A method for calculating the suspiciousness of offline check-in on mobile terminals

Abstract

This invention relates to a method for calculating the suspiciousness of offline check-in on mobile terminals, comprising the following steps: Step S1: When a check-in initiator user initiates a check-in, they obtain check-in-related information by calling a mobile terminal interface and submit it to the server; Step S2: The check-in user scans the check-in code on their mobile terminal and obtains the initiator user's information from the server; Step S3: After a successful check-in, the suspiciousness of the user is calculated on the mobile client to obtain the total suspiciousness; Step S4: The total suspiciousness is returned to the server, and the check-in initiator user obtains the server data to generate a suspiciousness list. This invention calculates the suspiciousness of check-in users by applying diverse judgment methods, making the evaluation more efficient and enabling check-in management personnel to more comprehensively and flexibly measure the quality of check-ins.

Description

Technical Field

[0001] This invention relates to a method for calculating the suspiciousness of offline check-in on mobile terminals. Background Technology

[0002] With the development of internet technology, scanning QR codes to check in via mobile devices such as smartphones has become widely used in various scenarios, including university classrooms, training sessions, and meetings. This check-in method is more convenient, efficient, and easier to track and manage than traditional methods. However, existing ordinary QR code check-in applications cannot fully meet the needs of various scenarios. In situations requiring accurate check-in, the anti-cheating mechanisms are inadequate. Most applications or mini-programs can be used to cheat on check-in through various methods, such as having someone else sign in on your behalf, using software to simulate geographical location for fake check-in, or remotely checking in by scanning QR codes passed from others. False check-in information can cause significant problems for administrators, directly or indirectly affecting their decision-making and leading to incorrect judgments.

[0003] Current methods for preventing sign-in fraud are limited and the evaluation results are relatively simple. For example, they can only determine whether fraud has occurred through facial recognition or user geographical distance. There is no anti-fraud mode that combines multiple anti-fraud methods. Furthermore, current sign-in fraud prevention methods are limited to determining whether a user has committed fraud, but cannot measure the degree of suspicion of a user's sign-in. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a method for calculating the suspiciousness of offline check-in on mobile terminals. By applying diversified judgment methods to calculate the suspiciousness of check-in users, the evaluation is made more efficient, enabling check-in managers to more comprehensively and flexibly measure the quality of check-in.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for calculating the suspiciousness of offline check-in on mobile terminals includes the following steps:

[0007] Step S1: When a user initiates a check-in, the relevant check-in information is obtained by calling the mobile terminal interface and submitted to the server.

[0008] Step S2: The user signs in by scanning the sign-in code with their mobile terminal and obtains the information of the initiating user from the server;

[0009] Step S3: After a user successfully signs in, the suspiciousness is calculated on the mobile client to obtain the total suspiciousness.

[0010] Step S4: Return the total suspiciousness to the server, and the user who initiated the check-in obtains the server data to generate a suspiciousness list.

[0011] Furthermore, step S1 also includes: when the user who initiated the check-in calls the location service interface, WiFi interface, and Bluetooth interface of the mobile APP or mini-program to obtain relevant information and submit it to the server, including the initiation time, latitude and longitude information, Bluetooth device list and WiFi device list, wherein the Bluetooth list and WiFi list are sorted according to signal strength.

[0012] Furthermore, step S2 also includes: the server stores the information submitted by the user who initiated the check-in into a cache or user database, and generates a check-in code that is updated in real time. The user who is checking in scans the check-in code through a mobile terminal and obtains the information submitted by the user who initiated the check-in, as well as the check-in time and check-in position in the mobile APP or mini-program. The user who is checking in calls the relevant interfaces of the mobile APP or user mini-program to obtain the check-in time, check-in position, latitude and longitude information, Bluetooth device list and WiFi device list, wherein the Bluetooth device list and WiFi device list are sorted according to signal strength.

[0013] Furthermore, after a user successfully signs in, the WGS84 latitude and longitude coordinates of the user and the initiating user are obtained as (x1, y1) and (x2, y2), respectively, where x represents longitude and y represents latitude. After being converted into radians by the calculation program, they are respectively (G 1x G 1y ),(G 2x G 2y The formula for converting radians is as follows.

[0014]

[0015] Then calculate the distance D1 using the following distance formula.

[0016] D1=R×arccos[cos(G 1y cos(G) 2y cos(G) 1x -G 2x )+sin(G 1y sin(G) 2y )]

[0017] The Earth's radius is R = 6371.0 km.

[0018] Set the distance threshold to D m ;

[0019] If D1>D m If the user is not within the geographical distance range of the check-in, then no suspicion level calculation is performed, and the total suspicion level SR is directly returned as 1; if D1 <D mIf so, retain the value of D1 and calculate the degree of suspicion.

[0020] Furthermore, the suspiciousness calculation includes suspiciousness calculation of check-in time, suspiciousness calculation of check-in distance, suspiciousness calculation of WiFi list, and suspiciousness calculation of Bluetooth list.

[0021] Furthermore, the calculation of the suspiciousness of the check-in time is specifically as follows: the check-in time initiated by the user is recorded as T1, the check-in time of the user is recorded as T2, and a time threshold T is set. m ;

[0022] If T2-T1>T m If the user's check-in time has expired, then all subsequent processes will be skipped, and the total suspicion level SR will be returned as 1 directly.

[0023] If T2-T1≤T m Proceed to the next step and begin calculating the suspiciousness level of the check-in time, SR1.

[0024] When a user signs in, they will receive their sign-in ranking (B) and the total number of sign-ins (N) along with the data. The suspiciousness of the sign-in time (SR1) is calculated using the following formula.

[0025]

[0026] Furthermore, the calculation of the suspiciousness of the check-in distance is as follows: based on the distance D1 and the distance threshold D... m The suspiciousness level SR2 of the check-in distance is calculated using the following formula:

[0027]

[0028] Furthermore, the calculation of the suspiciousness of the WiFi list is as follows:

[0029] (1) Sort the WiFi devices of the users who checked in according to their signal strength, initialize i=0, and set the initial value of the number of WiFi devices n to 0;

[0030] And set a threshold K for the number of matches and a threshold M for the number of traversals.

[0031] (2) Retrieve the i-th WiFi of the sign-in user and determine whether the WiFi exists in the initiating user's WiFi device list; if it exists, execute i = i + 1, n = n + 1; if it does not exist, retrieve the next WiFi, i.e., execute i = i + 1.

[0032] (3) Execute the judgment. If the number of matched WiFi is greater than or equal to the set matching number threshold K, that is, if n≥K, directly return the matching degree P1 as 1 and proceed to the next step to calculate the WiFi list suspicion degree; otherwise, repeat steps (2) and (3) until the WiFi list of the sign-in user is traversed or the number of traversals i is greater than or equal to the traversal number threshold M.

[0033] (4) Calculate the matching degree P1 using the following formula:

[0034]

[0035] (5) Calculate the WiFi list suspicion level SR3:

[0036] SR3 = 1 - P1.

[0037] Furthermore, the calculation of the suspiciousness of the Bluetooth list is as follows:

[0038] (1) Sort the list of Bluetooth devices of the users who have checked in according to the signal strength RSSI; let i=1, record the number of Bluetooth devices n that are matched as 0, and set the threshold K for the number of matches and the threshold M for the number of traversals;

[0039] (2) Retrieve the i-th Bluetooth of the sign-in user and determine whether the Bluetooth exists in the initiating user's Bluetooth list; if it exists, execute i = i + 1, n = n + 1; if it does not exist, retrieve the next Bluetooth in the Bluetooth list, i.e., execute i = i + 1.

[0040] (3) Execute the judgment. If the number of matched Bluetooth devices is greater than or equal to the matching number threshold K, that is, if n≥K, directly return the matching degree P2 as 1 and proceed to the next step to calculate the Bluetooth list suspicion degree; otherwise, repeat steps (2) and (3) until the Bluetooth list of the sign-in user is traversed or the number of traversed i is greater than the traversal number threshold M.

[0041] (4) Calculate the matching degree P2 using the following formula:

[0042]

[0043] (5) Calculate the Bluetooth list suspicion level SR4:

[0044] SR4 = 1 - P2.

[0045] Furthermore, the calculation of the total suspiciousness specifically involves: assigning weights to each factor in the total suspiciousness calculation, with the following weights: check-in time suspiciousness weight w1, check-in distance suspiciousness weight w2, WiFi list suspiciousness weight w3, and Bluetooth list suspiciousness weight w4; calculating the final total suspiciousness SR; and returning the result to the server.

[0046]

[0047] Compared with the prior art, the present invention has the following advantages:

[0048] This invention can screen out individuals with high suspicion levels and provides a quantitative standard for evaluating the suspicion level of user check-ins, enabling managers to more accurately and efficiently judge the quality of check-ins. At the same time, most of the calculations are completed on the client side, which can effectively reduce server load and improve check-in efficiency when there are many check-ins. Attached Figure Description

[0049] Figure 1 This is a timing diagram of the check-in process in one embodiment of the present invention;

[0050] Figure 2 This is a flowchart of a suspiciousness calculation procedure in one embodiment of the present invention;

[0051] Figure 3 This is a flowchart of WiFi list matching degree calculation in one embodiment of the present invention. Detailed Implementation

[0052] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0053] Please refer to Figures 1 - 3 This invention provides a method for calculating the suspiciousness of offline check-in on mobile terminals, comprising the following steps:

[0054] (1) Check-in Initiator: The user initiates the check-in process by calling the location service interface (e.g., wx.getLocation interface of WeChat Mini Program), WiFi interface (e.g., wx.getWifiList interface of WeChat Mini Program), and Bluetooth interface (e.g., wx.getBluetoothDevices interface of WeChat Mini Program) of the mobile APP or Mini Program to obtain relevant information and submit it to the server. This information includes the check-in initiation time, latitude and longitude information, and lists of WiFi and Bluetooth devices. The WiFi and Bluetooth device lists are sorted by signal strength. The obtained information is as follows:

[0055] Location information:

[0056] Latitude: A, Longitude: B Initiation Time: 14:58:26 WiFi Device List:

[0057] SSID BSSID Signal Strength lbj 04:5f:a7:84:af:89 0.9473683834075928 @Ruijie-sB356 ee:b9:70:82:b3:58 0.7382940053939819 CMCC-nb63 3c:cd:57:d9:8b:6a 0.5585205554962158 TP-LINK_503 f8:8c:21:2c:14:62 0.5117818117141724 feicuishijia 06:b8:e6:02:13:63 0.5117818117141724 Empty 2e:f2:2c:2c:b1:fa 0.4186046421527862 205 18:f2:2c:2c:b1:fa 0.2956398427486419 Xiaomi_703 3c:cd:57:e1:54:e4 0.2781965136528015 HUAWEI e2:66:ae:49:5c:19 0.2617059946060180 CMCC-305 28:77:77:fa:fa:6c 0.2461634576320648 CMCC-frvK 74:ad:b7:5d:ac:94 0.2461634576320648 DoubleXie a4:39:b3:05:66:12 0.2315525114536285 CMCC-mKud 18:f2:2c:6a:41:f2 0.2178475558757782 Empty 2e:f2:2c:6a:41:f2 0.2178475558757782 Empty 2e:f2:2c:2c:b1:fa 0.1818181872367859

[0058] Bluetooth device list:

[0059]

[0060]

[0061] Number of people to be signed in: 50

[0062] (2) The server stores the submitted information in the cache or user database and generates a real-time updated check-in code. Users check in by scanning the check-in code with their mobile devices and obtain the information submitted by the initiating user, as well as the check-in time and ranking. Users call the relevant interfaces of their mobile app or user mini-program to obtain the check-in time, latitude and longitude information, WiFi device list, and Bluetooth device list, where the WiFi and Bluetooth device lists are sorted by signal strength.

[0063] The data regarding users who checked in is as follows:

[0064] Location information:

[0065] Latitude: C, Longitude: D Check-in Time: 14:58:56 Number of Check-ins: 26 WiFi List:

[0066] SSID BSSID Signal Strength feicuishijia 06:0d:9e:f9:81:50 0.7684475183486938 Empty 6a:64:4a:b6:68:a6 0.7218034863471985 205 18:f2:2c:2c:b1:fa 0.7218034863471985 505 70:3a:a6:fd:38:0f 0.6666666865348816 lbj 04:5f:a7:84:af:89 0.6464840769767761 Feicuishijia505 f4:83:cd:cb:96:88 0.6254870891571045 Empty da:35:38:7b:03:73 0.5813953280448914 CMCC-xyuD 54:46:17:f2:43:73 0.5117818117141724 DoubleXie a4:39:b3:05:66:12 0.4882181584835052 Empty aa:39:b3:05:66:12 0.4647327065467834 CMCC-ytfe 78:5f:36:ab:b2:ff 0.4414794445037842 Mr.lin_5G 8c:de:f9:22:65:11 0.3535159230232239

[0067] Bluetooth list:

[0068]

[0069]

[0070] Number of people to be signed in: 50

[0071] (3) After a user successfully signs in, the suspiciousness is calculated on the mobile device. The longitude of the initiating user is B, and the latitude is A. The longitude of the sign-in user is D, and the latitude is C. The calculation program converts the longitude and latitude into radians and calculates the distance D1 according to the following formula.

[0072] D1=R×arccos[cos(G 1y cos(G) 2y cos(G) 1x -G 2x )+sin(G 1y sin(G) 2y The distance threshold for this example is approximately 65.240 meters. The Earth's radius R is 6371.0 km. Therefore, D1 is calculated to be approximately 65.240 meters and stored. m The distance is 500 meters, indicating that the users who checked in are located within a 500-meter radius of the initiating user. If D1 > D... m If the user who checked in is determined to be outside a 500m radius of the user who initiated the check-in, then all subsequent processes will not be performed, and the total suspicion level SR will be returned directly as 1.

[0073] (4) In this example, the check-in time T1 initiated by the user is 14:58:26, and the check-in time T2 of the user is 14:58:56. Let the time threshold T be... m It lasts for 300 seconds.

[0074] If T2-T1≤T m In this example, if this condition is met, then the calculation of the check-in time is considered suspicious.

[0075] SR1. Check-in

[0076] While receiving the data, users will receive information that the number of sign-ins B is 26 and the total number of sign-ins N is 50. SR1 is calculated using the following formula.

[0077]

[0078] If T2-T1>T m If the check-in time has expired, then all subsequent check-in procedures will not be completed.

[0079] The process will be returned directly.

[0080] The overall suspicion level (SR) is 1.

[0081] (5) Calculate the suspiciousness of the check-in distance SR2, based on the distance D1 obtained in step (3) and the distance threshold D. m SR2 is calculated using the following formula:

[0082]

[0083] (6) Calculate the WiFi list suspicion level SR3. Sort the WiFi device list of the sign-in users according to signal strength. Let i = 0, record the number of matched WiFi devices n as 0, and set the matching threshold K to 8 and the traversal threshold M to 15.

[0084] (7) Retrieve the i-th WiFi of the sign-in user and determine whether the BSSID of the WiFi exists in the initiating user's WiFi list. If it exists, execute i = i + 1, n = n + 1; if it does not exist, execute i = i + 1.

[0085] (8) Execute the judgment. If the number of matched WiFi is greater than or equal to the matching threshold of 8 set in this example, that is, when n≥8, directly return the matching degree P as 1 and proceed to the next step to calculate the WiFi list suspicion degree; otherwise, repeat steps (7) and (8) until the WiFi list of the sign-in user is traversed or the number of traversals i is greater than or equal to the traversal threshold of 15 set in this example. (9) In this example, the number of matched BSSIDs is 4, so n is 4. The total number of WiFis after traversal is 12. Finally, calculate the matching degree P1. The calculation formula is as follows:

[0086]

[0087] Finally, the WiFi list suspicion level SR3 was calculated:

[0088] SR3 = 1 - P1 ≈ 0.666

[0089] (10) Calculate the Bluetooth list suspicion score SR4. Sort the Bluetooth device list of the users who checked in according to the signal strength RSSI. Let i = 0, the number of matched Bluetooth devices n is also recorded as 0, and set the matching number threshold K to 5 and the traversal number threshold M to 15.

[0090] (11) Retrieve the i-th Bluetooth device from the list of the users who checked in, and determine whether the device ID of this Bluetooth device exists in the list of the initiating user's Bluetooth devices. If it exists, execute i = i + 1, n = n + 1; if it does not exist, execute i = i + 1.

[0091] (12) Execute the judgment. If the number of matched Bluetooth devices is greater than or equal to the matching threshold of 5 set in this example, that is, if n≥5, directly return the matching degree P2 as 1 and proceed to the next step to calculate the Bluetooth list suspicion degree; otherwise, repeat steps (11) and (12) until the Bluetooth device list of the sign-in user is traversed or the number of traversals i is greater than or equal to the traversal threshold of 15 set in this example. In the execution of this example, the fifth Bluetooth device matched the Bluetooth device in the initiating user's Bluetooth device list, so the Bluetooth matching degree P was directly obtained as 1.

[0092] (13) Calculate the Bluetooth pairing degree P2 using the following formula:

[0093]

[0094] Finally, the Bluetooth list suspicion level SR4 was calculated:

[0095] SR4 = 1 - P2 = 1 - 1 = 0

[0096] (14) Assign weights to each factor in the total suspicion level:

[0097] The weights of each factor are assigned as follows in this example:

[0098] The weighting ratio of suspicious check-in time (w1), suspicious check-in distance (w2), suspicious WiFi list (w3), and suspicious Bluetooth list (w4) is 2:4:2:2.

[0099] The total suspicion level (SR) is calculated using the following formula and returned to the server.

[0100]

[0101] The suspiciousness of the user who checked in was calculated to be 0.244.

[0102] (15) Finally, the client initiates a polling of the server to update the list of suspicious users who have signed in in real time. The client sorts each user according to the total suspiciousness SR. The larger the SR is and the closer it is to 1, the more suspicious the user's signing behavior is.

[0103] The above description is only a preferred embodiment of the present invention. All equivalent changes and modifications made within the scope of the claims of the present invention should be included in the scope of the present invention.

Claims

1. A method for calculating the suspiciousness of offline check-in on mobile terminals, characterized in that, Includes the following steps: Step S1: When a user initiates a check-in, the relevant check-in information is obtained by calling the mobile terminal interface and submitted to the server. Step S2: The user signs in by scanning the sign-in code with their mobile terminal and obtains the information of the initiating user from the server; Step S3: After a user successfully signs in, the suspiciousness is calculated on the mobile client to obtain the total suspiciousness. Step S4: Return the total suspiciousness score to the server, and the user who initiated the check-in obtains the server data to generate a suspiciousness score list; After a user successfully signs in, the WGS84 latitude and longitude coordinates of the user and the initiating user are obtained as (x1, y1) and (x2, y2), respectively, where x represents longitude and y represents latitude. After being converted into radians by the calculation program, they are respectively (G... 1x G 1y ),(G 2x G 2y The formula for converting radians is as follows: Then calculate the distance D1 using the following distance formula. D1=R×arccos[cos(G 1y )cos(G 2y )cos(G 1x -G 2x )+sin(G 1y )sin(G 2y )] The Earth's radius is R = 6371.0 km; Set the distance threshold to D m ; If D1>D m If the user is not within the geographical distance range of the check-in, then no suspicion level calculation is performed, and the total suspicion level SR is directly returned as 1; if D1 <D m If so, retain the value of D1 and calculate the degree of suspicion. The suspiciousness calculation includes suspiciousness calculation of check-in time, suspiciousness calculation of check-in distance, suspiciousness calculation of WiFi list, and suspiciousness calculation of Bluetooth list; The calculation of the suspiciousness of the check-in time is as follows: the check-in time initiated by the user is recorded as T1, the check-in time of the user is recorded as T2, and a time threshold T is set. m ; If T2-T1>T m If the user's check-in time has expired, then all subsequent processes will be skipped, and the total suspicion level SR will be returned as 1 directly. If T2-T1≤T m Proceed to the next step and begin calculating the suspiciousness level of the check-in time, SR1. When a user signs in, they will receive their sign-in ranking (B) and the total number of sign-ins (N) along with the data. The suspiciousness of the sign-in time (SR1) is calculated using the following formula.

2. The method for calculating the suspiciousness of offline check-in on mobile terminals according to claim 1, characterized in that, Step S1 further includes: when the user who initiates the check-in, the user calls the location service interface, WiFi interface, and Bluetooth interface of the mobile APP or mini-program to obtain relevant information and submit it to the server, including the initiation time, latitude and longitude information, Bluetooth device list and WiFi device list, wherein the Bluetooth list and WiFi list are sorted according to signal strength.

3. The method for calculating the suspiciousness of offline check-in on mobile terminals according to claim 2, characterized in that, Step S2 further includes: the server stores the information submitted by the user who initiated the check-in into the cache or user database, and generates a check-in code that is updated in real time. The user who is checking in scans the check-in code through a mobile terminal and obtains the information submitted by the user who initiated the check-in, as well as the check-in time and check-in position in the mobile APP or mini-program. The user who is checking in calls the relevant interfaces of the mobile APP or user mini-program to obtain the check-in time, check-in position, latitude and longitude information, Bluetooth device list and WiFi device list, wherein the Bluetooth device list and WiFi device list are sorted according to signal strength.

4. The method for calculating the suspiciousness of offline check-in on mobile terminals according to claim 1, characterized in that, The calculation of the suspiciousness of the check-in distance is as follows: based on the distance D1 and the distance threshold D... m The suspiciousness level SR2 of the check-in distance is calculated using the following formula:

5. The method for calculating the suspiciousness of offline check-in on mobile terminals according to claim 1, characterized in that, The calculation of the suspiciousness of the WiFi list is as follows: (1) Sort the WiFi devices of the users who checked in according to their signal strength, initialize i=0, and set the initial value of the number of WiFi devices n to 0; And set a threshold K for the number of matches and a threshold M for the number of traversals; (2) Retrieve the i-th WiFi of the sign-in user and determine whether the WiFi exists in the WiFi device list of the initiating user; If it exists, execute i = i + 1, n = n + 1; if it does not exist, retrieve the next WiFi, that is, execute i = i + 1. (3) Execute the judgment. If the number of matched WiFi is greater than or equal to the set matching number threshold K, that is, if n≥K, directly return the matching degree P1 as 1 and proceed to the next step to calculate the WiFi list suspicion degree; otherwise, repeat steps (2) and (3) until the WiFi list of the sign-in user is traversed or the number of traversals i is greater than or equal to the traversal number threshold M. (4) Calculate the matching degree P1 using the following formula: (5) Calculate the WiFi list suspicion level SR3: SR3 = 1 - P1.

6. The method for calculating the suspiciousness of offline check-in on mobile terminals according to claim 1, characterized in that, The calculation of the suspiciousness of the Bluetooth list is as follows: (1) Sort the list of Bluetooth devices of the users who have checked in according to the signal strength RSSI; let i=1, record the number of Bluetooth devices n that are matched as 0, and set the threshold K for the number of matches and the threshold M for the number of traversals; (2) Retrieve the i-th Bluetooth of the sign-in user and determine whether the Bluetooth exists in the initiating user's Bluetooth list; if it exists, execute i = i + 1, n = n + 1; if it does not exist, retrieve the next Bluetooth in the Bluetooth list, i.e., execute i = i + 1. (3) Execute the judgment. If the number of matched Bluetooth devices is greater than or equal to the matching number threshold K, that is, if n≥K, directly return the matching degree P2 as 1 and proceed to the next step to calculate the Bluetooth list suspicion degree; otherwise, repeat steps (2) and (3) until the Bluetooth list of the sign-in user is traversed or the number of traversed i is greater than the traversal number threshold M. (4) Calculate the matching degree P2 using the following formula: (5) Calculate the Bluetooth list suspicion level SR4: SR4 = 1 - P2.

7. The method for calculating the suspiciousness of offline check-in on mobile terminals according to claim 1, characterized in that, The calculation of the total suspicion level is specifically as follows: Each factor is assigned a weight in the total suspicion level calculation: check-in time suspicion level w1, check-in distance suspicion level w2, WiFi list suspicion level w3, and Bluetooth list suspicion level w4. The final total suspicion level SR is then calculated and returned to the server.

Images