Big data configuration method and system for smart campus management service platform

Abstract

The invention discloses a big data configuration method and system for an intelligent campus management service platform, relates to the field of intelligent campuses, solves the problem of poor teaching resource configuration effect of the existing intelligent campus management service platform, and comprises the following steps: S1, respectively obtaining a first learning resource matching coefficient and a second learning resource matching coefficient, the method comprises the steps of S1, obtaining student work and rest monitoring data, S2, carrying out online course concentration evaluation on a target student to obtain a third learning resource matching coefficient, and S3, obtaining a resource allocation evaluation coefficient by analyzing the first learning resource matching coefficient, the second learning resource matching coefficient and the third learning resource matching coefficient, and performing teaching resource configuration on the target student according to the resource configuration evaluation coefficient. According to the invention, the configuration pertinence of the teaching resources can be improved.

Description

Technical Field

[0001] The present invention belongs to the field of smart campus and relates to big data technology, and specifically to a big data configuration method and system for a smart campus management service platform. Background Art

[0002] The existing smart campus management service platform has the following specific defects when configuring courses:

[0003] 1. The existing smart campus management service platform cannot configure courses in a targeted manner according to students' personal habits, which leads to a lack of targeted teaching management services;

[0004] 2. The existing smart campus management service platform is unable to adjust the teaching method according to the students' concentration level in online courses, which leads to low teaching efficiency and is not conducive to improving students' academic performance.

[0005] To this end, we propose a big data configuration method and system for a smart campus management service platform. Summary of the invention

[0006] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a big data configuration method and system for a smart campus management service platform. The present invention aims to improve the configuration level of school educational resources and ensure the pertinence of teaching management services.

[0007] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a big data configuration method for a smart campus management service platform, comprising the following specific steps:

[0008] Step S1: monitor the autonomous learning period of the target student, and obtain the first learning resource matching coefficient according to the monitoring result; monitor the clock-in sleep period of the target student, and obtain the second learning resource matching coefficient according to the monitoring result, and obtain the student's work and rest monitoring data;

[0009] Step S2: randomly selecting a number of teaching hours in the online course that the target student receives teaching, obtaining multiple student focus evaluation coefficients by analyzing the student barrage interaction ratio, the course cumulative screen switching time ratio, and the student online time ratio corresponding to each teaching hour, and comprehensively processing the multiple student focus evaluation coefficients to obtain the third learning resource matching coefficient;

[0010] Step S3: Obtain a resource configuration evaluation coefficient by analyzing the first learning resource matching coefficient, the second learning resource matching coefficient, and the third learning resource matching coefficient, obtain a resource configuration evaluation coefficient threshold, perform numerical comparison with the resource configuration evaluation coefficient, and configure teaching resources for the target students according to the numerical comparison results.

[0011] Furthermore, the step S1 further includes the following specific steps:

[0012] Step S11: acquiring student users served by the smart campus management service platform, obtaining multiple platform student users, and randomly selecting a student user from the multiple platform student users as a target student;

[0013] Step S12: monitoring the autonomous learning period of the target student to obtain a first learning resource matching coefficient;

[0014] Step S13: monitoring the clock-in sleep period of the target student to obtain a second learning resource matching coefficient;

[0015] Step S14: define the first learning resource matching coefficient and the second learning resource matching coefficient as student work and rest monitoring data.

[0016] Furthermore, the step S12 further includes the following specific steps:

[0017] Step S121: Obtain the autonomous learning check-in data of the target student according to the smart campus management service platform, randomly select a number of learning check-in records from the autonomous learning check-in data of the target student, and name the obtained several learning check-in records as the first learning record to the zth learning record respectively;

[0018] Step S122: Analyze the first learning record in time periods to obtain the repetition rate of the first learning period and the first check-in learning duration;

[0019] Step S123: Obtain the punch-in study duration corresponding to the second learning record to the zth learning record, and obtain the second punch-in study duration to the zth punch-in study duration. Obtain the punch-in study duration corresponding to the second learning record to the zth learning record, and obtain the second learning period repetition rate to the zth learning period repetition rate;

[0020] Step S124: Calculate the first learning resource matching coefficient by combining the first check-in learning duration to the zth check-in learning duration and the first learning period repetition rate to the zth learning period repetition rate;

[0021] The first learning resource matching coefficient is calculated.

[0022] Furthermore, the step S122 further includes the following specific steps:

[0023] Step S1221: acquiring the time value corresponding to the start of the target student's study according to the first study record to obtain a first time value, and acquiring the time value corresponding to the end of the target student's study according to the first study record to obtain a second time value;

[0024] Step S1222: Name the time period between the first time value and the second time value as the first check-in study period;

[0025] Step S1223: Calculate the time difference between the first time value and the second time value to obtain the check-in study duration corresponding to the first study record, and name it as the first check-in study duration;

[0026] Step S1224: Obtain the school teaching timetable, and obtain multiple teaching periods corresponding to the school on the teaching working day according to the school teaching timetable;

[0027] Step S1225: creating a learning monitoring plane histogram through an existing spreadsheet program, marking a specific time period of the day as the coordinate y-axis of the learning monitoring plane histogram, and creating a first feature histogram column, a second feature histogram column, and a third feature histogram column in the coordinate x-axis of the learning monitoring plane histogram;

[0028] Step S1226: In the learning monitoring plane histogram, the first characteristic histogram column is filled with the first punch-in learning period to obtain the first sample filling area, the second characteristic histogram column is filled with multiple teaching periods to obtain the second sample filling area, and the overlapping area of ​​the first sample filling area and the second sample filling area on the coordinate y-axis is filled in the third characteristic histogram column to obtain the third sample filling area;

[0029] Step S1227: Obtain the length ratio of the third sample filling area in the third characteristic histogram to obtain the repetition rate of the first learning period.

[0030] Furthermore, the step S13 further includes the following specific steps:

[0031] Step S131: obtaining the target student's sleep check-in data according to the smart campus management service platform, randomly selecting a number of sleep check-in records from the target student's autonomous sleep check-in data, and processing the obtained several sleep check-in records to be named as the first sleep record to the sth sleep record respectively;

[0032] Step S132: Analyze the first sleep record in each period to obtain the repetition rate of the first sleep period and the first clock-in sleep duration;

[0033] Step S133: Acquire the clock-in sleep duration corresponding to the second sleep record to the s-th sleep record to obtain the second clock-in sleep duration to the s-th clock-in sleep duration, and acquire the clock-in sleep duration corresponding to the second sleep record to the s-th sleep record to obtain the second sleep period repetition rate to the s-th sleep period repetition rate;

[0034] Step S134: Calculate the second learning resource matching coefficient by combining the first clock-in sleep duration to the sth clock-in sleep duration and the first sleep period repetition rate to the sth sleep period repetition rate;

[0035] The second learning resource matching coefficient is calculated.

[0036] Furthermore, the step S132 further includes the following specific steps:

[0037] Step S1321: obtaining a sleep time value corresponding to the start of study of the target student according to the first sleep record to obtain a first sleep time value, and obtaining a sleep time value corresponding to the end of study of the target student according to the first sleep record to obtain a second sleep time value;

[0038] Step S1322: naming the time period between the first sleep time value and the second sleep time value as the first clock-in sleep period;

[0039] Step S1323: Calculate the time difference between the first sleep time value and the second sleep time value to obtain the clock-in sleep duration corresponding to the first sleep record, and name it the first clock-in sleep duration;

[0040] Step S1324: Obtain the school teaching timetable, and obtain multiple teaching periods corresponding to the school on the teaching working day according to the school teaching timetable;

[0041] Step S1325: creating a sleep monitoring plane histogram through an existing spreadsheet program, marking a specific time period of the day as the coordinate y-axis of the sleep monitoring plane histogram, and creating a first sample histogram column, a second sample histogram column, and a third sample histogram column in the coordinate x-axis of the sleep monitoring plane histogram;

[0042] A big data configuration system for a smart campus management service platform, comprising:

[0043] Life and work schedule module: used to monitor the target students' self-study period and obtain the first learning resource matching coefficient based on the monitoring results, monitor the target students' clock-in sleep period and obtain the second learning resource matching coefficient based on the monitoring results, and obtain the students' life and work schedule monitoring data;

[0044] Learning monitoring module: used to randomly select a number of teaching hours in the online course that the target students receive teaching, obtain multiple student focus evaluation coefficients by analyzing the student barrage interaction ratio, the course cumulative screen switching time ratio and the student online time ratio corresponding to each teaching hour, and perform comprehensive processing on multiple student focus evaluation coefficients to obtain the third learning resource matching coefficient;

[0045] Resource configuration module: used to obtain the resource configuration evaluation coefficient by analyzing the first learning resource matching coefficient, the second learning resource matching coefficient and the third learning resource matching coefficient, obtain the resource configuration evaluation coefficient threshold and compare it numerically with the resource configuration evaluation coefficient, and configure teaching resources for the target students according to the numerical comparison results.

[0046] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0047] 1. The present invention monitors the autonomous learning period of the target students and obtains the first learning resource matching coefficient according to the monitoring results, monitors the clock-in sleep period of the target students and obtains the second learning resource matching coefficient according to the monitoring results, and configures teaching resources according to the first learning resource matching coefficient and the second learning resource matching coefficient, which can effectively improve the configuration pertinence of teaching resources and improve the utilization efficiency of teaching resources;

[0048] 2. The present invention obtains multiple student concentration evaluation coefficients by analyzing the student barrage interaction ratio, the course cumulative screen switching time ratio and the student online time ratio corresponding to each teaching hour, and comprehensively processes the multiple student concentration evaluation coefficients to obtain the third learning resource matching coefficient, and configures teaching resources according to the third learning resource matching coefficient, which can effectively stimulate students' learning enthusiasm and learning efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0050] Figure 1 It is a diagram of the implementation steps of the present invention;

[0051] Figure 2 is the overall system block diagram of the present invention;

[0052] Figure 3 The histogram of the learning monitoring plane of the present invention. DETAILED DESCRIPTION

[0053] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. 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.

[0054] Embodiment 1

[0055] See also Figure 1 The present invention provides a technical solution: a big data configuration method for a smart campus management service platform, comprising the following specific steps:

[0056] Step S1: monitor the autonomous learning period of the target student, and obtain the first learning resource matching coefficient according to the monitoring result; monitor the clock-in sleep period of the target student, and obtain the second learning resource matching coefficient according to the monitoring result, and obtain the student's work and rest monitoring data;

[0057] The step S1 further includes the following specific steps:

[0058] Step S11: acquiring student users served by the smart campus management service platform, obtaining multiple platform student users, and randomly selecting a student user from the multiple platform student users as a target student;

[0059] Step S12: monitoring the autonomous learning period of the target student to obtain a first learning resource matching coefficient;

[0060] The step S12 further includes the following specific steps:

[0061] Step S121: Obtain the autonomous learning check-in data of the target student according to the smart campus management service platform, randomly select a number of learning check-in records from the autonomous learning check-in data of the target student, and name the obtained several learning check-in records as the first learning record to the zth learning record respectively;

[0062] Step S122: Analyze the first learning record in time periods to obtain the repetition rate of the first learning period and the first check-in learning duration;

[0063] The step S122 further includes the following specific steps:

[0064] Step S1221: acquiring the time value corresponding to the start of the target student's study according to the first study record to obtain a first time value, and acquiring the time value corresponding to the end of the target student's study according to the first study record to obtain a second time value;

[0065] Step S1222: Name the time period between the first time value and the second time value as the first check-in study period;

[0066] Step S1223: Calculate the time difference between the first time value and the second time value to obtain the check-in study duration corresponding to the first study record, and name it as the first check-in study duration;

[0067] Step S1224: Obtain the school teaching timetable, and obtain multiple teaching periods corresponding to the school on the teaching working day according to the school teaching timetable;

[0068] Step S1225: creating a learning monitoring plane histogram through an existing spreadsheet program, marking a specific time period of the day as the coordinate y-axis of the learning monitoring plane histogram, and creating a first feature histogram column, a second feature histogram column, and a third feature histogram column in the coordinate x-axis of the learning monitoring plane histogram;

[0069] Step S1226: In the learning monitoring plane histogram, use the first punch-in learning period to fill the first characteristic histogram column to obtain the first sample filling area, use multiple teaching periods to fill the second characteristic histogram column to obtain the second sample filling area, and fill the overlapping area of ​​the first sample filling area and the second sample filling area on the coordinate y-axis in the third characteristic histogram column to obtain the third sample filling area.

[0070] Step S1227: Obtain the length ratio of the third sample filling area in the third characteristic histogram to obtain the repetition rate of the first learning period;

[0071] Step S123: Obtain the punch-in study duration corresponding to the second learning record to the zth learning record, and obtain the second punch-in study duration to the zth punch-in study duration. Obtain the punch-in study duration corresponding to the second learning record to the zth learning record, and obtain the second learning period repetition rate to the zth learning period repetition rate;

[0072] Step S124: Calculate the first learning resource matching coefficient by combining the first check-in learning duration to the zth check-in learning duration and the first learning period repetition rate to the zth learning period repetition rate;

[0073] The first learning resource matching coefficient is calculated, and the specific formula is as follows:

[0074]

[0075] Among them, Zyx1 is the first learning resource matching coefficient, Dsc i is the learning time of the i-th check-in, Cf li is the repetition rate of the i-th learning period, and z is the number value corresponding to the learning check-in record.

[0076] Step S13: Monitor the target student's clock-in sleep period to obtain a second learning resource matching coefficient.

[0077] The step S13 further includes the following specific steps:

[0078] Step S131: obtaining the target student's sleep check-in data according to the smart campus management service platform, randomly selecting a number of sleep check-in records from the target student's autonomous sleep check-in data, and processing the obtained several sleep check-in records to be named as the first sleep record to the sth sleep record respectively;

[0079] Step S132: Analyze the first sleep record in each period to obtain the repetition rate of the first sleep period and the first clock-in sleep duration;

[0080] The step S132 further includes the following specific steps:

[0081] Step S1321: obtaining a sleep time value corresponding to the start of study of the target student according to the first sleep record to obtain a first sleep time value, and obtaining a sleep time value corresponding to the end of study of the target student according to the first sleep record to obtain a second sleep time value;

[0082] Step S1322: naming the time period between the first sleep time value and the second sleep time value as the first clock-in sleep period;

[0083] Step S1323: Calculate the time difference between the first sleep time value and the second sleep time value to obtain the clock-in sleep duration corresponding to the first sleep record, and name it the first clock-in sleep duration;

[0084] Step S1324: Obtain the school teaching timetable, and obtain multiple teaching periods corresponding to the school on the teaching working day according to the school teaching timetable;

[0085] Step S1325: creating a sleep monitoring plane histogram through an existing spreadsheet program, marking a specific time period of the day as the coordinate y-axis of the sleep monitoring plane histogram, and creating a first sample histogram column, a second sample histogram column, and a third sample histogram column in the coordinate x-axis of the sleep monitoring plane histogram;

[0086] Step S1326: In the sleep monitoring plane histogram, the first sample histogram column is filled with the first clock-in sleep period to obtain a first sample filling area, the second sample histogram column is filled with multiple teaching periods to obtain a second sample filling area, and the overlapping area of ​​the first sample filling area and the second sample filling area on the coordinate y-axis is filled in the third sample histogram column to obtain a third sample filling area;

[0087] Step S1327: acquiring the length ratio of the third sample filling area in the third sample histogram to obtain the repetition rate of the first sleep period;

[0088] Step S133: Acquire the clock-in sleep duration corresponding to the second sleep record to the s-th sleep record to obtain the second clock-in sleep duration to the s-th clock-in sleep duration, and acquire the clock-in sleep duration corresponding to the second sleep record to the s-th sleep record to obtain the second sleep period repetition rate to the s-th sleep period repetition rate;

[0089] Step S134: Calculate the second learning resource matching coefficient by combining the first clock-in sleep duration to the sth clock-in sleep duration and the first sleep period repetition rate to the sth sleep period repetition rate;

[0090] The second learning resource matching coefficient is calculated, and the specific formula is as follows:

[0091]

[0092] Among them, syx2 is the second learning resource matching coefficient, Msci is the sleep duration of the ith clock-in, Mfli is the repetition rate of the ith sleep period, and s is the number of sleep clock-in records.

[0093] Step S14: define the first learning resource matching coefficient and the second learning resource matching coefficient as student work and rest monitoring data.

[0094] Step S2: randomly selecting a number of teaching hours in the online course that the target student receives teaching, obtaining multiple student focus evaluation coefficients by analyzing the student barrage interaction ratio, the course cumulative screen switching time ratio, and the student online time ratio corresponding to each teaching hour, and comprehensively processing the multiple student focus evaluation coefficients to obtain the third learning resource matching coefficient;

[0095] The step S2 further includes the following specific steps:

[0096] Step S21: randomly selecting a number of teaching hours from the online course that the target student receives teaching, and selecting a sample teaching hour from the selected number of teaching hours;

[0097] Step S22: monitoring the sample teaching hours to obtain the student concentration evaluation coefficient corresponding to the sample teaching hours;

[0098] The step S22 further includes the following specific steps:

[0099] Step S221: Obtain the cumulative number of interactive bullet screens sent by the target students during the sample teaching hours to obtain the first bullet screen number, obtain the cumulative number of teaching interactive bullet screens corresponding to the sample teaching hours to obtain the second bullet screen number, calculate the ratio of the first bullet screen number to the second bullet screen number, and obtain the student bullet screen interaction ratio;

[0100] Step S222: Obtain the course duration corresponding to the sample teaching hours to obtain the first course duration;

[0101] Step S223: capturing multiple teaching screen switching of the target student in the sample teaching hours, obtaining the screen switching duration for each teaching screen switching, obtaining multiple teaching screen switching durations, and summing the obtained multiple teaching screen switching durations to obtain the cumulative screen switching duration of the course;

[0102] Step S224: Calculate the ratio of the cumulative screen switching time of the course to the course time to obtain the student screen switching time ratio;

[0103] Step S225: Obtain the cumulative online time of the target student in the sample teaching hours to obtain the student's cumulative online time, calculate the ratio of the student's cumulative online time to the first course time, and obtain the student's online time ratio;

[0104] Step S226: Calculate the student focus evaluation coefficient corresponding to the sample teaching hours by using the student bullet screen interaction ratio, the course cumulative screen switching time ratio, and the student online time ratio;

[0105] The student concentration evaluation coefficient corresponding to the sample teaching hours is calculated. The specific formula is as follows:

[0106]

[0107] Among them, Zzp is the student concentration evaluation coefficient corresponding to the sample teaching hours, Dmb is the student barrage interaction ratio, Qpb is the cumulative screen switching time ratio of the course, and Zxb is the student online time ratio.

[0108] Step S23: respectively obtain the student concentration evaluation coefficient corresponding to each teaching hour to obtain multiple student concentration evaluation coefficients, and average the obtained multiple student concentration evaluation coefficients to obtain the third learning resource matching coefficient.

[0109] Step S3: Obtain a resource configuration evaluation coefficient by analyzing the first learning resource matching coefficient, the second learning resource matching coefficient, and the third learning resource matching coefficient, obtain a resource configuration evaluation coefficient threshold value and compare the value with the resource configuration evaluation coefficient, and configure teaching resources for the target students according to the numerical comparison result;

[0110] The step S3 further includes the following specific steps:

[0111] Step S31: Obtain student work and rest monitoring data, and obtain a first learning resource matching coefficient and a second learning resource matching coefficient according to the student work and rest monitoring data;

[0112] Step S32: Obtaining a third learning resource matching coefficient;

[0113] Step S33: obtaining a resource configuration evaluation coefficient by calculating the first learning resource matching coefficient, the second learning resource matching coefficient, and the third learning resource matching coefficient;

[0114] The resource allocation evaluation coefficient is calculated as follows:

[0115]

[0116] Among them, Pzp is the resource configuration evaluation coefficient, Zyx1 is the first learning resource matching coefficient, Zyx2 is the second learning resource matching coefficient, and Zyx3 is the third learning resource matching coefficient;

[0117] Step S34: Obtain a resource configuration evaluation coefficient threshold, perform a numerical comparison between the resource configuration evaluation coefficient threshold and the resource configuration evaluation coefficient, and perform teaching resource configuration for the target students according to the numerical comparison result.

[0118] The step S34 further includes the following specific steps:

[0119] Step S341: respectively obtaining a first learning resource matching coefficient threshold, a second learning resource matching coefficient threshold, and a third learning resource matching coefficient threshold;

[0120] Step S342: Calculating the first learning resource matching coefficient threshold, the second learning resource matching coefficient threshold, and the third learning resource matching coefficient threshold to obtain a resource configuration evaluation coefficient threshold;

[0121] The resource configuration evaluation coefficient threshold is calculated, and the specific formula is as follows:

[0122]

[0123] Wherein, Pzp is the resource configuration evaluation coefficient threshold, Zyx1 is the first learning resource matching coefficient threshold, Zyx2 is the second learning resource matching coefficient threshold, and Zyx3 is the third learning resource matching coefficient threshold;

[0124] Step S343: when the resource configuration evaluation coefficient is greater than or equal to the resource configuration evaluation coefficient threshold, online teaching resources are preferentially configured for the target students;

[0125] Step S344: When the resource configuration evaluation coefficient is less than the resource configuration evaluation coefficient threshold, offline teaching resources are preferentially configured for the target students.

[0126] In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0127] Embodiment 2

[0128] See also Figure 2Based on another concept of the same invention, a big data configuration system for a smart campus management service platform is proposed, comprising a daily routine module, a learning monitoring module, a resource configuration module and a server, wherein the daily routine module, the learning monitoring module and the resource configuration module are respectively connected to the server, and the server controls the daily routine module, the learning monitoring module and the resource configuration module respectively;

[0129] The daily routine module monitors the target students' autonomous learning period and obtains the first learning resource matching coefficient based on the monitoring results. It also monitors the target students' clock-in sleep period and obtains the second learning resource matching coefficient based on the monitoring results to obtain the students' daily routine monitoring data.

[0130] Acquire student users served by the smart campus management service platform, obtain multiple platform student users, and randomly select a student user from the multiple platform student users as the target student;

[0131] It should be noted here that:

[0132] In the present application, the target student involved here is a sample in the smart campus management service platform. In the present application, the process of providing management services to the target students can be extended and applied to every student user served by the smart campus management service platform.

[0133] Monitor the target students' independent learning period to obtain the first learning resource matching coefficient;

[0134] The details are as follows:

[0135] Obtain the autonomous learning check-in data of the target students according to the smart campus management service platform, randomly select a number of learning check-in records from the autonomous learning check-in data of the target students, and name the obtained several learning check-in records as the first learning record to the zth learning record respectively;

[0136] It should be noted here that:

[0137] The z involved here is the number value corresponding to the learning check-in record, and z is an integer greater than 0;

[0138] The target students' independent learning check-in data involved here specifically refers to the target students' learning check-in data during non-teaching periods. The non-teaching periods involved here specifically refer to the learning periods outside the school teaching timetable. The non-teaching periods involved here do not include morning and evening self-study.

[0139] Perform time period analysis on the first learning record to obtain the repetition rate of the first learning period and the first punch-in learning duration;

[0140] The details are as follows:

[0141] According to the first learning record, the time value corresponding to when the target student starts learning is obtained to obtain a first time value, and according to the first learning record, the time value corresponding to when the target student ends learning is obtained to obtain a second time value;

[0142] The time period between the first time value and the second time value is named as the first check-in study period;

[0143] The time difference between the first time value and the second time value is calculated to obtain the check-in study time corresponding to the first study record, and the check-in study time is named the first check-in study time.

[0144] Obtain the school teaching timetable, and obtain multiple teaching periods corresponding to the school's teaching working days according to the school teaching timetable;

[0145] See also Figure 3 , create a learning monitoring plane histogram through an existing spreadsheet program, mark the specific time period of the day as the coordinate y-axis of the learning monitoring plane histogram, and create a first feature histogram column, a second feature histogram column, and a third feature histogram column in the coordinate x-axis of the learning monitoring plane histogram;

[0146] It should be noted here that:

[0147] The specific time period of a day involved here is 0-24 hours.

[0148] In the learning monitoring plane histogram, the first characteristic histogram column is filled with the first punch-in learning period to obtain the first sample filling area, the second characteristic histogram column is filled with multiple teaching periods to obtain the second sample filling area, and the overlapping area of ​​the first sample filling area and the second sample filling area on the coordinate y axis is filled in the third characteristic histogram column to obtain the third sample filling area;

[0149] The length ratio of the third sample filling area in the third characteristic histogram is obtained to obtain the repetition rate of the first learning period;

[0150] Repeat the process of time period analysis for the first learning record, obtain the check-in learning time corresponding to the second learning record to the zth learning record, and obtain the repetition rate of the second learning period to the zth learning period.

[0151] The first learning resource matching coefficient is obtained by calculating the first check-in learning time to the zth check-in learning time and the first learning period repetition rate to the zth learning period repetition rate;

[0152] The first learning resource matching coefficient is calculated, and the specific formula is as follows:

[0153]

[0154] Where Zyx1 is the first learning resource matching coefficient, Dsc i is the duration of the i-th check-in learning session, Cf li is the repetition rate of the i-th learning period, and z is the number of learning check-in records.

[0155] It should be noted here that:

[0156] In the present application, the i-th check-in study duration involved here can be any check-in study duration from the first check-in study duration to the z-th check-in study duration, and the i-th study period repetition rate involved here can be any study period repetition rate from the first study period repetition rate to the z-th study period repetition rate.

[0157] Monitor the target students’ clock-in sleep period to obtain the second learning resource matching coefficient;

[0158] The details are as follows:

[0159] Obtain the target student's sleep check-in data according to the smart campus management service platform, randomly select a number of sleep check-in records from the target student's autonomous sleep check-in data, and process the obtained sleep check-in records to be named the first sleep record to the sth sleep record respectively;

[0160] It should be noted here that:

[0161] The s involved here is the number value corresponding to the sleep clock-in record, and s is an integer greater than 0;

[0162] The target students' autonomous sleep check-in data involved here specifically refers to the target students' sleep check-in data during non-teaching periods.

[0163] Performing a period analysis on the first sleep record to obtain the repetition rate of the first sleep period and the first clock-in sleep duration;

[0164] The details are as follows:

[0165] According to the first sleep record, the sleep time value corresponding to the start of the target student's study is obtained to obtain a first sleep time value; according to the first sleep record, the sleep time value corresponding to the end of the target student's study is obtained to obtain a second sleep time value;

[0166] The time period between the first sleep time value and the second sleep time value is named as the first clock-in sleep period;

[0167] Calculate the time difference between the first sleep time value and the second sleep time value to obtain the clock-in sleep duration corresponding to the first sleep record, and name it the first clock-in sleep duration;

[0168] Obtain the school teaching timetable, and obtain multiple teaching periods corresponding to the school's teaching working days according to the school teaching timetable;

[0169] Create a sleep monitoring plane histogram through an existing spreadsheet program, mark the specific time period of the day as the coordinate y-axis of the sleep monitoring plane histogram, and create a first sample histogram, a second sample histogram, and a third sample histogram in the coordinate x-axis of the sleep monitoring plane histogram;

[0170] It should be noted here that:

[0171] The specific time period of a day involved here is 0-24 hours;

[0172] In the sleep monitoring plane histogram, the first sample histogram column is filled with the first clock-in sleep period to obtain the first sample filling area, the second sample histogram column is filled with multiple teaching periods to obtain the second sample filling area, and the overlapping area of ​​the first sample filling area and the second sample filling area on the coordinate y-axis is filled in the third sample histogram column to obtain the third sample filling area;

[0173] The length ratio of the third sample filling area in the third sample histogram is obtained to obtain the repetition rate of the first sleep period;

[0174] Repeat the process of period analysis for the first sleep record, obtain the clock-in sleep duration corresponding to the second sleep record to the s-th sleep record, and obtain the clock-in sleep duration corresponding to the second sleep record to the s-th sleep record, and obtain the repetition rate of the second sleep period to the repetition rate of the s-th sleep period;

[0175] The second learning resource matching coefficient is obtained by calculating the first clock-in sleep time to the s-th clock-in sleep time and the first sleep period repetition rate to the s-th sleep period repetition rate;

[0176] The second learning resource matching coefficient is calculated, and the specific formula is as follows:

[0177]

[0178] Among them, syx2 is the second learning resource matching coefficient, Msci is the sleep duration of the ith clock-in, Mfli is the repetition rate of the ith sleep period, and s is the number of sleep clock-in records.

[0179] It should be noted here that:

[0180] In the present application, the i-th clock-in sleep duration involved here may be any clock-in sleep duration from the first clock-in sleep duration to the s-th clock-in sleep duration, and the i-th sleep period repetition rate involved here may be any sleep period repetition rate from the first sleep period repetition rate to the s-th sleep period repetition rate;

[0181] The first learning resource matching coefficient and the second learning resource matching coefficient are defined as student work and rest monitoring data;

[0182] The daily routine module acquires the students' daily routine monitoring data and transmits it to the resource allocation module;

[0183] The learning monitoring module randomly selects several teaching hours from the online courses where the target students are taught, and obtains multiple student concentration evaluation coefficients by analyzing the student barrage interaction ratio, the course cumulative screen switching time ratio and the student online time ratio corresponding to each teaching hour. The multiple student concentration evaluation coefficients are comprehensively processed to obtain the third learning resource matching coefficient.

[0184] The details are as follows:

[0185] Randomly select a number of teaching hours in the online course where the target students receive teaching, and select a sample teaching hour from the selected number of teaching hours;

[0186] Monitor the sample teaching hours and obtain the student concentration evaluation coefficient corresponding to the sample teaching hours;

[0187] The details are as follows:

[0188] Obtain the cumulative number of interactive bullet screens sent by the target students during the sample teaching hours to obtain the first bullet screen number, obtain the cumulative number of teaching interactive bullet screens corresponding to the sample teaching hours to obtain the second bullet screen number, calculate the ratio of the first bullet screen number to the second bullet screen number to obtain the student bullet screen interaction ratio;

[0189] Get the course duration corresponding to the sample teaching hours and get the first course duration;

[0190] Capture multiple teaching screen switching of target students in the sample teaching hours, obtain the screen switching duration for each teaching screen switching, obtain multiple teaching screen switching durations, and sum up the obtained multiple teaching screen switching durations to obtain the cumulative screen switching duration of the course;

[0191] Calculate the ratio of the cumulative screen switching time of the course to the course duration to obtain the student screen switching time ratio;

[0192] Obtain the cumulative online time of the target students in the sample teaching hours to obtain the cumulative online time of the students, calculate the ratio of the cumulative online time of the students to the duration of the first course, and obtain the student online time ratio;

[0193] The student concentration evaluation coefficient corresponding to the sample teaching hours is obtained by calculating the student bullet screen interaction ratio, the course cumulative screen switching time ratio, and the student online time ratio;

[0194] The student concentration evaluation coefficient corresponding to the sample teaching hours is calculated. The specific formula is as follows:

[0195]

[0196] Among them, Zzp is the student concentration evaluation coefficient corresponding to the sample teaching hours, Dmb is the student barrage interaction ratio, Qpb is the course cumulative screen switching time ratio, and Zxb is the student online time ratio;

[0197] The process of obtaining the student concentration evaluation coefficient corresponding to the sample teaching hours is repeated, and the student concentration evaluation coefficient corresponding to each teaching hour is obtained respectively to obtain multiple student concentration evaluation coefficients, and the average of the multiple student concentration evaluation coefficients is calculated to obtain the third learning resource matching coefficient.

[0198] The learning monitoring module obtains the third learning resource matching coefficient and transmits it to the resource configuration module;

[0199] The resource allocation module obtains the resource allocation evaluation coefficient by analyzing the first learning resource matching coefficient, the second learning resource matching coefficient and the third learning resource matching coefficient, obtains the resource configuration evaluation coefficient threshold and compares it numerically with the resource configuration evaluation coefficient, and allocates teaching resources to the target students according to the numerical comparison results.

[0200] The details are as follows:

[0201] Obtain student work and rest monitoring data, and obtain a first learning resource matching coefficient and a second learning resource matching coefficient respectively according to the student work and rest monitoring data;

[0202] Obtain the third learning resource matching coefficient;

[0203] The first learning resource matching coefficient, the second learning resource matching coefficient and the third learning resource matching coefficient are calculated to obtain a resource configuration evaluation coefficient;

[0204] The resource allocation evaluation coefficient is calculated as follows:

[0205]

[0206] Among them, Pzp is the resource configuration evaluation coefficient, Zyx1 is the first learning resource matching coefficient, Zyx2 is the second learning resource matching coefficient, and Zyx3 is the third learning resource matching coefficient;

[0207] Obtaining a resource allocation evaluation coefficient threshold, comparing the resource allocation evaluation coefficient threshold with the resource allocation evaluation coefficient, and configuring teaching resources for target students according to the comparison result;

[0208] The details are as follows:

[0209] respectively obtaining a first learning resource matching coefficient threshold, a second learning resource matching coefficient threshold, and a third learning resource matching coefficient threshold;

[0210] It should be noted here that:

[0211] In the present application, the first learning resource matching coefficient threshold, the second learning resource matching coefficient threshold, and the third learning resource matching coefficient threshold involved herein are obtained by comprehensive analysis of student users in the smart campus management service platform through big data;

[0212] The first learning resource matching coefficient threshold, the second learning resource matching coefficient threshold and the third learning resource matching coefficient threshold are calculated to obtain a resource configuration evaluation coefficient threshold;

[0213] The resource configuration evaluation coefficient threshold is calculated, and the specific formula is as follows:

[0214]

[0215] Wherein, Pzp is the resource configuration evaluation coefficient threshold, Zyx1 is the first learning resource matching coefficient threshold, Zyx2 is the second learning resource matching coefficient threshold, and Zyx3 is the third learning resource matching coefficient threshold;

[0216] When the resource allocation evaluation coefficient is greater than or equal to the resource allocation evaluation coefficient threshold, online teaching resources are preferentially allocated to the target students;

[0217] When the resource allocation evaluation coefficient is less than the resource allocation evaluation coefficient threshold, offline teaching resources are preferentially allocated to the target students.

[0218] It should be noted here that:

[0219] In this application, the online teaching resources involved here include but are not limited to teaching videos, live courses and online tests, and the offline teaching resources involved here include but are not limited to extracurricular one-on-one tutoring, extracurricular practice and traditional classroom teaching.

[0220] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A big data configuration method for a smart campus management service platform, characterized in that: include: Step S1: monitor the autonomous learning period of the target student, and obtain the first learning resource matching coefficient according to the monitoring result; monitor the clock-in sleep period of the target student, and obtain the second learning resource matching coefficient according to the monitoring result, and obtain the student's work and rest monitoring data; Step S2: randomly selecting a number of teaching hours in the online course that the target student receives teaching, obtaining multiple student focus evaluation coefficients by analyzing the student barrage interaction ratio, the course cumulative screen switching time ratio, and the student online time ratio corresponding to each teaching hour, and comprehensively processing the multiple student focus evaluation coefficients to obtain the third learning resource matching coefficient; Step S3: Obtain a resource configuration evaluation coefficient by analyzing the first learning resource matching coefficient, the second learning resource matching coefficient, and the third learning resource matching coefficient, obtain a resource configuration evaluation coefficient threshold, perform numerical comparison with the resource configuration evaluation coefficient, and configure teaching resources for the target students according to the numerical comparison results.

2. A big data configuration method for a smart campus management service platform according to claim 1, characterized in that: The step S1 further includes the following specific steps: Step S11: acquiring student users served by the smart campus management service platform, obtaining multiple platform student users, and randomly selecting a student user from the multiple platform student users as a target student; Step S12: monitoring the autonomous learning period of the target student to obtain a first learning resource matching coefficient; Step S13: monitoring the clock-in sleep period of the target student to obtain a second learning resource matching coefficient; Step S14: define the first learning resource matching coefficient and the second learning resource matching coefficient as student work and rest monitoring data.

3. A big data configuration method for a smart campus management service platform according to claim 2, characterized in that: The step S12 further includes the following specific steps: Step S121: Obtain the autonomous learning check-in data of the target student, randomly select a number of learning check-in records from the autonomous learning check-in data of the target student, and name the obtained several learning check-in records as the first learning record to the zth learning record respectively; Step S122: Analyze the first learning record in time periods to obtain the repetition rate of the first learning period and the first check-in learning duration; Step S123: Obtain the punch-in study duration corresponding to the second learning record to the zth learning record, and obtain the second punch-in study duration to the zth punch-in study duration. Obtain the punch-in study duration corresponding to the second learning record to the zth learning record, and obtain the second learning period repetition rate to the zth learning period repetition rate; Step S124: Calculate the first learning resource matching coefficient by combining the first check-in learning duration to the zth check-in learning duration and the first learning period repetition rate to the zth learning period repetition rate; The first learning resource matching coefficient is calculated.

4. A big data configuration method for a smart campus management service platform according to claim 3, characterized in that: The step S122 further includes the following specific steps: Step S1221: acquiring the time value corresponding to the start of the target student's study according to the first study record to obtain a first time value, and acquiring the time value corresponding to the end of the target student's study according to the first study record to obtain a second time value; Step S1222: Name the time period between the first time value and the second time value as the first check-in study period; Step S1223: Calculate the time difference between the first time value and the second time value, obtain the check-in study duration corresponding to the first learning record, and name it the first check-in study duration.

5. A big data configuration method for a smart campus management service platform according to claim 4, characterized in that: The step S122 further includes the following specific steps: Step S1224: obtaining a school teaching schedule, and obtaining a plurality of teaching periods according to the school teaching schedule; Step S1225: creating a learning monitoring plane histogram through an existing spreadsheet program, marking a specific time period of the day as the coordinate y-axis of the learning monitoring plane histogram, and creating a first feature histogram column, a second feature histogram column, and a third feature histogram column in the coordinate x-axis of the learning monitoring plane histogram; Step S1226: In the learning monitoring plane histogram, the first characteristic histogram column is filled with the first punch-in learning period to obtain the first sample filling area, the second characteristic histogram column is filled with multiple teaching periods to obtain the second sample filling area, and the overlapping area of ​​the first sample filling area and the second sample filling area on the coordinate y-axis is filled in the third characteristic histogram column to obtain the third sample filling area; Step S1227: Obtain the length ratio of the third sample filling area in the third characteristic histogram to obtain the repetition rate of the first learning period.

6. A big data configuration method for a smart campus management service platform according to claim 5, characterized in that: The step S13 further includes the following specific steps: Step S131: obtaining the target student's sleep check-in data according to the smart campus management service platform, randomly selecting a number of sleep check-in records from the target student's autonomous sleep check-in data, and processing the obtained several sleep check-in records to be named as the first sleep record to the sth sleep record respectively; Step S132: Analyze the first sleep record for a certain period of time to obtain a repetition rate of the first sleep period and a first clock-in sleep duration.

7. A big data configuration method for a smart campus management service platform according to claim 6, characterized in that: The step S13 further includes the following specific steps: Step S133: Acquire the clock-in sleep duration corresponding to the second sleep record to the s-th sleep record to obtain the second clock-in sleep duration to the s-th clock-in sleep duration, and acquire the clock-in sleep duration corresponding to the second sleep record to the s-th sleep record to obtain the second sleep period repetition rate to the s-th sleep period repetition rate; Step S134: Calculate the second learning resource matching coefficient by combining the first clock-in sleep duration to the sth clock-in sleep duration and the first sleep period repetition rate to the sth sleep period repetition rate; The second learning resource matching coefficient is calculated.

8. The big data configuration method for a smart campus management service platform according to claim 7, characterized in that: The step S132 further includes the following specific steps: Step S1321: obtaining a sleep time value corresponding to the start of study of the target student according to the first sleep record to obtain a first sleep time value, and obtaining a sleep time value corresponding to the end of study of the target student according to the first sleep record to obtain a second sleep time value; Step S1322: Name the time period between the first sleep time value and the second sleep time value as the first clock-in sleep period.

9. A big data configuration method for a smart campus management service platform according to claim 8, characterized in that: The step S132 further includes the following specific steps: Step S1323: Calculate the time difference between the first sleep time value and the second sleep time value to obtain the clock-in sleep duration corresponding to the first sleep record, and name it the first clock-in sleep duration; Step S1324: Obtain the school teaching timetable, and obtain multiple teaching periods corresponding to the school on the teaching working day according to the school teaching timetable; Step S1325: Create a sleep monitoring plane histogram through an existing spreadsheet program, mark the specific time period of the day as the coordinate y-axis of the sleep monitoring plane histogram, and create a first sample histogram, a second sample histogram, and a third sample histogram in the coordinate x-axis of the sleep monitoring plane histogram.

10. A big data configuration system for a smart campus management service platform, applicable to a big data configuration method for a smart campus management service platform as described in any one of claims 1 to 9, characterized in that: The big data configuration system includes: Life and work schedule module: used to monitor the target students' self-study period and obtain the first learning resource matching coefficient based on the monitoring results, monitor the target students' clock-in sleep period and obtain the second learning resource matching coefficient based on the monitoring results, and obtain the students' life and work schedule monitoring data; Learning monitoring module: used to randomly select a number of teaching hours in the online course that the target students receive teaching, obtain multiple student focus evaluation coefficients by analyzing the student barrage interaction ratio, the course cumulative screen switching time ratio and the student online time ratio corresponding to each teaching hour, and perform comprehensive processing on multiple student focus evaluation coefficients to obtain the third learning resource matching coefficient; Resource configuration module: used to obtain the resource configuration evaluation coefficient by analyzing the first learning resource matching coefficient, the second learning resource matching coefficient and the third learning resource matching coefficient, obtain the resource configuration evaluation coefficient threshold and compare it numerically with the resource configuration evaluation coefficient, and configure teaching resources for the target students according to the numerical comparison results.

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