Life enrollment information management method, system and method

Abstract

The invention relates to the technical field of information management, in particular to a student enrollment information management system and method, and the system comprises a student enrollment data integration module, a student enrollment risk assessment module, a student enrollment resource optimization module, a data flow adjustment module and a student enrollment effect analysis module. And carrying out data matching on the basic information, the score and the professional intention. According to the method, when the data are screened and matched, the risk management capability is enhanced through identification and quantitative processing of the abnormal points, analysis of enrollment resources and demand differences and identification of performance bottlenecks caused by wrong decisions are avoided, the information processing flow is optimized, the system response time is shortened, the operation efficiency is improved, and the system reliability is improved. By monitoring the deviation between the data stream and the expected target in real time and adjusting the data stream processing process in time, the accuracy and real-time performance of the data are ensured, so that the transparency of the enrollment process and the evaluability of the execution effect are improved, and the management efficiency of the whole enrollment process is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of information management, and in particular to an enrollment information management system and method. Background Art

[0002] Information management technology involves the processes and systems for collecting, storing, managing, protecting and distributing information. This field uses modern information technology to process and analyze data to improve the decision-making ability and efficiency of organizations. Information management systems can include various forms such as database management systems, content management systems and knowledge management systems. These systems support structured processing and access to information. With the development of big data and artificial intelligence technologies, information management has gradually incorporated advanced data analysis and automation tools, making the extraction, processing and analysis of information more efficient and accurate. This field has a wide range of applications in many industries such as finance, education, and healthcare.

[0003] Among them, the enrollment information management and system focuses on optimizing the student enrollment process of educational institutions through information technology, which includes all aspects of student application, registration, evaluation and admission. By implementing this system, it can process a large amount of application materials more efficiently, reduce manual errors, speed up the decision-making process, and improve the overall enrollment quality and transparency. In addition, the system can also help educational institutions better analyze applicant data, optimize enrollment strategies and resource allocation, which not only improves management efficiency, but also provides a more convenient and direct communication channel between students and educational institutions.

[0004] Existing technologies exhibit problems of slow processing speed and inaccurate data integration when processing large-scale complex data. Especially in enrollment information management, there is a lack of effective tools to screen and evaluate abnormal application data, resulting in unsuitable students being mistakenly admitted. Insufficient resource allocation and data flow adjustment lead to uneven distribution of enrollment resources and low communication efficiency, which affects the quality of enrollment. These technical limitations are particularly evident in modern education management, resulting in the inadequacy of traditional systems in supporting complex management tasks. Summary of the invention

[0005] In order to solve the technical problems of information management in the prior art, the embodiment of the present invention provides an enrollment information management system and method. The technical solution is as follows:

[0006] On the one hand, an enrollment information management system is provided, the system comprising:

[0007] The enrollment data integration module matches and screens personal basic information, grades, and major intentions based on the student application form, selects key data items according to the enrollment standards, and sets up major intention standards for initial task screening, which are then integrated into an enrollment screening data set;

[0008] The enrollment risk assessment module identifies abnormal points in the information set based on the enrollment screening data set, quantifies the abnormal points, evaluates the data integrity and matching degree, determines abnormal behavior, calculates the risk frequency and impact degree, and obtains a summary of risk assessment indicators;

[0009] The enrollment resource optimization module analyzes the difference between existing enrollment resources and actual demand based on the risk assessment indicator summary, optimizes the processing flow and response time of enrollment information data by identifying key performance bottlenecks and utilizing hot spots, and obtains a resource allocation list;

[0010] The data flow adjustment module analyzes the matching degree between the enrollment management and the current demand based on the resource configuration list, and monitors the deviation between the real-time enrollment data and the expected data according to the matching analysis result, and optimizes the data flow processing process to obtain the data adjustment mapping record;

[0011] The enrollment effect analysis module adjusts the mapping records based on the data, collects the data generated after the implementation of enrollment management, including the admission ratio, the number of applications and the enrollment compliance, and evaluates the execution effect of the enrollment management to obtain the enrollment management analysis results.

[0012] On the other hand, the enrollment screening data set includes basic information data, performance data, and professional intention data. The risk assessment indicator summary includes outlier quantification indicators, data integrity indicators, data compliance indicators, and abnormal behavior assessment indicators. The resource allocation list includes key performance bottlenecks, resource allocation difference results, and data processing parameters. The data adjustment mapping records include real-time enrollment data deviations and expected data deviations. The enrollment management analysis results specifically include management implementation scores, effect optimization indicators, and management optimization results.

[0013] On the other hand, the enrollment data integration module includes:

[0014] The first data collection submodule collects data on personal information, grades, and major intentions based on the student application form, groups and categorizes the personal information, grades, and major intention data, removes duplicates, fills in missing values, corrects format errors, and generates information classification results;

[0015] The data screening submodule checks the matching degree between the personal information and the enrollment demand data based on the information classification results and the enrollment demand data standards, screens the enrollment key information that meets the standards, and performs content redundancy removal and optimization processing to obtain a set of key data points;

[0016] Based on the set of key data points, the information integration submodule establishes field correspondence through field mapping rules, partitions and organizes the key data, performs cross-correlation analysis and merging, reconstructs the complete data structure, and obtains the enrollment screening data set.

[0017] On the other hand, the above-mentioned combination of enrollment demand data standards checks the matching degree between personal information and enrollment demand data, and screens the enrollment key information that meets the standards, according to the formula:

[0018]

[0019] Calculate the matching score M;

[0020] Among them, s i represents the value of the i-th field in the personal information set, t i represents the value of the i-th field in the enrollment demand data, δ(s i ,t i ) is the indicator function, when the personal information set s i and enrollment demand data i If the ith field in the ith column is equal, it returns 1, otherwise it returns 0. i Represents the weight coefficient of the i-th field, and n represents the total number of fields involved in the match.

[0021] On the other hand, the enrollment risk assessment module includes:

[0022] The risk identification submodule performs data anomaly detection based on the enrollment screening data set and combined with enrollment requirements, identifies abnormal and inconsistent data points, and then detects the identified abnormal data, records the error type and abnormal situation of each data point, and generates preliminary risk information;

[0023] The risk analysis submodule organizes the risk data based on the preliminary risk information, determines the risk weight distribution and hierarchy by evaluating risk indicators and analyzing data relevance, and obtains a summary of risk assessment indicators.

[0024] On the other hand, the weight distribution and hierarchy of risks are optimized by evaluating risk indicators and analyzing data correlation, and the weight distribution and hierarchy of risks are determined according to the formula:

[0025]

[0026] Calculate the total risk weight W of each type of abnormal data;

[0027] Where K is the total number of anomaly types, e k represents the type of the k-th abnormality, δ G is the risk impact function, which returns the risk impact value corresponding to the anomaly. k Represents the weight of the k-th type of abnormal data.

[0028] On the other hand, the enrollment resource optimization module includes:

[0029] The resource difference analysis submodule groups the enrollment resource data based on the risk assessment index summary, labels the data according to the risk level, and compares the difference between the demand value and the existing enrollment resource allocation value to generate an enrollment resource demand difference table;

[0030] The performance bottleneck identification submodule extracts the resource allocation values ​​corresponding to the high-demand areas based on the enrollment resource demand difference table, performs data segmentation comparison and bottleneck screening according to the enrollment resource distribution density and the offset degree of uneven distribution, and analyzes the difference between the resource occupancy and demand of the bottleneck to generate key bottleneck distribution data;

[0031] The resource allocation optimization submodule adjusts the resource priority in the area according to the resource category corresponding to the bottleneck area through the key bottleneck distribution data, re-matches the resources, and generates a resource allocation list.

[0032] On the other hand, the data flow adjustment module includes:

[0033] The demand matching analysis submodule divides the enrollment management data into parameter categories based on the resource allocation list, compares it with the current demand data item by item, and marks the difference range of each parameter to generate a demand deviation distribution map;

[0034] The deviation monitoring submodule matches the time series parameters of the real-time enrollment data with the expected data based on the demand deviation distribution diagram, calculates the deviation values ​​in the time series calibration item by item, marks the deviation amplitude, and generates a deviation trend table;

[0035] The data flow optimization submodule compares the deviation trend table with the data flow priority, adjusts the priority order according to the comparison result, optimizes the data flow path, and generates a data adjustment mapping record.

[0036] On the other hand, the enrollment effect analysis module includes:

[0037] The second data collection submodule adjusts the mapping records based on the data, divides them by project category, calculates the ratio of the number of admissions to the number of applications, and marks them by time series to generate an enrollment data grouping table;

[0038] The effect evaluation submodule calculates the variation range of the admission ratio within the group based on the enrollment data grouping table, analyzes each item through the time series data of the number of applications, extracts the matching grouping data, calculates the deviation range, and generates enrollment execution effect data;

[0039] The process optimization submodule analyzes the compliance with large deviation range based on the enrollment execution effect data, screens the key points of the time series data, and adjusts the screened key points, including the admission ratio, number of applications and compliance, to generate the enrollment management analysis results.

[0040] On the other hand, an enrollment information management method is provided, which is applied to an enrollment information management system, comprising the following steps:

[0041] S1: Based on the student application form, extract personal basic information, application scores and major intentions, and then combine the age range, score range and major intention matching rules of the admissions standards to screen key data items and generate an admissions screening data set;

[0042] S2: Based on the enrollment screening data set, identify abnormal points in the information set, quantify the abnormal points, evaluate the data integrity and matching degree, determine abnormal behavior, calculate the risk frequency and impact degree, and obtain a summary of risk assessment indicators;

[0043] S3: Based on the summary of the risk assessment indicators, analyze the difference between the existing enrollment resources and the actual demand, optimize the processing flow and response time of the enrollment information data by identifying key performance bottlenecks and utilizing hot spots, and obtain a resource allocation list;

[0044] S4: Based on the resource allocation list, analyze the matching degree between enrollment management and current demand, and according to the matching degree analysis result, monitor the deviation between real-time enrollment data and expected data to obtain enrollment data matching adjustment record;

[0045] S5: Based on the enrollment data matching adjustment record, monitor the matching change trend between the real-time enrollment management data and the expected data, and count the deviation range in the change trend to obtain the data adjustment mapping record;

[0046] S6: Adjust the mapping records based on the data, collect data after the implementation of the admissions management, including the admission ratio, the number of applications and the admissions compliance, and evaluate the implementation effect of the admissions management to obtain the admissions management analysis results.

[0047] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0048] In the present invention, when screening and matching data, the risk management capability is enhanced by identifying and quantifying abnormal points, avoiding erroneous decisions in the analysis of enrollment resources and demand differences and the identification of performance bottlenecks, optimizing the information processing flow, improving the system response time and operating efficiency, and ensuring the accuracy and real-time nature of the data by real-time monitoring of the deviation between the data flow and the expected target and timely adjusting the data flow processing process, thereby improving the transparency of the enrollment process and the evaluability of the execution effect, and optimizing the management efficiency of the entire enrollment process. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0050] Figure 1 It is a system schematic diagram of the present invention;

[0051] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0052] Figure 3 It is a flow chart of the enrollment data integration module of the present invention;

[0053] Figure 4 A flow chart of the enrollment risk assessment module of the present invention;

[0054] Figure 5 It is a flow chart of the enrollment resource optimization module of the present invention;

[0055] Figure 6 It is a flow chart of the data flow adjustment module of the present invention;

[0056] Figure 7 This is a flow chart of the enrollment effect analysis module of the present invention;

[0057] Figure 8 The figure is a flow chart of the method steps of the present invention. DETAILED DESCRIPTION

[0058] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0059] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0060] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0061] In the embodiments of the present invention, sometimes the subscripts such as W 1 It may be written in non-subscript form such as W1. When the difference is not emphasized, the meaning is the same.

[0062] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0063] The embodiment of the present invention provides an enrollment information management and system, such as Figure 1 As shown, the system includes:

[0064] The enrollment data integration module matches and screens personal basic information, grades, and major intentions based on the student application form, selects key data items according to the enrollment standards, and sets up major intention standards for initial task screening, which are then integrated into an enrollment screening data set;

[0065] The enrollment risk assessment module is based on the enrollment screening data set, identifies anomalies in the information set, quantifies the anomalies, evaluates data integrity and matching, determines abnormal behavior, calculates risk frequency and impact, and obtains a summary of risk assessment indicators;

[0066] The enrollment resource optimization module analyzes the difference between existing enrollment resources and actual needs based on the summary of risk assessment indicators, optimizes the processing flow and response time of enrollment information data by identifying key performance bottlenecks and utilizing hot spots, and obtains a resource allocation list;

[0067] The data flow adjustment module analyzes the matching degree between enrollment management and current demand based on the resource allocation list, monitors the deviation between real-time enrollment data and expected data based on the matching analysis results, and optimizes the data flow processing process to obtain data adjustment mapping records;

[0068] The enrollment effect analysis module adjusts the mapping records based on the data, collects the data generated after the implementation of enrollment management, including the admission ratio, number of applications and enrollment compliance, and evaluates the implementation effect of enrollment management to obtain the enrollment management analysis results.

[0069] The enrollment screening data set includes basic information data, performance data, and major intention data. The risk assessment indicator summary includes outlier quantitative indicators, data integrity indicators, data compliance indicators, and abnormal behavior assessment indicators. The resource allocation list includes key performance bottlenecks, resource allocation difference results, and data processing parameters. The data adjustment mapping records include real-time enrollment data deviations and expected data deviations. The enrollment management analysis results are specifically management implementation scores, effect optimization indicators, and management optimization results.

[0070] like Figure 2 and Figure 3As shown, the enrollment data integration module includes a first data collection submodule, a data screening submodule, and an information integration submodule;

[0071] The first data collection submodule collects data on personal information, grades, and major intentions based on the student application form, groups and categorizes the personal information, grades, and major intention data, removes duplicates, fills in missing values, corrects format errors, and generates information classification results;

[0072] First, the format of personal information fields in the student application form, such as name, ID number and contact information, is checked by rule matching to see if they conform to the standard format. After eliminating data with incorrect format, the missing information is filled in by setting null value completion rules. For example, the place of origin is inferred based on the area code, and the missing date of birth is completed by matching the student ID or ID number with the existing database. For the grade field, it is divided into total score and subject score, and a range check is performed on each score field to eliminate abnormal values ​​that exceed the set range. The professional intention field uses keyword extraction method to standardize the text intention data into the preset professional classification. Then, the data is grouped and classified according to the field, for example, by school, region or grade. The removal of duplicates is achieved by performing a uniqueness test on the field combination. Finally, a grouped and classified result data set containing personal information, grades and professional intentions is formed.

[0073] The data screening submodule checks the matching degree between personal information and enrollment demand data based on the information classification results and the enrollment demand data standards, screens the enrollment key information that meets the standards, and performs content redundancy removal and optimization processing to obtain a set of key data points;

[0074] Key fields in personal information such as area code, gender, age, etc. are compared with corresponding conditions in enrollment requirements item by item. For example, logical rules are used to check whether regional restrictions are met, and mathematical intervals are used to determine whether the age is within the specified range. The degree of keyword matching of the target major in the enrollment requirements is compared with the professional intention field. After screening data points that meet the requirements, de-redundancy processing is performed to remove redundant fields such as duplicate contact information or irrelevant additional information. A standardized record format is formed by merging redundant information from multiple fields, such as merging multiple contact information fields into a standard telephone field. Optimization processing includes re-encoding classification fields such as gender and region into numerical codes to form a set of key data points.

[0075] Combined with the enrollment demand data standards, check the matching degree between personal information and enrollment demand data, screen the key enrollment information that meets the standards, and follow the formula:

[0076]

[0077] Calculate the matching score M;

[0078] Among them, s i represents the value of the i-th field in the personal information set, t i represents the value of the i-th field in the enrollment demand data, δ(s i ,t i ) is the indicator function, when the personal information set s i and enrollment demand data i If the ith field in the ith column is equal, it returns 1, otherwise it returns 0. i Represents the weight coefficient of the i-th field, and n represents the total number of fields involved in the match.

[0079] When the fields to be checked and corrected include name, grades and major intention, where the name weight is 0.3, the grade weight is 0.4, and the major intention weight is 0.3, assuming that the name, grade, and major intention in the personal information set are "Zhang San", 85 points, and "Computer Science and Technology", respectively, and the corresponding name, grade, and major intention in the enrollment demand data are "Zhang San", 82 points, and "Software Engineering", the calculation process is as follows:

[0080] δ("Zhang San","Zhang San")=1;

[0081] δ(85,82)=0 (because the scores do not match completely);

[0082] δ("Computer Science and Technology", "Software Engineering") = 0 (because the professional intention does not match);

[0083] Therefore, the matching score M is:

[0084] M=0.3×1+0.4×0+0.3×0=0.3;

[0085] The results show that the current matching score is low, mainly due to the mismatch between grades and major intentions, indicating that these fields need to be further revised and verified to improve the overall consistency of information.

[0086] The information integration submodule is based on the key data point set, establishes field correspondence through field mapping rules, partitions and organizes the key data, and then conducts cross-correlation analysis and merging to reconstruct the complete data structure and obtain the enrollment screening data set;

[0087] The relationship between fields is established through field mapping rules. For example, the grade field in the original data is mapped to the "academic ability score" field in the admissions standard. The mapping rules assign values ​​according to the score range through table lookup, and then the key data is partitioned and sorted according to admissions needs. For example, students' personal information is bound to academic data by school. Cross-correlation analysis determines the suitability of each major by matching the major intention field and admissions capacity information. The partitioned results are merged to form a standardized record structure. Finally, the complete data structure including field mapping, partition sorting and cross-analysis results is reconstructed and output as an admissions screening data set.

[0088] like Figure 2 and Figure 4 As shown, the enrollment risk assessment module includes a risk identification submodule and a risk analysis submodule;

[0089] The risk identification submodule performs data anomaly detection based on the enrollment screening data set and combined with enrollment requirements, identifies abnormal and inconsistent data points, and then detects the identified abnormal data, records the error type and abnormal situation of each data point, and generates preliminary risk information;

[0090] First, each field in the filtered data set is checked item by item to identify abnormal data points. For example, range checks are performed on numerical fields such as age and grades, and data that exceeds a reasonable range (such as age fields greater than 100 or grades less than 0) are marked as abnormal. For text fields such as professional intentions, blank values ​​or abnormal items that are not in the preset range are detected through keyword matching. Logical rules are used to check whether there are inconsistent data between fields. For example, when the verification rules for the gender field and the ID card number are inconsistent, the error type is recorded. The specific error type and field position of each abnormal data point are recorded by looping through the data set, and abnormal situations are classified according to the field type, such as missing values, mismatched values, or out-of-range values. After the abnormal classification is completed, preliminary risk information containing all abnormal records is formed.

[0091] The risk analysis submodule organizes the risk data based on the preliminary risk information, determines the risk weight distribution and hierarchy by evaluating risk indicators and analyzing data correlation, and obtains a summary of risk assessment indicators.

[0092] First, the error type field of each data point in the risk record is extracted, and a risk classification statistical table is generated by counting the frequency of various errors. The data points are grouped according to risk category and field type. Risk indicators such as missing value rate and error rate are set based on the risk classification results. The risk level of each data point is determined by calculating the value of each indicator. For example, the missing value rate indicator is obtained by counting the number of missing values ​​in a specific field and calculating the proportion. The error rate indicator is calculated by the ratio of the number of field errors to the total number of fields. Further, a risk weight allocation model is constructed based on the logical correlation between risk indicators and fields. The weight allocation determines the risk hierarchy through a hierarchical weighting method to generate a summary of risk assessment indicators that include risk indicators and a hierarchical structure.

[0093] By evaluating risk indicators and analyzing data correlation, the weight distribution and hierarchy of risks are optimized and determined according to the formula:

[0094]

[0095] Calculate the total risk weight W of each type of abnormal data;

[0096] Where K is the total number of anomaly types, e k represents the type of the k-th abnormality, δ G is the risk impact function, which returns the risk impact value corresponding to the anomaly. k Represents the weight of the k-th type of abnormal data.

[0097] In actual applications, there are three main types of anomalies in the system: ID card number anomaly, score field anomaly, and logical relationship error. The weight of each anomaly is set as follows: ID card number anomaly w 1 =0.5 The ID number is the core identifier of student information, and errors have the greatest impact. The grade field is abnormal. 2 =0.3 Grades are key data for academic evaluation, and their accuracy is crucial to student evaluation. Logical relationship error w 3 =0.2Logical errors lead to data processing and parsing problems, but the impact is smaller than identity or performance issues.

[0098] The risk impact function δ is set as follows: For abnormal ID card numbers, it is usually considered a serious risk, δ(e 1 )=1, the abnormality of the score field is considered as medium risk, δ(e 2 )=0.7, logical relationship errors are considered to be of low risk, δ(e 3 )=0.5.

[0099] Calculation formula:

[0100] W=0.5×1+0.3×0.7+0.2×0.5=0.5+0.21+0.1=0.81;

[0101] The result shows that in the current system, based on the set weights and risk assessment of abnormal data types, the overall risk weight is 0.81. The high value reflects the significant impact of ID card number anomalies on system security, ensuring that the most critical data receives primary attention and processing.

[0102] like Figure 2 and Figure 5 As shown, the enrollment resource optimization module includes a resource difference analysis submodule, a performance bottleneck identification submodule, and a resource allocation optimization submodule;

[0103] The resource difference analysis submodule groups the enrollment resource data based on the risk assessment index summary, labels the data according to the risk level, and compares the difference between the demand value and the existing enrollment resource allocation value to generate an enrollment resource demand difference table;

[0104] First, group and count the enrollment resources according to their types, such as faculty, teaching equipment, and venue capacity, extract the current allocation value of each group of resources, and mark resource groups of different risk levels through logical rules. For example, resources with a missing value rate higher than 50% in the risk indicator are marked as high-risk categories. At the same time, extract the target resource values ​​and existing resource allocation values ​​in the enrollment needs, and generate a demand gap data table by calculating the difference item by item. Record the difference of each group of resources and its risk level one by one. After completing the risk level labeling and difference calculation, summarize the data to generate an enrollment resource demand difference table.

[0105] The performance bottleneck identification submodule extracts the resource allocation values ​​corresponding to the high-demand areas based on the enrollment resource demand difference table, performs data segmentation comparison and bottleneck screening based on the enrollment resource distribution density and the degree of uneven distribution, and analyzes the difference between the resource occupancy and demand of the bottleneck to generate key bottleneck distribution data;

[0106] First, extract the current allocation values ​​of all resources in the high-demand area and refine them according to the resource type. For example, extract the data on the number of teaching equipment and venue area in the high-demand area, and calculate the resource distribution density. The distribution density is calculated by dividing the total amount of resources by the total area of ​​the region. Further extract the degree of deviation of the resource allocation value between regions. The degree of deviation is obtained by calculating the variance of the resource distribution density of each partition. Set the offset screening threshold to screen out partitions whose resource density deviates too much from the average value. At the same time, calculate the occupancy of bottleneck resources based on the difference between the resource allocation value and the demand value in the partition. The occupancy of bottleneck resources is the absolute difference between the demand value and the offset value. Finally, generate the key bottleneck distribution data.

[0107] The resource allocation optimization submodule uses key bottleneck distribution data to adjust the resource priority in the area according to the resource category corresponding to the bottleneck area, re-match resources, and generate a resource allocation list;

[0108] The resource categories in the bottleneck area are classified and processed. First, the corresponding resource allocation table is extracted according to the resource category, such as the number of personnel in the bottleneck area in the teaching staff, and the priority of each resource category is adjusted. The priority adjustment is based on the weight assigned by the severity of the bottleneck, and the resources are reallocated from high to low according to the weight. For example, by reducing the occupancy of low-priority resources to make room for high-priority resources, the adjusted resource matching table is summarized. After the resource reallocation is completed, a resource configuration list containing resource allocation weights, priority adjustments and reallocation values ​​is generated.

[0109] Extract the resource allocation values ​​corresponding to the high-demand areas, perform data segmentation comparison and bottleneck screening based on the enrollment resource distribution density and the degree of uneven distribution, according to the formula:

[0110]

[0111] Calculate resource distribution density.

[0112] In the formula, R density It indicates the amount of resources allocated per unit area in a specific area, expressed as the number of resources / square meter, R total Represents the total available resources of all categories in the area, such as the number of teaching equipment, the number of resources converted from the area of ​​classrooms, laboratories, etc. region Represents the total area of ​​the area in square meters, reflecting the geographic size of the high-demand area.

[0113] Detailed explanation of the formula and the process of formula calculation and derivation:

[0114] R total Obtained from specific resource data, including the number of teaching equipment and venue area in the area. region Measured by total area of ​​high demand areas or obtained from a geographic information system.

[0115] In practical applications, when the total amount of resources in a high-demand area R total =5000, and the total area of ​​the region is A region =2500m 2 , then the resource distribution density of the area is calculated as follows:

[0116]

[0117] The result 2 / square meter represents the distribution density of resources in the area per square meter.

[0118] like Figure 2 and Figure 6 As shown, the data flow adjustment module includes a demand matching analysis submodule, a deviation monitoring submodule, and a data flow optimization submodule;

[0119] The demand matching analysis submodule divides the enrollment management data into parameter categories based on the resource allocation list, compares it with the current demand data item by item, and marks the difference range of each parameter to generate a demand deviation distribution map;

[0120] The enrollment management data is divided according to parameter categories. First, the core parameters in the management data are extracted, including the number of students, teaching staff, venue capacity and professional distribution. These parameters are grouped and classified, and each parameter is compared item by item according to the enrollment demand data. For example, the data of the target number of students and the currently allocated number of students are extracted, and the difference between the two is calculated. The calculated difference is compared with the preset difference range. The difference range is set according to the historical data and the standard value in the enrollment plan, and the difference of each parameter is marked. For example, the difference is marked as positive, negative or zero. Finally, the difference distribution of each group of data is statistically analyzed according to the parameter category, and a demand deviation distribution map containing the difference range and marked results is generated.

[0121] The deviation monitoring submodule matches the time series parameters of the real-time enrollment data with the expected data based on the demand deviation distribution diagram, calculates the deviation values ​​in the time series calibration item by item, marks the deviation amplitude, and generates a deviation trend table;

[0122] Extract time series parameters from real-time enrollment data, including daily updated enrollment numbers, registration numbers, and resource allocation adjustment records, compare these time series data with expected data item by item, calculate the deviation value for each time point during the comparison process, for example, by calculating the difference between the actual enrollment number and the target number on a daily basis, extract and record the time points with larger deviation values, and mark the deviation amplitude at each time point, for example, mark it as slight deviation, medium deviation, and severe deviation, organize the overall deviation trend in the time series according to the classification of the deviation amplitude, summarize the deviation value and amplitude information at each time point, and generate a deviation trend table containing deviation value, amplitude annotation, and trend analysis.

[0123] The data flow optimization submodule compares the deviation trend table with the data flow priority, adjusts the priority order according to the comparison result, optimizes the data flow path, and generates data adjustment mapping records;

[0124] First, extract the corresponding priority information in each data flow, such as extracting high-priority data flows involved in core resource allocation, match the data flows with larger deviations in the deviation trend table with the priorities, and adjust the priority order by comparing the results. For example, adjust the data flows with high deviations to higher processing priorities. Then optimize the data flow path, extract the key nodes in each path, re-plan the data flow path for nodes that consume more resources, adjust the order of key nodes in the path, and map the optimized path information according to the corresponding deviation trend records. Finally, generate a data adjustment mapping record containing the adjusted priority and path information.

[0125] like Figure 2 and Figure 7 As shown, the enrollment effect analysis module includes a second data collection submodule, an effect evaluation submodule, and a process optimization submodule;

[0126] The second data collection submodule adjusts the mapping records based on the data, divides them by project category, calculates the ratio of the number of admissions to the number of applications, and annotates them by time series to generate an enrollment data grouping table;

[0127] First, extract the data on the number of admissions and the number of applications corresponding to each project category, and calculate the ratio of the two item by item. For example, divide the number of admissions in a certain category by the number of applicants to get the admission ratio of that category. Then mark these admission ratio data according to time series. For example, extract daily or weekly admission data and mark the time nodes. By classifying and arranging the data at different time nodes, summarize the admission ratios of each category in the same time series into corresponding groups. After completing the time series grouping of all category data, generate an admission data grouping table containing project categories, admission ratios and time labels.

[0128] The effect evaluation submodule calculates the variation range of the admission ratio within the group based on the enrollment data grouping table, analyzes each item through the time series data of the number of applications, extracts the matching grouping data, calculates the deviation range, and generates enrollment execution effect data;

[0129] Extract the admission ratio data within the group, compare the admission ratio in each group of data item by item, calculate the range of change in the admission ratio, such as comparing the admission ratio change value between the current time point and the previous time point, and perform time series analysis on the application quantity data in the group, check the change range of the number of applicants at different time points item by item, mark the differences in admission ratio and application number in each group data, and then filter the group data that meet specific standards, such as filtering the data with an admission ratio change range within a certain threshold, further calculate the deviation range of these data, classify and organize the deviation statistical results of the group data, and finally generate admission execution effect data including deviation range, admission ratio and application quantity analysis.

[0130] The process optimization submodule analyzes the compliance with large deviation range based on the enrollment execution effect data, screens the key points of the time series data, and adjusts the screened key points, including the admission ratio, the number of applications and the compliance, to generate the enrollment management analysis results;

[0131] First, we analyze the groups with larger deviations, extract the key point data related to the time series in these groups, including the admission ratio, number of applications and conformity at each time point, and screen the key points in the time series one by one, such as removing time points with smaller changes and retaining key points with larger deviations. We further adjust the screened key point data, such as recalculating the correlation value between the admission ratio and the number of applications, adjusting the logical rules for conformity annotation, and finally summarize the adjusted admission ratio, number of applications and conformity data to generate admission management analysis results that include optimized key points and adjustment results.

[0132] Analyze the conformity of the large deviation range, filter the key points of the time series data, and adjust the filtered key points according to the formula:

[0133]

[0134] Calculate the sum of squared deviations for each time point.

[0135] Where D represents the cumulative sum of squares of deviations between observed values ​​and expected values ​​at all time points in a given time series, i represents the observed value (admission ratio, number of applications, etc.) at each time point i, E i Represents the expected value at each time point (preset based on historical data or standard values).

[0136] O i It is the actual data measured at each time point, such as the admission ratio or the number of applications at a specific time point. i It can be an average of previous time points or a value predicted by other models.

[0137] In practical applications, when the observed admission rates at three time points are 30%, 40%, and 50%, and the expected admission rates are 35%, 35%, and 35%, the sum of squared deviations is calculated as follows:

[0138] D=(30-35) 2 +(40-35) 2 +(50-35) 2 =25+25+225=275;

[0139] The results show that the total sum of squared deviations of the calculated time points is 275. This high value indicates that there are significant deviations in the time series data, suggesting that enrollment management needs special attention and adjustment of strategies at these time points.

[0140] like Figure 8 As shown, an enrollment information management method includes the following steps:

[0141] S1: Based on the student application form, extract personal basic information, application scores and major intentions, and then combine the age range, score range and major intention matching rules of the admissions standards to screen key data items and generate an admissions screening data set;

[0142] S2: Based on the enrollment screening data set, identify the outliers in the information set, quantify the outliers, evaluate the data integrity and matching, determine abnormal behaviors, calculate the risk frequency and impact, and obtain a summary of risk assessment indicators;

[0143] S3: Based on the summary of risk assessment indicators, analyze the difference between existing enrollment resources and actual needs, identify key performance bottlenecks and utilize hot spots, optimize the processing flow and response time of enrollment information data, and obtain a resource allocation list;

[0144] S4: Based on the resource allocation list, analyze the matching degree between enrollment management and current needs, and monitor the deviation between real-time enrollment data and expected data according to the matching analysis results to obtain enrollment data matching adjustment records;

[0145] S5: Based on the enrollment data matching adjustment record, monitor the matching change trend between the real-time enrollment management data and the expected data, and count the deviation range in the change trend to obtain the data adjustment mapping record;

[0146] S6: Based on the data adjustment mapping records, collect data after the implementation of admissions management, including admission ratio, number of applications and admissions compliance, and evaluate the implementation effect of admissions management to obtain admissions management analysis results.

[0147] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0148] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0149] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes 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 the present invention.

[0150] 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 systems to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0151] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned system embodiments and will not be repeated here.

[0152] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and systems can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0153] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0154] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0155] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the system described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0156] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An enrollment information management and system, characterized in that: The system comprises: The enrollment data integration module matches and screens personal basic information, grades, and major intentions based on the student application form, selects key data items according to the enrollment standards, and sets up major intention standards for initial task screening, which are then integrated into an enrollment screening data set; The enrollment risk assessment module identifies abnormal points in the information set based on the enrollment screening data set, quantifies the abnormal points, evaluates the data integrity and matching degree, determines abnormal behavior, calculates the risk frequency and impact degree, and obtains a summary of risk assessment indicators; The enrollment resource optimization module analyzes the difference between existing enrollment resources and actual demand based on the risk assessment indicator summary, optimizes the processing flow and response time of enrollment information data by identifying key performance bottlenecks and utilizing hot spots, and obtains a resource allocation list; The data flow adjustment module analyzes the matching degree between the enrollment management and the current demand based on the resource configuration list, monitors the deviation between the real-time enrollment data and the expected data according to the matching analysis result, and optimizes the data flow processing process to obtain the data adjustment mapping record; The enrollment effect analysis module adjusts the mapping records based on the data, collects the data generated after the implementation of enrollment management, including the admission ratio, the number of applications and the enrollment compliance, and evaluates the execution effect of the enrollment management to obtain the enrollment management analysis results.

2. The enrollment information management and system according to claim 1, characterized in that: The enrollment screening data set includes basic information data, performance data, and major intention data. The risk assessment indicator summary includes outlier point quantification indicators, data integrity indicators, data compliance indicators, and abnormal behavior assessment indicators. The resource allocation list includes key performance bottlenecks, resource allocation difference results, and data processing parameters. The data adjustment mapping record includes real-time enrollment data deviations and expected data deviations. The enrollment management analysis results specifically include management implementation scores, effect optimization indicators, and management optimization results.

3. The enrollment information management and system according to claim 1, characterized in that: The enrollment data integration module includes: The first data collection submodule collects data on personal information, grades, and major intentions based on the student application form, groups and categorizes the personal information, grades, and major intention data, removes duplicates, fills in missing values, corrects format errors, and generates information classification results; The data screening submodule checks the matching degree between the personal information and the enrollment demand data based on the information classification results and the enrollment demand data standards, screens the enrollment key information that meets the standards, and performs content redundancy removal and optimization processing to obtain a set of key data points; Based on the set of key data points, the information integration submodule establishes field correspondence through field mapping rules, partitions and organizes the key data, performs cross-correlation analysis and merging, reconstructs the complete data structure, and obtains the enrollment screening data set.

4. The enrollment information management and system according to claim 3 is characterized in that: The above mentioned combination of enrollment demand data standard checks the matching degree between personal information and enrollment demand data, and selects enrollment key information that meets the standard, according to the formula: Calculate the matching score M; Among them, s i represents the value of the i-th field in the personal information set, t i represents the value of the i-th field in the enrollment demand data, δ(s i ,t i ) is the indicator function, when the personal information set s i and enrollment demand data i If the ith field in the ith column is equal, it returns 1, otherwise it returns 0. i Represents the weight coefficient of the i-th field, and n represents the total number of fields involved in the match.

5. The enrollment information management and system according to claim 1, characterized in that: The enrollment risk assessment module includes: The risk identification submodule performs data anomaly detection based on the enrollment screening data set and combined with enrollment requirements, identifies abnormal and inconsistent data points, and then detects the identified abnormal data, records the error type and abnormal situation of each data point, and generates preliminary risk information; The risk analysis submodule organizes the risk data based on the preliminary risk information, determines the risk weight distribution and hierarchy by evaluating risk indicators and analyzing data relevance, and obtains a summary of risk assessment indicators.

6. The enrollment information management and system according to claim 5, characterized in that: By evaluating risk indicators and analyzing data relevance, optimizing risk weight distribution and hierarchy, and determining risk weight distribution and hierarchy, according to the formula: Calculate the total risk weight W of each type of abnormal data; Where K is the total number of anomaly types, e k represents the type of the k-th abnormality, δ G is the risk impact function, which returns the risk impact value corresponding to the anomaly. k Represents the weight of the k-th type of abnormal data.

7. The enrollment information management and system according to claim 1, characterized in that: The enrollment resource optimization module includes: The resource difference analysis submodule groups the enrollment resource data based on the risk assessment index summary, labels the data according to the risk level, and compares the difference between the demand value and the existing enrollment resource allocation value to generate an enrollment resource demand difference table; The performance bottleneck identification submodule extracts the resource allocation values ​​corresponding to the high-demand areas based on the enrollment resource demand difference table, performs data segmentation comparison and bottleneck screening according to the enrollment resource distribution density and the offset degree of uneven distribution, and analyzes the difference between the resource occupancy and demand of the bottleneck to generate key bottleneck distribution data; The resource allocation optimization submodule adjusts the resource priority in the area according to the resource category corresponding to the bottleneck area through the key bottleneck distribution data, re-matches the resources, and generates a resource allocation list.

8. The enrollment information management and system according to claim 1, characterized in that: The data flow adjustment module includes: The demand matching analysis submodule divides the enrollment management data into parameter categories based on the resource allocation list, compares it with the current demand data item by item, and marks the difference range of each parameter to generate a demand deviation distribution map; The deviation monitoring submodule matches the time series parameters of the real-time enrollment data with the expected data based on the demand deviation distribution diagram, calculates the deviation values ​​in the time series calibration item by item, marks the deviation amplitude, and generates a deviation trend table; The data flow optimization submodule compares the deviation trend table with the data flow priority, adjusts the priority order according to the comparison result, optimizes the data flow path, and generates a data adjustment mapping record.

9. The enrollment information management and system according to claim 1, characterized in that: The enrollment effect analysis module includes: The second data collection submodule adjusts the mapping records based on the data, divides them by project category, calculates the ratio of the number of admissions to the number of applications, and marks them by time series to generate an enrollment data grouping table; The effect evaluation submodule calculates the variation range of the admission ratio within the group based on the enrollment data grouping table, analyzes each item through the time series data of the number of applications, extracts the matching grouping data, calculates the deviation range, and generates enrollment execution effect data; The process optimization submodule analyzes the compliance with large deviation range based on the enrollment execution effect data, screens the key points of the time series data, and adjusts the screened key points, including the admission ratio, number of applications and compliance, to generate the enrollment management analysis results.

10. An enrollment information management and method, the enrollment information management and method is used to implement the enrollment information management and system as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: Based on the student application form, extract personal basic information, application scores and major intentions, and then combine the age range, score range and major intention matching rules of the admissions standards to screen key data items and generate an admissions screening data set; Based on the enrollment screening data set, identify abnormal points in the information set, quantify the abnormal points, evaluate the data integrity and matching degree, determine abnormal behavior, calculate the risk frequency and impact degree, and obtain a summary of risk assessment indicators; Based on the summary of the risk assessment indicators, the difference between the existing enrollment resources and the actual demand is analyzed, and by identifying key performance bottlenecks and utilizing hot spots, the processing flow and response time of enrollment information data are optimized to obtain a resource allocation list; Based on the resource allocation list, analyze the matching degree between enrollment management and current demand, and according to the matching degree analysis result, monitor the deviation between real-time enrollment data and expected data to obtain enrollment data matching adjustment record; Based on the enrollment data matching adjustment record, monitor the matching change trend between the real-time enrollment management data and the expected data, and count the deviation range in the change trend to obtain the data adjustment mapping record; Based on the data, the mapping records are adjusted, and data after the implementation of the admissions management is collected, including the admission ratio, the number of applications and the admissions compliance, and the execution effect of the admissions management is evaluated to obtain the admissions management analysis results.

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