User class hour accounting method and device of teaching and training system and computer equipment
By identifying and analyzing the user's login and course learning behavior, combining the abnormal identification strategy, the user's real course learning time is calculated, and the problem of low class time accounting accuracy in the existing technology is solved, and the accuracy and efficiency of accounting are improved.
Patent Information
- Application Number
- CN202510021443.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-07
- Publication Date
- 2025-05-02
AI Technical Summary
When calculating user course learning sessions, there are illegal operations such as class swiping, multi-IP login and automatic backend swiping, resulting in low class review accuracy, poor manual calculation accuracy, high error rate, and low accounting efficiency.
By obtaining the user's course record information and login record information, identifying the login authentication information and login feature information, combining the course learning duration distribution information, using the login exception recognition strategy and the abnormal class time recognition strategy, identifying the abnormal login and abnormal class time information, and finally calculating the user's real course learning duration.
It improves the accounting accuracy and efficiency of users' actual course learning class hours, avoids the high error rate and low accuracy of manual analysis, and realizes multi-angle comprehensive analysis of class time abnormalities and login abnormalities, enhancing the comprehensiveness and accuracy of accounting.
Smart Images
Figure CN119918039A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of class time calculation, and in particular to a method, device and computer equipment for calculating user class time of a teaching and training system. Background Art
[0002] When calculating course grades, the education and training system will comprehensively calculate the user's course study hours and test scores to determine the user's final course grade. However, when calculating the user's course study hours, there are often users who cheat on the course, log in with multiple IP addresses, and automatically cheat on the course in the background through programs in order to ensure the course study hours, which makes the accuracy of the course study hours review low. Therefore, how to improve the accuracy of the actual course study hours of users in the education and training system is the current research focus.
[0003] In the existing technology, each user's actual class hours are often determined manually after manually querying the background's login time, login IP, and login duration. However, the accuracy of manual measurement is poor and the error rate of manual calculation is high, resulting in low efficiency in calculating the user's actual course learning hours. Summary of the invention
[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for calculating user class hours in a teaching and training system in response to the above-mentioned technical problems.
[0005] In a first aspect, the present application provides a method for calculating user class hours in a teaching and training system, including:
[0006] Obtaining user course record information of each user of the teaching and training system and user login record information of each user, and for each user, identifying the user's login authentication information and user login feature information based on the user's user login information;
[0007] Based on the user course record information of the user, the course study time distribution information of the user is identified, and based on the login authentication information of the user and the user login feature information of the user, the abnormal login information of the user is identified through the login abnormality identification strategy;
[0008] Based on the course learning time distribution information of the user, the abnormal class time information of the user is identified through an abnormal class time identification strategy, and based on the user's various abnormal login information and the user's abnormal class time information, the user's actual course learning time is identified.
[0009] Optionally, the identifying each login authentication information of the user and the user login feature information of the user based on each user login information of the user includes:
[0010] For each user login information, based on the user login information of the user, identifying the login time point of the user, the login address information of the user, and the login verification information of the user;
[0011] Based on the login verification information of the user, identifying the login authority information of the user and the login verification method of the user, and using the login authority information of the user and the login verification method of the user as the user login authentication information corresponding to the user login information;
[0012] Based on the user's login time points and the user's login address information, the user's login distribution information and the user's address change distribution information are identified, and the user's login distribution information and the user's address change distribution information are used as the user's login feature information.
[0013] Optionally, the identifying the course study time distribution information of the user based on the user course record information of the user includes:
[0014] Splitting the user course record information into sub-course record information of each course study, and identifying the course study duration of each sub-course record information and the course study distribution information of each sub-course record information;
[0015] According to the course learning starting time point of each sub-course record information, the course learning duration of each sub-course record information is time-distributed to obtain the initial course learning duration distribution information of the user; and based on the course learning distribution information of each sub-course record information, the initial course learning duration distribution information is adjusted to obtain the course learning duration distribution information of the user.
[0016] Optionally, the identifying each abnormal login information of the user based on each login authentication information of the user and the user login feature information of the user through a login abnormality identification strategy includes:
[0017] Based on each login authentication information of the user, abnormal authentication information is screened, and user login information corresponding to the abnormal authentication information is used as each first abnormal login information of the user;
[0018] Based on the login distribution information of the user and the address change distribution information of the user, identifying the login frequency change information of the user and the address change frequency change information of the user, and based on the login frequency change information of the user and the address change frequency change information of the user, a login abnormality identification strategy is used to identify each second abnormal login information of the user;
[0019] Each of the first abnormal login information and each of the second abnormal login information is used as each of the abnormal login information of the user.
[0020] Optionally, the identifying abnormal class time information of the user based on the course learning time distribution information of the user through an abnormal class time identification strategy includes:
[0021] Based on the course learning time distribution information of the user, a course distribution fitting curve of the user is generated through a distribution fitting strategy, and based on the course distribution fitting curve of the user, a course learning time range of the user and course learning time trend information of the user are identified;
[0022] Based on the course learning time range of the user and the course learning time trend information of the user, in each user login information, the first abnormal class time information of the user is screened, and based on the course distribution fitting curve, the actual course learning time of each user login information is identified;
[0023] Based on the actual course learning time of each user login information, the second abnormal class information of the user is identified through the abnormal class identification strategy, and the first abnormal class information and the second abnormal class information are used as the abnormal class information of the user.
[0024] Optionally, the identifying the actual course study time of the user based on each abnormal login information of the user and the abnormal class time information of the user includes:
[0025] Based on the abnormal login information of the user and the abnormal class information of the user, the real login information of the user is screened in the login information of each user, and the real course learning time of the user is calculated based on the actual course learning time of each real login information of the user.
[0026] In a second aspect, the present application also provides a user class time accounting device of a teaching and training system, comprising:
[0027] An acquisition module, used to acquire user course record information of each user of the teaching and training system and user login record information of each user, and for each user, based on the user login information of the user, identify the user's login authentication information and the user login feature information of the user;
[0028] A first identification module is used to identify the course learning time distribution information of the user based on the user course record information of the user, and to identify each abnormal login information of the user through a login abnormality identification strategy based on each login authentication information of the user and the user login feature information of the user;
[0029] The second identification module is used to identify the abnormal class information of the user based on the course learning time distribution information of the user and through the abnormal class identification strategy, and to identify the actual course learning time of the user based on the user's various abnormal login information and the user's abnormal class information.
[0030] Optionally, the acquisition module is specifically used to:
[0031] For each user login information, based on the user login information of the user, identifying the login time point of the user, the login address information of the user, and the login verification information of the user;
[0032] Based on the login verification information of the user, identifying the login authority information of the user and the login verification method of the user, and using the login authority information of the user and the login verification method of the user as the user login authentication information corresponding to the user login information;
[0033] Based on the user's login time points and the user's login address information, the user's login distribution information and the user's address change distribution information are identified, and the user's login distribution information and the user's address change distribution information are used as the user's login feature information.
[0034] Optionally, the first identification module is specifically used to:
[0035] Splitting the user course record information into sub-course record information of each course study, and identifying the course study duration of each sub-course record information and the course study distribution information of each sub-course record information;
[0036] According to the course learning starting time point of each sub-course record information, the course learning duration of each sub-course record information is time-distributed to obtain the initial course learning duration distribution information of the user; and based on the course learning distribution information of each sub-course record information, the initial course learning duration distribution information is adjusted to obtain the course learning duration distribution information of the user.
[0037] Optionally, the first identification module is specifically used to:
[0038] Based on each login authentication information of the user, abnormal authentication information is screened, and user login information corresponding to the abnormal authentication information is used as each first abnormal login information of the user;
[0039] Based on the login distribution information of the user and the address change distribution information of the user, identifying the login frequency change information of the user and the address change frequency change information of the user, and based on the login frequency change information of the user and the address change frequency change information of the user, a login abnormality identification strategy is used to identify each second abnormal login information of the user;
[0040] Each of the first abnormal login information and each of the second abnormal login information is used as each of the abnormal login information of the user.
[0041] Optionally, the second identification module is specifically used to:
[0042] Based on the course learning time distribution information of the user, a course distribution fitting curve of the user is generated through a distribution fitting strategy, and based on the course distribution fitting curve of the user, a course learning time range of the user and course learning time trend information of the user are identified;
[0043] Based on the course learning time range of the user and the course learning time trend information of the user, in each user login information, the first abnormal class time information of the user is screened, and based on the course distribution fitting curve, the actual course learning time of each user login information is identified;
[0044] Based on the actual course learning time of each user login information, the second abnormal class information of the user is identified through the abnormal class identification strategy, and the first abnormal class information and the second abnormal class information are used as the abnormal class information of the user.
[0045] Optionally, the second identification module is specifically used to:
[0046] Based on the abnormal login information of the user and the abnormal class information of the user, the real login information of the user is screened in the login information of each user, and the real course learning time of the user is calculated based on the actual course learning time of each real login information of the user.
[0047] In a third aspect, the present application provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any one of the methods in the first aspect are implemented.
[0048] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.
[0049] In a fifth aspect, the present application provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.
[0050] The user class hour accounting method, device and computer equipment of the above-mentioned teaching and training system obtain the user course record information of each user of the teaching and training system and the user login record information of each user, and for each user, identify the user's login authentication information and the user login feature information based on the user's login information; identify the user's course learning time distribution information based on the user's user course record information, and identify the user's abnormal login information through a login abnormality identification strategy based on the user's login authentication information and the user login feature information; identify the user's abnormal class hour information through an abnormal class hour identification strategy based on the user's course learning time distribution information, and identify the user's actual course learning time based on the user's abnormal login information and the user's abnormal class hour information. This solution, by identifying the user's login authentication information, login feature information, and course learning market segment information from the user's course record information and user login record information, comprehensively identifies the user's abnormal login information from the abnormal information in the course learning time and the abnormal login information when the user logs in, which not only avoids the high error rate and low precision of manual analysis, but also can comprehensively and comprehensively analyze the user's course time anomalies and login anomalies from multiple angles, avoiding the one-sidedness and omission of manual analysis. And by analyzing the user's behavior, a targeted method for distinguishing abnormal operations is designed, which improves the accuracy of identifying abnormal course hours and logins, thereby comprehensively improving the accuracy and efficiency of calculating the user's actual course learning hours. BRIEF DESCRIPTION OF THE DRAWINGS
[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0052] Figure 1 A schematic diagram of a flow chart of a method for calculating user class hours in a teaching and training system in one embodiment;
[0053] Figure 2 A schematic diagram of a process flow of an example of user class hour accounting of a teaching and training system in one embodiment;
[0054] Figure 3 This is a structural block diagram of a user class time calculation device of a teaching and training system in one embodiment;
[0055] Figure 4 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION
[0056] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0057] The user class time accounting method of the teaching and training system provided in the embodiment of the present application can be applied to the application environment of the user class time accounting of the teaching and training system. Among them, the method can be applied to the terminal, can also be applied to the server, and can also be applied to the system including the terminal and the server, and is realized through the interaction between the terminal and the server. Among them, the terminal can be, but not limited to, various personal computers, laptops, medium-sized computers, etc. Among them, the terminal identifies the user's login authentication information, login feature information, and course learning market segment information from the user's user course record information and user login record information, thereby comprehensively identifying the user's abnormal login information from the abnormal information existing in the course learning time and the abnormal login information existing when the user logs in, which not only avoids the high error rate of manual analysis and the low accuracy of manual analysis, but also this solution can comprehensively and comprehensively analyze the user's class time anomaly and login anomaly from multiple angles, avoiding the one-sidedness of manual analysis and the omission of analysis. And by analyzing the user's behavior, the judgment method of abnormal operation is designed in a targeted manner, which improves the accuracy of identifying abnormal class hours and logins, thereby comprehensively improving the accuracy and efficiency of calculating the user's actual course learning class hours.
[0058] In an exemplary embodiment, Figure 1 As shown, a method for calculating user class hours in a teaching and training system is provided, which is described by taking the method applied to a terminal as an example, and includes the following steps S101 to S103.
[0059] in:
[0060] Step S101, obtaining the user course record information of each user of the teaching and training system and the user login record information of each user, and for each user, identifying the user's login authentication information and the user login feature information based on the user's user login information.
[0061] In this embodiment, the terminal collects the user course record information of each user and the user login record information of each user by querying the user database of the teaching and training system, wherein the user course record information includes the course duration of each course study of the user. The user registration record information includes the user's login time point, the user's login address, and the user's login verification information. Among them, the login address includes the device signal of the user's login, the device identification code and other information, and the login IP address. The user's login verification information includes but is not limited to the user's login password, login fingerprint verification, login face verification, login random code verification and other verification information. Since login verification requires a combination of one or more login verifications, the order of the authenticity of the login verification from low to high is login password, login random code verification, login fingerprint verification, login face verification, and when the user actually logs in to the teaching and training system for login verification, although the user can enter the course learning through different login verification methods, the system cannot authenticate the user as the course learning. If the user needs to authenticate the course learning, the user needs to meet the preset verification requirement information before the user can be authenticated as the course learning. The preset verification requirement information can be the verification information that at least contains the login password, login fingerprint verification, login face verification and other verification information. Then, the terminal identifies the user's login authentication information and the user login feature information for each user based on the user's login information.
[0062] Step S102, based on the user's course record information, identify the user's course learning time distribution information, and based on the user's login authentication information and the user's login feature information, identify the user's abnormal login information through the login anomaly identification strategy.
[0063] In this embodiment, the terminal identifies the course learning time distribution information of the user based on the user's course record information, and identifies the user's abnormal login information based on the user's login authentication information and the user's login feature information through the login abnormality identification strategy. Among them, the course learning time distribution information is used to characterize the distribution information of the course learning time of the user who is authenticated as the user's course learning after each login. The login abnormality identification strategy is used to identify the user's address change abnormality, login verification abnormality and other situation information. The specific identification process will be described in detail later.
[0064] Step S103, based on the course learning time distribution information of the user, the abnormal course time identification strategy is used to identify the abnormal course time information of the user, and based on the abnormal login information of the user and the abnormal course time information of the user, the actual course learning time of the user is identified.
[0065] In this embodiment, the terminal identifies the abnormal class information of the user based on the course learning time distribution information of the user through the abnormal class identification strategy, and identifies the actual course learning time of the user based on the abnormal login information of the user and the abnormal class information of the user. Among them, the abnormal class identification strategy is used to identify abnormal class information such as abnormally long class time, abnormally short class time, or abnormal regularity of class interval frequency, and the specific identification process will be described in detail later.
[0066] Based on the above scheme, by identifying the user's login authentication information, login feature information, and course learning market segment information from the user's course record information and user login record information, the user's abnormal login information is comprehensively identified from the abnormal information in the course learning time and the abnormal login information when the user logs in. This not only avoids the high error rate and low precision of manual analysis, but also allows the scheme to comprehensively and comprehensively analyze the user's course time anomalies and login anomalies from multiple angles, avoiding the one-sidedness and omission of manual analysis. In addition, by analyzing the user's behavior and designing a method for distinguishing abnormal operations in a targeted manner, the accuracy of identifying abnormal course hours and logins is improved, thereby comprehensively improving the accuracy and efficiency of calculating the user's actual course learning hours.
[0067] Optionally, based on the user's various login information, identify the user's various login authentication information and the user's user login characteristic information, including: for each user login information, based on the user's user login information, identify the user's login time point, the user's login address information, and the user's login verification information; based on the user's login verification information, identify the user's login authority information and the user's login verification method, and use the user's login authority information and the user's login verification method as the user login authentication information corresponding to the user login information; based on the user's various login time points and the user's various login address information, identify the user's login distribution information and the user's address change distribution information, and use the user's login distribution information and the user's address change distribution information as the user's login characteristic information.
[0068] In this embodiment, the terminal identifies the user's login time point, the user's login address information, and the user's login verification information based on the user's login information for each user login information. Then, the terminal identifies the user's login authority information and the user's login verification method based on the user's login verification information, and uses the user's login authority information and the user's login verification method as the user login authentication information corresponding to the user login information. Among them, the login authority information is whether the user has the authority to access the account, and the user's login verification method is the verification type of the verification information adopted by the user. Each verification information corresponds to a verification type, and the verification type includes but is not limited to a login password type, a login random code verification type, a login fingerprint verification type, and a login face verification type.
[0069] Then, the terminal identifies the user's login distribution information and the user's address change distribution information based on the user's login time points and the user's login address information. The login distribution information is the distribution information of the login time points, which is used to characterize the user's daily login time distribution and the user's monthly login time distribution. The address change distribution information is used to characterize the address change information when the user logs in.
[0070] Finally, the terminal uses the user's login distribution information and the user's address change distribution information as the user's login feature information.
[0071] Based on the above scheme, the user's login authentication information and login feature information are analyzed through split identification, thereby improving the comprehensiveness of the identification and analysis of the login information.
[0072] Optionally, based on the user's course record information, identify the user's course learning time distribution information, including: splitting the user's course record information into sub-course record information for each course learning, and identifying the course learning time of each sub-course record information, and the course learning distribution information of each sub-course record information; performing time distribution processing on the course learning time of each sub-course record information according to the course learning starting time point of each sub-course record information to obtain the user's initial course learning time distribution information; and adjusting the initial course learning time distribution information based on the course learning distribution information of each sub-course record information to obtain the user's course learning time distribution information.
[0073] In this embodiment, the terminal divides the course record information of the user into sub-course record information of each course study, and identifies the course study time of each sub-course record information and the course study distribution information of each sub-course record information. Then, the terminal performs time distribution processing on the course study time of each sub-course record information according to the course study start time point of each sub-course record information, obtains the initial course study time distribution information of the user, and adjusts the initial course study time distribution information based on the course study distribution information of each sub-course record information to obtain the course study time distribution information of the user. Specifically, the course study distribution information of each sub-course record information includes the actual study time of the user in the course study time in each sub-course record information. The course study time is the time period between the login time of the user and the logout time, and the actual study time is the time the user studies according to the learning requirements. For example, if the system has a 30-minute token expiration limit, and the user does not perform online verification in accordance with the regulations, the subsequent study time will be suspended and will not be counted in the actual study time of the user. Therefore, the initial course learning time distribution information is adjusted according to the user's actual learning time, thereby improving the accuracy of the obtained course learning time distribution information.
[0074] Based on the above solution, by distributing the course learning time and then optimizing the distribution, the accuracy of the obtained course learning time distribution information is improved.
[0075] Optionally, based on the user's various login authentication information and the user's user login feature information, a login anomaly identification strategy is used to identify the user's various abnormal login information, including: based on the user's various login authentication information, filtering the abnormal authentication information, and using the user login information corresponding to the abnormal authentication information as the user's various first abnormal login information; based on the user's login distribution information and the user's address change distribution information, identifying the user's login frequency change information and the user's address change frequency change information, and based on the user's login frequency change information and the user's address change frequency change information, using a login anomaly identification strategy to identify the user's various second abnormal login information; using each first abnormal login information and each second abnormal login information as the user's various abnormal login information.
[0076] In this embodiment, the terminal screens abnormal authentication information based on each login authentication information of the user, and uses the user login information corresponding to the abnormal authentication information as each first abnormal login information of the user. Among them, the abnormal authentication information is the user login information that does not meet the login verification requirement information. Then, based on the user's login distribution information and the user's address change distribution information, the terminal identifies the user's login frequency change information and the user's address change frequency change information, and based on the user's login frequency change information and the user's address change frequency change information, the terminal logs in an abnormal identification strategy to identify each second abnormal login information of the user. Among them, the second abnormal login information is that the login frequency change is greater than the first frequency change threshold, the address change frequency is greater than the second frequency change threshold, and the login frequency change in a short period of time is greater than the maximum frequency change value. Among them, the first frequency change threshold, the second frequency change threshold, and the maximum frequency change value are threshold information preset in the terminal.
[0077] Finally, the terminal uses each first abnormal login information and each second abnormal login information as each abnormal login information of the user.
[0078] Based on the above solution, the accuracy and comprehensiveness of the analysis are improved by analyzing from the perspectives of login verification, login frequency, and address change frequency.
[0079] Optionally, based on the user's course learning time distribution information, an abnormal class time identification strategy is used to identify the user's abnormal class time information, including: based on the user's course learning time distribution information, generating the user's course distribution fitting curve through a distribution fitting strategy, and based on the user's course distribution fitting curve, identifying the user's course learning time range and the user's course learning time trend information; based on the user's course learning time range and the user's course learning time trend information, in each user's login information, screening the user's first abnormal class time information, and based on the course distribution fitting curve, identifying the actual course learning time of each user's login information; based on the actual course learning time of each user's login information, through the abnormal class time identification strategy, identifying the user's second abnormal class time information, and using each first abnormal class time information and each second abnormal class time information as the user's abnormal class time information.
[0080] In this embodiment, the terminal generates a course distribution fitting curve for the user based on the course learning time distribution information of the user through a distribution fitting strategy, and identifies the course learning time range and the course learning time trend information of the user based on the course distribution fitting curve of the user. Among them, the distribution fitting strategy is a fitting method for distribution fitting based on a curve distribution fitting algorithm. Then, based on the course learning time range and the course learning time trend information of the user, the terminal screens the first abnormal class information of the user in each user login information, and identifies the actual course learning time of each user login information based on the course distribution fitting curve. Among them, the actual course learning time is the actual learning time of each user login information obtained after fitting the distribution fitting curve. The actual learning time and the time obtained based on the terminal intelligent analysis described above can only exist in one of each user login information, that is, when the actual learning time obtained by the system analysis is abnormal or does not exist, the terminal obtains the actual learning time corresponding to the user login information in this fitting manner.
[0081] Afterwards, the terminal identifies the user's second abnormal class information based on the actual course learning time of each user's login information and through the abnormal class identification strategy, and uses the first abnormal class information and the second abnormal class information as the user's abnormal class information.
[0082] Based on the above scheme, after distribution fitting of course learning time, abnormal class information is identified, which improves the comprehensiveness and accuracy of identifying abnormal class information.
[0083] Optionally, based on the users' abnormal login information and the users' abnormal class information, identify the users' real course learning time, including: based on the users' abnormal login information and the users' abnormal class information, filter the users' real login information in the users' login information, and calculate the users' real course learning time based on the actual course learning time of the users' real login information.
[0084] In this embodiment, the terminal filters the user's real login information from each user's login information based on the user's abnormal login information and the user's abnormal class information, and takes the sum of the actual course learning time based on the user's real login information as the user's real course learning time.
[0085] Based on the above scheme, after screening abnormal login information and abnormal class information, the sum of the actual course learning time of each user's real login information is determined, which improves the accuracy of the determined user's real course learning time.
[0086] This application also provides an example of user class time calculation for a teaching and training system, such as Figure 2 As shown, the specific processing process includes the following steps:
[0087] Step S201, obtaining user course record information of each user of the teaching and training system and user login record information of each user.
[0088] Step S202: for each user login information, based on the user login information, identify the user's login time point, the user's login address information, and the user's login verification information.
[0089] Step S203, based on the user's login verification information, identifying the user's login authority information and the user's login verification method, and using the user's login authority information and the user's login verification method as user login authentication information corresponding to the user login information.
[0090] Step S204, based on the user's login time points and the user's login address information, identify the user's login distribution information and the user's address change distribution information, and use the user's login distribution information and the user's address change distribution information as the user's login feature information.
[0091] Step S205: split the user course record information into sub-course record information of each course study, and identify the course study duration of each sub-course record information and the course study distribution information of each sub-course record information.
[0092] Step S206, according to the course learning starting time point of each sub-course record information, the course learning time of each sub-course record information is time-distributed to obtain the user's initial course learning time distribution information, and based on the course learning distribution information of each sub-course record information, the initial course learning time distribution information is adjusted to obtain the user's course learning time distribution information.
[0093] Step S207: based on each login authentication information of the user, filter the abnormal authentication information, and use the user login information corresponding to the abnormal authentication information as each first abnormal login information of the user.
[0094] Step S208, based on the user's login distribution information and the user's address change distribution information, identify the user's login frequency change information and the user's address change frequency change information, and based on the user's login frequency change information and the user's address change frequency change information, log in an abnormality identification strategy to identify the user's second abnormal login information.
[0095] Step S209: taking each first abnormal login information and each second abnormal login information as each abnormal login information of the user.
[0096] Step S210, based on the user's course learning time distribution information, generate the user's course distribution fitting curve through a distribution fitting strategy, and based on the user's course distribution fitting curve, identify the user's course learning time range and the user's course learning time trend information.
[0097] Step S211, based on the user's course learning time range and the user's course learning time trend information, filter the user's first abnormal class time information in each user's login information, and identify the actual course learning time of each user's login information based on the course distribution fitting curve.
[0098] Step S212, based on the actual course learning time of each user's login information, identify the user's second abnormal class information through the abnormal class identification strategy, and use the first abnormal class information and the second abnormal class information as the user's abnormal class information.
[0099] Step S213, based on the users' abnormal login information and the users' abnormal class information, the users' real login information is screened in the users' login information, and the users' real course learning time is calculated based on the actual course learning time of the users' real login information.
[0100] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.
[0101] Based on the same inventive concept, the embodiment of the present application also provides a user class time accounting device for a teaching and training system for implementing the user class time accounting method of the teaching and training system involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the user class time accounting device for the teaching and training system provided below can refer to the limitations of the user class time accounting method for the teaching and training system above, and will not be repeated here.
[0102] In an exemplary embodiment, Figure 3 As shown, a user class time accounting device of a teaching and training system is provided, including: an acquisition module 310, a first identification module 320 and a second identification module 330, wherein:
[0103] The acquisition module 310 is used to acquire the user course record information of each user of the teaching and training system and the user login record information of each user, and for each user, based on the user login information of the user, identify the user's login authentication information and the user login feature information of the user;
[0104] The first identification module 320 is used to identify the course learning time distribution information of the user based on the user course record information of the user, and identify the abnormal login information of the user through the login abnormality identification strategy based on the login authentication information of the user and the user login feature information of the user;
[0105] The second identification module 330 is used to identify the abnormal class information of the user based on the course learning time distribution information of the user through the abnormal class identification strategy, and to identify the actual course learning time of the user based on the abnormal login information of the user and the abnormal class information of the user.
[0106] Optionally, the acquisition module 310 is specifically configured to:
[0107] For each user login information, based on the user login information of the user, identifying the login time point of the user, the login address information of the user, and the login verification information of the user;
[0108] Based on the login verification information of the user, identifying the login authority information of the user and the login verification method of the user, and using the login authority information of the user and the login verification method of the user as the user login authentication information corresponding to the user login information;
[0109] Based on the user's login time points and the user's login address information, the user's login distribution information and the user's address change distribution information are identified, and the user's login distribution information and the user's address change distribution information are used as the user's login feature information.
[0110] Optionally, the first identification module 320 is specifically configured to:
[0111] Splitting the user course record information into sub-course record information of each course study, and identifying the course study duration of each sub-course record information and the course study distribution information of each sub-course record information;
[0112] According to the course learning starting time point of each sub-course record information, the course learning duration of each sub-course record information is time-distributed to obtain the initial course learning duration distribution information of the user; and based on the course learning distribution information of each sub-course record information, the initial course learning duration distribution information is adjusted to obtain the course learning duration distribution information of the user.
[0113] Optionally, the first identification module 320 is specifically configured to:
[0114] Based on each login authentication information of the user, abnormal authentication information is screened, and user login information corresponding to the abnormal authentication information is used as each first abnormal login information of the user;
[0115] Based on the login distribution information of the user and the address change distribution information of the user, identifying the login frequency change information of the user and the address change frequency change information of the user, and based on the login frequency change information of the user and the address change frequency change information of the user, a login abnormality identification strategy is used to identify each second abnormal login information of the user;
[0116] Each of the first abnormal login information and each of the second abnormal login information is used as each of the abnormal login information of the user.
[0117] Optionally, the second identification module 330 is specifically configured to:
[0118] Based on the course learning time distribution information of the user, a course distribution fitting curve of the user is generated through a distribution fitting strategy, and based on the course distribution fitting curve of the user, a course learning time range of the user and course learning time trend information of the user are identified;
[0119] Based on the course learning time range of the user and the course learning time trend information of the user, in each user login information, the first abnormal class time information of the user is screened, and based on the course distribution fitting curve, the actual course learning time of each user login information is identified;
[0120] Based on the actual course learning time of each user login information, the second abnormal class information of the user is identified through the abnormal class identification strategy, and the first abnormal class information and the second abnormal class information are used as the abnormal class information of the user.
[0121] Optionally, the second identification module 330 is specifically configured to:
[0122] Based on the abnormal login information of the user and the abnormal class information of the user, the real login information of the user is screened in the login information of each user, and the real course learning time of the user is calculated based on the actual course learning time of each real login information of the user.
[0123] Each module in the user class time accounting device of the above-mentioned teaching and training system can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.
[0124] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 4As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless method can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a user class accounting method of a teaching and training system is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.
[0125] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.
[0126] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, steps of a method for calculating user class hours in a teaching and training system are implemented.
[0127] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of a method for calculating user class hours in a teaching and training system are implemented.
[0128] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps of a method for calculating user class hours in a teaching and training system.
[0129] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.
[0130] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.
[0131] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0132] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.
Claims
1. A method for calculating user class hours in a teaching and training system, characterized in that: The method comprises: Obtaining user course record information of each user of the teaching and training system and user login record information of each user, and for each user, identifying the user's login authentication information and user login feature information based on the user's user login information; Based on the user course record information of the user, the course study time distribution information of the user is identified, and based on the login authentication information of the user and the user login feature information of the user, the abnormal login information of the user is identified through the login abnormality identification strategy; Based on the course learning time distribution information of the user, the abnormal class time information of the user is identified through an abnormal class time identification strategy, and based on the user's various abnormal login information and the user's abnormal class time information, the user's actual course learning time is identified.
2. The method according to claim 1, characterized in that: The identifying each login authentication information of the user and the user login feature information of the user based on each user login information of the user includes: For each user login information, based on the user login information of the user, identifying the login time point of the user, the login address information of the user, and the login verification information of the user; Based on the login verification information of the user, identifying the login authority information of the user and the login verification method of the user, and using the login authority information of the user and the login verification method of the user as the user login authentication information corresponding to the user login information; Based on the user's login time points and the user's login address information, the user's login distribution information and the user's address change distribution information are identified, and the user's login distribution information and the user's address change distribution information are used as the user's login feature information.
3. The method according to claim 1, characterized in that The identifying the course study time distribution information of the user based on the user course record information of the user includes: Splitting the user course record information into sub-course record information of each course study, and identifying the course study duration of each sub-course record information and the course study distribution information of each sub-course record information; According to the course learning starting time point of each sub-course record information, the course learning duration of each sub-course record information is time-distributed to obtain the initial course learning duration distribution information of the user; and based on the course learning distribution information of each sub-course record information, the initial course learning duration distribution information is adjusted to obtain the course learning duration distribution information of the user.
4. The method according to claim 2, characterized in that: The identifying each abnormal login information of the user based on each login authentication information of the user and the user login feature information of the user through a login abnormality identification strategy includes: Based on each login authentication information of the user, abnormal authentication information is screened, and user login information corresponding to the abnormal authentication information is used as each first abnormal login information of the user; Based on the login distribution information of the user and the address change distribution information of the user, identifying the login frequency change information of the user and the address change frequency change information of the user, and based on the login frequency change information of the user and the address change frequency change information of the user, a login abnormality identification strategy is used to identify each second abnormal login information of the user; Each of the first abnormal login information and each of the second abnormal login information is used as each of the abnormal login information of the user.
5. The method according to claim 3, characterized in that: The identifying the abnormal class time information of the user based on the course learning time distribution information of the user through an abnormal class time identification strategy includes: Based on the course learning time distribution information of the user, a course distribution fitting curve of the user is generated through a distribution fitting strategy, and based on the course distribution fitting curve of the user, a course learning time range of the user and course learning time trend information of the user are identified; Based on the course learning time range of the user and the course learning time trend information of the user, in each user login information, the first abnormal class time information of the user is screened, and based on the course distribution fitting curve, the actual course learning time of each user login information is identified; Based on the actual course learning time of each user login information, the second abnormal class information of the user is identified through the abnormal class identification strategy, and the first abnormal class information and the second abnormal class information are used as the abnormal class information of the user.
6. The method according to claim 5, characterized in that The identifying the actual course study time of the user based on each abnormal login information of the user and the abnormal class time information of the user includes: Based on the abnormal login information of the user and the abnormal class information of the user, the real login information of the user is screened in the login information of each user, and the real course learning time of the user is calculated based on the actual course learning time of each real login information of the user.
7. A user class time calculation device for a teaching and training system, characterized in that: The device comprises: An acquisition module, used to acquire user course record information of each user of the teaching and training system and each user login record information of each user, and for each user, based on each user login information of the user, identify each login authentication information of the user and the user login feature information of the user; A first identification module is used to identify the course learning time distribution information of the user based on the user course record information of the user, and to identify each abnormal login information of the user through a login abnormality identification strategy based on each login authentication information of the user and the user login feature information of the user; The second identification module is used to identify the abnormal class information of the user based on the course learning time distribution information of the user through the abnormal class identification strategy, and to identify the actual course learning time of the user based on the user's various abnormal login information and the user's abnormal class information.
8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.
10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.