Data display method, device, computer equipment and medium based on artificial intelligence

Abstract

The present invention provides a data display method, device, computer equipment and storage medium based on artificial intelligence. The above method includes: obtaining various problems corresponding to the target business of the target platform; classifying all the problems based on a preset problem classification model to obtain corresponding simple problems and complex problems; calling the target page corresponding to the target business; obtaining the target standard answers corresponding to the simple questions based on a preset question library; displaying the simple questions and the target standard answers in the target page; constructing complex problem links corresponding to the complex problems; and displaying the complex problem links in the target page. The present invention realizes the intelligent hierarchical display processing of various problems corresponding to the target business of the target platform, effectively improves the flexibility and intelligence of problem display, and is conducive to improving the user experience.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an artificial intelligence-based data display method, device, computer equipment and storage medium. Background Art

[0002] In the field of modern logistics and transportation management, with the booming development of e-commerce and the increasing frequency of global trade, freight platforms, as the key bridge connecting shippers and carriers, are becoming increasingly important. Freight platforms not only provide a series of functions such as cargo matching, transportation scheduling, and in-transit monitoring, but also undertake the important mission of improving transportation efficiency, reducing logistics costs, and optimizing user experience. Among them, the convenience and efficiency of the ordering process is one of the important indicators for measuring the service quality of freight platforms, which is directly related to user experience and satisfaction.

[0003] In order to improve the user experience, freight platforms are usually equipped with a problem display and processing system to help users quickly solve various questions and problems encountered during use. This system is an important bridge for communication between users and the platform. Whether its design is reasonable is directly related to whether users can quickly and accurately obtain the required information, which in turn affects the conversion rate and user stickiness of the entire platform.

[0004] However, in the existing technology, the problem display method of freight platforms generally has certain limitations. Specifically, most freight platforms adopt a unified problem display method, that is, regardless of the nature and complexity of the problem and the specific needs of the user, all problems will be displayed in the same way on the same page. Although this "one-size-fits-all" display method ensures the simplicity and consistency of the interface to a certain extent, it ignores the diversity and differences of user needs.

[0005] In actual applications, since the types and complexity of questions vary, a unified question display method often leads to redundant and confusing information. When users are looking for answers to specific questions, they need to spend more time and energy to filter and identify relevant information, which not only reduces the user experience, but also may cause users to give up using the platform because they cannot find answers quickly, which in turn affects the user retention rate and conversion rate of the platform. Therefore, the unified question display method adopted by existing freight platforms lacks intelligence and flexibility. Summary of the invention

[0006] The main purpose of the present invention is to provide a data display method, device, computer equipment and storage medium based on artificial intelligence, aiming to solve the technical problem in the prior art that the unified problem display method adopted by the freight platform still lacks intelligence and flexibility.

[0007] To achieve the above object, the present invention provides a data display method based on artificial intelligence, the method comprising:

[0008] Obtain various issues corresponding to the target business of the target platform;

[0009] Classify all the problems based on a preset problem classification model to obtain corresponding simple problems and complex problems;

[0010] Calling a target page corresponding to the target business;

[0011] Obtaining target standard answers corresponding to the simple questions based on a preset question library;

[0012] Displaying the simple question and the target standard answer on the target page;

[0013] Constructing a complex problem link corresponding to the complex problem;

[0014] The complex question link is displayed in the target page.

[0015] Optionally, displaying the simple question and the target standard answer in the target page includes:

[0016] Get viewing frequency data for all said simple questions;

[0017] Adjusting the display order of all the complex questions based on the viewing frequency data to obtain a sorted list of simple questions;

[0018] Determining a preset simple question display area in the target page;

[0019] The simple question list and the target standard answers are displayed in the simple question display area.

[0020] Optionally, before classifying all the questions based on a preset question classification model to obtain corresponding simple questions and complex questions, the method further includes:

[0021] Get pre-constructed sample data of questions;

[0022] Dividing the problem sample data to obtain corresponding training sets and test sets;

[0023] Call the preset initial learning model;

[0024] Training the initial learning model based on the training set to obtain a corresponding first learning model;

[0025] Performing model evaluation on the first learning model based on the test set to obtain a corresponding evaluation result;

[0026] Optimizing the first learning model based on the evaluation result to obtain a second learning model that meets preset performance requirements;

[0027] The second learning model is used as the question classification model.

[0028] Optionally, obtaining pre-constructed sample question data includes:

[0029] Obtaining pre-collected initial problem data;

[0030] Preprocessing the initial question data to obtain corresponding first question data;

[0031] Performing labeling processing on the first question data to obtain corresponding second question data;

[0032] The second question data is used as the question sample data.

[0033] Optionally, after displaying the complex question link in the target page, the method further includes:

[0034] Determining whether a click operation on the complex question link is received;

[0035] If yes, jump to the complex question details page corresponding to the complex question link;

[0036] Obtaining a user portrait corresponding to the user, and obtaining function usage frequency data of the user from the user portrait;

[0037] Adjusting the display order of all the complex questions based on the function usage frequency data to obtain a sorted complex question list;

[0038] The complex question list is displayed in the complex question details page.

[0039] Optionally, after displaying the complex question list in the complex question details page, the method further includes:

[0040] Determining whether a click operation on a designated question in the complex question list is received, triggered by the user;

[0041] If yes, obtaining answer preference data of the user;

[0042] Determining a corresponding answer preference type based on the answer preference data;

[0043] Based on the answer preference type, obtaining a specified answer corresponding to the specified question from the question library;

[0044] The specified answer is displayed in the complex question details page.

[0045] Optionally, before acquiring the target standard answer corresponding to the simple question based on the preset question library, the method further includes:

[0046] Obtain standard answers corresponding to the simple questions;

[0047] Constructing simplified answers corresponding to the standard answers;

[0048] Call the preset question bank;

[0049] The simple questions, as well as the standard answers and simplified answers corresponding to the simple questions are stored in the question library.

[0050] In addition, to achieve the above-mentioned purpose, the present invention further provides a data display device based on artificial intelligence, and the data display device based on artificial intelligence includes:

[0051] A first acquisition module is used to acquire various issues corresponding to the target business of the target platform;

[0052] A classification module is used to classify all the problems based on a preset problem classification model to obtain corresponding simple problems and complex problems;

[0053] A first calling module, used to call a target page corresponding to the target business;

[0054] A second acquisition module is used to acquire a target standard answer corresponding to the simple question based on a preset question library;

[0055] A first display module, used for displaying the simple question and the target standard answer in the target page;

[0056] A first building module is used to build a complex problem link corresponding to the complex problem;

[0057] The second display module is used to display the complex question link in the target page.

[0058] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0059] The computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any one of the artificial intelligence-based data display methods proposed in the embodiments of the present application are implemented.

[0060] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0061] The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps of any one of the artificial intelligence-based data display methods proposed in the embodiments of the present application are implemented.

[0062] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0063] The present invention provides a data display method, device, computer equipment and storage medium based on artificial intelligence, and the method includes: obtaining various questions corresponding to the target business of the target platform; classifying all the questions based on a preset question classification model to obtain corresponding simple questions and complex questions; calling a target page corresponding to the target business; obtaining the target standard answers corresponding to the simple questions based on a preset question library; displaying the simple questions and the target standard answers in the target page; constructing a complex question link corresponding to the complex question; and finally displaying the complex question link in the target page. After obtaining various questions corresponding to the target business of the target platform, the present application can quickly and accurately classify and process all questions through the use of a question classification model, obtain corresponding simple questions and complex questions, and then obtain the target standard answers corresponding to the simple questions based on the question library, and construct complex question links corresponding to the complex questions, and then display the simple questions and the target standard answers in the target page, and display the complex question links, so as to achieve differentiated display according to the complexity of the questions, that is, simple questions and complex questions are respectively displayed directly and displayed by links to complex questions in the target page, thereby realizing intelligent layered display processing of various questions corresponding to the target business of the target platform, effectively improving the flexibility and intelligence of question display, and helping to improve user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0065] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0066] Figure 2 is a flow chart of a data display method based on artificial intelligence provided by an embodiment of the present invention;

[0067] Figure 3 is a structural schematic diagram of an embodiment of an artificial intelligence-based data display device according to the present application;

[0068] Figure 4 This is a basic structural block diagram of the computer device in this embodiment. DETAILED DESCRIPTION

[0069] The artificial intelligence-based data display method provided by the embodiment of the present invention is applied to the artificial intelligence-based data display device. Unless otherwise defined, all technical and scientific terms used in this document have the same meaning as those generally understood by technicians in the technical field of this application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned figures are used to distinguish different objects, not to describe a specific order.

[0070] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0071] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0072] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0073] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social online platform software, etc.

[0074] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, desktop computers, etc.

[0075] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0076] It should be noted that the artificial intelligence-based data display method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the artificial intelligence-based data display device is generally set in the server / terminal device.

[0077] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0078] In the field of modern logistics and transportation management, with the booming development of e-commerce and the increasing frequency of global trade, freight platforms, as the key bridge connecting shippers and carriers, are becoming increasingly important. Freight platforms not only provide a series of functions such as cargo matching, transportation scheduling, and in-transit monitoring, but also undertake the important mission of improving transportation efficiency, reducing logistics costs, and optimizing user experience. Among them, the convenience and efficiency of the ordering process is one of the important indicators for measuring the service quality of freight platforms, which is directly related to user experience and satisfaction.

[0079] In order to improve the user experience, freight platforms are usually equipped with a problem display and processing system to help users quickly solve various questions and problems encountered during use. This system is an important bridge for communication between users and the platform. Whether its design is reasonable is directly related to whether users can quickly and accurately obtain the required information, which in turn affects the conversion rate and user stickiness of the entire platform.

[0080] However, in the existing technology, the problem display method of freight platforms generally has certain limitations. Specifically, most freight platforms adopt a unified problem display method, that is, regardless of the nature and complexity of the problem and the specific needs of the user, all problems will be displayed in the same way on the same page. Although this "one-size-fits-all" display method ensures the simplicity and consistency of the interface to a certain extent, it ignores the diversity and differences of user needs.

[0081] In actual applications, since the types and complexity of questions vary, a unified question display method often leads to redundant and confusing information. When users are looking for answers to specific questions, they need to spend more time and energy to filter and identify relevant information, which not only reduces the user experience, but also may cause users to give up using the platform because they cannot find answers quickly, which in turn affects the user retention rate and conversion rate of the platform. Therefore, the unified question display method adopted by existing freight platforms lacks intelligence and flexibility.

[0082] Continue to refer Figure 2 , shows a flow chart of an embodiment of the data display method based on artificial intelligence proposed in this application. The embodiment of this application can acquire and process relevant data based on artificial intelligence technology.

[0083] The data display method based on artificial intelligence provided by the embodiment of the present invention comprises the following steps:

[0084] S210, obtaining various issues corresponding to the target business of the target platform.

[0085] In this step, the executor of the present invention may specifically be a problem display processing system, or simply referred to as a system. The present application may be applied to business scenarios of problem display on a freight platform. The above-mentioned target platform may specifically be a freight platform. The above-mentioned target business may specifically be a business of placing an order on a freight platform. Among them, various problems encountered by users during the ordering process on the freight platform may be collected in advance through various channels such as user feedback, customer service records, and online surveys.

[0086] S220, classify all the questions based on a preset question classification model to obtain corresponding simple questions and complex questions.

[0087] In this step, all the above questions can be input into the question classification model respectively, so as to classify each question through the question classification model, thereby dividing all the questions into simple questions and complex questions. Among them, simple questions refer to questions whose classification labels output by the model are simple questions, and complex questions refer to questions whose classification labels output by the model are complex questions. In addition, the specific construction process of the above question classification model will be further described in detail in the subsequent specific embodiments of this application, and will not be elaborated on here.

[0088] S230: Call a target page corresponding to the target business.

[0089] In this step, the target business may specifically be a business of placing an order on a freight platform. The target page may specifically refer to an ordering page of a freight platform.

[0090] S240, obtaining a target standard answer corresponding to the simple question based on a preset question library.

[0091] In this step, the question bank can be queried using simple questions to find the target standard answers corresponding to the simple questions. The construction process of the question bank will be described in further detail in the subsequent specific embodiments, and will not be elaborated here.

[0092] S250, displaying the simple question and the target standard answer in the target page.

[0093] In this step, the specific implementation process of displaying the simple question and the target standard answer in the target page will be described in further detail in subsequent specific embodiments of the present application, and will not be elaborated on here.

[0094] S260: Construct a complex question link corresponding to the complex question.

[0095] In this step, for complex questions, a corresponding complex question link is pre-built at the corresponding position of the target page, that is, the order page, and the user can jump to the corresponding complex question details page after clicking.

[0096] S270, displaying the complex question link in the target page.

[0097] In this step, a link display area corresponding to the complex question link can be determined in the target page, and the complex question link can be displayed in the link display area. There is no specific limitation on the area selection of the link display area, which can be determined according to actual business needs.

[0098] In an embodiment of the present invention, first, various questions corresponding to the target business of the target platform are obtained; then, all the questions are classified based on a preset question classification model to obtain corresponding simple questions and complex questions; then, a target page corresponding to the target business is called; and based on a preset question library, the target standard answers corresponding to the simple questions are obtained; subsequently, the simple questions and the target standard answers are displayed in the target page; further, a complex question link corresponding to the complex question is constructed; and finally, the complex question link is displayed in the target page. After obtaining various questions corresponding to the target business of the target platform, the present application can quickly and accurately classify and process all questions through the use of a question classification model, obtain corresponding simple questions and complex questions, and then obtain the target standard answers corresponding to the simple questions based on the question library, and construct complex question links corresponding to the complex questions, and then display the simple questions and the target standard answers in the target page, and display the complex question links, so as to achieve differentiated display according to the complexity of the questions, that is, simple questions and complex questions are respectively displayed directly and displayed by links to complex questions in the target page, thereby realizing intelligent layered display processing of various questions corresponding to the target business of the target platform, effectively improving the flexibility and intelligence of question display, and helping to improve user experience.

[0099] Optionally, displaying the simple question and the target standard answer in the target page includes:

[0100] Get viewing frequency data for all of the simple questions.

[0101] In this step, the viewing frequencies of all simple questions by all users may be collected in advance and counted to obtain viewing frequency data of the simple questions.

[0102] The display order of all the complex questions is adjusted based on the viewing frequency data to obtain a sorted list of simple questions.

[0103] In this step, the display order of all the complex questions may be adjusted in descending order of viewing frequency data, thereby obtaining a sorted list of simple questions.

[0104] A preset simple question display area is determined in the target page.

[0105] In this step, there is no specific limitation on the area selection for the simple question display area, which can be determined according to actual business needs. For example, a prominent position in the target page, such as the middle position, can be used.

[0106] The simple question list and the target standard answers are displayed in the simple question display area.

[0107] In this step, the generated simple question list and the corresponding target standard answers can be displayed in sequence in the simple question display area in the above target page.

[0108] In this embodiment, the viewing frequency data of all the simple questions are obtained; then the display order of all the complex questions is adjusted based on the viewing frequency data to obtain a sorted list of simple questions; then a preset simple question display area is determined in the target page; and subsequently the simple question list and the target standard answers are displayed in the simple question display area. This application obtains the viewing frequency data of all simple questions, and adjusts the display order of all simple questions based on the viewing frequency data to obtain a sorted list of simple questions, and then displays the simple question list and the target standard answers in the simple question display area, which effectively improves the display intelligence of the simple question list. And by directly displaying the simple question list and the target standard answers in the target page, no further operation is required by the user, which is conducive to improving the user experience and thus can improve the user's ordering efficiency.

[0109] Optionally, before classifying all the questions based on a preset question classification model to obtain corresponding simple questions and complex questions, the method further includes:

[0110] Get pre-constructed sample data of questions.

[0111] In this step, the specific implementation process of obtaining the pre-constructed question sample data will be further described in detail in the subsequent specific embodiments of this application, and will not be elaborated here.

[0112] The problem sample data is divided and processed to obtain corresponding training sets and test sets.

[0113] In this step, the problem sample data can be divided according to a preset division ratio to obtain corresponding training sets and test sets. There is no specific limitation on the numerical selection of the above division ratio, which can be set according to actual business needs, for example, 8:2.

[0114] Call the preset initial learning model.

[0115] In this step, a suitable machine learning algorithm can be selected to construct the above initial learning model according to the nature of the problem and the data scale. Specifically, the above initial learning model can be any one of support vector machine (SVM), decision tree, random forest, naive Bayes, convolutional neural network CNN, recurrent neural network RNN, and Transformer.

[0116] The initial learning model is trained based on the training set to obtain a corresponding first learning model.

[0117] In this step, the selected initial learning model can be trained using the training set. During the training process, the performance of the model is optimized by adjusting the parameters of the initial learning model (such as learning rate, regularization term, etc.), and cross-validation (such as K-fold cross-validation) is further used to evaluate the generalization ability of the model and avoid overfitting, thereby obtaining a trained first learning model.

[0118] Performing model evaluation on the first learning model based on the test set to obtain a corresponding evaluation result.

[0119] In this step, the test set can be used to evaluate the performance of the first learning model and obtain corresponding evaluation results. The evaluation indicators may include accuracy, precision, recall, F1 score, etc.

[0120] The first learning model is optimized based on the evaluation result to obtain a second learning model that meets preset performance requirements.

[0121] In this step, the parameters of the first learning model can be adjusted or other algorithms can be selected for training according to the evaluation results, until a second learning model is obtained whose evaluation index is greater than a preset index threshold. There is no specific limitation on the value of the index threshold, which can be set according to actual business needs.

[0122] The second learning model is used as the question classification model.

[0123] In this step, you can further use complex feature extraction methods, adjust model parameters, increase the size or diversity of the data set, etc. to optimize the question classification model. Or you can use ensemble learning methods (such as bagging, boosting, etc.) to improve the performance of the question classification model.

[0124] In this embodiment, by obtaining pre-constructed problem sample data; and dividing the problem sample data to obtain corresponding training sets and test sets; then calling the preset initial learning model; then training the initial learning model based on the training set to obtain the corresponding first learning model; subsequently, the first learning model is evaluated based on the test set to obtain the corresponding evaluation result; further based on the evaluation result, the first learning model is optimized to obtain a second learning model that meets the preset performance requirements; finally, the second learning model is used as the problem classification model. This application divides the pre-constructed problem sample data to obtain the corresponding training set and test set, and then trains the initial learning model based on the use of the training set to obtain the corresponding first learning model, and evaluates the first learning model based on the use of the test set to obtain the corresponding evaluation result, and then optimizes the first learning model based on the evaluation result, so that the problem classification model that meets the preset performance requirements can be constructed efficiently and accurately, the construction efficiency of the problem classification model is improved, and the model effect of the obtained problem classification model is guaranteed. It is conducive to the subsequent use of the problem classification model to accurately classify all problems, so as to accurately obtain the corresponding simple and complex problems.

[0125] Optionally, obtaining pre-constructed sample question data includes:

[0126] Get pre-collected initial question data.

[0127] In this step, a large number of problems related to the ordering process of the freight platform can be collected from user feedback, customer service records, online surveys, etc. The collected problems are cleaned and irrelevant information such as garbled characters and advertisements are removed to obtain the corresponding initial problem data.

[0128] The initial question data is preprocessed to obtain corresponding first question data.

[0129] In this step, the above preprocessing may at least include: word segmentation, stop word removal, lemmatization, and feature extraction processing. Specifically, word segmentation includes: using a word segmentation tool (such as jieba, NLTK, etc.) to perform word segmentation on the problem text, splitting the text into individual words or phrases. Stop word removal includes: removing stop words in the text, such as "de", "le", "zai", etc., which frequently appear in the text but contribute little to classification. Lemmatization includes: reducing words to their basic forms, such as reducing "running" to "run", which helps to reduce lexical diversity. Feature extraction includes: converting the text into a feature vector, which can be specifically achieved by methods including the Bag of Words model, TF-IDF (term frequency-inverse document frequency), etc. Or word embedding techniques (such as Word2Vec, BERT, etc.) can also be used to convert the text into a higher-dimensional and richer feature vector.

[0130] Perform annotation processing on the first problem data to obtain corresponding second problem data.

[0131] In this step, the preprocessed first problem data can be automatically annotated based on the use of an annotation tool to classify the first problem data into two categories: simple problems and complex problems. Among them, in order to ensure the accuracy and consistency of the annotation, cross-validation can also be performed on the annotation results.

[0132] Use the second problem data as the problem sample data.

[0133] In this embodiment, initial problem data collected in advance is obtained; then the initial problem data is preprocessed to obtain corresponding first problem data; then annotation processing is performed on the first problem data to obtain corresponding second problem data; subsequently, the second problem data is used as the problem sample data. This application obtains the initial problem data collected in advance, and then performs preprocessing and annotation processing on the initial problem data, so as to quickly and accurately construct the required problem sample data, ensuring the data accuracy of the obtained problem sample data.

[0134] Optionally, after the display processing of the complex problem link in the target page, the method further includes:

[0135] Determine whether a click operation on the complex problem link triggered by the user is received.

[0136] In this step, for complex problems, a corresponding complex problem link is provided in advance at a corresponding position on the target page, that is, the order placement page. After the user clicks, they can jump to the corresponding complex problem details page. Among them, the complex problem link is concise and clear data that can accurately reflect the nature and content of the problem.

[0137] If yes, jump to the complex problem details page corresponding to the complex problem link.

[0138] In this step, an independent detail page corresponding to the complex question is created in advance, that is, the complex question detail page, which is used to display the complex question and the answer to the complex question. The complex question detail page includes the complex question, and may also include detailed explanations, steps, charts or other auxiliary information corresponding to the complex question to help users gain a deeper understanding of the problem. In addition, the design of the load question detail page is concise and clear, and easy for users to understand and operate.

[0139] A user portrait corresponding to the user is obtained, and function usage frequency data of the user is obtained from the user portrait.

[0140] In this step, the process of constructing the above user portrait includes: collecting user behavior data, such as click counts, browsing time, order frequency, preference settings, etc., through the user's operation records on the freight platform. And use identifiers such as cookies and user IDs to track the user's unique identity to ensure data continuity. Then collect user preference data, such as common functions, preferred answer methods, language preferences, etc., through user-filled questionnaires, user feedback, historical purchase records, etc. The collected user behavior data and preference data are subsequently integrated to form a complete user portrait. And a data warehouse or big data platform can be used to store and manage user portraits to ensure the accuracy and timeliness of the data.

[0141] After obtaining the user portrait corresponding to the user, the user's usage frequency of different functions can be analyzed based on the user portrait to obtain corresponding function usage frequency data.

[0142] The display order of all the complex questions is adjusted based on the function usage frequency data to obtain a sorted complex question list.

[0143] In this step, according to the obtained function usage frequency data, for functions frequently used by users, the display order of related questions corresponding to the functions frequently used by users in complex questions is advanced so that users can find the required information more quickly. For functions that are not frequently used, the display order of questions corresponding to the functions that are not frequently used by users in complex questions is postponed or hidden to complete the adjustment of the display order of complex questions, thereby obtaining a sorted complex question list.

[0144] The complex question list is displayed in the complex question details page.

[0145] In this step, a preset complex problem display area can be determined in the complex problem details page, and the complex problem list can be displayed in the complex problem display area to improve the display standardization and intelligence of the complex problem list. There is no specific limitation on the selection of the complex problem display area, which can be determined according to actual business needs.

[0146] In this embodiment, it is determined whether a click operation on the complex question link triggered by the user is received; if so, jump to the complex question details page corresponding to the complex question link; then obtain the user portrait corresponding to the user, and obtain the function usage frequency data of the user from the user portrait; then adjust the display order of all the complex questions based on the function usage frequency data to obtain a sorted complex question list; and then display the complex question list in the complex question details page. After receiving a click operation on a complex question link triggered by the user, the application will automatically jump to the complex question details page corresponding to the complex question link, and obtain the user portrait corresponding to the user, and obtain the function usage frequency data of the user from the user portrait, and then adjust the display order of all complex questions based on the function usage frequency data to obtain a sorted complex question list, and finally display the complex question list in the complex question details page, thereby intelligently and accurately realizing the display processing of personalized complex questions for users according to the user's function usage frequency data, effectively improving the display intelligence of complex questions, which is conducive to improving the user's experience, and thus helps to improve the user's ordering efficiency.

[0147] Optionally, after displaying the complex question list in the complex question details page, the method further includes:

[0148] Determine whether a click operation on a designated question in the complex question list is received, triggered by the user.

[0149] In this step, the above-mentioned designated question refers to any one of all the complex questions included in the above-mentioned complex question list. After the user clicks on the designated question, the subsequent system will automatically obtain the answer to the designated question and display it for the user to review.

[0150] If yes, obtain the answer preference data of the user.

[0151] In this step, the user's initial answer preference data is collected in advance through user questionnaires, user feedback, historical answer records, etc. The above initial answer preference data is cleaned and sorted using data analysis tools to form the user's answer preference data. Among them, the answer preference data includes a preference for concise and clear answers, or a preference for detailed explanation answers.

[0152] A corresponding answer preference type is determined based on the answer preference data.

[0153] In this step, if the answer preference data is a preference for concise and clear answers, the corresponding answer preference type is a concise and clear type. If the answer preference data is a preference for detailed explanation answers, the corresponding answer preference type is a detailed explanation type.

[0154] Based on the answer preference type, a specified answer corresponding to the specified question is obtained from the question library.

[0155] In this step, the answer preference type of the concise and clear type has a corresponding relationship with the simplified answer, and the answer preference type of the detailed explanation type has a corresponding relationship with the standard answer. According to the above answer preference type, the specified question and the specified answer corresponding to the answer preference type can be obtained from the question library.

[0156] The specified answer is displayed in the complex question details page.

[0157] In this step, a preset answer display area can be determined in the complex question details page, and the specified answer can be displayed in the complex question display area to improve the standardization and intelligence of the answer display. There is no specific limitation on the selection of the above answer display area, which can be determined according to actual business needs.

[0158] In this embodiment, it is determined whether a click operation triggered by the user for a specified question in the complex question list is received; if so, the answer preference data of the user is obtained; then the corresponding answer preference type is determined based on the answer preference data; then, based on the answer preference type, the specified answer corresponding to the specified question is obtained from the question library; and subsequently, the specified answer is displayed in the complex question details page. After receiving a click operation triggered by the user for a specified question in the complex question list, the present application will intelligently obtain the answer preference data of the user, and determine the corresponding answer preference type based on the answer preference data, and then, based on the answer preference type, obtain the specified answer corresponding to the specified question from the question library, and finally display the specified answer in the complex question details page, so that the user can be automatically and intelligently provided with a more personalized answer method and interactive experience based on the user's answer preference data, thereby improving the intelligence of the answer display and facilitating user satisfaction.

[0159] Optionally, before acquiring the target standard answer corresponding to the simple question based on the preset question library, the method further includes:

[0160] Get the standard answers corresponding to the simple questions.

[0161] In this step, the above-mentioned standard answers refer to answers that provide comprehensive solutions and background information for the questions.

[0162] Construct simplified answers that correspond to the standard answers.

[0163] In this step, the simplified answer refers to a short and direct answer to the question. The corresponding simplified answer can be obtained by removing background information and irrelevant information from the standard answer.

[0164] Call up a preset question bank.

[0165] In this step, the question library is a pre-built database for storing various questions corresponding to the target business of the target platform, as well as standard answers and simplified answers corresponding to the various questions.

[0166] The simple questions, as well as the standard answers and simplified answers corresponding to the simple questions are stored in the question library.

[0167] In this step, a correspondence between the simple question, the standard answer corresponding to the simple question and the simplified answer can be constructed, and then based on the correspondence, the simple question, and the standard answer and simplified answer corresponding to the simple question can be stored in the question library.

[0168] In this embodiment, the standard answer corresponding to the simple question is obtained; then the simplified answer corresponding to the standard answer is constructed; then the preset question library is called; and subsequently the simple question, and the standard answer and simplified answer corresponding to the simple question are stored in the question library. This application obtains the standard answer corresponding to the simple question, constructs the simplified answer corresponding to the standard answer, and then stores the simple question, and the standard answer and simplified answer corresponding to the simple question in the question library, so that the construction of the question library can be completed quickly and accurately, which improves the intelligence and standardization of the construction of the question library, and is conducive to the subsequent use of the question library to quickly and accurately query the answers corresponding to the relevant questions.

[0169] Compared with the prior art, this application has the following advantages:

[0170] 1. Improve user experience: This application optimizes user experience by dividing questions into simple and complex categories. The answers to simple questions can be displayed directly on the current page without the need for users to click additionally, thereby significantly reducing the number of operation steps and improving order efficiency. Compared with existing products on the market, users can obtain the required information in a shorter time, improving the ordering experience.

[0171] 2. Meeting the needs of different users: The design of this application not only meets the needs of different groups of people, ensuring that users can choose the appropriate answer method according to their own situation, but also reduces the burden on customer service. By enhancing the interactive experience, this application aims to effectively help users quickly solve their questions, improve overall satisfaction, and promote the conversion rate of the freight platform.

[0172] 3. Optimize resource utilization: This application optimizes resource utilization by displaying simple and complex questions in categories. The direct display of simple questions reduces information redundancy, avoids the trouble of users looking for answers in a large amount of information, and improves the efficiency of information acquisition. At the same time, for complex questions, users can click on the link to enter the details page to obtain more comprehensive information, which not only meets the user's demand for detailed information, but also avoids information interference from simple questions.

[0173] 4. Personalized answer method: This application provides users with differentiated display methods through a layered display processing system, and provides personalized answers based on the complexity of the question, which undoubtedly improves the user experience.

[0174] Further references Figure 3 , as a response to the above Figure 2 In order to realize the method shown in the figure, the present application provides an embodiment of a data display device 300 based on artificial intelligence, and the device embodiment is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0175] An embodiment of the present invention provides an artificial intelligence-based data display device 300, and the artificial intelligence-based data display device 300 includes:

[0176] A first acquisition module 310 is used to acquire various issues corresponding to the target business of the target platform;

[0177] A classification module 320 is used to classify all the questions based on a preset question classification model to obtain corresponding simple questions and complex questions;

[0178] A first calling module 330, configured to call a target page corresponding to the target business;

[0179] A second acquisition module 340 is used to acquire a target standard answer corresponding to the simple question based on a preset question library;

[0180] A first display module 350 is used to display the simple question and the target standard answer in the target page;

[0181] A first construction module 306 is used to construct a complex question link corresponding to the complex question;

[0182] The second display module 370 is used to display the complex question link in the target page.

[0183] Optionally, the first display module 350 includes:

[0184] A first acquisition submodule is used to acquire viewing frequency data of all the simple questions;

[0185] An adjustment submodule, used for adjusting the display order of all the complex questions based on the viewing frequency data to obtain a sorted list of simple questions;

[0186] A first determination submodule is used to determine a preset simple question display area in the target page;

[0187] The display submodule is used to display the simple question list and the target standard answers in the simple question display area.

[0188] Optionally, the artificial intelligence-based data display device 300 further includes:

[0189] A third acquisition module is used to obtain pre-constructed question sample data;

[0190] A partitioning module is used to partition the problem sample data to obtain corresponding training sets and test sets;

[0191] The second calling module is used to call a preset initial learning model;

[0192] A training module, used for training the initial learning model based on the training set to obtain a corresponding first learning model;

[0193] An evaluation module, used to perform model evaluation on the first learning model based on the test set to obtain a corresponding evaluation result;

[0194] An optimization module, configured to optimize the first learning model based on the evaluation result to obtain a second learning model that meets preset performance requirements;

[0195] The first determining module is used to use the second learning model as the question classification model.

[0196] Optionally, the third acquisition module includes:

[0197] The second acquisition submodule is used to acquire the initial question data collected in advance;

[0198] A preprocessing submodule, used for preprocessing the initial question data to obtain corresponding first question data;

[0199] A labeling submodule, used for labeling the first question data to obtain corresponding second question data;

[0200] The second determining submodule is used to use the second question data as the question sample data.

[0201] Optionally, the artificial intelligence-based data display device 300 further includes:

[0202] A first judgment module is used to judge whether a click operation on the complex question link triggered by a user is received;

[0203] A jump module, for jumping to a complex question details page corresponding to the complex question link if yes;

[0204] A fourth acquisition module, used to acquire a user portrait corresponding to the user, and acquire function usage frequency data of the user from the user portrait;

[0205] An adjustment module, used for adjusting the display order of all the complex questions based on the function usage frequency data to obtain a sorted complex question list;

[0206] The third display module is used to display the complex question list in the complex question details page.

[0207] Optionally, the artificial intelligence-based data display device 300 further includes:

[0208] A second judgment module is used to judge whether a click operation on a designated question in the complex question list triggered by the user is received;

[0209] A fifth acquisition module, configured to acquire answer preference data of the user if yes;

[0210] A second determination module, configured to determine a corresponding answer preference type based on the answer preference data;

[0211] A sixth acquisition module, configured to acquire, from the question library, a specified answer corresponding to the specified question based on the answer preference type;

[0212] The fourth display module is used to display the specified answer in the complex question details page.

[0213] Optionally, the artificial intelligence-based data display device 300 further includes:

[0214] A seventh acquisition module, used to acquire a standard answer corresponding to the simple question;

[0215] A second building module is used to build a simplified answer corresponding to the standard answer;

[0216] The third calling module is used to call the preset question library;

[0217] The storage module is used to store the simple questions, and the standard answers and simplified answers corresponding to the simple questions in the question library.

[0218] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0219] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0220] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0221] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk equipped on the computer device 4, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as the program code of the data display method based on artificial intelligence, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0222] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run the program code stored in the memory 41 or process data, such as running the program code of the artificial intelligence-based data display method.

[0223] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0224] The present application also provides another implementation, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores the application crash processing program, and the application crash processing program can be executed by at least one processor to enable the at least one processor to perform the steps of the artificial intelligence-based data display method as described above.

[0225] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware online platform, and of course, by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0226] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0227] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A data display method based on artificial intelligence, characterized in that: include: Obtain various issues corresponding to the target business of the target platform; Classify all the problems based on a preset problem classification model to obtain corresponding simple problems and complex problems; Calling a target page corresponding to the target business; Obtaining target standard answers corresponding to the simple questions based on a preset question library; Displaying the simple question and the target standard answer on the target page; Constructing a complex problem link corresponding to the complex problem; The complex question link is displayed in the target page.

2. The method according to claim 1, characterized in that: The displaying process of the simple question and the target standard answer in the target page includes: Get viewing frequency data for all said simple questions; Adjusting the display order of all the complex questions based on the viewing frequency data to obtain a sorted list of simple questions; Determining a preset simple question display area in the target page; The simple question list and the target standard answers are displayed in the simple question display area.

3. The method according to claim 1, characterized in that Before classifying all the questions based on the preset question classification model to obtain corresponding simple questions and complex questions, the method further includes: Get pre-constructed sample data of questions; Dividing the problem sample data to obtain corresponding training sets and test sets; Call the preset initial learning model; Training the initial learning model based on the training set to obtain a corresponding first learning model; Performing model evaluation on the first learning model based on the test set to obtain a corresponding evaluation result; Optimizing the first learning model based on the evaluation result to obtain a second learning model that meets preset performance requirements; The second learning model is used as the question classification model.

4. The method according to claim 3, characterized in that The obtaining of pre-constructed sample question data includes: Obtaining pre-collected initial problem data; Preprocessing the initial question data to obtain corresponding first question data; Performing labeling processing on the first question data to obtain corresponding second question data; The second question data is used as the question sample data.

5. The method according to claim 1, characterized in that: After displaying the complex question link in the target page, the method further includes: Determining whether a click operation on the complex question link is received; If yes, jump to the complex question details page corresponding to the complex question link; Obtaining a user portrait corresponding to the user, and obtaining function usage frequency data of the user from the user portrait; Adjusting the display order of all the complex questions based on the function usage frequency data to obtain a sorted complex question list; The complex question list is displayed in the complex question details page.

6. The method according to claim 5, characterized in that After displaying the complex question list in the complex question details page, the method further includes: Determining whether a click operation on a designated question in the complex question list is received, triggered by the user; If yes, obtaining answer preference data of the user; Determining a corresponding answer preference type based on the answer preference data; Based on the answer preference type, obtaining a specified answer corresponding to the specified question from the question library; The specified answer is displayed in the complex question details page.

7. The method according to claim 1, characterized in that Before obtaining the target standard answer corresponding to the simple question based on the preset question library, the method further includes: Obtain standard answers corresponding to the simple questions; Constructing simplified answers corresponding to the standard answers; Call the preset question bank; The simple questions, as well as the standard answers and simplified answers corresponding to the simple questions are stored in the question library.

8. A data display device based on artificial intelligence, characterized in that: include: A first acquisition module is used to acquire various issues corresponding to the target business of the target platform; A classification module is used to classify all the problems based on a preset problem classification model to obtain corresponding simple problems and complex problems; A first calling module, used to call a target page corresponding to the target business; A second acquisition module is used to acquire a target standard answer corresponding to the simple question based on a preset question library; A first display module, used for displaying the simple question and the target standard answer in the target page; A first building module is used to build a complex problem link corresponding to the complex problem; The second display module is used to display the complex question link in the target page.

9. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the artificial intelligence-based data presentation method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the artificial intelligence-based data presentation method according to any one of claims 1 to 7.

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