Overseas study demand prediction system and method

Abstract

The invention provides an overseas study demand prediction system and method. The overseas study demand prediction system comprises a data acquisition module, a data processing module and a demand prediction module, the data acquisition module is used for acquiring overseas study demand social data of overseas students, and the overseas study demand social data is used for describing a plurality of first overseas study demand categories extracted from question data generated in the mutual communication process of the overseas students; the data processing module is used for performing data expansion on the overseas study demand social data of the overseas students to obtain overseas study demand expansion data of the overseas students, and the overseas study demand expansion data comprises a plurality of first overseas study demand categories and a second overseas study demand category obtained after at least one first overseas study demand category is expanded; and the demand prediction module is used for performing data processing on the overseas study demand expansion data of the overseas students to obtain an overseas study demand prediction result of the overseas students. Therefore, the prediction accuracy of overseas study demands is effectively improved.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of service management technology, and in particular, to a system and method for predicting demand for studying abroad. Background Art

[0002] Predicting the needs of overseas students is an important issue. Accurately predicting the needs of overseas students is of great significance to improving the service experience. Accurately predicting the needs of overseas students can avoid the phenomenon of insufficient service experience and improve the accuracy of service in the entire communication service process.

[0003] In related technologies, traditional methods for predicting demand for overseas students are usually based on subjective judgment or simple data analysis based on historical data and experience, and lack in-depth exploration of complex patterns and associations in the data.

[0004] However, using the existing methods, the accuracy of predicting overseas study demand is not high. Summary of the invention

[0005] The embodiments described herein provide a system and method for predicting demand for studying abroad, which overcomes the above-mentioned problems.

[0006] In a first aspect, according to the content of the present disclosure, there is provided a system for forecasting demand for studying abroad, including: a data collection module, a data processing module and a demand forecasting module;

[0007] The data collection module is used to collect the social data of overseas students' study abroad needs, where the social data of study abroad needs is used to describe a plurality of first study abroad needs categories extracted from the problem data generated during the communication process between the overseas students;

[0008] The data processing module is used to perform data expansion on the overseas student's study abroad demand social data to obtain the overseas student's study abroad demand expansion data, wherein the study abroad demand expansion data includes: a plurality of the first study abroad demand categories and at least one second study abroad demand category obtained by expanding the first study abroad demand category;

[0009] The demand prediction module is used to process the expanded data of the overseas students' study abroad demand to obtain the prediction results of the overseas students' study abroad demand;

[0010] The study abroad demand prediction result includes: study abroad category object data corresponding to the first study abroad demand category and study abroad category object data corresponding to the second study abroad demand category.

[0011] In a second aspect, according to the content of the present disclosure, a method for predicting demand for studying abroad is provided, comprising:

[0012] Collecting social data on overseas students' study abroad needs, where the social data on study abroad needs is used to describe a plurality of first study abroad need categories extracted from question data generated during communication between the overseas students;

[0013] Performing data expansion on the overseas student's study abroad demand social data to obtain the overseas student's study abroad demand expansion data, wherein the study abroad demand expansion data includes: a plurality of the first study abroad demand categories and at least one second study abroad demand category obtained by expanding the first study abroad demand category;

[0014] Processing the expanded data of the overseas students' study abroad needs to obtain a prediction result of the overseas students' study abroad needs;

[0015] The study abroad demand prediction result includes: study abroad category object data corresponding to the first study abroad demand category and study abroad category object data corresponding to the second study abroad demand category.

[0016] In a third aspect, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the method for predicting demand for overseas study as in any of the above embodiments are implemented.

[0017] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for predicting demand for overseas study as in any of the above embodiments are implemented.

[0018] The overseas study demand prediction system provided by the embodiment of the present application includes: a data collection module, a data processing module and a demand prediction module; the data collection module is used to collect the overseas students' social data on their study demand, and the social data on their study demand is used to describe the multiple first study demand categories extracted from the problem data generated during the communication process between overseas students; the data processing module is used to expand the overseas students' social data on their study demand, and obtain the overseas students' study demand expansion data, and the study demand expansion data includes: multiple first study demand categories and at least one second study demand category obtained after the first study demand category is expanded; the demand prediction module is used to process the overseas students' study demand expansion data, and obtain the overseas students' study demand prediction results; wherein the study demand prediction results include: the study category object data corresponding to the first study demand category and the study category object data corresponding to the second study demand category. In this way, by expanding the data of the collected multiple first study demand categories, a second study demand category with richer categories is obtained to predict the overseas study demand, so as to effectively improve the prediction accuracy of the overseas study demand.

[0019] The above description is only an overview of the technical solution of the embodiment of the present application. In order to more clearly understand the technical means of the embodiment of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiment of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments will be briefly described below. It should be noted that the drawings described below only relate to some embodiments of the present disclosure, but are not intended to limit the present disclosure, wherein:

[0021] Figure 1 It is a structural diagram of an overseas study demand prediction system provided by the present invention.

[0022] Figure 2 It is a flow chart of a method for predicting demand for studying abroad provided by the present invention.

[0023] Figure 3 It is a structural schematic diagram of a computer device provided by the present disclosure.

[0024] It should be noted that the elements in the drawings are schematic and not drawn to scale. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution of the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present disclosure, rather than all of the embodiments. Based on the described embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without creative work also fall within the scope of protection of the present disclosure.

[0026] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by a person skilled in the art to which the subject matter of the present disclosure belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the specification and the relevant art, and will not be interpreted in an idealized or overly formal form unless otherwise explicitly defined herein. As used herein, a statement that two or more parts are "connected" or "coupled" together shall mean that the parts are joined together directly or through one or more intermediate components.

[0027] 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 "embodiments" 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.

[0028] The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists, A and B exist at the same time, and B exists. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship. Terms such as "first" and "second" are only used to distinguish one component (or a part of a component) from another component (or another part of a component).

[0029] In the description of the present application, unless otherwise specified, "plurality" means more than two (including two), and similarly, "multiple groups" means more than two groups (including two).

[0030] 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.

[0031] Figure 1 is a schematic diagram of the structure of a system for predicting demand for studying abroad provided by an embodiment of the present disclosure, such as Figure 1As shown, the overseas study demand forecasting system includes: a data collection module 110, a data processing module 120 and a demand forecasting module 130. The data collection module 110 is connected to the data processing module 120, and the data processing module 120 is connected to the demand forecasting module 130.

[0032] The data collection module 110 is used to collect the social data of overseas students' study abroad needs. The social data of study abroad needs is used to describe multiple first study abroad demand categories extracted from the problem data generated during the communication process of overseas students.

[0033] Among them, overseas students can communicate about their study abroad needs through the overseas student communication system and publish their own communication questions (i.e., question data) on the system. The data collection module 110 can collect question data generated during the communication process from the overseas student communication system by calling the API (Application Program Interface) of the overseas student communication system, and extract categories from these question data to obtain multiple first study abroad demand categories.

[0034] For example, the data collection module 110 collects customer service interaction information (i.e., interaction information between overseas students and system customer service) from the overseas student communication system, performs semantic recognition on the customer service interaction information through a natural semantic recognition model, and classifies the semantic recognition through a preset question classification model to obtain multiple first study abroad demand categories.

[0035] The data collection module 110 can pre-determine the various types of data generated by the project management tool in the overseas student communication system, and formulate a data collection plan based on the various data types. The data collection plan includes: how to obtain data, data format and standards, and data acquisition time; according to the data collection plan, call the API of the project management tool to extract multiple first study abroad demand categories.

[0036] The data processing module 120 is used to expand the social data of overseas students' study abroad needs to obtain expanded data of overseas students' study abroad needs.

[0037] The expanded data on the demand for studying abroad includes: a plurality of first demand categories for studying abroad and a second demand category for studying abroad obtained by expanding at least one first demand category for studying abroad.

[0038] The category of the second study abroad demand category and the category of the first study abroad demand category can be the same category. For example, the first study abroad demand category is fruit, and the second study abroad demand category obtained by expanding the first study abroad demand category is vegetables. In this case, the category of the second study abroad demand category and the category of the first study abroad demand category both belong to the same category of food. The category of the second study abroad demand category and the category of the first study abroad demand category can be different categories. For example, the first study abroad demand category is fruit, and the second study abroad demand category obtained by expanding the first study abroad demand category is accommodation.

[0039] It should be noted that before expanding the social data on overseas students' study abroad needs, data cleaning, data conversion, and data specification can also be performed on the social data on study abroad needs to facilitate improving the content integrity and format uniformity of the data.

[0040] In some embodiments, the data processing module 120 includes: an acquisition unit, a matching unit, a determination unit and an expansion unit. The acquisition unit is connected to the pre-matching unit, the matching unit is connected to the determination unit, and the determination unit is connected to the expansion unit.

[0041] An acquisition unit is used to acquire a preset knowledge graph, which includes: multiple third study abroad demand categories; a matching unit is used to match each first study abroad demand category included in the study abroad demand social data with each third study abroad demand category included in the preset knowledge graph; a determination unit is used to determine that the third study abroad demand category that matches the first study abroad demand category is the second study abroad demand category corresponding to the first study abroad demand category; an expansion unit is used to expand the data of the corresponding first study abroad demand category based on the second study abroad demand category corresponding to the first study abroad demand category, so as to obtain expanded study abroad demand data for overseas students.

[0042] Among them, when the matching unit matches each first studying abroad demand category included in the social data of studying abroad demand with each third studying abroad demand category included in the preset knowledge graph, if the matching degree between the first studying abroad demand category and a third studying abroad demand category is greater than the preset matching threshold, it is determined that the first studying abroad demand category matches the third studying abroad demand category.

[0043] It should be noted that when matching the study abroad demand categories, a first study abroad demand category may have a matching relationship with one or more third study abroad demand categories at the same time, that is, after matching, one study abroad demand category may correspond to one or more second study abroad demand categories.

[0044] When the expansion unit expands the data of the first study abroad demand category, it can merge multiple first study abroad demand categories and the second study abroad demand categories corresponding to the first study abroad demand categories to obtain the expanded study abroad demand data of overseas students, so as to enrich the data categories of the expanded study abroad demand data.

[0045] In some embodiments, the expansion unit is specifically used to:

[0046] From the category database, obtain multiple candidate study abroad demand categories associated with the second study abroad demand category corresponding to the first study abroad demand category; determine the category association characterization value of each candidate study abroad demand category and the second study abroad demand category corresponding to the first study abroad demand category; determine the candidate study abroad demand category whose category association characterization value is greater than a preset characterization threshold as the target study abroad demand category corresponding to the first study abroad demand category; based on the multiple first study abroad demand categories and the target study abroad demand category corresponding to the first study abroad demand category, determine the expanded study abroad demand data of overseas students.

[0047] The category database may be a database composed of multiple demand categories classified from historical exchange data of overseas students.

[0048] The multiple candidate study abroad demand categories associated with the second study abroad demand category corresponding to the first study abroad demand category are multiple candidate study abroad demand categories that have an association relationship with the second study abroad demand category corresponding to the first study abroad demand category. The association relationship means that the second study abroad demand category corresponding to the first study abroad demand category and the candidate study abroad demand category belong to the same category (such as belonging to the category of food) or there is a life correlation between the categories (such as the life correlation between the category of food and the category of sleep).

[0049] The category association characterization value between the candidate study abroad demand category and the second study abroad demand category corresponding to the first study abroad demand category may be the category matching degree between the candidate study abroad demand category and the second study abroad demand category corresponding to the first study abroad demand category. The data processing module 120 may set an identification value for each specific demand category to facilitate the determination of the category matching degree between each two demand categories. The category matching degree may be determined based on the difference between the identification values ​​between each two demand categories. The smaller the difference, the higher the category matching degree.

[0050] When determining the expanded data on overseas students' study abroad needs based on multiple first study abroad needs categories and the target study abroad needs categories corresponding to the first study abroad needs categories, multiple first study abroad needs categories and the target study abroad needs categories corresponding to the first study abroad needs categories may be combined to obtain the expanded data on overseas students' study abroad needs, or multiple first study abroad needs categories, second study abroad needs categories corresponding to the first study abroad needs categories, and target study abroad needs categories corresponding to the first study abroad needs categories may be combined.

[0051] Therefore, the first category of overseas study demand is extended and expanded through the category database, further effectively improving the category diversity of overseas study demand categories.

[0052] The demand prediction module 130 is used to process the expanded data of overseas students' study abroad demand to obtain the prediction results of overseas students' study abroad demand.

[0053] Among them, the study abroad demand prediction result includes: the study abroad category object data corresponding to the first study abroad demand category and the study abroad category object data corresponding to the second study abroad demand category.

[0054] The study abroad category object data may be the representation data of the specific objects involved in the corresponding study abroad demand category. For example, the first study abroad demand category is fruit, and the study abroad category object data corresponding to the first study abroad demand category may be the number of apples (where apples are objects and the number is object data); the second study abroad demand category is vegetables, and the study abroad category object data corresponding to the second study abroad demand category may be the cooking method of potatoes (where potatoes are objects and the cooking method is object data).

[0055] In some embodiments, the demand forecasting module 130 includes: an extraction unit, a selection unit, and a forecasting unit. The extraction unit is connected to the selection unit, and the selection unit is connected to the forecasting unit.

[0056] The extraction unit is used to extract features from the expanded data of overseas students' study-abroad demand to obtain the overseas students' study-abroad related features; the selection unit is used to select the study-abroad demand prediction model with the highest matching degree with the overseas students' study-abroad related features from the model database; the prediction unit is used to input the overseas students' study-abroad related features into the study-abroad demand prediction model, and determine the overseas students' study-abroad demand prediction results based on the output results of the study-abroad demand prediction model.

[0057] Therefore, by selecting the study abroad demand prediction model with the highest matching degree with the study abroad related characteristics of overseas students, the demand prediction is carried out based on the study abroad related characteristics of overseas students, so as to further improve the prediction accuracy of the overseas study demand prediction.

[0058] In addition, the study abroad demand prediction model is obtained by training the linear regression model based on historical related features. The output result of the study abroad demand prediction model can be shown in formula (1).

[0059] y=β0+β1x1+β2x2+…+β n x n +ε (1)

[0060] In formula (1), y is the forecast result of overseas students’ study abroad demand, x nis the nth study abroad related feature, (β1,β2,…,βn) are the parameters of the linear regression model, and ε is the error term.

[0061] The demand prediction module 130 is also used to evaluate the trained model by comparing the error index between the test results and the actual results during the training of the linear regression model, so as to effectively obtain the overseas study demand prediction model.

[0062] In some embodiments, the output result of the study abroad demand prediction model includes: initial category object data corresponding to the first study abroad demand category and initial category object data corresponding to the second study abroad demand category.

[0063] The prediction unit is specifically used for:

[0064] Obtain historical demand forecast results; based on the study abroad category object data corresponding to the historical study abroad demand categories, update the initial category object data corresponding to the first study abroad demand category and the initial category object data corresponding to the second study abroad demand category to obtain the study abroad demand forecast results for overseas students.

[0065] Among them, the historical demand forecast results include: study abroad category object data corresponding to the historical study abroad demand categories.

[0066] When updating the initial category object data corresponding to the first study abroad demand category and the initial category object data corresponding to the second study abroad demand category based on the study abroad category object data corresponding to the historical study abroad demand categories, the study abroad category object data with the lowest frequency appearing in the study abroad category object data corresponding to the historical study abroad demand categories can be deleted from the initial category object data corresponding to the first study abroad demand category and the initial category object data corresponding to the second study abroad demand category. Since overseas students have a relatively low demand for this part of the object data, deleting these data facilitates improving the applicability of the overseas students' study abroad demand prediction results.

[0067] The overseas study demand prediction system in this embodiment includes: a data collection module, a data processing module and a demand prediction module; the data collection module is used to collect the social data of overseas students' study demand, and the social data of study demand is used to describe multiple first study demand categories extracted from the problem data generated in the process of overseas students' mutual communication; the data processing module is used to expand the social data of overseas students' study demand to obtain the expanded data of overseas students' study demand, and the expanded data of study demand includes: multiple first study demand categories and at least one second study demand category obtained after the expansion of the first study demand category; the demand prediction module is used to process the expanded data of overseas students' study demand to obtain the overseas students' study demand prediction results; wherein the study demand prediction results include: the study category object data corresponding to the first study demand category and the study category object data corresponding to the second study demand category. In this way, by expanding the data of the collected multiple first study demand categories, a second study demand category with richer categories is obtained to predict the overseas study demand, so as to effectively improve the prediction accuracy of the overseas study demand.

[0068] In addition, the overseas study demand forecasting system also includes: a report generation module; the report generation module is used to generate a demand analysis report based on the first study demand category, the second study demand category and the overseas students' study demand forecasting results, so as to facilitate unified viewing.

[0069] In some embodiments, the overseas study demand forecasting system further includes: a region creation module and a data display module. The region creation module is connected to the data display module.

[0070] An area creation module is used to create a social data display area, an extended data display area and a prediction result display area in a user interface; a data display module is used to display multiple first study abroad demand categories included in the social data of study abroad demand in the social data display area, display multiple second study abroad demand categories included in the extended data of study abroad demand in the extended data display area, and display the study abroad demand prediction results of overseas students in the prediction result display area.

[0071] Among them, the social data display area, the expanded data display area and the prediction result display area can be located in different display areas in the same user interface, so that the first study abroad demand category, the second study abroad demand category and the study abroad demand prediction results of overseas students can be viewed at the same time in the same user interface.

[0072] In some embodiments, the overseas study demand forecasting system also includes: a demand adjustment module.

[0073] The demand adjustment module is used to adjust the overseas students' study abroad demand forecast results in response to the adjustment instructions of the overseas students' study abroad demand forecast results; the data display module is used to update and display the overseas students' study abroad demand forecast results in the forecast result display area.

[0074] Among them, the adjustment instruction for the overseas students' study abroad demand forecast results can be a demand adjustment request sent by the overseas students, and the demand adjustment request includes: demand adjustment data, which is convenient for adjusting the overseas students' study abroad demand forecast results through the demand adjustment data.

[0075] Therefore, by collecting the demand adjustment suggestions of overseas students, adjusting and displaying the forecast results of overseas students' study abroad demand, it is possible to effectively improve the fit between the forecast results of study abroad demand and the satisfaction of overseas students.

[0076] This embodiment can integrate various aspects of the service management process, including data sources of time, demand, solutions, etc., and perform data analysis, as well as use artificial intelligence technology to conduct comprehensive and in-depth analysis and mining to extract features that are useful for predicting demand for overseas students.

[0077] Through the system of this embodiment, it is possible to integrate various aspects of the project management process, including data sources of time management, demand management, quality management, etc., and perform data analysis, as well as use artificial intelligence technology for comprehensive and in-depth analysis and mining, extract features that are useful for manpower demand forecasting, provide comprehensive manpower demand forecasting reports and decision support, and improve the accuracy and precision of forecasts.

[0078] Figure 2 The present embodiment provides a flow chart of a method for predicting demand for overseas study. The method for predicting demand for overseas study may include:

[0079] S210. Collect social data on overseas students’ study-abroad needs.

[0080] Among them, the social data on study abroad needs is used to describe multiple first study abroad demand categories extracted from the problem data generated during the communication process of overseas students.

[0081] Overseas students can communicate about their study abroad needs through the overseas student communication system and publish their own communication questions (i.e., question data) on the system. For example, when collecting social data on overseas students' study abroad needs, the API (Application Program Interface) of the overseas student communication system can be called to collect question data generated during the communication process from the overseas student communication system, and these question data can be categorized to obtain multiple first study abroad need categories.

[0082] For example, customer service interaction information (i.e., interaction information between overseas students and system customer service) is collected from the overseas student communication system, semantic recognition is performed on the customer service interaction information through a natural semantic recognition model, and the semantic recognition is classified through a preset question classification model to obtain multiple first study abroad demand categories.

[0083] The various data types generated by the project management tool in the overseas student communication system can be determined in advance, and a data collection plan can be formulated based on the various data types. The data collection plan includes: how to obtain data, data format and standards, and data acquisition time; according to the data collection plan, the API of the project management tool is called to extract multiple first study abroad demand categories.

[0084] S220. Expand the social data of overseas students' study-abroad needs to obtain expanded data of overseas students' study-abroad needs.

[0085] The expanded data on the demand for studying abroad includes: a plurality of first demand categories for studying abroad and a second demand category for studying abroad obtained by expanding at least one first demand category for studying abroad.

[0086] The category of the second study abroad demand category and the category of the first study abroad demand category can be the same category. For example, the first study abroad demand category is fruit, and the second study abroad demand category obtained by expanding the first study abroad demand category is vegetables. In this case, the category of the second study abroad demand category and the category of the first study abroad demand category both belong to the same category of food. The category of the second study abroad demand category and the category of the first study abroad demand category can be different categories. For example, the first study abroad demand category is fruit, and the second study abroad demand category obtained by expanding the first study abroad demand category is accommodation.

[0087] It should be noted that before expanding the social data on overseas students' study abroad needs, data cleaning, data conversion, and data specification can also be performed on the social data on study abroad needs to facilitate improving the content integrity and format uniformity of the data.

[0088] In some embodiments, data expansion is performed on the social data of overseas students' study abroad needs to obtain expanded data of overseas students' study abroad needs, including:

[0089] Obtain a preset knowledge graph, which includes: multiple third study abroad demand categories; match each first study abroad demand category included in the study abroad demand social data with each third study abroad demand category included in the preset knowledge graph; determine that the third study abroad demand category that matches the first study abroad demand category is the second study abroad demand category corresponding to the first study abroad demand category; based on the second study abroad demand category corresponding to the first study abroad demand category, expand the data of the corresponding first study abroad demand category to obtain expanded study abroad demand data for overseas students.

[0090] Among them, when each first studying abroad demand category included in the social data of studying abroad demand is matched with each third studying abroad demand category included in the preset knowledge graph, if the matching degree between the first studying abroad demand category and a third studying abroad demand category is greater than the preset matching threshold, it is determined that the first studying abroad demand category matches the third studying abroad demand category.

[0091] It should be noted that when matching the study abroad demand categories, a first study abroad demand category may have a matching relationship with one or more third study abroad demand categories at the same time, that is, after matching, one study abroad demand category may correspond to one or more second study abroad demand categories.

[0092] When expanding the data of the first study abroad demand category, multiple first study abroad demand categories and the second study abroad demand categories corresponding to the first study abroad demand categories can be merged to obtain the expanded study abroad demand data of overseas students, so as to enrich the data categories of the expanded study abroad demand data.

[0093] In some embodiments, based on the second study abroad demand category corresponding to the first study abroad demand category, data expansion is performed on the corresponding first study abroad demand category to obtain expanded study abroad demand data of overseas students, including:

[0094] From the category database, obtain multiple candidate study abroad demand categories associated with the second study abroad demand category corresponding to the first study abroad demand category; determine the category association characterization value of each candidate study abroad demand category and the second study abroad demand category corresponding to the first study abroad demand category; determine the candidate study abroad demand category whose category association characterization value is greater than a preset characterization threshold as the target study abroad demand category corresponding to the first study abroad demand category; based on the multiple first study abroad demand categories and the target study abroad demand category corresponding to the first study abroad demand category, determine the expanded study abroad demand data of overseas students.

[0095] The category database may be a database composed of multiple demand categories classified from historical exchange data of overseas students.

[0096] The multiple candidate study abroad demand categories associated with the second study abroad demand category corresponding to the first study abroad demand category are multiple candidate study abroad demand categories that have an association relationship with the second study abroad demand category corresponding to the first study abroad demand category. The association relationship means that the second study abroad demand category corresponding to the first study abroad demand category and the candidate study abroad demand category belong to the same category (such as belonging to the category of food) or there is a life correlation between the categories (such as the life correlation between the category of food and the category of sleep).

[0097] The category association characterization value between the candidate study abroad demand category and the second study abroad demand category corresponding to the first study abroad demand category may be the category matching degree between the candidate study abroad demand category and the second study abroad demand category corresponding to the first study abroad demand category. An identification value may be set for each specific demand category to facilitate the determination of the category matching degree between each two demand categories. The category matching degree may be determined based on the difference in the identification value between each two demand categories. The smaller the difference, the higher the category matching degree.

[0098] Determining expanded study abroad demand data for overseas students based on multiple first study abroad demand categories and target study abroad demand categories corresponding to the first study abroad demand categories may include: combining multiple first study abroad demand categories and target study abroad demand categories corresponding to the first study abroad demand categories to obtain expanded study abroad demand data for overseas students, or combining multiple first study abroad demand categories, second study abroad demand categories corresponding to the first study abroad demand categories, and target study abroad demand categories corresponding to the first study abroad demand categories.

[0099] Therefore, the first category of overseas study demand is extended and expanded through the category database, further effectively improving the category diversity of overseas study demand categories.

[0100] S230, processing the expanded data on overseas students' study-abroad demand to obtain a forecast result of overseas students' study-abroad demand.

[0101] Among them, the study abroad demand prediction result includes: the study abroad category object data corresponding to the first study abroad demand category and the study abroad category object data corresponding to the second study abroad demand category.

[0102] The study abroad category object data may be the representation data of the specific objects involved in the corresponding study abroad demand category. For example, the first study abroad demand category is fruit, and the study abroad category object data corresponding to the first study abroad demand category may be the number of apples (where apples are objects and the number is object data); the second study abroad demand category is vegetables, and the study abroad category object data corresponding to the second study abroad demand category may be the cooking method of potatoes (where potatoes are objects and the cooking method is object data).

[0103] Therefore, by expanding the data of multiple first study abroad demand categories collected, a second study abroad demand category with richer categories is obtained to predict the demand for studying abroad, which is convenient for effectively improving the prediction accuracy of the demand for studying abroad.

[0104] In some embodiments, data processing is performed on the expanded data of overseas students' study abroad needs to obtain the overseas students' study abroad needs prediction results, including:

[0105] Feature extraction is performed on the expanded data of overseas students' study abroad demand to obtain overseas students' study abroad related features; a study abroad demand prediction model with the highest matching degree with overseas students' study abroad related features is selected from the model database; overseas students' study abroad related features are input into the study abroad demand prediction model, and the study abroad demand prediction results of overseas students are determined based on the output results of the study abroad demand prediction model.

[0106] Therefore, by selecting the study abroad demand prediction model with the highest matching degree with the study abroad related characteristics of overseas students, the demand prediction is carried out based on the study abroad related characteristics of overseas students, so as to further improve the prediction accuracy of the overseas study demand prediction.

[0107] In addition, the study abroad demand prediction model is obtained by training the linear regression model based on historical related features. The output result of the study abroad demand prediction model can be shown in formula (1) shown above. In the process of training the linear regression model, the trained model can be evaluated by comparing the error index of the test results with the actual results, so as to effectively obtain the study abroad demand prediction model.

[0108] In some embodiments, the output result of the study abroad demand prediction model includes: initial category object data corresponding to the first study abroad demand category and initial category object data corresponding to the second study abroad demand category.

[0109] Based on the output results of the study abroad demand prediction model, the study abroad demand prediction results of overseas students are determined, including:

[0110] Obtain historical demand forecast results, which include: study abroad category object data corresponding to historical study abroad demand categories; based on the study abroad category object data corresponding to historical study abroad demand categories, update the initial category object data corresponding to the first study abroad demand category and the initial category object data corresponding to the second study abroad demand category to obtain study abroad demand forecast results for overseas students.

[0111] Among them, the historical demand forecast results include: study abroad category object data corresponding to the historical study abroad demand categories.

[0112] Based on the study abroad category object data corresponding to the historical study abroad demand categories, the initial category object data corresponding to the first study abroad demand category and the initial category object data corresponding to the second study abroad demand category are updated to obtain the study abroad demand prediction results of overseas students, which may include: deleting the study abroad category object data with the lowest frequency in the study abroad category object data corresponding to the historical study abroad demand categories from the initial category object data corresponding to the first study abroad demand category and the initial category object data corresponding to the second study abroad demand category, to obtain the study abroad demand prediction results of overseas students.

[0113] Since overseas students have a relatively low demand for this part of the data, deleting this data will help improve the applicability of the overseas students' study abroad demand forecast results.

[0114] In addition, after obtaining the forecast results of overseas students' study abroad demand, a demand analysis report can be generated based on the first study abroad demand category, the second study abroad demand category and the forecast results of overseas students' study abroad demand for unified viewing.

[0115] In some embodiments, the method of this embodiment further includes:

[0116] A social data display area, an extended data display area and a prediction result display area are created in the user interface; multiple first study abroad demand categories included in the study abroad demand social data are displayed in the social data display area, multiple second study abroad demand categories included in the study abroad demand extended data are displayed in the extended data display area, and the study abroad demand prediction results of overseas students are displayed in the prediction result display area.

[0117] Among them, the social data display area, the expanded data display area and the prediction result display area can be located in different display areas in the same user interface, so that the first study abroad demand category, the second study abroad demand category and the study abroad demand prediction results of overseas students can be viewed at the same time in the same user interface.

[0118] In some embodiments, the method of this embodiment further includes:

[0119] In response to an instruction to adjust the overseas students' study abroad demand forecast result, the overseas students' study abroad demand forecast result is adjusted; and the overseas students' study abroad demand forecast result is updated and displayed in the forecast result display area.

[0120] Among them, the adjustment instruction for the overseas students' study abroad demand forecast results can be a demand adjustment request sent by the overseas students, and the demand adjustment request includes: demand adjustment data, which is convenient for adjusting the overseas students' study abroad demand forecast results through the demand adjustment data.

[0121] Therefore, by collecting the demand adjustment suggestions of overseas students, adjusting and displaying the forecast results of overseas students' study abroad demand, it is possible to effectively improve the fit between the forecast results of study abroad demand and the satisfaction of overseas students.

[0122] The present application also provides a computer device. Figure 3 , Figure 3 This is a basic structural block diagram of the computer device in this embodiment.

[0123] The computer device includes a memory 310 and a processor 320 that are connected to each other through a system bus. It should be noted that the figure only shows a computer device with a memory 310 and a processor 320, 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.

[0124] Computer devices can be computing devices such as desktop computers, notebooks, PDAs, and cloud servers. Computer devices can interact with users through keyboards, mice, remote controls, touch pads, or voice control devices.

[0125] The memory 310 includes at least one type of readable storage medium, and the readable storage medium includes a non-volatile memory or a volatile memory, for example, a flash memory, a hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc., and the RAM may include a static RAM or a dynamic RAM. In some embodiments, the memory 310 may be an internal storage unit of a computer device, for example, a hard disk or a memory of the computer device. In other embodiments, the memory 310 may also be an external storage device of the computer device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, or a flash card (FlashCard) equipped on the computer device. Of course, the memory 310 may also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the memory 310 is generally used to store the operating system and various application software installed on the computer device, such as the program code of the above method. In addition, the memory 310 may also be used to temporarily store various data that have been output or are to be output.

[0126] The processor 320 is generally used to perform the overall operation of the computer device. In this embodiment, the memory 310 is used to store program codes or instructions, the program code includes computer operation instructions, and the processor 320 is used to execute the program codes or instructions stored in the memory 310 or process data, such as running the program code of the above method.

[0127] In this article, the bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus system can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0128] Another embodiment of the present application also provides a computer-readable medium, which may be a computer-readable signal medium or a computer-readable medium. A processor in a computer reads a computer-readable program code stored in the computer-readable medium, so that the processor can execute the functional actions specified in each step or a combination of steps in the above method; and generate a device for implementing the functional actions specified in each block or a combination of blocks in the block diagram.

[0129] Computer-readable media include but are not limited to electronic, magnetic, optical, electromagnetic, infrared memory or semiconductor systems, devices or apparatuses, or any appropriate combination of the foregoing, the memory is used to store program codes or instructions, the program codes include computer operating instructions, and the processor is used to execute the program codes or instructions of the above methods stored in the memory.

[0130] For the definitions of memory and processor, please refer to the description of the aforementioned computer device embodiment and will not be repeated here.

[0131] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0132] Each functional unit or module in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0133] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program code.

[0134] In the claims, any reference symbols placed between brackets shall not be construed as limiting the claims. The word "comprising" described in the present application does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present application can be implemented with the aid of hardware comprising several different elements and with the aid of a suitably programmed computer. In a unit claim that lists a number of devices, several units of these devices may be embodied by the same hardware item. The use of first, second, and third, etc. does not indicate any order, and these words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

[0135] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A system for predicting demand for studying abroad, characterized in that: include: Data collection module, data processing module and demand forecasting module; The data collection module is used to collect the social data of overseas students' study abroad needs, where the social data of study abroad needs is used to describe a plurality of first study abroad needs categories extracted from the problem data generated during the communication process between the overseas students; The data processing module is used to perform data expansion on the overseas student's study abroad demand social data to obtain the overseas student's study abroad demand expansion data, wherein the study abroad demand expansion data includes: a plurality of the first study abroad demand categories and at least one second study abroad demand category obtained by expanding the first study abroad demand category; The demand prediction module is used to process the expanded data of the overseas students' study abroad demand to obtain the prediction results of the overseas students' study abroad demand; The study abroad demand prediction result includes: study abroad category object data corresponding to the first study abroad demand category and study abroad category object data corresponding to the second study abroad demand category.

2. The system according to claim 1, characterized in that The data processing module includes: an acquisition unit, a matching unit, a determination unit and an expansion unit: The acquisition unit is used to acquire a preset knowledge graph, wherein the preset knowledge graph includes: a plurality of third study abroad demand categories; The matching unit is used to match each of the first study abroad demand categories included in the study abroad demand social data with each of the third study abroad demand categories included in the preset knowledge graph; The determining unit is used to determine that the third study abroad demand category matching the first study abroad demand category is the second study abroad demand category corresponding to the first study abroad demand category; The expansion unit is used to expand the data of the corresponding first study abroad demand category based on the second study abroad demand category corresponding to the first study abroad demand category, so as to obtain the expanded study abroad demand data of the overseas students.

3. The system according to claim 2, characterized in that The expansion unit is specifically used for: Acquire, from a category database, a plurality of candidate overseas study demand categories associated with a second overseas study demand category corresponding to the first overseas study demand category; Determine a category association representation value of each of the candidate study abroad demand categories and a second study abroad demand category corresponding to the first study abroad demand category; Determine the candidate study abroad demand category whose category association representation value is greater than a preset representation threshold as the target study abroad demand category corresponding to the first study abroad demand category; Based on a plurality of the first study abroad demand categories and a target study abroad demand category corresponding to the first study abroad demand category, the study abroad demand expansion data of the overseas student is determined.

4. The system according to claim 1, characterized in that The demand forecasting module includes: an extraction unit, a selection unit and a forecasting unit; The extraction unit is used to extract features from the expanded data of the overseas students' study-abroad needs to obtain study-abroad-related features of the overseas students; The selection unit is used to select the study abroad demand prediction model with the highest matching degree with the study abroad related characteristics of the overseas student from the model database; The prediction unit is used to input the study-related characteristics of the overseas students into the study-abroad demand prediction model, and determine the study-abroad demand prediction result of the overseas students based on the output result of the study-abroad demand prediction model.

5. The system according to claim 4, characterized in that The output result of the overseas study demand prediction model includes: initial category object data corresponding to the first overseas study demand category and initial category object data corresponding to the second overseas study demand category; The prediction unit is specifically used for: Obtaining historical demand forecast results, wherein the historical demand forecast results include: study abroad category object data corresponding to historical study abroad demand categories; Based on the study abroad category object data corresponding to the historical study abroad demand category, the initial category object data corresponding to the first study abroad demand category and the initial category object data corresponding to the second study abroad demand category are updated to obtain the study abroad demand prediction result of the overseas students.

6. The system according to claim 1, characterized in that Also includes: Area creation module and data display module; The area creation module is used to create a social data display area, an extended data display area and a prediction result display area in the user interface; The data display module is used to display multiple first study abroad demand categories included in the study abroad demand social data in the social data display area, display multiple second study abroad demand categories included in the study abroad demand expanded data in the expanded data display area, and display the study abroad demand prediction results of the overseas students in the prediction result display area.

7. The system according to claim 6, characterized in that Also includes: Demand adjustment module; The demand adjustment module is used to adjust the overseas study demand prediction result of the overseas study students in response to an adjustment instruction of the overseas study demand prediction result; The data display module is used to update and display the overseas students' study-abroad demand forecast results in the forecast result display area.

8. A method for predicting demand for studying abroad, characterized in that: include: Collecting social data on overseas students' study abroad needs, where the social data on study abroad needs is used to describe a plurality of first study abroad need categories extracted from question data generated during communication between the overseas students; Performing data expansion on the overseas student's study abroad demand social data to obtain the overseas student's study abroad demand expansion data, wherein the study abroad demand expansion data includes: a plurality of the first study abroad demand categories and at least one second study abroad demand category obtained by expanding the first study abroad demand category; Processing the expanded data of the overseas students' study abroad needs to obtain a prediction result of the overseas students' study abroad needs; The study abroad demand prediction result includes: study abroad category object data corresponding to the first study abroad demand category and study abroad category object data corresponding to the second study abroad demand category.

9. The method according to claim 8, characterized in that The step of expanding the social data of the overseas students' study abroad needs to obtain the expanded data of the overseas students' study abroad needs includes: Obtaining a preset knowledge graph, wherein the preset knowledge graph includes: a plurality of third study abroad demand categories; Perform category matching on each of the first study abroad demand categories included in the study abroad demand social data and each of the third study abroad demand categories included in the preset knowledge graph; Determine the third study abroad demand category that matches the first study abroad demand category, which is the second study abroad demand category corresponding to the first study abroad demand category; Based on the second study abroad demand category corresponding to the first study abroad demand category, data expansion is performed on the corresponding first study abroad demand category to obtain the study abroad demand expansion data of the overseas students.

10. The method according to claim 9, characterized in that The second study abroad demand category corresponding to the first study abroad demand category is based on data expansion of the corresponding first study abroad demand category to obtain the study abroad demand expansion data of the overseas students, including: Acquire, from a category database, a plurality of candidate overseas study demand categories associated with a second overseas study demand category corresponding to the first overseas study demand category; Determine a category association representation value of each of the candidate study abroad demand categories and a second study abroad demand category corresponding to the first study abroad demand category; Determine the candidate study abroad demand category whose category association representation value is greater than a preset representation threshold as the target study abroad demand category corresponding to the first study abroad demand category; Based on a plurality of the first study abroad demand categories and a target study abroad demand category corresponding to the first study abroad demand category, the study abroad demand expansion data of the overseas student is determined.

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