A method and system for processing employment tracking survey information of internship and training bases

Abstract

This application applies to the field of data processing technology, and in particular relates to a method and system for processing information on employment tracking surveys at internship and training bases. The method comprises: obtaining student information to be processed; extracting the student's personal characteristics from the student's personal information, and extracting the professional characteristics of each of the student's occupations from the professional information of each occupation in the student's internship and employment information; determining the student's professional interest characteristics based on the professional characteristics of each of the student's occupations and the student's internship and employment information; determining the student's career change characteristics based on the student's personal characteristics and the professional characteristics of each of the student's occupations; and predicting the student's next occupation based on the student's professional interest characteristics and the student's career change characteristics to obtain the student's prediction result. This method can provide more personalized career predictions for students and internship and training bases.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and in particular relates to a method and system for processing employment tracking and investigation information of an internship and training base. Background Art

[0002] The primary purpose of employment tracking surveys at internship and training bases is to understand students' post-internship and post-training experience in the job market and assess the effectiveness of internships in developing students' professional skills and employability. These surveys not only provide schools with first-hand data on students' career development, helping them optimize curriculum, internship arrangements, and other educational processes, but also inform subsequent career guidance and foster close ties between schools, businesses, and students.

[0003] In existing technology, internship and training bases typically collect students' internship and employment information when tracking and investigating their employment status. However, traditional career recommendations or career path predictions are often based solely on students' internship and employment information or simple career correlations, failing to fully consider students' individual characteristics. This means that internship and training bases are unable to provide students with personalized career recommendations, limiting their ability to meet their individual career development needs.

[0004] In summary, in the process of processing employment tracking survey information from internship and training bases, there is a problem of lack of predictive ability for students' personalized career development. Summary of the Invention

[0005] The embodiments of the present application provide a method and system for processing employment tracking and investigation information of internship and training bases, which can solve the problem of lack of predictive ability for students' personalized career development when processing employment tracking and investigation information of internship and training bases in related technologies.

[0006] In a first aspect, an embodiment of the present application provides a method for processing employment tracking survey information of an internship and training base, comprising:

[0007] Obtaining the student's pending information; wherein the student's pending information is used to represent information obtained by the internship training base from a follow-up investigation of the student's employment situation, and the student's pending information includes the student's personal information and the student's internship and employment information;

[0008] extracting personal characteristics of the student and occupational characteristics of each occupation of the student from the information to be processed of the student;

[0009] Determining the student's career interest characteristics based on the student's career characteristics and internship employment information;

[0010] determining the occupational change characteristics of the student based on the student's personal characteristics and the occupational characteristics of each of the student's occupations;

[0011] Based on the student's career interest characteristics and the student's career change characteristics, the student's next career is predicted to obtain a prediction result for the student.

[0012] The above technical solutions of the embodiments of the present application have at least the following technical effects:

[0013] The present application provides a method for processing information of an internship and training base employment tracking survey. First, by obtaining the student's pending information, it is beneficial to learn the student's personal information and the student's internship and employment information. Then, the student's personal characteristics are extracted from the student's personal information, and the occupational characteristics of each occupation of the student are extracted from the occupational information of each occupation in the student's internship and employment information. Then, based on the occupational characteristics of each occupation of the student and the student's internship and employment information, the student's occupational interest characteristics are determined. Based on the student's personal characteristics and the occupational characteristics of each occupation of the student, the student's occupational change characteristics are determined. Finally, based on the student's occupational interest characteristics and the student's occupational change characteristics, the student's next occupation is predicted and processed to obtain the student's prediction result. By analyzing the student's occupational interest and occupational change characteristics, the method can effectively identify the student's occupational interest tendency and change possibility, so that the predicted next occupation is more in line with the student's personality and development needs, and improve the student's matching with the future occupation. The method provides strong data support for internship and training bases and educational institutions. Educational institutions and training bases can provide more personalized and targeted employment guidance and career planning suggestions based on the student's occupational interest characteristics and occupational change characteristics, helping students find the right career path faster.

[0014] In a possible implementation of the first aspect, determining the student's career interest characteristics based on the professional characteristics of each of the student's occupations and the student's internship and employment information includes:

[0015] According to the time information of each occupation in the internship employment information of the student, each occupation is arranged in ascending time order to obtain an occupation time sequence; wherein the occupation time sequence includes the time information of each occupation; the time information of each occupation includes the start time and end time of each occupation;

[0016] Determine a first feature of each occupation based on the occupation time series and the occupational features of each occupation of the student; wherein the first feature is used to represent a fusion feature between the position feature of each occupation in the occupation time series and the occupational features of each occupation;

[0017] According to the first characteristic of each occupation, the occupational interest characteristics of the student are determined.

[0018] In a possible implementation of the first aspect, determining the first characteristic of each occupation based on the occupation time series and the occupational characteristics of each occupation of the student includes:

[0019] Determine the positional characteristics of each occupation according to the position of each occupation in the occupational time series and the time information of each occupation in the occupational time series;

[0020] The position feature of each occupation and the occupation feature of each occupation of the students are fused to obtain the first feature of each occupation.

[0021] In a possible implementation of the first aspect, determining the position feature of each occupation based on the position of each occupation in the occupation time series and the time information of each occupation in the occupation time series includes:

[0022] Determine the position index of each occupation according to the position of each occupation in the occupation time series; wherein the position index is used to represent the absolute position of the occupation in the occupation time series;

[0023] Calculating the duration of each occupation according to the time information of each occupation in the occupation time series;

[0024] Calculating the occupational interval of each occupation based on the time information of each occupation in the occupational time series; wherein the occupational interval is used to represent the time interval between two previous occupations;

[0025] Calculate the relative position of each occupation in the occupation time series based on the time information of each occupation in the occupation time series to obtain the relative time proportion of each occupation;

[0026] The position index of each occupation, the duration of each occupation, the occupational interval of each occupation and the relative time proportion of each occupation are integrated to obtain the position characteristics of each occupation.

[0027] In a possible implementation of the first aspect, determining the student's career interest characteristic based on the first characteristic of each occupation includes:

[0028] Extracting correlation information from the student's internship and employment information, and calculating the correlation between each occupation in the occupation time series and the previous occupation based on the correlation information, to obtain correlation features of each occupation in the occupation time series; wherein the correlation information is used to characterize the correlation between each occupation;

[0029] Fusing the associated features of each occupation in the occupation time series with the first features of each occupation to obtain the second features of each occupation;

[0030] The second feature of the last occupation in the occupation time series, the associated feature of each occupation, and the first feature of each occupation are fused to obtain the third feature of each occupation;

[0031] The second feature of the last occupation in the occupation time series and the third feature of the last occupation in the occupation time series are fused to obtain the occupational interest feature of the student.

[0032] In a possible implementation of the first aspect, determining the student's occupation change characteristics based on the student's personal characteristics and the occupational characteristics of each of the student's occupations includes:

[0033] constructing a career directed graph based on the personal characteristics of the student, the professional characteristics of each of the student's careers, and the association information;

[0034] Based on the occupation directed graph, a graph neural network is used to calculate the attention features of each occupation;

[0035] The attention features of each occupation are integrated to obtain the occupation change features of the student.

[0036] In a possible implementation of the first aspect, constructing a career directed graph based on the personal characteristics of the student, the professional characteristics of each of the student's careers, and the association information includes:

[0037] Determine each occupation of the student as a node of the occupation directed graph, and determine the personal characteristics of the student and the occupational characteristics of each occupation of the student as initial characteristics of the node corresponding to each occupation;

[0038] Determining edges of the occupation directed graph based on the association information;

[0039] Determining edge weights of the occupation directed graph based on the association information;

[0040] The occupation directed graph is constructed based on the nodes of the occupation directed graph, the edges of the occupation directed graph and the edge weights of the occupation directed graph.

[0041] In a possible implementation of the first aspect, calculating the attention feature of each occupation using a graph neural network based on the occupation directed graph includes:

[0042] According to the initial features of each node of the occupation directed graph, the attention weight between each node and each node's adjacent nodes is calculated using the attention mechanism of the graph neural network to obtain the attention weight of each node's adjacent nodes;

[0043] Normalize the attention weights of the adjacent nodes of each node to obtain the normalized attention weights of the adjacent nodes of each node;

[0044] According to the normalized attention weights of the neighboring nodes of each node, the initial features of the neighboring nodes of each node are weighted summed to obtain the attention features of each node;

[0045] The attention feature of each node is determined as the attention feature of the profession corresponding to each node.

[0046] In a possible implementation of the first aspect, the predicting the student's next career based on the student's career interest characteristics and the student's career change characteristics to obtain the student's prediction result includes:

[0047] Extracting the occupational characteristics of the last occupation in the occupational time series;

[0048] Fusing the occupational characteristics of the last occupation in the occupational time series, the personal characteristics of the student, the occupational interest characteristics of the student, and the occupational change characteristics of the student to obtain the overall characteristics of the student;

[0049] Based on the overall characteristics of the student, a prediction model is used to predict the student's next career to obtain a prediction result for the student; wherein the prediction model is a deep learning model.

[0050] In a second aspect, an embodiment of the present application provides an internship and training base employment tracking and investigation information processing system, comprising:

[0051] An acquisition unit is configured to acquire information to be processed by a student; wherein the information to be processed by the student is used to represent information obtained by the internship training base from a follow-up investigation of the employment status of the student, and the information to be processed by the student includes the personal information of the student and the internship and employment information of the student;

[0052] an extraction unit, configured to extract the personal characteristics of the student from the personal information of the student, and to extract the professional characteristics of each occupation of the student from the professional information of each occupation in the internship and employment information of the student;

[0053] a career interest characteristic obtaining unit, configured to determine the career interest characteristics of the student based on the career characteristics of each of the student's careers and the student's internship and employment information;

[0054] a career change characteristic obtaining unit, configured to determine the career change characteristics of the student based on the personal characteristics of the student and the career characteristics of each of the student's careers;

[0055] The prediction unit is used to predict the student's next career based on the student's career interest characteristics and the student's career change characteristics, and obtain the student's prediction result.

[0056] In a third aspect, an embodiment of the present application provides an information processing device for employment tracking and investigation of an internship and training base, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any one of the embodiments of the first aspect when executing the computer program.

[0057] It can be understood that the beneficial effects of the second to third aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0059] Figure 1 This is a flowchart of an employment tracking survey information processing method for an internship and training base provided in one embodiment of the present application;

[0060] Figure 2 This is a schematic diagram of the implementation flow of steps S310 and S320 in the method for processing employment tracking survey information of an internship and training base provided in an embodiment of the present application;

[0061] Figure 3 This is a schematic diagram of the implementation flow of step S330 in the method for processing employment tracking survey information of an internship and training base provided in an embodiment of the present application;

[0062] Figure 4 This is a flowchart illustrating the implementation of step S400 in the method for processing employment tracking and investigation information of an internship and training base provided in an embodiment of the present application;

[0063] Figure 5 This is a schematic diagram of the structure of the internship and training base employment tracking and investigation information processing system provided in an embodiment of the present application;

[0064] Figure 6 It is a structural diagram of the employment tracking and investigation information processing equipment of the internship and training base provided in the embodiment of the present application. DETAILED DESCRIPTION

[0065] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0066] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0067] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0068] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0069] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0070] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0071] In the related art, internship and training bases typically collect students' internship and employment information when tracking and investigating their employment status. However, traditional career recommendations or career path predictions are often based solely on students' internship and employment information or simple career correlations, failing to fully consider students' individual characteristics. This means that internship and training bases are unable to provide students with personalized career recommendations, limiting their ability to meet their individual career development needs.

[0072] To solve the above problems, the embodiment of the present application provides an internship training base employment tracking survey information processing method and system. In this method, by first obtaining the student's pending information, it is beneficial to know the student's personal information and the student's internship employment information, then extract the student's personal characteristics from the student's personal information, and extract the student's occupational characteristics from the occupational information of each occupation in the student's internship employment information, then determine the student's occupational interest characteristics based on the occupational characteristics of each occupation and the student's internship employment information, and determine the student's occupational change characteristics based on the student's personal characteristics and the occupational characteristics of each occupation, and finally, based on the student's occupational interest characteristics and the student's occupational change characteristics, predict the student's next occupation and obtain the student's prediction result. By analyzing the student's occupational interest and occupational change characteristics, this method can effectively identify the student's occupational interest tendency and change possibility, so that the predicted next occupation is more in line with the student's personality and development needs, and improve the student's matching with the future occupation. This method provides strong data support for internship training bases and educational institutions. Educational institutions and training bases can provide more personalized and targeted employment guidance and career planning suggestions based on the student's occupational interest characteristics and occupational change characteristics, helping students find the right career path faster.

[0073] The employment tracking and investigation information processing method for internship and training bases provided in the embodiment of the present application can be applied to the employment tracking and investigation information processing equipment for internship and training bases. At this time, the employment tracking and investigation information processing equipment for internship and training bases is the executor of the employment tracking and investigation information processing method for internship and training bases provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of the employment tracking and investigation information processing equipment for internship and training bases.

[0074] For example, the information processing device can be a mobile phone, a tablet computer, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), a desktop computer, a computer, a laptop computer, a handheld computing device, customer premise equipment (CPE) and / or other devices for communicating on a wireless system and a next-generation communication system, for example, a mobile terminal in a 5G network or a mobile terminal in a future evolved public land mobile network (PLMN).

[0075] In order to better understand the employment tracking and investigation information processing method of the internship and training base provided in the embodiment of the present application, the specific implementation process of the employment tracking and investigation information processing method of the internship and training base provided in the embodiment of the present application is exemplarily introduced below.

[0076] Figure 1 A schematic flow chart of an internship and training base employment tracking survey information processing method provided in an embodiment of the present application is shown. The internship and training base employment tracking survey information processing method includes:

[0077] S100: Obtaining the student's pending information. The student's pending information is used to represent the information obtained by the internship training base's follow-up investigation of the student's employment situation. The student's pending information includes the student's personal information and the student's internship and employment information.

[0078] It is understood that a student's personal information may include name, age, gender, educational background (e.g., major, degree, etc.), personal skills and certifications, etc. A student's internship or employment information may include detailed information about each internship or job, such as company name, position title, job description, internship or employment start and end dates, work location, industry type, salary, and performance evaluation information during the internship or job.

[0079] For example, the student's pending information can be obtained from the database of the internship training base. The database of the internship training base can be constructed by collecting each student's personal information, internship employment information, and tracking data: each student's personal information can be collected from the student's academic information system or archives; each student's internship employment information can be collected from the internship base, employer feedback, and student self-reports; each student's tracking data can be collected from student feedback reports, and tracking data includes students' satisfaction with different internships or jobs, performance evaluations, and subsequent career changes (such as resignation, promotion). Data cleaning and preprocessing are performed on all collected information to ensure that the data has no duplication, missing values, or outliers, and each student's personal information and internship employment information are linked to form a complete pending information entry. The processed pending information is stored in the database of the internship training base to facilitate subsequent query and analysis.

[0080] S200, extracting the student's personal characteristics from the student's personal information, and extracting the professional characteristics of each of the student's occupations from the professional information of each occupation in the student's internship and employment information.

[0081] For example, personal information and internship and employment information can be standardized to facilitate the unification of data formats.

[0082] The age field can be directly read from personal information or calculated using the date of birth; the gender field can be directly read from personal information; educational background information can be extracted from personal information and encoded as features, such as high school, undergraduate, and master's degrees; professional information in personal information can be analyzed to extract professional characteristics, such as computer science, electronic engineering, and finance; personal skills in personal information can be analyzed based on keyword matching (such as Python, Java, and C++) or using natural language processing (NLP) technology, using specific skills as features; for interest fields, keyword extraction or sentiment analysis can be used to identify areas of interest, such as programming and design. The extracted features are converted into numerical vectors, and different feature vectors are combined into a personal feature vector. For example, features such as educational background, major, and skill characteristics can be encoded as numerical values or labels for further analysis.

[0083] Keyword matching or NLP can be used to extract job types (such as software development, project management, and data analysis) from internship job postings. Job descriptions in internship job postings can be segmented and analyzed for word frequency to extract the core responsibilities and skill requirements of the position. TF-IDF or word embedding models can be used to extract key skills and tasks (such as programming, data analysis, and management) within the job description. Topic models such as LDA can be used to analyze the job content and identify the primary areas of responsibility for the occupation. Classification models or predefined rule sets can be used to categorize companies into internet companies, financial companies, and manufacturing, based on company name or type fields. Occupations can be categorized into specific industries (such as IT, finance, manufacturing, and healthcare) based on industry fields or job content. Key skills can be extracted from job descriptions and used as occupational characteristics (such as SQL, data modeling, and project management). These features can be converted into feature vectors (for example, using one-hot encoding or word embeddings), and the different features can be combined into vectors to obtain occupational characteristics for each occupation.

[0084] The personal characteristics of students and the professional characteristics of each profession can be stored in a database or data table to facilitate subsequent query and analysis.

[0085] S300, determining the student's career interest characteristics based on the career characteristics of each of the student's careers and the student's internship and employment information.

[0086] For example, based on students' internship and employment information, the frequency of students' appearance in different types of occupations can be calculated. For example, if a student repeatedly chooses data analysis or software development positions, it can be preliminarily concluded that the student is more interested in data analysis or software development positions; the number of internships or working hours of students in specific industries (such as IT, finance, and manufacturing) can be counted to determine the students' interest preferences in the industry; the frequency of skill requirements appearing in students' work content can be calculated to find out the skills that students frequently come into contact with (such as Python, data analysis, etc.), and infer students' interests at the skill level.

[0087] If the student's internship employment information contains a satisfaction score or work experience feedback, the satisfaction score or work experience feedback can be used to judge the student's interest in different positions or industries. Career types with high satisfaction can reflect the student's interest tendencies; interest tendencies can be judged by the student's performance in different jobs. Students will perform better in positions that interest them, and this can be verified through performance scores.

[0088] The occupational interest features obtained above may be uniformly coded or normalized to obtain an occupational interest feature vector.

[0089] We can cluster the occupational feature vectors of all students' occupations to identify the occupational groups that the students tend to favor. For example, if a student's internship experience is concentrated in technology research and development and data analysis positions, we can conclude that the student has a strong interest in technology research and development and data analysis positions.

[0090] Machine learning models (such as KNN, SVM, and decision trees) can be used to predict students' career interest characteristics. The model uses the career characteristics of each student's job as input, and student satisfaction and performance scores from internship and employment information as labels. The model is trained to predict students' interests and preferences, and the model outputs the student's career interest characteristics.

[0091] Through this step, career interest characteristics can be extracted from the occupational characteristics of each student's occupation and internship employment information, which can help internship training bases or career guidance departments better understand students' career tendencies and provide students with personalized career planning suggestions.

[0092] In one possible implementation, S300 determines the student's career interest characteristics based on the career characteristics of each of the student's jobs and the student's internship and employment information, including:

[0093] S310: Arrange each occupation in ascending time order based on the time information of each occupation in the student's internship and employment information to obtain an occupation time sequence, wherein the occupation time sequence includes the time information of each occupation, and the time information of each occupation includes the start time and end time of each occupation.

[0094] For example, we can extract the start and end times of each occupation from the student's internship and employment information, sort all occupations in ascending order by start time, and generate a time series containing occupations. Each entry includes information such as occupation name, start time, end time, and duration. For example, the occupation time series can have a structure of [(occupation 1, start time, end time), (occupation 2, start time, end time), ...].

[0095] S320: Determine a first feature of each occupation based on the occupation time series and the occupational features of each student's occupation. The first feature is used to represent a fusion feature between the position feature of each occupation in the occupation time series and the occupational features of each occupation.

[0096] For example, the relative position of each occupation in a time series can be calculated. For example, for a time series containing five occupations, the relative position of the first occupation is 1 / 5, the relative position of the second occupation is 2 / 5, and so on. The time weight of each occupation in the entire occupation time series can be determined based on the duration of each occupation (i.e., the end time minus the start time), representing the proportion of each occupation in the entire occupational experience. For example, if a certain occupation lasts for 6 months and the entire occupational experience is 24 months, the time weight is 6 / 24 = 0.25. The relative position of each occupation in the time series and the time weight of each occupation are used as the positional features of each occupation in the occupational time series.

[0097] The location and occupational features of each occupation can be quantized and concatenated to form a fused feature vector, for example, [relative position, time weight, job type code, industry type code, key skill code, ...]. The first feature of each occupation forms a multidimensional vector that includes both time location and occupation information.

[0098] Optionally, see Figure 2 , S320, based on the occupation time series and the occupational characteristics of each occupation of the student, determine the first characteristic of each occupation, including:

[0099] S321, determining the position feature of each occupation according to the position of each occupation in the occupation time series and the time information of each occupation in the occupation time series.

[0100] For example, the time evolution trend characteristics and distance-based characteristics of each occupation can be determined based on the position of each occupation in the occupation time series and the time information of each occupation in the occupation time series, and the time evolution trend characteristics and distance-based characteristics of each occupation can be used as the position characteristics of each occupation.

[0101] The time evolution trend characteristics of each occupation can include the difference characteristics before and after and the time proportion of adjacent occupations. The time difference between the current occupation and the previous occupation can be calculated, that is, the start time of the current occupation is subtracted from the end time of the previous occupation (for the first occupation, it can be set to zero) to obtain the difference characteristics before and after. The difference between the proportion of the current occupation and the previous occupation in the entire time series can be calculated to obtain the time proportion of adjacent occupations. The time proportion of adjacent occupations can represent the evolution trend of occupations in the time dimension. The calculation formula for the time proportion of adjacent occupations can be ,in, Indicates the time proportion of adjacent occupations, Indicates the duration of the current occupation, indicates the duration of the previous occupation, Indicates the total career experience time.

[0102] The distance-based features of each occupation may include a starting time distance feature and an average time interval. The starting time distance of each occupation relative to the first occupation can be calculated, that is, the starting time of the current occupation is subtracted from the starting time of the first occupation to obtain the starting time distance feature. The starting time distance feature can represent the time span between the current occupation and the initial occupation. The average time interval of all occupations before the current occupation can be calculated to obtain the average time interval. The average time interval calculation formula can be ,in, represents the average time interval, Indicates that the current occupation is a career, Indicates the The start time of the career, Indicates the The end time of a career.

[0103] For example, S321, based on the position of each occupation in the occupation time series and the time information of each occupation in the occupation time series, determines the position feature of each occupation, including:

[0104] S3211: Determine a position index for each occupation based on the position of each occupation in the occupation time series, wherein the position index is used to represent the absolute position of the occupation in the occupation time series.

[0105] For example, since the occupation time series is arranged in ascending order, the The position index of an occupation can be , indicating the The absolute position of an occupation in the occupation time series. For example, if the occupation time series is [occupation 1, occupation 2, occupation 3], then the position index is [1, 2, 3].

[0106] S3212, calculating the duration of each occupation based on the time information of each occupation in the occupation time series.

[0107] For example, the start and end time of each occupation can be used to calculate the duration of each occupation. The duration can be expressed in days, months, or years, depending on the time granularity of the data. For example, if Occupation 1 starts in January 2020 and ends in December 2020, then the duration is 12 months.

[0108] S3213: Calculate the occupational interval of each occupation based on the time information of each occupation in the occupational time series, wherein the occupational interval is used to represent the time interval between two consecutive occupations.

[0109] For example, the career interval represents the time difference between the end time of the previous career and the start time of the current career. The career interval can be calculated by subtracting the end time of the previous career from the start time of the current career. For the first career, the career interval can be defined as zero because there is no previous career. For example, if Career 1 ends in December 2020 and Career 2 starts in March 2021, the career interval is 3 months.

[0110] S3214, based on the time information of each occupation in the occupation time series, calculate the relative position of each occupation in the occupation time series to obtain the relative time ratio of each occupation.

[0111] For example, the relative time proportion of each occupation is used to characterize the relative proportion of the duration of each occupation in the entire occupational experience. The total duration of all occupations can be calculated based on the duration of each occupation, and the relative time proportion of each occupation can be calculated based on the duration of each occupation and the total duration. The calculation formula can be: ,in, Relative time ratio, Indicates the duration of the job, = represents the total duration of all occupations. For example, if the duration of occupation 1 is 12 months, the duration of occupation 2 is 6 months, and the total duration is 18 months, then the relative time proportion of occupation 1 is 12 / 18 = 0.67, and the relative time proportion of occupation 2 is 6 / 18 = 0.33.

[0112] S3215, integrating the position index of each occupation, the duration of each occupation, the occupational interval of each occupation, and the relative time ratio of each occupation to obtain the position characteristics of each occupation.

[0113] For example, the position index, duration, occupation interval, and relative time ratio of each occupation can be integrated to form a position feature for each occupation. The position feature of each occupation can be represented as a vector, for example, position feature = [position index, duration, occupation interval, relative time ratio]. The position feature of each occupation includes its absolute position in the time series, duration, interval with the previous occupation, and relative position in the entire occupation time series.

[0114] S322, feature fusion of the position feature of each occupation and the occupation feature of each occupation of the students to obtain the first feature of each occupation.

[0115] For example, all position features and occupation features may be normalized or standardized, which is beneficial for integrating features of different scales and preventing certain features from dominating in terms of values.

[0116] For each occupation, you can perform feature crossover on the position and occupation characteristics. For example, you can crossover the time share difference feature with the job type feature to create a new feature, time share-job type, which represents the change in time share within the current job type. This feature crossover increases the expressive power of features and captures more detailed correlations between position and occupation characteristics.

[0117] The location characteristics and occupational characteristics (including cross-characteristics) of each occupation can be spliced into a vector (first eigenvector). The first eigenvector of each occupation ultimately contains multidimensional information of the location characteristics and occupational characteristics in the time series.

[0118] Through the above steps, we can capture the changing patterns of occupations in the time series, which helps to more accurately analyze students' career interests and career development trends.

[0119] S330, determining the student's career interest characteristics based on the first characteristics of each career.

[0120] For example, a time-weighted average can be performed on the first feature of each occupation to highlight features that persist for a long time in the student's career. For example, the time weight of each occupation can be multiplied by the position type, industry type, and skill code, and then accumulated to generate a student's career interest feature vector.

[0121] We can cluster the first eigenvectors of each student's occupation to identify groups with similar characteristics. By analyzing the centers of these groups, we can derive the student's occupational interest characteristics. For example, if a cluster contains multiple occupations related to data analysis, we can conclude that the student has a strong interest in data analysis.

[0122] By analyzing the career time series, we can determine the first characteristic of each career. Based on the first characteristic, we can comprehensively analyze the career interest characteristics of students and provide data support for further career recommendations and career development planning.

[0123] Optionally, see Figure 3 , S330, based on the first characteristic of each occupation, determine the student's career interest characteristics, including:

[0124] S331 , extracting correlation information from the student's internship and employment information, and calculating the correlation between each occupation in the occupation time series and the previous occupation based on the correlation information, thereby obtaining correlation features for each occupation in the occupation time series. The correlation information is used to characterize the correlation between each occupation.

[0125] It can be understood that the associated information may include: similarity in job type, the similarity between each occupation and other occupations in job type, such as whether both occupations belong to technical positions, management positions or sales positions, etc.; similarity in industry type, the similarity between each occupation and other occupations in industry category, such as whether both occupations are in IT, finance, manufacturing and other industries; similarity in professional skills, the degree of overlap between the skills required for each occupation and other occupations, the more the skills overlap, the stronger the correlation between the two; similarity in geographical location, the same or similar geographical location may reflect a certain connection between occupations, for example, working in the same area may mean lower migration costs.

[0126] For example, based on the job type similarity, industry type similarity, professional skill similarity, and geographic location similarity in the association information, the job type similarity, industry type similarity, skill overlap, and geographic location similarity of each job in the occupation time series (starting from the second job, as the first job has no predecessor) can be calculated. The job type similarity, industry type similarity, skill overlap, and geographic location similarity for each job can be integrated to obtain the association characteristics for each job. Job type similarity and industry type similarity can be represented as binary values (1 for the same job and 0 for different jobs) or using embedded cosine similarity. Skill overlap can be represented by the Jaccard similarity coefficient, calculating the ratio of the intersection to the union of the skill sets. Geographic location similarity can be represented as binary values (1 for the same city and 0 for different cities), or similarity can be calculated based on geographic distance.

[0127] Assume that the students’ occupation information is as shown in the following table:

[0128]

[0129] Calculate the correlation characteristics between occupations 1 and 2: If occupations 1 and 2 have the same job type, then the job type similarity is assigned a value of 1; if occupations 1 and 2 have the same industry type, then the industry type similarity is assigned a value of 1; based on the professional skills of occupations 1 and 2, the Jaccard similarity (skill overlap) can be calculated, that is, Occupation 1 and Occupation 2 have different locations, and the geographical location similarity is assigned to 0. Then the associated features of Occupation 2 = [1, 1, 0.33, 0].

[0130] Calculate the correlation characteristics of occupations 2 and 3: If occupations 2 and 3 have different job types, then the job type similarity is assigned to 0; if occupations 2 and 3 have different industry types, then the industry type similarity is assigned to 0; based on the professional skills of occupations 2 and 3, the Jaccard similarity (skill overlap) can be calculated, that is, Occupation 2 and Occupation 3 have different locations, and the geographical location similarity is assigned to 0. Then the associated features of Occupation 3 = [0, 0, 0.25, 0].

[0131] S332: Fusing the associated features of each occupation in the occupation time series with the first features of each occupation to obtain the second features of each occupation.

[0132] For example, the first feature and the associated feature can be fused by directly concatenating the two feature vectors to obtain the second feature of each occupation, that is, the second feature = [first feature, associated feature]. For example, the first feature of a certain occupation is [1, 12, 0, 0.33], and the associated feature is [1, 1, 0.33, 0]. By concatenating the first feature and the associated feature of the occupation, the second feature of the occupation is obtained as [1, 12, 0, 0.33, 1, 1, 0.33, 0].

[0133] S333: Fusing the second feature of the last occupation in the occupation time series, the associated feature of each occupation, and the first feature of each occupation to obtain the third feature of each occupation.

[0134] For example, we can find the last occupation (i.e., the most recent one) in the time series and extract its second feature. The second feature of the last occupation, the associated features of each occupation, and the first feature of each occupation are concatenated and fused to obtain the third feature of each occupation: [second feature of the last occupation, first feature, associated features]. For example, if the second feature of the last occupation is [1, 12, 0, 0.33, 0.8, 1, 0.5, 1, 0.2], the first feature of a particular occupation is [2, 10, 3, 0.28], and its associated features are [0, 0, 0.25, 0], then the third feature of the occupation is [1, 12, 0, 0.33, 0.8, 1, 0.5, 1, 0.2, 2, 10, 3, 0.28, 0, 0, 0.25, 0].

[0135] S334, performing feature fusion on the second feature of the last occupation in the occupation time series and the third feature of the last occupation in the occupation time series to obtain the student's occupational interest feature.

[0136] For example, the second eigenvector of the last occupation in the occupation time series and the third eigenvector of the last occupation in the occupation time series are concatenated to generate a eigenvector representing the student's career interest, that is, career interest feature = [second feature of the last occupation, third feature of the last occupation].

[0137] The career interest characteristics obtained through the above steps can be used for further analysis and prediction, such as recommending suitable career paths and analyzing students' career tendencies.

[0138] S400, determining the student's career change characteristics based on the student's personal characteristics and the career characteristics of each of the student's careers.

[0139] For example, factors that influence career change can be identified by analyzing students' personal characteristics. The degree of match between students' skills and the skill requirements of each occupation can be analyzed. If students frequently switch from positions with low skill match to positions with high skill match, skill match may be a key factor in career change. Students' age and work experience may also influence the frequency and direction of career change; younger students or those with less experience may explore different occupations more frequently.

[0140] By analyzing the characteristics of each student's career, we can identify factors that trigger career changes. We can calculate the frequency of career changes and analyze the average time between changes to determine whether students tend to maintain stable employment or frequently switch careers. We can also identify the direction of career change by looking at the student's career change path, for example, whether the student gradually shifts from a technical position to a management position, or makes a lateral move within a specific field. Data analysis can also identify common characteristics before each career change (such as low salary) to infer possible triggers for the change.

[0141] The identified career change factors can be identified as the student's career change characteristics. These factors can be converted into vector representations to generate career change feature vectors for model training. The student's personal characteristics, the career characteristics of each job, and the career change characteristics can be input into a machine learning model (such as logistic regression or random forest), which can then predict the student's next possible career change.

[0142] Through this step, a comprehensive analysis of students' personal and career characteristics can determine the students' career change characteristics, help predict students' future career development trends, and provide personalized career advice to improve career matching and satisfaction.

[0143] In one possible implementation, see Figure 4 , S400, determines the student's career change characteristics based on the student's personal characteristics and the career characteristics of each of the student's careers, including:

[0144] S410, constructing a career directed graph based on the student's personal characteristics, the professional characteristics of each of the student's careers, and related information.

[0145] For example, a career directed graph is a graph structure used to represent a student's career path and its associated relationships. Each node in the career directed graph represents a student's career. The professional characteristics and personal characteristics of each career can be combined to form a feature vector for each node. Based on this association information, directed edges can be established between each node (each career). Edge features can also be incorporated to represent inter-career associations, such as job type similarity, industry type similarity, skill overlap, and geographic location similarity.

[0146] Optionally, at S410 , a career directed graph is constructed based on the student's personal characteristics, the professional characteristics of each of the student's careers, and related information, including:

[0147] S411, determining each of the students' occupations as a node of the occupational directed graph, and determining the students' personal characteristics and the occupational characteristics of each of the students' occupations as initial characteristics of the node corresponding to each occupation.

[0148] For example, a student's career can be considered a node. For example, if a student has n career experiences, then the career directed graph has n nodes. The individual characteristics can be integrated with the professional characteristics of each career to obtain the initial feature vector for each node.

[0149] S412: Determine the edges of the occupation directed graph based on the association information.

[0150] For example, the edges of the career directed graph can represent the correlation between each occupation and each occupation. According to the association information, the nodes are connected to obtain the edges of the career directed graph.

[0151] S413: Determine the edge weights of the occupation directed graph based on the association information.

[0152] For example, edge weights can measure the strength of the association between each occupation. Based on the association information, the job type similarity between two nodes (occupations) connected by an edge in the occupational directed graph can be calculated. This can be done using a binary representation (1 for the same, 0 for different) or cosine similarity of the job type embedding. Based on the association information, the industry type similarity between two nodes (occupations) connected by an edge in the occupational directed graph can be calculated. This can be done using a binary or embedded similarity representation. Based on the association information, the skill overlap between two nodes (occupations) connected by an edge in the occupational directed graph can be calculated using Jaccard similarity or cosine similarity. If the two nodes (occupations) connected by an edge in the occupational directed graph have the same geographical location, a higher association score can be assigned, or the similarity can be calculated based on the geographical distance between the two nodes.

[0153] You can use a weighted summation approach to assign weights to each similarity calculated above and calculate the sum to obtain the edge weight of each edge. For example, if the job type similarity between two nodes in a directed occupational graph is 0.8, the industry type similarity is 1, the skill overlap is 0.5, and the location similarity is 0, and the weight parameter for each similarity is 1, then the edge weight of this edge is 1 × 0.8 + 1 × 1 + 1 × 0.5 + 1 × 0 = 2.3.

[0154] S414: Construct a career directed graph based on the nodes, edges, and edge weights of the career directed graph.

[0155] For example, a career directed graph can be constructed using nodes, edges, and edge weights. This graph can be used to represent a student's career path and the connections between careers. A career directed graph can be represented using an adjacency matrix or an adjacency list. In an adjacency matrix, each element represents the strength of the connection between two careers (edge weight); in an adjacency list, each node contains the nodes connected to it and the corresponding edge weights.

[0156] S420: Calculate the attention features of each occupation using a graph neural network based on the occupation directed graph.

[0157] For example, a graph neural network (e.g., a Graph Attention Network (GAT)) can be used to perform message passing on a directed graph of occupations to compute the attentional features of each occupation. For each node, GAT calculates an attention weight based on the features of its neighboring nodes. The attention weight indicates the degree of association between each node and its neighboring nodes. A higher weight indicates a greater influence of the node by its neighboring nodes.

[0158] Optionally, at S420, the attention feature of each occupation is calculated using a graph neural network based on the occupation directed graph, including:

[0159] S421, based on the initial features of each node in the career directed graph, the attention weight between each node and each node's adjacent nodes is calculated using the attention mechanism of the graph neural network to obtain the attention weight of each node's adjacent nodes.

[0160] For example, it can be assumed that each node The initial characteristics are , you can do this for each node The initial characteristics Apply a linear transformation to obtain the transformed features ,in Represents a learnable weight matrix. Extract each node Each adjacent node of The transformed features , according to each node The transformed features , each node Each adjacent node of The transformed features And each node With each node Each adjacent node of The edge weights between , calculate each node Each adjacent node of The attention weight , the calculation formula is ,in, is a learnable weight vector used to calculate attention, Represents a feature concatenation operation.

[0161] S422, normalize the attention weights of the adjacent nodes of each node to obtain the normalized attention weights of the adjacent nodes of each node.

[0162] For example, each node obtained by calculation can be Each adjacent node of The attention weight Normalize and get each node Each adjacent node of The normalized attention weight The calculation formula is ,in, Representation node The set of all adjacent nodes of . After normalization, the sum of the attention weights of all adjacent nodes of each node is 1.

[0163] S423 , performing weighted summation on the initial features of the neighboring nodes of each node according to the normalized attention weights of the neighboring nodes of each node to obtain the attention features of each node.

[0164] For example, each node can be used Each adjacent node of The normalized attention weight For each node Each adjacent node of The transformed features Perform weighted summation to obtain the attention feature of each node. The calculation formula is: ,in, represents the attention feature of each node, is an activation function (e.g. ReLU) used to add nonlinearity.

[0165] S424, determining the attention feature of each node as the attention feature of the occupation corresponding to each node.

[0166] For example, the attention feature of each node That is, the attention characteristics of the occupation corresponding to each node.

[0167] S430, integrating the attention features of each occupation to obtain the student's occupation change features.

[0168] For example, the average pooling method can be used to average the attention features of all occupations to generate the student's career change features. Average pooling will equally consider the features of all nodes and is suitable for situations where all career changes are equally important to the student's career change features.

[0169] The maximum pooling method can be used to take the element-level maximum value of the attention features of all occupations to generate occupation change features. Maximum pooling can retain the key changes in the features and emphasize the important features in the occupation directed graph.

[0170] The weighted pooling method can be used to assign different weights to each occupation according to its position or importance in the time series, and the attention features of each occupation are weighted summed according to the weight of each occupation.

[0171] Through the above steps, the correlation between different occupations can be adaptively learned, thereby effectively capturing students' career development trends. The final career change characteristics can serve as the basis for further analysis and prediction, and be used to generate personalized career development suggestions or path recommendations.

[0172] S500, based on the student's career interest characteristics and the student's career change characteristics, the student's next career is predicted to obtain the student's prediction result.

[0173] For example, we can identify the career categories that best match a student's career interests (e.g., job type, industry preference, and skill requirements). For example, if a student has a strong interest in data analysis, data analysis positions may be prioritized. We can assign weights to career interest characteristics (e.g., 30% for industry preference, 40% for job type, and 30% for skill matching), and calculate scores based on the degree of matching between these characteristics.

[0174] Based on a student's career change characteristics, such as change frequency, salary sensitivity, and job or industry preferences, we can determine the likely direction of their next career change. For example, if a student's career change history indicates a strong sensitivity to salary, higher-paying career categories will be prioritized. If a student exhibits a clear trend in career change (such as a gradual shift from a technical position to a management position), management positions may also be prioritized when recommending positions. Career change characteristics can be weighted, and scores calculated based on the degree of compatibility between different characteristics.

[0175] The interest matching score and the change propensity score can be weighted and superimposed, and the occupation category with the highest score is the predicted result. Threshold conditions can be set to avoid recommending positions that are too far away from the student's interests.

[0176] In one possible implementation, S500 predicts the student's next career based on the student's career interest characteristics and the student's career change characteristics, and obtains the student's prediction result, including:

[0177] S510, extracting the occupational characteristics of the last occupation in the occupational time series.

[0178] For example, the occupational characteristics of the last occupation can be extracted from the occupational time series.

[0179] S520, the occupational characteristics of the last occupation in the occupational time series, the personal characteristics of the student, the occupational interest characteristics of the student, and the occupational change characteristics of the student are integrated to obtain the overall characteristics of the student.

[0180] For example, feature fusion can be performed by vector splicing, where the occupational feature vector of the last occupation, the student's personal feature vector, the student's occupational interest feature vector, and the student's occupational change feature vector are spliced together to form a complete feature vector. If the dimensions of the features are unbalanced, standardization or dimensionality reduction of the features can be considered to ensure the consistency of the prediction model input.

[0181] S530: Based on the overall characteristics of the student, the prediction model is used to predict the student's next career to obtain a prediction result for the student. The prediction model is a deep learning model.

[0182] For example, a suitable deep learning model can be selected, which may include: a multi-layer perceptron (MLP), in which the MLP input layer receives the overall features, undergoes nonlinear transformations in several hidden layers, and finally outputs the prediction results. The model structure is simple and suitable for scenarios where features have been extracted and fused; a Transformer model, in which the student's overall feature vector can be input into the Transformer model, which is processed and output with the self-attention mechanism of the Transformer model. The Transformer model can flexibly process long sequences and capture global dependencies.

[0183] Prediction model training process: A dataset can be constructed by extracting the pending information of multiple students from the internship training base database. Each student's actual next career move can be labeled. The labeled dataset can be divided into a training set, a validation set, and a test set, with a ratio of 70% training, 15% validation, and 15% test.

[0184] After selecting a suitable deep learning model, you can choose the model's training configuration: you can use the cross-entropy loss function. You can use the Adam or SGD optimizer. Adam is a more commonly used optimizer due to its fast convergence speed and automatic learning rate adjustment. You can also set the model's hyperparameters. You can set the model's learning rate, starting from 0.001 and adjusting it based on the validation set's performance; you can set the model's batch size, such as 32, 64, or 128, the specific size can be determined through experimentation; and you can set the model's number of epochs, ranging from 10 to 100, and decide whether to stop early based on the validation set's performance.

[0185] The training set is input into the model. After multiple layers of calculation, the output probabilities are obtained. The model's predictions are compared with the true labels, and the loss value is calculated. Based on the loss value, the gradient is calculated, and the optimizer is used to update the model parameters to gradually reduce the loss. After each epoch, the model's performance is evaluated on the validation set, monitoring the loss and accuracy of the validation set. If the loss on the validation set no longer decreases, consider using early stopping to terminate training to prevent overfitting. After training, the model is evaluated on the test set, calculating metrics such as accuracy, precision, recall, and F1 score to understand the model's generalization performance.

[0186] After model training and optimization, the trained prediction model can be used to predict new data. Specifically, the student's overall characteristics are input, processed by the prediction model, and the output is a prediction of the student's next career. The prediction model outputs a probability distribution of career categories, representing the possible categories for the student's next career. The category with the highest probability is selected as the prediction result.

[0187] Through the above steps, we can comprehensively consider students' career interests, career change patterns and personal backgrounds to provide more accurate career predictions.

[0188] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0189] Corresponding to the internship and training base employment tracking and investigation information processing method described in the above embodiment, the embodiment of the present application also provides an internship and training base employment tracking and investigation information processing system, and each unit of the system can implement each step of the internship and training base employment tracking and investigation information processing method. Figure 5 A structural block diagram of the internship and training base employment tracking and investigation information processing system provided in an embodiment of the present application is shown. For the sake of convenience, only the parts related to the embodiment of the present application are shown.

[0190] Reference Figure 5 , the system comprises:

[0191] The acquisition unit is used to acquire the student's pending information, wherein the student's pending information is used to represent the information obtained by the internship training base from the follow-up investigation of the student's employment situation, and the student's pending information includes the student's personal information and the student's internship and employment information.

[0192] The extraction unit is used to extract the personal characteristics of the students from the personal information of the students, and to extract the professional characteristics of each occupation of the students from the professional information of each occupation in the internship and employment information of the students.

[0193] The occupational interest characteristic obtaining unit is used to determine the occupational interest characteristics of students based on the occupational characteristics of each of their occupations and the students' internship and employment information.

[0194] The occupation change characteristic obtaining unit is used to determine the occupation change characteristics of the student based on the student's personal characteristics and the occupation characteristics of each of the student's occupations.

[0195] The prediction unit is used to predict the student's next career based on the student's career interest characteristics and the student's career change characteristics, and obtain the student's prediction result.

[0196] It should be noted that the information interaction, execution process, etc. between the above-mentioned units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0197] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0198] The embodiment of the present application also provides an information processing device for employment tracking and investigation of internship and training bases, Figure 6 This is a schematic diagram of the structure of the internship training base employment tracking survey information processing device provided in one embodiment of the present application. Figure 6 As shown, the internship training base employment tracking survey information processing device 6 of this embodiment includes: at least one processor 60 ( Figure 6 Only one is shown), at least one memory 61 ( Figure 6 Only one is shown in the figure) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the internship and training base employment tracking survey information processing device 6 implements the steps of any of the above-mentioned internship and training base employment tracking survey information processing method embodiments, or implements the functions of each unit in the above-mentioned device embodiments.

[0199] For example, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to implement the present application. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program 62 in the internship and training base employment tracking survey information processing device 6.

[0200] The internship training base employment tracking survey information processing device 6 can be a computing device such as a desktop computer, a notebook, a palmtop computer, a cloud server, etc. The internship training base employment tracking survey information processing device can include, but is not limited to, a processor 60 and a memory 61. It can be understood by those skilled in the art that Figure 6 It is only an example of the internship and training base employment tracking survey information processing device 6 and does not constitute a limitation of the internship and training base employment tracking survey information processing device 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, buses, etc.

[0201] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0202] In some embodiments, the memory 61 may be an internal storage unit of the internship and training base employment tracking survey information processing device 6, such as the hard drive or memory of the internship and training base employment tracking survey information processing device 6. In other embodiments, the memory 61 may also be an external storage device of the internship and training base employment tracking survey information processing device 6, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped with the internship and training base employment tracking survey information processing device 6. Furthermore, the memory 61 may include both the internal storage unit of the internship and training base employment tracking survey information processing device 6 and an external storage device. The memory 61 is used to store the operating system, application programs, boot loaders, data, and other programs, such as the program code of the computer program. The memory 61 may also be used to temporarily store data that has been output or is about to be output.

[0203] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0204] An embodiment of the present application provides a computer program product. When the computer program product runs on an internship and training base employment tracking and investigation information processing device, the internship and training base employment tracking and investigation information processing device implements the steps of any of the above-mentioned method embodiments.

[0205] If the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process steps in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to the information processing equipment of the internship training base employment tracking survey, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, removable hard drives, magnetic disks, or optical disks. In some jurisdictions, based on legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0206] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0207] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0208] In the embodiments provided in the present application, it should be understood that the disclosed internship and training base employment tracking survey information processing system, device and method can be implemented in other ways. For example, the internship and training base employment tracking survey information processing system and device embodiments described above are merely schematic. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, 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 an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0209] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0210] The above-described 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. 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 various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for processing employment tracking survey information of an internship and training base, characterized in that: include: Obtaining the student's pending information; wherein the student's pending information is used to represent information obtained by the internship training base from a follow-up investigation of the student's employment situation, and the student's pending information includes the student's personal information and the student's internship and employment information; Extracting the personal characteristics of the student from the personal information of the student, and extracting the occupational characteristics of each occupation of the student from the occupational information of each occupation in the internship employment information of the student; Determining the student's career interest characteristics based on the student's career characteristics and internship employment information; determining the occupational change characteristics of the student based on the student's personal characteristics and the occupational characteristics of each of the student's occupations; Based on the student's career interest characteristics and the student's career change characteristics, predicting the student's next career to obtain a prediction result for the student; The step of determining the student's career interest characteristics based on the student's career characteristics and internship information includes: According to the time information of each occupation in the internship employment information, each occupation is arranged in ascending time order to obtain an occupation time sequence; wherein the occupation time sequence includes the time information of each occupation, and the time information of each occupation includes the start time and end time of each occupation; Determine a first feature of each occupation based on the occupation time series and the occupational characteristics of each occupation; wherein the first feature is used to represent a fusion feature between the position feature of each occupation in the occupation time series and the occupational characteristics of each occupation; Determining the student's career interest characteristics based on the first characteristic of each career; Determining the student's career interest characteristics based on the first characteristic of each occupation includes: Extracting correlation information from the internship employment information, and calculating the correlation between each occupation in the occupation time series and the previous occupation based on the correlation information to obtain correlation features of each occupation in the occupation time series; wherein the correlation information is used to characterize the correlation between each occupation; Fusing the associated features of each occupation in the occupation time series with the first features of each occupation to obtain the second features of each occupation; The second feature of the last occupation in the occupation time series, the associated feature of each occupation, and the first feature of each occupation are fused to obtain the third feature of each occupation; The second feature of the last occupation in the occupation time series and the third feature of the last occupation in the occupation time series are fused to obtain the occupational interest feature of the student.

2. The method for processing employment tracking survey information of an internship and training base according to claim 1, characterized in that: The determining of the first characteristic of each occupation according to the occupation time series and the occupation characteristics of each occupation of the student includes: Determine the positional characteristics of each occupation according to the position of each occupation in the occupational time series and the time information of each occupation in the occupational time series; The position feature of each occupation and the occupation feature of each occupation of the students are fused to obtain the first feature of each occupation.

3. The method for processing employment tracking survey information of an internship and training base according to claim 2, characterized in that: Determining the position feature of each occupation according to the position of each occupation in the occupation time series and the time information of each occupation in the occupation time series includes: Determine the position index of each occupation according to the position of each occupation in the occupation time series; wherein the position index is used to represent the absolute position of the occupation in the occupation time series; Calculating the duration of each occupation according to the time information of each occupation in the occupation time series; Calculating the occupational interval of each occupation based on the time information of each occupation in the occupational time series; wherein the occupational interval is used to represent the time interval between two previous occupations; Calculate the relative position of each occupation in the occupation time series based on the time information of each occupation in the occupation time series to obtain the relative time proportion of each occupation; The position index of each occupation, the duration of each occupation, the occupational interval of each occupation and the relative time proportion of each occupation are integrated to obtain the position characteristics of each occupation.

4. The method for processing employment tracking survey information of an internship and training base according to claim 1, characterized in that: Determining the student's occupation change characteristics based on the student's personal characteristics and the occupational characteristics of each of the student's occupations includes: constructing a career directed graph based on the personal characteristics of the student, the professional characteristics of each of the student's careers, and the association information; Based on the occupation directed graph, a graph neural network is used to calculate the attention features of each occupation; The attention features of each occupation are integrated to obtain the occupation change features of the student.

5. The method for processing employment tracking survey information of an internship and training base according to claim 4, characterized in that: The constructing of a career directed graph based on the personal characteristics of the student, the professional characteristics of each of the student's careers, and the association information includes: Determine each occupation of the student as a node of the occupation directed graph, and determine the personal characteristics of the student and the occupational characteristics of each occupation of the student as initial characteristics of the node corresponding to each occupation; Determining edges of the occupation directed graph based on the association information; Determining edge weights of the occupation directed graph based on the association information; The occupation directed graph is constructed based on the nodes of the occupation directed graph, the edges of the occupation directed graph and the edge weights of the occupation directed graph.

6. The method for processing employment tracking survey information of an internship and training base according to claim 5, characterized in that: The method of calculating the attention features of each occupation using a graph neural network based on the occupation directed graph includes: According to the initial features of each node of the occupation directed graph, the attention weight between each node and each node's adjacent nodes is calculated using the attention mechanism of the graph neural network to obtain the attention weight of each node's adjacent nodes; Normalize the attention weights of the adjacent nodes of each node to obtain the normalized attention weights of the adjacent nodes of each node; According to the normalized attention weights of the neighboring nodes of each node, the initial features of the neighboring nodes of each node are weighted summed to obtain the attention features of each node; The attention feature of each node is determined as the attention feature of the profession corresponding to each node.

7. The method for processing employment tracking survey information of an internship and training base according to claim 1, characterized in that: The step of predicting the student's next career based on the student's career interest characteristics and the student's career change characteristics to obtain the student's prediction result includes: Extracting the occupational characteristics of the last occupation in the occupational time series; Fusing the occupational characteristics of the last occupation in the occupational time series, the personal characteristics of the student, the occupational interest characteristics of the student, and the occupational change characteristics of the student to obtain the overall characteristics of the student; Based on the overall characteristics of the student, a prediction model is used to predict the student's next career to obtain a prediction result for the student; wherein the prediction model is a deep learning model.

8. An employment tracking survey information processing system for internship and training bases, characterized by: Used to implement the internship and training base employment tracking survey information processing method according to any one of claims 1 to 7, the internship and training base employment tracking survey information processing system comprises: An acquisition unit is configured to acquire information to be processed by a student; wherein the information to be processed by the student is used to represent information obtained by the internship training base from a follow-up investigation of the employment status of the student, and the information to be processed by the student includes the personal information of the student and the internship and employment information of the student; an extraction unit, configured to extract the personal characteristics of the student from the personal information of the student, and to extract the professional characteristics of each occupation of the student from the professional information of each occupation in the internship and employment information of the student; a career interest characteristic obtaining unit, configured to determine the career interest characteristics of the student based on the career characteristics of each of the student's careers and the student's internship and employment information; a career change characteristic obtaining unit, configured to determine the career change characteristics of the student based on the personal characteristics of the student and the career characteristics of each of the student's careers; The prediction unit is used to predict the student's next career based on the student's career interest characteristics and the student's career change characteristics, and obtain the student's prediction result.

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