Data processing method and device, equipment, storage medium and computer program product

Abstract

The invention discloses a data processing method and device, equipment, a storage medium and a computer program product, which are used for solving the problem that an existing intelligent question and answer platform needs to determine historical hotspot problems in a manual screening and sorting mode, so that the updating frequency of the existing intelligent question and answer platform for the hotspot problems is relatively low. The method comprises the steps of generating a plurality of question and answer groups corresponding to a question and answer platform according to collected historical business data of the question and answer platform; screening and sorting the question and answer groups according to a preset screening dimension, and determining at least one hotspot question and answer group; determining a question keyword corresponding to the hotspot question and answer group, and searching a standard answer matched with the question keyword; according to the standard reply, reply results in the hotspot question and answer group are updated, and a standard hotspot question and answer group is obtained; and obtaining service data corresponding to a user account logging in the question and answer platform, determining a priority order corresponding to the standard hotspot question and answer group, and pushing and displaying the standard hotspot question and answer group according to the priority order.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular, to a data processing method, apparatus, device, storage medium, and computer program product. Background Art

[0002] With the in-depth development of computer and artificial intelligence technologies and their rapid implementation in various industries, the customer service industry has become a key application scenario in artificial intelligence technology.

[0003] Among them, the intelligent question-and-answer technology is a technology for industry applications developed on the basis of large-scale knowledge processing, and is applicable to various fields such as large-scale knowledge processing, natural language understanding, knowledge management, automatic question-and-answer systems, enterprise disaster recovery management, and unified information services. Relying on the intelligent question-and-answer technology, many enterprises have built intelligent question-and-answer databases, and based on the intelligent question-and-answer databases, they have built intelligent customer service platforms, using the intelligent customer service platforms to replace most of the manual customer service, reducing labor costs and improving service efficiency at the same time. The intelligent question-and-answer database not only provides enterprises with fine-grained knowledge management technology, but also establishes a fast and effective technical means based on natural language for communication between enterprises and a large number of users, and can also provide statistical analysis information required for refined management of enterprises.

[0004] In order to improve the service accuracy and service efficiency of the intelligent question-and-answer platform, in related technologies, it is often necessary to perform statistics on the historical data of the intelligent question-and-answer platform (such as a unified information service platform) in the past period of time (such as the past week or month), and then screen out the high-heat questions in the past period of time, and then perform targeted optimization on these high-heat questions to improve the reply accuracy and reply efficiency of the intelligent question-and-answer product for these high-heat questions.

[0005] However, in related technologies, the intelligent question-and-answer platform (such as a unified information service platform) cannot automatically summarize and count historical hot questions, but needs to manually screen and sort historical questions through manual means, resulting in a low update frequency of the existing intelligent question-and-answer platform for hot questions, and further resulting in poor question-and-answer accuracy of the intelligent question-and-answer platform.

[0006] Therefore, there is an urgent need for a method that can automatically and efficiently implement statistical screening of hot questions in the intelligent question-and-answer system. Summary of the Invention

[0007] Embodiments of this application provide a data processing method to solve the problem that the existing intelligent question-and-answer platform needs to determine historical hot questions by manual screening and sorting, resulting in a low update frequency of the existing intelligent question-and-answer platform for hot questions, and further resulting in poor question-and-answer accuracy of the intelligent question-and-answer platform.

[0008] The embodiment of the present application also provides a data processing device, which is used to solve the problem that the existing intelligent Q&A platform needs to manually screen and sort to determine historical hot issues, resulting in a low update frequency of the existing intelligent Q&A platform for hot issues, and further resulting in poor Q&A accuracy of the intelligent Q&A platform.

[0009] The embodiment of the present application also provides a data processing device, which is used to solve the problem that the existing intelligent Q&A platform needs to manually screen and sort to determine historical hot issues, resulting in a low update frequency of the existing intelligent Q&A platform for hot issues, and further resulting in poor Q&A accuracy of the intelligent Q&A platform.

[0010] The embodiment of the present application also provides a computer-readable storage medium, which is used to solve the problem that the existing intelligent Q&A platform needs to manually screen and sort to determine historical hot issues, resulting in a low update frequency of the existing intelligent Q&A platform for hot issues, and further resulting in poor Q&A accuracy of the intelligent Q&A platform.

[0011] A computer program product, which is used to solve the problem that the existing intelligent Q&A platform needs to manually screen and sort to determine historical hot issues, resulting in a low update frequency of the existing intelligent Q&A platform for hot issues, and further resulting in poor Q&A accuracy of the intelligent Q&A platform.

[0012] The embodiment of the present application adopts the following technical solutions: A data processing method includes: generating multiple groups of Q&A groups corresponding to the Q&A platform according to the collected historical business data of the Q&A platform, where the Q&A group includes a historical question and a corresponding answer result; screening and sorting the multiple groups of Q&A groups according to a preset screening dimension to determine at least one hot Q&A group corresponding to the Q&A platform; determining a question keyword corresponding to the hot Q&A group, and searching for a standard answer matching the question keyword in a standard answer database preset in the Q&A platform according to the question keyword; when a standard answer matching the question keyword is found, updating the answer result in the hot Q&A group according to the standard answer to obtain a standard hot Q&A group; obtaining business data corresponding to a user account logged in to the Q&A platform, determining a priority ranking corresponding to the standard hot Q&A group according to the business data, and pushing and displaying the standard hot Q&A group according to the priority ranking.

[0013] A data processing device, comprising: a Q&A group generation unit for generating multiple groups of Q&A groups corresponding to the Q&A platform according to the collected historical business data of the Q&A platform, wherein the Q&A groups include historical questions and the corresponding answer results of the historical questions; a hot Q&A group screening unit for screening and sorting the multiple groups of Q&A groups according to a preset screening dimension to determine at least one hot Q&A group corresponding to the Q&A platform; a question group matching unit for determining the question keywords corresponding to the hot Q&A group and searching for a standard answer matching the question keywords in a standard answer database preset in the Q&A platform according to the question keywords; a question group updating unit for, when a standard answer matching the question keywords is found, updating the answer results in the hot Q&A group according to the standard answer to obtain a standard hot Q&A group; a push unit for obtaining the business data corresponding to the user account logged in to the Q&A platform, determining the priority ranking corresponding to the standard hot Q&A group according to the business data, and pushing and displaying the standard hot Q&A group according to the priority ranking.

[0014] A data processing device, comprising: a processor; and a memory arranged to store computer-executable instructions that, when executed, cause the processor to perform the following operations: generating multiple groups of Q&A groups corresponding to the Q&A platform according to the collected historical business data of the Q&A platform, wherein the Q&A groups include historical questions and the corresponding answer results of the historical questions; screening and sorting the multiple groups of Q&A groups according to a preset screening dimension to determine at least one hot Q&A group corresponding to the Q&A platform; determining the question keywords corresponding to the hot Q&A group and searching for a standard answer matching the question keywords in a standard answer database preset in the Q&A platform according to the question keywords; when a standard answer matching the question keywords is found, updating the answer results in the hot Q&A group according to the standard answer to obtain a standard hot Q&A group; obtaining the business data corresponding to the user account logged in to the Q&A platform, determining the priority ranking corresponding to the standard hot Q&A group according to the business data, and pushing and displaying the standard hot Q&A group according to the priority ranking.

[0015] A computer-readable storage medium stores one or more programs. When the one or more programs are executed by an electronic device including a plurality of application programs, the electronic device is caused to perform the following operations: generating multiple groups of question-and-answer groups corresponding to the Q&A platform according to the collected historical business data of the Q&A platform, wherein each question-and-answer group includes a historical question and a corresponding answer result; screening and sorting the multiple groups of question-and-answer groups according to a preset screening dimension to determine at least one hot question-and-answer group corresponding to the Q&A platform; determining a question keyword corresponding to the hot question-and-answer group, and searching for a standard answer matching the question keyword in a standard answer database preset in the Q&A platform according to the question keyword; when a standard answer matching the question keyword is found, updating the answer result in the hot question-and-answer group according to the standard answer to obtain a standard hot question-and-answer group; obtaining the business data corresponding to the user account logged in to the Q&A platform, determining a priority ranking corresponding to the standard hot question-and-answer group according to the business data, and pushing and displaying the standard hot question-and-answer group according to the priority ranking.

[0016] A computer program product includes a computer program which, when executed by a processor, implements: generating multiple groups of question-and-answer groups corresponding to the Q&A platform according to the collected historical business data of the Q&A platform, wherein each question-and-answer group includes a historical question and a corresponding answer result; screening and sorting the multiple groups of question-and-answer groups according to a preset screening dimension to determine at least one hot question-and-answer group corresponding to the Q&A platform; determining a question keyword corresponding to the hot question-and-answer group, and searching for a standard answer matching the question keyword in a standard answer database preset in the Q&A platform according to the question keyword; when a standard answer matching the question keyword is found, updating the answer result in the hot question-and-answer group according to the standard answer to obtain a standard hot question-and-answer group; obtaining the business data corresponding to the user account logged in to the Q&A platform, determining a priority ranking corresponding to the standard hot question-and-answer group according to the business data, and pushing and displaying the standard hot question-and-answer group according to the priority ranking.

[0017] The above at least one technical solution adopted in the embodiments of the present application can achieve the following beneficial effects: By adopting the data processing method provided in the embodiment of the present application, the data processing system can collect the historical business data generated by the Q&A platform within a preset data processing cycle, and generate multiple groups of Q&A groups corresponding to the Q&A platform, which are composed of historical questions and the corresponding answer results according to the collected historical business data of the Q&A platform. At the same time, the data processing system can screen and sort multiple groups of Q&A groups according to a preset screening dimension to determine at least one hot Q&A group corresponding to the Q&A platform; after completing the screening of the hot Q&A group, the data processing system can determine the question keywords corresponding to the hot Q&A group, and search for the standard answers matching the question keywords in the standard answer database preset in the Q&A platform according to the question keywords. When the standard answers matching the question keywords are found, the answer results in the hot Q&A group are updated according to the standard answers to obtain the standard hot Q&A group, so as to ensure that the standard answers to the hot questions are stored in the hot Q&A group and avoid the problem of inaccurate answers to the hot questions due to AI hallucinations; finally, the data processing system can obtain the business data corresponding to the user account logged in to the Q&A platform, determine the priority ranking corresponding to the standard hot Q&A group according to the business data, and push and display the standard hot Q&A group according to the priority ranking, so as to display the hot Q&A group for the user according to the priority of user attention. The user can conveniently find the hot questions recently concerned by the user in the hot Q&A group display area and directly view the standard answers corresponding to the hot questions, which greatly improves the business processing efficiency and the Q&A accuracy of the Q&A platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments and descriptions thereof of the present application are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings: Figure 1 It is a specific flow schematic diagram of a data processing method provided by an embodiment of the present application; Figure 2 It is a specific structural schematic diagram of a data processing device provided by an embodiment of the present application; Figure 3 It is a specific structural schematic diagram of a data processing device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments of this application and the corresponding drawings. Apparently, the described embodiments are only a part of the embodiments of this application, rather than all of them. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application.

[0020] To solve the problem that the existing intelligent Q&A platform needs to determine historical hot issues by manual screening and sorting, resulting in a low update frequency of the existing intelligent Q&A platform for hot issues, and further resulting in poor Q&A accuracy of the intelligent Q&A platform, the embodiments of this application provide a data processing system and a data processing method based on the data processing system.

[0021] The execution subject of the data processing method provided by the embodiments of this application can be, but is not limited to, at least one of an intelligent Q&A server, a unified information service platform, a data management server, or an intelligent customer service server, etc.; alternatively, the execution subject of this method can also be an intelligent Q&A platform running on a server; in addition, the execution subject of this method can also be a terminal device used by a user or a background administrator, or even an intelligent Q&A application installed on the terminal device, etc. For ease of description, the embodiments of the present invention are all described by taking the execution subject as a data processing system running on an intelligent Q&A server as an example.

[0022] The above-mentioned execution subject does not constitute a limitation to this application. For ease of description, the following takes the execution subject of this method as a data processing system running on an intelligent Q&A server as an example to introduce the implementation manner of this method. It can be understood that taking the execution subject of this method as a data processing system running on an intelligent Q&A server is only an exemplary description and should not be construed as a limitation to this method.

[0023] The specific implementation process schematic diagram of the data processing method provided by this application is as Figure 1 shown and mainly includes the following steps: Step 11, generating multiple groups of Q&A groups corresponding to the Q&A platform according to the collected historical business data of the Q&A platform; In the embodiments of this application, the data processing system can collect the business data generated by the Q&A platform during the collection period according to a preset collection period to obtain historical business data, and generate multiple groups of Q&A groups received and processed by the Q&A platform during the collection period by analyzing and processing the collected historical business data.

[0024] Among them, the Q&A group includes the historical questions uploaded by the user received by the Q&A platform and the corresponding reply results made by the Q&A platform for the received historical questions.

[0025] Specifically, in the embodiments of the present application, the data processing system can specifically collect historical business data according to the following sub-steps, and generate multiple groups of question-and-answer groups based on the collected historical business data, including: Sub-step 1101: Collect data from the question-and-answer platform according to a preset collection period; It should be noted here that existing intelligent question-and-answer platforms generally use JIRA list interfaces to implement functions related to the tracking of integrated questions (for example, through JIRA list interfaces, historical interaction data of intelligent question-and-answer platforms can be recorded, including question descriptions, question answers, processing times, etc.) and data analysis. Therefore, in the embodiments of the present application, the data processing system can connect to the JIRA list interface of the intelligent question-and-answer platform through a data acquisition interface, construct JQL (Jira Query Language) query conditions according to business requirements and a preset collection period (for example, the time range of data collection and question types can be set according to the collection period), construct a complete request URL according to the constructed JQL query conditions, and send a data collection request to the JIRA interface, so that the intelligent question-and-answer platform queries historical business data from the database according to the received data collection request, and returns the queried historical business data to the JIRA list interface. Furthermore, the data processing system can obtain data from the JIRA list interface through data scraping technology (such as Scrapy).

[0026] Sub-step 1102: Perform data preprocessing on the collected data to obtain historical business data; In the embodiments of the present application, the data processing system can perform data cleaning, duplicate removal, missing value supplementation, unified text format (such as full-width / half-width conversion, case unification), and processing of special characters, spelling mistakes, abbreviation expansion, etc. on the collected historical business data to ensure the quality and integrity of the collected historical business data, which is convenient for subsequent processing.

[0027] After the above processing is completed, the data processing system will perform word segmentation and annotation on the text in the historical business data, and perform word segmentation on questions / answers. In one implementation, the data processing system can use the Jieba component to perform word segmentation on Chinese text, and use the Natural Language Toolkit (NLTK) to perform word segmentation on English text. At the same time, the data processor will remove stop words (such as meaningless words like "de", "en", etc.) from the text data, standardize the part-of-speech of the text data (such as verbs, nouns), and perform entity naming (such as person names, place names, etc.).

[0028] Sub-step 1103: Identify the historical business data preprocessed in sub-step 1102 according to the natural language processing technology NLP; In the embodiment of the present application, when processing the historical business text data obtained after executing sub-step 1102 based on the natural language processing (NLP) technology, first, a pre-trained model (such as BERT or RoBERTa) can be used to classify the intent of the questions in the text data, and then output the question type label and confidence level.

[0029] At the same time, judge the sentiment tendency (positive, neutral, negative) of the user's question through sentiment analysis to identify potential dissatisfaction or urgent problems. Then, extract the key information from the historical text data, and use the term frequency–inverse document frequency (TF-IDF) algorithm to extract high-frequency keywords. And extract entities (such as names of people, place names, product models, geographical locations, commodities) and key phrases (such as "refund process", "installation failure"). Finally, cluster the questions with similar semantics through a text embedding model (such as Sentence-BERT), merge duplicate or similar question groups, and then obtain the question-and-answer groups.

[0030] Sub-step 1104: Use the large language model to process the result obtained by executing sub-step 1102 to generate multiple groups of question-and-answer groups corresponding to the question-and-answer platform; After completing the extraction of the question-and-answer groups of the intelligent question-and-answer platform within the collection period, in order to avoid the intelligent question-and-answer model from generating content that does not conform to the facts due to "AI hallucinations", resulting in inaccurate or incorrect question answers, in the embodiment of the present application, the obtained question-and-answer groups can be input into a pre-trained large language model (LLM), and then the LLM model can score from dimensions such as accuracy, integrity, and logic to identify low-quality answers.

[0031] For example, the generation ability of the LLM model can be used to generate a summary of complex questions or refine potential user needs (for example: the user's question "How to reset the password?" may imply concerns about account security). Then, according to the extracted potential user needs, analyze and evaluate the accuracy of the answer results in the question-and-answer groups, and eliminate the answer results with poor accuracy; or the entity relationships in the content of the question-and-answer groups can also be parsed through the LLM model to construct a domain knowledge graph, and then optimize the answer results of the question-and-answer groups through the constructed question-and-answer knowledge graph to obtain multiple groups of optimized question-and-answer groups corresponding to the intelligent question-and-answer platform.

[0032] It should be noted here that optimizing the response results of the Q&A model through the LLM model is a commonly used technical means in the relevant field. The embodiments of the present application do not limit the specific implementation manners of using the LLM model to optimize the response results.

[0033] Step 12, according to the preset screening dimensions, screen and sort the multiple groups of Q&A groups obtained by executing Step 11 to determine at least one hot Q&A group corresponding to the intelligent Q&A platform; In the embodiments of the present application, the data processing system can screen the multiple groups of Q&A groups according to multiple dimensions such as question type, question priority, and question creation time, and then determine the hot Q&A groups corresponding to the intelligent Q&A platform during the collection period. Specifically, in one implementation manner, the specific implementation manner of Step 12 may include: respectively determining the question type, question priority, and question creation time corresponding to each group of the Q&A groups; screening and sorting the multiple groups of Q&A groups according to the question type, the question priority, and the question creation time to determine at least one hot Q&A group corresponding to the Q&A platform.

[0034] Specifically, in one implementation manner, the data processing system can calculate the heat values corresponding to each group of Q&A groups according to the following sub-steps, and then screen out at least one hot Q&A group corresponding to the intelligent Q&A platform during the collection period according to the order from high to low of the heat values: Sub-step 1201, according to the question type of each Q&A group, determine the basic heat score of the Q&A group; In the embodiments of the present application, the data processing system can calculate according to the number of occurrences S of each question type during the collection period, the growth rate Q of the number of occurrences of this question type relative to the previous collection period, and the average number of occurrences in the historical collection period and the historical weight q, and determine the basic heat score of each Q&A group according to the following formula [1]: Basic heat score V = S×Q + ×q [1] Sub-step 1202, determine the priority corresponding to each question type, and correct the basic heat score of each group of Q&A groups according to the priority weight; V2 = Base heat score V × Priority weight coefficient [2] Sub-step 1203: Determine the question creation time corresponding to each group of Q&A groups. According to the question creation time, perform time decay adjustment on the priority correction score V2 of each Q&A group to obtain the final heat score corresponding to each Q&A group; In the embodiment of the present application, the time decay factor can be determined according to the following formula [3] : = e^(- ×(Current time - Question creation time)) [3] Where, is the decay rate. Assume that, = 0.1 means a 10% decay per day.

[0035] Furthermore, according to the time decay factor , according to the following formula [4], the final heat score V3 corresponding to each Q&A group can be determined: V3 = × V2 [4] Sub-step 1204: According to the order of the final heat scores V3 of each group of Q&A groups determined by executing sub-step 1203 from high to low, screen out at least one hot Q&A group corresponding to the intelligent Q&A platform during the collection period.

[0036] Step 13: Determine the question keywords corresponding to each hot Q&A group. According to the question keywords, search for the standard answers matching the question keywords in the standard answer database preset in the Q&A platform; It should be noted here that in order to further avoid the problem that the answer results generated by the intelligent Q&A model for hot questions are inaccurate, in the embodiment of the present application, the data processing system can search for the standard answers matching the question keywords in the preset standard answer library according to the question keywords corresponding to the hot Q&A group. When a standard answer matching the question keywords is found, step 14 is executed. When no matching standard answer is found, step 15 is executed.

[0037] Step 14: When a standard answer matching the question keywords is found by executing step 13, update the answer results in the hot Q&A group according to the standard answer to obtain a standard hot Q&A group.

[0038] Step 15: When no standard answer matching the question keywords is found by executing step 13, retrieve the associated data corresponding to the question keywords in the external database based on the Retrieval-Augmented Generation (RAG) model; In the case where there is no standard reply result in the preset standard reply database, the data processing system can search for associated data corresponding to the question keywords by retrieving the question keywords in an external database, and then generate auxiliary reply data corresponding to the question keywords based on the retrieved associated data, and update the reply results in the hot Q&A group based on the auxiliary reply data to obtain a standard hot Q&A group.

[0039] Step 16: Obtain the business data corresponding to the user account that logs in to the Q&A platform, determine the priority ranking corresponding to the standard hot Q&A group according to the business data, and push and display the standard hot Q&A group according to the priority ranking.

[0040] In the embodiment of the present application, in order to accurately recommend the hot Q&A group for different users according to the business needs of individual users, in one implementation, the data processing system can obtain the business data corresponding to the user account according to the user account that logs in to the Q&A platform (for example, the user's order information, the user's insurance policy information, the user's customer service information, etc.), and determine the business type and business creation timestamp corresponding to each business data, and then can determine the standard hot Q&A group corresponding to each user account according to the business type corresponding to each business data; then determine the priority ranking corresponding to the standard hot Q&A group according to the sequence of the business creation timestamps of the business data corresponding to the standard hot Q&A group, and then when the user logs in to the intelligent Q&A platform through this user account, the intelligent Q&A platform's most recent hot Q&A group can be recommended and displayed for the user according to this priority ranking.

[0041] For example, if the business data collected by the user account that logs in to the intelligent Q&A platform determines that the question that the user has recently paid attention to is the order refund question, then when recommending hot questions for the user, the hot Q&A group related to refunds can be preferentially displayed.

[0042] By using the data processing method provided in the embodiments of the present application, the data processing system can collect the historical business data generated by the Q&A platform within a preset data processing cycle according to the preset data processing cycle, and generate multiple groups of Q&A groups corresponding to the Q&A platform, which are composed of historical questions and the corresponding answer results of the historical questions based on the collected historical business data of the Q&A platform. At the same time, the data processing system can screen and sort the multiple groups of Q&A groups according to the preset screening dimension to determine at least one hot Q&A group corresponding to the Q&A platform. After completing the screening of the hot Q&A group, the data processing system can determine the question keywords corresponding to the hot Q&A group, and search for the standard answers matching the question keywords in the standard answer database preset in the Q&A platform according to the question keywords. When the standard answers matching the question keywords are found, the answer results in the hot Q&A group are updated according to the standard answers to obtain the standard hot Q&A group, so as to ensure that the standard answers to the hot questions are stored in the hot Q&A group and avoid the problem of inaccurate answers to the hot questions due to AI hallucinations. Finally, the data processing system can obtain the business data corresponding to the user account logged in to the Q&A platform, determine the priority ranking of the standard hot Q&A group according to the business data, and push and display the standard hot Q&A group according to the priority ranking, so that the hot Q&A group can be displayed for the user according to the priority of the user's attention. The user can conveniently find the hot questions recently concerned by the user in the hot Q&A group display area and directly view the standard answers corresponding to the hot questions, which greatly improves the business processing efficiency and the Q&A accuracy of the Q&A platform.

[0043] In one implementation manner, the embodiments of the present application further provide a data processing device, which is used to solve the problem that the existing intelligent Q&A platform needs to manually screen and sort historical questions in a manual manner to determine historical hot questions, resulting in a low update frequency of the existing intelligent Q&A platform for hot questions and thus a poor Q&A accuracy of the intelligent Q&A platform. The specific structural schematic diagram of the data processing device is as Figure 2 shown, including: a Q&A group generation unit 21, a hot Q&A group screening unit 22, a question group matching unit 23, a question group update unit 24, and a push unit 25.

[0044] Among them, the Q&A group generation unit 21 is specifically configured to generate multiple groups of Q&A groups corresponding to the Q&A platform according to the collected historical business data of the Q&A platform, where the Q&A group includes a historical question and the corresponding answer result of the historical question; The hot Q&A group screening unit 22 is specifically configured to screen and sort the multiple groups of Q&A groups according to a preset screening dimension to determine at least one hot Q&A group corresponding to the Q&A platform; The question group matching unit 23 is specifically configured to determine the question keywords corresponding to the hot Q&A group, and based on the question keywords, search for standard answers that match the question keywords in the standard answer database preset in the Q&A platform; The question group updating unit 24 is specifically configured to, when a standard answer that matches the question keywords is found, update the answer result in the hot Q&A group according to the standard answer to obtain a standard hot Q&A group; The pushing unit 25 is specifically configured to obtain the service data corresponding to the user account logged in to the Q&A platform, determine the priority ranking corresponding to the standard hot Q&A group according to the service data, and perform push display on the standard hot Q&A group according to the priority ranking; In one implementation manner, the Q&A group generating unit 21 is specifically configured to: perform data collection on the Q&A platform according to a preset collection period, and perform data preprocessing on the collected data to obtain historical service data; identify the historical service data according to the natural language processing technology NLP, and use a large language model to process the identification result to generate multiple groups of Q&A groups corresponding to the Q&A platform.

[0045] In one implementation manner, the hot Q&A group screening unit 22 is specifically configured to: respectively determine the question type, question priority, and question creation time corresponding to each group of the Q&A groups; perform screening and sorting on the multiple groups of Q&A groups according to the question type, the question priority, and the question creation time to determine at least one hot Q&A group corresponding to the Q&A platform.

[0046] In one implementation manner, the question group updating unit 24 is specifically configured to: retrieve associated data corresponding to the question keywords in an external database based on the retrieval-augmented generation (RAG) model; generate auxiliary answer data corresponding to the question keywords according to the retrieved associated data; update the answer result in the hot Q&A group according to the auxiliary answer data to obtain a standard hot Q&A group.

[0047] In one implementation manner, the pushing unit 25 is specifically configured to: respectively determine the service type and service creation timestamp corresponding to each of the service data; respectively determine the standard hot Q&A group corresponding to each user account according to the service type corresponding to each of the service data; determine the priority ranking corresponding to the standard hot Q&A group according to the sequence of the service creation timestamps of the service data corresponding to the standard hot Q&A group.

[0048] Using the data processing device provided by the embodiment of the present application, the data processing system can collect the historical business data generated by the Q&A platform during a preset data processing cycle, and generate multiple groups of Q&A groups corresponding to the Q&A platform, which are composed of historical questions and the corresponding answer results according to the collected historical business data of the Q&A platform. At the same time, the data processing system can screen and sort the multiple groups of Q&A groups according to a preset screening dimension to determine at least one hot Q&A group corresponding to the Q&A platform; after completing the screening of the hot Q&A group, the data processing system can determine the question keywords corresponding to the hot Q&A group, and search for the standard answers matching the question keywords in the standard answer database preset in the Q&A platform according to the question keywords. When a standard answer matching the question keywords is found, the answer results in the hot Q&A group are updated according to the standard answer to obtain a standard hot Q&A group, so as to ensure that the standard answers to the hot questions are stored in the hot Q&A group and avoid the problem of inaccurate answers to the hot questions due to AI hallucinations; finally, the data processing system can obtain the business data corresponding to the user account logging in to the Q&A platform, determine the priority ranking corresponding to the standard hot Q&A group according to the business data, and push and display the standard hot Q&A group according to the priority ranking, so that the hot Q&A group can be displayed for the user according to the priority of user attention. The user can easily find the hot questions recently concerned by the user in the hot Q&A group display area and directly view the standard answers corresponding to the hot questions, which greatly improves the business processing efficiency and the Q&A accuracy of the Q&A platform.

[0049] Figure 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Please refer to Figure 3 , at the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. Among them, the memory may include a memory, such as a high-speed random access memory (Random-Access Memory, RAM), and may also include a non-volatile memory, such as at least one disk memory, etc. Of course, the electronic device may also include other hardware required for other services.

[0050] The processor, network interface, and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 3 only a bidirectional arrow is used in Figure 3 , but it does not mean that there is only one bus or one type of bus.

[0051] A memory for storing programs. Specifically, the program can include program code, and the program code includes computer operation instructions. The memory can include a memory and a non-volatile memory, and provide instructions and data to the processor.

[0052] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a data processing device at the logical level. The processor executes the program stored in the memory and is specifically used to perform the following operations: Generate multiple groups of Q&A groups corresponding to the Q&A platform according to the collected historical business data of the Q&A platform. Among them, the Q&A group includes a historical question and a reply result corresponding to the historical question; screen and sort the multiple groups of Q&A groups according to a preset screening dimension to determine at least one hot Q&A group corresponding to the Q&A platform; determine the question keywords corresponding to the hot Q&A group, and according to the question keywords, search for a standard reply matching the question keywords in the standard reply database preset in the Q&A platform; when a standard reply matching the question keywords is found, update the reply result in the hot Q&A group according to the standard reply to obtain a standard hot Q&A group; obtain the business data corresponding to the user account logging in to the Q&A platform, determine the priority ranking corresponding to the standard hot Q&A group according to the business data, and push and display the standard hot Q&A group according to the priority ranking.

[0053] The above as in this application Figure 3The method executed by the data processing electronic device disclosed in the illustrated embodiment can be applied to or implemented by a processor. The processor may be an integrated circuit chip with the ability to process signals. During implementation, the steps of the above method can be completed by the integrated logic circuit of the hardware in the processor or the instructions in the form of software. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by the combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method.

[0054] Of course, in addition to the software implementation, the electronic device of the present application does not exclude other implementation manners, such as a logic device or a combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or a logic device.

[0055] The embodiments of the present application also propose a computer-readable storage medium. The computer-readable storage medium stores one or more programs. The one or more programs include instructions that, when executed by a portable electronic device including a plurality of application programs, can enable the portable electronic device to execute Figure 1 the method of the illustrated embodiment, and specifically used to perform the following operations: Generate multiple groups of Q&A sets corresponding to the Q&A platform according to the collected historical business data of the Q&A platform, where each Q&A set includes a historical question and the corresponding answer result of the historical question; screen and sort the multiple groups of Q&A sets according to a preset screening dimension to determine at least one hot Q&A set corresponding to the Q&A platform; determine the question keywords corresponding to the hot Q&A set, and search for a standard answer matching the question keywords in the standard answer database preset in the Q&A platform according to the question keywords; when a standard answer matching the question keywords is found, update the answer result in the hot Q&A set according to the standard answer to obtain a standard hot Q&A set; obtain the business data corresponding to the user account logged in to the Q&A platform, determine the priority sorting corresponding to the standard hot Q&A set according to the business data, and push and display the standard hot Q&A set according to the priority sorting.

[0056] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0057] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0058] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0059] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0060] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0061] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0062] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0063] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0064] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0065] The above are only the embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A data processing method, characterized in that, Including: Generating multiple groups of Q&A sets corresponding to the Q&A platform according to the collected historical business data of the Q&A platform, where each Q&A set includes a historical question and a corresponding answer result for the historical question; Screening and sorting the multiple groups of Q&A sets according to a preset screening dimension to determine at least one hot Q&A set corresponding to the Q&A platform; Determining the question keywords corresponding to the hot Q&A set, and searching for a standard answer matching the question keywords in the standard answer database preset in the Q&A platform according to the question keywords; When a standard answer matching the question keywords is found, updating the answer result in the hot Q&A set according to the standard answer to obtain a standard hot Q&A set; Obtaining the business data corresponding to the user account logged in to the Q&A platform, determining the priority ranking corresponding to the standard hot Q&A set according to the business data, and pushing and displaying the standard hot Q&A set according to the priority ranking.

2. The method according to claim 1, wherein The generating multiple groups of Q&A sets corresponding to the Q&A platform according to the collected historical business data of the Q&A platform specifically includes: Collecting data from the Q&A platform according to a preset collection period, and performing data preprocessing on the collected data to obtain historical business data; Identifying the historical business data according to the natural language processing technology NLP, and processing the identification result by using a large language model to generate multiple groups of Q&A sets corresponding to the Q&A platform.

3. The method according to claim 1, wherein The screening and sorting the multiple groups of Q&A sets according to a preset screening dimension to determine at least one hot Q&A set corresponding to the Q&A platform specifically includes: Respectively determining the question type, question priority, and question creation time corresponding to each group of Q&A sets; Screening and sorting the multiple groups of Q&A sets according to the question type, the question priority, and the question creation time to determine at least one hot Q&A set corresponding to the Q&A platform.

4. The method according to claim 1, characterized in that, When a standard answer matching the question keywords is not found in the standard answer database preset in the Q&A platform according to the question keywords, it further includes: Retrieving associated data corresponding to the question keywords in an external database based on the Retrieval-Augmented Generation (RAG) model; Generating auxiliary answer data corresponding to the question keywords according to the retrieved associated data; Updating the answer result in the hot Q&A set according to the auxiliary answer data to obtain a standard hot Q&A set.

5. The method according to claim 1, wherein The obtaining the business data corresponding to the user account logged in to the Q&A platform, and determining the priority ranking corresponding to the standard hot Q&A set according to the business data specifically includes: Respectively determining the business type and business creation timestamp corresponding to each piece of business data; Respectively determining the standard hot Q&A sets corresponding to each user account according to the business type corresponding to each piece of business data; Determining the priority ranking corresponding to the standard hot Q&A set according to the chronological order of the business creation timestamps of the business data corresponding to the standard hot Q&A set.

6. A data processing device, characterized in that, Including: A Q&A group generation unit, configured to generate multiple groups of Q&A groups corresponding to the Q&A platform according to the historical business data collected from the Q&A platform, where the Q&A groups include historical questions and the corresponding answer results of the historical questions; A hot Q&A group screening unit, configured to screen and sort the multiple groups of Q&A groups according to a preset screening dimension to determine at least one hot Q&A group corresponding to the Q&A platform; A question group matching unit, configured to determine the question keywords corresponding to the hot Q&A group, and search for standard answers matching the question keywords in the standard answer database preset in the Q&A platform according to the question keywords; A question group updating unit, configured to, when a standard answer matching the question keywords is found, update the answer results in the hot Q&A group according to the standard answer to obtain a standard hot Q&A group; A pushing unit, configured to obtain the business data corresponding to the user account logged in to the Q&A platform, determine the priority sorting corresponding to the standard hot Q&A group according to the business data, and push and display the standard hot Q&A group according to the priority sorting.

7. The device according to claim 6, characterized in that, The Q&A group generation unit is specifically configured to: Collect data from the Q&A platform according to a preset collection period, and perform data preprocessing on the collected data to obtain historical business data; Identify the historical business data according to the natural language processing technology NLP, and process the identification results using a large language model to generate multiple groups of Q&A groups corresponding to the Q&A platform.

8. A data processing device, comprising: A processor; And A memory arranged to store computer-executable instructions, which when executed cause the processor to perform the following operations: Generate multiple groups of Q&A groups corresponding to the Q&A platform according to the historical business data collected from the Q&A platform, where the Q&A groups include historical questions and the corresponding answer results of the historical questions; Screen and sort the multiple groups of Q&A groups according to a preset screening dimension to determine at least one hot Q&A group corresponding to the Q&A platform; Determine the question keywords corresponding to the hot Q&A group, and search for standard answers matching the question keywords in the standard answer database preset in the Q&A platform according to the question keywords; When a standard answer matching the question keywords is found, update the answer results in the hot Q&A group according to the standard answer to obtain a standard hot Q&A group; Obtain the business data corresponding to the user account logged in to the Q&A platform, determine the priority sorting corresponding to the standard hot Q&A group according to the business data, and push and display the standard hot Q&A group according to the priority sorting.

9. A computer-readable storage medium, where the computer-readable storage medium stores one or more programs, and when the one or more programs are executed by an electronic device including multiple application programs, the electronic device is caused to execute the data processing method according to any one of claims 1-5.

10. A computer program product, characterized in that, Comprising a computer program which, when executed by a processor, implements the data processing method according to any one of claims 1-5.

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