Construction method of problem analysis tool based on front-end webpage plug-in and AI-Agent

Abstract

The embodiment of the invention provides a method for constructing a problem analysis tool based on a front-end webpage plug-in and an AI-Agent. The method can solve the technical problems of low efficiency and poor quality of manual processing of customer complaints of telecom operators. The method comprises the following steps: constructing an interactive interface plug-in in a webpage application; the interactive interface plug-in comprises a question label; the question label is used for being clicked by a user to trigger the action of unfolding the question input area; the question input area comprises a question input box; the question input box is used for inputting and displaying complaint request content of a user; in response to an input confirmation operation on the complaint request content of the user, starting a problem analysis action; obtaining user information of a user; inputting the user information and the user complaint request content into an intelligent processing unit to obtain a user intention; according to the user intention, query is carried out in the strategy database to obtain a question analysis result, a question answering result and a question suggestion result, customer complaints of telecom operators are intelligently processed, and efficiency and quality are improved.

Description

Technical Field

[0001] The present application relates to the technical field of computer application development, and in particular to a method for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent. Background Art

[0002] Telecom operator customer complaints usually involve network failures, service interruptions, fee disputes, or insufficient technical support. When customers encounter difficulties or dissatisfaction, they submit complaints by phone, online channels, or business halls, and operators need to respond quickly and solve the problems. The importance of complaint handling is reflected in maintaining customer satisfaction and brand image. Effectively resolving complaints can not only restore customer trust, but also provide important feedback for companies to optimize services. At the same time, complaint data analysis can help identify potential problems, improve service quality and operational efficiency, and thus achieve a win-win situation for companies and customers.

[0003] At present, when telecom operators make complaints, customer service personnel or front-line operation and maintenance personnel need to switch query interfaces of multiple different systems to query and analyze the problem, explain the problem, and suggest solutions based on their own experience. Once a problem cannot be analyzed and solved by the front-line personnel, it needs to be transferred to the second-line operation and maintenance or back-end professional technical support personnel for verification and feedback to the user.

[0004] However, this manual processing model places very high demands on the personal business knowledge reserves of front-line and second-line operation and maintenance personnel. Moreover, due to the long processing process and frequent turnover of front-line operation and maintenance personnel, the problem solving time is long and the customer experience is poor. Therefore, how to achieve intelligent processing of customer complaints of telecom operators has become an urgent problem to be solved. Summary of the invention

[0005] The embodiment of the present application provides a method for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent, which can solve the technical problem of low efficiency and poor quality in manually handling customer complaints from telecom operators.

[0006] To achieve the above objectives, the embodiments of the present application adopt the following technical solutions:

[0007] In the first aspect, an embodiment of the present application provides a method for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent, and the method for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent includes: in a web page application, constructing an interactive interface plug-in; the interactive interface plug-in includes a question tag; the question tag is used for users to click to trigger the action of expanding the question input area; the question input area includes a question input box; the question input box is used to input and display the user complaint request content; in response to the input confirmation operation of the user complaint request content, start the problem analysis action; and, obtain the user information of the user; the user information includes the user account, login status and complaint service; input the user information and the user complaint request content into the intelligent processing unit together to obtain the user intention; according to the user intention, query in the policy database to obtain the problem analysis result, the question answer result and the question suggestion result.

[0008] Based on the above description of the construction method of the problem analysis tool based on the front-end web page plug-in and AI-Agent provided in the embodiment of the present application, it can be known that the construction method of the problem analysis tool based on the front-end web page plug-in and AI-Agent includes directly embedding the AI ​​customer service capability by constructing an interactive interface plug-in as a load in the web application. By obtaining the user's intention, the user's problems are intelligently classified, and the business scenarios corresponding to the user's problems are automatically identified. By querying in the policy database, the corresponding problem automation scenario analysis process is called to locate the problem, and the answer to the problem and the solution are given. The application of intelligent processing units and policy databases assists front-line and operation and maintenance personnel to quickly resolve daily customer-related questions, automatically verify and analyze problems such as business rule restrictions, data anomalies, and process anomalies, and provide corresponding solutions. In this way, customer complaints from telecom operators are handled intelligently to improve efficiency and quality.

[0009] In a feasible implementation of the first aspect, the question input area includes a voice input option; the method for constructing a question analysis tool based on a front-end web page plug-in and an AI-Agent also includes: in response to an input confirmation operation of the voice input option, calling a voice recognition function to receive input voice data information; converting the voice data information into text information; and displaying the text information in the question input box.

[0010] In a feasible implementation of the first aspect, the interactive interface plug-in also includes a result display area, and the method for constructing a problem analysis tool based on the front-end web page plug-in and AI-Agent also includes: displaying question analysis results, question answering results and question suggestion results in the result display area.

[0011] In a feasible implementation of the first aspect, the method for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent also includes: marking the problem types of historical customer service problems; problem types include query type, business processing type, fault complaint type, and value-added business consultation; marking the target solutions to historical customer service problems; building a solution knowledge base based on the problem type, historical customer service problems, and target solutions; marking business documents based on historical customer service problems; encapsulating multiple functional query tools to obtain tool encapsulation; multiple functional query tools include business knowledge query tools and billing rule query tools; encapsulating the solution knowledge base, business documents, and tools, and storing business rules, product information, and service processes in the form of a policy library to build a policy database.

[0012] In a feasible implementation of the first aspect, the method for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent also includes: using natural language processing technology to pre-process user complaint requests to obtain semantic results, structural results, and keyword results; fusing the semantic results, structural results, and keyword results to obtain user intent.

[0013] In a feasible implementation of the first aspect, a method for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent also includes: if the problem type is a query type, then querying the specific tables and fields of the policy database, and constructing a query statement to obtain data; if the problem type is a business processing type, then querying the qualification conditions of the policy database, and constructing the business processing results; if the problem type is a fault complaint type, then querying the fault diagnosis of the policy database, and constructing the troubleshooting method results.

[0014] In a feasible implementation of the first aspect, the method for constructing a question analysis tool based on a front-end web page plug-in and an AI-Agent further includes: updating a strategy database according to question analysis results, question answering results, and question suggestion results.

[0015] In a feasible implementation of the first aspect, the method for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent also includes: the interactive interface plug-in is embedded in the customer service system in a floating box or the like.

[0016] In a second aspect, an embodiment of the present application provides a system for building a problem analysis tool based on a front-end web page plug-in and an AI-Agent, and the system for building a problem analysis tool based on a front-end web page plug-in and an AI-Agent includes: at least one processor; a memory communicatively connected to at least one processor; wherein the memory stores instructions executable by at least one processor, and the instructions are executed by at least one processor so that at least one processor can execute the method provided in the first aspect.

[0017] The construction system of the problem analysis tool based on the front-end web page plug-in and AI-Agent embeds the AI ​​customer service capability directly by executing the method provided in the first aspect, by building an interactive interface plug-in as a load in the web application. By obtaining the user's intention, the user's problems are intelligently classified, and the business scenarios corresponding to the user's problems are automatically identified. By querying in the policy database, the corresponding problem automation scenario analysis process is called to locate the problem, and the answer to the problem and the solution are given. The application of intelligent processing units and policy databases assists front-line and operation and maintenance personnel to quickly resolve daily customer-related questions, automatically verify and analyze problems such as business rule restrictions, data anomalies, and process anomalies, and provide corresponding solutions. In this way, customer complaints from telecom operators are handled intelligently to improve efficiency and quality.

[0018] In a third aspect, an embodiment of the present application provides a computer-readable medium having computer program instructions stored thereon, and the computer program instructions can be executed by a processor to implement the method provided in the first aspect.

[0019] The computer program instructions in the computer-readable medium implement the method provided in the first aspect, and directly embed the AI ​​customer service capability by building an interactive interface plug-in as a load in the web application. By obtaining the user's intention, the user's problems are intelligently classified, and the business scenarios corresponding to the user's problems are automatically identified. By querying in the policy database, the corresponding problem automation scenario analysis process is called to locate the problem, and the answer to the problem and the solution are given. The application of intelligent processing units and policy databases assists front-line and operation and maintenance personnel to quickly resolve daily customer-related questions, automatically verify and analyze problems such as business rule restrictions, data anomalies, and process anomalies, and provide corresponding solutions. In this way, customer complaints from telecom operators are handled intelligently to improve efficiency and quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic diagram of the structure of a system for building a problem analysis tool based on a front-end web page plug-in and an AI-Agent provided in an embodiment of the present application;

[0021] Figure 2 A flowchart of a method for constructing a problem analysis tool based on a front-end web page plug-in and AI-Agent provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present invention will be described below in conjunction with the accompanying drawings in the embodiments of the present invention. In the description of the embodiments of the present invention, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0023] In addition, in order to facilitate the clear description of the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, the words "first", "second" and the like are used to distinguish the same items or similar items with substantially the same functions and effects. Those skilled in the art will understand that the words "first", "second" and the like do not limit the quantity and execution order, and the words "first", "second" and the like do not necessarily limit the difference. At the same time, in the embodiments of the present invention, the words "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or design. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way for easy understanding.

[0024] The principles and features of the present application are described below. The examples given are only used to explain the present application and are not used to limit the scope of the present application.

[0025] The embodiment of the present application provides a method for constructing a problem analysis tool based on a front-end web page plug-in and AI-Agent, which is suitable for handling complaint handling scenarios. The embodiment of the present application uses a web page plug-in as a load, based on speech recognition and AI large model technology, to construct an intelligent problem analysis tool for front-line operation and maintenance personnel. Intelligent scene recognition is performed on complaint work order problems, and scenario-based problem analysis and problem location are performed based on the recognition results, and answers and solutions to the problems are given, providing users of telecom operators with convenient, efficient, and accurate problem answering and business consulting services, improving user experience and service quality, while also helping to improve operators' operational efficiency and customer satisfaction.

[0026] The embodiment of the present application provides a system for constructing a problem analysis tool based on a front-end web page plug-in and AI-Agent, which can execute the method for constructing a problem analysis tool based on a front-end web page plug-in and AI-Agent provided in the embodiment of the present application. Figure 1 A structural diagram of a system for building a problem analysis tool based on a front-end web page plug-in and an AI-Agent provided in an embodiment of the present application.

[0027] like Figure 1 As shown, the construction system 001 of the problem analysis tool based on the front-end web page plug-in and AI-Agent includes at least one processor 011 and a memory 012 communicatively connected to the at least one processor; wherein the memory 012 stores instructions executable by the at least one processor 011, and the instructions are executed by the at least one processor 011 so that the at least one processor 011 can execute the construction method of the problem analysis tool based on the front-end web page plug-in and AI-Agent provided in an embodiment of the present application.

[0028] Figure 2 The present invention provides a flowchart of a method for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent. Figure 2 As shown, in some embodiments, the method for constructing the problem analysis tool based on the front-end web page plug-in and AI-Agent includes the following steps:

[0029] S1, build interactive interface plug-ins in web applications.

[0030] The interactive interface plug-in can be a front-end web page plug-in that can be easily integrated into various web page applications, such as customer service web pages and business handling web pages of telecom operators.

[0031] The interactive interface plug-in includes a question tag. Exemplarily, the interactive interface plug-in is embedded in the customer service system in the form of a floating box or the like. The question tag is used for a user to click to trigger an action of expanding the question input area. The question input area includes a question input box.

[0032] In this way, the plug-in presents a simple and intuitive interactive interface on the web page, such as a small floating icon or a fixed consultation bar, and the user can expand the question input area after clicking.

[0033] The question input box is used to input and display the content of the user's complaint request.

[0034] In some embodiments, the question input area includes a voice input option. When executing step S1, the method for constructing a question analysis tool based on a front-end web page plug-in and an AI-Agent further includes the following steps:

[0035] S111, in response to a confirmation operation input to a voice input selection item, calling a voice recognition function to receive input voice data information.

[0036] Exemplarily, the voice input option may be a voice input button. After the user clicks the voice input button, the voice recognition function of the device is called, such as the voice application programming interface (API) provided by the browser or an integrated third-party voice recognition engine, and the voice is converted into text and displayed in the input box, so that the user can ask questions in a natural language, and the problem analysis process can be quickly started regardless of typing or voice input.

[0037] Integrate a speech recognition engine into the front-end web plug-in to optimize the recognition of professional terms and common expressions in the telecommunications field. For example, train the speech recognition model to recognize the special pronunciation and usage of telecommunications-related terms such as "traffic package" and "phone bill".

[0038] S112, converting the voice data information into text information.

[0039] S113, displaying text information in the question input box.

[0040] S2, in response to the input confirmation operation of the user's complaint request content, starting the problem analysis action, and obtaining the user's user information.

[0041] User information includes user account, login status and complaint service. This provides more context for problem analysis and passes this information along with the questions raised by the user to the backend AI-Agent, which helps to understand the user's intention more accurately.

[0042] S3, input the user information and the user complaint request content into the intelligent processing unit to obtain the user intention.

[0043] The intelligent processing unit identifies the intention of the user's question based on the information fused by the perception module. The user's intention is classified into common telecom business intention categories, such as inquiry (call fee inquiry, traffic inquiry, etc.), business handling (package change, value-added service activation, etc.), fault complaint (network failure, signal problem, etc.), consultation (business rule consultation, preferential activity consultation, etc.), etc.

[0044] For example, if a user asks a question on the package change page, AI-Agent can conduct a comprehensive analysis based on relevant business knowledge of the package change and user account information.

[0045] In some embodiments, when executing step S3, the method for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent further includes the following steps:

[0046] S311, using natural language processing technology, pre-process the user complaint request to obtain semantic results, structural results and keyword results.

[0047] S312, integrating the semantic results, structural results and keyword results to obtain the user intention.

[0048] S4, according to the user's intention, query the policy database to obtain the question analysis results, question answering results and question suggestion results.

[0049] After identifying the user's question intention, the relevant knowledge nodes and relationships are queried in the policy database to obtain background knowledge and solution information related to the problem, providing data support for the decision-making module. For example, if a user asks about the traffic usage rules of a certain package, the traffic rule node corresponding to the package is searched in the policy database.

[0050] According to the user intentions identified by the cognitive module, the information in the policy database retrieved, and the conversation history, the historical policy memory is referenced to conduct new policy planning and generate the capability call policy required for problem solving. If it is a query problem, the strategy may be to determine the specific tables and fields of the query database, construct a query statement to obtain data and return it to the user; if it is a business handling problem, the strategy may be to check the user's eligibility conditions for handling business, generate the process steps for handling business and guide the user's operation; if it is a fault complaint problem, the strategy may be to conduct preliminary fault diagnosis first, provide some simple troubleshooting methods to the user, or arrange for technicians to come to the door for repair according to the severity of the fault.

[0051] Generate text content to answer user questions based on the strategy generated by the decision-making module. The answer should not only accurately convey the solution to the problem, but also be presented to the user in easy-to-understand and friendly language. Generate targeted answers by using methods such as template filling and natural language generation technology, combined with business knowledge and personalized information. For example, in response to a user's package change question, the answer may be "Hello, you can change your package by following the steps below: log in to our mobile business hall APP, click the 'Package Change' button, select the type of package you want to change, and confirm the submission. The package change may take effect in the next month. If you have any other questions, please feel free to contact us."

[0052] In some embodiments, before executing step S4, the method for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent further includes:

[0053] S401, marking the problem type of the historical customer service problem.

[0054] Historical customer service issues, including a large number of historical customer service issues collected from telecom operators' customer service records, online Q&A platforms, user feedback channels, etc. Issue types include inquiry, business handling, fault complaint, and value-added service consultation. Inquiry includes call fee inquiry. Business handling includes package changes. Fault complaint includes network failure.

[0055] In some embodiments, high-quality solutions are marked and a solution knowledge base corresponding to the problem scenario is quickly formed.

[0056] S402, marking target solutions for historical customer service issues.

[0057] For example, for hot issues for which no high-quality solutions are collected, manual solution entry is performed.

[0058] S403, building a solution knowledge base based on the problem type, historical customer service problems and target solutions.

[0059] S404, marking business documents based on historical customer service issues.

[0060] In this way, the corresponding solutions, business rule documents (such as package descriptions, charging standards, service agreements) and other relevant materials serve as the knowledge basis for understanding and answering questions.

[0061] By executing steps S401 to S404, the problem is marked, and the marking content may include the problem type (such as cost-related, business handling, technical failure), the business field involved (such as mobile data business, fixed-line phone business), the urgency of the problem (such as urgent, general, non-urgent), etc. This helps the subsequent Agent to classify and prioritize the problems.

[0062] S405, encapsulating multiple function query tools to obtain tool encapsulation.

[0063] Multiple functional query tools include business knowledge query tools and billing rules query tools.

[0064] Basic capabilities such as business knowledge query, billing rule query, and API capabilities such as bill query, cumulative query, balance query, SMS record query, payment information query, and refund record query are encapsulated and put into the tool library as basic tools.

[0065] S406, encapsulate the solution knowledge base, business documents and tools, store business rules, product information, service processes in the form of a policy library, and construct a policy database.

[0066] In some embodiments, if the problem type is a query type, the specific tables and fields of the policy database are queried, and a query statement is constructed to obtain data. If the problem type is a business handling type, the eligibility conditions of the policy database are queried, and the business handling process results are constructed. If the problem type is a fault complaint type, the fault diagnosis of the policy database is queried, and the troubleshooting method results are constructed.

[0067] In some embodiments, after executing step S406, the method for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent further includes:

[0068] S407, updating the strategy database according to the question analysis result, the question answer result and the question suggestion result.

[0069] Build a telecom business policy database, store business rules, product information, service processes and other knowledge in the form of a policy library, and when encountering a problem, query the policy database to obtain relevant knowledge to assist in problem analysis. Tool-based decomposition of business rules, product information, service processes, etc. is performed in advance, and the tool combination required for various scenarios is used as the original policy memory input as the preliminary basis for policy generation. For example, the policy database stores information such as the charging standards of different packages and the included business content, which is used to answer questions about packages.

[0070] In some embodiments, after executing step S407, the method for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent further includes:

[0071] S408: Establish an AI work order analysis model based on the policy database.

[0072] According to the user's intention, query the AI ​​work order analysis model to obtain the problem analysis results, question answer results and question suggestion results.

[0073] In some embodiments, the interactive interface plug-in further includes a result display area. When executing step S4, the method for constructing a problem analysis tool based on the front-end web page plug-in and the AI-Agent further includes:

[0074] S411, in the result display area, display the question analysis results, question answer results and question suggestion results.

[0075] For example, the question analysis results, answers and suggestions returned by the backend AI-Agent are received and displayed to the user in a clear and friendly manner on the front-end plug-in interface. The results include text answers, related business link recommendations, operation step prompts, etc.

[0076] The AI-Agent plug-in is embedded into the customer service system of telecom operators in the form of a floating box, etc., without affecting existing common functions, and connected with existing customer service channels (such as operation and maintenance platforms, online customer service platforms). When a user asks a question, the plug-in system sends the question to the AI-Agent for analysis and answering.

[0077] In actual use, the performance of AI-Agent is monitored in real time to collect user feedback and new types of customer service questions. The model is updated and optimized regularly to adapt to changing business needs and user problems. For example, when an operator launches a new package or service, the policy database and related model parameters are updated in a timely manner.

[0078] In the embodiments of the present application, by using AI-Agent technology, AI customer service moves towards the role of intelligent body or intelligent agent (Agent), and has more complex cognitive and behavioral capabilities. This transformation marks the evolution of AI customer service from passive response to active service, from single function to multi-dimensional collaboration, bringing new possibilities for improving customer experience.

[0079] The embodiment of the present application abandons the traditional way of building a new system and directly embeds AI customer service capabilities through web page plug-ins as loads, greatly reducing system deployment, transformation, and learning costs.

[0080] The embodiment of the present application semantically analyzes the user's intention in asking questions, whether it is to seek solutions, obtain information, complain, or other purposes; and extracts key information, such as the subject of the question (phone bill), time range (this month), nature of the problem (sudden increase), etc.

[0081] The embodiment of the present application identifies and resolves problems based on key information extracted by the perception module, such as call charge inquiry, call charge anomalies, uninformed ordering, activity disputes, etc.

[0082] The embodiment of the present application uses model capabilities and refers to historical strategy memory to find similar scenarios, and plans new strategies based on the current problem to generate the capability call strategy required to solve the problem.

[0083] The embodiment of the present application automatically updates, supplements, and optimizes the strategy memory according to the result of the current strategy execution.

[0084] Based on the same application concept, a system for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent is also provided in an embodiment of the present application. The method corresponding to the system for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent may be the method for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent in the aforementioned embodiment, and its principle of solving the problem is similar to that of the method. The system for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent provided in an embodiment of the present application includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the methods and / or technical solutions of the aforementioned multiple embodiments of the present application.

[0085] Another embodiment of the present application further provides a computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the methods and / or technical solutions of any one or more embodiments of the present application described above.

[0086] Specifically, the present embodiment may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device, or device.

[0087] Computer readable signal media may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer readable program code. Such propagated data signals may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than a computer readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0088] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0089] The computer program code for performing the operation of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer, partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0090] The flow chart or block diagram in the accompanying drawings shows the possible architecture, function and operation of the equipment, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated system for hardware that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0091] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0092] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or page components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0093] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0094] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0095] The above integrated units implemented in the form of software functional units can be stored in a computer-readable storage medium. The above software functional units stored in a storage medium include several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute some steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

[0097] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in a device claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

Claims

1. A method for constructing a problem analysis tool based on a front-end web page plug-in and AI-Agent, characterized in that: include: In a web application, an interactive interface plug-in is constructed; the interactive interface plug-in includes a question tag; the question tag is used for a user to click to trigger an action of expanding a question input area; the question input area includes a question input box; the question input box is used to input and display user complaint request content; In response to the input confirmation operation of the user complaint request content, starting a problem analysis action; And, obtaining user information of the user; The user information includes user account, login status and complaint service; Inputting the user information and the user complaint request content into an intelligent processing unit to obtain the user intention; According to the user intention, a query is made in the policy database to obtain question analysis results, question answering results and question suggestion results.

2. The method for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent according to claim 1, characterized in that: The question input area includes a voice input option; The method for constructing the problem analysis tool based on the front-end web page plug-in and AI-Agent also includes: In response to a confirmation operation inputted to the voice input selection item, calling a voice recognition function to receive inputted voice data information; Converting the voice data information into text information; The text information is displayed in the question input box.

3. The method for constructing a problem analysis tool based on a front-end web page plug-in and AI-Agent according to claim 1 or 2, characterized in that: The interactive interface plug-in also includes a result display area, and the method for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent also includes: In the result display area, the question analysis result, the question answer result and the question suggestion result are displayed.

4. The method for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent according to claim 1 or 2, characterized in that: The method for constructing the problem analysis tool based on the front-end web page plug-in and AI-Agent also includes: Mark the types of historical customer service questions; the types of questions include inquiry, business handling, fault complaint and value-added business consultation; Marking a target solution to the historical customer service issue; Building a solution knowledge base according to the problem type, the historical customer service problems and the target solution; Marking business documents based on the historical customer service issues; Encapsulating multiple function query tools to obtain tool encapsulation; the multiple function query tools include a business knowledge query tool and a billing rule query tool; The solution knowledge base, the business document and the tool are encapsulated, and business rules, product information and service processes are stored in the form of a policy library to construct the policy database.

5. The method for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent according to claim 1 or 2, characterized in that: The method for constructing the problem analysis tool based on the front-end web page plug-in and AI-Agent also includes: Using natural language processing technology, pre-process the user complaint request to obtain semantic results, structural results and keyword results; The semantic results, the structural results and the keyword results are integrated to obtain the user intention.

6. The method for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent according to claim 4, characterized in that: The method for constructing the problem analysis tool based on the front-end web page plug-in and AI-Agent also includes: If the question type is a query type, query the specific tables and fields of the policy database, and construct a query statement to obtain data; If the problem type is business handling, query the eligibility conditions of the policy database and construct a business handling process result; If the problem type is a fault complaint type, the fault diagnosis of the strategy database is queried to construct a troubleshooting method result.

7. The method for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent according to claim 1 or 2, characterized in that: The method for constructing the problem analysis tool based on the front-end web page plug-in and AI-Agent also includes: The strategy database is updated according to the question analysis result, the question answer result and the question suggestion result.

8. The method for constructing a problem analysis tool based on a front-end web page plug-in and an AI-Agent according to claim 6, characterized in that: The method for constructing the problem analysis tool based on the front-end web page plug-in and AI-Agent also includes: The interactive interface plug-in is embedded in the customer service system in the form of a floating frame or the like.

9. A system for constructing a problem analysis tool based on a front-end web page plug-in and AI-Agent, characterized in that: include: at least one processor; a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1 to 8.

10. A computer readable medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the method according to any one of claims 1 to 8.

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