CN115456481A - Attendance data processing method applied to enterprise management and attendance server - Google Patents
Attendance data processing method applied to enterprise management and attendance server Download PDFInfo
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- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
According to the attendance data processing method and the attendance server applied to enterprise management, aiming at the selected attendance judgment indexes with small number of the employee attendance data of the authenticated enterprise, the strategy variables of the attendance data processing strategies of the selected attendance judgment indexes are obtained through a strategy variable positioning algorithm, and then the attendance data processing strategies of the selected attendance judgment indexes are deployed according to the strategy variables, so that the personnel attendance analysis and processing of the selected attendance judgment indexes are realized. By the design, the annotation resource overhead of the authenticated enterprise employee attendance data is reduced, and the defect that attendance analysis conditions are too strict due to the fact that a few parts of the authenticated enterprise employee attendance data are used for forcibly debugging the attendance data processing strategy can be overcome. In addition, the technical scheme can be compatible with the addition of additional attendance judgment indexes, so that the comprehensiveness and the intelligent degree of attendance analysis are improved, and the flexibility of attendance data analysis is improved.
Description
Technical Field
The invention relates to the technical field of enterprise management, in particular to an attendance data processing method and an attendance server applied to enterprise management.
Background
As an important link of enterprise management at present, attendance processing of enterprise employees is concerned by more and more enterprises. With the development of science and technology, the attendance modes of enterprise employees also undergo a series of changes, such as handwriting attendance, attendance by a punch card machine and fingerprint attendance. However, even the advanced attendance technology in the air outlets of digital enterprises and intelligent enterprises still has certain disadvantages, such as poor attendance flexibility and difficulty in comprehensively and intelligently analyzing and processing attendance data of enterprise employees.
Disclosure of Invention
The invention at least provides an attendance data processing method and an attendance server applied to enterprise management.
The invention provides an attendance data processing method applied to enterprise management, which is applied to an attendance server and at least comprises the following steps: deploying an attendance data processing strategy of a selected attendance judgment index, and performing personalized biological knowledge mining on the acquired employee attendance data of the enterprise through the attendance data processing strategy of the selected attendance judgment index to obtain an attendance analysis report of the acquired employee attendance data of the enterprise; wherein: the strategy variables of the attendance data processing strategy of the selected attendance judgment indexes are determined by loading the authenticated enterprise employee attendance data of the selected attendance judgment indexes into a strategy variable positioning algorithm.
In some possible examples, the method further comprises: obtaining at least one authenticated employee attendance data record from the enterprise employee attendance data records, wherein each authenticated employee attendance data record comprises authenticated enterprise employee attendance data of W attendance determination indexes, each attendance determination index comprises B authenticated enterprise employee attendance data, and W is a positive integer; and debugging the strategy variable positioning algorithm according to the attendance data records of the authenticated employees.
In some possible examples, the B authenticated enterprise employee attendance data comprises F enterprise employee attendance data references and G enterprise employee attendance data samples, F and G being positive integers; the debugging of the strategy variable positioning algorithm according to the attendance data records of the authenticated employees comprises the following steps:
for each authenticated employee attendance data record:
loading the employee attendance data of each enterprise recorded by the authenticated employee attendance data into a strategy variable positioning algorithm to be debugged to obtain a strategy variable of a pervasive attendance data processing strategy recorded by the authenticated employee attendance data, and deploying the pervasive attendance data processing strategy recorded by the authenticated employee attendance data according to the strategy variable of the pervasive attendance data processing strategy;
loading each enterprise employee attendance data sample of the authenticated employee attendance data record to a biological description mining unit to be debugged to obtain a biological description knowledge relationship network of each enterprise employee attendance data sample of the authenticated employee attendance data record;
respectively loading the biological description knowledge relationship network of each enterprise employee attendance data sample into the pervasive attendance data processing strategy to obtain attendance test conclusion statistical information of each enterprise employee attendance data sample;
determining attendance staff analysis errors of the pervasive attendance data processing strategy by combining attendance test conclusion statistical information and prior attendance conclusions of the enterprise staff attendance data samples;
and debugging the strategy variable positioning algorithm to be debugged by combining the analysis error of the attendance personnel of the universal attendance data processing strategy.
In some possible examples, loading each enterprise employee attendance data reference of the authenticated employee attendance data record into a policy variable positioning algorithm to be debugged to obtain a policy variable of a pervasive attendance data processing policy of the authenticated employee attendance data record, including:
loading the enterprise employee attendance data references recorded by the authenticated employee attendance data records to a strategy variable positioning algorithm to be debugged respectively to obtain strategy variables of an attendance data processing strategy corresponding to the enterprise employee attendance data references;
determining the strategy variable of the attendance data processing strategy of each attendance determination index recorded by the authenticated employee attendance data by utilizing the strategy variable of the attendance data processing strategy corresponding to each enterprise employee attendance data reference and the known attendance determination index of each enterprise employee attendance data reference;
and determining the strategy variables of the universal attendance data processing strategies recorded by the attendance data of the authenticated employee according to the strategy variables of the attendance data processing strategies of the attendance judgment indexes recorded by the attendance data of the authenticated employee.
In some possible examples, the method further comprises: and debugging the biological description mining unit to be debugged by combining the analysis error of the attendance personnel of the universal attendance data processing strategy.
In some possible examples, in conjunction with attendance personnel analysis errors of the pervasive attendance data processing policy, debugging the biological description mining unit to be debugged includes:
obtaining a candidate attendance data processing strategy of the authenticated employee attendance data record;
respectively loading the biological description knowledge relationship network of the employee attendance data samples of each enterprise into the candidate attendance data processing strategy to obtain the supervision attendance conclusion statistical information of the employee attendance data samples of each enterprise;
determining attendance staff analysis errors of the candidate attendance data processing strategies by combining the monitoring attendance conclusion statistical information and the prior attendance conclusion of the enterprise staff attendance data samples;
and debugging the biological description mining unit to be debugged by combining the analysis error of the attendance personnel of the universal attendance data processing strategy and the analysis error of the attendance personnel of the candidate attendance data processing strategy.
In some possible examples, obtaining policy variables of the candidate attendance data processing policies for the authenticated employee attendance data record includes:
acquiring an attendance data processing strategy which completes default processing;
debugging the attendance data processing strategy which is subjected to default processing by means of all enterprise employee attendance data samples recorded by the authenticated employee attendance data;
and determining the strategy variable of the debugged attendance data processing strategy as a candidate attendance data processing strategy of the authenticated employee attendance data record.
In some possible examples, in combination with attendance personnel analysis errors of the pervasive attendance data processing policy, debugging the policy variable positioning algorithm to be debugged includes:
determining a strategy performance cost function of the pervasive attendance data processing strategy according to the strategy variable of the pervasive attendance data processing strategy of the authenticated employee attendance data record and the strategy variable of the candidate attendance data processing strategy of the authenticated employee attendance data record;
and debugging the strategy variables of the strategy variable positioning algorithm to be debugged by combining the analysis error and the strategy performance cost function of the attendance personnel of the pervasive attendance data processing strategy.
In some possible examples, the method further comprises: determining a cross entropy cost function of the pervasive attendance data processing strategy; and debugging the strategy variable positioning algorithm to be debugged by combining the cross entropy cost function of the universal attendance data processing strategy.
In some possible examples, the attendance data processing policy to deploy the selected attendance determination metric comprises: obtaining the authenticated enterprise employee attendance data of the selected attendance judgment index; loading the attendance data of the authenticated enterprise employees of the selected attendance judgment index into the strategy variable positioning algorithm respectively to obtain strategy variables of the attendance data processing strategy corresponding to each debugging basis of the selected attendance judgment index; determining the strategy variables of the attendance data processing strategies of the selected attendance judgment indexes in combination with the strategy variables of the attendance data processing strategies corresponding to each debugging basis of the selected attendance judgment indexes; and deploying the attendance data processing strategy of the selected attendance judgment index by combining the strategy variables of the attendance data processing strategy of the selected attendance judgment index.
The invention also provides an attendance checking server, which comprises a processor and a memory; the processor is in communication connection with the memory, and the processor is used for reading the computer program from the memory and executing the computer program to realize the method.
The invention also provides a computer-readable storage medium, on which a computer program is stored, which computer program, when executed, carries out the above-mentioned method.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects: aiming at the selected attendance judgment indexes with less attendance data number of the authenticated enterprise staff, the strategy variables of the attendance data processing strategies of the selected attendance judgment indexes can be obtained through a strategy variable positioning algorithm, and then the attendance data processing strategies of the selected attendance judgment indexes are deployed according to the strategy variables, so that the staff attendance analysis processing of the selected attendance judgment indexes is realized. By the design, the annotation resource overhead of the authenticated enterprise employee attendance data is reduced, and the defect that attendance analysis conditions are too strict due to the fact that a few parts of the authenticated enterprise employee attendance data are used for forcibly debugging the attendance data processing strategy can be overcome. In addition, the technical scheme can be compatible with the addition of additional attendance judgment indexes, so that the comprehensiveness and the intelligent degree of attendance analysis are improved, and the flexibility of attendance data analysis is improved.
For the description of the effects of the attendance server and the computer-readable storage medium, reference is made to the description of the method.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solution of the embodiments of the present invention, the drawings required for the embodiments will be briefly described below, and the drawings herein incorporated in and forming a part of the specification illustrate embodiments consistent with the present invention and, together with the description, serve to explain the technical solution of the present invention. It is appreciated that the following drawings depict only some embodiments of the invention and are therefore not to be considered limiting of its scope, for those skilled in the art will be able to derive additional related drawings therefrom without the benefit of the inventive faculty.
Fig. 1 is a block diagram of an attendance checking server according to an embodiment of the present invention.
Fig. 2 is a schematic flowchart of an attendance data processing method applied to enterprise management according to an embodiment of the present invention.
Fig. 3 is a block diagram of an attendance data processing apparatus applied to enterprise management according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
Fig. 1 is a schematic structural diagram of an attendance checking server 10 according to an embodiment of the present invention, which includes a processor 102, a memory 104, and a bus 106. The memory 104 is used for storing an execution instruction, and includes a memory and an external memory, where the memory may also be understood as an internal memory, and is used for temporarily storing operation data in the processor 102 and data exchanged with the external memory such as a hard disk, and the processor 102 exchanges data with the external memory through the memory, and when the attendance server 10 runs, the processor 102 and the memory 104 communicate through the bus 106, so that the processor 102 executes the attendance data processing method applied to enterprise management according to the embodiment of the present invention.
Referring to fig. 2, fig. 2 is a schematic flowchart of an attendance data processing method applied to enterprise management, which is applied to an attendance server, and the method exemplarily includes the following steps.
And Step11, deploying an attendance data processing strategy for selecting the attendance judgment indexes.
Step12, performing personalized biological knowledge mining on the acquired employee attendance data of the enterprise through the attendance data processing strategy of the selected attendance judgment index to obtain an attendance analysis report of the acquired employee attendance data of the enterprise.
Furthermore, the policy variable of the attendance data processing policy of the selected attendance judgment index is determined by loading the authenticated employee attendance data of the enterprise, which is combined with the selected attendance judgment index, into a policy variable positioning algorithm.
In the embodiment of the invention, for the selected attendance judgment indexes with less number of the employee attendance data of the authenticated enterprise, the strategy variables of the attendance data processing strategies of the selected attendance judgment indexes can be obtained through the strategy variable positioning algorithm, and then the attendance data processing strategies of the selected attendance judgment indexes are deployed according to the strategy variables, so that the personnel attendance analysis processing of the selected attendance judgment indexes is realized. Due to the design, the annotation resource overhead of the authenticated enterprise employee attendance data is reduced, and the defect that the attendance analysis conditions are too strict due to the fact that a few parts of the authenticated enterprise employee attendance data forcibly debug the attendance data processing strategy can be overcome. In addition, the technical scheme can be compatible with the addition of additional attendance judgment indexes, so that the comprehensiveness and the intelligent degree of attendance analysis are improved.
For example, the selected attendance determination index may also be an attendance determination index that matches a large number of employee attendance data of an authenticated enterprise, and the attendance data processing method applied to enterprise management provided by the embodiment of the present invention may also be applied to matching an attendance determination index that matches a large number of employee attendance data of an authenticated enterprise. The attendance judgment index can be understood as an attendance judgment category, such as a fingerprint characteristic attendance index, a voice characteristic attendance index, a facial characteristic attendance index, an iris characteristic attendance index and the like.
In Step11, the selected attendance judgment index can represent the attendance judgment index to be subjected to the attendance analysis processing of the personnel. For some embodiments, the selected attendance determination indicator may be an attendance determination indicator that matches a smaller number of the authenticated employee attendance data, for example, the selected attendance determination indicator may be an attendance determination indicator that matches one or more of the authenticated employee attendance data. For some possible scenarios, the selected attendance determination indicator may be an additional attendance determination indicator newly added in real time.
The attendance data processing strategy can represent an artificial intelligence model for personnel attendance analysis processing. For some embodiments, the architecture type of the attendance data processing strategy may be a convolutional neural network, a residual neural network, a cyclic neural network, a deep learning neural network, or the like.
The attendance data processing strategy for selecting the attendance judgment index can represent an artificial intelligence model for carrying out personnel attendance analysis processing on the selected attendance judgment index. In other words, whether the biometric items of the selected attendance judgment indexes exist in the acquired employee attendance data of the enterprise can be determined by selecting the attendance data processing strategy of the attendance judgment indexes. In the embodiment of the invention, the policy variables (such as the model variables or the configuration weights) of the attendance data processing policies of the selected attendance judgment indexes can be obtained first, and then the attendance data processing policies of the selected attendance judgment indexes are deployed (that is, the corresponding artificial intelligence models are generated or built) by means of the policy variables of the attendance data processing policies of the selected attendance judgment indexes. The strategy variables of the attendance data processing strategy of the selected attendance judgment indexes are determined by loading the authenticated enterprise employee attendance data of the selected attendance judgment indexes into a strategy variable positioning algorithm.
Correspondingly, the strategy variable positioning algorithm can be used for calculating the strategy variables of the attendance data processing strategy, and the strategy variable positioning algorithm can also be an artificial intelligence model, and the structure and the configuration of the strategy variable positioning algorithm are not limited. The strategy variable positioning algorithm takes the authenticated enterprise employee attendance data (enterprise employee attendance data training sample) as input, takes the strategy variable of the attendance data processing strategy as output, loads the authenticated enterprise employee attendance data with the selected attendance judgment index into the strategy variable positioning algorithm, and can obtain the strategy variable of the attendance data processing strategy with the selected attendance judgment index.
For some embodiments, authenticated enterprise employee attendance data of the selected attendance determination index may be obtained, and each authenticated enterprise employee attendance data of the selected attendance determination index is loaded into the policy variable positioning algorithm, so as to obtain a policy variable of an attendance data processing policy corresponding to each debugging criterion (training sample) of the selected attendance determination index; determining the strategy variables of the attendance data processing strategies of the selected attendance judgment indexes in combination with the strategy variables of each debugging basis corresponding attendance data processing strategies of the selected attendance judgment indexes; and deploying the attendance data processing strategy of the selected attendance judgment index by combining the strategy variables of the attendance data processing strategy of the selected attendance judgment index.
In the embodiment of the invention, the authenticated enterprise employee attendance data of the selected attendance judgment index can be loaded into the strategy variable positioning algorithm respectively to obtain the strategy variable of the attendance data processing strategy corresponding to each authenticated enterprise employee attendance data of the selected attendance judgment index. Based on the selected attendance judgment indexes, the strategy variables of the attendance data processing strategies of the selected attendance judgment indexes can be determined according to the strategy variables of the attendance data processing strategies corresponding to the authenticated enterprise employee attendance data.
For some embodiments, the policy variables of the attendance data processing policies corresponding to the authenticated employee attendance data of the enterprise of the selected attendance determination index may be averaged, and the policy variable of the attendance data processing policy whose averaging is completed may be determined as the policy variable of the attendance data processing policy of the selected attendance determination index.
For other embodiments, the importance coefficient of each authenticated enterprise employee attendance data of the selected attendance determination index may be determined according to the distribution condition or the data size of the target data set (the data set corresponding to the biometric feature item of the selected attendance determination index) in the authenticated enterprise employee attendance data; and then based on the importance coefficient, calculating the strategy variables of the attendance data processing strategies corresponding to the attendance data of the authenticated enterprise employees of the selected attendance judgment index, and determining the strategy variables of the attendance data processing strategies completing the weighted average calculation as the strategy variables of the attendance data processing strategies of the selected attendance judgment index.
After the policy variables of the attendance data processing policy are obtained, the corresponding attendance data processing policy can be deployed based on the framework of the attendance data processing policy. In other words, after the policy variables of the attendance data processing policy for the selected attendance determination index are obtained, the attendance data processing policy for the selected attendance determination index may be deployed based on the framework of the attendance data processing policy, so as to focus on attendance analysis processing of the selected attendance determination index based on the attendance data processing policy.
For some embodiments, the attendance data processing policy may be determined as the selected attendance determination index based on the attendance data processing policy set to the policy variable of the attendance data processing policy of the selected attendance determination index. By the design, after the authenticated enterprise employee attendance data with the selected attendance judgment index is loaded to the strategy variable positioning algorithm, the attendance data processing strategy with the selected attendance judgment index can be quickly and accurately obtained.
For some embodiments, the attendance data processing policy of the policy variable set as the policy variable of the attendance data processing policy of the selected attendance determination index may be first determined as the original attendance data processing policy of the selected attendance determination index; and then, the original attendance data processing strategy is changed to obtain an attendance data processing strategy which is a selected attendance judgment index.
For some embodiments, the raw attendance data processing policy may be modified by a cost function min rule. The cost function may include an attendance personnel analysis error and a cross entropy cost function of the raw attendance data processing policy. The analysis error of the attendance staff of the original attendance data processing strategy can be determined according to attendance test conclusion statistical information generated after the authenticated enterprise staff attendance data of the selected attendance judgment index is loaded into the original attendance data processing strategy and a corresponding prior attendance conclusion (a real label used for attendance analysis, such as a label 1 representing that the result of attendance analysis performed by the attendance judgment index 1 is attendance success, and a label 2 representing that the result of attendance analysis performed by the attendance judgment index 2 is attendance failure).
By the design, the improved attendance data processing strategy can be quickly and efficiently obtained, so that the precision and the reliability of the attendance data processing strategy for selecting the attendance judgment index are improved.
In Step12, the acquired employee attendance data of the enterprise can be loaded to the attendance data processing strategy of the selected attendance judgment index to obtain an attendance analysis report of the acquired employee attendance data of the enterprise. For some embodiments, the attendance analysis report may include a likelihood that the collected employee attendance data is the selected attendance determination indicator and a distribution of biometric items of the selected attendance determination indicator in the collected employee attendance data.
In the embodiment of the invention, firstly, the strategy variable of the attendance data processing strategy of the selected attendance judgment index is obtained based on the strategy variable positioning algorithm, and then the attendance data processing strategy of the selected attendance judgment index is deployed according to the strategy variable of the attendance data processing strategy of the selected attendance judgment index, so that the personnel attendance analysis processing based on the selected attendance judgment index is realized. The policy variable positioning algorithm is a core thought of the attendance data processing method applied to enterprise management in the embodiment of the invention.
For some embodiments, the debugging concept of the policy variable positioning algorithm may include: obtaining at least one authenticated employee attendance data record from the enterprise employee attendance data records; and debugging the strategy variable positioning algorithm according to the attendance data records of the authenticated employees.
The method is introduced by taking the example that the enterprise employee attendance data records comprise authenticated enterprise employee attendance data of J attendance judgment indexes (regarded as J basic indexes), and each attendance judgment index comprises T authenticated enterprise employee attendance data. The idea of obtaining an authenticated employee attendance data record from the enterprise employee attendance data record may include: and randomly selecting W attendance judgment indexes from the J attendance judgment indexes, and randomly selecting B authenticated enterprise employee attendance data from the T authenticated enterprise employee attendance data of each attendance judgment index. In this case, the authenticated employee attendance data record includes authenticated enterprise employee attendance data for W attendance determination indicators, each attendance determination indicator including B authenticated enterprise employee attendance data. By repeatedly implementing the content, a plurality of authenticated employee attendance data records can be obtained from the employee attendance data records of the enterprise. Wherein J, T, W and B are positive integers, J is more than W, and T is more than B.
Further, the number of W and B can be flexibly adjusted. In view of the fact that the attendance data processing method applied to enterprise management in the embodiment of the invention focuses on attendance analysis of the attendance judgment indexes with fewer authenticated enterprise employee attendance data numbers, based on the method, when the strategy variable positioning algorithm is debugged, the number of the passing attendance judgment indexes is fewer, and the number of the authenticated enterprise employee attendance data of each attendance judgment index is also fewer. For some embodiments, W may be 3; b may be 9, 12, 18, etc. And the corresponding J may be 800 or 1500, etc., and T may be 3000 or 8000, etc. For example, the idea of the debugging policy variable positioning algorithm provided by the embodiment of the invention is still suitable for attendance judgment indexes with a large number, based on which the number of the employee attendance data of the authenticated enterprise of each attendance judgment index can be large, and B can be 300 or 8000 and the like.
Generally speaking, for each attendance judgment index of the authenticated employee attendance data records, the B authenticated enterprise employee attendance data included in the attendance judgment index may include F enterprise employee attendance data references and G enterprise employee attendance data samples, where F and G are positive integers, and B is greater than or equal to F + G. On the basis that B = F + G, for each attendance judgment index of the authenticated employee attendance data records, F authenticated enterprise employee attendance data can be randomly selected from B authenticated enterprise employee attendance data of the attendance judgment index to serve as an enterprise employee attendance data reference, and the remaining authenticated enterprise employee attendance data of the attendance judgment index serves as an enterprise employee attendance data sample. On the basis that B is larger than F + G, F authenticated enterprise employee attendance data can be randomly selected from B authenticated enterprise employee attendance data of the attendance determination indexes as enterprise employee attendance data references according to each attendance determination index of the authenticated employee attendance data records, and G authenticated enterprise employee attendance data can be randomly selected from the rest authenticated enterprise employee attendance data of the attendance determination indexes as an enterprise employee attendance data sample.
Taking an authenticated employee attendance data record as an example, a debugging idea of a strategy variable positioning algorithm is introduced. The idea of recording and debugging the strategy variable positioning algorithm by a plurality of authenticated staff attendance data is essentially the idea of repeatedly recording and debugging the strategy variable positioning algorithm by one authenticated staff attendance data.
For some embodiments, debugging the policy variable positioning algorithm based on an authenticated employee attendance data record may include: loading the employee attendance data of each enterprise recorded by the authenticated employee attendance data into a strategy variable positioning algorithm to be debugged to obtain a strategy variable of a pervasive attendance data processing strategy recorded by the authenticated employee attendance data, and deploying the pervasive attendance data processing strategy recorded by the authenticated employee attendance data according to the strategy variable of the pervasive attendance data processing strategy; loading each enterprise employee attendance data sample of the authenticated employee attendance data record to a biological description mining unit to be debugged to obtain a biological description knowledge relationship network of each enterprise employee attendance data sample of the authenticated employee attendance data record; respectively loading the biological description knowledge relationship network of each enterprise employee attendance data sample into the pervasive attendance data processing strategy to obtain attendance test conclusion statistical information of each enterprise employee attendance data sample; determining the analysis error of the attendance personnel of the universal attendance data processing strategy (a general attendance data processing strategy or an attendance data processing strategy with a wider application range) by combining the attendance test conclusion statistical information and the prior attendance conclusion of the enterprise employee attendance data samples; and (4) analyzing errors (errors for judging whether attendance is successful) by the attendance personnel combined with the pervasive attendance data processing strategy, and debugging the strategy variable positioning algorithm to be debugged.
Further, loading the employee attendance data of each enterprise recorded by the authenticated employee attendance data into a policy variable positioning algorithm to be debugged to obtain a policy variable of a pervasive attendance data processing policy of the authenticated employee attendance data record, which may include: loading the enterprise employee attendance data references recorded by the authenticated employee attendance data records to a strategy variable positioning algorithm to be debugged respectively to obtain strategy variables of an attendance data processing strategy corresponding to the enterprise employee attendance data references; determining the strategy variable of the attendance data processing strategy of each attendance determination index recorded by the authenticated employee attendance data by utilizing the strategy variable of the attendance data processing strategy corresponding to each enterprise employee attendance data reference and the known attendance determination index of each enterprise employee attendance data reference; and determining the strategy variables of the universal attendance data processing strategy of the authenticated employee attendance data record according to the strategy variables of the attendance data processing strategies of the attendance judgment indexes of the authenticated employee attendance data record.
For some embodiments, the policy variables of the attendance data processing policy corresponding to the employee attendance data references of the same attendance determination index may be averaged or calculated (the importance coefficient value may be determined according to the distribution of the target data set in the employee attendance data references of the enterprise or the size of the data volume) according to the known attendance determination index referenced by the employee attendance data references of each enterprise, so as to obtain the policy variables of the attendance data processing policy corresponding to the attendance determination index. And then, combining the strategy variables of the attendance data processing strategies of the attendance judgment indexes into the strategy variables of the universal attendance data processing strategies of the authenticated employee attendance data records.
In some possible examples, an exemplary configuration corresponding to the above-described attendance data processing policy may include a policy variable positioning algorithm algorithmm _ a and a biological description mining unit _ b. The strategy variable of the strategy variable positioning algorithm algorithma is a, and the strategy variable of the biological description mining unit _ b is b.
An authenticated employee attendance data record R = [ data1_ s, data2_ s) v, (data 1_ q, data2_ q) u ] is further obtained from the employee attendance data record of the enterprise, the authenticated employee attendance data record comprising an employee attendance data reference set Rs = [ (data 1_ s, data2_ s) v ] and an employee attendance data sample set Rq = [ data1_ q, data2_ q) u ].
The enterprise employee attendance data reference set Rs comprises enterprise employee attendance data references of W attendance judgment indexes, and each attendance judgment index comprises F enterprise employee attendance data references. data1_ s represents a target data set in the enterprise employee attendance data reference, data2_ s represents a priori attendance conclusion of the data1_ s, and (data 1_ s, data2_ s) v represents a target data set and a priori attendance conclusion of the vth enterprise employee attendance data reference in the enterprise employee attendance data reference set Rs, wherein v is greater than or equal to 1 and less than or equal to W x F.
Further, the enterprise employee attendance data sample set Rq includes enterprise employee attendance data samples of W attendance determination indexes, and each attendance determination index includes G enterprise employee attendance data samples. data1_ q represents a target data set in the employee attendance data sample of the enterprise, data2_ q represents a priori attendance conclusion of the data1_ q, (data 1_ q, data2_ q) u represents the target data set and the priori attendance conclusion of the u-th employee attendance data sample in the employee attendance data sample set Rq of the enterprise, and u is greater than or equal to 1 and less than or equal to W x G.
Based on the above, through the authenticated employee attendance data record R, the idea of debugging the policy variable positioning algorithm may include the following.
(1) The method comprises the steps of deploying a pervasive attendance data processing strategy for recording authenticated employee attendance data, exemplarily, disassembling each enterprise employee attendance data reference in an enterprise employee attendance data reference set Rs to obtain a target data set data1_ s of each enterprise employee attendance data reference, loading the target data set data1_ s of each enterprise employee attendance data reference into a strategy variable positioning algorithm algorithmic _ a to be debugged, obtaining strategy variables of the attendance data processing strategy corresponding to each enterprise employee attendance data reference, averaging (or weighted averaging) strategy variables of the attendance data processing strategies corresponding to the enterprise employee attendance data reference data1_ s of the same attendance judgment index, and obtaining the strategy variables of the attendance data processing strategy of the attendance judgment index. In other words, the attendance determination index of the attendance data processing policy may be the same as the attendance determination index referenced by the employee attendance data of the enterprise.
Furthermore, strategy variables of attendance data processing strategies of the W attendance judgment indexes are combined to obtain strategy variables of the universal attendance data processing strategies, and then the universal attendance data processing strategies recorded by the attendance data of the authenticated staff can be deployed according to the strategy variables of the universal attendance data processing strategies.
(2) The method includes the steps of obtaining a biological description knowledge relationship network of enterprise employee attendance data samples, illustratively, disassembling each enterprise employee attendance data sample in an enterprise employee attendance data sample set Rq to obtain a target data set data1_ q of each enterprise employee attendance data sample, and loading the target data set data1_ q into a biological description mining unit _ b to obtain a biological description knowledge relationship network unit _ b (data 1_ q) of each enterprise employee attendance data sample.
(3) Determining analysis errors of attendance personnel of the pervasive attendance data processing strategy, exemplarily, loading the biological description knowledge relationship net unit _ b (data 1_ q) of each enterprise employee attendance data sample into the pervasive attendance data processing strategy with the strategy variable of yes, and obtaining attendance test conclusion statistical information of each enterprise employee attendance data sample. And obtaining the analysis error of the attendance personnel of the universal attendance data processing strategy by using the attendance test conclusion statistical information and the prior attendance conclusion data2_ q of the attendance data samples of the enterprise personnel.
(4) And debugging a strategy variable positioning algorithm algorithma to be debugged according to the analysis error loss1 of the attendance personnel of the universal attendance data processing strategy.
And changing the strategy variable a of the strategy variable positioning algorithm _ a to realize debugging of the strategy variable positioning algorithm _ a by taking the min rule of the analysis error of attendance personnel of the universal attendance data processing strategy as an expectation.
By the design, the strategy variable positioning algorithm algorithmhm _ a debugged by a few examples can be used for determining the strategy variables of the attendance data processing strategy of the extra attendance judgment index, and the processing attention points of the attendance data processing strategy can be transferred to the extra attendance judgment index.
For some embodiments, the method further comprises: and debugging the biological description mining unit to be debugged by combining the analysis error of the attendance personnel of the universal attendance data processing strategy.
In some examples, the unit b of the biological description mining may be debugged in parallel in the idea of debugging the policy variable positioning algorithm algorithma. In other words, the strategy variable b of the biological description mining unit _ b can be updated with the analysis error min of the attendance personnel of the pervasive attendance data processing strategy as an expectation.
For some embodiments, debugging the to-be-debugged biological description mining unit in conjunction with attendance personnel analysis errors of the pervasive attendance data processing policy comprises: obtaining a strategy variable of a candidate attendance data processing strategy of the authenticated employee attendance data record; deploying the candidate attendance data processing strategy of the authenticated employee attendance data record in combination with the strategy variable of the candidate attendance data processing strategy of the authenticated employee attendance data record; respectively loading the biological description knowledge relationship network of the employee attendance data samples of each enterprise into the candidate attendance data processing strategy to obtain the supervision attendance conclusion statistical information of the employee attendance data samples of each enterprise; determining the analysis error of the attendance personnel of the candidate attendance data processing strategy by combining the supervision attendance conclusion statistical information and the prior attendance conclusion of the enterprise employee attendance data samples; and debugging the biological description mining unit to be debugged by combining the analysis error of the attendance personnel of the universal attendance data processing strategy and the analysis error of the attendance personnel of the candidate attendance data processing strategy.
The candidate attendance data processing strategy can be used for representing the authenticated enterprise employee attendance data of all attendance judgment indexes based on the enterprise employee attendance data records, and debugging the obtained attendance data processing strategy.
When the candidate attendance data processing strategy and the biological description mining unit are debugged through the certified staff attendance data record, one round of debugging thought only relates to W attendance judgment indexes, and multiple rounds of debugging are still restricted in the limited number of attendance judgment indexes. By the design, the identification performance of the biological description mining unit _ b debugged by noise is limited in attendance judgment indexes related to the attendance data records of the authenticated employees, so that the biological characteristic analysis performance of the biological description mining unit _ b is poor. Furthermore, when the candidate attendance data processing strategy and the biological description mining unit are debugged through the authenticated staff attendance data records, the number of the authenticated enterprise staff attendance data related to the debugging thought is small. And compared with the personnel attendance analysis and processing performance of the attendance data processing strategy obtained through massive authenticated enterprise employee attendance data, the ubiquitous attendance data processing strategy obtained through debugging the attendance data of a few authenticated enterprise employees is poorer. Based on this, in the embodiment of the invention, a candidate attendance data processing strategy obtained by debugging massive authenticated enterprise employee attendance data with various attendance judgment indexes is newly added, and the debugging of the strategy variable positioning algorithm _ a and the biological description mining unit _ b is optimized.
For other possible policy configurations, a candidate attendance data processing policy with a policy variable n can be added on the basis of the above. And loading the biological description knowledge relationship network unit _ b (data 1_ q) of each enterprise employee attendance data sample into a candidate attendance data processing strategy with a strategy variable of n to obtain the monitoring attendance conclusion statistical information of each enterprise employee attendance data sample. And the attendance staff analysis error of the candidate attendance data processing strategy can be obtained by utilizing the monitoring attendance conclusion statistical information and the prior attendance conclusion data2_ q of the enterprise staff attendance data samples.
Due to the design, the candidate attendance data processing strategy is debugged by combining the authenticated enterprise employee attendance data of all attendance judgment indexes, and based on the combination of the analysis error of the attendance person of the universal attendance data processing strategy and the analysis error of the attendance person of the candidate attendance data processing strategy, the biological description mining unit to be debugged is debugged in a combined manner, so that the biological description identification performance of the biological description mining unit can be improved.
For some embodiments, obtaining policy variables for the candidate attendance data processing policies for the authenticated employee attendance data record may include: acquiring an attendance data processing strategy which completes default processing; debugging the attendance data processing strategy which is subjected to default processing by means of all enterprise employee attendance data samples recorded by the authenticated employee attendance data; and determining the strategy variable of the debugged attendance data processing strategy as a candidate attendance data processing strategy of the attendance data record of the authenticated employee.
Firstly, randomly initializing an attendance data processing strategy as an attendance data processing strategy to be debugged, and then processing the attendance data processing strategy to be debugged by means of all enterprise employee attendance data samples recorded by the authenticated employee attendance data to obtain a candidate attendance data processing strategy recorded by the authenticated employee attendance data. The candidate attendance data processing strategy and strategy variable positioning algorithm _ a and biological description mining unit _ b of the authenticated employee attendance data record can be debugged in parallel. The idea of debugging the attendance data processing strategy which is subjected to default processing by means of all enterprise employee attendance data samples recorded by the authenticated employee attendance data can be combined with a debugging method for debugging the attendance data processing strategy in the traditional scheme.
Furthermore, the strategy variables of the candidate attendance data processing strategies recorded by the attendance data of the authenticated staff are obtained by combining the strategy variables of the attendance data processing strategies of the W attendance judgment indexes. The candidate attendance data processing strategy of the authenticated employee attendance data record can be deployed based on the strategy variable of the candidate attendance data processing strategy of the authenticated employee attendance data record. Strategy variables of candidate attendance data processing strategies of W attendance judgment indexes are combined to obtain strategy variables of candidate attendance data processing strategies recorded by the attendance data of the authenticated staff
Generally speaking, in the embodiment of the present invention, a data set including W attendance determination indicators may be deployed again to perform the debugging of the candidate attendance data processing policy. The debugging thought can be combined with the thought for debugging through the employee attendance data sample of the enterprise.
For some embodiments, debugging the policy variable positioning algorithm to be debugged in conjunction with attendance personnel analysis errors of the pervasive attendance data processing policy comprises: determining a strategy performance cost function of the pervasive attendance data processing strategy according to the strategy variable of the pervasive attendance data processing strategy of the authenticated employee attendance data record and the strategy variable of the candidate attendance data processing strategy of the authenticated employee attendance data record; and debugging the strategy variables of the strategy variable positioning algorithm to be debugged by combining the analysis error and the strategy performance cost function of the attendance personnel of the pervasive attendance data processing strategy.
For some embodiments, the policy performance cost function of the ubiquitous attendance data processing policy can be obtained through the above related contents.
Due to the design, the candidate attendance data processing strategy is debugged by combining the attendance data of the authenticated enterprise staff with all attendance judgment indexes, and on the basis, the strategy variables of the strategy variable positioning algorithm to be debugged are debugged together by combining the analysis error and the strategy performance cost function of the attendance staff of the pervasive attendance data processing strategy, so that the precision and the reliability of the attendance data processing strategy obtained based on the strategy variable positioning algorithm can meet the actual requirements as much as possible.
For some embodiments, the method may further comprise: determining a cross entropy cost function of the pervasive attendance data processing strategy; and debugging the strategy variable positioning algorithm to be debugged by combining the cross entropy cost function of the universal attendance data processing strategy.
In view of the above, the policy variable positioning algorithm _ a, the biological description mining unit _ b and the candidate attendance data processing policy can be debugged in parallel. Based on this, a global cost function Lwhole = loss1+ loss2+ h loss3+ x loss g may be determined.
Wherein Lwhole represents a global cost function, loss1 represents an analysis error of attendance personnel of a general attendance data processing strategy, loss2 represents an analysis error of attendance personnel of a candidate attendance data processing strategy, loss3 represents a strategy performance cost function of the general attendance data processing strategy, and losSG represents a cross entropy cost function of the general attendance data processing strategy. h and x are specified learning parameters (hyper-parameters such as learning rate). h and x can be flexibly adjusted. For some embodiments, h may be 0.01, and x may be 1.
In the embodiment of the invention, a strategy variable positioning algorithm _ a, a biological description mining unit _ b and a candidate attendance data processing strategy can be debugged simultaneously based on Lwrite, and strategy variables a, b and n are adjusted.
In some independent embodiments, biological characteristics of collected employee attendance data of an enterprise can be mined by using an attendance data processing strategy of a selected attendance judgment index, and then whether attendance is successful or not is judged based on the mined personalized biological knowledge characteristics, for example, the mined personalized biological knowledge characteristics are matched with the knowledge characteristics in a preset attendance database, if the matching is successful, an attendance analysis report can reflect that the attendance result of a person corresponding to the related personalized biological knowledge characteristics passes through, in view of the fact that the scheme can be compatible with the newly added attendance judgment index, the intelligent degree of attendance processing can be improved, and the biological characteristic detection requirements of different attendance persons can be met.
In some independent embodiments, the method may further include: responding to the attendance checking conclusion of the attendance checking personnel analysis report representing the target personalized biological knowledge characteristics to be late, and determining office behavior data of the target enterprise staff corresponding to the target personalized biological knowledge characteristics; determining the remote office feasibility of the target enterprise employee based on the office behavior data; and if the remote office feasibility degree is greater than the set credibility, configuring a remote office label for the target personalized biological knowledge characteristic.
For example, if the attendance conclusion of the target enterprise employee corresponding to the target personalized biological knowledge characteristic is late, whether the target enterprise employee is suitable for remote office or not can be further analyzed, so that the enterprise office can be flexibly realized, and the enterprise management intelligent degree of attendance analysis and office mode adjustment is improved.
In some independent embodiments, determining the remote office feasibility of the target enterprise employee based on the office activity data may include the following: determining an office business operation information sequence from the office behavior data, wherein the office business operation information sequence comprises a plurality of uninterrupted office business operation information groups; acquiring a non-office business operation information sequence according to the office business operation information sequence, wherein the non-office business operation information sequence comprises uninterrupted multiple groups of non-office business operation information; on the basis of the office business operation information sequence, a subnet is mined through a first behavior vector included in a remote office analysis network to obtain an office business behavior vector sequence, wherein the office business behavior vector sequence comprises a plurality of office business behavior vectors; on the basis of the non-office business operation information sequence, a subnet is mined through a second behavior vector included by the remote office analysis network to obtain a non-office business behavior vector sequence, wherein the non-office business behavior vector sequence comprises a plurality of non-office business behavior vectors; acquiring a feasible label corresponding to the office business operation information through a feasible degree detection subnet included in the teleworking analysis network based on the office business behavior vector sequence and the non-office business behavior vector sequence; and determining the remote office feasibility of the office business operation information sequence according to the feasible label.
By the design, the feasible label can be accurately positioned by considering the office business behavior and the non-office business behavior, so that the remote office feasibility is accurately determined according to the preset label-reliability mapping relation.
On the basis, please refer to fig. 3, the present invention further provides a block diagram of an attendance data processing apparatus 30 applied to enterprise management, where the apparatus includes the following functional modules: the strategy deployment module 31 is used for deploying attendance data processing strategies for selecting attendance judgment indexes; and the knowledge mining module 32 is used for performing personalized biological knowledge mining on the acquired employee attendance data of the enterprise through the attendance data processing strategy of the selected attendance judgment index to obtain an attendance analysis report of the acquired employee attendance data of the enterprise.
Further, a readable storage medium is provided, on which a program is stored, which when executed by a processor implements the method described above.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working process of the system and the apparatus exemplarily described above may refer to the corresponding process in the foregoing method embodiment, and is not described herein again. In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the units into only one type of logical function may be implemented in other ways, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
Claims (10)
1. An attendance data processing method applied to enterprise management is characterized by being applied to an attendance server, and the method at least comprises the following steps:
deploying an attendance data processing strategy of a selected attendance judgment index, and performing personalized biological knowledge mining on the acquired employee attendance data of the enterprise through the attendance data processing strategy of the selected attendance judgment index to obtain an attendance analysis report of the acquired employee attendance data of the enterprise;
wherein: the strategy variables of the attendance data processing strategy of the selected attendance judgment indexes are determined by loading the authenticated enterprise employee attendance data of the selected attendance judgment indexes into a strategy variable positioning algorithm.
2. The method of claim 1, further comprising:
obtaining at least one authenticated employee attendance data record from the enterprise employee attendance data records, wherein each authenticated employee attendance data record comprises authenticated enterprise employee attendance data of W attendance determination indexes, each attendance determination index comprises B authenticated enterprise employee attendance data, and W is a positive integer;
and debugging the strategy variable positioning algorithm according to the attendance data records of the authenticated personnel.
3. The method of claim 2, wherein the B authenticated employee attendance data comprises F enterprise employee attendance data references and G enterprise employee attendance data samples, F and G being positive integers; the debugging of the strategy variable positioning algorithm according to the attendance data records of the authenticated employees comprises the following steps:
for each authenticated employee attendance data record:
loading the employee attendance data of each enterprise recorded by the authenticated employee attendance data into a strategy variable positioning algorithm to be debugged to obtain a strategy variable of a pervasive attendance data processing strategy recorded by the authenticated employee attendance data, and deploying the pervasive attendance data processing strategy recorded by the authenticated employee attendance data according to the strategy variable of the pervasive attendance data processing strategy;
loading each enterprise employee attendance data sample of the authenticated employee attendance data record to a biological description mining unit to be debugged to obtain a biological description knowledge relationship network of each enterprise employee attendance data sample of the authenticated employee attendance data record;
respectively loading the biological description knowledge relationship network of the employee attendance data samples of each enterprise to the pervasive attendance data processing strategy to obtain attendance test conclusion statistical information of the employee attendance data samples of each enterprise;
determining the analysis error of the attendance personnel of the pervasive attendance data processing strategy by combining the attendance test conclusion statistical information and the prior attendance conclusion of the enterprise employee attendance data samples;
and debugging the strategy variable positioning algorithm to be debugged by combining the analysis error of the attendance personnel of the universal attendance data processing strategy.
4. The method of claim 3, wherein loading the enterprise employee attendance data references of the authenticated employee attendance data record into a policy variable positioning algorithm to be debugged to obtain the policy variables of the pervasive attendance data processing policy of the authenticated employee attendance data record, comprises:
loading the enterprise employee attendance data references recorded by the authenticated employee attendance data records to a strategy variable positioning algorithm to be debugged respectively to obtain strategy variables of an attendance data processing strategy corresponding to the enterprise employee attendance data references;
determining the strategy variable of the attendance data processing strategy of each attendance judgment index of the authenticated employee attendance data record by utilizing the strategy variable of the attendance data processing strategy corresponding to each enterprise employee attendance data reference and the known attendance judgment index of each enterprise employee attendance data reference;
determining the strategy variables of the universal attendance data processing strategies of the authenticated employee attendance data records according to the strategy variables of the attendance data processing strategies of the attendance judgment indexes of the authenticated employee attendance data records;
wherein the method further comprises: and debugging the biological description mining unit to be debugged by combining the analysis error of the attendance personnel of the universal attendance data processing strategy.
5. The method of claim 4, wherein debugging the to-be-debugged biological description mining unit in conjunction with attendance personnel analysis errors of the pervasive attendance data processing policy comprises:
obtaining a candidate attendance data processing strategy of the authenticated employee attendance data record;
respectively loading the biological description knowledge relationship network of each enterprise employee attendance data sample into the candidate attendance data processing strategy to obtain the monitoring attendance conclusion statistical information of each enterprise employee attendance data sample;
determining attendance staff analysis errors of the candidate attendance data processing strategies by combining the monitoring attendance conclusion statistical information and the prior attendance conclusion of the enterprise staff attendance data samples;
and debugging the biological description mining unit to be debugged by combining the analysis error of the attendance personnel of the universal attendance data processing strategy and the analysis error of the attendance personnel of the candidate attendance data processing strategy.
6. The method of claim 5, wherein obtaining policy variables for the candidate attendance data processing policies for the authenticated employee attendance data record comprises:
acquiring an attendance data processing strategy which completes default processing;
debugging the attendance data processing strategy which is subjected to default processing by means of all enterprise employee attendance data samples recorded by the authenticated employee attendance data;
and determining the strategy variable of the debugged attendance data processing strategy as a candidate attendance data processing strategy of the authenticated employee attendance data record.
7. The method of claim 5 or 6, wherein debugging the strategy variable positioning algorithm to be debugged in combination with analyzing errors by an attendance personnel of the pervasive attendance data processing strategy comprises:
determining a strategy performance cost function of the universal attendance data processing strategy according to the strategy variable of the universal attendance data processing strategy recorded by the authenticated employee attendance data and the strategy variable of the candidate attendance data processing strategy recorded by the authenticated employee attendance data;
and debugging the strategy variables of the strategy variable positioning algorithm to be debugged by combining the analysis error and the strategy performance cost function of the attendance personnel of the universal attendance data processing strategy.
8. The method of claim 7, further comprising:
determining a cross entropy cost function of the pervasive attendance data processing strategy;
and debugging the strategy variable positioning algorithm to be debugged by combining the cross entropy cost function of the universal attendance data processing strategy.
9. The method of claim 2, wherein deploying the attendance data processing policy for the selected attendance determination metric comprises:
obtaining the authenticated enterprise employee attendance data of the selected attendance judgment index;
loading the attendance data of the authenticated enterprise employees of the selected attendance judgment indexes into the strategy variable positioning algorithm respectively to obtain strategy variables of the attendance data processing strategies corresponding to each debugging basis of the selected attendance judgment indexes;
determining the strategy variables of the attendance data processing strategies of the selected attendance judgment indexes in combination with the strategy variables of each debugging basis corresponding attendance data processing strategies of the selected attendance judgment indexes;
and deploying the attendance data processing strategy of the selected attendance judgment index by combining the strategy variables of the attendance data processing strategy of the selected attendance judgment index.
10. An attendance server is characterized by comprising a processor and a memory; the processor is connected in communication with the memory, and the processor is configured to read the computer program from the memory and execute the computer program to implement the method of any one of claims 1 to 9.
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| CN116503968B (en) * | 2023-06-28 | 2024-01-02 | 南方电网调峰调频发电有限公司信息通信分公司 | Remote punching method and device for power generation enterprises |
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