Sign-in and sign-out anti-cheating method

Abstract

The invention relates to the technical field of computers, in particular to a sign-in and sign-out anti-cheating method which comprises the following steps: S1, distributing an activity sign-in code; s2, acquiring a sign-in request of a user, and judging whether the activity sign-in code input by the user is correct or not; s3, traversing a user blacklist maintained by a preset relational database, and turning to step S4 when the user is not in the user blacklist; s4, judging whether the sign-in frequency corresponding to the user and the sign-in frequency corresponding to the client in the NoSQL database exceed a preset threshold value or not; s5, the user sign-in is successful; s6, initializing an activity check-out process; and S7, obtaining a sign-out request of the user, and judging whether the sign-in record of the user and the sign-in record of the corresponding client exist in the NoSQL database or not. According to the method, rapid sign-in and sign-out of a large number of users in a large activity range can be realized, and illegal and cheating behaviors such as unauthorized willingness service duration and the like can be effectively counteracted.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a sign-in and sign-out anti-cheating method. Background Art

[0002] With the development of computer technology and network technology, more and more activity records are turning to data records. Therefore, when some activities that can obtain rewards or other benefits are held, some people will forge participation records in batches to obtain rewards or other benefits. For example, when holding volunteer activities, some cheating behaviors such as stealing volunteer service time often occur. In order to improve the anti-cheating efforts, the existing technology generally adopts a method of increasing the difficulty of sign-in, such as the method, device and system for anti-cheating disclosed in a Chinese patent (Announcement No.: CN111079140B). The method in the patented technology includes running a sign-in application for face-swiping sign-in on a mobile device, and detecting whether the sign-in application is running on a virtualized system. The method also includes responding to the detection. The sign-in application is running on a virtualized system. To determine whether there is cheating on a mobile device, it is possible to detect whether the sign-in application is running in a virtualized environment and determine whether there is cheating in the sign-in application, thereby preventing users from cheating in. There are also other methods to increase the difficulty of sign-in, such as audio verification, fingerprint punching, and ID card punching. However, this method of increasing the difficulty of sign-in will consume a lot of computing power resources in the verification work when actually used. At the same time, in some activities, such as volunteer service activities, there are a large number of participants, including the elderly, children and other groups, and the venue of the activity covers a large area. The use of the above verification method will increase the difficulty of use for participants, increase the threshold for participation in the activity, and is not conducive to the development of the activity. Summary of the invention

[0003] The technical problem to be solved by the present invention is that the existing anti-cheating methods are relatively backward and difficult to verify, which increases the difficulty of use for participants and raises the threshold for activity participation, which is not conducive to the development of activities.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solution: a sign-in and sign-out anti-cheating method, comprising the following steps: S1: Initialize the activity sign-in process and distribute the activity sign-in code; S2: Access the client, obtain the user's sign-in request, and determine whether the activity sign-in code entered by the user is correct. If it is correct, proceed to step S3; otherwise, it is determined that the sign-in fails; S3: Call the user's relevant information and traverse the user blacklist maintained by the preset relational database. If the user is not in the user blacklist, go to step S4. Otherwise, it is determined that the sign-in fails. S4: Obtain relevant information of the user, query the NoSQL database, and determine whether the number of check-ins corresponding to the user in the NoSQL database and the number of check-ins corresponding to the client exceed a preset threshold. If not, proceed to step S5; otherwise, it is determined that the check-in fails. S5: The user signs in successfully, and the relational database and NoSQL database are updated to record the user's relevant information. When the activity ends, proceed to step S6; S6: Initialize the activity sign-out process; S7: Obtain the user's sign-out request, query the NoSQL database, and determine whether there is a sign-in record of the user and a sign-in record of the corresponding client in the NoSQL database. If there is a sign-in record, determine that the user has successfully signed out, and record the user's relevant information. Otherwise, determine that the sign-out has failed.

[0005] When the present invention is working, it can realize the rapid sign-in and sign-out of a large number of users within a large activity range, and at the same time can realize a series of tasks such as the judgment of wrong activity sign-in codes, automatic identification of blacklisted users, and binding of users to clients, which can effectively counter illegal and cheating behaviors such as stealing volunteer service time, greatly improve the participation and effect of activities, reduce the number of false activities, and reduce the situation where the actual number of participants in activities is insufficient, which leads to difficulties in carrying out activities, and at the same time crack down on the gray industrial chain that replaces sign-in, sign-out, and steals volunteer service time to obtain profits.

[0006] Preferably, step S2 further includes the following steps: when a user fails to sign in, atomic self-increment data is stored in a preset NoSQL database to record the number of times the same user incorrectly enters the activity sign-in code; when the number of times the user incorrectly enters the activity sign-in code stored in the NoSQL database exceeds a preset upper limit on the number of trial and error attempts, the user is switched to a locked state, and the relevant information of the user is updated to a user blacklist.

[0007] Preferably, in step S4, when obtaining relevant information of the user, querying the NoSQL database, and determining whether the number of check-ins corresponding to the user and the number of check-ins corresponding to the client in the NoSQL database exceed a preset threshold, the following steps are adopted: obtaining relevant information of the user, including the user's ID and the unique identifier of the client, querying the NoSQL database through the user's ID and the unique identifier of the client, and determining whether the number of check-ins corresponding to the user and the number of check-ins corresponding to the client in the NoSQL database exceed a preset threshold.

[0008] Preferably, in step S5, when the user signs in successfully, the relational database and the NoSQL database are updated to record the user's relevant information, the following steps are adopted: when the user signs in successfully, the user's relevant information, including the user's ID and the client's unique identifier, is written into the relational database, and the NoSQL database is double-written.

[0009] Preferably, in step S7, a sign-out request of the user is obtained, a NoSQL database is queried, and it is determined whether there is a sign-in record of the user and a sign-in record of the corresponding client in the NoSQL database. When a sign-in record exists, it is determined that the user has successfully signed out, and the relevant information of the user is recorded. Otherwise, when it is determined that the sign-out fails, the following steps are adopted: when the user signs out, a unique identifier of the client used for the user's sign-out is obtained, and the unique identifier of the client used for the user's sign-in is obtained from the NoSQL database through the user's ID for comparison. When the unique identifiers are consistent, it is determined that there is a sign-in record and the user has successfully signed out. Otherwise, it is determined that the sign-out fails.

[0010] As a preference, the method further comprises the following steps: A1: Perform anti-cheating verification work through preset trigger conditions; A2: Obtain all activity data within a unit of time. The activity data includes the time information, area information, and relevant information of the users participating in the activity. A3: Obtain the time range of the preset cross-region violation operation rules, and obtain the user ID list of the user who has signed in and signed out in a unit time in the relational database; A4: Traverse the user ID list, query the check-in and check-out record list of each user in a unit time from the relational database according to the user ID, arrange several records in the check-in and check-out record list in the order of creation time of each record, compare the region information of adjacent records, and when the region information of adjacent records is different and the time interval between adjacent records is less than the time range of the preset cross-region violation operation rule, mark the record as a violation record and mark the user as a violating user.

[0011] When the present invention is working, by comparing the regional information of adjacent records, the users corresponding to the records with different regional information of adjacent records and the time interval of adjacent records being less than the time range of the preset cross-regional illegal operation rules are marked as illegal users. The illegal records of replacing others or letting others participate in activities in different areas on their behalf can be distinguished, thereby realizing anti-cheating verification of illegal users. At the same time, the anti-cheating verification work occupies less computing power resources, has high verification efficiency and good verification effect, and can further counter illegal and cheating behaviors such as stealing volunteer service time.

[0012] Preferably, in step A4, when a user is marked as an illegal user, the relevant information of the user is synchronized to a user blacklist.

[0013] Preferably, in step A4, the following steps are also included: C1: After marking the offending users, trace back the historical check-in and check-out records of all offending users and the corresponding activity information, and store them in structured data; C2: After obtaining the historical sign-in and sign-out records of all violating users and preprocessing them, the preset feature rules are used to create the time interval features of intensive activity participation, the area features of the activity participation of the violating users, the identity features of the violating users, and the associated features of the activities participated by the violating users. C3: Through cluster analysis, the historical sign-in and sign-out records of all violating users are processed according to the characteristics of the time interval of intensive participation in activities, the characteristics of the area where the violating users participate in activities, the characteristics of the identity of the violating users, and the characteristics of the association of the activities participated by the violating users, so as to divide several violating users into different groups; C4: Perform association rule mining on the features of illegal users corresponding to each group, obtain abnormal activity information, cheating group information, high-incidence cheating area information and high-incidence cheating time information respectively, and then perform anti-cheating tracing work on the related activities or users based on the obtained abnormal activity information, cheating group information, high-incidence cheating area information and high-incidence cheating time information.

[0014] When the present invention is working, by tracing back the historical sign-in and sign-out records of all illegal users and the corresponding activity information, it is possible to obtain abnormal activity information, cheating group information, cheating high-incidence area information and cheating high-incidence time information, which can further improve the anti-cheating detection efforts for past cheating users, thereby effectively combating cheating behavior.

[0015] Preferably, step C4 further includes the following steps: when performing anti-cheating tracing work, a suspected violating user determination rule is added, and users who cannot be determined as violating users among users associated with abnormal activity information, cheating group information, high-incidence cheating area information, and high-incidence cheating time information, and some users whose partial feature information deviates from that of normal users by more than a preset threshold, are determined as suspected violating users, and relevant information of some users determined as suspected violating users is synchronized to the secondary verification user list in the relational database.

[0016] Preferably, the step S3 also includes the following steps: calling the user's relevant information, traversing the secondary verification user list maintained by the relational database, and when the user is in the secondary verification user list, enabling the secondary verification process, and when the verification is passed, proceeding to step S4, otherwise, it is determined that the sign-in failed.

[0017] When the present invention is working, it can realize the screening of users suspected of violating the rules. At the same time, when signing in for subsequent activities, the suspected users of violations can be verified twice, which can further enhance the anti-cheating verification efforts and counter illegal and cheating behaviors such as stealing volunteer service time.

[0018] The beneficial technical effects of the present invention include: 1. The present invention can realize the rapid sign-in and sign-out of a large number of users within a large activity range, and can also realize a series of tasks such as the judgment of wrong activity sign-in codes, automatic identification of blacklisted users, and binding of users to clients, which can effectively counter illegal and cheating behaviors such as stealing volunteer service time, greatly improve the participation and effect of activities, reduce the number of false activities, and reduce the situation where the actual number of participants in activities is insufficient, which leads to difficulties in carrying out activities. At the same time, it combats the gray industry chain of signing in and out on behalf of others, stealing volunteer service time, and obtaining profits.

[0019] 2. The present invention compares the regional information of adjacent records, marks the users corresponding to the records with different regional information and the time interval of adjacent records less than the time range of the preset cross-regional illegal operation rules as illegal users, and can distinguish the illegal records of participating in activities in different regions on behalf of others or letting others participate in activities on their behalf, thereby realizing anti-cheating verification of illegal users. At the same time, the anti-cheating verification work occupies less computing power resources, has high verification efficiency and good verification effect, and can further counter illegal and cheating behaviors such as stealing volunteer service time.

[0020] 3. The present invention can obtain abnormal activity information, cheating group information, high-incidence cheating area information and high-incidence cheating time information by tracing the historical sign-in and sign-out records of all illegal users and the corresponding activity information, which can further improve the anti-cheating detection efforts for past cheating users, thereby effectively combating cheating behavior.

[0021] 4. The present invention can screen out users suspected of violating regulations, and at the same time, conduct secondary verification on users suspected of violating regulations when signing in for subsequent activities, which can further enhance the anti-cheating verification efforts and counter illegal and cheating behaviors such as stealing volunteer service time.

[0022] Other features and advantages of the present invention will be disclosed in detail in the following specific embodiments and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The present invention will be further described below in conjunction with the accompanying drawings: Figure 1 A workflow for a sign-in and sign-out anti-cheating method Figure 1 ; Figure 2 A workflow for a sign-in and sign-out anti-cheating method Figure 2 ; Figure 3 A workflow for a sign-in and sign-out anti-cheating method Figure 3 . DETAILED DESCRIPTION

[0024] The technical solutions of the embodiments of the present invention are explained and described below in conjunction with the drawings of the embodiments of the present invention, but the following embodiments are only preferred embodiments of the present invention, not all. Based on the embodiments in the implementation mode, other embodiments obtained by those skilled in the art without creative work are all within the protection scope of the present invention.

[0025] In the following description, terms such as "inside", "outside", "up", "down", "left", "right", etc. that indicate directions or positional relationships are only used to facilitate the description of the embodiments and simplify the description, and do not indicate or imply that the referred device or element must have a specific direction, be constructed and operate in a specific direction. Therefore, it should not be understood as a limitation of the present invention. Embodiment 1:

[0026] Please refer to the attached Figure 1 This embodiment discloses a sign-in and sign-out anti-cheating method, comprising the following steps: S1: Initialize the activity sign-in process and distribute the activity sign-in code; S2: Access the client, obtain the user's sign-in request, and determine whether the activity sign-in code entered by the user is correct. If it is correct, proceed to step S3; otherwise, it is determined that the sign-in fails; S3: Call the user's relevant information and traverse the user blacklist maintained by the preset relational database. If the user is not in the user blacklist, go to step S4. Otherwise, it is determined that the sign-in fails. S4: Obtain relevant information of the user, query the NoSQL database, and determine whether the number of check-ins corresponding to the user in the NoSQL database and the number of check-ins corresponding to the client exceed a preset threshold. If not, proceed to step S5; otherwise, it is determined that the check-in fails. S5: The user signs in successfully, and the relational database and NoSQL database are updated to record the user's relevant information. When the activity ends, proceed to step S6; S6: Initialize the activity sign-out process; S7: Obtain the user's sign-out request, query the NoSQL database, and determine whether there is a sign-in record of the user and a sign-in record of the corresponding client in the NoSQL database. If there is a sign-in record, determine that the user has successfully signed out, and record the user's relevant information. Otherwise, determine that the sign-out has failed.

[0027] When this embodiment is working, it can realize the rapid sign-in and sign-out of a large number of users within a larger activity range. At the same time, it can realize a series of tasks such as judging the wrong activity sign-in code, automatically identifying blacklisted users, and binding users with clients. It can effectively counter illegal and cheating behaviors such as stealing volunteer service time, greatly improve the participation and effect of activities, reduce the number of false activities, and reduce the situation where the actual number of participants in activities is insufficient, which leads to difficulties in carrying out activities. At the same time, it cracks down on the gray industry chain that replaces sign-in and sign-out, steals volunteer service time, and obtains profits.

[0028] Preferably, step S2 further includes the following steps: when a user fails to sign in, atomic self-increment data is stored in a preset NoSQL database to record the number of times the same user incorrectly enters the activity sign-in code; when the number of times the user incorrectly enters the activity sign-in code stored in the NoSQL database exceeds a preset upper limit on the number of trial and error attempts, the user is switched to a locked state, and the relevant information of the user is updated to a user blacklist; for example, the number of times the user incorrectly enters the activity sign-in code is set to five times; when the incorrect activity sign-in code is entered for the sixth time, the user is switched to a locked state, and the relevant information of the user is updated to a user blacklist, thereby avoiding brute force cracking of the activity sign-in code.

[0029] Preferably, in step S4, when obtaining relevant information of the user, querying the NoSQL database, and determining whether the number of check-ins corresponding to the user and the number of check-ins corresponding to the client in the NoSQL database exceed a preset threshold, the following steps are adopted: obtaining relevant information of the user, including the user's ID and the unique identifier of the client, querying the NoSQL database through the user's ID and the unique identifier of the client, and determining whether the number of check-ins corresponding to the user and the number of check-ins corresponding to the client in the NoSQL database exceed a preset threshold. In specific implementation, the threshold is generally set to zero. When the number of check-ins corresponding to the user and the number of check-ins corresponding to the client exceed zero, it can be determined that the user has signed in repeatedly or the client has signed in for multiple users. At this time, it is determined that the sign-in failed, which can avoid a single client from signing in for multiple users.

[0030] In the specific implementation, in step S5, when the user signs in successfully, the relational database and the NoSQL database are updated to record the relevant information of the user, the following steps are adopted: when the user signs in successfully, the relevant information of the user, including the user's ID and the unique identifier of the client, is written into the relational database, and the NoSQL database is double-written.

[0031] Preferably, in step S7, the user's sign-out request is obtained, the NoSQL database is queried, and it is determined whether there is a sign-in record of the user and a sign-in record of the corresponding client in the NoSQL database. When there is a sign-in record, it is determined that the user has successfully signed out, and the relevant information of the user is recorded. Otherwise, when it is determined that the sign-out fails, the following steps are adopted: when the user signs out, the unique identifier of the client used for the user's sign-out is obtained, and the unique identifier of the client used for the user's sign-in is obtained from the NoSQL database through the user's ID, and a comparison is performed. When the unique identifiers are consistent, it is determined that there is a sign-in record and the user has successfully signed out. Otherwise, it is determined that the sign-out fails, thereby achieving a single user signing in and out only through the same client, thereby avoiding others signing in or signing out on behalf of the user. Embodiment 2:

[0032] Please refer to the attached Figure 2 This embodiment provides a sign-in and sign-out anti-cheating method. The similarities with other embodiments are not repeated here, and the differences are described in detail below.

[0033] Please refer to the attached Figure 2 In this embodiment, the following steps are also included: A1: Perform anti-cheating verification work through preset trigger conditions; A2: Obtain all activity data within a unit of time. The activity data includes the time information, area information, and relevant information of the users participating in the activity. A3: Obtain the time range of the preset cross-region violation operation rules, and obtain the user ID list of the user who has signed in and signed out in a unit time in the relational database; A4: Traverse the user ID list, query the check-in and check-out record list of each user in a unit time from the relational database according to the user ID, arrange several records in the check-in and check-out record list in the order of creation time of each record, compare the region information of adjacent records, and when the region information of adjacent records is different and the time interval between adjacent records is less than the time range of the preset cross-region violation operation rule, mark the record as a violation record and mark the user as a violating user.

[0034] When this embodiment is working, by comparing the regional information of adjacent records, the users corresponding to the records with different regional information of adjacent records and the time interval of adjacent records being less than the time range of the preset cross-regional illegal operation rules are marked as illegal users. This can distinguish the illegal records of participating in activities in different regions on behalf of others or letting others participate in activities in different regions on their behalf, thereby realizing anti-cheating verification of illegal users. At the same time, the anti-cheating verification work occupies less computing power resources, has high verification efficiency and good verification effect, and can further counter illegal and cheating behaviors such as stealing volunteer service time.

[0035] Preferably, in step A4, when a user is marked as an illegal user, the relevant information of the user is synchronized to the user blacklist. Embodiment three:

[0036] Please refer to the attached Figure 3 This embodiment provides a sign-in and sign-out anti-cheating method. The similarities with other embodiments are not repeated here, and the differences are described in detail below.

[0037] In the specific implementation, the step A4 further includes the following steps: C1: After marking the offending users, trace back the historical check-in and check-out records of all offending users and the corresponding activity information, and store them in structured data; C2: After obtaining the historical sign-in and sign-out records of all violating users and preprocessing them, the preset feature rules are used to create the time interval features of intensive activity participation, the area features of the activity participation of the violating users, the identity features of the violating users, and the associated features of the activities participated by the violating users. C3: Through cluster analysis, the historical sign-in and sign-out records of all violating users are processed according to the characteristics of the time interval of intensive participation in activities, the characteristics of the area where the violating users participate in activities, the characteristics of the identity of the violating users, and the characteristics of the association of the activities participated by the violating users, so as to divide several violating users into different groups; C4: Perform association rule mining on the features of illegal users corresponding to each group, obtain abnormal activity information, cheating group information, high-incidence cheating area information and high-incidence cheating time information respectively, and then perform anti-cheating tracing work on the related activities or users based on the obtained abnormal activity information, cheating group information, high-incidence cheating area information and high-incidence cheating time information.

[0038] When this embodiment is working, by tracing the historical sign-in and sign-out records of all illegal users and the corresponding activity information, it is possible to obtain abnormal activity information, cheating group information, high-incidence cheating area information and high-incidence cheating time information, which can further improve the anti-cheating detection efforts for past cheating users, thereby effectively combating cheating behavior.

[0039] In the specific implementation, in the step C2, after obtaining the historical sign-in and sign-out records of all violating users and preprocessing them, the following steps are adopted to obtain the historical sign-in and sign-out records of all violating users and preprocess them. When creating the time interval feature of intensive participation in activities for all violating users, the time information of each violating user participating in activities is obtained, and after time sorting several activities corresponding to each violating user, the time interval of adjacent activities is obtained. At least one intensive activity time interval feature of each violating user is determined after dynamic correction through a preset threshold. In the specific implementation, the threshold can be set to a fixed time or number of days. At the same time, the threshold can be corrected during special time periods such as holidays. For example, multiple participation in activities during holidays can be dynamically corrected to concentrated activities, which can avoid misjudgment to a certain extent. When creating regional features of illegal users participating in activities for all illegal users, obtain regional information of activities participated in by each illegal user, perform arithmetic averaging on the regional distribution of several activities corresponding to each illegal user, and determine the regional features of illegal users participating in activities for each illegal user through a preset threshold; When creating violating user identity features for all violating users, obtain the relevant information of each violating user, standardize the numerical features in the relevant information of each violating user, and convert the non-numerical attributes in the relevant information into coded categorical variables, thereby obtaining the violating user identity features of each violating user; When creating the associated features of the illegal users participating in activities for all illegal users, the type information of the activities participated by each illegal user is obtained, the type information of several activities corresponding to each illegal user is counted, and the high-frequency activity types are used to determine the associated features of the illegal users participating in activities for each illegal user through a preset ratio.

[0040] Preferably, in step C3, the historical check-in and check-out records of all violating users are processed by cluster analysis according to the characteristics of the time interval of intensive participation in activities, the characteristics of the area of ​​activity participation of violating users, the identity characteristics of violating users and the characteristics associated with the activities participated by violating users, so as to divide a number of violating users into different groups, and the following steps are adopted: the historical check-in and check-out records of all violating users are processed by cluster analysis according to the characteristics of the time interval of intensive participation in activities, the characteristics of the area of ​​activity participation of violating users, the identity characteristics of violating users and the characteristics associated with the activities participated by violating users, so as to divide a number of violating users into different groups according to the above characteristics; in the specific implementation, user identity characteristics, activity time characteristics, activity area characteristics and activity type characteristics can be selected as aggregation indicators as needed.

[0041] Preferably, in step C4, after obtaining abnormal activity information, cheating group information, cheating high-incidence area information and cheating high-incidence time information respectively, anti-cheating tracing work is performed on the associated activities or users according to the obtained abnormal activity information, cheating group information, cheating high-incidence area information and cheating high-incidence time information, the following steps are adopted: performing association rule mining on the features of the illegal users corresponding to each group. In specific implementation, the association rule mining can be performed through similarity judgment, set operation or SQL query, or a pre-trained AI model can be deployed to perform association rule mining on the features of the illegal users of each group, so as to obtain abnormal Activity information, cheating group information, cheating high-incidence area information and cheating high-incidence time information. For example, in volunteer activities, in order to meet the demand for volunteer service time during the graduation season of college students, a large number of students often steal volunteer service time. At this time, through this embodiment, it can be analyzed that the cheating group information of these illegal users is students, the cheating high-incidence area is the area where the school is located, and the cheating high-incidence time is March to June. At this time, the obtained information can be used to conduct anti-cheating tracing work on users with student identities in all activities in the area where the school is located from March to mid-June. By applying the technical solution in Example 2, cheating behavior can be judged, thereby further improving the detection range of anti-cheating and combating cheating behavior.

[0042] As a further improvement of this embodiment, in the step C4, the following steps are also included: when performing anti-cheating tracing work, a suspected violating user determination rule is added, and users who cannot be determined as violating users among users associated with abnormal activity information, cheating group information, high-incidence cheating area information and high-incidence cheating time information, and some users whose partial feature information deviates from that of normal users by more than a preset threshold, are determined as suspected violating users, and relevant information of users determined as suspected violating users is synchronized to the secondary verification user list in the relational database.

[0043] In the specific implementation, in the step S3, the following steps are also included, calling the relevant information of the user, traversing the secondary verification user list maintained by the relational database, when the user is in the secondary verification user list, enabling the secondary verification process, when the verification is passed, proceeding to step S4, otherwise, it is judged that the sign-in failed. Preferably, the secondary verification can be performed through facial information, fingerprint information or voiceprint information reserved when the user registers, such as face check-in, audio verification or fingerprint punching. Of course, the activity administrator can also perform secondary verification. For users who fail the verification, when the number of verification failures exceeds a preset threshold, the user will be moved from the secondary verification user list to the user blacklist.

[0044] The beneficial technical effects of this embodiment include: the present invention can realize the rapid sign-in and sign-out of a large number of users within a large activity range, and at the same time can realize a series of tasks such as the judgment of incorrect activity sign-in codes, automatic identification of blacklisted users, and binding of users to clients. It can effectively counter illegal and cheating behaviors such as stealing volunteer service time, greatly improve the participation and effect of activities, reduce the number of false activities, and reduce the situation where the actual number of participants in activities is insufficient, which leads to difficulties in carrying out activities. At the same time, it cracks down on the gray industrial chain that replaces sign-in, sign-out, and steals volunteer service time to obtain profits.

[0045] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that the present invention includes but is not limited to the contents described in the drawings and the above specific embodiments. Any modification that does not deviate from the functional and structural principles of the present invention will be included in the scope of the claims.

Claims

1. A sign-in and sign-out anti-cheating method, characterized in that: The following steps are involved: S1: Initialize the activity sign-in process and distribute the activity sign-in code; S2: Access the client, obtain the user's sign-in request, and determine whether the activity sign-in code entered by the user is correct. If it is correct, proceed to step S3; otherwise, it is determined that the sign-in fails; S3: Call the user's relevant information and traverse the user blacklist maintained by the preset relational database. If the user is not in the user blacklist, go to step S4. Otherwise, it is determined that the sign-in fails. S4: Obtain relevant information of the user, query the NoSQL database, and determine whether the number of check-ins corresponding to the user in the NoSQL database and the number of check-ins corresponding to the client exceed a preset threshold. If not, proceed to step S5; otherwise, it is determined that the check-in fails. S5: The user signs in successfully, and the relational database and NoSQL database are updated to record the user's relevant information. When the activity ends, proceed to step S6; S6: Initialize the activity sign-out process; S7: Obtain the user's sign-out request, query the NoSQL database, and determine whether there is a sign-in record of the user and a sign-in record of the corresponding client in the NoSQL database. If there is a sign-in record, determine that the user has successfully signed out, and record the user's relevant information. Otherwise, determine that the sign-out has failed.

2. A sign-in and sign-out anti-cheating method according to claim 1, characterized in that: In the step S2, the following steps are also included: when the user fails to sign in, the atomic self-increment data is stored in a preset NoSQL database to record the number of times the same user incorrectly enters the activity sign-in code; when the number of times the user incorrectly enters the activity sign-in code stored in the NoSQL database exceeds the preset upper limit of the number of trial and error, the user is switched to a locked state, and the relevant information of the user is updated to the user blacklist list.

3. A sign-in and sign-out anti-cheating method according to claim 1, characterized in that: In step S4, when obtaining relevant information of the user, querying the NoSQL database, and determining whether the number of check-ins corresponding to the user and the number of check-ins corresponding to the client in the NoSQL database exceed a preset threshold, the following steps are adopted: obtaining relevant information of the user, including the user's ID and the unique identifier of the client, querying the NoSQL database through the user's ID and the unique identifier of the client, and determining whether the number of check-ins corresponding to the user and the number of check-ins corresponding to the client in the NoSQL database exceed a preset threshold.

4. A sign-in and sign-out anti-cheating method according to claim 1, characterized in that: In step S5, when the user signs in successfully, the relational database and the NoSQL database are updated to record the user's relevant information, the following steps are adopted: when the user signs in successfully, the user's relevant information, including the user's ID and the client's unique identifier, is written into the relational database, and the NoSQL database is double-written.

5. A sign-in and sign-out anti-cheating method according to claim 4, characterized in that: In step S7, the user's sign-out request is obtained, and the NoSQL database is queried to determine whether there is a sign-in record of the user and a sign-in record of the corresponding client in the NoSQL database. When there is a sign-in record, it is determined that the user has signed out successfully, and the relevant information of the user is recorded. Otherwise, when it is determined that the sign-out fails, the following steps are adopted: when the user signs out, the unique identifier of the user's sign-out client is obtained, and the unique identifier of the user's sign-in client is obtained from the NoSQL database through the user's ID, and a comparison is performed. When the unique identifiers are consistent, it is determined that there is a sign-in record and the user has signed out successfully. Otherwise, it is determined that the sign-out fails.

6. A sign-in and sign-out anti-cheating method according to claim 1, characterized in that: The following steps are also included: A1: Perform anti-cheating verification work through preset trigger conditions; A2: Obtain all activity data within a unit of time. The activity data includes the time information, area information, and relevant information of the users participating in the activity. A3: Obtain the time range of the preset cross-region violation operation rules, and obtain the user ID list of the user who has signed in and signed out in a unit time in the relational database; A4: Traverse the user ID list, query the check-in and check-out record list of each user in a unit time from the relational database according to the user ID, arrange several records in the check-in and check-out record list in the order of creation time of each record, compare the region information of adjacent records, and when the region information of adjacent records is different and the time interval between adjacent records is less than the time range of the preset cross-region violation operation rule, mark the record as a violation record and mark the user as a violating user.

7. A sign-in and sign-out anti-cheating method according to claim 6, characterized in that: In step A4, when a user is marked as an illegal user, the relevant information of the user is synchronized to the user blacklist.

8. A sign-in and sign-out anti-cheating method according to claim 6, characterized in that: In the step A4, the following steps are also included: C1: After marking the offending users, trace back the historical check-in and check-out records of all offending users and the corresponding activity information, and store them in structured data; C2: After obtaining the historical sign-in and sign-out records of all violating users and preprocessing them, the preset feature rules are used to create the time interval features of intensive activity participation, the area features of the activity participation of the violating users, the identity features of the violating users, and the associated features of the activities participated by the violating users. C3: Through cluster analysis, the historical sign-in and sign-out records of all violating users are processed according to the characteristics of the time interval of intensive participation in activities, the characteristics of the area where the violating users participate in activities, the characteristics of the identity of the violating users, and the characteristics of the association of the activities participated by the violating users, so as to divide several violating users into different groups; C4: Perform association rule mining on the features of illegal users corresponding to each group, obtain abnormal activity information, cheating group information, high-incidence cheating area information and high-incidence cheating time information respectively, and then perform anti-cheating tracing work on the related activities or users based on the obtained abnormal activity information, cheating group information, high-incidence cheating area information and high-incidence cheating time information.

9. A sign-in and sign-out anti-cheating method according to claim 8, characterized in that: In the step C4, the following steps are also included: when performing anti-cheating tracing work, a suspected violating user determination rule is added, and users who cannot be determined as violating users among the users associated with abnormal activity information, cheating group information, high-incidence cheating area information and high-incidence cheating time information, and some users whose partial feature information deviates from that of normal users by more than a preset threshold are determined as suspected violating users, and the relevant information of some users determined as suspected violating users is synchronized to the secondary verification user list in the relational database.

10. A sign-in and sign-out anti-cheating method according to claim 9, characterized in that: In the step S3, the following steps are also included: calling the user's relevant information, traversing the secondary verification user list maintained by the relational database, and when the user is in the secondary verification user list, enabling the secondary verification process, and when the verification is passed, proceeding to step S4, otherwise, it is determined that the sign-in failed.

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