CN114626694A - Network course planning management system based on internet - Google Patents
Network course planning management system based on internet Download PDFInfo
- Publication number
- CN114626694A CN114626694A CN202210170811.0A CN202210170811A CN114626694A CN 114626694 A CN114626694 A CN 114626694A CN 202210170811 A CN202210170811 A CN 202210170811A CN 114626694 A CN114626694 A CN 114626694A
- Authority
- CN
- China
- Prior art keywords
- data
- course
- time
- class
- password
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000007726 management method Methods 0.000 title claims abstract description 33
- 238000012795 verification Methods 0.000 claims abstract description 33
- 238000004458 analytical method Methods 0.000 claims abstract description 29
- 238000012545 processing Methods 0.000 claims abstract description 28
- 230000011664 signaling Effects 0.000 claims abstract description 22
- 238000004891 communication Methods 0.000 claims abstract description 4
- 238000004364 calculation method Methods 0.000 claims description 49
- 238000012163 sequencing technique Methods 0.000 claims description 42
- 238000005259 measurement Methods 0.000 claims description 38
- 238000013475 authorization Methods 0.000 claims description 25
- 238000000034 method Methods 0.000 claims description 16
- 238000012360 testing method Methods 0.000 claims description 9
- 230000001105 regulatory effect Effects 0.000 claims description 6
- 230000005540 biological transmission Effects 0.000 claims description 3
- 238000009795 derivation Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 238000003672 processing method Methods 0.000 claims description 3
- 238000013139 quantization Methods 0.000 claims description 3
- 230000001788 irregular Effects 0.000 claims 1
- 238000007405 data analysis Methods 0.000 abstract description 4
- 230000010354 integration Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 230000009191 jumping Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/602—Providing cryptographic facilities or services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06316—Sequencing of tasks or work
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Educational Administration (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Health & Medical Sciences (AREA)
- Quality & Reliability (AREA)
- General Health & Medical Sciences (AREA)
- Development Economics (AREA)
- Computer Security & Cryptography (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Educational Technology (AREA)
- Bioethics (AREA)
- Computer Hardware Design (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of course planning, in particular to an internet-based network course planning management system, which comprises a course planning management platform, wherein a server is arranged in the course planning management platform, and the server is in communication connection with a time interval teaching analysis unit, a learning time interval processing unit, a teaching account management and control unit and a course planning unit; a user logs in and verifies an account number through a teaching account management and control unit, generates a safety passing signaling after logging in is successful, and skips to a time period teaching analysis unit according to the safety passing signaling; according to the invention, the security of the account of the user is increased by inputting and verifying the authorized account and the corresponding password, information leakage caused by account loss is avoided, and data analysis is carried out according to the login habit of the user and the conventional login data in protection verification, so that the account is prevented from being used by other people, and the deviation of the result of the recommended course of the system is influenced.
Description
Technical Field
The invention relates to the technical field of course planning, in particular to an internet-based network course planning management system.
Background
The network course is the sum of the teaching content of a certain subject and the implemented teaching activities expressed through the network, is a new expression form of the course under the condition of an information era, and comprises the teaching content organized according to a certain teaching target and a teaching strategy and a network teaching supporting environment, wherein the network teaching supporting environment refers to software tools and teaching resources supporting network teaching and the teaching activities implemented on a network teaching platform, and the network course has basic characteristics of interactivity, shareability, openness, collaboration, autonomy and the like;
at present, network courses are gradually popularized, students or social personnel can learn through a network, people can learn at home only by registering a learning account number belonging to the users, but the attention degree of each platform on the safety of the learning account number is not high, so that information leakage of the users is caused, the deviation degree is large and the working progress is influenced when the system recommends the courses, meanwhile, for the social personnel, the idle time is often drawn out for learning, the selection of the course duration and the type of the courses need to spend a large amount of time for selection, and therefore, the learning efficiency of the students or the social personnel is low;
therefore, an internet-based network course planning management system is provided.
Disclosure of Invention
The invention aims to provide an internet-based network course planning management system, which is characterized in that an authorized account and a corresponding password are input and verified, so that the safety of the user account is improved, information leakage caused by account loss is avoided, and data analysis is performed on the login habit of the user and the previous login data in protection verification, so that the account is prevented from being used by others, and the deviation of the course recommending result of the system is influenced; the learning condition of students in the authorized account and the network course information are subjected to period teaching analysis, regular judgment is carried out according to the learning type of the user, so that whether the learning of the user is regular learning or not and the time consumed by the user in each actual learning is judged, the judgment is carried out according to the result of all data integration analysis, a plurality of recommended courses are selected and recommended, the courses required by the user are planned better, the time consumed by course selection of the user is saved, and the working efficiency is improved.
The purpose of the invention can be realized by the following technical scheme:
a network course planning management system based on the Internet comprises a course planning management platform, wherein a server is arranged in the course planning management platform, and the server is in communication connection with a time interval teaching analysis unit, a learning time interval processing unit, a teaching account management and control unit and a course planning unit;
the user logs in and verifies an account number through the teaching account management and control unit, generates a safety passing signaling after logging in is successful, and jumps to the time-interval teaching analysis unit according to the safety passing signaling;
the network course information that the network course is relevant is stored in the server, with network course information transmission to period teaching analysis unit, carry out period teaching analysis to student's study condition in this authorization account number and network course information through period teaching analysis unit, the server generates period learning signaling and sends to study period processing unit, and study period processing unit carries out period learning processing to the user study time that the authorization account number corresponds according to study period processing unit, the server generates the class and pushes away the signaling and transmit to the course planning unit, the course planning unit pushes away the signaling according to the class and carries out the course and recommends.
Further, the user logs in the authorization account number and the corresponding personal password required by the network course in the teaching account management and control unit, and the authorization account number and the corresponding personal password required by the network course and the record account number and the record password required by the user to log in the network course in the past are stored in the teaching account management and control unit for account number verification, a security verification signal or a password error signal is generated or not generated according to a verification result, the security verification signal or the password error signal is identified, if the password error signal is identified, the user jumps to a login interface, and when the security verification signal is identified, protection verification is performed, specifically:
collecting the interval time between every two password inputs during password input and marking as input time data, collecting the force received by the case corresponding to each password during password input and marking as input force data, collecting the distance between every two password cases during password input and marking as input distance data, collecting the digit number of the password during password input and marking as secret data, collecting the type of each password during password input and marking as secret data, wherein the secret data comprises a numeric password, a letter password and a punctuation password, and the punctuation password comprises all characters except letters and numbers, for example: "@", "β", "/", "-", etc.;
extracting secret data and secret bit data, sequentially marking a digital password, a letter password and a punctuation password in the secret data as Sm, Zm and Bm, marking each character in a personal password according to the secret bit data as Sm, Zm and Bm, thereby obtaining a password combination mark Mz which is similar to that of the personal password: sm, Zm, Bm;
extracting a plurality of time-transmission data, carrying out mean value calculation on the plurality of time-transmission data, calculating a time-transmission mean value, carrying out difference value calculation on the plurality of time-transmission data and the time-transmission mean value respectively, calculating a plurality of time-transmission difference values, and carrying out positive and negative value marking on the plurality of time-transmission difference values, wherein the specific steps are as follows: marking the time-input difference value which is more than or equal to zero as a positive difference value, marking the time-input difference value which is less than zero as a negative difference value, extracting the secret data of two passwords corresponding to each time-input difference value, and judging the influence on the positive difference value, the negative difference value and the secret data, specifically:
when two adjacent passwords are the same type, the two adjacent passwords are marked as a first-class password, when the two adjacent passwords are different types, the two adjacent passwords are marked as a second-class password, the positive difference value and the negative difference value are matched with the second-class passwords of the first-class password, the times of simultaneous occurrence of the second-class password and the positive difference value are matched, the positive difference values corresponding to the times of simultaneous occurrence are subjected to mean value calculation, a positive difference mean value is calculated, and the first-class password and the negative difference value are processed according to the same processing method to obtain a negative difference mean value;
extracting a plurality of force input data and a plurality of distance input data, and bringing the plurality of force input data and the plurality of distance input data into a preset relational expression: distance data, namely distance influence values = force data, calculating a plurality of distance influence values, carrying out average calculation on the plurality of distance influence values, and calculating a distance influence average;
according to the calculation formula:
calculating password input score values Dpi, Ssi are represented as input time data, ZCi is represented as a positive difference mean value, Fci is represented as a negative difference mean value, e1 is represented as a weight coefficient of actual input time, Sji is represented as input distance data, Jji is represented as a distance influence mean value, Sli is represented as input force data, e2 is represented as an input deviation adjustment factor, i =1,2,3.. n, and n is a positive integer, and the calculated numerical values of the calculation formula are all numbers subjected to quantization processing, do not carry units, and ensure that the dimension quantities are uniform;
and comparing the password input score value Dpi with a preset value M1, and judging that the password input is safe when the Dpi is larger than M1 to generate a safety pass signaling.
Further, the specific analysis process in the time interval teaching analysis unit is as follows:
collecting courses learned by a user during learning in an authorization account and marking the courses as course name data, collecting the duration of the courses learned by the user during learning in the authorization account and marking the duration as class time data, collecting the time length required by the courses during learning of the user in the authorization account and marking the time length as learning time data, collecting the duration of post-course testing of the user during learning in the authorization account and marking the duration as time measuring data, and collecting the scores of post-course testing of the user in the authorization account and marking the scores as score measuring data;
the network course information comprises course classification data, course name data and course duration data, the course classification data internally comprises a plurality of classes, and the course name data belongs to the corresponding course classification data;
selecting class name data, class time data, time measurement data and score measurement data learned by a user in an authorized account within a period of time, matching the class name data with the class classification data, matching corresponding class classification data and marking as selected class data, and counting all selected class data and class name data, wherein the period of time refers to a time period from the first day of the last month to the last day of the last month, counting class name learning times of different selected class data, sorting the selected class data from large to small according to the class name learning times to obtain selected class data sorting data, and sorting the selected class data corresponding to the class name data according to the time of learning different class name data of the user in the authorized account within the period of time, specifically: selecting selected class data corresponding to class name data learned for the first time, marking the selected class data as i, sequentially identifying backwards according to time sequence, selecting class name data corresponding to the selected class data appearing for the second time, the third time and the fourth time and corresponding learning times, marking the corresponding learning times as A1, marking the appearance times corresponding to the selected i as A2, and substituting A1 and A2 into a calculation formula according to a rule calculation formula A1= A2 u1+ n1, when A1 and A2 accord with the rule calculation formula, judging the learning class rule of the user to generate a rule signal, otherwise, judging the learning irregularity of the user to generate an irregularity signal, wherein u1 and n1 are preset values;
selecting class hour data and school hour data corresponding to the authorized account within a period of time according to a calculation formula: class ratio = class data/class data, calculates a plurality of class ratios, calculates the mean value of the class ratios, calculates the class ratio mean value, and sorts the time measurement data and the score measurement data from large to small, thereby obtaining time measurement sorting data and score measurement sorting data.
Further, the specific processing procedure of the learning period processing unit is:
the method comprises the steps of selecting course duration data corresponding to course name data learned by an authorized account every time within a period of time, calculating the sum of the duration of the course duration data, eliminating the day with the learning duration of zero for a user within the period of time, selecting the remaining days, calibrating the remaining days as the learning days, calculating the total duration of the learning courses of the user each day, calculating the mean value of the total duration of the learning courses of the user within the period of time and the learning days, and calculating the learning time mean value.
Further, the specific recommendation processing procedure in the course planning unit is as follows:
according to the rule signals and the irrational signals, class recommendation is carried out, and when the rule signals are identified, class data are selected according to the corresponding rule calculation formula for recommendation, and the method specifically comprises the following steps:
collecting the time point of the current day and the time point of the first learning of the authorized account, calculating the difference between the time point of the current day and the time point of the first learning of the authorized account, calculating the time difference, substituting the time difference for A1 into a rule calculation formula A1= A2 × u1+ n1, calculating the selected class data needed to be learned by the client, and marking the calculated selected class data as recommended class data;
selecting corresponding course name data according to the recommended course data, removing the course name data which are learned by the user in an authorized account in the course name data, selecting the residual course name data and marking as the recommended course data, extracting the course duration data corresponding to the recommended course data and marking as the recommended duration, and according to the calculation formula: mean value of learning hour = total of recommended duration of learning class is compared mean value, carry out the combination derivation one by one to recommended course data, thereby calculate a plurality of course name data in the recommended course data, carry out numerical processing with the sequencing of a plurality of course name data in time measurement sequencing data and branch measurement sequencing data, the sequencing number in the selected a plurality of course name data, and sum and mark as recommended sequencing value sequencing number with the sequencing number of both, thereby obtain a plurality of recommended sequencing value, a plurality of corresponding course name data that the numerical value of selected recommended sequencing value is minimum, concrete formula of calculation does: the recommended ranking value = sequence number of the measured ranking data, a first weight coefficient and a sequence number of the measured sorting data, and a second weight coefficient, wherein the first weight coefficient refers to the influence ratio of the sequence number of the measured ranking data to the recommended ranking value, the second weight coefficient refers to the influence ratio of the sequence number of the measured sorting data to the recommended ranking value, the first weight coefficient and the second weight coefficient are preset values, and the corresponding plurality of course name data with the minimum numerical value of the recommended ranking value are marked as the current recommended course data;
when an illegal signal is identified, carrying out course recommendation according to the selected course data sorting data, which specifically comprises the following steps:
the course name data and the course duration data in every classification in the class data sequencing data are selected in the extraction, and the course name data is matched with the course name data, when the matching result is consistent, the corresponding course name data is proposed, when the matching result is inconsistent, the corresponding course name data is extracted and marked as a preselection course according to the calculation formula: the learning time mean = the total of the time of the pre-selected courses and the learning course proportion mean, the pre-selected courses and the corresponding course time data are calculated, the total of the time of the pre-selected courses is represented as the time total of the course time data corresponding to the pre-selected courses, and therefore the course name data in a plurality of pre-selected courses are calculated and deduced and marked as qualified courses;
calculating the planning course of the qualified course according to the time measurement sequencing data, the test score sequencing data and the selected course data sequencing data, wherein the specific calculation formula is as follows: the planning sorting value = (sequence number of time measurement sorting data + sequence number of minute sorting data + sequence number of course selection data + fifth weight coefficient) deviation regulating factor, wherein the third weight coefficient refers to the influence ratio of the sequence number of the time measurement sorting data to the planning sorting value, the fourth weight coefficient refers to the influence ratio of the sequence number of the minute sorting data to the planning sorting value, the fifth weight coefficient refers to the influence ratio of the course selection data sorting data to the planning sorting value, the third weight coefficient, the fourth weight coefficient and the fifth weight coefficient are preset values, the deviation regulating factor is a preset value, and the first qualified course in the planning sorting value is marked as the current planning course data.
The invention has the beneficial effects that:
(1) the safety of the user account is improved by inputting and verifying the authorized account and the corresponding password, information leakage caused by account loss is avoided, and data analysis is carried out according to the login habit of the user and the previous login data in protection verification, so that the account is prevented from being used by others, and the deviation of the result of the recommended course of the system is influenced;
(2) the learning condition of students in the authorized account and the network course information are subjected to period teaching analysis, and regular judgment is carried out according to the learning types of the users, so that whether the learning of the users is regular learning or not and the time consumed by the users in each actual learning are judged, a plurality of recommended courses are selected and recommended according to the result of all data integration analysis, the courses required by the users are planned better, the time consumed by the users in course selection is saved, and the working efficiency is improved.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a system block diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention is an internet-based network course planning management system, including a course planning management platform, a server is disposed in the course planning management platform, and the server is connected to a time period teaching analysis unit, a learning time period processing unit, a teaching account management and control unit, and a course planning unit in a communication manner;
the teaching account management and control unit is internally provided with a login unit, a verification unit, a recording unit and a safety protection unit, the login unit is used for a user to login an authorization account and a corresponding personal password required by a network course, the authorization account and the corresponding personal password required by the network course are transmitted to the verification unit, a recording account and a recording password required by the user to login the network course in the past are stored in the recording unit, the verification unit acquires the recording account and the recording password required by the user to login the network course in the past from the recording unit, and account verification is performed on the recording account and the authorization account and the corresponding personal password required by the network course, and the specific process of account verification is as follows:
matching the authorized account number with the recorded account number, judging that the authorized account number does not exist when the account number corresponding to the authorized account number is not matched from the recorded account number, generating an account number registration signal, judging that the authorized account number exists when the account number corresponding to the authorized account number is matched from the recorded account number, generating a password verification signal, identifying the account number registration signal and the password verification signal, skipping to a login unit for account number registration when the account number registration signal is identified, and extracting a corresponding recorded password and a personal password and performing password verification when the password verification signal is identified, specifically:
matching the recorded password with the personal password, judging that the password is correct when the matching result of the recorded password and the personal password is completely consistent, generating a safety verification signal, judging that the password is wrong when the matching result of the recorded password and the personal password is inconsistent, generating a password error signal, identifying the safety verification signal and the password error signal, automatically jumping to a login unit for logging in again when the password error signal is identified, and transmitting the safety verification signal to a safety protection unit when the safety verification signal is identified;
the safety protection unit carries out input data acquisition on the personal password according to the safety verification signal and carries out protection verification according to the input data, and the method specifically comprises the following steps:
collecting the interval time between every two password inputs during password input and marking as input time data, collecting the force received by the case corresponding to each password during password input and marking as input force data, collecting the distance between every two password cases during password input and marking as input distance data, collecting the digit of the password during password input and marking as secret data, collecting the type of each password during password input and marking as secret data, the secret data comprises a numeric password, a letter password and a punctuation password, and the punctuation password comprises all characters except letters and numbers, for example: "@", "β", "/", "-", etc.;
extracting secret data and secret bit data, sequentially marking a digital password, a letter password and a punctuation password in the secret data as Sm, Zm and Bm, and marking each character in a personal password according to the secret bit data as Sm, Zm and Bm, so that a password combination mark Mz is obtained, and the Mz is similar: sm, Zm, Bm;
extracting a plurality of time-transmission data, carrying out mean value calculation on the plurality of time-transmission data, calculating a time-transmission mean value, carrying out difference value calculation on the plurality of time-transmission data and the time-transmission mean value respectively, calculating a plurality of time-transmission difference values, and carrying out positive and negative value marking on the plurality of time-transmission difference values, wherein the specific steps are as follows: the method comprises the following steps of marking the time-input difference value which is more than or equal to zero as a positive difference value, marking the time-input difference value which is less than zero as a negative difference value, extracting secret data of two passwords corresponding to each time-input difference value, and judging the influence of the positive difference value, the negative difference value and the secret data, wherein the method specifically comprises the following steps:
when two adjacent passwords are the same type, the two adjacent passwords are marked as a first-class password, when the two adjacent passwords are different types, the two adjacent passwords are marked as a second-class password, the positive difference and the negative difference are matched with the second-class passwords of the first-class password, the times of simultaneous occurrence of the second-class password and the positive difference are matched, the positive differences corresponding to the times of simultaneous occurrence are subjected to mean value calculation, a positive difference mean value is calculated, and the first-class password and the negative difference value are processed according to the same processing method to obtain a negative difference mean value;
extracting a plurality of force input data and a plurality of distance input data, and bringing the plurality of force input data and the plurality of distance input data into a preset relational expression: distance data, namely distance influence values = force data, calculating a plurality of distance influence values, carrying out average calculation on the plurality of distance influence values, and calculating a distance influence average;
according to the calculation formula:
calculating a password input scoreThe values Dpi, Ssi are expressed as input time data, Zci is expressed as a positive difference mean value, Fci is expressed as a negative difference mean value, e1 is expressed as a weight coefficient of an actual input time, Sji is expressed as input distance data, Jji is expressed as a distance influence mean value, Sli is expressed as input force data, e2 is expressed as an input deviation adjustment factor, i =1,2,3.... n, and n is a positive integer, and the calculated values of the calculation formula are all numbers processed through quantization and do not carry units, so that the class quantities are uniform;
comparing the password input score value Dpi with a preset value M1, judging that the password input is safe when the Dpi is larger than M1, generating a safety passing signaling, and transmitting the safety passing signaling to a server;
the server automatically jumps to the period teaching analysis unit through the signaling according to the safety, and the storage has the relevant network course information of network course in the server, with network course information transmission to period teaching analysis unit, carries out period teaching analysis, specifically is to student's the study condition and network course information in this authorization account number through period teaching analysis unit:
collecting courses learned by a user during learning in an authorization account and marking the courses as course name data, collecting the duration of the courses learned by the user during learning in the authorization account and marking the duration as class time data, collecting the time length required by the courses during learning of the user in the authorization account and marking the time length as learning time data, collecting the duration of post-course testing of the user during learning in the authorization account and marking the duration as time measuring data, and collecting the scores of post-course testing of the user in the authorization account and marking the scores as score measuring data;
the network course information comprises course classification data, course name data and course duration data, the course classification data internally comprises a plurality of classes, and the course name data belongs to the corresponding course classification data;
the method comprises the steps of selecting class name data, class hour data, time measurement data and score measurement data learned by a user in an authorized account within a period of time, matching the class name data with class classification data, matching corresponding class classification data and marking as selected class data, and counting all selected class data and class name data, wherein the period of time refers to a time period from the first day of the month to the last day of the month, counting class name learning times of different selected class data, sequencing the selected class data according to the class name learning times from large to small to obtain selected class data sequencing data, and sequencing the selected class data corresponding to the class name data according to the time for the user to learn different class name data in the authorized account within the period of time, wherein the sequence is as follows: selecting selected class data corresponding to class name data learned for the first time, marking the selected class data as i, sequentially identifying backwards according to time sequence, selecting class name data corresponding to the selected class data appearing for the second time, the third time and the fourth time and corresponding learning times, marking the corresponding learning times as A1, marking the appearance times corresponding to the selected i as A2, and substituting A1 and A2 into a calculation formula according to a rule calculation formula A1= A2 u1+ n1, when A1 and A2 accord with the rule calculation formula, judging the learning class rule of the user to generate a rule signal, otherwise, judging the learning irregularity of the user to generate an irregularity signal, wherein u1 and n1 are preset values;
selecting class hour data and school hour data corresponding to the authorized account within a period of time according to a calculation formula: the class learning ratio = class learning data/class time data, a plurality of class learning ratios are calculated, the average value of the class learning ratios is calculated, the class learning ratio average value is calculated, and the time measurement data and the score measurement data are sorted from big to small, so that time measurement sorting data and score measurement sorting data are obtained;
the server generates a time interval learning signaling and sends the time interval learning signaling to the learning time interval processing unit, and the learning time interval processing unit performs time interval learning processing on user learning time corresponding to the authorized account according to the learning time interval processing unit, specifically:
selecting course time length data corresponding to course name data learned by an authorized account every time within a period of time, calculating the sum of the time lengths of the course time length data, eliminating the day with the learning time length of a user being zero within the period of time, selecting the remaining days, calibrating the remaining days as the learning days, calculating the total time length of the learning courses of the user each day, calculating the mean value of the total time length of the learning courses of the user within the period of time and the learning days, and calculating the mean value of learning time;
the server generates a class pushing signaling and transmits the class pushing signaling to the course planning unit, and the course planning unit carries out course recommendation according to the class pushing signaling, specifically comprising:
according to the rule signals and the irrational signals, class recommendation is carried out, and when the rule signals are identified, class data are selected according to the corresponding rule calculation formula for recommendation, and the method specifically comprises the following steps:
collecting the time point of the current day and the time point of the first learning of the authorized account, calculating the difference between the time point of the current day and the time point of the first learning of the authorized account, calculating the time difference, substituting the time difference for A1 into a rule calculation formula A1= A2 × u1+ n1, calculating the selected class data required to be learned by the client, and marking the calculated selected class data as recommended class data;
selecting corresponding course name data according to the recommended course data, removing the course name data which are learned by the user in an authorized account in the course name data, selecting the residual course name data and marking as the recommended course data, extracting the course duration data corresponding to the recommended course data and marking as the recommended duration, and according to the calculation formula: mean value of learning hour = total of recommended duration of learning class is compared mean value, carry out the combination derivation one by one to recommended course data, thereby calculate a plurality of course name data in the recommended course data, carry out numerical processing with the sequencing of a plurality of course name data in time measurement sequencing data and branch measurement sequencing data, the sequencing number in the selected a plurality of course name data, and sum and mark as recommended sequencing value sequencing number with the sequencing number of both, thereby obtain a plurality of recommended sequencing value, a plurality of corresponding course name data that the numerical value of selected recommended sequencing value is minimum, concrete formula of calculation does: the recommended ranking value = sequence number of the measured ranking data, a first weight coefficient and a sequence number of the measured sorting data, and a second weight coefficient, wherein the first weight coefficient refers to the influence ratio of the sequence number of the measured ranking data to the recommended ranking value, the second weight coefficient refers to the influence ratio of the sequence number of the measured sorting data to the recommended ranking value, the first weight coefficient and the second weight coefficient are preset values, and the corresponding plurality of course name data with the minimum numerical value of the recommended ranking value are marked as the current recommended course data;
when an illegal signal is identified, carrying out course recommendation according to the selected course data sorting data, which specifically comprises the following steps:
the course name data and the course duration data in every classification in the class data sequencing data are selected in the extraction, and the course name data is matched with the course name data, when the matching result is consistent, the corresponding course name data is proposed, when the matching result is inconsistent, the corresponding course name data is extracted and marked as a preselection course according to the calculation formula: the learning time mean = the total of the time of the pre-selected courses and the learning course proportion mean, the pre-selected courses and the corresponding course time data are calculated, the total of the time of the pre-selected courses is represented as the time total of the course time data corresponding to the pre-selected courses, and therefore the course name data in a plurality of pre-selected courses are calculated and deduced and marked as qualified courses;
calculating the planning course of the qualified course according to the time measurement sequencing data, the test score sequencing data and the selected course data sequencing data, wherein the specific calculation formula is as follows: the planning sorting value = (the serial number of the time measurement sorting data is the third weight coefficient, the serial number of the time measurement sorting data is the fourth weight coefficient, the serial number of the selection class data is the fifth weight coefficient), the deviation regulating factor is set, wherein the third weight coefficient refers to the influence ratio of the serial number of the time measurement sorting data to the planning sorting value, the fourth weight coefficient refers to the influence ratio of the serial number of the detection sorting data to the planning sorting value, the fifth weight coefficient refers to the influence ratio of the selection class data sorting data to the planning sorting value, the third weight coefficient, the fourth weight coefficient and the fifth weight coefficient are preset values, the deviation regulating factor is a preset value, and the first qualified course in the planning sorting value is marked as the current planning course data;
the server carries out course recommendation according to the course recommendation data or the planning course data;
all numerical values of the calculation formula are processed in a quantification mode, only corresponding numerical values are extracted, units are not carried, meanwhile, related data are collected in advance through a collection unit, and the collection method can be achieved through the prior art.
When the invention works, a user inputs an authorized account and a corresponding password through the teaching account management and control unit, the verification unit performs form matching of the account and the password according to the authorized account and the corresponding password input by the user, re-logs in when the matching result is inconsistent, extracts the related data when the authorized account and the corresponding password log in when the matching result is consistent, performs security verification of the account according to the related data, thereby increasing the security of the account, performs time-interval teaching analysis on the learning condition of students in the authorized account and network course information through the time-interval teaching analysis unit after the verification is passed, performs rule judgment according to the learning type of the user, thereby judging whether the learning of the user is regular learning or not, and performs time-interval learning processing on the user learning time corresponding to the authorized account through the learning time-interval processing unit, the time consumed by each practical learning of the user is selected, the course planning unit carries out integration analysis and judgment according to the data analysis results of the time period teaching analysis unit and the learning time period processing unit, a plurality of recommended courses are selected, the recommended sequence value and the planned sequence value are calculated according to the positions of the recommended courses in the corresponding sequence, and the courses are recommended according to the recommended sequence value and the planned sequence value.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (5)
1. A network course planning management system based on the Internet is characterized by comprising a course planning management platform, wherein a server is arranged in the course planning management platform, and the server is in communication connection with a time interval teaching analysis unit, a learning time interval processing unit, a teaching account management and control unit and a course planning unit;
the user logs in and verifies an account number through the teaching account management and control unit, generates a safety passing signaling after logging in is successful, and jumps to the time-interval teaching analysis unit according to the safety passing signaling;
the network course information that the network course is relevant is stored in the server, with network course information transmission to period teaching analysis unit, carry out period teaching analysis to student's study condition in this authorization account number and network course information through period teaching analysis unit, the server generates period learning signaling and sends to study period processing unit, and study period processing unit carries out period learning processing to the user study time that the authorization account number corresponds according to study period processing unit, the server generates the class and pushes away the signaling and transmit to the course planning unit, the course planning unit pushes away the signaling according to the class and carries out the course and recommends.
2. The internet-based network course planning management system of claim 1, wherein a user logs in an authorized account and a corresponding personal password required for network courses in a teaching account management and control unit, performs account verification on the authorized account and the corresponding personal password required for logging in the network courses and a recorded account and a recorded password required for logging in the network courses previously stored in the teaching account management and control unit, generates a security verification signal or a password error signal according to a verification result, identifies the security verification signal or the password error signal, jumps to a login interface if the password error signal is identified, and performs protection verification when the security verification signal is identified, specifically:
collecting the interval time between every two password inputs during password input and marking as input time data, collecting the force received by the case corresponding to each password during password input and marking as input force data, collecting the distance between every two password cases during password input and marking as input distance data, collecting the digit of the password during password input and marking as secret data, collecting the type of each password during password input and marking as secret data, the secret data comprises a numeric password, a letter password and a punctuation password, and the punctuation password comprises all characters except letters and numbers, for example: "@", "β", "/", "-";
extracting secret data and secret bit data, sequentially marking a digital password, a letter password and a punctuation password in the secret data as Sm, Zm and Bm, and marking each character in a personal password according to the secret bit data as Sm, Zm and Bm, so that a password combination mark Mz is obtained, and the Mz is similar: sm, Zm, Bm;
extracting a plurality of time-transmission data, carrying out mean value calculation on the plurality of time-transmission data, calculating a time-transmission mean value, carrying out difference value calculation on the plurality of time-transmission data and the time-transmission mean value respectively, calculating a plurality of time-transmission difference values, and carrying out positive and negative value marking on the plurality of time-transmission difference values, wherein the specific steps are as follows: marking the time-input difference value which is more than or equal to zero as a positive difference value, marking the time-input difference value which is less than zero as a negative difference value, extracting the secret data of two passwords corresponding to each time-input difference value, and judging the influence on the positive difference value, the negative difference value and the secret data, specifically:
when two adjacent passwords are the same type, the two adjacent passwords are marked as a first-class password, when the two adjacent passwords are different types, the two adjacent passwords are marked as a second-class password, the positive difference value and the negative difference value are matched with the second-class passwords of the first-class password, the times of simultaneous occurrence of the second-class password and the positive difference value are matched, the positive difference values corresponding to the times of simultaneous occurrence are subjected to mean value calculation, a positive difference mean value is calculated, and the first-class password and the negative difference value are processed according to the same processing method to obtain a negative difference mean value;
extracting a plurality of force input data and a plurality of distance input data, and bringing the plurality of force input data and the plurality of distance input data into a preset relational expression: distance data, namely distance influence values = force data, calculating a plurality of distance influence values, carrying out average calculation on the plurality of distance influence values, and calculating a distance influence average;
according to the calculation formula:
calculating password input score values, wherein Ssi represents input time data, Zci represents a positive difference mean value, Fci represents a negative difference mean value, e1 represents a weight coefficient of actual input time, Sji represents input distance data, Jji represents a distance influence mean value, Sli represents input force data, e2 represents an input deviation adjustment factor, i =1,2,3.. once.n, and n is a positive integer, and the calculated numerical values of the calculation formula are numbers subjected to quantization processing and do not carry units, so that the class quantities are uniform;
and comparing the password input score value Dpi with a preset value M1, and judging that the password input is safe when the Dpi is larger than M1 to generate a safety pass signaling.
3. The system as claimed in claim 1, wherein the time-interval teaching analysis unit comprises a specific analysis process:
collecting courses learned by a user during learning in an authorization account and marking the courses as course name data, collecting the duration of the courses learned by the user during learning in the authorization account and marking the duration as class time data, collecting the time length required by the courses during learning of the user in the authorization account and marking the time length as learning time data, collecting the duration of post-course testing of the user during learning in the authorization account and marking the duration as time measuring data, and collecting the scores of post-course testing of the user in the authorization account and marking the scores as score measuring data;
the network course information comprises course classification data, course name data and course duration data, the course classification data comprises a plurality of classifications, and the course name data belongs to the corresponding course classification data;
selecting class name data, class time data, time measurement data and score measurement data learned by a user in an authorized account within a period of time, matching the class name data with the class classification data, matching corresponding class classification data and marking as selected class data, and counting all selected class data and class name data, wherein the period of time refers to a time period from the first day of the last month to the last day of the last month, counting class name learning times of different selected class data, sorting the selected class data from large to small according to the class name learning times to obtain selected class data sorting data, and sorting the selected class data corresponding to the class name data according to the time of learning different class name data of the user in the authorized account within the period of time, specifically: selecting selected class data corresponding to class name data learned for the first time, marking the selected class data as i, sequentially identifying backwards according to time sequence, selecting class name data corresponding to the selected class data appearing for the second time, the third time and the fourth time and corresponding learning times, marking the corresponding learning times as A1, marking the appearance times corresponding to the selected i as A2, and substituting A1 and A2 into a calculation formula according to a rule calculation formula A1= A2 u1+ n1, when A1 and A2 accord with the rule calculation formula, judging the learning class rule of the user to generate a rule signal, otherwise, judging the learning irregularity of the user to generate an irregularity signal, wherein u1 and n1 are preset values;
selecting class hour data and school hour data corresponding to the authorized account within a period of time according to a calculation formula: class ratio = class data/class data, calculates a plurality of class ratios, calculates the mean value of the class ratios, calculates the class ratio mean value, and sorts the time measurement data and the score measurement data from large to small, thereby obtaining time measurement sorting data and score measurement sorting data.
4. The internet-based network lesson planning management system as claimed in claim 1, wherein said learning period processing unit comprises the following steps:
the method comprises the steps of selecting course duration data corresponding to course name data learned by an authorized account every time within a period of time, calculating the sum of the duration of the course duration data, eliminating the day with the learning duration of zero for a user within the period of time, selecting the remaining days, calibrating the remaining days as the learning days, calculating the total duration of the learning courses of the user each day, calculating the mean value of the total duration of the learning courses of the user within the period of time and the learning days, and calculating the learning time mean value.
5. The internet-based network curriculum planning management system of claim 1, wherein the specific recommendation process in the curriculum planning unit is:
the course class recommendation is carried out according to the regular signals and the irregular signals, and when the regular signals are identified, course class data are selected according to the corresponding regular calculation formula for recommendation, and the method specifically comprises the following steps:
collecting the time point of the current day and the time point of the first learning of the authorized account, calculating the difference between the time point of the current day and the time point of the first learning of the authorized account, calculating the time difference, substituting the time difference for A1 into a rule calculation formula A1= A2 × u1+ n1, calculating the selected class data needed to be learned by the client, and marking the calculated selected class data as recommended class data;
selecting corresponding course name data according to the recommended course data, removing the course name data which are learned by the user in an authorized account in the course name data, selecting the residual course name data and marking as the recommended course data, extracting the course duration data corresponding to the recommended course data and marking as the recommended duration, and according to the calculation formula: mean value of learning hour = total of recommended duration of learning class is compared mean value, carry out the combination derivation one by one to recommended course data, thereby calculate a plurality of course name data in the recommended course data, carry out numerical processing with the sequencing of a plurality of course name data in time measurement sequencing data and branch measurement sequencing data, the sequencing number in the selected a plurality of course name data, and sum and mark as recommended sequencing value sequencing number with the sequencing number of both, thereby obtain a plurality of recommended sequencing value, a plurality of corresponding course name data that the numerical value of selected recommended sequencing value is minimum, concrete formula of calculation does: the recommended ranking value = sequence number of the ranking data under measurement x a first weight coefficient + sequence number of the ranking data under measurement x a second weight coefficient, wherein the first weight coefficient refers to the influence ratio of the sequence number of the ranking data under measurement to the recommended ranking value, the second weight coefficient refers to the influence ratio of the sequence number of the ranking data under measurement to the recommended ranking value, the first weight coefficient and the second weight coefficient are both preset values, and the corresponding plurality of course name data with the minimum numerical value of the recommended ranking value are marked as the current recommended course data;
when an illegal signal is identified, carrying out course recommendation according to the selected course data sorting data, which specifically comprises the following steps:
the course name data and the course duration data in every classification in the class data sequencing data are selected in the extraction, and the course name data is matched with the course name data, when the matching result is consistent, the corresponding course name data is proposed, when the matching result is inconsistent, the corresponding course name data is extracted and marked as a preselection course according to the calculation formula: the learning time mean = the total of the time of the pre-selected courses and the learning course proportion mean, the pre-selected courses and the corresponding course time data are calculated, the total of the time of the pre-selected courses is represented as the time total of the course time data corresponding to the pre-selected courses, and therefore the course name data in a plurality of pre-selected courses are calculated and deduced and marked as qualified courses;
calculating the planning course of the qualified course according to the time measurement sequencing data, the test score sequencing data and the selected course data sequencing data, wherein the specific calculation formula is as follows: the planning sorting value = (sequence number of time measurement sorting data + sequence number of minute sorting data + sequence number of course selection data + fifth weight coefficient) deviation regulating factor, wherein the third weight coefficient refers to the influence ratio of the sequence number of the time measurement sorting data to the planning sorting value, the fourth weight coefficient refers to the influence ratio of the sequence number of the minute sorting data to the planning sorting value, the fifth weight coefficient refers to the influence ratio of the course selection data sorting data to the planning sorting value, the third weight coefficient, the fourth weight coefficient and the fifth weight coefficient are preset values, the deviation regulating factor is a preset value, and the first qualified course in the planning sorting value is marked as the current planning course data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210170811.0A CN114626694B (en) | 2022-02-24 | 2022-02-24 | Network course planning management system based on internet |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210170811.0A CN114626694B (en) | 2022-02-24 | 2022-02-24 | Network course planning management system based on internet |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114626694A true CN114626694A (en) | 2022-06-14 |
| CN114626694B CN114626694B (en) | 2022-11-08 |
Family
ID=81900451
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210170811.0A Active CN114626694B (en) | 2022-02-24 | 2022-02-24 | Network course planning management system based on internet |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114626694B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115082269A (en) * | 2022-07-18 | 2022-09-20 | 华北理工大学 | Big data based teaching planning method and system |
| CN116070885A (en) * | 2023-04-03 | 2023-05-05 | 深圳市摩天之星企业管理有限公司 | User course learning progress monitoring system for online learning platform |
| CN117196909A (en) * | 2023-11-03 | 2023-12-08 | 湖南强智科技发展有限公司 | University course arrangement method, system, equipment and medium based on custom classification |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201674507U (en) * | 2009-10-19 | 2010-12-15 | 北京微通新成网络科技有限公司 | Personal identification system based on keystroke dynamics of users |
| CN108182489A (en) * | 2017-12-25 | 2018-06-19 | 浙江工业大学 | Method is recommended in a kind of individualized learning based on on-line study behavioural analysis |
| CN111163087A (en) * | 2019-12-30 | 2020-05-15 | 南宁一站网网络技术有限公司 | Database safety protection system based on data acquisition |
| CN111445365A (en) * | 2020-04-13 | 2020-07-24 | 申珊珊 | Online distance education system |
| AU2020102927A4 (en) * | 2020-10-21 | 2020-12-17 | The Trustee for TOP INVESTMENT TRUST | Method and process for delivering a rewards based online cyber security education course via an integrated automated learning management system |
| CN112214670A (en) * | 2020-10-09 | 2021-01-12 | 平安国际智慧城市科技股份有限公司 | Online course recommendation method and device, electronic equipment and storage medium |
| CN113254978A (en) * | 2021-06-24 | 2021-08-13 | 国能大渡河大数据服务有限公司 | Data security management system based on machine learning |
| CN113256464A (en) * | 2021-05-28 | 2021-08-13 | 智慧校园(广东)教育科技有限公司 | Online education course automatic distribution system and method based on cloud computing |
| CN113792248A (en) * | 2021-11-16 | 2021-12-14 | 深圳华埔之星科技有限公司 | Online education course sharing and distributing system based on Internet and mobile terminal |
-
2022
- 2022-02-24 CN CN202210170811.0A patent/CN114626694B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201674507U (en) * | 2009-10-19 | 2010-12-15 | 北京微通新成网络科技有限公司 | Personal identification system based on keystroke dynamics of users |
| CN108182489A (en) * | 2017-12-25 | 2018-06-19 | 浙江工业大学 | Method is recommended in a kind of individualized learning based on on-line study behavioural analysis |
| CN111163087A (en) * | 2019-12-30 | 2020-05-15 | 南宁一站网网络技术有限公司 | Database safety protection system based on data acquisition |
| CN111445365A (en) * | 2020-04-13 | 2020-07-24 | 申珊珊 | Online distance education system |
| CN112214670A (en) * | 2020-10-09 | 2021-01-12 | 平安国际智慧城市科技股份有限公司 | Online course recommendation method and device, electronic equipment and storage medium |
| AU2020102927A4 (en) * | 2020-10-21 | 2020-12-17 | The Trustee for TOP INVESTMENT TRUST | Method and process for delivering a rewards based online cyber security education course via an integrated automated learning management system |
| CN113256464A (en) * | 2021-05-28 | 2021-08-13 | 智慧校园(广东)教育科技有限公司 | Online education course automatic distribution system and method based on cloud computing |
| CN113254978A (en) * | 2021-06-24 | 2021-08-13 | 国能大渡河大数据服务有限公司 | Data security management system based on machine learning |
| CN113792248A (en) * | 2021-11-16 | 2021-12-14 | 深圳华埔之星科技有限公司 | Online education course sharing and distributing system based on Internet and mobile terminal |
Non-Patent Citations (2)
| Title |
|---|
| X.JIN等: "Problem-Based Practical Training Awared Course Recommendation System", 《2021 9TH INTERNATIONAL CONFERENCE ON ORANGE TECHNOLOGY (ICOT)》 * |
| 胡园园 等: "一种结合用户适合度和课程搭配度的在线课程推荐方法", 《计算机研究与发展》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115082269A (en) * | 2022-07-18 | 2022-09-20 | 华北理工大学 | Big data based teaching planning method and system |
| CN116070885A (en) * | 2023-04-03 | 2023-05-05 | 深圳市摩天之星企业管理有限公司 | User course learning progress monitoring system for online learning platform |
| CN117196909A (en) * | 2023-11-03 | 2023-12-08 | 湖南强智科技发展有限公司 | University course arrangement method, system, equipment and medium based on custom classification |
| CN117196909B (en) * | 2023-11-03 | 2024-04-05 | 湖南强智科技发展有限公司 | University course arrangement method, system, equipment and medium based on custom classification |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114626694B (en) | 2022-11-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114626694B (en) | Network course planning management system based on internet | |
| CN113256464B (en) | Online education course automatic distribution system and method based on cloud computing | |
| CN112862643A (en) | Multimedia remote education platform system | |
| US6751440B2 (en) | Apparatus and method for selecting target educational course | |
| CN116797267B (en) | Distributed market data acquisition management system for equity investment | |
| CN113792248B (en) | Online education course sharing and distributing system based on Internet and mobile terminal | |
| CN110751861B (en) | Network remote education system based on cloud platform | |
| CN106652621A (en) | Automotive soldier driving subject checking and judging system | |
| CN113947270A (en) | Method for improving crowdsourcing task labeling quality | |
| CN115659369A (en) | User unified management system based on user operation habits | |
| CN116896452B (en) | Computer network information security management method and system based on data processing | |
| Clark et al. | Condensed mastery profile method for setting standards for diagnostic assessment systems | |
| CN116596395B (en) | Operation quality control platform for engineering project evaluation unit guidance and detection | |
| CN112951022A (en) | Multimedia interactive education training system | |
| CN116384947A (en) | Publication issuing monitoring management system and method based on big data | |
| KR101129601B1 (en) | A system providing a setting question by an incorrect answer and method thereof | |
| CN113780798B (en) | Key index display system based on cloud computing | |
| CN112700356B (en) | Method and system for managing information of training personnel for real-time online education and training | |
| CN113094404B (en) | Big data acquisition multi-core parameter self-adaptive time-sharing memory driving method and system | |
| CN114943625A (en) | Remote education system based on block chain technology | |
| CN114881827A (en) | Remote online education training method and system based on Internet and storage medium | |
| CN103514288B (en) | Client-class recognition methods and system | |
| CN111209588A (en) | Teaching and cultivating management method and system based on block chain | |
| CN114978414B (en) | Data transmission method and system based on big data and non-orthogonal multiple access | |
| CN111538982B (en) | Multistage multi-element serial authentication method and system for smart education cloud platform |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20230609 Address after: 518000 9G04, Block A, Building 1, Anhongji Tianyao Square, Daling Community, Minzhi Street, Longhua District, Shenzhen, Guangdong Patentee after: Shenzhen Renrenshi Network Technology Co.,Ltd. Address before: 518000 9g01, block a, building 1, anhongji TIANYAO Plaza, Daling community, Minzhi street, Longhua District, Shenzhen, Guangdong Province Patentee before: SHENZHEN HUASHI BROTHERS EDUCATION TECHNOLOGY Co.,Ltd. |