendering the interactive flowchart, a node expand / collapse function is set for the specified node element corresponding to the target node in the target tree structure. This function allows customer service personnel to trigger it through predetermined operations on the interactive flowchart (such as clicking, detecting that the indicator stays at the specified node element for more than a predetermined time, etc.). Specifically, when the specified node element is in the expanded state, the corresponding response script for the specified node element is displayed in the interactive flowchart; when the specified node element is in the collapsed state, the corresponding response script for the specified node element is not displayed in the interactive flowchart.

[0062] In this way, after displaying the interactive flowchart that represents the target tree structure, customer service personnel can perform predetermined operations on any specified node element in the interactive flowchart according to their own needs, so that the interactive flowchart can display the corresponding response script for that specified node element. This helps customer service personnel to respond to user inquiries in a timely manner based on the response script they see, thereby reducing the response time to user inquiries and improving the service quality and user experience.

[0063] Optionally, in one implementation, the specified node element in the interactive flowchart is a node element that is displayed according to a predetermined display effect; wherein, the predetermined display effect is used to guide customer service personnel to have corresponding response scripts associated with the specified node element.

[0064] In this implementation, to highlight specific node elements in the interactive flowchart and guide customer service personnel to provide corresponding response scripts for those elements, the specified node elements can, for example, have predetermined display effects. These effects could include different font colors, font weights, or graphic identifiers. This embodiment does not limit the specific format of these predetermined display effects; the effect should simply help customer service personnel intuitively distinguish between specified node elements and other node elements in the interactive flowchart.

[0065] In this implementation, by setting a predefined display effect for a specified node element, customer service personnel can directly identify the specified node element in the interactive flowchart by observing the display effect of the node element.

[0066] As can be seen from the above, the data processing method for product malfunction issues provided in this application is applied to a customer service agent server. After the customer service personnel input the question to be consulted into the customer service agent server, the customer service agent server calls the first major model to search for target candidate questions that match the question to be consulted based on the first candidate question library. It should be noted that each candidate question in the first candidate question library is a product malfunction issue that requires multiple rounds of troubleshooting. The troubleshooting process required for this type of product malfunction issue is relatively complex. Therefore, in this application, a tree structure is set up to associate with each candidate question in the first candidate question library. The node hierarchy of the tree structure is used to represent the troubleshooting process required for the candidate question, and each target node is associated with a reply script that matches the content represented by the target node and is used to reply to the user. Then, in response to finding the target candidate question in the first candidate question database, the target tree structure associated with the target candidate question is determined. Next, an interactive flowchart representing the target tree structure is displayed. This interactive flowchart contains designated node elements, each representing a target node. The corresponding response script is the response script associated with the target node represented by that designated element. Thus, customer service personnel can display the corresponding response script for any designated node element in the interactive flowchart through a predefined operation. In other words, the interactive flowchart intuitively demonstrates the troubleshooting process involved in the target candidate question matching the inquiry, thereby helping customer service personnel quickly locate the problem raised by the user. Furthermore, the provided response script allows customer service personnel to quickly provide feedback to the user. Therefore, this solution can reduce response time to user inquiries and improve customer service quality and user experience.

[0067] Alternatively, in another embodiment of this application, such as Figure 2 As shown, the step of calling the first model to find target candidate questions that match the customer service personnel's input inquiry based on the first candidate question library may include the following step S201:

[0068] S201: Input the question to be consulted and the first prompt word into the first large model so that the first large model can output the target candidate question based on the question to be consulted and the first prompt word;

[0069] The first prompt word instructs the primary model to perform semantic analysis on the customer service representative's input question and, based on a predetermined analysis dimension and a primary candidate question library, search for target candidate questions that match the analysis results obtained from the semantic analysis. The primary model can be a Deepseek-V3.1 model, a Qwen plus model, etc., and this application does not impose any specific limitations. Furthermore, in some scenarios, the question to be consulted and the first prompt word can be collectively referred to as the prompt words input to the primary model; that is, the input content to the primary model can be used as the prompt words provided to the primary model, which is also reasonable.

[0070] In this implementation, during the process of searching for target candidate questions that match the question to be consulted, the question to be consulted and each candidate question in the first candidate question library can be analyzed from one or more analytical dimensions to improve the accuracy of matching the found target candidate questions with the question to be consulted. For example, the above-mentioned analytical dimensions may include the analytical object dimension, fault classification dimension, and condition scenario dimension, etc. In this embodiment, no specific limitation is made.

[0071] It should be noted that the so-called object dimension refers to the description of a specific hardware or software entity explicitly mentioned in the problem. For example, when troubleshooting a camera, the object dimension description can be the physical components of the camera itself, such as the "camera lens" in "blurry camera lens" and the "camera microphone" in "no sound from camera microphone"; it can also be the software or functional modules linked to the camera, such as the "camera driver cannot be installed" in "camera driver cannot be installed" and the camera access module of the video conferencing software in "video conferencing software cannot recognize the camera," etc. When the analysis dimension includes the object dimension, it can clarify the starting point of troubleshooting and the core subject to be investigated. The so-called fault classification dimension can be understood as the categorized description of the fault manifestations in the problem. For example, the fault manifestation of "wireless camera cannot connect to the Internet" is classified as a connection fault, and the fault manifestation of "camera cannot start after new system update" is classified as a compatibility fault, etc. When the analysis dimension includes the fault classification dimension, it can clarify the direction of fault resolution, with different classifications corresponding to different troubleshooting paths. In practical applications, the same fault may have different causes in different scenarios. The above-mentioned conditional scenario dimension can be understood as the description of the background information such as environment, operation, and event when the fault occurs. Combining the description related to the conditional scenario dimension can further narrow down the scope of investigation. Taking camera screen lag as an example, the differences between different conditional scenarios can be: (1) The fault is triggered by a specific operation, for example, "The camera screen lags as long as 4K resolution recording is turned on; it is normal when switched to 1080P"; (2) The fault is related to the external environment, for example, "The wireless camera screen does not lag when there are many people in the conference room and the signal is full; it lags when the signal is weak in the corridor"; (3) The fault occurs at a specific time point, for example, "The camera screen is normal for the first 10 minutes after the computer is turned on; it starts to lag after 10 minutes, and it recovers after restarting the computer".

[0072] Therefore, in this embodiment, a first prompt word is preset, and after the agent server receives the question to be consulted, the question to be consulted and the first prompt word are used as input content and input into the first large model, so that the first large model performs semantic analysis on the question to be consulted according to the prompt word, and searches for target candidate questions that match the analysis results obtained from the semantic analysis in each candidate question in the first candidate question library according to one or more predetermined analysis dimensions.

[0073] In this embodiment, by setting the first prompt word, a clear task objective is provided to the first main model, allowing it to understand the type of task it needs to complete. This guides the first main model to analyze and obtain the expected content. Specifically, through semantic analysis of the question to be consulted, and according to one or more analytical dimensions, the target candidate question that matches the analysis results of the semantic analysis of the question to be consulted is found in the first candidate question library. This implementation method allows for the rapid and accurate retrieval of target candidate questions.

[0074] Alternatively, in another embodiment of this application, such as Figure 3 As shown, the steps described above, which involve calling the first major model to find target candidate questions that match the customer service representative's input question based on the first candidate question library, may include the following steps:

[0075] S301: Invoke the first major model to find target candidate questions that match the questions entered by customer service personnel, based on the first and second candidate question libraries;

[0076] Among them, each candidate question in the second candidate question pool is a question related to product failure consultation and does not belong to the multi-round investigation category. Each candidate question in the second candidate question pool is associated with a predetermined response script.

[0077] Accordingly, the data processing method for product failure problems provided in this application embodiment may further include the following steps:

[0078] S302: In response to finding the target candidate question in the second candidate question library, retrieve and display the pre-defined response script associated with the target candidate question.

[0079] In this embodiment, considering that the user's inquiry may be related to product malfunction but not to the category of multi-round troubleshooting, for example, the inquiry may be "the QR code is missing." Device QR codes are usually located on the device body or base, or can be found by instructing the user to locate them in a specific location in the device's user manual. This type of problem has a relatively simple cause and can be resolved without a complex troubleshooting process. Therefore, in this embodiment, corresponding response scripts can be pre-set for product malfunction inquiries that are not related to multi-round troubleshooting. These related but non-multi-round troubleshooting questions are then merged to obtain a second candidate question library. The pre-set response scripts are then associated with each candidate question in the second candidate question library.

[0080] However, considering that when the agent server receives a question, it cannot determine whether the question is a product malfunction requiring multiple rounds of troubleshooting or a related product malfunction but not requiring multiple rounds of troubleshooting, a third prompt word can be optionally set. After receiving the question, the agent server can input the question and the third prompt word into the first main model. This allows the first model to perform semantic analysis on the question according to the prompt word, and search for target candidate questions matching the semantic analysis results in the first candidate question library according to one or more predetermined analysis dimensions. It also searches for target candidate questions matching the question input by the customer service representative in the second candidate question library. It should be noted that in some scenarios, the question and the third prompt word can be collectively referred to as prompt words input into the first main model; that is, the input content into the first main model can be used as prompt words provided for the first main model, which is also reasonable.

[0081] In this way, by setting the third prompt word, a clear task objective is provided to the first main model, allowing it to understand the type of task it needs to complete. This guides the first main model to analyze and obtain the expected content. Specifically, through semantic analysis of the question to be consulted, and according to one or more analytical dimensions, it searches for target candidate questions in the first candidate question library that match the semantic analysis results of the question to be consulted, or in the second candidate question library that match the question to be consulted entered by the customer service representative. This implementation method allows for the rapid and accurate retrieval of target candidate questions.

[0082] In this way, when the target candidate question is found to be in the second candidate question library, the predetermined response script associated with the target candidate question is obtained and displayed in response to the target candidate question being found in the second candidate question library.

[0083] For example, the question to be consulted is "the QR code is missing", and the target candidate question found is "the camera QR code is lost" in the second candidate question library. The pre-defined reply to the target candidate question is "the device's QR code / verification code can be found in two places: (1) the device body or base; (2) the device quick operation guide (instruction manual) home page -- only applicable to some older devices. Some new devices cannot be obtained from the instruction manual home page. If the device is online and the device version number is upgraded to 5.3.8 or above, check [Cloud Video - Device Settings - Device Information - Verification Code at the bottom of the page]. If it cannot be found in either of these places, you can send the device back for repair. After-sales service will help you find the tag information (if the device has been out of service, it cannot be processed)".

[0084] Optionally, to improve the service quality of customer service personnel, the aforementioned pre-planned response script may also include other information. For example, when dealing with equipment repair, the pre-planned response script may include information about repair costs and procedures for applying for repair; when dealing with mail delivery, the pre-planned response script may include information about shipping rules, etc. This embodiment does not impose specific limitations on this.

[0085] In this embodiment, two candidate question libraries are set up: the first candidate question library is set up for product failure issues that require multiple rounds of troubleshooting, and the second candidate question library is set up for issues related to product failure inquiries but not belonging to the multiple rounds of troubleshooting category. This comprehensively covers all kinds of issues related to product failure inquiries, and corresponding response scripts are set up for each type of issue. While assisting customer service personnel in quickly locating failure issues, the provided response scripts can help customer service personnel quickly provide feedback to users, thereby reducing the response time to user inquiries and improving the service quality and user experience of customer service.

[0086] Alternatively, in another embodiment of this application, such as Figure 4 As shown in the embodiments of this application, a data processing method for product failure problems may further include the following steps:

[0087] S401: If no target candidate question matching the question to be consulted is found, the question to be consulted and the second prompt word are input to call the second model so that the second model outputs the answer to the question to be consulted;

[0088] The second major model is a pre-set expert model for product failure analysis; the second prompt word is used to instruct the second major model to generate the corresponding answer to the question based on the semantic content of the question to be consulted.

[0089] S402: Display the answers to the questions you are asking.

[0090] In this embodiment, considering that the first and second candidate question databases may not store target candidate questions matching the question to be consulted, meaning that the user's question does not have a pre-set corresponding response script, in order to ensure the accuracy and professionalism of the customer service personnel's responses while responding to user inquiries in a timely manner, a pre-set expert model for product fault analysis can be invoked as the second major model in the agent server. This second major model can be a RAG (Retrieval Augmented Generation) model, a REALM (Retrieval-Augmented Language Model), a RETRO (Retrieval-Enhanced Transformer), etc., and this embodiment does not specifically limit its application.

[0091] Furthermore, a second prompt word is pre-set so that when the agent server cannot find a matching candidate question in either the first or second candidate question database, it inputs the question to be consulted and the second prompt word into the second main model. The second main model then generates the corresponding answer to the question based on the semantic content of the question, according to the prompt word. Finally, the answer to the question output by the second main model is displayed.

[0092] In this embodiment, when the agent server fails to find a target candidate question that matches the question to be consulted in either the first candidate question database or the second candidate question database, it can generate the corresponding question answer based on the semantic content of the question to be consulted by using the set second big model and the second prompt word.

[0093] For example, if the question is "How to adjust the gimbal?", the second model, based on the semantic content of the question, could generate the answer as follows: "Hello! I'm happy to provide you with adjustment guidance for the XX gimbal camera. As a human customer service representative, I will provide you with the operating steps according to the product instructions. You can adjust the gimbal in the following two ways: First method: Open the cloud video software on your mobile phone and log in to your account; select the gimbal camera you need to adjust from the device list; after entering the live preview interface, you will see the gimbal control icon. Then, click the directional arrows to control the gimbal to rotate up, down, left, and right, and use the zoom buttons to adjust the lens focus (some models support preset position settings, which can quickly rotate to a specified position). Second method: Local device adjustment; some gimbal cameras support manual adjustment. You can directly touch the device lens or gimbal part to make physical adjustments (please note: do not use excessive force when manually adjusting to avoid damaging the device)."

[0094] It should be noted that although the answers obtained are generated by calling the second major model based on the semantic content of the question, the answers provided by the second major model may not be suitable for customer service personnel to directly copy and paste to reply to users. Therefore, customer service personnel can adopt all or part of the answers generated by the second major model according to their own needs to obtain text that can be used as a reply to users.

[0095] Optionally, the answers to the above questions can be rich text data, that is, the answers to the questions can be a composite data form containing text content and formatted elements, and its core features include multimedia integration, stylized expression and cross-platform compatibility.

[0096] In this embodiment, when no target candidate question matching the question to be consulted is found in either the first candidate question library or the second candidate question library, the question answer corresponding to the question to be consulted is generated and displayed through the set second major model and second prompt words. This not only assists customer service personnel in quickly locating the fault problem, but also helps customer service personnel respond to user inquiries in a timely manner, thereby improving the service quality and user experience of customer service.

[0097] Optionally, in another embodiment of this application, step S103 above, displaying the corresponding response script for the specified node element, may include the following step A1:

[0098] Step A1: Display the corresponding response message for the specified node element in a non-streaming output mode.

[0099] In this embodiment, considering that the reply script corresponding to the specified node element is essentially pre-set or pre-generated text content, when outputting the reply script corresponding to the specified node element, the complete reply script is returned to the customer service personnel in a non-streaming output mode.

[0100] Optionally, in another embodiment of this application, step S302 above, displaying the predetermined response script associated with the target candidate question, may include the following step A2:

[0101] Step A2: Output the pre-defined response script associated with the target candidate question in a streaming output manner.

[0102] In this embodiment, the corresponding reply scripts for the specified node elements are the same as those for the target candidate questions found in the second candidate question library. The predetermined reply scripts are also pre-set or pre-generated text content. However, considering that the text content involved in this type of question is more than the reply script content of the specified node elements in the first candidate question library, for example, the predetermined reply scripts associated with the target candidate questions in the second candidate question library are: "The device's QR code / verification code can be found in two places: (1) the device body or base; (2) the device quick operation guide (instruction manual) homepage -- only applicable to some older devices, some newer devices cannot be found in the instruction manual." If the device is online and its version number is upgraded to 5.3.8 or higher, you can find the tag information by checking the verification code at the bottom of the page under "Cloud Video - Device Settings - Device Information". If you cannot find it in either of these places, you can send the device back for repair. After-sales service will help you retrieve the tag information (if the device is out of service, this cannot be processed). The response message for the specified node element corresponding to the target candidate question in the first candidate question library is "Hello, it seems that your device's memory card is not inserted properly. We suggest you power off the device and reinsert the card. If convenient, please take a picture after inserting it so we can show you if it is inserted correctly." Therefore, in order to reduce the waiting time for customer service personnel, some results are output quickly in a streaming manner.

[0103] Optionally, in another embodiment of this application, step S402 above, displaying the answer to the question to be consulted, may include the following step A3:

[0104] Step A3: Output the answer to the question to be consulted in the streaming output mode.

[0105] In this embodiment, considering that the answers to the questions generated by the second model are not like the aforementioned reply scripts, which are text content that customer service personnel can directly copy and send to the user, the generated answers may not meet expectations. When customer service personnel see the rapidly output partial results, if they find that the partial results do not meet expectations, they can promptly interrupt or stop the generation to avoid wasting time waiting for the complete results.

[0106] In this embodiment, to improve the efficiency of text output to customer service personnel, a text output method suitable for the corresponding application scenario is set for the three different text contents to be displayed to customer service personnel.

[0107] Optionally, in another embodiment of this application, after the step of inputting the question to be consulted and the second prompt word to call the second large model so that the second large model outputs the answer to the question to be consulted, the data processing for product failure problems provided in this application embodiment may further include the following steps B1-B3:

[0108] Step B1: Identify whether the problem to be consulted is a fault that requires multiple rounds of troubleshooting; if so, proceed to step B2; otherwise, proceed to step B3.

[0109] Step B2: Based on the answers to the questions to be consulted, construct a tree structure for the questions to be consulted, and add a candidate question to the first candidate question library to represent the content of the questions to be consulted, and associate the added candidate question with the tree structure for the questions to be consulted;

[0110] Step B3: In the second candidate question library, add a candidate question to represent the content of the question to be consulted, and associate the answer to the question to be consulted as a predetermined reply with the added candidate question.

[0111] In this embodiment, considering the supplementation of the first and second candidate question databases, after inputting the question to be consulted and the second prompt word to call the second large model, so that the second large model outputs the answer to the question to be consulted, the agent server can identify whether the question to be consulted is a fault problem of the multi-round troubleshooting type. Therefore, based on the identification result, as well as the question to be consulted and the corresponding answer, the first or second candidate question database is supplemented and updated. Specifically:

[0112] When the identification results indicate that the problem to be consulted is a fault problem of the multi-round investigation type, the agent server can construct a tree structure for the problem to be consulted based on the answer to the problem to be consulted, add a candidate question to the first candidate question library to represent the problem content of the problem to be consulted, and associate the added candidate question with the tree structure constructed for the problem to be consulted.

[0113] When the identification results indicate that the problem to be consulted is not a multi-round troubleshooting type of fault, the agent server can add a candidate question to the second candidate question library to represent the content of the problem to be consulted, and associate the answer to the problem to be consulted as a predetermined response script with the added candidate question. The associated answer to the problem to be consulted can be a portion of the answers generated by the second model, or it can be all of the answers generated by the second model; both are reasonable.

[0114] In this embodiment, based on the question to be consulted and the answer generated by the second model, the first or second candidate question database is supplemented and updated to improve the real-time applicability of the agent server.

[0115] Optionally, the agent server includes a feedback mechanism. When displaying response scripts or answers to questions to customer service representatives, a button for them to provide feedback can also be shown. Clicking this button displays a designated feedback interface, which can contain multiple feedback questions of a specified type (e.g., no matching process, incorrect answer, semantically similar terms, presence of sensitive information, incorrect term scheduling, etc.). This interface can also include editable sections for customer service representatives. This feedback mechanism helps maintenance personnel collect user feedback, enabling targeted updates and maintenance of the agent server based on this feedback, thus achieving continuous optimization.

[0116] To facilitate understanding of the embodiments of this application, specific examples are described below. Figure 5 This is a flowchart illustrating a specific example provided in an embodiment of this application. This specific example may include the following steps S501-S506:

[0117] S501: Receive inquiries from customer service personnel;

[0118] S502: Artificial intelligence semantic recognition; that is, the step in this application embodiment of calling the first major model to perform semantic analysis on the question to be consulted;

[0119] S503: Receiving feedback; i.e., the feedback mechanism set in the embodiments of this application.

[0120] S504: Match the target candidate question in the specified knowledge base and determine the tree structure corresponding to the target candidate question; wherein, the specified knowledge base is the first candidate question base in the embodiments of this application;

[0121] S505: Front-end rendering flowchart; that is, the steps shown in the embodiments of this application to illustrate an interactive flowchart for representing the target tree structure;

[0122] S506: Display the node of the question to be consulted, and in response to the customer service personnel's click operation, display the reply script for that node; that is, in this embodiment of the application, in response to the customer service personnel's predetermined operation on any specified node element in the interactive flowchart, display the corresponding reply script for that specified node element.

[0123] In this specific example, customer service personnel input the user's inquiry into the system (i.e., the agent server in this embodiment, also known as the dual-mode troubleshooting system, specifically referring to a composite knowledge base system that simultaneously employs artificial intelligence semantic recognition and flowchart visualization technology for problem localization). The system invokes the first major model, combining artificial intelligence to perform semantic recognition on the inquiry. If a target candidate question is matched in the specified knowledge base, the corresponding tree structure (hierarchical relationship) is determined. Then, the front-end renders the flowchart of this tree structure, enabling customer service personnel to find the node of the user's inquiry in the flowchart and obtain the corresponding response script by clicking on that node. If no target candidate question is matched in the specified knowledge base, the problem can be reported through the feedback mechanism, allowing system maintenance personnel to optimize and maintain the system based on the received feedback.

[0124] Alternatively, the tree structure (also known as a troubleshooting knowledge tree, which is a fault-finding logic system organized in a tree structure, where nodes contain decision conditions and standard solutions) can be constructed in the following way:

[0125] The system uses a tree-like hierarchical structure (treeList) created in the backend to add answers at different levels. It employs Vue (view-driven layer), Element (UI component library), and jsmind (a professional visualization plugin) technologies. Within the vue-cli (Vue Command Line Interface) architecture, it imports the jsmind plugin and uses HTTP fetch (a technology or process for exchanging data via the HTTP protocol) to request and return data in a push-stream format (SSE, Streaming SIMD Extensions). The returned data is then processed via GET requests. The `Reader()` method (which retrieves the character input stream of the requested entity content) parses the `eventStream` returned by the push stream, i.e., the streaming data. It uses the `read()` method to obtain the status of the stream nodes (whole, completed) and their values ​​(value). From the `value`, it retrieves the data of message nodes where the event is `message`, and determines the type of data returned by matching tags (tree node data (i.e., the response script for the specified node element in this application), AI answer data (i.e., the pre-defined response script in this application), rich text data (i.e., the question answer in this application)). If it is tree node data, then `new jsMind(init JSMind(id))` (creates jsMind). (Mind map example), generates a jsMind instance object, which is the unique name of the mind map for that tree node. initJSMind initializes the mind map, with the name set on the page to the corresponding instance object name. After calling the show() method of the instance object, the mind map of that tree node will be rendered. This transforms a normal tree structure into a mind map similar to XMind. Clicking on a tree node allows you to copy and view questions and answers, and the node can be configured to display question images.

[0126] For example, a tree-like hierarchical relationship is constructed for each candidate question through various editing operations (adding subclasses, editing, editing answers, and deleting, etc.) included in the predefined problem management interface. Taking the candidate question "No recording" as an example, the first-level relationship corresponding to this candidate question contains the node "Get Serial Number". This node is associated with two nodes in the second-level relationship (Memory card no recording: BOSS checks memory card status, and cloud storage no recording: status and whether the device is online). For each node in the second-level relationship, there is at least one node in the third-level relationship, and so on. According to the actual needs, each branch related to "No recording" is constructed to obtain the tree-like hierarchical relationship corresponding to the "No recording" question.

[0127] The following is combined with Figure 6 The process of constructing the tree hierarchy corresponding to one of the branches in the above-mentioned "no recording" problem will be explained. Specifically, regarding the "no recording" issue, the process involves first adding a subclass to the "Get Serial Number" node in the first-level relationship of this issue. Then, for the "Get Serial Number" node, a subclass is added to associate it with the second-level relationship node "Memory Card Not Recording: BOSS Checks Memory Card Status." Next, for the "Memory Card Not Recording: BOSS Checks Memory Card Status" node, a subclass is added to associate it with the third-level relationship nodes "Communication Error + Media Error," "Unformatted," "No Card Inserted: Power Off and Reinsert," and "Normal Use." Finally, for the "Communication Error + Media Error" node, a subclass is added to associate it with the fourth-level relationship node "Unresolved: Replace Card." For the "Unformatted" node, a subclass is added to associate it with the fourth-level relationship node "Failure: Card Meets Requirements." Finally, for the "Normal Use" node, a subclass is added to associate it with the fourth-level relationship node "Recording All Day."

[0128] It should be noted that the edit operation allows you to edit the category name, the edit answer operation allows you to edit the reply text corresponding to the node, the delete operation allows you to delete the subcategory, and the view answer operation allows you to view the reply text corresponding to the node. By performing the above operations in the predefined problem management interface, you can build a tree-like hierarchical relationship of candidate problems.

[0129] In this way, when maintenance personnel create a customer service question database (i.e., the first candidate question database in this application embodiment) in the background, they can associate the created tree-like hierarchical relationship with "no recording". Thus, when the found candidate question (also known as the troubleshooting question) is "no recording", it can be rendered and displayed based on the tree-like hierarchical relationship created in the background in advance. Figure 7 The interactive flowchart shown.

[0130] like Figure 7 As shown, the interactive flowchart of this candidate question can include the following multi-level relationships, which are interconnected through nodes. Among them, "Get Serial Number" is a node in the first-level relationship. This node "Get Serial Number" is associated with two nodes in the second-level relationship ("Memory Card Not Recording: BOSS Checks Memory Card Status", and "Cloud Storage Not Recording: Status and Device Online Status").

[0131] The first node in the second-level relationship is "Memory card not recording: BOSS checks memory card status". This node "Memory card not recording: BOSS checks memory card status" is associated with four nodes in the third-level relationship ("Communication error + media error", "Unformatted", "No card inserted: Power off and reinsert", and "Normal use").

[0132] The node "Communication Error + Media Error" is associated with a fourth-level node "Unresolved: Replace Card"; the node "Unformatted" is associated with a fourth-level node "Failed: Card Compliance"; the node "In Normal Use" is associated with two fourth-level nodes ("24-Hour Recording" and "Event Recording").

[0133] The node "24 / 7 Recording" is associated with a fifth-level node "Unresolved: Replace Card". This node "Unresolved: Replace Card" is associated with a sixth-level node "Still Abnormal: Reset Network". The node "Still Abnormal: Reset Network" is associated with a seventh-level node "Still Unable to Record After Operation: Device After-Sales Service".

[0134] The node "Event Recording" is associated with a fifth-level node "Still Invalid," which in turn is associated with two sixth-level nodes ("Detection alert / device local sound, but memory card does not record: memory card malfunction," and "No detection / device local sound"). The node "Detection alert / device local sound, but memory card does not record: memory card malfunction" is associated with a seventh-level node "No power outage / reboot / upgrade"; this node "No power outage / reboot / upgrade" is associated with an eighth-level node "Unresolved," which is associated with a ninth-level node "Problem not resolved after replacement." The node "No detection / device local sound" is associated with two seventh-level nodes ("Reset and reconnect to the network, problem resolved: troubleshooting complete," and "Reset and reconnect to the network, problem not resolved: device after-sales service").

[0135] The second node in the second-level relationship is "Cloud Storage No Recording: Status and Device Online Status". This node "Cloud Storage No Recording: Status and Device Online Status" is associated with four nodes in the third-level relationship ("Not Activated", "Expired", "Suspended and in Use: Check Device Signal"). Among them, the node "Not Activated" is associated with one node in the fourth-level relationship "User Does Not Accept"; the node "Expired" is associated with one node in the fourth-level relationship "User Does Not Accept"; and the node "In Use: Check Device Signal" is associated with two nodes in the fourth-level relationship ("Weak Signal (Below 70)" and "Strong Signal (Above 70)").

[0136] The node "Weak signal (below 70)" is associated with two nodes of the fifth level relationship ("Easy to adjust: cloud storage cannot record after adjustment, jump to strong signal", and "Inconvenient to adjust").

[0137] The node "Signal strong (above 70)" is associated with a fifth-level node "Still not triggering cloud storage recording". This node "Still not triggering cloud storage recording" is associated with a sixth-level node "Turn on device prompt sound / detection reminder. If there is a device prompt sound / detection reminder, send an upgrade work order to the second line".

[0138] In this way, when a customer service representative enters the question "The camera cannot record," the system can utilize artificial intelligence to score and identify the question against various candidate questions in the customer service question database. Specifically, it performs semantic similarity analysis between "The camera cannot record" and each candidate question, considering dimensions such as object, fault category, and conditional scenario. Candidate questions with a semantic similarity score higher than 90 (meaning "Do not record") are output as target candidate questions. Then, the interactive flowchart corresponding to the hierarchical relationship of the "Do not record" question is rendered on the front end. Figure 7 As shown in the interactive flowchart, customer service personnel can obtain corresponding response scripts by clicking on the black square nodes in the flowchart. It should be noted that nodes can also be linked to screenshots of questions to be consulted, and the interactive flowchart can be zoomed in or out as the customer service personnel interact with it.

[0139] Furthermore, considering the read-heavy, write-light nature of the tree hierarchy in this design, the traditional Nested Set Model suffers from disadvantages in operational complexity and query efficiency. Querying subtrees or ancestor nodes requires recursive traversal, resulting in a time complexity of O(n). In contrast, the Modified Preorder Tree Traversal model transforms the tree structure into a linear interval by assigning two values ​​(left and right) to each node, achieving O(1) range queries. This enables efficient querying of the entire tree or subtrees. In the Modified Preorder Tree Traversal model, the left and right values ​​of each node satisfy the following rules: the left value is always less than the right value; there are no other identical values ​​between the left and right values; and the left and right values ​​of child nodes are always nested within the left and right values ​​of their parent nodes.

[0140] In this specific example, for complex troubleshooting issues, the system avoids the traditional step-by-step selection mechanism. It can directly map user questions to nodes in the flowchart and render the flowchart in real time as a dynamic decision-making interface. Furthermore, when no troubleshooting-related questions are identified, the system uses the RAG model to search for relevant knowledge in the vector database as background information to answer customer service personnel. For parameter-based or simple product questions, the RAG model generates answers based on the product database and provides the basis for the answers to help customer service personnel answer the user's inquiries.

[0141] Based on the above method embodiments, this application provides a seat service system, such as... Figure 8 As shown, the system includes a client 810, a client 820, and a server 830.

[0142] The client 810 is used to receive questions input by the user.

[0143] The client 820 is used to obtain the user-inputted question sent by the client 810, and in response to the operation of the customer service personnel, revise the obtained question and send it to the server 830 or send the obtained question to the server 830.

[0144] The server 830 is used to execute a data processing method for product failure problems provided in this application embodiment based on the received inquiry.

[0145] In the aforementioned agent service system, after acquiring a question to be consulted, client 820 can output the acquired question (e.g., through a user interface) to display it to customer service personnel. Customer service personnel can then interact with the question acquired by client 820, allowing client 820 to respond accordingly. For example, if the customer service personnel determine that the semantics of the question acquired and output by client 820 are clear, they can issue an instruction to send the question. Client 820, in response to this instruction, directly sends the acquired question to server 830. Alternatively, if the customer service personnel determine that the semantics of the question acquired and output by client 820 are unclear (e.g., the question is ambiguous or incomplete), they can issue an instruction to revise and send the acquired question. Client 820, in response to this instruction, revises the acquired question and sends it to server 830. The revision process for the obtained questions to be consulted can be carried out by the client 820 after performing semantic analysis on the obtained questions to be consulted, or it can be carried out according to the revision method given by the customer service personnel. Both of these are reasonable.

[0146] In this way, after receiving the question to be consulted, the server 830 can execute the data processing method for product failure problems provided in this application embodiment based on the received question to reduce the response time to user inquiries and improve the service quality and user experience of customer service.

[0147] Corresponding to the above method embodiments, this application also provides a data processing device for product failure problems, applied to a call center server, such as... Figure 9 As shown, the device includes:

[0148] The search module 910 is used to call the first model to search for target candidate questions that match the questions to be consulted by customer service personnel based on the first candidate question library; wherein, each candidate question in the first candidate question library is a product failure question that has undergone multiple rounds of investigation.

[0149] The tree structure determination module 920 is used to determine the target tree structure associated with the target candidate question in response to finding the target candidate question in the first candidate question library; wherein, each candidate question in the first candidate question library is associated with a tree structure, the node hierarchy of the tree structure is used to characterize the troubleshooting process required for the candidate question, and each target node is associated with a reply script for replying to the user that matches the content represented by the target node, and the content represented by the target node is: node content that requires user interaction and question answering;

[0150] The first display module 930 is used to display an interactive flowchart representing the target tree structure; and, in response to a predetermined operation by the customer service personnel on any designated node element in the interactive flowchart, to display the corresponding response script for that designated node element; wherein, the designated node element is a node element representing a target node and the corresponding response script is the response script associated with the target node represented by the designated node element.

[0151] Optionally, in one implementation, the lookup module 910 is specifically used for:

[0152] The question to be consulted and the first prompt word are input into the first large model, so that the first large model outputs target candidate questions based on the question to be consulted and the first prompt word; wherein, the first prompt word is used to instruct the first large model to perform semantic analysis on the question to be consulted input by the customer service personnel and, according to one or more predetermined analysis dimensions, search for target candidate questions that match the analysis results obtained from the semantic analysis based on the first candidate question library.

[0153] Optionally, in one implementation, the lookup module 910 is specifically used for:

[0154] The first model is invoked to find target candidate questions that match the inquiry entered by the customer service personnel, based on the first candidate question library and the second candidate question library. Each candidate question in the second candidate question library is related to product failure consultation and does not belong to the multi-round investigation category. Each candidate question in the second candidate question library is associated with a predetermined response script.

[0155] The device further includes:

[0156] The second display module is used to retrieve and display the predetermined response script associated with the target candidate question in response to finding the target candidate question in the second candidate question library.

[0157] Optionally, in one implementation, the apparatus further includes:

[0158] The generation module is used to, if no target candidate question matching the question to be consulted is found, input the question to be consulted and the second prompt word to call the second large model, so that the second large model outputs the question answer corresponding to the question to be consulted; wherein, the second large model is a preset expert large model for product failure analysis; the second prompt word is used to instruct the second large model to generate the question answer corresponding to the question to be consulted based on the semantic content of the question to be consulted;

[0159] The third display module is used to display the answers to the questions to be consulted.

[0160] Optionally, in one implementation, the designated node element in the interactive flowchart is a node element that is displayed according to a predetermined display effect; wherein, the predetermined display effect is used to guide the customer service personnel to have corresponding response scripts associated with the designated node element.

[0161] Optionally, in one implementation, the first display module 930 is specifically used for:

[0162] Display the corresponding response message for the specified node element in a non-streaming output format;

[0163] And / or,

[0164] The second display module is specifically used for:

[0165] The predetermined response script associated with the target candidate question is output in a streaming output manner.

[0166] And / or,

[0167] The second display module is specifically used for:

[0168] Output the answers to the questions asked in a streaming manner.

[0169] Optionally, in one implementation, the apparatus further includes:

[0170] The identification module is used to identify whether the question to be consulted is a multi-round troubleshooting type of fault problem after the second prompt word is input and the second prompt word is called to make the second prompt word output the answer to the question to be consulted. If so, the construction module is triggered; otherwise, the addition module is triggered.

[0171] The construction module is used to construct a tree structure for the question to be consulted based on the question answer corresponding to the question to be consulted, and to add a candidate question to the first candidate question library to represent the question content of the question to be consulted, and associate the added candidate question with the tree structure for the question to be consulted;

[0172] The adding module is used to add a candidate question to the second candidate question library to represent the question content of the question to be consulted, and to associate the added candidate question with the corresponding question answer of the question to be consulted.

[0173] This application also provides an electronic device, such as... Figure 10 As shown, it includes:

[0174] Memory 1001 is used to store computer programs;

[0175] When the processor 1002 executes the program stored in the memory 1001, it implements the data processing method for product failure problems described in any of the above embodiments.

[0176] Furthermore, the aforementioned electronic device may also include a communication bus and / or a communication interface, with the processor 1002, the communication interface, and the memory 1001 communicating with each other via the communication bus.

[0177] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0178] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0179] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0180] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0181] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores a computer program that, when executed by a processor, implements the steps of any of the above-described data processing methods for product failure problems.

[0182] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the data processing methods for product failure problems described in the above embodiments.

[0183] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a solid-state drive (SSD), etc.

[0184] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0185] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device embodiments, system embodiments, electronic device embodiments, computer-readable storage medium embodiments, and computer program product embodiments are basically similar to the method embodiments, and therefore the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0186] The above description is merely a preferred embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application are included within the scope of protection of this application.

Claims

1. A data processing method for product failure problems, characterized in that, Applied to the agent server, the method includes: The first major model is invoked to search for target candidate questions that match the inquiry questions entered by customer service personnel, based on the first candidate question library; wherein, each candidate question in the first candidate question library is a product failure question that has undergone multiple rounds of troubleshooting. In response to finding the target candidate question in the first candidate question library, a target tree structure associated with the target candidate question is determined; wherein, each candidate question in the first candidate question library is associated with a tree structure, the node hierarchy of the tree structure is used to characterize the troubleshooting process required for the candidate question, and each target node is associated with a reply script for replying to the user that matches the content represented by the target node, the content represented by the target node is: node content that has a need for user interaction and question answering; Display an interactive flowchart representing the target tree structure; and, in response to a pre-defined operation by the customer service representative on any designated node element in the interactive flowchart, display the corresponding response script for that designated node element; wherein the designated node element is a node element representing a target node and the corresponding response script is the response script associated with the target node represented by the designated node element.

2. The method according to claim 1, characterized in that, The process of calling the first major model to find target candidate questions that match the questions entered by customer service personnel, based on the first candidate question library, includes: The question to be consulted and the first prompt word are input into the first large model, so that the first large model outputs the target candidate question based on the question to be consulted and the first prompt word; The first prompt word is used to instruct the first large model to perform semantic analysis on the inquiry input by the customer service personnel and, according to one or more predetermined analysis dimensions, search for target candidate questions that match the analysis results obtained from the semantic analysis based on the first candidate question library.

3. The method according to claim 1, characterized in that, The process of calling the first major model to find target candidate questions that match the questions entered by customer service personnel, based on the first candidate question library, includes: The first model is invoked to find target candidate questions that match the inquiry entered by the customer service personnel, based on the first candidate question library and the second candidate question library. Each candidate question in the second candidate question library is related to product failure consultation and does not belong to the multi-round investigation category. Each candidate question in the second candidate question library is associated with a predetermined response script. The method further includes: In response to finding the target candidate question in the second candidate question library, the predetermined response script associated with the target candidate question is obtained and displayed.

4. The method according to claim 3, characterized in that, The method further includes: If no target candidate question matching the question to be consulted is found, the question to be consulted and the second prompt word are input to call the second large model, so that the second large model outputs the question answer corresponding to the question to be consulted; wherein, the second large model is a preset expert large model for product failure analysis; the second prompt word is used to instruct the second large model to generate the question answer corresponding to the question to be consulted based on the semantic content of the question to be consulted; Display the answers to the questions you are asking.

5. The method according to any one of claims 1-3, characterized in that, The specified node element in the interactive flowchart is a node element that is displayed according to a predetermined display effect; The predetermined display effect is used to guide the customer service personnel to have corresponding response scripts for the specified node elements.

6. The method according to claim 4, characterized in that, The display of the corresponding response text for the specified node element includes: Display the corresponding response message for the specified node element in a non-streaming output format; And / or, The presentation of the predetermined response script associated with the target candidate question includes: The predetermined response script associated with the target candidate question is output in a streaming manner. And / or, The display of the answers to the questions to be consulted includes: Output the answers to the questions asked in a streaming manner.

7. The method according to claim 4, characterized in that, After inputting the question to be consulted and the second prompt word to call the second large model, so that the second large model outputs the answer to the question to be consulted, the method further includes: Identify whether the problem to be consulted is a fault that requires multiple rounds of troubleshooting; If so, based on the answer to the question to be consulted, a tree structure for the question to be consulted is constructed, and a candidate question is added to the first candidate question library to represent the question content of the question to be consulted, and the added candidate question is associated with the tree structure for the question to be consulted; Otherwise, in the second candidate question pool, a candidate question is added to represent the content of the question to be consulted, and the answer to the question to be consulted is associated with the added candidate question as a predetermined reply.

8. A data processing device for product malfunction problems, characterized in that, The device, applied to a seat server, includes: The search module is used to call the first model to search for target candidate questions that match the questions entered by customer service personnel, based on the first candidate question library; wherein, each candidate question in the first candidate question library is a product fault question that has undergone multiple rounds of investigation. The tree structure determination module is used to determine the target tree structure associated with the target candidate question in response to finding the target candidate question in the first candidate question library; wherein, each candidate question in the first candidate question library is associated with a tree structure, the node hierarchy of the tree structure is used to represent the troubleshooting process required for the candidate question, and each target node is associated with a reply script for replying to the user that matches the content represented by the target node, and the content represented by the target node is: node content that requires user interaction and question answering; The first display module is used to display an interactive flowchart representing the target tree structure; and, in response to a predetermined operation by the customer service personnel on any designated node element in the interactive flowchart, to display the corresponding response script for that designated node element; wherein, the designated node element is a node element representing a target node and the corresponding response script is the response script associated with the target node represented by the designated node element.

9. A customer service system, characterized in that, Includes client, client, and server: The client is used to receive questions input by the user. The client terminal is used to obtain the user-inputted inquiry sent by the client, and in response to the operation of the customer service personnel, revise the obtained inquiry and send it to the server or send the obtained inquiry to the server. The server is configured to execute the method described in any one of claims 1-7 based on the received inquiry.

10. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method described in any one of claims 1-7.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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