Attendance data processing method and device

Abstract

The invention relates to an attendance data processing method and device. The method comprises the following steps: acquiring card punching data and passing data of employees; the method further comprises the step of determining an attendance record table based on the card punching data and the passing data, and the attendance record table comprises attendance data of a plurality of employees and a plurality of first attendance judgment results corresponding to the attendance data. Besides, the method further comprises the step of determining a second attendance judgment result based on the attendance data in the attendance record table, the travel data and the leave cancellation data in response to the fact that the first attendance judgment result in the attendance record table is abnormal, so that the first attendance judgment result in the attendance record table is adjusted to be the second attendance judgment result. Through the mode, the effective attendance record of the employee is determined by utilizing the two attendance data, and the business trip and leave record is determined by combining the business trip and leave data, so that the attendance data processing correctness and efficiency are improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of attendance management, and more specifically, to an attendance data processing method and device. Background Art

[0002] Enterprises generally use access control systems to manage employee attendance, such as swiping cards, face scans, or fingerprints to record attendance time. In addition, some enterprises also enable mobile phone positioning clocking in, so that employees can complete the clocking in operation anytime and anywhere through mobile devices such as smartphones, which is convenient and fast.

[0003] However, the above-mentioned clocking-in method has certain limitations. For example, the gates or access control devices of the access control system are fixedly set up, and their coverage is limited, so they cannot fully record the attendance of employees. In addition, the data of the access control system needs to be manually synchronized with the attendance management system, which is cumbersome and error-prone. For another example, an employee may clock in with his mobile phone in advance before arriving at the workplace, or continue to clock in with his mobile phone after leaving the workplace, thereby falsely reporting attendance. Summary of the invention

[0004] The embodiments of the present disclosure provide a method and device for processing attendance data.

[0005] In a first aspect of the present disclosure, a method for processing attendance data is provided. The method includes obtaining employee punch-in data and pass data. The method also includes determining an attendance record table based on the punch-in data and pass data, wherein the attendance record table includes attendance data of multiple employees and multiple first attendance judgment results corresponding to the attendance data, wherein the first attendance judgment results include normal, early leave, late arrival, out-of-office, and card-missing to be confirmed. In addition, the method also includes, in response to the first attendance judgment result in the attendance record table being abnormal, determining a second attendance judgment result based on the attendance data and travel data and leave application and cancellation data in the attendance record table, so as to adjust the first attendance judgment result in the attendance record table to a second attendance judgment result, wherein the second attendance judgment result includes leave application but not cancelled, normal, business trip, and card-missing to be confirmed.

[0006] In a second aspect of the present disclosure, an attendance data processing device is provided. The device includes an acquisition module configured to acquire employee punch-in data and pass data. The device also includes a first determination module configured to determine an attendance record table based on the punch-in data and pass data, the attendance record table including attendance data of multiple employees, and multiple first attendance judgment results corresponding to the attendance data, the first attendance judgment results including normal, early leave, late, and missing card to be confirmed. In addition, the device also includes a second determination module configured to determine a second attendance judgment result based on the attendance data, travel data, and leave application and cancellation data in the attendance record table in response to the first attendance judgment result in the attendance record table being abnormal, so as to adjust the first attendance judgment result in the attendance record table to the second attendance judgment result, the second attendance judgment result including leave not cancelled, normal, business trip, and missing card to be confirmed.

[0007] In a third aspect of the present disclosure, a computer program product is provided, comprising a computer program, the computer program being executed by a processor to implement the method according to the first aspect.

[0008] In a fourth aspect of the present disclosure, a machine-readable storage medium is provided, wherein the machine-readable storage medium stores machine-executable instructions, wherein the machine-executable instructions are executed by a processor to implement the method provided according to the first aspect of the present disclosure.

[0009] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein: Figure 1 A schematic diagram showing an example environment in which some embodiments of the present disclosure may be implemented; Figure 2 A flowchart of a method for processing attendance data according to some embodiments of the present disclosure is shown; Figure 3 A schematic diagram showing a process of determining an attendance record sheet according to some embodiments of the present disclosure is shown; Figure 4 A flowchart showing a process of determining attendance data and attendance judgment results in an attendance record table according to some embodiments of the present disclosure is shown; Figure 5 A schematic diagram showing an example process in which abnormal attendance data in an attendance record table requires confirmation by a user terminal according to some embodiments of the present disclosure; Figure 6 A block diagram showing an attendance data processing apparatus according to some embodiments of the present disclosure; and Figure 7 A block diagram of an electronic device in which various embodiments of the present disclosure may be implemented is shown. DETAILED DESCRIPTION

[0011] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0012] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0013] As mentioned above, although the mobile clock-in method is convenient, there is a problem of false attendance. Enterprise managers cannot identify false attendance records from mobile clock-in records, and abnormal attendance records (including abnormal records of lateness, early departure and field work, and empty records of missing cards to be confirmed) need to be verified one by one. The verification process requires employees to provide proof information and be approved by leaders at all levels. The large amount of verification data greatly reduces the efficiency of attendance data processing. In particular, if the approving user fails to respond in time, the approval time will be greatly extended.

[0014] The attendance data of the access control system depends on the access control equipment or gates. These devices need to be set up in fixed locations within the enterprise, with limited coverage areas, and cannot fully reflect the attendance of employees. Once the equipment fails, employee attendance data cannot be counted. Enterprise managers cannot fully obtain the attendance of all employees, and need to verify abnormal attendance records one by one, which also has the problem of slow data processing efficiency and low accuracy.

[0015] To this end, an embodiment of the present disclosure proposes an attendance data processing method, which utilizes mobile terminal punch-in data and access control pass data to record employee attendance data in an all-round and multi-dimensional manner. The attendance method of the two data sources can assist in verifying employee attendance and automatically determine employee valid attendance records, thereby improving the accuracy of attendance data and the efficiency of attendance data processing, and greatly reducing the amount of data that needs to be verified by employees and approved by leaders.

[0016] In this way, compared with the traditional single punch-in or manual recording method, the inaccurate attendance data caused by human factors such as missed punch-ins and wrong punch-ins can be significantly reduced, ensuring the authenticity and reliability of attendance data. Through the efficient comparison of punch-in data and gate pass data by intelligent algorithms, attendance reports can be quickly generated, reducing manual intervention and improving work efficiency. At the same time, it can also automatically identify abnormal attendance situations, such as lateness, early departure, and absenteeism, and provide timely and accurate attendance information for managers. Furthermore, enterprises can more accurately understand the attendance and work status of employees, so as to reasonably arrange work tasks and personnel scheduling. This helps to optimize the allocation of enterprise resources and improve production efficiency and economic benefits. At the same time, accurate attendance data can also provide strong support for the company's performance appraisal, salary calculation, etc.

[0017] Figure 1 1 is a schematic diagram of an example environment 100 in which some embodiments of the present disclosure may be implemented. Figure 1As shown, the environment 100 includes a gate 102 of the access control system, and employees 102-1 and 102-2 pass through the gate 102 to form a pass record of the corresponding employees. The gate identifies the passing personnel information, pass time, pass location, and pass type, and stores them in the access control database 112, such as a MySQL database. The access control database 112 records the pass records of all employees. If the pass records of employees are not collected, it will be displayed as a null value. In the environment 100, user 104-1 enters the range of the dotted circle and clocks in using mobile phone 106-1. After entering the range of the dotted circle, user 104-2 clocks in using mobile phone 106-2. The dotted circle is the allowable range for clocking in and out, that is, clocking in within this range is normal clocking in, and clocking in outside the dotted circle means that the clocking in location is abnormal. The clocking in data of users 104-1 and 104-2 will be uploaded to the cloud server 108, such as using JSON format for storage. In the environment 100, the cloud server 108 synchronizes the received punch-in data to the enterprise database 114 in real time, and the access control database 112 can transmit the pass data to the enterprise database 114 at a fixed time, for example, uploading the data to the enterprise database 114 after get off work every day. In some embodiments, the enterprise database 114 can send an HTTP request to the cloud server 108 through an API interface call to obtain the punch-in data, and also use the SQL query method to extract the pass data from the access control database 112 by writing an SQL statement.

[0018] like Figure 1As shown, the environment 100 includes a computing unit 110, and the computing unit 110 can be any device with computing or processing capabilities. For example, the computing unit 110 can include but is not limited to a mobile phone, a tablet computer, a desktop computer, a server, etc. The computing unit 110 is configured with an attendance management system and can execute an attendance data processing flow. The computing unit 110 extracts a punch-in record table 114-1 and a pass record table 114-2 from an enterprise database 114. The punch-in record table 114-1 records the attendance data of multiple employees, mainly mobile phone punch-in data, including but not limited to personnel number, personnel name, punch-in time, punch-in location, punch-in type, and attendance status. Among them, the attendance status is determined by judging the punch-in time, punch-in type, and punch-in location according to the enterprise attendance rules. For example, the enterprise attendance rules are set as: at 8 o'clock during working hours, any position within the dotted circle is a normal place for attendance punch-in. For record 1, Zhang San punched in at 7:59 within the attendance range and punched in at work, which is considered to comply with the enterprise attendance rules, and the attendance status is determined to be normal. For record 2, Li Si punched in and out within the attendance range after 8 o'clock, and punched in and out for work, which is considered not in compliance with the company's attendance rules, and the attendance status is determined to be late. For record 3, Wang Wu's punch-in data is empty, so it is impossible to determine whether it complies with the company's attendance rules, and the attendance status is determined to be missing card pending confirmation. For records 4 and 5, if the punch-in location is not within the attendance range, the attendance status is confirmed as off-duty. Similarly, the pass record table 114-2 records the attendance data of multiple employees, mainly the pass data through the gate, including but not limited to personnel number, personnel name, pass time, pass location (such as gate number), pass type, and attendance status. Among them, the attendance status is confirmed according to the company's attendance rules by judging the pass time, pass type, and pass location.

[0019] like Figure 1As shown, the calculation unit 110 determines the attendance record table 120 based on the punching data and the pass data in the punching record table 114-1 and the pass record table 114-2. The attendance record table 120 records the attendance data and attendance judgment results (hereinafter referred to as the first attendance judgment result) of multiple employees. Among them, the attendance data can be punching data or pass data, which can be determined by comparing the relevant records in the two punching record tables. In some embodiments, based on the punching record of a certain employee on a certain day in the punching record table 114-1, the pass record of the same employee on the same day is matched from the pass record table 114-2. When the pass data and the punching data are both normal, the punching data shall prevail and be recorded in the attendance record table 120, such as record 1. When the pass data and the punching data are both abnormal, the punching data shall prevail and be recorded in the attendance record table 120, such as record 2. When the punch data is abnormal or null, and the pass data is normal, the pass data is used as the standard and recorded in the attendance record table 120, such as records 3 and 5. When the punch data is abnormal and the pass data is null, the punch data is used as the standard and recorded in the attendance record table 120.

[0020] Next, the calculation unit 110 performs a second confirmation on some abnormal records in the attendance record table 120 based on the travel data 116 and the leave application and cancellation data 118, confirming which records match the travel data or leave application and cancellation data, and can adjust the abnormal attendance status to normal or business trip or leave application and uncancelled attendance judgment results (hereinafter referred to as the second attendance judgment result), forming an adjusted attendance record table 122. For example, in record 2, Li Si has a leave application, so its attendance judgment result can be adjusted to leave application and uncancelled leave. For another example, in record 4, Zheng Liu has a business trip application and the field location matches the business trip location, so its attendance judgment result is adjusted to business trip.

[0021] In addition, the calculation unit 110 can also return the attendance records that need to be confirmed by the employees in the adjusted attendance record table 122 to the mobile phone of the corresponding employees. If the employees declare the abnormal records as normal records, they need to initiate verification and approval. After the superior leader approves, the attendance judgment result of the abnormal records in the attendance record table 122 can be adjusted to normal. The superior leader rejects the declaration, or the employee does not declare the abnormal records, and the original attendance judgment result is maintained.

[0022] In this way, the calculation unit uses the punch card record table 114-1 and the pass record table 114-2 to assist each other in verification, first preliminarily determines a relatively accurate attendance judgment result, and forms an attendance record table, and then combines the travel data and leave cancellation data to perform a secondary determination on the abnormal records to adjust the attendance judgment result to be more accurate. If the punch card data is used alone for attendance statistics, there are four abnormal records in records 1-5, and the four abnormal records are generally returned to the employee end for verification and approval. After the calculation unit 110 combines the pass data and the punch card data to form the attendance record table, there are two abnormal records in the five records. After comparing the travel data 116 and the leave cancellation data 118, the two abnormal records are clearly leave without cancellation and business trip. Records 1-5 confirm the attendance judgment result after automatic data comparison, and there is no record that needs to be returned to the employee end for verification and approval. Even if there are records that need to be confirmed by employees, they are relatively few, which greatly improves the efficiency of attendance data processing. Moreover, the two attendance methods complement each other's shortcomings and can more accurately determine the true attendance situation of employees.

[0023] It should be understood that the architecture and functions in the example environment 100 are presented for exemplary purposes only and do not imply any limitation on the scope of the present disclosure. Embodiments of the present disclosure may also be applied to other environments with different structures and / or functions.

[0024] Figure 2 FIG. 2 is a flowchart of a method 200 for processing attendance data according to some embodiments of the present disclosure. The method 200 may be, for example, Figure 1 The computing unit 110 in the environment 100 shown is executed. Figure 2As shown, in box 202, method 200 can obtain the punch-in data and pass data of the employee. In some embodiments, the punch-in data on the punch-in server and the pass data of the access control system are obtained. For example, a data synchronization strategy is pre-set, and the punch-in server and the access control system transmit data in real time. If real-time data transmission is not supported, a reasonable synchronization time interval can be set (such as every hour, every half day or every day, etc.). Then, based on the data cleaning rules, the employee's punch-in data and pass data are determined. In one example, the data duplication processing rules are used to deduplicate the duplicate records in the punch-in data and delete the duplicate data. Similarly, the duplicate records in the pass data are deduplicated and deleted. After processing, it is stored in the enterprise database. For example, if duplicate data appears in the personnel number, date and time field, etc., the database query statement or data processing program is used to remove the punch-in data and the pass data, leaving a unique record stored in the enterprise database. In one example, the missing value processing rules of data are used to identify missing values ​​in the punch-in data and the pass data, such as the name of the person, department in the punch-in data, or the pass time in the pass data, and the appropriate processing method is selected according to the business logic, such as using historical data to fill the missing values, or marking them as missing and retaining the original data. In one example, the data outlier processing rules are used to identify and process outliers in the punch-in data, such as unreasonable punch-in time (such as 1 a.m.), invalid punch-in location, etc., and outliers in the pass data, such as unreasonable pass time (such as 1 a.m.), invalid pass location, can also be identified and processed. The above abnormal data chooses to delete, modify or mark outliers according to the business logic. The data is processed by one or more of the above cleaning rules to ensure the accuracy, completeness and consistency of the data. The cleaned data is stored in the enterprise database. In addition, since the punch-in data and the pass data come from different systems and have different storage formats. Method 200 can perform data format consistency processing on the cleaned data, and uniformly convert the pass data and the punch-in data and load them into the database. For example, the personnel number should be a number or a string, the time should be in the unified date and time format "YYYY-MM-DDHH:MM:SS", and the punch-in locations and punch-in types should be standardized to ensure that all locations and types have unified naming and coding. In some examples, data formatting tools such as the DecimalFormat class in Java and Python built-in methods can be used to convert raw data into the required format, use logical checks and data validation rules to ensure data accuracy and consistency, mark or delete data that does not comply with the rules, and output the formatted data to the enterprise database.

[0025] In box 204, method 200 can determine an attendance record table based on the punch-in data and the pass data. The attendance record table includes the attendance data of multiple employees and multiple first attendance judgment results corresponding to the attendance data. The first attendance judgment results include normal, early leave, late, out-of-work, and card-missing to be confirmed. In some embodiments, based on the punch-in data and pass data sorted within the integrated unified time range, they are classified in the enterprise database based on the personnel number, punch-in type, and pass type to obtain the full punch-in time and full pass time of each employee. For example, the punch-in data and pass data belonging to the same month are sorted and classified to obtain the punch-in data and pass data of each employee going to work and leaving get off work every day. Afterwards, the punch-in data and pass data of the employees are matched. In one example, the attendance records are matched based on the employee number. Each employee generally has at least two records on the same day, the records of going to work and leaving get off work, and the corresponding records need to be matched based on the time window. Since clocking in is generally done within the clocking range, you can clock in before passing through the access control system, you can clock in while passing through the access control system, or you can clock in after passing through the access control system. Based on the time window, the clocking in record and the pass record are matched. In this way, any clocking in and pass record occurring within the time window will be regarded as records within the same time period. It can be understood that the interval between the clocking in time and the pass time needs to be within the time window, and the time window can be set to 5-30 minutes. For example, if the clocking in time in the clocking in record is 7:51, and the pass time in the pass record is 7:58, and the time window is 10 minutes, then the clocking in record and the pass record can be identified as the attendance record of the same employee in the same time period. Finally, based on the matching situation, the attendance record table is determined. When matching the records, if the clocking in data is abnormal (referring to the clocking in time and the clocking in location are abnormal), and the pass data is normal, the attendance of the employee is determined based on the pass data. When a record is matched, if both the punch-in data and the access data are normal, the access data assists in proving the accuracy of the punch-in data, and the attendance of the employee is determined based on the punch-in data. When a record is matched, if both the punch-in data and the access data are abnormal (the attendance location and attendance time are abnormal), it is necessary to reconfirm. Currently, it can be recorded in the attendance record table based on the punch-in data. When a record is matched, if the punch-in data, or the punch-in data and the access data are both empty, the attendance of the employee is determined based on the access data. If the punch-in data is not empty, but the access data is empty, the attendance of the employee is determined based on the punch-in data. When no record is matched, it is necessary to reconfirm. Currently, it can be recorded in the attendance record table based on the punch-in data. The above matching process can traverse the access data in the access record table and match it with the punch-in data of each record in the punch-in record table, or it can traverse the punch-in data in the punch-in record table and match it with the access data of each record in the access record table.

[0026] In box 206, in response to the first attendance judgment result in the attendance record being abnormal, the method 200 can determine a second attendance judgment result based on the attendance data, travel data, and leave cancellation data in the attendance record, so as to adjust the first attendance judgment result in the attendance record to a second attendance judgment result, wherein the second attendance judgment result includes leave not cancelled, normal, business trip, and card missing to be confirmed. In some embodiments, the abnormal first attendance judgment result in the attendance record determined in box 204 is early leave, late arrival, out-of-town work, and card missing to be confirmed. The attendance records involving such abnormal first attendance judgment results need to be confirmed twice through box 206. By comparing the leave data, it is determined that the employee's leave date in the leave data matches the date of the abnormal attendance record, and the employee's leave cancellation data has not been received, then the first attendance judgment result can be adjusted to the second attendance judgment result, and the leave has not been cancelled. If the leave cancellation data is received, the first attendance judgment result can be adjusted to the second attendance judgment result, which is normal. If no match is found, the first attendance judgment result is maintained, which may be late, early leave, or missing card to be confirmed. By comparing the travel data, it is determined that the employee's business trip date in the travel data matches the date of the abnormal attendance record, and the punch-in location also matches the business trip location, then the first attendance judgment result can be adjusted to the second attendance judgment result, business trip. If no match is found, the first attendance judgment result is maintained, which may be late, early leave, or missing card to be confirmed.

[0027] In this way, the attendance record table is first determined by using two types of attendance data together, which can form a table that reflects the actual attendance of employees more accurately, and reduce the amount of data records that need to be confirmed by employees, thereby improving the efficiency of attendance data processing. In addition, based on leave application and cancellation data and travel data, the abnormal attendance records in the attendance record table are re-determined, and the attendance records are matched with any one of the leave application and cancellation data and travel data. Only when the match is successful can the attendance judgment result be adjusted. This not only improves the accuracy of the attendance records, but also further reduces the amount of data records that need to be confirmed by employees after the automatic comparison is successful, thereby improving the efficiency of attendance data processing.

[0028] Figure 3 FIG. 3 is a schematic diagram of an attendance record sheet determination process 300 according to some embodiments of the present disclosure. Figure 3As shown, the cleaning module 306 cleans the punch-in data 302 obtained from the cloud server and the pass data 304 obtained from the gate database according to the data cleaning rules. In addition, the punch-in data 302 and the pass data 304 can also be converted into a unified format according to the format processing rules and stored in the enterprise database. The punch-in record table 308-1 is a table after data cleaning and format conversion, and the pass record table 308-2 is a table after data cleaning and format conversion. The first determination module 310 determines the attendance record table 312 based on the punch-in data of the punch-in record table 308-1 and the pass data of the pass record table 308-2, and the attendance judgment result in the attendance record table is the first attendance judgment result. Afterwards, the second determination module 314 determines which abnormal attendance data's first attendance judgment result needs to be adjusted to the second attendance judgment result based on the attendance record table 312, the travel data 312 and the leave application and cancellation data 316, and adjusts it to a new attendance record table 318.

[0029] like Figure 3As shown, the first punch-in record in the punch-in record table 308-1 is obtained, and the pass record table 308-2 is traversed to match the first pass record in the punch-in record table 308-2. Zhang San's punch-in and pass attendance status are both normal, and Zhang San's punch-in data is determined as the attendance data in the attendance record table 312. Accordingly, based on the attendance status in the punch-in data, the first attendance judgment result is determined. Then the second punch-in record in the punch-in record table 308-1 is obtained, and the pass record table 308-2 is traversed to match the second pass record. Li Si's punch-in and pass attendance status are both late (i.e., abnormal), and the punch-in data is first determined as the attendance data in the attendance record table 312. Accordingly, based on the attendance status in the punch-in data, the first attendance judgment result is determined. The record needs to be determined by the second determination module later. When the leave data is matched and there is no leave cancellation data, the second attendance judgment result can be determined as leave without cancellation, and the attendance record table 312 is updated according to the second attendance judgment result. In one example, based on the punch-in data, the employee's leave document is matched from the leave cancellation data 316, and it is determined that the leave date matches the punch-in date in the punch-in data, and there is no leave cancellation data, then the second attendance judgment result is leave without cancellation. If there is leave cancellation data, the second attendance judgment result is normal. Next, the third punch-in record in the punch-in record table 308-1 is obtained, and the pass record table 308-2 is traversed to match the third pass record related to Wang Wu. Wang Wu's punch-in data is empty, and the pass data is not empty and normal. Then Wang Wu's pass data is determined as the attendance data in the attendance record table 312, and the first attendance judgment result is determined based on the attendance status in the pass data. Next, obtain the fourth punch-in record in the punch-in record table 308-1, traverse the pass record table 308-2, and match the pass record related to Zheng Liu. The punch-in location in Zheng Liu's punch-in data is abnormal, the attendance status is off-duty, and Zheng Liu's pass data is empty. First, determine the punch-in data as the attendance data in the attendance record table 312, and accordingly, determine the first attendance judgment result based on the attendance status in the punch-in data. This record needs to be determined by the second determination module later. When the travel data is matched, the business trip order of the person is matched from the travel data to determine whether the attendance date and the business trip date in the pass record match. The business trip location and the attendance punch-in location are fuzzy matched in the city. If they match, the second attendance judgment result is marked as a business trip. If they do not match, it is marked as missing card to be confirmed. Next, the fifth punch-in record in the punch-in record table 308-1 is obtained, and the pass record table 308-2 is traversed to match the fifth pass record in the pass record table 308-2. The punch-in record shows that the punch-in location is abnormal, the attendance status is off-duty, and the pass attendance status is normal, then the pass data is determined as the attendance data in the attendance record table 312, and the first attendance judgment result is determined based on the attendance status of the pass data.Next, the sixth punch-in record in the punch-in record table 308-1 is obtained, and the pass record table 308-2 is traversed to match the pass record related to Jiaba, but the punch-in data and the pass data are both empty. The punch-in data is first determined as the attendance data in the attendance record table 312, and accordingly, the first attendance judgment result is determined based on the attendance status in the punch-in data. This record needs to be determined by the second determination module later. When the leave data is matched and there is no leave cancellation data, the second attendance judgment result is determined to be leave not cancelled, and the attendance record table 318 is adjusted based on the second attendance judgment result.

[0030] Through the above example, by comparing the punch record table 308-1 and the pass record table 308-2, the adjusted attendance record table 318 determines the attendance data of most employees, or even all of them. In the final attendance record table 318, there are very few records of missing cards that need to be confirmed by employees, which greatly improves the efficiency of attendance data processing. Figure 4 FIG. 4 is a flowchart showing a process 400 for determining attendance data and attendance judgment results in an attendance record table according to some embodiments of the present disclosure. Figure 4 As shown, the process 400 includes a box 402, traversing the pass record table to determine whether there is a pass record that matches the punch record. When a match is found, enter box 404 and box 406 to respectively determine whether the attendance status is normal. If the attendance status is normal, box 408 determines the punch data as the attendance data in the attendance record table, and determines the first attendance judgment result. If the punch attendance status is abnormal, and the pass attendance status is normal, box 410 determines the pass data as the attendance data in the attendance record table, and determines the first attendance judgment result. If the attendance status is abnormal, enter a more detailed judgment. Determine whether the punch attendance status or the pass attendance status is an abnormal empty state or an abnormal state, and the abnormality refers to off-duty, lateness, and early departure. If the attendance status of the punch-in record is determined to be abnormal in box 414 and the attendance status of the pass record is determined to be abnormal in box 416, the punch-in data is determined as the attendance data in the attendance record table in box 420, and the first attendance judgment result is determined. Figure 3The second determination module in performs a secondary determination. If box 414 determines that the punch-in data is abnormal and box 418 determines that the pass record is empty, box 420 determines the punch-in data as the attendance data in the attendance record table, and determines the first attendance determination result, and a secondary determination is required later. If box 412 determines that the punch-in record is empty and box 416 determines that the pass record is abnormal, box 422 determines the pass data as the attendance data in the attendance record table, and determines the first attendance determination result, and a secondary determination is required later. If box 412 determines that the punch-in record is empty and box 418 determines that the pass record is empty, box 422 determines the pass data as the attendance data in the attendance record table, and determines the first attendance determination result, and a secondary determination is required later. The process 400 includes, when the punch-in record does not match the pass record, box 424 first determines the punch-in data as the attendance data in the attendance record table and determines the first attendance determination result, and a secondary determination is required later.

[0031] Figure 5 A schematic diagram of an example process 500 of requiring user terminal confirmation of abnormal attendance data in an attendance record table according to some embodiments of the present disclosure is shown. Figure 5As shown, the process 500 can return the record corresponding to the abnormal attendance status to the employee's mobile phone through the third determination module 506-1 or 506-2 for notification. The employee can appeal or not appeal through the appeal confirmation module 508. In some embodiments, the third determination module 506-1 sends the abnormal record 504-1 of employee 1's card shortage to be confirmed to the mobile phone 502-1 of employee 1, and employee 1 sends the appeal request to the mobile phone 510 of the superior leader through the appeal confirmation module 508, and the appeal request includes the supplementary leave form. The superior leader can have one or more superiors at different levels. After accepting the reason for the appeal, the appeal confirmation module 504 returns the information to the mobile phone 502-1 of employee 1. The third determination module 506-1 adjusts the second attendance judgment result in the abnormal record 504-1 to the third attendance judgment result, that is, the leave is not cancelled (see attendance record 510-1). In some embodiments, the third determination module 506-2 sends the abnormal record 504-2 of employee 2's lateness to employee 2's mobile phone 502-2. Employee 2 initiates a complaint request and sends it to the mobile phone 510 of the superior leader through the complaint confirmation module 508. The superior leader can have one or more superior leaders at different levels. After accepting the reason for the complaint, the complaint confirmation module 504 returns the rejection information to employee 2's mobile phone 502-2. The third determination module 506-2 does not adjust the abnormal record 504-2 of lateness, and outputs the final attendance record (see attendance record 510-2). The third determination module returns the finalized attendance record to the enterprise database 512, and the attendance record table stored in the enterprise database is the final attendance record table. Among them, there can be one third determination module, not limited to the two shown in the figure. The third determination module can push the abnormal record to the employee, and can also output the final confirmed record to adjust the record in the attendance record table in the enterprise database 512 or not record it.

[0032] like Figure 5 As shown, the process 500 can send the determined final attendance record table to the push module 514. The push module 514 can push the attendance results 516-1, ..., 516-N formed according to the attendance data of each employee every month, every quarter, or every period of time to the corresponding employees 518-1, ..., 518-N. For example, the attendance results are pushed to the employee's personal mobile device (for example, a mobile phone application) or a personal computer (PC). The pushed attendance result information includes but is not limited to the employee's unique work number, employee name, specific attendance date, accurate records of on-duty and off-duty time, and attendance status (the attendance status can be determined using the attendance judgment result) and other key information.

[0033] In addition, in some embodiments, the final attendance record table automatically summarizes and generates the final attendance results within the attendance cycle. This process ensures the accuracy and completeness of the attendance data and provides reliable data support for subsequent salary calculations, performance evaluations, etc. In some embodiments, the final attendance record table is used to generate an attendance report to display information such as employee attendance, abnormal attendance records, etc. In some examples, based on data statistical analysis algorithms, abnormal attendance records are identified, such as being late for multiple consecutive days, frequent leave, etc., and the detailed information and frequency of these abnormal behaviors are recorded. In some examples, descriptive statistical analysis is performed, including calculating statistics such as the average punch-in time, number of late arrivals, and number of days of leave for each employee. Visualization tools such as box plots and histograms are used to display the distribution of data in order to identify outliers. In some examples, data statistical analysis is performed based on a rule engine, and employee attendance data is matched and judged according to the set rules. If an employee's attendance behavior violates the rules, such as the number of late arrivals exceeds the set threshold, the system automatically marks it as an abnormal record. In some examples, the attendance trends of employees or teams are displayed through charts, such as monthly, quarterly, and annual attendance rate changes, to help managers understand the overall trend of attendance data.

[0034] Figure 6 A block diagram of an attendance data processing device 600 according to some embodiments of the present disclosure is shown. The device 600 includes an acquisition module 602, which is configured to acquire employee punch-in data and pass data. The device 600 also includes a first determination module 604, which is configured to determine an attendance record table based on the punch-in data and pass data, the attendance record table including attendance data of multiple employees, and multiple first attendance judgment results corresponding to the attendance data, the first attendance judgment results including normal, early leave, late, and missing card to be confirmed. In addition, the device 600 also includes a second determination module 606, which is configured to determine a second attendance judgment result based on the attendance data, travel data, and leave cancellation data in the attendance record table in response to the first attendance judgment result in the attendance record table being abnormal, so as to adjust the first attendance judgment result in the attendance record table to the second attendance judgment result, the second attendance judgment result including leave not cancelled, normal, business trip, and missing card to be confirmed.

[0035] In some embodiments, the first determination module 604 includes a first response unit configured to determine the employee's attendance data and the first attendance judgment result based on the punch-in data in response to the punch-in data matching the pass data and the attendance status in both matching data being normal. The first determination module 604 also includes a second response unit configured to determine the employee's attendance data and the first attendance judgment result based on the pass data in response to the punch-in data matching the pass data and the attendance status of the pass data in both matching data being normal and the attendance status of the punch-in data being abnormal. The first determination module 604 also includes a third response unit configured to determine the employee's attendance data and the first attendance judgment result based on the pass data in response to the punch-in data matching the pass data and at least the punch-in data in both matching data being empty. In addition, the first determination module 604 also includes a fourth response unit configured to determine the employee's attendance data and the first attendance judgment result based on the punch-in data in response to the punch-in data not matching the pass data.

[0036] In some embodiments, the second determination module 600 includes a determination unit configured to determine a second attendance judgment result based on the matching data in response to a match between the attendance data in the attendance record table and one of the travel data and the leave application and cancellation data.

[0037] In some embodiments, the device 600 further includes a third determination module, which is configured to send the first attendance judgment result or the second attendance judgment result in the adjusted attendance record table to the employee, and the employee initiates an appeal request. If the superior leader approves it, the attendance judgment result of the corresponding record is adjusted. If not, the original attendance judgment result of the record is maintained. Finally, the attendance record table adjusted by the third determination module is the final attendance record table, which is used for subsequent performance statistics, personnel attendance rate analysis, etc.

[0038] Figure 7 Schematic block diagram of an example device 700 that can be used to implement embodiments of the present disclosure is shown. Figure 7 As shown, the device 700 includes a processor 701, which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) 702 and loaded into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 can also be stored. The processor 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0039] The various processes and processing described above, such as method 200, may be performed by processor 701. For example, in some embodiments, method 200 may be implemented as a software program that is tangibly contained in a machine-readable medium. In some embodiments, part or all of the software program may be loaded and / or installed on device 700 via ROM 702. When the software program is loaded into RAM 703 and executed by processor 701, one or more actions of method 200 described above may be performed.

[0040] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip systems (SOCs), load programmable logic devices (CPLDs), and the like.

[0041] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0042] The present disclosure may be a method, an apparatus, a system and / or a program product. The program product may include a machine-readable storage medium on which are loaded machine-readable program instructions for executing various aspects of the present disclosure. The machine-readable program instructions described herein may be downloaded from the machine-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network and / or a wireless network. The network may include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or a network interface in each computing / processing device receives a machine-readable program instruction from the network, and forwards the machine-readable program instruction for storage in a machine-readable storage medium in each computing / processing device.

[0043] The machine program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The machine-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect through the Internet). In some embodiments, by using the state information of the machine-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit may execute the machine-readable program instructions, thereby implementing various aspects of the present disclosure.

[0044] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In addition, although each operation is depicted in a specific order, this should be understood as requiring such operations to be performed in the specific order shown or in a sequential order, or requiring that all illustrated operations should be performed to obtain the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Certain features described in the context of separate embodiments can also be implemented in a single implementation in combination. On the contrary, the various features described in the context of a single implementation can also be implemented in multiple implementations individually or in any suitable sub-combination.

[0045] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A method for processing attendance data, characterized in that: include: Obtain employees’ clock-in and pass data; Based on the punch-in data and the pass data, an attendance record table is determined, wherein the attendance record table includes attendance data of multiple employees and multiple first attendance judgment results corresponding to the attendance data, wherein the first attendance judgment results include normal, early leave, late, out-of-town, and card-missing to be confirmed; In response to the first attendance judgment result in the attendance record being abnormal, a second attendance judgment result is determined based on the attendance data, travel data, and leave data in the attendance record to adjust the first attendance judgment result in the attendance record to the second attendance judgment result, wherein the second attendance judgment result includes leave not yet cancelled, normal, business trip, and card missing awaiting confirmation.

2. The method according to claim 1, wherein obtaining the employee's punch-in data and pass data comprises: Obtain the punch-in data on the punch-in server and the access data of the access control system; Based on data cleaning rules, the punch-in data and the pass data of the employee are determined.

3. The method according to claim 1, wherein determining the attendance record table based on the punch-in data and the pass data comprises: In response to the punch-in data matching the pass data and the attendance status in both matching data being normal, determining the employee's attendance data and the first attendance determination result based on the punch-in data; In response to the punching data matching the pass data and the attendance status of the pass data being normal while the attendance status of the punching data being abnormal, the attendance data of the employee and the first attendance judgment result are determined based on the pass data.

4. The method according to claim 3, wherein determining the attendance record table based on the punch-in data and the pass data further comprises: In response to the punch-in data matching the pass data and the attendance status in both matching data being abnormal, the attendance data of the employee and the first attendance determination result are determined based on the punch-in data.

5. The method according to claim 3, wherein determining the attendance record table based on the punch-in data and the pass data further comprises: In response to the punch-in data matching the pass data and at least the punch-in data of the two matching data being empty, the attendance data of the employee and the first attendance determination result are determined based on the pass data.

6. The method according to claim 3, 4 or 5, wherein determining the attendance record table based on the punch-in data and the pass data further comprises: In response to the punch-in data not matching the pass data, the attendance data of the employee and the first attendance determination result are determined based on the punch-in data.

7. The method according to claim 6, wherein determining the second attendance judgment result based on the attendance data, travel data and leave application and cancellation data in the attendance record table comprises: In response to the attendance data in the attendance record table matching one of the travel data and the leave application and cancellation data, a second attendance judgment result is determined based on the matching data.

8. The method according to claim 7, wherein in response to the attendance data in the attendance record table matching one of the travel data and the leave application and cancellation data, determining the second attendance judgment result based on the matching data comprises: In response to the date of the attendance data in the attendance record table matching the business trip date in the travel data and the location of the attendance data in the attendance record table matching the business trip location, determining that the second attendance judgment result is a business trip; In response to the date of the attendance data in the attendance record table matching the leave date in the leave application and leave cancellation data, it is determined that the second attendance judgment result is that the leave has not been cancelled or is normal.

9. The method according to claim 1, further comprising: Send the adjusted attendance data of the first attendance determination result or the second attendance determination result as abnormal in the attendance record table to the employee to obtain the verification result to generate the final attendance determination result.

10. An attendance data processing device, characterized in that: include: An acquisition module, configured to acquire employee punch-in data and pass data; A first determination module is configured to determine an attendance record table based on the punch-in data and the pass data, wherein the attendance record table includes attendance data of multiple employees and multiple first attendance judgment results corresponding to the attendance data, wherein the first attendance judgment results include normal, early leave, late, and card-missing to be confirmed; The second determination module is configured to determine a second attendance determination result in response to the first attendance determination result in the attendance record being abnormal, based on the attendance data, travel data, and leave application and cancellation data in the attendance record, so as to adjust the first attendance determination result in the attendance record to the second attendance determination result, wherein the second attendance determination result includes leave not cancelled, normal, business trip, and card missing to be confirmed.

Images