Abnormal attendance management method and system based on artificial intelligence, medium and equipment

Abstract

The invention provides an abnormal attendance management method and system based on artificial intelligence, a medium and equipment. The method comprises the following steps: acquiring employee attendance data; obtaining employee body index data; based on the acquired employee attendance data, performing attendance abnormity judgment by using an abnormity judgment model; and based on the obtained employee body index data, utilizing an abnormity judgment model to judge the body condition, based on the currently judged body condition, judging whether the current sick and false application takes effect, and meanwhile, carrying out attendance abnormity judgment in combination with hospital bill information.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an abnormal attendance management method, system, medium and equipment based on artificial intelligence. Background Art

[0002] Traditional attendance management methods have problems such as missed clocking in, clocking in for others, and low efficiency in attendance data processing, which makes it difficult to meet the modern enterprises' requirements for efficient and accurate attendance management.

[0003] Patent document CN115346280A discloses an unmanned artificial intelligence attendance device, comprising the following steps: determining the group to which a first employee belongs, and determining the shift information of the group; determining the shift of the first employee based on the attendance data of all employees in the group in the previous statistical period and the shift information of the group; obtaining the attendance data of the first employee on the current day; comparing the attendance data of the first employee on the current day with the shift of the first employee, and confirming whether the first employee has abnormal attendance based on the comparison result.

[0004] The present invention aims to utilize artificial intelligence technology to propose an automated and intelligent abnormal attendance management method to solve the problems existing in traditional attendance management methods. Summary of the invention

[0005] The main purpose of the present invention is to solve the problems in the prior art including missed punching, punching in on behalf of others, low efficiency in attendance data processing, etc., which are difficult to meet the efficient and accurate requirements of modern enterprises for attendance management.

[0006] The first aspect of the present invention provides an abnormal attendance management method based on artificial intelligence, comprising:

[0007] Get employee attendance data;

[0008] Obtain employee physical index data;

[0009] Based on the acquired employee attendance data, the abnormality judgment model is used to judge attendance abnormalities;

[0010] Based on the acquired employee physical indicator data, the abnormal judgment model is used to judge the physical condition. Based on the current physical condition, it is determined whether the current sick leave application is effective. At the same time, attendance abnormality judgment is performed in combination with hospital bill information.

[0011] Optionally, in a first implementation of the first aspect of the present invention, obtaining employee attendance data includes:

[0012] When the WIFI device within the attendance range is connected to the employee's mobile device, the employee's attendance data is collected using the WIFI device;

[0013] The employee's attendance data includes: clock-in records and location information;

[0014] The clock-in record is the earliest time when the employee's mobile device is connected to the WIFI device within the attendance range, and the latest time when it is disconnected;

[0015] The location information is when the employee's mobile device is connected to a WIFI device within the attendance range, and the current employee's location information is within the attendance range;

[0016] Obtain employee attendance data based on WIFI devices.

[0017] Optionally, in a second implementation of the first aspect of the present invention, obtaining employee physical indicator data includes:

[0018] Based on the Big Health APP, AI facial recognition technology is used to obtain body index data including oxygen saturation, facial temperature, heart rate and respiratory rate;

[0019] Obtain employees' physical index data based on connecting to the big health APP.

[0020] Optionally, in a third implementation of the first aspect of the present invention, the abnormality judgment of attendance based on the acquired employee attendance data using an abnormality judgment model includes:

[0021] Use the abnormal judgment model to compare the clock-in time with the day's scheduled time to determine whether there is abnormal attendance;

[0022] When the WIFI device within the attendance range is not connected to the employee's mobile device, the abnormal judgment model is used to compare the GPS location with the address of the out-of-office application to determine whether there is abnormal attendance;

[0023] When the WI FI device within the attendance range is connected to the employee's mobile device and the connection time is longer than the preset value or the GPS positioning is in one position for a long time without moving, it is judged that there is abnormal attendance.

[0024] Optionally, in a fourth implementation of the first aspect of the present invention, judging the physical condition based on the acquired physical indicator data of the employee using an abnormality judgment model, and judging whether the current sick leave application is effective based on the currently judged physical condition, includes:

[0025] Preprocessing the acquired employee physical index data to obtain preprocessed employee physical index data;

[0026] The pre-processed employee physical indicator data is compared with the threshold using the abnormal judgment model. When the employee's physical indicator data exceeds the threshold, the sick leave application takes effect.

[0027] Optionally, in the fifth implementation method of the first aspect of the present invention, the attendance anomaly judgment based on hospital ticket information includes: obtaining hospital tickets, using OCI recognition technology to identify hospital ticket information, and using an anomaly judgment model to determine whether there is an anomaly in the identified hospital ticket information; if there is an anomaly, it is considered that the sick leave application is abnormal.

[0028] Optionally, in a sixth implementation of the first aspect of the present invention, the method further comprises: sending a warning message to a monitoring terminal based on the identified attendance abnormality;

[0029] Based on the identified attendance anomalies, analysis reports are generated regularly and transmitted to the monitoring end.

[0030] The second aspect of the present invention provides an abnormal attendance management system based on artificial intelligence, comprising:

[0031] Attendance data acquisition module, used to obtain employee attendance data;

[0032] Physical index acquisition module, used to obtain employee physical index data;

[0033] The attendance anomaly judgment module is used to judge attendance anomalies based on the acquired employee attendance data using an anomaly judgment model;

[0034] The sick leave application module is used to judge the physical condition of employees based on the acquired physical indicator data using an abnormality judgment model, and to judge whether the current sick leave application is effective based on the current physical condition. At the same time, it combines hospital bill information to make attendance anomaly judgments.

[0035] Optionally, in a first implementation of the second aspect of the present invention, the attendance data acquisition module includes:

[0036] When the WIFI device within the attendance range is connected to the employee's mobile device, the employee's attendance data is collected using the WIFI device;

[0037] The employee's attendance data includes: clock-in records and location information;

[0038] The clock-in record is the earliest time when the employee's mobile device is connected to the WIFI device within the attendance range, and the latest time when it is disconnected;

[0039] The location information is when the employee's mobile device is connected to a WIFI device within the attendance range, and the current employee's location information is within the attendance range;

[0040] Obtain employee attendance data based on WIFI devices.

[0041] Optionally, in a second implementation of the second aspect of the present invention, the body indicator acquisition module includes:

[0042] Based on the Big Health APP, AI facial recognition technology is used to obtain body index data including oxygen saturation, facial temperature, heart rate and respiratory rate;

[0043] Obtain employees' physical index data based on connecting to the big health APP.

[0044] Optionally, in a third implementation of the second aspect of the present invention, the attendance abnormality judgment module includes:

[0045] Use the abnormal judgment model to compare the clock-in time with the day's scheduled time to determine whether there is abnormal attendance;

[0046] When the WIFI device within the attendance range is not connected to the employee's mobile device, the abnormal judgment model is used to compare the GPS location with the address of the out-of-office application to determine whether there is abnormal attendance;

[0047] When the WIFI device within the attendance range is connected to the employee's mobile device and the connection time is longer than the preset value or the GPS positioning is in one position for a long time without moving, it is judged that there is abnormal attendance.

[0048] Optionally, in a fourth implementation of the second aspect of the present invention, the sick leave application module includes:

[0049] Preprocessing the acquired employee physical index data to obtain preprocessed employee physical index data;

[0050] The pre-processed employee physical indicator data is compared with the threshold using the abnormal judgment model. When the employee's physical indicator data exceeds the threshold, the sick leave application takes effect.

[0051] Optionally, in the fifth implementation method of the second aspect of the present invention, the sick leave application module also includes: obtaining hospital bills, using OCI recognition technology to identify hospital bill information, and using an abnormality judgment model to determine whether there is an abnormality in the identified hospital bill information. If there is an abnormality, it is considered that the sick leave application has an abnormality.

[0052] Optionally, in a sixth implementation of the second aspect of the present invention, the system further includes:

[0053] The warning module is used to send warning information to the monitoring end based on the identified abnormal attendance situation;

[0054] The report generation module is used to regularly generate analysis reports based on the identified attendance anomalies and transmit the analysis reports to the monitoring end.

[0055] A third aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-mentioned abnormal attendance management method based on artificial intelligence.

[0056] A fourth aspect of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the above-mentioned abnormal attendance management method based on artificial intelligence when executed by the processor.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] 1. The present invention establishes an abnormal attendance judgment method, reduces manual intervention, and improves the efficiency of attendance management;

[0059] 2. The present invention uses artificial intelligence technology to accurately identify most abnormal attendance situations;

[0060] 3. The present invention can monitor the attendance of employees in real time and promptly detect and handle abnormal situations;

[0061] 4. The present invention provides enterprises with deeper data insights and management decision support by analyzing attendance data, sending warning information regularly, and generating abnormal reports regularly. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0063] Figure 1 A first flow chart of an abnormal attendance management method based on artificial intelligence provided in an embodiment of the present invention.

[0064] Figure 2 A second flow chart of the abnormal attendance management method based on artificial intelligence provided in an embodiment of the present invention.

[0065] Figure 3 A structural diagram of an abnormal attendance management system based on artificial intelligence provided by an embodiment of the present invention.

[0066] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0068] The embodiment of the present invention provides an abnormal attendance management method, system, medium and equipment based on artificial intelligence, which obtains employee attendance data; obtains employee physical index data; uses an abnormal judgment model to judge attendance abnormalities based on the obtained employee attendance data; uses an abnormal judgment model to judge physical conditions based on the obtained employee physical index data, and judges whether the current sick leave application is effective based on the currently judged physical condition. At the same time, it judges attendance abnormalities in combination with hospital bill information; sends warning information to the monitoring end based on the identified attendance abnormalities; generates analysis reports regularly based on the identified attendance abnormalities, and transmits the analysis reports to the monitoring end. The present invention solves the problems in the prior art including missing punch-ins, punch-ins on behalf of others, and low efficiency in attendance data processing.

[0069] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0070] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 , the first embodiment of the abnormal attendance management method based on artificial intelligence in the embodiment of the present invention includes:

[0071] 101. Obtain employee attendance data;

[0072] In this embodiment, when a WIFI device within the attendance range is connected to an employee's mobile device, the employee's attendance data is collected using the WIFI device;

[0073] The employee's attendance data includes: clock-in records and location information;

[0074] The clock-in record is the earliest time when the employee's mobile device is connected to the WIFI device within the attendance range, and the latest time when it is disconnected;

[0075] The location information is when the employee's mobile device is connected to a WI FI device within the attendance range, and the current employee's location information is within the attendance range;

[0076] Obtain employee attendance data based on WI FI devices.

[0077] 102. Obtain employee physical index data;

[0078] In this embodiment, based on the Big Health APP, AI face recognition technology is used to obtain body index data including oxygen saturation, facial temperature, heart rate and respiratory rate;

[0079] Obtain employees' physical index data based on connecting to the big health APP.

[0080] 103. Based on the acquired employee attendance data, use the abnormality judgment model to judge attendance abnormalities;

[0081] In this embodiment, the abnormality judgment model is used to compare the clock-in time with the scheduled time of the day to determine whether there is abnormal attendance;

[0082] When the WIFI device within the attendance range is not connected to the employee's mobile device, the abnormal judgment model is used to compare the GPS location with the address of the out-of-office application to determine whether there is abnormal attendance;

[0083] When the WIFI device within the attendance range is connected to the employee's mobile device and the connection time is longer than the preset value or the GPS positioning is in one position for a long time without moving, it is judged that there is abnormal attendance.

[0084] 104. Based on the acquired employee physical indicator data, the abnormal judgment model is used to judge the physical condition. Based on the current physical condition, it is determined whether the current sick leave application is effective. At the same time, the attendance abnormality judgment is performed in combination with the hospital bill information.

[0085] In this embodiment, the acquired employee physical index data is preprocessed to obtain the preprocessed employee physical index data;

[0086] The pre-processed employee physical indicator data is compared with the threshold value using the abnormal judgment model. When the employee's physical indicator data exceeds the threshold value, the sick leave application takes effect; if the employee's physical indicator data does not exceed the threshold value, the sick leave application is invalid and the attendance is abnormal.

[0087] At the same time, hospital receipts are obtained, and the hospital receipt information is identified using OCI recognition technology. The anomaly judgment model is used to determine whether there are any anomalies in the identified hospital receipt information. If there are any anomalies, it is considered that there are anomalies in the sick leave application and the current attendance is abnormal.

[0088] See also Figure 2 Another embodiment of the abnormal attendance management method based on artificial intelligence in the embodiment of the present invention includes:

[0089] 201. Obtain employee attendance data;

[0090] In this embodiment, a WIFI device is used to bind the device ID of an employee's mobile phone to collect the employee's attendance data, including clock-in records, location information, etc.

[0091] 202. Obtain employee physical index data;

[0092] In this embodiment, the big health APP is connected and AI face recognition technology is used to judge the real health status based on indicators such as oxygen saturation, facial temperature, heart rate, and respiratory rate.

[0093] 203. Based on the acquired employee attendance data, use the abnormality judgment model to judge attendance abnormality;

[0094] In this embodiment, the abnormal judgment model is used to compare the earliest and latest time of the bound device connecting and disconnecting the WIFI with the day's scheduled time to determine whether there is lateness, early departure, or absenteeism, and whether it is abnormal attendance from the time dimension;

[0095] Abnormal judgment based on location information: set up a whitelist of office locations; for users who have applied for going out, they need to declare their going out in the attendance system in advance and fill in the detailed address of going out. On the day of going out, you need to use the bound device to punch in the GPS at the address, and use the abnormal judgment model to determine whether the punch-in address information is consistent with the filled-in address, otherwise it will be judged as abnormal attendance. For users who have not applied for going out, if the WIFI disconnection time is long and the GPS location is not in the whitelist, it will be judged as abnormal attendance.

[0096] In this embodiment, if the mobile phone is connected to WIFI for a long time or the GPS positioning is in one position for a long time without moving, it is judged as abnormal attendance.

[0097] 204. Based on the acquired employee physical index data, use the abnormality judgment model to judge the physical condition, and based on the current physical condition, judge whether the current sick leave application is effective. At the same time, combine the hospital bill information to judge the attendance abnormality;

[0098] In this embodiment, users who apply for sick leave need to perform face recognition and other operations in the big health APP when applying. After obtaining various physical indicators, the abnormal judgment model is used to determine whether they are within the range of sick leave. If they are within the range, they can take sick leave normally. If not, the sick leave record will be marked with a problem label. At the same time, the hospital bill entry function is opened, and medical information can be entered after taking sick leave. Based on OCI recognition and other technologies, it is determined whether the bill information is correct. If correct, the sick leave application will be valid.

[0099] 205. Send warning information to the monitoring terminal based on the identified attendance abnormality;

[0100] 206. Based on the identified attendance anomalies, analysis reports are generated regularly and transmitted to the monitoring terminal.

[0101] In this embodiment, according to the identified abnormal situation, corresponding treatment measures are taken, and warning information is regularly sent to the mobile phones of users and attendance specialists. The system will regularly generate abnormal reports and send them to the human resources department.

[0102] The above describes the abnormal attendance management method based on artificial intelligence in the embodiment of the present invention. The following describes the abnormal attendance management system based on artificial intelligence in the embodiment of the present invention. Figure 3 In one embodiment of the present invention, an abnormal attendance management system based on artificial intelligence includes:

[0103] Attendance data acquisition module 301, used to obtain employee attendance data;

[0104] In this embodiment, when a WIFI device within the attendance range is connected to an employee's mobile device, the employee's attendance data is collected using the WIFI device;

[0105] The employee's attendance data includes: clock-in records and location information;

[0106] The clock-in record is the earliest time when the employee's mobile device is connected to the WIFI device within the attendance range, and the latest time when it is disconnected;

[0107] The location information is when the employee's mobile device is connected to a WIFI device within the attendance range, and the current employee's location information is within the attendance range;

[0108] Obtain employee attendance data based on WIFI devices.

[0109] The physical index acquisition module 302 is used to obtain the physical index data of the employee;

[0110] In this embodiment, based on the Big Health APP, AI face recognition technology is used to obtain body index data including oxygen saturation, facial temperature, heart rate and respiratory rate;

[0111] Obtain employees' physical index data based on connecting to the big health APP.

[0112] The attendance abnormality judgment module 303 is used to judge attendance abnormality based on the acquired employee attendance data using an abnormality judgment model;

[0113] In this embodiment, the abnormality judgment model is used to compare the clock-in time with the scheduled time of the day to determine whether there is abnormal attendance;

[0114] When the WIFI device within the attendance range is not connected to the employee's mobile device, the abnormal judgment model is used to compare the GPS location with the address applied for going out to determine whether there is abnormal attendance.

[0115] In this embodiment, if the WIFI device within the attendance range is disconnected from the employee's mobile device for a long time, and the GPS positioning is not within the preset attendance range, it is judged as abnormal attendance;

[0116] When the WIFI device within the attendance range is connected to the employee's mobile device and the connection time is too long or the GPS positioning is in one position for a long time without moving, it is judged as abnormal attendance.

[0117] The sick leave application module 304 is used to judge the physical condition of the employee based on the acquired physical indicator data using an abnormality judgment model, and to judge whether the current sick leave application is effective based on the current judged physical condition. At the same time, it combines the hospital bill information to make attendance abnormality judgment.

[0118] In this embodiment, users who apply for sick leave need to perform face recognition and other operations in the Big Health APP when applying. After obtaining various physical indicators, it is determined whether they are within the sick leave range. If they are within the range, they can apply for sick leave normally. If not, this sick leave record will be marked with a problem label. At the same time, the hospital bill entry function is opened, and medical information can be entered after applying for sick leave. Based on technologies such as OCI recognition, it is determined whether the bill information is correct. If it is correct, the sick leave application will be set valid.

[0119] The warning module 305 is used to send a warning message to the monitoring terminal based on the identified abnormal attendance situation;

[0120] The report generation module 306 is used to regularly generate analysis reports based on the identified attendance anomalies, and transmit the analysis reports to the monitoring terminal.

[0121] above Figure 3The artificial intelligence-based abnormal attendance management system in the embodiment of the present invention is described in detail from the perspective of modular functional entities, and the electronic device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0122] Figure 4 7 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device 700 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 710 (for example, one or more processors) and a memory 720, and one or more storage media 730 (for example, one or more mass storage devices) storing application programs 733 or data 732. Among them, the memory 720 and the storage medium 730 can be temporary storage or permanent storage. The program stored in the storage medium 730 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the electronic device 700. Furthermore, the processor 710 can be configured to communicate with the storage medium 730 to execute a series of instruction operations in the storage medium 730 on the electronic device 700.

[0123] The electronic device 700 may also include one or more power supplies 740, one or more wired or wireless network interfaces 750, one or more input and output interfaces 750, and / or one or more operating systems 731, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 4 The structure of the electronic device shown does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine some components, or arrange the components differently.

[0124] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of an abnormal attendance management method based on artificial intelligence.

[0125] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0126] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0127] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An abnormal attendance management method based on artificial intelligence, characterized in that: include: Get employee attendance data; Obtain employee physical index data; Based on the acquired employee attendance data, the abnormality judgment model is used to judge attendance abnormalities; Based on the acquired employee physical indicator data, the abnormal judgment model is used to judge the physical condition. Based on the current physical condition, it is determined whether the current sick leave application is effective. At the same time, attendance abnormality judgment is performed in combination with hospital bill information.

2. The abnormal attendance management method based on artificial intelligence according to claim 1 is characterized in that: The acquisition of employee attendance data includes: When the WIFI device within the attendance range is connected to the employee's mobile device, the employee's attendance data is collected using the WIFI device; The employee's attendance data includes: clock-in records and location information; The clock-in record is the earliest time when the employee's mobile device is connected to the WIFI device within the attendance range, and the latest time when it is disconnected; The location information is when the employee's mobile device is connected to a WIFI device within the attendance range, and the current employee's location information is within the attendance range; Obtain employee attendance data based on WIFI devices.

3. The abnormal attendance management method based on artificial intelligence according to claim 1 is characterized in that: The acquisition of employee physical index data includes: Based on the Big Health APP, AI facial recognition technology is used to obtain body index data including oxygen saturation, facial temperature, heart rate and respiratory rate; Obtain employees' physical index data based on the connection with the big health APP.

4. The abnormal attendance management method based on artificial intelligence according to claim 1 is characterized in that: The abnormality judgment model is used to judge attendance abnormality based on the acquired employee attendance data, including: Use the abnormal judgment model to compare the clock-in time with the day's scheduled time to determine whether there is abnormal attendance; When the WIFI device within the attendance range is not connected to the employee's mobile device, the abnormal judgment model is used to compare the GPS location with the address of the out-of-office application to determine whether there is abnormal attendance; When the WIFI device within the attendance range is connected to the employee's mobile device and the connection time is longer than the preset value or the GPS positioning is in one position for a long time without moving, it is judged that there is abnormal attendance.

5. The abnormal attendance management method based on artificial intelligence according to claim 1 is characterized in that: The method of judging the physical condition of the employee based on the acquired physical index data by using an abnormality judgment model, and judging whether the current sick leave application is effective based on the currently judged physical condition, includes: Preprocessing the acquired employee physical index data to obtain preprocessed employee physical index data; The pre-processed employee physical index data is compared with the threshold using the abnormal judgment model. When the employee's physical index data exceeds the threshold, the sick leave application takes effect.

6. The abnormal attendance management method based on artificial intelligence according to claim 5 is characterized in that: The abnormal attendance judgment based on the hospital receipt information includes: obtaining hospital receipts, identifying hospital receipt information using OCI recognition technology, and using an abnormal judgment model to determine whether the identified hospital receipt information has abnormalities. If there are abnormalities, it is considered that the sick leave application has abnormalities.

7. The abnormal attendance management method based on artificial intelligence according to claim 1 is characterized in that: The method further includes: sending a warning message to a monitoring terminal based on the identified attendance abnormality; Based on the identified attendance anomalies, analysis reports are generated regularly and transmitted to the monitoring end.

8. An abnormal attendance management system based on artificial intelligence, characterized in that: include: Attendance data acquisition module, used to obtain employee attendance data; Physical index acquisition module, used to obtain employee physical index data; The attendance anomaly judgment module is used to judge attendance anomalies based on the acquired employee attendance data using an anomaly judgment model; The sick leave application module is used to judge the physical condition of employees based on the acquired physical indicator data using an abnormality judgment model, and to judge whether the current sick leave application is effective based on the current physical condition. At the same time, it combines hospital bill information to make attendance anomaly judgments.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the abnormal attendance management method based on artificial intelligence described in any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the computer program is executed by a processor, the steps of the abnormal attendance management method based on artificial intelligence described in any one of claims 1 to 7 are implemented.

Images