A remote teaching system account login authentication method
Patent Information
- Application Number
- CN202611157041.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-31
- Publication Date
- 2026-09-25
AI Technical Summary
[0003]现有的教学系统账号登录认证技术通常采用传统的账号登录认证技术，即采用账户密码、验证码和人脸识别等多重验证来确定登录者的身份信息，但这些方式中，账户密码和验证码无法确认登录者是否由持有者帮助登录，而人脸识别认证仅能确保认证的那一刻为持有者登录，认证结束后无法知晓操作教学系统的用户的身份，而在远程教学系统中通常存在学习和考试测验，若无法知晓操作教学系统的用户的身份，则可能出现替学和替考等问题，现有的教学系统账号登录认证技术还存在未针对教学系统的特殊性构建专用的账号登录认证方式，而是采用传统的账号登录认证方式，导致易出现替学和替考的问题
[0037]本发明的有益效果：1、本发明通过在用户初次登录远程教学系统时，记录用户与触屏交互时产生的触屏数据，然后根据触屏数据中的点击数据提取用户在交互时的点击特征，同时根据触屏数据中的滑动数据提取用户在交互时的滑动特征，再对点击特征和滑动特征进行学习，得到专属于用户的交互标准特征，优势在于，大多数教学系统均倾向于移动端，而在移动端中则涉及触摸式操作，而每个人的手指大小不同，且发力方式不同，这就导致用户在触屏时具有不同的操作特征，通过提取用户点击和滑动的特征并学习，最终形成交互标准特征，此时每个人的交互标准特征均有所不同，以此来分辨使用者的身份，提高了教学系统账号登录认证的准确性以及有效性；
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Figure CN122824482A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of account login authentication technology for teaching systems, specifically a method for account login authentication in a remote teaching system. Background Technology
[0002] The teaching system account login authentication technology refers to the application of unified identity authentication and single sign-on technology in the education field. Its core is to ensure that the login user is only the person himself / herself, and that others cannot use the corresponding account to enter the teaching system even if they have the account password.
[0003] Existing account login authentication technologies for teaching systems typically employ traditional methods, using multiple verification methods such as account passwords, verification codes, and facial recognition to determine the login user's identity. However, these methods cannot verify whether the login is being assisted by the account holder, and facial recognition authentication only ensures that the account holder is logged in at the moment of authentication; after authentication, the identity of the user operating the teaching system remains unknown. In remote teaching systems, learning and testing are common activities. If the identity of the user operating the teaching system cannot be known, problems such as proxy learning and proxy testing may occur. Furthermore, existing account login authentication technologies for teaching systems do not have a dedicated account login authentication method tailored to the specific needs of teaching systems; instead, they rely on traditional methods, making them prone to proxy learning and proxy testing. Summary of the Invention
[0004] This invention aims to at least partially solve one of the technical problems in the prior art. When a user first logs into a remote teaching system, the system records touchscreen data generated during the user's interaction with the touchscreen. Then, it extracts the user's click features based on the click data and swipe features based on the swipe data. By learning from these click and swipe features, it obtains user-specific interaction standard features. Afterward, the user logs into the remote teaching system using their account and password. Upon login, the user is granted basic permissions. The system analyzes whether the user's interaction features meet the interaction standard features and determines whether to grant the user the highest permissions based on the analysis results. This addresses the problem that existing teaching system account login authentication technologies fail to develop a dedicated account login authentication method for the specific needs of teaching systems, instead relying on traditional account login authentication methods, which easily leads to problems such as proxy learning and proxy testing.
[0005] To achieve the above objectives, firstly, this application provides a method for account login authentication in a remote teaching system, comprising the following steps:
[0006] When a user logs into the remote teaching system for the first time, the system records the touch screen data generated when the user interacts with the touch screen.
[0007] Based on touch screen data, the interaction features of users interacting with the touch screen are extracted and the interaction features are learned to obtain standard interaction features;
[0008] Users log in to the remote teaching system using an account and password, and are granted basic privileges after logging in;
[0009] Analyze whether the user's interaction characteristics meet the interaction standard characteristics, and decide whether to grant the user the highest privileges based on the analysis results.
[0010] Furthermore, the step of recording the touchscreen data generated when the user interacts with the touchscreen upon first logging into the remote teaching system also includes the following sub-steps:
[0011] When a user logs into the remote teaching system for the first time, the system grants the user the highest level of access and records the touch screen data generated when the user interacts with the touch screen.
[0012] The highest level of access can execute all operations within the remote teaching system, and the touch screen data includes click data and swipe data.
[0013] Furthermore, the step of extracting interaction features when the user interacts with the touchscreen based on the touchscreen data and learning the interaction features to obtain standard interaction features also includes the following sub-steps:
[0014] Extract user click features during interaction based on click data from touchscreen data;
[0015] Extract user swipe features during interaction based on swipe data from touchscreen data;
[0016] By learning click and swipe features, we can obtain user-specific standard interaction features.
[0017] Furthermore, the step of extracting user click features during interaction based on click data in the touchscreen data also includes the following sub-steps:
[0018] Both the click data and the swipe data are represented as a capacitance matrix output by the capacitive touchscreen sensor. The capacitance signal generated by the user's touch in the capacitance matrix is named the touch signal, and the touch signal output by the capacitive touchscreen sensor at the nth row and mth column is labeled as TS. n,m Where n and m are both non-zero natural numbers and are subscripts of TS, and TS is... n,m The corresponding capacitive touchscreen sensor is marked as SP. n,m ;
[0019] Based on the capacitive touchscreen sensor SP n,m Touch signal TS at the location n,mThe click characteristics of user clicks on capacitive touchscreens are analyzed, including click width and capacitance distribution trend.
[0020] Furthermore, the step of extracting the user's swipe features during interaction based on the swipe data in the touchscreen data also includes the following sub-steps:
[0021] The sliding data is obtained by combining different numbers of click data. If in the same SP n,m There are different TS at this location n,m The maximum value among the possible values is taken as the value in the capacitance matrix.
[0022] Rename the touch signal in the swipe data to a swipe signal, and simultaneously change the corresponding TS. n,m Change to SS n,m ;
[0023] For sliding signal SS n,m Cluster analysis was performed to divide the sliding signal into different capacitance analysis groups. The average value of the sliding signal in the capacitance analysis group was calculated and named the group mean capacitance. The capacitance analysis group with the largest group mean capacitance was taken as the contact center group, and the sliding signal in the contact center group was named the sliding center signal.
[0024] The sliding characteristics of the sliding data generated by the user during interaction are analyzed based on the positional distribution of the sliding center signal. The sliding characteristics include the width fluctuation parameter.
[0025] Furthermore, the step of learning click and swipe features to obtain user-specific interaction standard features also includes the following sub-steps:
[0026] The range of the click width is statistically analyzed and named the standard click width range. At the same time, the range of the width fluctuation parameter is statistically analyzed and named the standard width fluctuation range.
[0027] All capacitance distribution trend features are placed in the same capacitance distribution analysis coordinate system. The two endpoints of the capacitance distribution trend features are named Endpoint 1 and Endpoint 2, respectively. Endpoint 1 is located on the left side of the capacitance distribution trend feature, and Endpoint 2 is located on the right side of the capacitance distribution trend feature.
[0028] Connect adjacent endpoint 1 and adjacent endpoint 2. Name the connected line segment as the sealing area auxiliary line. Name the area enclosed by the capacitance distribution trend feature and the sealing area auxiliary line as the capacitance distribution trend area. Extract the outer contour of the capacitance distribution trend area and name it the capacitance distribution standard feature. The click standard width range, width fluctuation standard range and capacitance distribution standard feature together constitute the interactive standard feature.
[0029] Furthermore, the step of granting basic permissions to a user after they log in to the remote teaching system using their account and password also includes the following sub-steps:
[0030] Users log in to the remote teaching system using an account and password, and are granted basic permissions upon each login except for the first login.
[0031] The basic permissions only allow access to instructional videos, but do not allow viewing users' private data within the remote teaching system.
[0032] Furthermore, the step of analyzing whether the user's interaction characteristics meet the interaction standard characteristics and deciding whether to grant the user the highest privileges based on the analysis results also includes the following sub-steps:
[0033] Collect one instance each of the user's click width, capacitance distribution trend feature, and width fluctuation parameter, and name them as click width to be identified, capacitance distribution trend feature to be identified, and width fluctuation parameter to be identified, respectively.
[0034] If the width to be identified is within the standard width range, the capacitance distribution trend to be identified is within the standard capacitance distribution characteristics, and the width fluctuation to be identified parameter is within the standard width fluctuation range, then the user's permission will be upgraded from basic permission to the highest permission; otherwise, the user's permission will remain at the basic permission level.
[0035] A second aspect of the present invention provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect above.
[0036] A third aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect above.
[0037] The beneficial effects of this invention are as follows: 1. This invention records the touch screen data generated when a user interacts with the touch screen during their first login to the remote teaching system. Then, it extracts the user's click features during the interaction based on the click data in the touch screen data, and extracts the user's swipe features during the interaction based on the swipe data in the touch screen data. The click features and swipe features are then learned to obtain user-specific interaction standard features. The advantage is that most teaching systems tend to be mobile devices, which involve touch operations. Since everyone's fingers are different in size and force application, users have different operation features when touching the screen. By extracting and learning the user's click and swipe features, interaction standard features are finally formed. At this time, each person's interaction standard features are different, thereby distinguishing the user's identity and improving the accuracy and effectiveness of the teaching system's account login authentication.
[0038] 2. This invention allows users to log in to the remote teaching system using an account and password. After logging in, users are granted basic permissions. The system analyzes the user's interaction characteristics to determine whether they meet the interaction standard characteristics and decides whether to grant the user the highest permissions based on the analysis results. The advantage is that basic permissions only allow viewing the teaching videos that have been studied, and cannot access the holder's private data. At the same time, it cannot view the teaching videos that have not been studied or take exams on behalf of the holder. However, when the user's interaction characteristics meet the interaction standard characteristics, it means that the user is the holder at this time, and the highest permissions are then granted to the user. At this time, the user can use all the functions of the remote teaching system, which further improves the accuracy and effectiveness of the teaching system account login authentication. Attached Figure Description
[0039] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0040] Figure 1 This is a flowchart illustrating the steps of the method of the present invention;
[0041] Figure 2 This is a schematic diagram of the click data of the present invention;
[0042] Figure 3 This is a schematic diagram of the clickable area of the present invention;
[0043] Figure 4 This is a schematic diagram of the signal trend circle of the present invention;
[0044] Figure 5 This is a schematic diagram of the coordinate system for capacitance distribution analysis according to the present invention;
[0045] Figure 6 This is a schematic diagram of the sliding area corresponding to the sliding data of the present invention;
[0046] Figure 7 This is a schematic diagram of the sliding profile and sliding center sensor of the present invention;
[0047] Figure 8 This is a schematic diagram of the width auxiliary lines of the present invention;
[0048] Figure 9 This is a schematic diagram of the present invention, which places all capacitance distribution trend characteristics in the same capacitance distribution analysis coordinate system.
[0049] Figure 10 This is a schematic diagram of the sealing area auxiliary line of the present invention. Detailed Implementation
[0050] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0051] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can cover implementations in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0052] Remote learning systems typically include functions such as learning and testing. The most significant characteristic of these functions is that they must be completed by the individual. Traditional account login authentication technologies can only ensure that the user is authorized by the account holder, but cannot guarantee that the user is indeed the account holder. If the user's identity cannot be verified, the account holder may seek someone else to study or take exams on their behalf, especially given the current problem of online courses being used for cheating. To address these issues, Embodiment 1 of this application provides a remote learning system account login authentication method. Please refer to... Figure 1 As shown, it includes the following steps:
[0053] Step S100: When a user logs into the remote teaching system for the first time, record the touch screen data generated during the user's interaction with the touch screen; Step S100 includes the following sub-steps:
[0054] Step S101: When a user logs into the remote teaching system for the first time, the user is granted the highest privileges, and the touch screen data generated when the user interacts with the touch screen is recorded.
[0055] Step S102: The highest level of privilege can execute all operations within the remote teaching system, and the touch screen data includes click data and swipe data;
[0056] In practice, the remote teaching system is set up so that the full functionality can only be used on mobile devices. When a user logs in for the first time, it is usually when the account is first created. At this time, the user must be the holder. During this login process, the user is given the highest privileges, and the touch screen data is recorded.
[0057] Step S200 involves extracting interaction features from the touchscreen data and learning these features to obtain standard interaction features. Step S200 includes the following sub-steps:
[0058] Step S201: Extract the user's click features during interaction based on the click data in the touch screen data;
[0059] Step S201 includes the following sub-steps:
[0060] Step S2011: Both click data and swipe data are represented as a capacitance matrix output by the capacitive touchscreen sensor. The capacitance signal generated by the user's touch in the capacitance matrix is named the touch signal, and the touch signal output by the capacitive touchscreen sensor at the nth row and mth column is labeled as TS. n,m n and m are both non-zero natural numbers and are indices of TS. TS is also... n,m The corresponding capacitive touchscreen sensor is marked as SP. n,m ;
[0061] In practical implementation, the capacitance matrix is represented as follows: In this matrix, a value of 0 represents a change in capacitance detected by the capacitive touchscreen sensor at this location being 0, and 3 represents a change in capacitance detected by the capacitive touchscreen sensor at this location being 3pF. The other values follow the same logic. For example, the value 1 at the top of the capacitance matrix corresponds to the marker TS. 2,3 This represents the capacitance change value located in the 2nd row and 3rd column of the capacitance matrix; the other values are calculated similarly. Furthermore, because the capacitive touchscreen sensors are arranged in an array, SP... 2,3 This represents the capacitive touchscreen sensor located in the 2nd row and 3rd column, whose output is TS. 2,3 .
[0062] Step S2012, based on the capacitive touchscreen sensor SP n,m Touch signal TS at the locationn,m Analyze the click characteristics of user click data generated by tapping a capacitive touchscreen.
[0063] Please see Figures 2 to 3 As shown, in step S2013, SP n,m The area is named the click area. Draw the outer circle of the click area and name it the click range circle. Get the diameter of the click range circle and name it the click width. The click width belongs to the click feature.
[0064] In practice, steps S2013 to S2018 are actually further supplementary explanations of step S2012. For example, the click data for a certain instance is as follows: Figure 2 As shown, each square represents the monitoring range of a capacitive touchscreen sensor, thus obtaining the click area as follows: Figure 3 As shown, Figure 3 The gray area in the diagram represents the click area. The outer circle of the click area is drawn to obtain the click range circle, and then its diameter is obtained to get the click width, which represents the size of the user's finger touching the screen. Since the drawing of the outer circle is too simple, it is not shown as a separate image in this embodiment.
[0065] Please see Figure 4 As shown, in step S2014, draw concentric circles of the click range circle, name them signal trend circles, name the diameter of the signal trend circle the signal trend diameter, and name the arc of the signal trend circle the signal trend arc.
[0066] Step S2015: Calculate the TS along which the signal trend arc passes. n,m The average value is named the capacitance trend mean. The signal trend diameter is gradually increased from zero, with a maximum not exceeding the click width. At the same time, the capacitance trend mean is recorded in real time during the process of increasing the signal trend diameter.
[0067] In practice, the signal trend circle is drawn as follows: Figure 4 As shown, Figure 4 The circle inside the clickable area circle is the signal trend circle. The signal trend arc of this circle passes through a total of 5 time intervals: 6.2, 3.7, 3.6, 3.8, and 3.8. n,m The average of these values is calculated to yield a capacitance trend mean of 4.22. Similarly, the average capacitance trend of all values is calculated as the signal trend diameter gradually increases from zero. It should be noted that when the signal trend arc passes through TS... n,m The capacitance trend mean is recorded only when a change occurs. If the signal trend arc passes through TS during the process of increasing the signal trend diameter... n,m If no change has occurred, there is no need to record additional capacitance trend mean values. Furthermore, Figure 4The example shown has a small number of capacitive touchscreen sensors inside the click area circle, which may seem insufficient to accurately distinguish the size of each person's finger. However, this is just an example for the sake of illustration and data understanding in this embodiment. In actual applications, capacitive touchscreen sensors are distributed very densely, usually with hundreds of them. In this case, the click data generated when a finger touches the screen will not appear as regular as in the example shown in this embodiment, and the accuracy in extracting the click width will be higher.
[0068] Step S2016: Sort the capacitance trend mean values according to the order in which they appear, using the symbol CT. h This means that h is a non-zero natural number and is the subscript of CT;
[0069] Please see Figure 5 As shown, in step S2017, CT... h The subscript h is the X-axis, and the capacitance trend mean CT is used as the X-axis. h Construct a capacitance distribution analysis coordinate system for the Y-axis, and use CT... h Enter the capacitance distribution analysis coordinate system according to h;
[0070] Step S2018: Perform curve regression on the capacitance distribution analysis coordinate system, and name the curve obtained by curve regression as capacitance distribution trend feature. The capacitance distribution trend feature belongs to click feature.
[0071] In practice, CT1 to CT6 are sorted, and a capacitance distribution analysis coordinate system is constructed as follows: Figure 5 As shown, Figure 5 The curve in the figure represents the capacitance distribution trend, which shows the average change trend of capacitance value as the center of the touchscreen expands outward in a circle when the user clicks the touchscreen. This depends not only on the size of the user's finger, but also on the user's touch pressure and the fit between the finger and the touchscreen, among other factors.
[0072] Step S202: Extract the user's swipe features during interaction based on the swipe data in the touch screen data;
[0073] Step S202 includes the following sub-steps:
[0074] Step S2021, the sliding data is obtained by combining different numbers of click data. If in the same SP n,m There are different TS at this location n,m The maximum value among the possible values is taken as the value in the capacitance matrix.
[0075] Step S2022: Rename the touch signal in the swipe data to a swipe signal, and simultaneously change the corresponding TS. n,m Change to SS n,m ;
[0076] In practice, the sliding data is obtained by combining multiple frames of click data. For example, a certain sliding data may only have two frames of click data, where the click data of the first frame is... The click data for the second frame is The combination of the two yields the sliding data. The value 1 in the second row and third column corresponds to SS. 2,3 The value 1 in the 2nd row and 4th column corresponds to SS. 2,4 And so on.
[0077] Step S2023, for the sliding signal SS n,m Cluster analysis was performed to divide the sliding signal into different capacitance analysis groups. The average value of the sliding signal in the capacitance analysis group was calculated and named the group mean capacitance. The capacitance analysis group with the largest group mean capacitance was taken as the contact center group, and the sliding signal in the contact center group was named the sliding center signal.
[0078] In practice, as the user's finger moves on the touchscreen during the swiping process, the center point of each finger press corresponds to the point with the highest capacitance change value. The capacitance signal change value will gradually decrease as it moves away from the center of the press. Therefore, cluster analysis is used to determine the cluster with the largest average capacitance. This cluster is the swiping signal corresponding to the center of the press, i.e., the swiping center signal.
[0079] Step S2024: Analyze the sliding characteristics of the sliding data generated by the user during interaction based on the positional distribution of the sliding center signal.
[0080] Please see Figures 6 to 7 As shown, in step S2025, the area where the capacitive touch screen sensor to which the sliding signal belongs is named the sliding area, the outline of the sliding area is named the sliding outline, and the capacitive touch screen sensor to which the sliding center signal belongs is named the sliding center sensor.
[0081] Please see Figure 8 As shown, in step S2026, a line segment is drawn through the geometric center of the sliding center sensor and named the width auxiliary line. The endpoint of the width auxiliary line is always on the sliding profile.
[0082] Step S2027: Rotate the width auxiliary line with the geometric center of the sliding center sensor as the rotation center, and at the same time, obtain the length of the width auxiliary line in real time, name it as the test width, and take the minimum value of the test width as the sliding width.
[0083] Step S2028: Sort the sliding widths in ascending order, using the symbol W. i This means that i is a non-zero natural number and i is the index of W, where W is the index of the natural number. iThe subscript i represents the horizontal axis, and the sliding width W i Establish a width fluctuation analysis coordinate system for the vertical axis, and set W... i According to the coordinate system for width fluctuation analysis, enter i.
[0084] Step S2029: Perform linear regression on the width fluctuation analysis coordinate system to obtain the slope of the regression function, which is named the width fluctuation parameter. The width fluctuation parameter is the sliding characteristic.
[0085] In practice, steps S2025 to S2029 are actually further supplementary explanations of step S2024. For example, the sliding area corresponding to a certain set of sliding data is as follows: Figure 6 As shown, Figure 6 The gray area in the image represents the sliding region. Simultaneously, the sliding profile and sliding center sensor are extracted. Figure 7 As shown, each sliding center sensor is rectangular, with its geometric center being the intersection of its diagonals. The width auxiliary lines are drawn to find the width of the sliding signal along its path, representing the degree of contact between the user's finger and the touchscreen during the sliding process. The width auxiliary lines are drawn as follows: Figure 8 As shown, Figure 8 The dashed lines in the diagram are width auxiliary lines. The obtained sliding width is 2.98. Simultaneously, W1 to W4 are sorted. It should be noted that if multiple sliding widths exist, only one needs to be retained. This embodiment... Figure 8 For the sake of illustration in this embodiment, it appears that all four sliding widths are equal. However, in actual use, it is impossible for all sliding widths to be equal. That is, the sliding width will definitely fluctuate. Therefore, a width fluctuation analysis coordinate system is established and the sliding widths are introduced in ascending order. Finally, the width fluctuation parameter is obtained as 0.11 through linear regression. In addition, in practical applications, the user's sliding angle can be distinguished, such as horizontal sliding, diagonal sliding, and vertical sliding. The width fluctuation parameter of each angle is analyzed independently to evaluate the degree of fluctuation in the degree of contact between the user's finger and the touch screen at each sliding angle. Although this embodiment does not make detailed distinctions, it does not mean that this embodiment does not consider the different contact characteristics of the finger under different sliding angles.
[0086] Step S203: Learn the click features and swipe features to obtain user-specific interaction standard features;
[0087] Step S203 includes the following sub-steps:
[0088] Step S2031: Calculate the range of click width and name it as the standard click width range. At the same time, calculate the range of width fluctuation parameters and name it as the standard width fluctuation range.
[0089] In practice, the user will perform several operations during the first login, thus obtaining several click width and width fluctuation parameters. By statistically analyzing their ranges, the standard click width range and the standard width fluctuation range can be obtained. Since the range statistics are relatively simple, this embodiment will not provide a detailed explanation.
[0090] Please see Figure 9 As shown, in step S2032, all capacitance distribution trend features are placed in the same capacitance distribution analysis coordinate system, and the two endpoints of the capacitance distribution trend features are named endpoint one and endpoint two, respectively. Endpoint one is located on the left side of the capacitance distribution trend feature, and endpoint two is located on the right side of the capacitance distribution trend feature.
[0091] Please see Figure 10 As shown, in step S2033, connect the adjacent first endpoint and connect the adjacent second endpoint. Name the connected line segment as the sealing area auxiliary line. Name the area enclosed by the capacitance distribution trend feature and the sealing area auxiliary line as the capacitance distribution trend area. Extract the outer contour of the capacitance distribution trend area and name it the capacitance distribution standard feature. Click the standard width range, width fluctuation standard range and capacitance distribution standard feature to form the interactive standard feature.
[0092] In practical implementation, taking three capacitance distribution trend features as an example, all capacitance distribution trend features are placed in the same capacitance distribution analysis coordinate system, such as... Figure 10 As shown, Figure 10 It contains three different capacitance distribution trend characteristics, which are connected to obtain the auxiliary line of the sealing area, as shown below. Figure 10 As shown, Figure 10 It does not exist in Figure 9 The black line segment in the diagram is the auxiliary line for sealing off the area, which is ultimately determined by... Figure 10 The three capacitance distribution trend features and the sealing area auxiliary line together form a closed area. The outermost outline of this closed area is the standard capacitance distribution characteristic. The standard capacitance distribution characteristic represents the commonality of different capacitance distribution trend features of holders. That is, no matter how the capacitance distribution trend features of holders change, they are all within the standard capacitance distribution characteristic.
[0093] Step S300: The user logs into the remote teaching system using their account and password. After logging in, the user is granted basic permissions. Step S300 includes the following sub-steps:
[0094] Step S301: The user logs into the remote teaching system using an account and password, and is granted basic permissions after each login except for the first login.
[0095] Step S302: Basic permissions only allow opening teaching videos, and do not allow viewing users' private data within the remote teaching system;
[0096] In practice, users log in to the remote teaching system using an account and password. After each login except the first login, users are granted basic permissions. At this time, users can only watch the teaching videos that they have already completed, and cannot view personal settings, personal data, or exam scores and other private data. They also cannot watch teaching videos that they have not yet completed or take any exams or quizzes.
[0097] Step S400 involves analyzing whether the user's interaction characteristics meet the interaction standard characteristics and deciding whether to grant the user the highest privileges based on the analysis results. Step S400 includes the following sub-steps:
[0098] Step S401: Collect one instance each of the user's click width, capacitance distribution trend feature, and width fluctuation parameter, and name them as click width to be identified, capacitance distribution trend feature to be identified, and width fluctuation parameter to be identified, respectively.
[0099] Step S402: If the width to be identified is within the standard width range, the capacitance distribution trend to be identified is within the standard capacitance distribution characteristics, and the width fluctuation to be identified parameter is within the standard width fluctuation range, then the user's permission is raised from primary permission to the highest permission; otherwise, the user's permission remains at the primary permission level.
[0100] In practice, after logging into the remote teaching system, users will perform click and swipe actions. The system acquires the click width and capacitance distribution trend characteristics of the user's first click, resulting in the click width and capacitance distribution trend characteristics to be identified. Simultaneously, it acquires the width fluctuation parameters of the user's first swipe, resulting in the width fluctuation parameters to be identified. If the click width to be identified is within the standard click width range, the capacitance distribution trend characteristics to be identified are within the standard capacitance distribution characteristics, and the width fluctuation parameters to be identified are within the standard width fluctuation range, then the user's privileges are upgraded from basic to highest privileges. Otherwise, the user's privileges remain at basic privileges. Furthermore, during subsequent use, the user's click width to be identified, capacitance distribution trend characteristics to be identified, and width fluctuation parameters to be identified need to be monitored in real time. If any data does not meet the above judgment conditions, the user's privileges are adjusted back from highest privileges to basic privileges until all data are analyzed again and all judgment conditions are met before granting highest privileges.
[0101] Embodiment 2 of this application provides an electronic device. The electronic device may include a processor and a memory communicatively connected to the processor. The memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method embodiment as described in Embodiment 1 above. The specific implementation and technical effects are similar and will not be repeated here.
[0102] In this embodiment, the memory and processor are connected via a bus. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be categorized into address bus, data bus, control bus, etc.
[0103] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.
[0104] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.
[0105] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.
[0106] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.
[0107] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.
[0108] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.
[0109] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.
[0110] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.
[0111] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.
Claims
1. A method for account login authentication in a remote teaching system, characterized in that, include: When a user logs into the remote teaching system for the first time, the system records the touch screen data generated when the user interacts with the touch screen. Based on touch screen data, the interaction features of users interacting with the touch screen are extracted and the interaction features are learned to obtain standard interaction features; Users log in to the remote teaching system using an account and password, and are granted basic privileges after logging in; Analyze whether the user's interaction characteristics meet the interaction standard characteristics, and decide whether to grant the user the highest privileges based on the analysis results.
2. The method according to claim 1, characterized in that, The step of recording touchscreen data generated during user interaction with the touchscreen when the user first logs into the remote teaching system also includes the following sub-steps: When a user logs into the remote teaching system for the first time, the system grants the user the highest level of access and records the touch screen data generated when the user interacts with the touch screen. The highest level of access can execute all operations within the remote teaching system, and the touch screen data includes click data and swipe data.
3. The method according to claim 2, characterized in that, The step of extracting interaction features of user interaction with the touchscreen based on touchscreen data and learning these interaction features to obtain standard interaction features further includes the following sub-steps: Extract user click features during interaction based on click data from touchscreen data; Extract user swipe features during interaction based on swipe data from touchscreen data; By learning click and swipe features, we can obtain user-specific standard interaction features.
4. The method according to claim 3, characterized in that, The step of extracting user click features during interaction based on click data in the touchscreen data further includes the following sub-steps: Both the click data and the swipe data are represented as a capacitance matrix output by the capacitive touchscreen sensor. The capacitance signal generated by the user's touch in the capacitance matrix is named the touch signal, and the touch signal output by the capacitive touchscreen sensor at the nth row and mth column is labeled as TS. n,m Where n and m are both non-zero natural numbers and are subscripts of TS, and TS is... n,m The corresponding capacitive touchscreen sensor is marked as SP. n,m ; Based on the capacitive touchscreen sensor SP n,m Touch signal TS at the location n,m The click characteristics of user clicks on capacitive touchscreens are analyzed, including click width and capacitance distribution trend.
5. The method according to claim 4, characterized in that, The step of extracting the user's swipe features during interaction based on the swipe data in the touch screen data further includes the following sub-steps: The sliding data is obtained by combining different numbers of click data. If in the same SP n,m There are different TS at this location n,m The maximum value among the possible values is taken as the value in the capacitance matrix. Rename the touch signal in the swipe data to a swipe signal, and simultaneously change the corresponding TS. n,m Change to SS n,m ; For sliding signal SS n,m Cluster analysis was performed to divide the sliding signal into different capacitance analysis groups. The average value of the sliding signal in the capacitance analysis group was calculated and named the group mean capacitance. The capacitance analysis group with the largest group mean capacitance was taken as the contact center group, and the sliding signal in the contact center group was named the sliding center signal. The sliding characteristics of the sliding data generated by the user during interaction are analyzed based on the positional distribution of the sliding center signal. The sliding characteristics include the width fluctuation parameter.
6. The method according to claim 5, characterized in that, The step of learning click and swipe features to obtain user-specific interaction standard features also includes the following sub-steps: The range of the click width is statistically analyzed and named the standard click width range. At the same time, the range of the width fluctuation parameter is statistically analyzed and named the standard width fluctuation range. All capacitance distribution trend features are placed in the same capacitance distribution analysis coordinate system. The two endpoints of the capacitance distribution trend features are named Endpoint 1 and Endpoint 2, respectively. Endpoint 1 is located on the left side of the capacitance distribution trend feature, and Endpoint 2 is located on the right side of the capacitance distribution trend feature. Connect adjacent endpoint 1 and adjacent endpoint 2. Name the connected line segment as the sealing area auxiliary line. Name the area enclosed by the capacitance distribution trend feature and the sealing area auxiliary line as the capacitance distribution trend area. Extract the outer contour of the capacitance distribution trend area and name it the capacitance distribution standard feature. The click standard width range, width fluctuation standard range and capacitance distribution standard feature together constitute the interactive standard feature.
7. The method according to claim 6, characterized in that, The process of granting basic permissions to users after they log in to the remote teaching system using their account and password also includes the following sub-steps: Users log in to the remote teaching system using an account and password, and are granted basic permissions upon each login except for the first login. The basic permissions only allow access to instructional videos, but do not allow viewing users' private data within the remote teaching system.
8. The method according to claim 7, characterized in that, The step of analyzing whether the user's interaction characteristics meet the interaction standard characteristics and deciding whether to grant the user the highest privileges based on the analysis results also includes the following sub-steps: Collect one instance each of the user's click width, capacitance distribution trend feature, and width fluctuation parameter, and name them as click width to be identified, capacitance distribution trend feature to be identified, and width fluctuation parameter to be identified, respectively. If the width to be identified is within the standard width range, the capacitance distribution trend to be identified is within the standard capacitance distribution characteristics, and the width fluctuation to be identified parameter is within the standard width fluctuation range, then the user's permission will be upgraded from basic permission to the highest permission; otherwise, the user's permission will remain at the basic permission level.
9. An electronic device, characterized in that, The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method as described in any one of claims 1-8.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1-8.