Student enrollment qualification input method and system based on government affair big data

Abstract

The invention provides a student enrollment qualification input method and system based on government affair big data, and relates to the technical field of education management, and the method comprises the steps: obtaining the basic information of a target student group from a government affair big data platform; performing data cleaning and data correction on the basic information to obtain standardized data; performing text classification on the standardized data based on the context relationship to obtain standardized data of each data category; verifying the standardized data of each data category to obtain verification data, and performing data access control and authority management on the verification data; checking the verification data according to a preset enrollment qualification standard so as to judge whether the student meets an enrollment condition or not; and automatically inputting the verification data meeting the enrollment condition into the student management system of the corresponding school. According to the method, the data quality is improved, the data integration process is simplified, and the integration complexity caused by non-uniform data formats is reduced.

Description

Technical Field

[0001] The present invention relates to the field of education management technology, and in particular to a method and system for entering student admission qualifications based on government affairs big data. Background Art

[0002] With the development of information technology, the application of government big data is becoming more and more extensive. In the field of education, the review and entry of student admission qualifications is a tedious and error-prone process. Traditional methods rely on manual review and manual entry, which is inefficient and prone to errors. Therefore, there is an urgent need for an automated method to improve efficiency and accuracy.

[0003] The invention patent with application number 202210525483.1 discloses a method for determining admission qualifications based on population data, which belongs to the field of big data information processing; the specific steps of the method are as follows: S1 sorts out the corresponding data resources according to the data demand list and the actual situation of the project; S2 shares resources in a timely manner according to the data aggregation requirements; S3 establishes an admission qualification special service model according to the admission qualification review conditions; S4 provides a service list based on standard data, develops corresponding data interfaces and tests them; the technical admission qualification review method is to integrate the data of various commissions and bureaus within the government, conduct data modeling analysis, and provide query and verification services to obtain high-accuracy relationship result data, so as to better serve the society, parents and students. However, the data formats and standards between government departments are not unified, which increases the complexity of data integration, and data sharing authority management and privacy protection become unavoidable obstacles. Summary of the invention

[0004] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a student admission qualification entry method and system based on government big data, so as to improve the efficiency and accuracy of admission qualification review and entry.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for entering student admission qualifications based on government affairs big data, comprising:

[0007] Obtain basic information of the target student group from the government big data platform;

[0008] Using data middle platform technology, the basic information is cleaned and corrected to obtain standardized data;

[0009] Performing text classification on the standardized data based on the contextual relationship to obtain standardized data of each data category;

[0010] Verify the standardized data of each data category to ensure the authenticity and integrity of the data, obtain verified data, and perform data access control and authority management on the verified data to ensure the security and compliance of data sharing;

[0011] Reviewing the verification data according to the preset admission qualification standards to determine whether the student meets the admission requirements;

[0012] The verification data that meets the admission requirements will be automatically entered into the student management system of the corresponding school.

[0013] Preferably, it also includes:

[0014] The verification data that does not meet the admission requirements will be processed manually.

[0015] Preferably, the basic information includes: identity card information, household registration information, student registration information and family information.

[0016] Preferably, the data cleaning step includes: data deduplication, abnormal data checking and data standardization checking; the data correction step includes: data completion and data coding.

[0017] Preferably, text classification is performed on the standardized data based on the contextual relationship to obtain standardized data of each data category, including:

[0018] Inputting the standardized data into a text context feature extraction layer to extract context feature information;

[0019] Inputting the standardized data into a global feature extraction layer to extract global feature information;

[0020] Fusing the contextual feature information and the global feature information to obtain a fused text feature;

[0021] Constructing a loss function using the fused text features;

[0022] Continuously optimizing the loss function to obtain a text classification model;

[0023] The text classification model is used to complete text classification and obtain standardized data of each data category.

[0024] Preferably, the standardized data is input into a text context feature extraction layer to extract context feature information, including:

[0025] Use the pre-trained language model to extract the initial feature information of the standardized data;

[0026] Inputting the initial feature information into a forward gated recurrent unit and a reverse gated recurrent unit;

[0027] The outputs of the forward gated recurrent unit and the reverse gated recurrent unit are concatenated to obtain contextual relationship feature information.

[0028] Preferably, the outputs of the forward gated recurrent unit and the reverse gated recurrent unit are concatenated to obtain contextual feature information, including:

[0029] Using the formula:

[0030]

[0031]

[0032] The outputs of the forward gated recurrent unit and the reverse gated recurrent unit are concatenated to obtain contextual feature information; wherein, H t ={H 1 ,H 2 ,…H l} represents the initial feature information input at time t, represents the output of the positive gated recurrent unit at time t, Represents the output of the reverse gated recurrent unit at time t, GRU represents the gated recurrent unit, and G represents the contextual relationship feature information.

[0033] Preferably, the standardized data is input into a global feature extraction layer to extract global feature information, including:

[0034] The initial feature information of the standardized data is input into the convolution layer and the pooling layer in sequence to obtain the global feature information; the global feature information extraction formula is:

[0035] c i =f(ω●H+b)

[0036]

[0037] In the formula, f is the activation function, ω is the convolution kernel, h is the convolution kernel size, b is the bias value, c is i is the feature vector extracted by the i-th convolutional layer, It represents the value of the features extracted by three different convolution kernels after the maximum pooling layer, and C represents the extracted global feature information.

[0038] A student admission qualification entry system based on government affairs big data, comprising:

[0039] The information acquisition module is used to obtain the basic information of the target student group from the government big data platform;

[0040] A data processing module is used to perform data cleaning and data correction on the basic information using data middle platform technology to obtain standardized data;

[0041] A data classification module, used to perform text classification on the standardized data based on contextual relationships to obtain standardized data of various data categories;

[0042] A security verification module is used to verify the standardized data of each data category to ensure the authenticity and integrity of the data, obtain the verified data, and perform data access control and authority management on the verified data to ensure the security and compliance of data sharing;

[0043] An audit module is used to audit the verification data according to the preset admission qualification standards to determine whether the student meets the admission requirements;

[0044] The entry module is used to automatically enter the verification data that meets the admission requirements into the student management system of the corresponding school.

[0045] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0046] The present invention provides a method and system for entering student admission qualifications based on government big data, the method comprising: obtaining basic information of a target student group from a government big data platform; using data middle platform technology to perform data cleaning and data correction on the basic information to obtain standardized data; text classification of the standardized data based on contextual relationships to obtain standardized data of each data category; verifying the standardized data of each data category to ensure the authenticity and integrity of the data, obtaining verification data, and performing data access control and authority management on the verification data to ensure the security and compliance of data sharing; reviewing the verification data according to preset admission qualification standards to determine whether the student meets the admission conditions; and automatically entering the verification data that meets the admission conditions into the student management system of the corresponding school. The present invention obtains the basic information of the target student group from the government big data platform, and uses data middle platform technology to perform data cleaning and correction, so as to standardize data from different sources and in different formats. This standardization not only improves the quality of data, but also simplifies the process of data integration and reduces the integration complexity caused by inconsistent data formats. And text classification of standardized data based on contextual relationships can more accurately identify and classify different types of data. This classification method can improve the efficiency and accuracy of data processing, making the subsequent verification and audit process more efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0048] Figure 1 A flow chart of a method provided by an embodiment of the present invention;

[0049] Figure 2 A schematic diagram of the system structure provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] The purpose of the present invention is to provide a student admission qualification entry method and system based on government big data, which can improve the quality of data, simplify the data integration process, and reduce the integration complexity caused by inconsistent data formats.

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0053] Figure 1 A flow chart of a method provided by an embodiment of the present invention, such as Figure 1 As shown, the present invention provides a method for entering student admission qualifications based on government affairs big data, comprising:

[0054] Step 100: Obtain basic information of the target student group from the government big data platform;

[0055] Step 200: Using data middle platform technology, the basic information is cleaned and corrected to obtain standardized data;

[0056] Step 300: performing text classification on the standardized data based on the contextual relationship to obtain standardized data of each data category;

[0057] Step 400: verifying the standardized data of each data category to ensure the authenticity and integrity of the data, obtaining the verified data, and performing data access control and authority management on the verified data to ensure the security and compliance of data sharing;

[0058] Step 500: reviewing the verification data according to the preset admission qualification standard to determine whether the student meets the admission requirements;

[0059] Step 600: Automatically enter the verification data that meets the admission requirements into the student management system of the corresponding school.

[0060] Preferably, it also includes:

[0061] The verification data that does not meet the admission requirements will be processed manually.

[0062] Specifically, this embodiment first establishes a secure connection with the government big data platform to ensure the security and compliance of data transmission. Then, using the API (application programming interface) or data interface protocol, a request is sent to the government big data platform to specify the required student basic information fields, such as name, ID number, household registration address, family information, student status, etc. The request contains necessary identity authentication information to ensure that only authorized users can access the data. After receiving the request, the data platform will retrieve data according to the preset query conditions (such as age, region, year of enrollment, etc.) and return the basic information data set of students that meet the conditions. Subsequently, the system will perform a preliminary format check and integrity verification on the acquired data to ensure the validity of the data. Finally, the basic information is stored in the local database for subsequent data cleaning, correction and analysis. Through this series of steps, this embodiment can efficiently and accurately obtain the basic information of the target student group from the government big data platform, providing a reliable data basis for the subsequent admission qualification review.

[0063] Optionally, the data center serves as a centrally managed data processing platform that can integrate basic student information data from different sources and unify data formats and standards. Through the data center, predefined data cleaning rules are applied, such as removing duplicate records, filling missing values, standardizing field names and data types, etc., to ensure data consistency and accuracy. In addition, the data center can also use machine learning algorithms to intelligently correct data, identify and correct potential errors, such as verifying the authenticity of information by comparing historical data and external data sources. Ultimately, the standardized data that has been cleaned and corrected will be stored in the data center for subsequent analysis and application, thereby improving the quality and availability of the data.

[0064] Preferably, the basic information includes: identity card information, household registration information, student registration information and family information.

[0065] Optionally, for the government big data platform, this embodiment also sorts out the data resource directory therein, sorts out government data resources by conducting a large-scale survey of the data resource directory, and establishes a government data resource directory system. The data asset directory survey and sorting includes the production of the following survey templates, sorting out survey data, and determining the data aggregation directory.

[0066] (1) Research template: Provides Excel templates and research specifications for researching each department’s business systems, databases, and data tables.

[0067] (2) Organize research data: collect and organize the business systems, databases, and data table directories of each unit.

[0068] (3) Determine the aggregation directory: According to relevant regulations, determine the data directory to be aggregated by each department.

[0069] Furthermore, in this embodiment, the government data resource registration is performed through a unified platform, and the registration content at least includes:

[0070] 1) Application system information: information about the application system that generates or manages data resources, including the name of the business system, operating network, applicability, and system classification.

[0071] 2) Database information: database information that generates or manages data resources, including the database name, database type, etc.

[0072] 3) Data scope: the regional scope covered by government data, such as "the whole province", "provincial level", "all cities", "the whole city", "city level", "the whole county (city, district)", and "county (city, district) level".

[0073] 4) Source item name: the name of the item that generates the government data resources.

[0074] 5) Data resource name: a title that describes the content of government data resources.

[0075] 6) Government data resource code: The unique and unchanging identification code of government data resources.

[0076] 7) Data resource provider: the department that provides government data resources.

[0077] 8) Data resource provider code: the unified social credit code of the department providing data resources.

[0078] 9) Data resource summary: A brief description of the content (or key fields) of government data resources.

[0079] 10) Data resource format: Description of the storage format of government data resources, including data resource storage methods and corresponding storage formats.

[0080] 11) Update cycle: The frequency of updating government data resources is divided into real-time, daily, weekly, monthly, quarterly, semi-annual, annual, and other.

[0081] 12) Release date: the date on which the public data resource provider releases the government data resources.

[0082] 13) Sharing type: divided into three types: unconditional sharing, conditional sharing, and temporary non-sharing, and fill in the corresponding description.

[0083] Government data that can be shared with all data production and application units belongs to the unconditional sharing category. Government data that can only be shared with relevant data production and application units or only part of the content can be shared with relevant data production and application units belongs to the conditional sharing category. Government data that is not yet available for sharing with other data production and application units belongs to the temporarily non-sharing category.

[0084] 14) Data item information: a detailed description of structured data resources, including data item name, data type, data length, field value range, whether it is a primary key, field sharing attributes, open attributes, associated dictionaries, etc.

[0085] Sharing attributes: Description of the types and conditions of sharing of government data resources, including sharing types, sharing conditions, and sharing methods.

[0086] Open attributes: description of the opening of government data resources to the public and the conditions for opening, including the type of opening and the conditions for opening.

[0087] Open type, including: general openness and authorized openness. Open conditions, a description of the conditions for opening public data resources to the society.

[0088] Preferably, the data cleaning step includes: data deduplication, abnormal data checking and data standardization checking; the data correction step includes: data completion and data coding.

[0089] Furthermore, in this embodiment, data cleaning is a procedure for discovering identifiable errors in data files. By configuring cleaning rules for valid range information, business logic cleaning is performed to ensure that data complies with data standards and business specifications. The main tasks of data cleaning include at least:

[0090] 1) Data deduplication: including primary key matching and full field matching;

[0091] 2) Abnormal data check: null value check, type check, length check, format check, dictionary check, value range check, business logic check, etc.;

[0092] 3) Data standardization check: date and time standardization, currency value standardization, mobile phone number verification, email format verification, postal code format verification, etc.

[0093] Furthermore, data completion solves the problem of missing key fields in the data table (such as ID number, unified social credit code, etc.), making it impossible to determine who the data in the data table belongs to. Data completion relies on data tables containing complete identification information and key field information of natural persons, and completes the key field data required for each data table in the process of data archiving and relationship mining, so that any data table can exist independently without relying on other tables. Data completion work at least includes:

[0094] The data table related to natural persons is missing the ID number or name and needs to be supplemented;

[0095] Furthermore, data coding is to identify the unique and unchanging code of the data, which corresponds to its content. Through coding, data is integrated and processed to ensure the consistency of data science organization, storage and business application. Data coding work at least:

[0096] Determine the corresponding reference table according to the defined coding type.

[0097] Optionally, this embodiment also performs data desensitization, which is also called data de-privacy, and refers to the deformation of certain sensitive information through desensitization rules to achieve reliable protection of sensitive privacy data. Given desensitization rules and policies, the program automatically identifies sensitive data, and the sensitive fields that can be automatically identified include: telephone number, name, address, email, ID number, bank card number, etc. The method of identifying data can use technologies such as regular expressions and keyword recognition. In cases involving personal security data or some sensitive data, such real data is displayed with data desensitization without violating the rules.

[0098] Specifically, this embodiment also carries out data error correction. The information platform establishes a data error correction mechanism, clarifies the division of responsibilities among all parties, ensures that registration is implemented, grasps each link of the data source, processing process and application scenario, analyzes the causes of data problems, continuously improves the problem data processing process, measures and tools, and continuously improves the quality of government data.

[0099] Data error correction work includes:

[0100] (1) Problem data reception: Problem data reception is mainly through data cleaning and feedback from data application units.

[0101] (2) Problem data feedback: A tracking mechanism needs to be established to promptly feedback problem data to the data source unit for processing;

[0102] (3) Problem data processing: The data source unit analyzes the causes of data problems and updates and corrects the problematic data in a timely manner;

[0103] (4) Verification of problem data results: Verify and confirm the problem data through system verification, manual confirmation, etc.;

[0104] (5) Feedback of problem data results: Feedback of problem data results to the data application unit in a timely manner through interfaces, pages, etc.

[0105] Preferably, text classification is performed on the standardized data based on the contextual relationship to obtain standardized data of each data category, including:

[0106] Inputting the standardized data into a text context feature extraction layer to extract context feature information;

[0107] Inputting the standardized data into a global feature extraction layer to extract global feature information;

[0108] Fusing the contextual feature information and the global feature information to obtain a fused text feature;

[0109] Constructing a loss function using the fused text features;

[0110] Continuously optimizing the loss function to obtain a text classification model;

[0111] The text classification model is used to complete text classification and obtain standardized data of each data category.

[0112] Preferably, the standardized data is input into a text context feature extraction layer to extract context feature information, including:

[0113] Use the pre-trained language model to extract the initial feature information of the standardized data;

[0114] Inputting the initial feature information into a forward gated recurrent unit and a reverse gated recurrent unit;

[0115] The outputs of the forward gated recurrent unit and the reverse gated recurrent unit are concatenated to obtain contextual relationship feature information.

[0116] Preferably, the outputs of the forward gated recurrent unit and the reverse gated recurrent unit are concatenated to obtain contextual feature information, including:

[0117] Using the formula:

[0118]

[0119]

[0120] The outputs of the forward gated recurrent unit and the reverse gated recurrent unit are concatenated to obtain contextual feature information; wherein, H t ={H 1 ,H 2 ,…H l} represents the initial feature information input at time t, represents the output of the positive gated recurrent unit at time t, Represents the output of the reverse gated recurrent unit at time t, GRU represents the gated recurrent unit, and G represents the contextual relationship feature information.

[0121] Specifically, this embodiment can more fully understand the context relationship of the text and obtain the context information by splicing the outputs of the forward gated recurrent unit and the reverse gated recurrent unit.

[0122] Preferably, the standardized data is input into a global feature extraction layer to extract global feature information, including:

[0123] The initial feature information of the standardized data is input into the convolution layer and the pooling layer in sequence to obtain the global feature information; the global feature information extraction formula is:

[0124] c i =f(ω·H+b)

[0125]

[0126] In the formula, f is the activation function, ω is the convolution kernel, h is the convolution kernel size, b is the bias value, c is i is the feature vector extracted by the i-th convolutional layer, It represents the value of the features extracted by three different convolution kernels after the maximum pooling layer, and C represents the extracted global feature information.

[0127] Specifically, the steps of data security and verification in this embodiment include:

[0128] 1. Data classification and identification:

[0129] First, the standardized data is classified into different categories (such as personal information, family information, student information, etc.), and a unique identifier is assigned to each data category, which helps with subsequent verification and management.

[0130] 2. Data verification rule formulation:

[0131] According to business requirements and data characteristics, a series of data validation rules are formulated. These rules include format validation (such as ID card number format), range validation (such as age range), logic validation (such as the consistency of home address and household registration information), etc.

[0132] 3. Automated verification process:

[0133] Use data processing tools or scripts to automate the data verification process. The system compares the standardized data with the preset verification rules, identifies data records that do not meet the rules, and marks them as "pending verification" or "abnormal".

[0134] 4. Manual review and feedback:

[0135] For data marked as abnormal during the automated verification process, the system will generate an abnormality report and notify relevant staff to conduct manual review. Staff can correct or confirm the data based on actual conditions to ensure the authenticity and integrity of the data.

[0136] 5. Data access control and authority management:

[0137] After data verification is completed, the system will implement strict data access control and permission management on the verified data. Through the role permission management system, the data access rights of different user roles (such as administrators, auditors, ordinary users, etc.) are defined to ensure that only authorized users can access sensitive data. This embodiment adopts a role-based access control (RBAC) mechanism to ensure that users can only access data categories that match their roles to prevent data leakage and abuse.

[0138] 6. Audit and compliance inspection:

[0139] The system conducts data access audits regularly to record users’ access to verification data to ensure the security and compliance of data sharing. The audit logs will be used for subsequent compliance checks and problem tracking.

[0140] 7. Data sharing and updating:

[0141] After ensuring that the data has been verified and meets access control requirements, the system will allow compliant sharing operations. The shared data will be encrypted for transmission and decrypted and stored at the receiving end to ensure the security of the data during the sharing process.

[0142] Through the above steps, this embodiment can effectively verify the authenticity and integrity of the data, while implementing strict data access control and authority management to ensure the security and compliance of data sharing.

[0143] Furthermore, the steps in the process of reviewing the verification data according to the preset admission qualification standards in this embodiment are as follows:

[0144] 1. Definition of Admission Qualification Criteria:

[0145] First, the education administration will formulate a set of clear admission qualification standards based on relevant policies and regulations. These standards include age requirements, household registration requirements, student status, family economic conditions, etc. The system stores these standards in the form of rules in the database for subsequent review and use.

[0146] 2. Data review process:

[0147] The system will extract relevant information about each student from the verification data and compare it with the preset admission eligibility criteria. The review process will use an automated algorithm to check whether each student's data meets the admission requirements one by one. For example, the system will check whether the student's date of birth is within the specified age range, whether the household registration information complies with local admission policies, etc.

[0148] During the review process, the system will record the results of each review step and mark the qualified and ineligible students. For ineligible students, the system will generate a detailed review report, stating the specific reasons for non-compliance, for subsequent manual review or appeal processing.

[0149] 3. Automatically enter qualified data:

[0150] For students who have passed the review, the system will automatically enter their verification data into the student management system of the corresponding school. This process will be implemented through API or data interface to ensure seamless data transmission. The system will organize the qualified student information (such as name, ID number, year of admission, etc.) according to the required format of the school management system and send it to the target system.

[0151] During the data entry process, the system will perform a completeness check to ensure that all necessary fields have been filled and the data format meets the requirements of the target system. After successful entry, the system will generate a confirmation log to record the time, operator and student information of each data entry for subsequent audit and tracking.

[0152] 4. Exception handling and feedback mechanism:

[0153] If any abnormality occurs during the automatic entry process (such as data format mismatch, network transmission error, etc.), the system will immediately issue an alarm and generate an error report to notify relevant technical personnel to handle it. At the same time, the system will retain the student information that has not been successfully entered for subsequent manual review and entry.

[0154] In addition, this embodiment will also provide a feedback mechanism to allow school administrators to review and confirm the entered data to ensure the accuracy and completeness of the data.

[0155] Through the above steps, this embodiment can efficiently and accurately review student data according to the preset admission qualification standards, and automatically enter the qualified student information into the school's student management system, thereby improving the efficiency and accuracy of admission qualification review.

[0156] Corresponding to the above method, this embodiment also provides a student admission qualification entry system based on government big data, such as Figure 2 As shown, including:

[0157] The information acquisition module is used to obtain the basic information of the target student group from the government big data platform;

[0158] A data processing module is used to perform data cleaning and data correction on the basic information using data middle platform technology to obtain standardized data;

[0159] A data classification module, used to perform text classification on the standardized data based on contextual relationships to obtain standardized data of various data categories;

[0160] A security verification module is used to verify the standardized data of each data category to ensure the authenticity and integrity of the data, obtain the verified data, and perform data access control and authority management on the verified data to ensure the security and compliance of data sharing;

[0161] An audit module is used to audit the verification data according to the preset admission qualification standards to determine whether the student meets the admission requirements;

[0162] The entry module is used to automatically enter the verification data that meets the admission requirements into the student management system of the corresponding school.

[0163] The beneficial effects of the present invention are as follows:

[0164] (1) The present invention obtains the basic information of the target student group from the government big data platform and uses the data middle platform technology to clean and correct the data, so as to standardize the data from different sources and in different formats. The standardization of the present invention not only improves the quality of the data, but also simplifies the process of data integration and reduces the integration complexity caused by the inconsistency of data formats.

[0165] (2) The present invention can more accurately identify and classify different types of data by performing text classification on standardized data based on contextual relationships. The classification method of the present invention can improve the efficiency and accuracy of data processing, making the subsequent verification and audit process more efficient.

[0166] (3) The present invention verifies the standardized data of each data category to ensure the authenticity and integrity of the data. At the same time, data access control and authority management are implemented to ensure the security and compliance of data sharing. The dual protection mechanism of the present invention can effectively prevent data leakage and abuse.

[0167] (4) The present invention reviews the verification data according to the preset admission qualification standards, determines whether the student meets the admission requirements, and automatically enters the qualified verification data into the student management system of the corresponding school. The automated process of the present invention greatly improves work efficiency and reduces the need for manual intervention.

[0168] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0169] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for entering student admission qualifications based on government affairs big data, characterized in that: include: Obtain basic information of the target student group from the government big data platform; Using data middle platform technology, the basic information is cleaned and corrected to obtain standardized data; Performing text classification on the standardized data based on the contextual relationship to obtain standardized data of each data category; Verify the standardized data of each data category to ensure the authenticity and integrity of the data, obtain verified data, and perform data access control and authority management on the verified data to ensure the security and compliance of data sharing; Reviewing the verification data according to the preset admission qualification standards to determine whether the student meets the admission requirements; The verification data that meets the admission requirements will be automatically entered into the student management system of the corresponding school.

2. The method for entering student admission qualifications based on government affairs big data according to claim 1 is characterized in that: Also includes: The verification data that does not meet the admission requirements will be processed manually.

3. The method for entering student admission qualifications based on government affairs big data according to claim 1 is characterized in that: The basic information includes: ID card information, household registration information, student registration information and family information.

4. The method for entering student admission qualifications based on government affairs big data according to claim 1 is characterized in that: The data cleaning step includes: data deduplication, abnormal data checking and data standardization checking; the data correction step includes: data completion and data coding.

5. The method for entering student admission qualifications based on government affairs big data according to claim 1 is characterized in that: The standardized data is subjected to text classification based on the contextual relationship to obtain standardized data of each data category, including: Inputting the standardized data into a text context feature extraction layer to extract context feature information; Inputting the standardized data into a global feature extraction layer to extract global feature information; Fusing the contextual feature information and the global feature information to obtain a fused text feature; Constructing a loss function using the fused text features; Continuously optimizing the loss function to obtain a text classification model; The text classification model is used to complete text classification and obtain standardized data of each data category.

6. The method for entering student admission qualifications based on government affairs big data according to claim 5 is characterized in that: Inputting the standardized data into the text context feature extraction layer to extract context feature information includes: Use the pre-trained language model to extract the initial feature information of the standardized data; Inputting the initial feature information into a forward gated recurrent unit and a reverse gated recurrent unit; The outputs of the forward gated recurrent unit and the reverse gated recurrent unit are concatenated to obtain contextual relationship feature information.

7. The method for entering student admission qualifications based on government affairs big data according to claim 6 is characterized in that: The outputs of the forward gated recurrent unit and the reverse gated recurrent unit are concatenated to obtain contextual feature information, including: Using the formula: The outputs of the forward gated recurrent unit and the reverse gated recurrent unit are concatenated to obtain contextual feature information; wherein, H t ={H1,H2,…H l } represents the initial feature information input at time t, represents the output of the positive gated recurrent unit at time t, Represents the output of the reverse gated recurrent unit at time t, GRU represents the gated recurrent unit, and G represents the contextual relationship feature information.

8. The method for entering student admission qualifications based on government affairs big data according to claim 7 is characterized in that: Inputting the standardized data into the global feature extraction layer to extract global feature information includes: The initial feature information of the standardized data is input into the convolution layer and the pooling layer in sequence to obtain the global feature information; the global feature information extraction formula is: c i =f(ω●H+b) In the formula, f is the activation function, ω is the convolution kernel, h is the convolution kernel size, b is the bias value, c is i is the feature vector extracted by the i-th convolutional layer, It represents the value of the features extracted by three different convolution kernels after the maximum pooling layer, and C represents the extracted global feature information.

9. A student admission qualification entry system based on government affairs big data, characterized in that: include: The information acquisition module is used to obtain the basic information of the target student group from the government big data platform; A data processing module is used to perform data cleaning and data correction on the basic information using data middle platform technology to obtain standardized data; A data classification module, used to perform text classification on the standardized data based on contextual relationships to obtain standardized data of various data categories; A security verification module is used to verify the standardized data of each data category to ensure the authenticity and integrity of the data, obtain the verified data, and perform data access control and authority management on the verified data to ensure the security and compliance of data sharing; An audit module is used to audit the verification data according to the preset admission qualification standards to determine whether the student meets the admission requirements; The entry module is used to automatically enter the verification data that meets the admission requirements into the student management system of the corresponding school.

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