A punch card processing method and apparatus

Abstract

This application provides a check-in processing method and apparatus, relating to the field of big data technology. The method includes: using the base station connected to the target user's mobile phone during the check-in operation as the target base station; acquiring the base station information of the target base station and the base station information of multiple associated base stations; determining the location of the target user's mobile phone during the check-in based on the base station information of the target base station and the base station information of the multiple base stations associated with the target user's mobile phone; determining the credibility score of the check-in based on the determined location information and the identifier of the base station connected to the check-in, combined with a preset rule engine; and determining whether the check-in is an abnormal check-in based on the credibility score. This solution solves the technical problem of low accuracy in check-in data caused by the inability to identify abnormal check-in operations, achieving the technical effect of effectively identifying abnormal check-ins and improving the accuracy of check-in data.

Description

Technical Field

[0001] This application belongs to the field of big data technology, and in particular relates to a method and apparatus for clocking in / out. Background Technology

[0002] For all enterprises and institutions, employee clocking in and out is a crucial aspect of corporate management. Existing clocking-in methods include mechanical clocking, card swiping, and manual check-in. However, these methods suffer from cumbersome operation and low efficiency. Therefore, an online clocking-in method is proposed, using an app for online clocking in and out. For example, by setting up clocking-in areas, the company's internal app's attendance module can be used for clocking in and out.

[0003] However, online check-in has the following problems:

[0004] 1) Easily cracked: After obtaining the highest level of access to the phone through some means, users can modify the data on the phone terminal, such as modifying the phone's GPS location, so that the user can clock in even if they are not in the work area;

[0005] 2) Having someone else clock in for them: Some users may log into their company account through other colleagues and then clock in for them.

[0006] 3) Automated clocking in and out: Some employees with strong hands-on skills will purchase multiple mobile phones, place one of them in the office area, and set up automatic tasks through automation software. When the designated time arrives, the mobile phone will automatically trigger the automated task, simulate the user's operation, and automatically clock in and out through the clocking-in APP to complete the automatic check-in and check-out operations.

[0007] It is evident that existing online check-in systems have many risks and problems. Currently, there is no effective solution to the problems of cumbersome operation and easy forgery in existing check-in methods. Summary of the Invention

[0008] The purpose of this application is to provide a method and apparatus for processing attendance records, which can effectively identify abnormal attendance records.

[0009] This application provides a method and apparatus for processing attendance records, which are implemented as follows:

[0010] A clock-in / clock-out processing method includes:

[0011] The base station connected to the target user's mobile phone when performing this check-in operation is used as the target base station;

[0012] Obtain the base station information of the target base station, and the base station information of multiple base stations associated with the target user's mobile phone;

[0013] Based on the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone, the location of the target user's mobile phone for this check-in is determined;

[0014] Based on the determined location information and the identifier of the base station connected to this check-in, combined with the preset rule engine, the credibility score of this check-in is determined.

[0015] Based on the credibility score of this check-in, determine whether this check-in is abnormal.

[0016] In one implementation, determining the location of the target user's mobile phone for this check-in, based on the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone, includes:

[0017] Obtain the location information and signal strength of the target base station;

[0018] Obtain the location information and signal strength of each of the multiple base stations associated with the target base station;

[0019] Based on the signal strength of the target base station and the signal strength of each of the multiple base stations associated with the target user's mobile phone, the distance between the target base station and each of the multiple base stations is calculated.

[0020] Based on the location information of each of the multiple base stations and the distance between the target base station and each of the multiple base stations, the location of the target user's mobile phone for this check-in is determined.

[0021] In one implementation, the location of the target user's mobile phone for this check-in is determined based on the location information of each of the plurality of base stations and the distance between the target base station and each of the plurality of base stations, including:

[0022] Using the location information of the target base station as one vertex of a triangle, the location information of two base stations from the plurality of base stations are selected one by one as two vertices of a triangle to generate multiple triangles;

[0023] Based on the location information of each of the multiple base stations and the distance between the target base station and each of the multiple base stations, calculate the area of ​​each of the multiple triangles;

[0024] The area containing the triangle with the smallest area among the multiple triangles is selected as the location where the target user's mobile phone checks in for this time.

[0025] In one implementation, based on the determined location information of this check-in and the identifier of the base station connected to this check-in, and in conjunction with a preset rule engine, a credibility score for this check-in is determined, including:

[0026] Based on the location information of this check-in, the distance between this check-in point and historically frequently used check-in points is determined as the first dimension of data;

[0027] Based on the identifier of the base station connected to this check-in, the result of determining whether the base station connected to this check-in is a commonly used base station is used as the second dimension of data.

[0028] Determine whether the MAC address of the mobile phone used for this check-in is a commonly used MAC address, and use the judgment result as the third dimension data;

[0029] Determine whether the mobile phone number used for this check-in is the user's own mobile phone number, and use the judgment result as the fourth dimension data;

[0030] Based on the weight values ​​of each dimension, the data in the first, second, third, and fourth dimensions are weighted to obtain the credibility score for this check-in.

[0031] In one implementation, the data in the first, second, third, and fourth dimensions are weighted according to the weight values ​​of each dimension to obtain the credibility score for this check-in, including:

[0032] Scores are assigned to the first, second, third, and fourth dimension data according to preset rules;

[0033] Based on the assigned scores, the results are weighted and accumulated according to the weight values ​​of each dimension to obtain the credibility score for this check-in.

[0034] In one implementation, the base station connected to the target user's mobile phone when performing this check-in operation is used as the target base station, including:

[0035] Establish a communication connection with the target user's mobile phone;

[0036] Based on the communication connection, obtain the user's current mobile phone signal;

[0037] Based on the current mobile phone signal, determine the base station that the target user's mobile phone is connected to when performing this check-in operation.

[0038] A time clock processing device, comprising:

[0039] The first determining module is used to identify the base station connected to the target user's mobile phone when performing this check-in operation as the target base station;

[0040] The acquisition module is used to acquire the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone;

[0041] The second determining module is used to determine the location of the target user's mobile phone for this check-in based on the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone;

[0042] The third determination module is used to determine the credibility score of this check-in based on the determined location information of this check-in and the identifier of the base station connected to this check-in, combined with the preset rule engine.

[0043] The fourth determination module is used to determine whether the current check-in is an abnormal check-in based on the credibility score of the current check-in.

[0044] An electronic device includes a processor and a memory for storing processor-executable instructions, wherein the processor, when executing the instructions, implements the steps of the method described above.

[0045] A computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0046] A computer program product includes a computer program / instructions that, when executed by a processor, implement the steps of the above-described method.

[0047] The check-in processing method provided in this application uses the base station connected to the target user's mobile phone when performing the check-in operation as the target base station. Then, by using the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone, the location of the target user's mobile phone during the check-in is determined. Furthermore, based on the determined location information and the identifier of the base station connected to the check-in, combined with a preset rule engine, a credibility score for the check-in is determined, thereby determining whether the check-in is abnormal. This solution solves the technical problem of low accuracy in check-in data caused by the inability to identify abnormal check-in operations, achieving the technical effect of effectively identifying abnormal check-ins and improving the accuracy of check-in data. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart of one embodiment of the attendance processing method provided in this application;

[0050] Figure 2 This application provides a schematic diagram illustrating the locational relationship between the target base station and similar base stations;

[0051] Figure 3 This is a hardware structure block diagram of an electronic device for a card-swiping processing method provided in this application;

[0052] Figure 4 This is a schematic diagram of the module structure of one embodiment of the attendance processing device provided in this application. Detailed Implementation

[0053] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0054] Figure 1 This is a flowchart of one embodiment of the attendance processing method provided in this application. Although this application provides method operation steps or device structures as shown in the following embodiments or figures, more or fewer operation steps or module units may be included in the method or device based on conventional or non-inventive effort. In steps or structures where there is no logically necessary causal relationship, the execution order of these steps or the module structure of the device is not limited to the execution order or module structure described in the embodiments and figures of this application. When the method or module structure is applied in actual devices or terminal products, it can be executed sequentially or in parallel according to the method or module structure shown in the embodiments or figures (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed processing environment).

[0055] Specifically, such as Figure 1 As shown, the above-mentioned check-in processing method may include the following steps:

[0056] Step 101: Use the base station connected to the target user's mobile phone when performing this check-in operation as the target base station;

[0057] After receiving the user's check-in information, it can trigger a determination to determine whether the check-in is abnormal. Based on this, it can determine whether the user checked in via mobile phone. If the check-in was via mobile phone, the base station connected at the time of check-in is obtained as the target base station.

[0058] Step 102: Obtain the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone;

[0059] The multiple base stations associated with the target user's mobile phone mentioned above can be base stations that can receive signals when the mobile phone is used for check-in. These base stations are all considered as multiple base stations associated with the target user's mobile phone, and can also be called similar base stations.

[0060] Step 103: Determine the location of the target user's mobile phone for this check-in based on the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone;

[0061] When implementing this, the location for mobile phone check-in can be determined based on the distance between base stations. Specifically, this can include the following steps:

[0062] S1: Obtain the location information and signal strength of the target base station;

[0063] S2: Obtain the location information and signal strength of each of the multiple base stations associated with the target base station;

[0064] S3: Based on the signal strength of the target base station and the signal strength of each of the multiple base stations associated with the target user's mobile phone, calculate the distance between the target base station and each of the multiple base stations;

[0065] S4: Based on the location information of each of the multiple base stations and the distance between the target base station and each of the multiple base stations, determine the location of the target user's mobile phone for this check-in.

[0066] Specifically, such as Figure 2 As shown, the location information of the target base station can be used as one vertex of a triangle, and the location information of two base stations from the multiple base stations can be selected one by one as two vertices of the triangle to generate multiple triangles; the area of ​​each triangle in the multiple triangles can be calculated based on the location information of each base station in the multiple base stations and the distance between the target base station and each base station in the multiple base stations; the area of ​​the triangle with the smallest area in the multiple triangles is selected as the location of the target user's mobile phone check-in this time.

[0067] For example, taking the current device's location at base station (x1, y1) as a fixed point, and considering other similar base stations (similar base station 1, similar base station 2) as two other vertices (x2, y2) and (x3, y3), where similar base stations can be understood as base stations that can receive signals when the phone is used for check-in, these base stations are all considered as base stations associated with the target user's phone, i.e., similar base stations. Then, the distance from the current device's location to the other two vertices can be calculated using the following formula:

[0068]

[0069]

[0070] The distance between vertices formed by two similar base stations is calculated using the following formula:

[0071]

[0072] Then, calculate the area S of the first triangle using the following formula:

[0073] p = (d1 + d2 + d3) / 2

[0074]

[0075] The area of ​​multiple triangles can be calculated using the above method, and the area enclosed by the triangle with the smallest area can be selected as the location for mobile phone check-in.

[0076] Step 104: Based on the determined location information of this check-in and the identifier of the base station connected to this check-in, and in conjunction with the preset rule engine, determine the credibility score of this check-in;

[0077] Step 105: Determine whether this check-in is an abnormal check-in based on the credibility score of this check-in.

[0078] For example, if the credibility threshold is set to 80 points, a check-in is considered normal if the credibility score exceeds 80 points, and abnormal if the credibility score does not exceed 80 points.

[0079] In the example above, the base station connected to the target user's mobile phone when performing this check-in operation is designated as the target base station. Then, by using the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone, the location of the target user's mobile phone during this check-in is determined. Furthermore, based on the determined location information and the identifier of the base station connected to this check-in, combined with a preset rule engine, the credibility score of this check-in is determined, thereby determining whether this check-in is abnormal. This solution can solve the technical problem of low accuracy of check-in data caused by the inability to identify abnormal check-in operations, achieving the technical effect of effectively identifying abnormal check-ins and improving the accuracy of check-in data.

[0080] When calculating the credibility score, data can be acquired and calculated from multiple dimensions. For example: based on the location information of this check-in, the distance between the current check-in point and historically frequently used check-in points is determined as the first dimension data; based on the identifier of the base station connected to this check-in, it is determined whether the base station connected to this check-in is a frequently used base station, which is the second dimension data; it is determined whether the MAC (Media Access Control Address) address of the mobile phone used for this check-in is a frequently used MAC address, and the result is the third dimension data; it is determined whether the mobile phone number used for this check-in is the user's own mobile phone number, and the result is the fourth dimension data; then, based on the weight values ​​of each dimension, the first, second, third, and fourth dimension data are weighted to obtain the credibility score of this check-in.

[0081] That is, using the above four dimensions as calculation dimensions, scores are assigned to the first, second, third, and fourth dimension data according to preset rules; based on the scores assigned, the scores are weighted and accumulated according to the weight values ​​of each dimension to obtain the credibility score of this check-in.

[0082] For example, if the first dimension data is between 0 and 10, the score is 100; if it's between 10 and 50, the score is 60; if it's between 50 and 100, the score is 30; and if it's greater than 100, the score is 0. If the second dimension data is yes, the score is 100; if it's no, the score is 30. If the third dimension data is yes, the score is 100; if it's no, the score is 0. If the fourth dimension data is yes, the score is 100; if it's no, the score is 10. The weights of the four dimensions are summed to 1.

[0083] However, it is worth noting that the assignment rules, weight values, and confidence thresholds given in the above example are merely illustrative descriptions, and this application does not impose any limitations on them.

[0084] In implementation, the base station connected to the target user's mobile phone when performing this check-in operation is used as the target base station. This can be achieved by establishing a communication connection with the target user's mobile phone; based on the communication connection, the user's current mobile phone signal is obtained; and based on the current mobile phone signal, the base station connected to the target user's mobile phone when performing this check-in operation is determined.

[0085] The above method will be described below with reference to a specific embodiment. However, it should be noted that this specific embodiment is only for better illustration of this application and does not constitute an improper limitation of this application.

[0086] This example provides a check-in method that uses big data to analyze user check-in behavior in real time, thereby improving the accuracy and reliability of the check-in process. Specifically, a check-in processing system is provided, which may include: a data acquisition module, a real-time analysis module, and a check-in determination module, wherein:

[0087] 1) Data acquisition module, used to collect site data of each base station, as well as data such as operator IP, including base station information of 4G network users and IP information of WIFI network users.

[0088] 2) The real-time analysis module analyzes the collected data in real time to determine whether the user's check-in operation is normal. Specifically, if the user's check-in device is confirmed to be a mobile phone, the module obtains the base station information connected to the user's mobile phone and determines whether the mobile phone is within the designated check-in area based on the base station information. If the base station information connected to the user's mobile phone is inconsistent with the base station information within the enterprise's designated check-in area, then it can be determined that the user has engaged in abnormal check-in.

[0089] Specifically, calculations can be performed based on signal strength and base station location information. By comparing the base station information of the base station to which the user's mobile phone is connected with the base station database, the user's location range can be determined.

[0090] In this example, base station-based positioning can include:

[0091] Method 1) Collect signal strength and location information of mobile communication base stations, then upload the collected information to a network positioning server. The network positioning server compares the collected base station ID information connected to the user's mobile phone with the base station database to determine the user's location range. Specifically, the stronger the signal strength, the closer the user is to the base station. By collecting signal strength information from multiple base stations, the user's location range is determined.

[0092] Method 2) By collecting base station signal strength and location information, as well as map data, and then calculating and analyzing this information, the user's location information can be determined. Specifically, the user's location information can be determined by collecting base station signal strength and location information, as well as map data, and then calculating and analyzing this information.

[0093] Specifically, determining a user's location can include:

[0094] S1: Obtain the location information (x1, y1) and signal strength RSSI1 of the base station where the user's mobile phone is located;

[0095] S2: Retrieve the location information and signal strength information of all similar base stations from the database, namely (x2, y2, RSSI2), (x3, y3, RSSI3), ... (xn, yn, RSSIn).

[0096] S3: Based on RSSI1 and RSSI2, calculate the distance D1 between the current device's base station and the first similar base station using the following formula:

[0097]

[0098] Where m represents the environmental factor, which can be fine-tuned according to the actual situation, and is generally taken as 2.

[0099] S4: Repeat step S3 to calculate the distances D2, D3, D4...Dn between the current base station and other similar base stations.

[0100] S5: Based on the locations of similar base stations and the calculated distances, a triangle is formed to calculate the current location of the device. Specifically:

[0101] Taking the base station where the current device is located as a fixed point, and considering other similar base stations as two other vertices, the distance from the current device's location to the other two vertices is calculated using the following formula:

[0102]

[0103]

[0104] Then, calculate the area S of the triangle using the following formula:

[0105] p = (d1 + d2 + d3) / 2

[0106]

[0107] Where d3 is the distance between similar base stations.

[0108] Repeat the above steps to calculate the area of ​​all triangles, and select the triangle with the smallest area as the location of the mobile phone used by the user to check in.

[0109] Furthermore, determining the check-in location can be achieved through model recognition. However, the accuracy of base station positioning is affected by various factors, such as base station signal strength, base station location, terrain, and buildings. Therefore, in practical applications, data values ​​uploaded by multiple users can be used as samples for training, and optimization and adjustments can be made according to specific circumstances to improve positioning accuracy.

[0110] Specifically, training this model may include the following steps:

[0111] S1: Data Preprocessing: Collect data uploaded by each user, such as base station signal strength, base station location, terrain, and buildings. Clean and process this data, including data deduplication, missing value imputation, and outlier handling.

[0112] S2: Feature selection: Select appropriate features based on the actual situation, such as base station signal strength, base station location, terrain, buildings, etc.

[0113] S3: Model Selection: In this example, KNN (k-Nearest Neighbor) is selected as the model. First, the distance between the predicted sample and each training sample is calculated. Then, the K closest samples are selected and a voting mechanism is used for classification prediction.

[0114] S4: Model Training: Divide the sample dataset into training and test sets, use the training set to train the model, and optimize and adjust the model through methods such as cross-validation to improve positioning accuracy.

[0115] S5: Model Evaluation: Evaluate the model using the test set, including metrics such as precision, recall, and F1 score.

[0116] S6: Model Application: Apply the trained model to real-world scenarios to locate new data.

[0117] Specifically, machine learning algorithms can be used to analyze historical data, build models, and predict and judge user check-in behavior. Alternatively, a rule engine can be incorporated to analyze simulated user profiles in real time, and then combined with the rule engine for a comprehensive judgment. For example, feature values ​​in the following dimensions can be calculated and obtained in real time:

[0118] P1: The range of this user within meters of their frequently used check-in points;

[0119] P2: Was the base station used by this user during check-in a frequently used base station?

[0120] P3: Is the user's MAC address a frequently used address?

[0121] P4: Is the user's mobile phone number their own?

[0122] By implementing the calculated feature values ​​and combining them with a rule engine, the system can automatically determine whether a user's check-in behavior is abnormal. Specifically, it can automatically determine whether a user's check-in is a normal operation. Specifically, it can calculate the score for each dimension according to the following rules:

[0123] P1: 0-10, score 100; 10-50, score 60; 50-100, score 30; >100, score 0;

[0124] P2: Yes, score 100; No, score 30.

[0125] P3: Yes, score 100; No, score 0.

[0126] P4: Yes, score 100; No, score 10.

[0127] Based on the specific values ​​of each dimension mentioned above, a weighted calculation is performed according to the following formula to obtain the final score D:

[0128]

[0129] Among them, s i Let w represent the score of the i-th dimension, n represent the number of dimensions, and w represent the score of the ith dimension. i This represents the weight of the i-th dimension, where the weight value of each dimension can be determined through methods such as expert evaluation and statistical analysis.

[0130] In other words, the weight of each dimension is different. For example, important dimensions will have a relatively high weight, while unimportant dimensions will have a relatively low weight. Even if some dimensions have a value of 100, because their weight is very small, their impact on the final result will not be significant. For example, the calculated scores and weights for each dimension are as follows:

[0131] Dimension 1, score 60, weight 0.6;

[0132] Dimension 2, score 100, weight 0.2;

[0133] Dimension 3, score 0, weight 0.1;

[0134] Dimension 4, score 100, weight 0.1;

[0135] Therefore, the total score is:

[0136]

[0137] When implementing this feature, you can set a threshold for judging the score. For example, you can set a score of 80 or above to be considered a normal check-in operation, and a score of 80 or below to be considered an abnormal check-in operation.

[0138] 3) The attendance tracking module receives the results from the real-time analysis module and records and verifies attendance tracking. If the attendance tracking is deemed normal, the module records the attendance time and location and uploads the record to the enterprise attendance management system. If the attendance tracking is deemed abnormal, the module records the abnormal information and uploads it to the enterprise attendance management system, alerting the administrator for processing.

[0139] In the example above, real-time data stream analysis enables real-time judgment of user check-in operations, improving the accuracy and reliability of the operation. Furthermore, based on the characteristics of real-time calculations, user behavior can be predicted, determining whether abnormal methods were used to complete the check-in process. Algorithms such as base station positioning prevent data tampering. Historical behavioral data allows for real-time calculation of user behavioral characteristics. These characteristics, combined with custom rule engine conditions, enable behavior judgment. By judging the results and adjusting parameters, the accuracy of calculations and judgments can be effectively improved.

[0140] The methods and embodiments provided in the above-described embodiments of this application can be executed in a mobile terminal, computer terminal, or similar computing device. Taking operation on an electronic device as an example... Figure 3 This is a hardware structure block diagram of an electronic device for a time clock processing method provided in this application. For example... Figure 3 As shown, the electronic device 10 may include one or more (only one is shown in the figure) processors 02 (processors 02 may include, but are not limited to, processing devices such as microprocessors (MCUs) or programmable logic devices (FPGAs), a memory 04 for storing data, and a transmission module 06 for communication functions. Those skilled in the art will understand that... Figure 3 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device described above. For example, electronic device 10 may also include... Figure 3 The more or fewer components shown, or having the same Figure 3 The different configurations shown.

[0141] The memory 04 can be used to store software programs and modules of application software, such as the program instructions / modules corresponding to the attendance processing method in this embodiment. The processor 02 executes various functional applications and data processing by running the software programs and modules stored in the memory 04, thereby implementing the attendance processing method of the aforementioned application. The memory 04 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 04 may further include memory remotely located relative to the processor 02, and these remote memories can be connected to the electronic device 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0142] The transmission module 06 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the electronic device 10. In one example, the transmission module 06 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission module 06 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0143] At the software level, the aforementioned attendance processing device, such as Figure 4 As shown, it may include:

[0144] The first determining module 401 is used to take the base station connected to the target user's mobile phone when performing this check-in operation as the target base station;

[0145] The acquisition module 402 is used to acquire the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone;

[0146] The second determining module 403 is used to determine the location of the target user's mobile phone for this check-in based on the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone;

[0147] The third determining module 404 is used to determine the credibility score of this check-in based on the determined location information of this check-in and the identifier of the base station connected to this check-in, combined with a preset rule engine.

[0148] The fourth determination module 405 is used to determine whether the current check-in is an abnormal check-in based on the credibility score of the current check-in.

[0149] In one embodiment, the second determining module 403 may specifically acquire the location information and signal strength of the target base station; acquire the location information and signal strength of each of the multiple base stations associated with the target base station; calculate the distance between the target base station and each of the multiple base stations based on the signal strength of the target base station and the signal strength of each of the multiple base stations associated with the target user's mobile phone; and determine the location of the target user's mobile phone for this check-in based on the location information of each of the multiple base stations and the distance between the target base station and each of the multiple base stations.

[0150] In one implementation, determining the location of the target user's mobile phone check-in based on the location information of each of the plurality of base stations and the distance between the target base station and each of the plurality of base stations may include: using the location information of the target base station as one vertex of a triangle, selecting the location information of two base stations from the plurality of base stations one by one as two vertices of a triangle to generate multiple triangles; calculating the area of ​​each of the multiple triangles based on the location information of each of the plurality of base stations and the distance between the target base station and each of the plurality of base stations; and selecting the area of ​​the triangle with the smallest area from the multiple triangles as the location of the target user's mobile phone check-in for this time.

[0151] In one implementation, the third determining module 404 can specifically determine the distance between the current check-in point and historically frequently used check-in points as the first dimension data based on the location information of the current check-in; determine whether the base station connected to the current check-in is a frequently used base station based on the identifier of the base station connected to the current check-in, as the second dimension data; determine whether the MAC address of the mobile phone used for the current check-in is a frequently used MAC address, and use the judgment result as the third dimension data; determine whether the mobile phone number used for the current check-in is the user's own mobile phone number, and use the judgment result as the fourth dimension data; and weight the first dimension data, second dimension data, third dimension data and fourth dimension data according to the weight values ​​of each dimension to obtain the credibility score of the current check-in.

[0152] In one implementation, the data of the first dimension, the second dimension, the third dimension, and the fourth dimension are weighted according to the weight values ​​of each dimension to obtain the credibility score of this check-in. This may include: assigning scores to the data of the first dimension, the second dimension, the third dimension, and the fourth dimension according to preset rules; and weighting and summing the results of the score assignment according to the weight values ​​of each dimension to obtain the credibility score of this check-in.

[0153] In one implementation, the first determining module 401 may specifically establish a communication connection with the target user's mobile phone; obtain the user's current mobile phone signal based on the communication connection; and determine the base station to which the target user's mobile phone is connected when performing this check-in operation based on the current mobile phone signal.

[0154] This application also provides a specific implementation of an electronic device capable of implementing all steps of the attendance processing method in the above embodiments. The electronic device specifically includes: a processor, a memory, a communication interface, and a bus; wherein the processor, memory, and communication interface communicate with each other via the bus; the processor is used to call a computer program in the memory, and when the processor executes the computer program, it implements all steps of the attendance processing method in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0155] Step 1: Use the base station that the target user's mobile phone connects to when performing this check-in operation as the target base station;

[0156] Step 2: Obtain the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone;

[0157] Step 3: Determine the location of the target user's mobile phone for this check-in based on the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone;

[0158] Step 4: Based on the determined location information of this check-in and the identifier of the base station connected to this check-in, and in conjunction with the preset rule engine, determine the credibility score of this check-in;

[0159] Step 5: Determine whether this check-in is an abnormal check-in based on the credibility score of this check-in.

[0160] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the attendance processing method in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the attendance processing method in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0161] Step 1: Use the base station that the target user's mobile phone connects to when performing this check-in operation as the target base station;

[0162] Step 2: Obtain the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone;

[0163] Step 3: Determine the location of the target user's mobile phone for this check-in based on the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone;

[0164] Step 4: Based on the determined location information of this check-in and the identifier of the base station connected to this check-in, and in conjunction with the preset rule engine, determine the credibility score of this check-in;

[0165] Step 5: Determine whether this check-in is an abnormal check-in based on the credibility score of this check-in.

[0166] As described above, this embodiment of the application uses the base station connected to the target user's mobile phone when performing the check-in operation as the target base station. Then, by using the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone, the location of the target user's mobile phone during this check-in is determined. Furthermore, based on the determined location information of this check-in and the identifier of the base station connected to this check-in, combined with a preset rule engine, the credibility score of this check-in is determined, thereby determining whether this check-in is an abnormal check-in. This solution solves the technical problem of low accuracy in check-in data caused by the inability to identify abnormal check-in operations, achieving the technical effect of effectively identifying abnormal check-ins and improving the accuracy of check-in data.

[0167] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0168] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, hardware + program embodiments are relatively simple in description because they are fundamentally similar to method embodiments; relevant parts can be referred to the descriptions in the method embodiments.

[0169] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0170] While this application provides the method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive labor. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual device or client product execution, the methods shown in the embodiments or drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0171] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, a laptop computer, an in-vehicle human-machine interaction device, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0172] While this specification provides method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual device or end product execution, the methods shown in the embodiments or drawings may be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment). The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitations, the presence of other identical or equivalent elements in the process, method, product, or apparatus that includes said elements is not excluded.

[0173] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing the embodiments of this specification, the functions of each module can be implemented in one or more software and / or hardware components, or a module that performs the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0174] Those skilled in the art will also know that, besides implementing the controller using purely computer-readable program code, the same functions can be achieved by logically programming the method steps, making the controller function as logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers (PLCs), and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the devices within it used to implement various functions can also be considered structures within that hardware component. Alternatively, the devices used to implement various functions can be considered as both software modules implementing the method and structures within a hardware component.

[0175] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0176] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0177] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0178] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0179] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0180] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0181] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of computer program products implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0182] The embodiments described in this specification can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. The embodiments of this specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0183] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, system embodiments are basically similar to method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. In the description of this specification, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the embodiments in this specification. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0184] The above description is merely an embodiment of the present specification and is not intended to limit the embodiments of the present specification. For those skilled in the art, various modifications and variations can be made to the embodiments of the present specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the embodiments of the present specification should be included within the scope of the claims of the embodiments of the present specification.

Claims

1. A punch-in processing method characterized by comprising: include: The base station connected to the target user's mobile phone when performing this check-in operation is used as the target base station; Obtain the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone, wherein the multiple base stations associated with the target user's mobile phone are base stations that can receive signals when the mobile phone is used for check-in, and these base stations are all regarded as multiple base stations associated with the target user's mobile phone. Based on the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone, the location of the target user's mobile phone for this check-in is determined; Based on the determined location information and the identifier of the base station connected to this check-in, combined with the preset rule engine, the credibility score of this check-in is determined. Based on the credibility score of this check-in, determine whether this check-in is abnormal; Specifically, determining the location of the target user's mobile phone for this check-in, based on the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone, includes: Using the location information of the target base station as one vertex of a triangle, the location information of two base stations from the plurality of base stations are selected one by one as two vertices of a triangle to generate multiple triangles; Based on the location information of each of the multiple base stations and the distance between the target base station and each of the multiple base stations, calculate the area of ​​each of the multiple triangles; The area containing the triangle with the smallest area among the multiple triangles is selected as the location where the target user's mobile phone checks in for this time.

2. The method of claim 1, wherein, Based on the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone, the location of the target user's mobile phone for this check-in is determined, including: Obtain the location information and signal strength of the target base station; Obtain the location information and signal strength of each of the multiple base stations associated with the target base station; Based on the signal strength of the target base station and the signal strength of each of the multiple base stations associated with the target user's mobile phone, the distance between the target base station and each of the multiple base stations is calculated. Based on the location information of each of the multiple base stations and the distance between the target base station and each of the multiple base stations, the location of the target user's mobile phone for this check-in is determined.

3. The method of claim 1, wherein, Based on the determined location information and the identifier of the base station connected to this check-in, and in conjunction with the preset rule engine, the credibility score of this check-in is determined, including: Based on the location information of this check-in, the distance between this check-in point and historically frequently used check-in points is determined as the first dimension of data; Based on the identifier of the base station connected to this check-in, the result of determining whether the base station connected to this check-in is a commonly used base station is used as the second dimension of data. Determine whether the MAC address of the mobile phone used for this check-in is a commonly used MAC address, and use the judgment result as the third dimension data; Determine whether the mobile phone number used for this check-in is the user's own mobile phone number, and use the judgment result as the fourth dimension data; Based on the weight values ​​of each dimension, the data in the first, second, third, and fourth dimensions are weighted to obtain the credibility score for this check-in.

4. The method of claim 3, wherein, Based on the weight values ​​of each dimension, the data in the first, second, third, and fourth dimensions are weighted to obtain the credibility score for this check-in, including: Scores are assigned to the first, second, third, and fourth dimension data according to preset rules; Based on the assigned scores, the results are weighted and accumulated according to the weight values ​​of each dimension to obtain the credibility score for this check-in.

5. The method of claim 1, wherein, The target base station is defined as the base station to which the target user's mobile phone connects when performing this check-in operation, including: Establish a communication connection with the target user's mobile phone; Based on the communication connection, obtain the user's current mobile phone signal; Based on the current mobile phone signal, determine the base station that the target user's mobile phone is connected to when performing this check-in operation.

6. A punch processing apparatus characterized by comprising: include: The first determining module is used to identify the base station connected to the target user's mobile phone when performing this check-in operation as the target base station; The acquisition module is used to acquire the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone. The multiple base stations associated with the target user's mobile phone are base stations that can receive signals when the mobile phone is used for check-in. All of these base stations are considered as multiple base stations associated with the target user's mobile phone. The second determining module is used to determine the location of the target user's mobile phone for this check-in based on the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone; The third determination module is used to determine the credibility score of this check-in based on the determined location information of this check-in and the identifier of the base station connected to this check-in, combined with the preset rule engine. The fourth determination module is used to determine whether the current check-in is an abnormal check-in based on the credibility score of the current check-in. Specifically, determining the location of the target user's mobile phone for this check-in, based on the base station information of the target base station and the base station information of multiple base stations associated with the target user's mobile phone, includes: Using the location information of the target base station as one vertex of a triangle, the location information of two base stations from the plurality of base stations are selected one by one as two vertices of a triangle to generate multiple triangles; Based on the location information of each of the multiple base stations and the distance between the target base station and each of the multiple base stations, calculate the area of ​​each of the multiple triangles; The area containing the triangle with the smallest area among the multiple triangles is selected as the location where the target user's mobile phone checks in for this time.

7. An electronic device comprising a processor and a memory for storing processor-executable instructions, the electronic device characterized by: When the processor executes the instructions, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 5.

9. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 5.

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