A method and system for generating attendance records based on artificial intelligence

Abstract

The invention relates to the field of artificial intelligence technology, and provides an attendance generation method and system based on artificial intelligence. The method comprises: obtaining punching information, obtaining a punching time information set and a punching location information set; obtaining standard punching time information and standard punching location information, inputting them into an attendance analysis model for data processing and analysis, obtaining a punching time processing result set and a punching location processing result set, inputting them into the attendance analysis model, obtaining a time attendance analysis result and a location attendance analysis result, inputting them into an attendance generation database, and generating an attendance data set, solving the technical problems of fixed employee attendance punching conditions, inability to synchronize attendance data in real time, and low efficiency of enterprise attendance management, achieving the technical effect of improving the comprehensiveness and intelligence of attendance analysis, setting punching conditions according to employee attendance scenarios, improving the flexibility of attendance data analysis, ensuring real-time synchronization of attendance data, and improving the efficiency of enterprise attendance management.

Description

Technical Field

[0001] The present invention relates to the technical field related to artificial intelligence, and in particular to an attendance generation method and system based on artificial intelligence. Background Art

[0002] At present, the common way of employee attendance is to use access cards for integrated attendance management. However, this method is cumbersome, with poor transparency and real-time performance, which increases the company's human resource costs. At the same time, it cannot solve the problems of proxy exams and absenteeism (access cards can be swiped on behalf of others). For field staff, attendance management records cannot be kept, which is not conducive to the company's personnel management.

[0003] The rapid increase in the number of employees in modern enterprises and the frequent changes in employees have made the attendance statistics management work of enterprises increasingly complicated. Traditional enterprise attendance management methods are difficult to ensure the accuracy and real-time nature of data. Modern information technology means such as communication technology are used to realize enterprise informatization construction and new modern management, which is an urgent task that enterprises must face in the inevitable survival competition.

[0004] In summary, the existing technology has the technical problems of fixed employee attendance punching conditions, inability to synchronize attendance data in real time, and low efficiency of enterprise attendance management. Summary of the invention

[0005] This application provides an attendance generation method and system based on artificial intelligence, aiming to solve the technical problems of fixed employee attendance punching conditions, inability to synchronize attendance data in real time, and low efficiency of enterprise attendance management.

[0006] In view of the above problems, an embodiment of the present application provides an attendance generation method and system based on artificial intelligence.

[0007] The first aspect disclosed in the present application provides an attendance generation method based on artificial intelligence, wherein the method includes: obtaining the punch-in information of a first employee within a preset time period, obtaining a punch-in time information set and a punch-in location information set; obtaining the standard punch-in time information and standard punch-in location information for the first employee to punch in; performing data processing and analysis on the punch-in time information set and the punch-in location information set according to the standard punch-in time information and the standard punch-in location information, and obtaining a punch-in time processing result set and a punch-in location processing result set; inputting the punch-in time processing result set and the punch-in location processing result set into an attendance analysis model, and obtaining a time attendance analysis result and a location attendance analysis result, wherein the attendance analysis model includes an attendance time analysis unit and an attendance location analysis unit; inputting the punch-in time information set, the punch-in location information set, the time attendance analysis result and the location attendance analysis result into an attendance generation database, and generating an attendance data set for the first employee within the current preset time period.

[0008] Another aspect disclosed in the present application provides an attendance generation system based on artificial intelligence, wherein the system includes: a punch-in information acquisition module, which is used to obtain the punch-in information of a first employee within a preset time period, and obtain a punch-in time information set and a punch-in location information set; a standard punch-in information acquisition module, which is used to obtain the standard punch-in time information and standard punch-in location information for the first employee to punch in; a data processing and analysis module, which is used to perform data processing and analysis on the punch-in time information set and the punch-in location information set according to the standard punch-in time information and the standard punch-in location information, and obtain the punch-in time information set and the standard punch-in location information set. a punch-in time processing result set and a punch-in location processing result set; an attendance analysis result obtaining module, used for inputting the punch-in time processing result set and the punch-in location processing result set into the attendance analysis model, and obtaining the time attendance analysis result and the location attendance analysis result, wherein the attendance analysis model includes an attendance time analysis unit and an attendance location analysis unit; an attendance data set generating module, used for inputting the punch-in time information set, the punch-in location information set, the time attendance analysis result and the location attendance analysis result into the attendance generation database, and generating the attendance data set of the first employee in the current preset time period.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] The method adopts the method of obtaining the punch-in information of the first employee within a preset time period, obtaining a punch-in time information set and a punch-in location information set; obtaining the standard punch-in time information and the standard punch-in location information for the first employee to punch in, performing data processing and analysis on the punch-in time information set and the punch-in location information set, obtaining a punch-in time processing result set and a punch-in location processing result set, inputting them into the attendance analysis model, obtaining a time attendance analysis result and a location attendance analysis result; inputting the punch-in time information set, the punch-in location information set, the time attendance analysis result and the location attendance analysis result into the attendance generation database, and generating an attendance data set of the first employee within the current preset time period, thereby achieving the technical effect of improving the comprehensiveness and intelligence of the attendance analysis, setting the punch-in conditions according to the employee attendance scenario, improving the flexibility of the attendance data analysis, ensuring the real-time synchronization of the attendance data, and improving the efficiency of the enterprise attendance management.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1A possible flow chart of an attendance generation method based on artificial intelligence is provided for the embodiment of the present application;

[0013] Figure 2 A possible flow chart of constructing an attendance time analysis unit in an attendance generation method based on artificial intelligence is provided for an embodiment of the present application;

[0014] Figure 3 A possible flow chart of generating and obtaining an attendance data set in an attendance generation method based on artificial intelligence is provided for an embodiment of the present application;

[0015] Figure 4 A possible structural diagram of an attendance generation system based on artificial intelligence is provided for the embodiment of the present application.

[0016] Explanation of reference numerals: punch-in information acquisition module 100 , standard punch-in information acquisition module 200 , data processing and analysis module 300 , attendance analysis result acquisition module 400 , attendance data set generation module 500 . DETAILED DESCRIPTION

[0017] This application provides an attendance generation method and system based on artificial intelligence, which solves the technical problems of fixed employee attendance punching conditions, inability to synchronize attendance data in real time, and low efficiency of enterprise attendance management. It achieves the technical effect of improving the comprehensiveness and intelligence of attendance analysis, setting punching conditions according to employee attendance scenarios, improving the flexibility of attendance data analysis, ensuring real-time synchronization of attendance data, and improving the efficiency of enterprise attendance management.

[0018] Embodiment 1

[0019] like Figure 1 As shown, the embodiment of the present application provides an attendance generation method based on artificial intelligence, wherein the method comprises:

[0020] S10: Obtain the clock-in information of the first employee within a preset time period, and obtain a clock-in time information set and a clock-in location information set;

[0021] Step S10 includes the steps of:

[0022] S11: within the preset time period, when the first employee clocks in, obtaining the time information of the clocking in through the server, and obtaining the clocking in time information set;

[0023] S12: When the first employee clocks in, the location information of the clocking in is obtained through a positioning service to obtain the clocking in location information set.

[0024] Specifically, the first employee is an attendance target object, the preset time period can be set to a natural month, the punch-in time information set includes all punch-in time records within the preset time period, and the punch-in location information set includes all punch-in location records within the preset time period;

[0025] Obtain the punch-in information of the first employee within a preset time period, and obtain a punch-in time information set and a punch-in location information set, specifically including: within the preset time period (generally the working days of a natural month), when the first employee punches in, obtain the time information when punching in through the server (the server is connected to the cloud, and the time information directly synchronized has high accuracy), integrate the time information when punching in, and obtain the punch-in time information set (if the preset time period is a natural month, the punch-in time information set is also the monthly punch-in time information set); when the first employee punches in, obtain the location information when punching in through the positioning service (generally, a positioning chip, an amplification matching circuit (LNA+SAW, etc.), a GPS Beidou antenna, made into a PCBA, or other chips such as DR are added and packaged into a module to obtain a positioning module to realize a positioning service), integrate the location information when punching in, and obtain the punch-in location information set (if the preset time period is a natural month, the punch-in location information set is also the monthly punch-in location information set), so as to provide data support for attendance.

[0026] S20: Obtaining standard clock-in time information and standard clock-in location information for the first employee to clock in;

[0027] S30: performing data processing and analysis on the punch-in time information set and the punch-in location information set according to the standard punch-in time information and the standard punch-in location information to obtain a punch-in time processing result set and a punch-in location processing result set;

[0028] Step S30 includes the steps of:

[0029] S31: Calculate the difference between the punch-in time information in the punch-in time information set and the standard punch-in time information to obtain a time difference information set;

[0030] S32: Calculate the difference between the punch-in location information in the punch-in location information set and the standard punch-in location information to obtain a location difference information set;

[0031] S33: Calculate the mean of the data in the time difference information set and the position difference information set to obtain time difference mean information and position difference mean information;

[0032] S34: Calculate the variance of the data in the time difference information set and the position difference information set to obtain time difference variance information and position difference variance information;

[0033] S35: taking the time difference mean information and the time difference variance information as the time attendance analysis result, and taking the position difference mean information and the position difference variance information as the position attendance analysis result.

[0034] Specifically, the standard punch-in time information and standard punch-in location information of the first employee for punching in are obtained, the standard punch-in time information may be 9:00:00 (clock-in time for work) and 17:30:00 (clock-in time for leaving get off work), and the standard punch-in location information may be a company location area within the company boundary;

[0035] According to the standard punch-in time information and the standard punch-in location information, data processing and analysis are performed on the punch-in time information set and the punch-in location information set to obtain a punch-in time processing result set and a punch-in location processing result set, specifically including: calculating the difference between the punch-in time information in the punch-in time information set and the standard punch-in time information (e.g., if the punch-in time information is 8:53:20, the corresponding difference can be set to the time difference information of 0:6:40; if the punch-in time information is 9:3:15, the corresponding difference can be set to the time difference information of -0:3:15; if there is a "-" mark, it is a late punch-in), traversing and calculating the difference between each punch-in time information in the punch-in time information set and the standard punch-in time information, integrating the difference between each punch-in time information in the punch-in time information set and the standard punch-in time information, to obtain a time difference information set;

[0036] Calculate the difference between the punch-in location information in the punch-in location information set and the standard punch-in location information (for example, the door on the first floor of the office building where the company is located is set as the coordinate origin, the maximum horizontal dimension corresponding to the building area is used as the horizontal axis, and the maximum vertical dimension corresponding to the building area is used as the vertical axis, and a spatial coordinate system is established to frame the company location area within the company boundary, obtain the company boundary frame coordinate set, determine the direction vector with the shortest vector length between the punch-in space coordinates and the company boundary frame coordinate set, and set the direction vector as the difference between the punch-in location information and the standard punch-in location information. For example, the boundary coordinates of the company corresponding to the company location area are (3.24-9.28, 5.68-15.32, 10.56-13.44), and the punch-in location coordinates are (3, 3.5, 0.9), then the difference is The coordinate data are all in units of m, which means that the first user punches in on the first floor as soon as he enters the office building, and does not follow the standard punching rules). The difference between each punching location information in the punching location information set and the standard punching location information is traversed and calculated, and the difference between each punching location information in the punching location information set and the standard punching location information is integrated to obtain a location difference information set.

[0037] Calculate the mean of the data in the time difference information set to obtain the time difference mean information; calculate the mean of the data in the position difference information set to obtain the position difference mean information; calculate the variance of the data in the time difference information set to obtain the time difference variance information; calculate the variance of the data in the position difference information set to obtain the position difference variance information; use the time difference mean information and the time difference variance information as the time attendance analysis result, and use the position difference mean information and the position difference variance information as the position attendance analysis result. On the one hand, support is provided to ensure the clarity of employee attendance information. On the other hand, staggered attendance management and multi-location synchronous attendance management can also be performed (the standard punch-in time information 9:00:00, 9:10:00, 9:20:00 can be grouped for calculation to achieve staggered attendance management) (the company location area, the first field location area, and the second field location area within the company boundary of the standard punch-in location information can be grouped for calculation to achieve multi-location synchronous attendance management), thereby improving the flexibility of attendance management.

[0038] S40: Inputting the punch-in time processing result set and the punch-in location processing result set into the attendance analysis model to obtain a time attendance analysis result and a location attendance analysis result, wherein the attendance analysis model includes an attendance time analysis unit and an attendance location analysis unit;

[0039] like Figure 2 As shown, step S40 includes the steps of:

[0040] S41: Obtaining the mean information of multiple sample time differences and the variance information of multiple sample time differences;

[0041] S42: combining the plurality of sample time difference mean information and the plurality of sample time difference variance information in a one-to-one correspondence manner, and performing a punch-in time qualification level assessment to obtain a plurality of sample time qualification analysis results;

[0042] S43: constructing the attendance time analysis unit by using the plurality of sample time difference mean information, the plurality of sample time difference variance information and the plurality of sample time qualification analysis results.

[0043] Specifically, the punch-in time processing result set and the punch-in location processing result set are input into the attendance analysis model to obtain the time attendance analysis result and the location attendance analysis result, which specifically includes: the attendance analysis model includes an attendance time analysis unit and an attendance location analysis unit, the multiple sample time difference mean information is the time difference mean information of multiple sampled employees (random sampling) of the attendance generation system, and the multiple sample time difference variance information is the time difference variance information of multiple sampled employees of the attendance generation system;

[0044] Based on the attendance generation system, data is randomly extracted to obtain multiple sample time difference mean information and multiple sample time difference variance information; the multiple sample time difference mean information and multiple sample time difference variance information are combined one by one, and the punch-in time qualification level is evaluated (the time is a positive value (there is no "-" mark, which means not late); the larger the mean value, the earlier the arrival, and the higher the punch-in time qualification level; the smaller the variance, the more stable the attendance, and the higher the punch-in time qualification level), to obtain multiple sample time qualification analysis results; the multiple sample time difference mean information, multiple sample time difference variance information and multiple sample time qualification analysis results are used to construct the attendance time analysis unit to provide model support for attendance time analysis.

[0045] Step S43 includes the steps of:

[0046] S431: using the time difference mean information as the first decision feature and the plurality of sample time difference mean information to construct a first part of multi-layer decision nodes in the attendance time analysis unit;

[0047] S432: using the time difference variance information as the second decision feature and using the plurality of sample time difference variance information to construct a second part of multi-layer decision nodes in the attendance time analysis unit;

[0048] S433: Connecting the multi-layer decision nodes of the first part and the second part, wherein the top-level decision node of the multi-layer decision nodes in the first part is connected to the bottom-level decision node of the multi-layer decision nodes in the second part;

[0049] S434: Using the multiple sample time qualification analysis results as multiple final decision division results, marking the multiple final division results of the multi-layer decision nodes of the first part and the second part after connection, to obtain the attendance time analysis unit.

[0050] Specifically, the attendance time analysis unit is constructed by using the plurality of sample time difference mean information, the plurality of sample time difference variance information and the plurality of sample time qualification analysis results, which specifically includes: after the punch-in time processing result set is input into the attendance generation system, the decision tree is used as the model basis, the time difference mean information is used as the first decision feature (first-level classification feature), the plurality of sample time difference mean information is used to classify the punch-in time processing result set, and the first part of the multi-layer decision nodes in the attendance time analysis unit are constructed; the time difference variance information is used as the second decision feature (secondary classification feature), the plurality of sample time difference variance information is used to classify the punch-in time processing result set, and the second part of the multi-layer decision nodes in the attendance time analysis unit are constructed; the The multiple sample time qualification analysis results are supervision data; the top decision node of the multi-layer decision nodes in the first part is connected to the bottom decision node of the multi-layer decision nodes in the second part, and based on this, the multi-layer decision nodes of the first part and the second part are connected; the multiple sample time qualification analysis results are used as multiple final decision division results, and the multiple final division results of the multi-layer decision nodes of the first part and the second part after connection are marked to obtain the attendance time analysis unit (the attendance time analysis unit fully satisfies the rule that the time is a positive value (there is no "-" mark, and it is not late), the larger the mean, the earlier the arrival, and the higher the qualified level of the punch-in time; the smaller the variance, the more stable the attendance, and the higher the qualified level of the punch-in time), to provide support for rapid attendance time analysis.

[0051] Step S40 includes the steps of:

[0052] S44: Obtaining the mean information of multiple sample position differences and the variance information of multiple sample check-in position differences;

[0053] S45: combining the plurality of sample position difference mean information and the plurality of sample punch-in position difference variance information one by one, and performing punch-in position qualification level assessment to obtain a plurality of sample position qualification analysis results;

[0054] S46: constructing the attendance position analysis unit by using the plurality of sample position difference mean information, the plurality of sample punch-in position difference variance information and the plurality of sample position qualification analysis results, and combining with the attendance time analysis unit to obtain the attendance analysis model;

[0055] S47: inputting the punch-in time processing result set and the punch-in location processing result set into the attendance time analysis unit and the attendance location analysis unit respectively, to obtain the time attendance analysis result and the location attendance analysis result.

[0056] Specifically, the punch-in time processing result set and the punch-in location processing result set are input into the attendance analysis model to obtain the time attendance analysis result and the location attendance analysis result, which specifically includes: the attendance analysis model includes an attendance time analysis unit and an attendance location analysis unit, and the attendance location analysis unit is constructed: multiple sample location difference mean information, based on the attendance generation system, data is randomly extracted to obtain multiple sample location difference mean information and multiple sample punch-in location difference variance information; the multiple sample location difference mean information and multiple sample punch-in location difference variance information are combined one by one, and the punch-in location qualification level is evaluated (the smaller the distance, the closer to the company when punching in, the higher the level; the smaller the attendance positioning variance, the more stable the attendance positioning, the higher the level), to obtain multiple sample location qualification analysis results; based on the multiple sample location difference mean information and multiple sample punch-in location difference The variance information and the qualification analysis results of multiple sample positions are based on the decision tree as the model basis, and the position difference mean information is used as the third decision feature (first-level classification feature). The punch-in position processing result set is classified by using the punch-in position difference variance information as the fourth decision feature (secondary classification feature), and the punch-in position processing result set is classified by using the variance information of multiple sample punch-in position differences; the qualification analysis results of the multiple sample positions are used as supervision data to construct the attendance position analysis unit; based on the attendance position analysis unit, combined with the attendance time analysis unit, the attendance analysis model is obtained; the punch-in time processing result set and the punch-in position processing result set are respectively input into the attendance time analysis unit and the attendance position analysis unit to obtain the time attendance analysis result and the position attendance analysis result, so as to provide a reference for subsequent substitution operation / verification.

[0057] S50: inputting the punch-in time information set, the punch-in location information set, the time attendance analysis result and the location attendance analysis result into an attendance generation database, and generating an attendance data set of the first employee in a current preset time period.

[0058] like Figure 3 As shown, step S50 includes the steps of:

[0059] S51: traverse and filter the punch-in time information set and the punch-in location information set to obtain the qualified punch-in times and unqualified punch-in times that meet the standard punch-in time information and the standard punch-in location information;

[0060] S52: constructing an employee data index based on the first employee;

[0061] S53: Based on the number of qualified punch-in times, the number of unqualified punch-in times, the punch-in time information, the punch-in location information, the time attendance analysis result and the location attendance analysis result, construct multiple punch-in data indexes to obtain the attendance generation database;

[0062] S54: inputting the qualified punch-in times, unqualified punch-in times, punch-in time information set, punch-in location information set, time attendance analysis results and location attendance analysis results of the first employee into the attendance generation database to generate the attendance data set.

[0063] Specifically, the punch-in time information set, the punch-in location information set, the time attendance analysis result and the location attendance analysis result are input into the attendance generation database, and an attendance data set of the first employee in the current preset time period is generated, specifically including: traversing and screening the punch-in time information set and the punch-in location information set through the time attendance analysis result and the location attendance analysis result to obtain the qualified punch-in times and the unqualified punch-in times that meet the standard punch-in time information and the standard punch-in location information; based on the first employee, setting an exclusive index symbol for the first employee, traversing the above-mentioned exclusive index symbol setting process, and constructing an employee data index; based on the qualified punch-in times, the unqualified punch-in times, the punch-in time information, the punch-in location information, the time attendance analysis result and the location attendance analysis result, As a result, a qualified punch-in sub-directory, an unqualified punch-in sub-directory, a punch-in time sub-directory, a punch-in location sub-directory, a time attendance analysis result sub-directory and a location attendance analysis result sub-directory are set, and multiple punch-in data indexes are constructed (the multiple punch-in data indexes include but are not limited to qualified punch-in index symbols, unqualified punch-in index symbols, punch-in time index symbols, punch-in location index symbols, time attendance analysis result index symbols and location attendance analysis result index symbols), and the attendance generation database is obtained; the qualified punch-in times, unqualified punch-in times, punch-in time information set, punch-in location information set, time attendance analysis results and location attendance analysis results of the first employee are graded and input into the attendance generation database to generate the attendance data set to provide support for flexible management of employee attendance.

[0064] In summary, the attendance generation method and system based on artificial intelligence provided by the embodiments of the present application have the following technical effects:

[0065] 1. By obtaining the punch-in information of the first employee within a preset time period, a punch-in time information set and a punch-in location information set are obtained; the standard punch-in time information and standard punch-in location information for the first employee to punch in are obtained, and data processing and analysis are performed on the punch-in time information set and the punch-in location information set to obtain a punch-in time processing result set and a punch-in location processing result set, which are input into the attendance analysis model to obtain a time attendance analysis result and a location attendance analysis result; the punch-in time information set, the punch-in location information set, the time attendance analysis result and the location attendance analysis result are input into the attendance generation database, and an attendance data set of the first employee within the current preset time period is generated. This application provides an attendance generation method and system based on artificial intelligence, thereby achieving the technical effect of improving the comprehensiveness and intelligence of attendance analysis, setting punch-in conditions according to employee attendance scenarios, improving the flexibility of attendance data analysis, ensuring real-time synchronization of attendance data, and improving the efficiency of enterprise attendance management.

[0066] 2. Due to the traversal and screening to obtain the qualified punch-in times and unqualified punch-in times that meet the standard punch-in time information and standard punch-in location information, an employee data index is constructed, multiple punch-in data indexes are constructed, an attendance generation database is obtained, the attendance generation database is input, and an attendance data set is generated to provide support for flexible management of employee attendance.

[0067] Embodiment 2

[0068] Based on the same inventive concept as the attendance generation method based on artificial intelligence in the aforementioned embodiment, Figure 4 As shown, the embodiment of the present application provides an attendance generation system based on artificial intelligence, wherein the system includes:

[0069] The clock-in information acquisition module 100 is used to acquire the clock-in information of the first employee within a preset time period, and obtain a clock-in time information set and a clock-in location information set;

[0070] A standard clock-in information acquisition module 200 is used to acquire the standard clock-in time information and standard clock-in location information of the first employee;

[0071] The data processing and analysis module 300 is used to perform data processing and analysis on the punch-in time information set and the punch-in location information set according to the standard punch-in time information and the standard punch-in location information, and obtain a punch-in time processing result set and a punch-in location processing result set;

[0072] The attendance analysis result obtaining module 400 is used to input the punch-in time processing result set and the punch-in location processing result set into the attendance analysis model to obtain the time attendance analysis result and the location attendance analysis result, wherein the attendance analysis model includes an attendance time analysis unit and an attendance location analysis unit;

[0073] The attendance data set generation module 500 is used to input the punch-in time information set, the punch-in location information set, the time attendance analysis result and the location attendance analysis result into the attendance generation database, and generate the attendance data set of the first employee in the current preset time period.

[0074] Furthermore, the system comprises:

[0075] A time information set obtaining module, used to obtain the time information of the punch-in time by using the server to obtain the punch-in time information set when the first employee punches in during the preset time period;

[0076] The location information set obtaining module is used to obtain the location information of the first employee when punching in through the positioning service to obtain the punching in location information set.

[0077] Furthermore, the system comprises:

[0078] A time difference information set obtaining module, used to calculate the difference between the punch-in time information in the punch-in time information set and the standard punch-in time information, and obtain the time difference information set;

[0079] A position difference information set obtaining module, used for calculating the difference between the punch-in position information in the punch-in position information set and the standard punch-in position information, to obtain a position difference information set;

[0080] A difference mean information obtaining module, used to calculate the mean of the data in the time difference information set and the position difference information set, and obtain the time difference mean information and the position difference mean information;

[0081] A difference variance information obtaining module, used to calculate the variance of the data in the time difference information set and the position difference information set, and obtain the time difference variance information and the position difference variance information;

[0082] The location attendance analysis result determination module is used to use the time difference mean information and the time difference variance information as the time attendance analysis result, and use the position difference mean information and the position difference variance information as the location attendance analysis result.

[0083] Furthermore, the system comprises:

[0084] A first sample information acquisition module is used to acquire the mean information of multiple sample time differences and the variance information of multiple sample time differences;

[0085] A first corresponding combination module is used to combine the plurality of sample time difference mean information and the plurality of sample time difference variance information one by one, and to evaluate the punch-in time qualification level to obtain a plurality of sample time qualification analysis results;

[0086] The attendance time analysis unit construction module is used to construct the attendance time analysis unit by using the plurality of sample time difference mean information, the plurality of sample time difference variance information and the plurality of sample time qualification analysis results.

[0087] Furthermore, the system comprises:

[0088] The second sample information acquisition module is used to obtain the mean information of multiple sample position differences and the variance information of multiple sample check-in position differences;

[0089] The second corresponding combination module is used to combine the plurality of sample position difference mean information and the plurality of sample punch-in position difference variance information one by one, and perform punch-in position qualification level assessment to obtain a plurality of sample position qualification analysis results;

[0090] An attendance analysis model acquisition module is used to construct the attendance position analysis unit by using the plurality of sample position difference mean information, the plurality of sample punch-in position difference variance information and the plurality of sample position qualification analysis results, and obtain the attendance analysis model in combination with the attendance time analysis unit;

[0091] The attendance analysis result obtaining module is used to input the punching time processing result set and the punching position processing result set into the attendance time analysis unit and the attendance position analysis unit respectively to obtain the time attendance analysis result and the position attendance analysis result.

[0092] Furthermore, the system comprises:

[0093] A first multi-layer decision node construction module, used to use the time difference mean information as the first decision feature and the plurality of sample time difference mean information to construct a first part of multi-layer decision nodes in the attendance time analysis unit;

[0094] A second multi-layer decision node construction module, used to use the time difference variance information as the second decision feature and the plurality of sample time difference variance information to construct a second part of multi-layer decision nodes in the attendance time analysis unit;

[0095] A multi-layer decision node connection module, used to connect the multi-layer decision nodes of the first part and the second part, wherein the top-level decision node of the multi-layer decision nodes in the first part is connected to the bottom-level decision node of the multi-layer decision nodes in the second part;

[0096] The attendance time analysis unit acquisition module is used to use the multiple sample time qualification analysis results as multiple final decision division results, mark the multiple final division results of the multi-layer decision nodes of the first part and the second part after connection, and obtain the attendance time analysis unit.

[0097] Furthermore, the system comprises:

[0098] A qualified punch-in and unqualified punch-in statistics module is used to traverse and screen the punch-in time information set and the punch-in location information set to obtain the qualified punch-in times and unqualified punch-in times that meet the standard punch-in time information and the standard punch-in location information;

[0099] An employee data index building module, used to build an employee data index based on the first employee;

[0100] An attendance generation database acquisition module is used to construct multiple punch-in data indexes based on the number of qualified punch-in times, the number of unqualified punch-in times, the punch-in time information, the punch-in location information, the time attendance analysis results and the location attendance analysis results to obtain the attendance generation database;

[0101] The attendance data set acquisition module is used to input the qualified punch-in times, unqualified punch-in times, punch-in time information set, punch-in location information set, time attendance analysis results and location attendance analysis results of the first employee into the attendance generation database to generate the attendance data set.

[0102] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0103] Furthermore, the first or second mentioned above may not only represent an order relationship, but may also represent a specific concept, and / or refer to multiple elements that can be selected individually or in full. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. An attendance generation method based on artificial intelligence, characterized in that: The method comprises: Obtain the clock-in information of the first employee within a preset time period, and obtain a clock-in time information set and a clock-in location information set; Obtaining standard clock-in time information and standard clock-in location information for the first employee to clock in; According to the standard punch-in time information and the standard punch-in location information, data processing and analysis are performed on the punch-in time information set and the punch-in location information set to obtain a punch-in time processing result set and a punch-in location processing result set; Inputting the punch-in time processing result set and the punch-in location processing result set into the attendance analysis model to obtain the time attendance analysis result and the location attendance analysis result, wherein the attendance analysis model includes an attendance time analysis unit and an attendance location analysis unit; Input the punch-in time information set, the punch-in location information set, the time attendance analysis result and the location attendance analysis result into an attendance generation database, and generate an attendance data set for the first employee within a current preset time period; Obtain the clock-in information of the first employee within a preset time period, and obtain a clock-in time information set and a clock-in location information set, including: During the preset time period, when the first employee clocks in, the time information of the clocking in is obtained through the server, and the clocking in time information set is obtained; When the first employee clocks in, the location information of the clocking in is obtained through a positioning service to obtain the clocking in location information set; According to the standard punch-in time information and the standard punch-in location information, data processing and analysis are performed on the punch-in time information set and the punch-in location information set, including: Calculate the difference between the punch-in time information in the punch-in time information set and the standard punch-in time information to obtain a time difference information set; Calculate the difference between the punch-in location information in the punch-in location information set and the standard punch-in location information to obtain a location difference information set; Calculating the mean of the data in the time difference information set and the position difference information set to obtain time difference mean information and position difference mean information; Calculating the variance of the data in the time difference information set and the position difference information set to obtain time difference variance information and position difference variance information; The time difference mean information and the time difference variance information are used as the time attendance analysis result, and the position difference mean information and the position difference variance information are used as the position attendance analysis result; According to the time difference mean information, the larger the time difference mean is, the earlier the person arrives and the higher the pass level of the clock-in time is; According to the time difference variance information, the smaller the time difference variance is, the more stable the attendance is, and the higher the pass level of the punch-in time is; According to the position difference mean information, the smaller the position difference mean is, the closer the person is to the company when clocking in, and the higher the level is; According to the position difference square value information, the smaller the position difference square value information is, the more stable the attendance positioning is and the higher the level is.

2. The method according to claim 1, characterized in that: The construction process of the attendance analysis model includes constructing an attendance time analysis unit and constructing an attendance location analysis unit. The construction of the attendance time analysis unit includes: Obtaining the mean information of multiple sample time differences and the variance information of multiple sample time differences; Combining the plurality of sample time difference mean information and the plurality of sample time difference variance information in a one-to-one correspondence, and performing a punch-in time qualification level assessment to obtain a plurality of sample time qualification analysis results; The attendance time analysis unit is constructed by using the plurality of sample time difference mean information, the plurality of sample time difference variance information and the plurality of sample time qualification analysis results.

3. The method according to claim 1, characterized in that The construction process of the attendance analysis model includes constructing an attendance time analysis unit and constructing an attendance location analysis unit. The construction of the attendance location analysis unit includes: Obtain the mean information of multiple sample position differences and the variance information of multiple sample check-in position differences; Combining the plurality of sample position difference mean information and the plurality of sample punch-in position difference variance information in a one-to-one correspondence, and performing punch-in position qualification level assessment to obtain a plurality of sample position qualification analysis results; The attendance position analysis unit is constructed by using the plurality of sample position difference mean information, the plurality of sample punch-in position difference variance information and the plurality of sample position qualification analysis results, and the attendance analysis model is obtained by combining the attendance time analysis unit; The punch-in time processing result set and the punch-in location processing result set are input into the attendance time analysis unit and the attendance location analysis unit respectively to obtain the time attendance analysis result and the location attendance analysis result.

4. The method according to claim 2, characterized in that: The attendance time analysis unit is constructed by using the plurality of sample time difference mean information, the plurality of sample time difference variance information and the plurality of sample time qualification analysis results, including: Taking the time difference mean information as the first decision feature, using the plurality of sample time difference mean information, constructing the first part of the multi-layer decision nodes in the attendance time analysis unit; Taking the time difference variance information as the second decision feature, using the plurality of sample time difference variance information, constructing the second part of the multi-layer decision nodes in the attendance time analysis unit; A multi-layer decision node connecting the first part and the second part, wherein the top-layer decision node of the multi-layer decision node in the first part is connected to the bottom-layer decision node of the multi-layer decision node in the second part; The plurality of sample time qualification analysis results are used as a plurality of final decision division results, and the plurality of final division results of the multi-layer decision nodes of the first part and the second part after being connected are marked to obtain the attendance time analysis unit.

5. The method according to claim 1, characterized in that Inputting the punch-in time information set, the punch-in location information set, the time attendance analysis result and the location attendance analysis result into the attendance generation database includes: Traverse and filter the punch-in time information set and the punch-in location information set to obtain the qualified punch-in times and unqualified punch-in times that meet the standard punch-in time information and the standard punch-in location information; Based on the first employee, build an employee data index; Based on the number of qualified punch-in times, the number of unqualified punch-in times, the punch-in time information, the punch-in location information, the time attendance analysis results and the location attendance analysis results, a plurality of punch-in data indexes are constructed to obtain the attendance generation database; The qualified punch-in times, unqualified punch-in times, punch-in time information set, punch-in location information set, time attendance analysis results and location attendance analysis results of the first employee are input into the attendance generation database to generate the attendance data set.

6. An attendance generation system based on artificial intelligence, characterized in that: An attendance generation method based on artificial intelligence for implementing any one of claims 1 to 5, comprising: A clock-in information acquisition module, used to acquire the clock-in information of the first employee within a preset time period, and obtain a clock-in time information set and a clock-in location information set; A standard clock-in information acquisition module, used to acquire the standard clock-in time information and standard clock-in location information of the first employee; A data processing and analysis module, configured to perform data processing and analysis on the punch-in time information set and the punch-in location information set according to the standard punch-in time information and the standard punch-in location information, and obtain a punch-in time processing result set and a punch-in location processing result set; An attendance analysis result obtaining module, used for inputting the punch-in time processing result set and the punch-in location processing result set into the attendance analysis model to obtain the time attendance analysis result and the location attendance analysis result, wherein the attendance analysis model includes an attendance time analysis unit and an attendance location analysis unit; An attendance data set generation module, used to input the punch-in time information set, the punch-in location information set, the time attendance analysis result and the location attendance analysis result into an attendance generation database, and generate an attendance data set for the first employee in a current preset time period; A time difference information set obtaining module, used to calculate the difference between the punch-in time information in the punch-in time information set and the standard punch-in time information, and obtain the time difference information set; A position difference information set obtaining module, used for calculating the difference between the punch-in position information in the punch-in position information set and the standard punch-in position information, to obtain a position difference information set; A difference mean information obtaining module, used to calculate the mean of the data in the time difference information set and the position difference information set, and obtain the time difference mean information and the position difference mean information; A difference variance information obtaining module, used to calculate the variance of the data in the time difference information set and the position difference information set, and obtain the time difference variance information and the position difference variance information; The location attendance analysis result determination module is used to use the time difference mean information and the time difference variance information as the time attendance analysis result, and use the position difference mean information and the position difference variance information as the location attendance analysis result.

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