Human resource management intelligent optimization method and system based on data mining

Abstract

The invention discloses a human resource management intelligent optimization method and system based on data mining, and relates to the field of human resource management optimization. According to the invention, management of employee information in human resources in an enterprise is optimized in two aspects of selection of a storage mode of employee information in a human resource department and reduction of employee information loss times; wherein the storage mode prediction data of the current enterprise is obtained by constructing a final storage mode mapping equation, and then the storage mode prediction data is adopted to correct and adjust the employee information storage mode of the current enterprise; by constructing the final employee information loss frequency mapping model, the employee information loss frequency prediction data of the current enterprise is obtained, and then the employee storage cluster of the current enterprise is adjusted subsequently to ensure that the employee information loss frequency is minimum, so that the risk of enterprise employee information loss is reduced, and the enterprise employee information loss efficiency is improved. And the information security and integrity of the enterprise are ensured.

Description

Technical Field

[0001] The present invention belongs to the field of human resource management optimization, and in particular, relates to a human resource management intelligent optimization method and system based on data mining. Background Art

[0002] A Chinese patent with announcement number CN111401841B discloses a human resources management system and method thereof, which implements information sharing management through an information access unit and a salary and benefits module. When an applicant needs to know about the company's salary and benefits, he or she can submit an access application to the information access unit. After approval by the system administrator, he or she can directly access the detailed information of the salary and benefits module.

[0003] In the human resources department of an enterprise, the management of enterprise employee information is an important work content; employee information is divided into sensitive information and non-sensitive information; therefore, it is necessary to choose the storage method for these two types of information, separate storage or centralized storage; if the appropriate storage method is not selected, not only will the security direction of employee information be affected, but also the access efficiency of enterprise employee information will be reduced; secondly, if the characteristic parameters of the cluster storing employee information cannot be set appropriately, the risk of loss of stored employee information will increase because the cluster may fail due to various reasons, such as downtime or power outage. Summary of the invention

[0004] In view of the problems in the related technology, the present invention proposes a human resource management intelligent optimization method and system based on data mining to overcome the above technical problems existing in the existing related technology.

[0005] To solve the above technical problems, the present invention is achieved through the following technical solutions: The present invention is a human resource management intelligent optimization method based on data mining, comprising the following steps: S1. Collect employee data, storage method data, storage method influencing factor data, characteristic data of stored employee information clusters, and the number of times employee information is lost during the statistical period from multiple companies to obtain the company employee data matrix, company storage method data set, company storage method influencing factor data matrix, company cluster characteristic data matrix, and company employee information loss times data set.

[0006] S2. Use the enterprise employee data matrix, enterprise storage method data set and enterprise storage method influencing factor data matrix to construct the final storage method mapping equation; use the enterprise cluster feature data matrix and enterprise employee information loss times data set to construct the final employee information loss times mapping model.

[0007] S3. Use the final storage method mapping equation to map the current enterprise's employee data and storage method influencing factor data to obtain the current storage method data; use the final employee information loss frequency mapping model to map the characteristic data of the current enterprise's stored employee information cluster to obtain the current enterprise's employee information loss frequency data.

[0008] S4, storing the employee information of the current enterprise according to the current storage mode data; optimizing the current cluster feature data set according to the number of times the employee information of the current enterprise is lost, to obtain the current final cluster feature data set; By collecting employee data, storage method data, storage method influencing factor data, characteristic data of employee information clusters stored, and the number of times employee information was lost during the statistical period from multiple companies, data support is provided for the subsequent construction of the final storage method mapping equation and the final employee information loss frequency mapping model; by constructing the final storage method mapping equation, a mapping tool is provided for the subsequent mapping of the collected employee data and storage method influencing factor data of the current company, so as to obtain the storage method prediction data of the current company, and then use the storage method prediction data to correct and adjust the storage method of employee information of the current company; by constructing the final employee information loss frequency mapping model, a mapping tool is provided for the subsequent mapping of the characteristic data of the current company storage employee information clusters collected, so as to obtain the employee information loss frequency prediction data of the current company, and then adjust the employee storage cluster of the current company to ensure that the number of employee information losses is minimized, thereby reducing the risk of employee information loss in the company and ensuring the information security and integrity of the company. END Preferably, multiple types of common information and sensitive information of employees are set to obtain a set of common information types of employees. And employee sensitive information type set , a 1i 、a 2i They represent the set i-th type of employee general information and employee sensitive information respectively. , They respectively represent the set employee general information types and the total amount of employee sensitive information.

[0009] Set employee information collection type set , employee information storage method set And the storage method influencing factor type set ; , , They represent the number of common information types in employee information, the number of sensitive information types in employee information, and the number of employees respectively; , They represent the separate storage of employee general information and sensitive information and the centralized storage of employee general information and sensitive information, which are represented by 0 and 1 respectively; Indicates setting the type of factors affecting the i-th storage method. Indicates the total number of factors affecting the storage method.

[0010] S12: Collect employee data, storage method data and storage method influencing factor data stored by multiple companies according to the employee general information type set and employee sensitive information type set and the employee information collection type set, employee information storage method set and storage method influencing factor type set to obtain the enterprise employee data matrix , Enterprise Storage Method Dataset And the data matrix of factors affecting enterprise storage methods ; represents the storage method data of the employee data collected from the ith enterprise, and b represents the total number of enterprises collected; , They are as follows: ; ; in, , , They respectively represent the quantity data of common information types in the employee information collected from the i-th enterprise, the quantity data of sensitive information types in the employee information, and the number of employees; Represents the j-th type of storage method influencing factor data collected from the ith enterprise.

[0011] S13. Setting employee information storage cluster feature type set , represents the i-th employee information storage cluster feature type in the set employee information storage cluster feature type set, It indicates the total number of employee information storage cluster feature types in the set employee information storage cluster feature type set; the employee information storage cluster feature type set includes the number of servers, performance level, cost performance level, scalability level, availability level, load balancing capability level, error recovery capability level, transparency level, manageability level and programmability level of the employee information storage cluster.

[0012] Set a statistical period; collect characteristic data of employee information clusters stored in multiple enterprises and the number of times employee information is lost during the statistical period according to the employee information storage cluster characteristic type set, and obtain the enterprise cluster characteristic data matrix And the data set of the number of times enterprise employee information is lost , Indicates the number of times the employee information of the i-th enterprise is lost; as follows, ; in, Represents the characteristic data of the j-th type of employee information storage cluster collected from the ith enterprise.

[0013] The set of employee general information types includes basic personal information, educational background, work experience, position and title, health status, emergency contacts, family member information and training records, etc.; the set of employee sensitive information types includes fingerprints, facial recognition, iris scanning and other biometric information used to identify personal identity, religious belief information, medical health information, financial account information, whereabouts information, genetic information, blood type and personal property information, etc.; because the proportion of sensitive information types in employee information collected by the company's human resources department and employee information data will have a great impact on the company's decision on the storage method of employee information, such as storing sensitive information separately from general information will better protect the security of sensitive information, but will increase storage costs and reduce the efficiency of accessing employee information; while centralized storage of sensitive information and general information will increase the security risk of employee sensitive information, but reduce storage costs and improve the efficiency of accessing employee information; the types of factors affecting storage methods include compliance, user experience, cost, technical capabilities, internal policies and culture, etc.

[0014] Preferably, S2 comprises the following steps: S21, construct the initial storage mode mapping equation; as follows, ; in, The dependent variable representing the initial storage method mapping equation represents the employee information storage method data; represents the i-th independent variable of the initial storage mode mapping equation, represents the i-th type of enterprise employee data, express The coefficient of the independent variable ; represents the i+3th independent variable of the initial storage method mapping equation, represents the influencing factor data of the i-th enterprise employee information storage method, express The coefficient of the independent variable ; represents the offset of the initial storage mapping equation, and ceil represents the rounding function.

[0015] S22. Optimize the initial storage mode mapping equation using the enterprise employee data matrix, the enterprise storage mode data set, and the enterprise storage mode influencing factor data matrix to obtain a final storage mode mapping equation.

[0016] S23, constructing an initial employee information loss frequency mapping model; using the enterprise cluster feature data matrix and the enterprise employee information loss frequency data set to train and test the initial employee information loss frequency mapping model; after the training and testing are completed, a final employee information loss frequency mapping model is obtained; By using the enterprise employee data matrix, the enterprise storage method data set and the enterprise storage method influencing factor data matrix to optimize the independent variable coefficients and bias in the initial storage method mapping equation, the final storage method mapping equation is more accurate in mapping between enterprise employee data, enterprise storage method influencing factor data and enterprise storage method; by training and testing the initial employee information loss times mapping model, the final employee information loss times mapping model is more accurate in mapping between enterprise cluster characteristic data and enterprise employee information loss times data.

[0017] Preferably, the S22 comprises the following steps: S221, input each row of data in the enterprise employee data matrix and the enterprise storage mode influencing factor data matrix into the initial storage mode mapping equation for mapping, and obtain the initial storage mode mapping data set. , c i Indicates that the enterprise employee data matrix And the data matrix of factors affecting enterprise storage methods The i-th row of data in is input into the initial storage mode mapping equation for mapping to obtain the storage mode mapping data.

[0018] S222, when there is storage mode mapping data in the initial storage mode mapping data set that is different from the corresponding enterprise storage mode data in the enterprise storage mode data set, optimizing the initial storage mode mapping equation until there is no storage mode mapping data in the initial storage mode mapping data set that is different from the corresponding enterprise storage mode data in the enterprise storage mode data set, and obtaining a final storage mode mapping equation; otherwise, there is no need to optimize the initial storage mode mapping equation, and the initial storage mode mapping equation is used as the final storage mode mapping equation; By comparing the initial storage method mapping data with the actual enterprise storage method data collected, when some of the data is inconsistent with the actual data, it means that the mapping accuracy of the initial storage method mapping equation does not meet the requirements, and it is necessary to adjust and optimize the independent variable coefficient and bias of the initial storage method mapping equation.

[0019] Preferably, S3 comprises the following steps: S31, according to the employee general information type set and the employee sensitive information type set and according to the employee information collection type set and the storage method influencing factor type set, collect the employee data and storage method influencing factor data of the enterprise that currently needs to optimize human resource management, and obtain the current employee data set And the current storage method affects the data set ;e 11 、e 12 、e 13 They respectively represent the quantity data of common information types in the employee information of the enterprise that currently needs to optimize human resource management, the quantity data of sensitive information types in the employee information, and the quantity data of employees; 3i Represents the i-th type of storage method influencing factor data of the enterprise that currently needs to optimize human resource management.

[0020] S32: Collect characteristic data of the employee information storage cluster of the enterprise that currently needs to optimize human resource management according to the employee information storage cluster characteristic type set to obtain a current cluster characteristic data set. , e 4i Represents the characteristic data of the i-th type of employee information storage cluster of an enterprise that needs to optimize its human resource management.

[0021] S33, inputting the current employee data set, the current storage mode data, and the current storage mode influencing factor data set into the final storage mode mapping equation for mapping, and obtaining the current storage mode data set e 2 ; The current cluster feature dataset Input it into the final employee information loss frequency mapping model for mapping, and obtain the current enterprise employee information loss frequency data e 5 ; By mapping the current enterprise's employee data, storage method data, and storage method influencing factor data, the corresponding storage method data can be obtained. The storage method data comprehensively considers various requirements, including the number of employees, the proportion of sensitive information, and storage costs, so that the storage method data can be used as a reference for subsequent storage of the corresponding employee information of the current enterprise; by mapping the characteristic data of the current enterprise's employee information storage cluster, the corresponding number of employee information losses can be obtained, which provides a basis for determining whether the cluster needs to be adjusted.

[0022] Preferably, S4 comprises the following steps: S41, according to the current storage mode data e 2 Store the employee information of the current enterprise.

[0023] S42, setting a threshold value of the number of times information is lost within the statistical period; when the number of times the information of the current enterprise employees is lost is 5 When the number of times the information is lost is greater than or equal to the threshold value, the current cluster feature data set is adjusted until the number of times the information of the current enterprise employees is lost is equal to or greater than the threshold value. 5 When the number of times of information loss is less than the threshold, the current final cluster feature data set is obtained; otherwise, the current cluster feature data set does not need to be adjusted, and the current cluster feature data set is used as the current final cluster feature data set; By setting a threshold for the number of information losses, a quantitative basis is provided for determining whether the current number of employee information losses in the enterprise meets the requirements, making the determination more objective and accurate.

[0024] Preferably, in S42, the current cluster feature data set is adjusted until the current enterprise employee information loss times data e 5 When the number of information loss times is less than the threshold, obtaining the current final cluster feature data set includes the following steps: S421. Constructing the second dragonfly population , represents the i-th dragonfly in the second dragonfly population, represents the size of the second dragonfly population; sets the maximum number of iterations of the second dragonfly population to And the current number of iterations is , respectively recorded as the second maximum number of iterations and the second current number of iterations; the search space dimension of the second dragonfly population is .

[0025] S422, setting the value interval of the data corresponding to each employee information storage cluster feature type in the employee information storage cluster feature type set to obtain a cluster feature value interval set d 3 ;as follows, ; in, , respectively represent the lower limit and upper limit of the data corresponding to the i-th employee information storage cluster feature type in the employee information storage cluster feature type set; The initial position of each dragonfly in the second dragonfly population is set according to the cluster feature value interval set to obtain a second initial position matrix ;as follows, ; in, represents the component of the initial position of the kth dragonfly in the second dragonfly population in the i-th dimension; the calculation formula is as follows, ; In the formula, rand 2ki Indicates for Generates a random number between 0 and 1.

[0026] S423, according to the current enterprise employee information loss times data e 5 Construct the fitness function of the second dragonfly population ;as follows,

[0027] S424, start iteration, before iteration, set the second current iteration number Set to 1; the fitness function of the second dragonfly population is used in the first round of iteration The fitness value of the initial position of each dragonfly in the second initial position matrix is ​​calculated in conjunction with the final employee information loss times mapping model to obtain a third fitness value set; the maximum fitness value in the third fitness value set and the corresponding initial position of the dragonfly are respectively used as the third global optimal fitness and the third global optimal position; the initial position of each dragonfly in the second initial position matrix is ​​updated according to the third global optimal fitness and the third global optimal position; after the update is completed, the second current iteration number is Add 1 and enter the next iteration; The fitness function of the second dragonfly population is used in each other iteration process And cooperate with the final employee information loss times mapping model to calculate the fitness value of the position of each dragonfly in the second dragonfly population updated in the previous iteration process to obtain a fourth fitness value set; the maximum fitness value in the fourth fitness value set and the corresponding dragonfly position are used as the fourth global optimal fitness and the fourth global optimal position respectively; according to the fourth global optimal fitness and the fourth global optimal position, the position of each dragonfly in the second dragonfly population updated in the previous iteration process is updated again; after the update is completed, the second current iteration number Add 1 and enter the next iteration.

[0028] S425, when When , stop the iteration and get the second final global optimal position; otherwise, continue to iterate until until the second final global optimal position is used as the optimized current cluster feature data set; the optimized current cluster feature data set is input into the final employee information loss times mapping model for mapping, and the optimized current enterprise employee information loss times data is obtained; When the optimized current enterprise employee information loss times data is less than the information loss times threshold, the optimized current cluster feature data set is used as the current final cluster feature data set; otherwise, return to S424 and continue to iterate until the optimized current enterprise employee information loss times data is less than the information loss times threshold; By adopting the dragonfly optimization algorithm, multiple iterative optimizations are performed on each characteristic data of the current enterprise's employee information storage cluster at the same time, and the number of employee information losses obtained by adopting and finally mapping the number of employee information loss models is used as the fitness function; therefore, as the iteration proceeds, the number of employee information losses corresponding to each characteristic data of the employee information storage cluster obtained by the iteration becomes less and less, indicating that the direction of the current iteration is correct and ultimately meets the requirements for storing employee information.

[0029] A human resource management intelligent optimization system based on data mining includes an employee information type setting module, an enterprise employee information storage mode data collection module, an enterprise employee information storage mode data collection module, a storage mode mapping equation construction module, an employee information loss times mapping model construction module, a current enterprise employee storage data collection module, a first mapping module, a second mapping module, a storage mode management module and a cluster feature optimization module.

[0030] The present invention has the following beneficial effects: 1. The present invention optimizes the management of employee information in the human resources of an enterprise by selecting the storage method of employee information in the human resources department and reducing the number of times employee information is lost; wherein, by constructing a final storage method mapping equation, storage method prediction data of the current enterprise is obtained, and then the storage method prediction data of the current enterprise for employee information is corrected and adjusted; by constructing a final employee information loss frequency mapping model, employee information loss frequency prediction data of the current enterprise is obtained, and then the employee storage cluster of the current enterprise is subsequently adjusted to ensure that the number of times employee information is lost is minimized, thereby reducing the risk of employee information loss in the enterprise and ensuring the information security and integrity of the enterprise; 2. The present invention maps the characteristic data of the employee information storage cluster of the current enterprise to obtain the corresponding number of employee information loss, which provides a basis for determining whether the cluster needs to be adjusted in the future; 3. In the present invention, the dragonfly optimization algorithm is used to perform multiple iterations of optimization on each characteristic data of the current enterprise's employee information storage cluster at the same time, and the number of employee information losses obtained by adopting and finally mapping the number of employee information loss models is used as the fitness function; therefore, as the iteration proceeds, the number of employee information losses corresponding to each characteristic data of the employee information storage cluster obtained by the iteration becomes smaller and smaller, indicating that the direction of the current iteration is correct and ultimately meets the requirements for storing employee information.

[0031] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying creative work.

[0033] Figure 1 The present invention is a flowchart of an intelligent optimization method for human resource management based on data mining. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the invention to clearly and completely describe the technical solutions in the embodiments of the invention. Obviously, the described embodiments are only part of the embodiments of the invention, not all of the embodiments. Based on the embodiments in the invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the invention.

[0035] In the description of the present invention, it is necessary to understand that the terms "opening", "upper", "lower", "top", "middle", "inside" and the like indicating orientation or positional relationship are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the invention.

[0036] Embodiment 1 See also Figure 1 , this embodiment is a human resource management intelligent optimization method based on data mining, comprising the following steps: S1. Collect employee data, storage method data, storage method influencing factor data, characteristic data of stored employee information clusters, and the number of times employee information is lost during the statistical period saved by multiple companies, and obtain enterprise employee data matrix, enterprise storage method data set, enterprise storage method influencing factor data matrix, enterprise cluster characteristic data matrix, and enterprise employee information loss number data set; Set the common information types and sensitive information types of multiple employees to obtain the common information type set of employees And employee sensitive information type set , a 1i 、a 2i They represent the set i-th type of employee general information and employee sensitive information respectively. , They respectively represent the set employee general information types and the total amount of employee sensitive information; Set employee information collection type set , employee information storage method set And the storage method influencing factor type set ; , , They represent the number of common information types in employee information, the number of sensitive information types in employee information, and the number of employees respectively; , They represent the separate storage of employee general information and sensitive information and the centralized storage of employee general information and sensitive information, which are represented by 0 and 1 respectively; Indicates setting the type of factors affecting the i-th storage method. Indicates the total number of factors affecting the storage method.

[0037] S12: Collect employee data, storage method data and storage method influencing factor data stored by multiple companies according to the employee general information type set and employee sensitive information type set and the employee information collection type set, employee information storage method set and storage method influencing factor type set to obtain the enterprise employee data matrix , Enterprise Storage Method Dataset And the data matrix of factors affecting enterprise storage methods ; represents the storage method data of the employee data collected from the ith enterprise, and b represents the total number of enterprises collected; , They are as follows: ; ; in, , , They respectively represent the quantity data of common information types in the employee information collected from the i-th enterprise, the quantity data of sensitive information types in the employee information, and the number of employees; Represents the j-th type of storage method influencing factor data collected from the ith enterprise.

[0038] S13. Setting employee information storage cluster feature type set , represents the i-th employee information storage cluster feature type in the set employee information storage cluster feature type set, Indicates the total number of employee information storage cluster feature types in the set employee information storage cluster feature type set; the employee information storage cluster feature type set includes the number of servers, performance level, cost performance level, scalability level, availability level, load balancing capability level, error recovery capability level, transparency level, manageability level and programmability level of the employee information storage cluster; Set a statistical period; collect characteristic data of employee information clusters stored in multiple enterprises and the number of times employee information is lost during the statistical period according to the employee information storage cluster characteristic type set, and obtain the enterprise cluster characteristic data matrix And the data set of the number of times enterprise employee information is lost , Indicates the number of times the employee information of the i-th enterprise is lost; as follows, ; in, Represents the characteristic data of the j-th type of employee information storage cluster collected from the ith enterprise.

[0039] S2, using the enterprise employee data matrix, enterprise storage method data set and enterprise storage method influencing factor data matrix to construct the final storage method mapping equation; using the enterprise cluster feature data matrix and enterprise employee information loss times data set to construct the final employee information loss times mapping model; The S2 comprises the following steps: S21, construct the initial storage mode mapping equation; as follows, ; in, The dependent variable representing the initial storage method mapping equation represents the employee information storage method data; represents the i-th independent variable of the initial storage mode mapping equation, represents the i-th type of enterprise employee data, express The coefficient of the independent variable ; represents the i+3th independent variable of the initial storage method mapping equation, represents the influencing factor data of the i-th enterprise employee information storage method, express The coefficient of the independent variable ; represents the offset of the initial storage mapping equation, and ceil represents the rounding function.

[0040] S22. Optimize the initial storage mode mapping equation using the enterprise employee data matrix, the enterprise storage mode data set, and the enterprise storage mode influencing factor data matrix to obtain a final storage mode mapping equation.

[0041] The S22 comprises the following steps: S221, input each row of data in the enterprise employee data matrix and the enterprise storage mode influencing factor data matrix into the initial storage mode mapping equation for mapping, and obtain the initial storage mode mapping data set. , c i Indicates that the enterprise employee data matrix And the data matrix of factors affecting enterprise storage methods The i-th row of data in is input into the initial storage mode mapping equation for mapping to obtain the storage mode mapping data.

[0042] S222. When the initial storage mode mapping data set contains storage mode mapping data that is different from the corresponding enterprise storage mode data in the enterprise storage mode data set, the initial storage mode mapping equation is optimized until the initial storage mode mapping data set contains storage mode mapping data that is different from the corresponding enterprise storage mode data in the enterprise storage mode data set, and a final storage mode mapping equation is obtained; otherwise, there is no need to optimize the initial storage mode mapping equation, and the initial storage mode mapping equation is used as the final storage mode mapping equation.

[0043] The optimization of the initial storage mode mapping equation in S222 includes the following steps: S2221. Constructing the first dragonfly population , represents the i-th dragonfly in the first dragonfly population, represents the size of the first dragonfly population; sets the maximum number of iterations of the first dragonfly population to And the current number of iterations is , respectively recorded as the first maximum number of iterations and the first current number of iterations; the search space dimension of the first dragonfly population is .

[0044] S2222, set the initial storage mode to map the value intervals of each sub-variable coefficient and the offset in the equation, and obtain the independent variable value interval matrix d 1 And the bias value range [d 21 ,d 22 ];d 21 d 22 Respectively represent the lower limit and upper limit of the bias value in the initial storage mode mapping equation; d 1 as follows, ; in, , , They respectively represent the lower limits of the first, second and third independent variable coefficients in the initial storage mode mapping equation, , , Respectively represent the upper limits of the first, second, and third independent variable coefficients in the initial storage mode mapping equation; , respectively represent the lower limit and upper limit of the value of the i+3th independent variable coefficient of the initial storage mode mapping equation; The initial position of each dragonfly in the first dragonfly population is set according to the independent variable value interval matrix and the bias value interval, and the first initial position matrix set is obtained. , represents the initial position matrix of the kth dragonfly in the first dragonfly population; as follows, ; in, , , They respectively represent the components of the initial position of the kth dragonfly in the first dragonfly population in the first, second and third independent variable coefficient dimensions in the initial storage mapping equation, represents the component of the initial position of the kth dragonfly in the first dragonfly population in the i+3th independent variable coefficient dimension of the initial storage mode mapping equation, A component representing the initial position of the kth dragonfly in the first dragonfly population in the offset dimension of the initial storage mapping equation; , , , , The calculation formulas are as follows: ; ; ; ; ; In the formula, rand 1k11 、rand 1k12 、rand 1k13 、rand 1k2i 、rand 1k3 Respectively for , , , , Generates a random number between 0 and 1.

[0045] S2223, mapping the data set c={c 1 ,c 2 ,…,c i ,…,c b} and enterprise storage method data sets Construct the fitness function of the first dragonfly population ;as follows, ; In the formula, β is a positive number, indicating the correction parameter.

[0046] S2224, start iteration, before iteration, set the first current iteration number Set to 1; the fitness function of the first dragonfly population is used in the first round of iteration Calculate the fitness value of the initial position matrix of each dragonfly in the first initial position matrix set to obtain a first fitness value set; use the maximum fitness value in the first fitness value set and the corresponding initial position matrix of the dragonfly as the first global optimal fitness and the first global optimal position respectively; update the initial position matrix of each dragonfly in the first initial position matrix set according to the first global optimal fitness and the first global optimal position; after the update is completed, the first current iteration number Add 1 and enter the next iteration; In each other iteration, the fitness function of the first dragonfly population is used Calculate the fitness value of the position matrix of each dragonfly in the first dragonfly population updated in the previous round of iterations to obtain a second fitness value set; use the maximum fitness value in the second fitness value set and the corresponding dragonfly position matrix as the second global optimal fitness and the second global optimal position respectively; update the position matrix of each dragonfly in the first dragonfly population updated in the previous round of iterations according to the second global optimal fitness and the second global optimal position; after the update is completed, the first current iteration number Add 1 and enter the next iteration.

[0047] S2225, when When , stop the iteration and get the first final global optimal position; otherwise, continue to iterate until until the time; substitute the first final global optimal position into the initial storage mode mapping equation to obtain the optimized storage mode mapping equation; input each row of data in the enterprise employee data matrix and the enterprise storage mode influencing factor data matrix into the optimized storage mode mapping equation for mapping, and obtain the optimized storage mode mapping data set; When there is no storage mode mapping data in the optimized storage mode mapping data set that is different from the corresponding enterprise storage mode data in the enterprise storage mode data set, the optimized storage mode mapping equation is used as the final storage mode mapping equation; otherwise, return to S2224 to continue iterating until there is no storage mode mapping data in the optimized storage mode mapping data set that is different from the corresponding enterprise storage mode data in the enterprise storage mode data set, and the final storage mode mapping equation is obtained; The dragonfly optimization algorithm can effectively perform global exploration in the search space by simulating the dynamic and static clustering behaviors of dragonflies, which helps to escape from the local optimal solution and find the global optimal solution. The number of parameters is small, which makes the adjustment and optimization process of the algorithm simpler and has good robustness. Based on the above advantages, the dragonfly optimization algorithm is used in this scheme to perform multiple iterative optimizations on the coefficients of each independent variable and the bias of the initial storage mode mapping equation at the same time, and the sum of the differences between the data mapped by the initial storage mode mapping equation and the actual data is used as the fitness function. Therefore, as the iteration proceeds, the difference between the data mapped by the initial storage mode mapping equation and the actual data will become smaller and smaller, and finally meet the accuracy requirements.

[0048] S23, constructing an initial employee information loss frequency mapping model; using the enterprise cluster feature data matrix and the enterprise employee information loss frequency data set to train and test the initial employee information loss frequency mapping model; after the training and testing are completed, obtaining the final employee information loss frequency mapping model.

[0049] The S23 comprises the following steps: S231. Construct an initial SVM classification model and set the training data ratio; use the initial SVM classification model as an initial employee information loss times mapping model; divide the enterprise cluster characteristic data matrix and the enterprise employee information loss times data set according to the training data ratio to obtain the enterprise cluster characteristic training data matrix, the enterprise employee information loss times training data set, the enterprise cluster characteristic test data matrix and the enterprise employee information loss times test data set.

[0050] S232. Set a training error threshold; input the enterprise cluster feature training data matrix and the enterprise employee information loss times training data set as training data and training labels respectively into the initial employee information loss times mapping model for training; during the training process, when the training error is less than the training error threshold, stop the training and obtain the trained employee information loss times mapping model; otherwise, continue the training until the training error is less than the training error threshold.

[0051] S233, setting a test accuracy threshold; inputting the enterprise cluster feature test data matrix and the enterprise employee information loss times test data set as test data and test labels respectively into the trained employee information loss times mapping model for testing; after the test is completed, obtaining the test accuracy; when the test accuracy is greater than or equal to the test accuracy threshold, using the trained employee information loss times mapping model as the final employee information loss times mapping model; otherwise, returning to S232 to continue training the trained employee information loss times mapping model until the test accuracy is greater than or equal to the test accuracy threshold; By training and testing the initial employee information loss times mapping model using the enterprise cluster feature training data matrix, the enterprise employee information loss times training data set, the enterprise cluster feature test data matrix, and the enterprise employee information loss times test data set respectively, the final employee information loss times mapping model obtained has better mapping and classification capabilities between enterprise cluster feature data and enterprise employee information loss times data.

[0052] S3. Use the final storage method mapping equation to map the current enterprise's employee data and storage method influencing factor data to obtain the current storage method data; use the final employee information loss frequency mapping model to map the characteristic data of the current enterprise's stored employee information cluster to obtain the current enterprise's employee information loss frequency data.

[0053] The S3 comprises the following steps: S31, according to the employee general information type set and the employee sensitive information type set and according to the employee information collection type set and the storage method influencing factor type set, collect the employee data and storage method influencing factor data of the enterprise that currently needs to optimize human resource management, and obtain the current employee data set e 1 ={e 11 ,e 12 ,e 13} and the current storage method influencing factor dataset ;e 11 、e 12 、e 13They respectively represent the quantity data of common information types in the employee information of the enterprise that currently needs to optimize human resource management, the quantity data of sensitive information types in the employee information, and the quantity data of employees; 3i Represents the i-th type of storage method influencing factor data of the enterprise that currently needs to optimize human resource management.

[0054] S32: Collect characteristic data of the employee information storage cluster of the enterprise that currently needs to optimize human resource management according to the employee information storage cluster characteristic type set to obtain a current cluster characteristic data set. , e 4i Represents the characteristic data of the i-th type of employee information storage cluster of an enterprise that needs to optimize its human resource management.

[0055] S33, inputting the current employee data set, the current storage mode data, and the current storage mode influencing factor data set into the final storage mode mapping equation for mapping, and obtaining the current storage mode data set e 2 ; The current cluster feature dataset Input it into the final employee information loss frequency mapping model for mapping, and obtain the current enterprise employee information loss frequency data e 5 .

[0056] S4. Store the employee information of the current enterprise according to the current storage mode data; optimize the current cluster feature data set according to the number of times the employee information of the current enterprise is lost, to obtain the current final cluster feature data set.

[0057] The S4 comprises the following steps: S41, according to the current storage mode data e 2 Store the employee information of the current enterprise.

[0058] S42, setting a threshold value of the number of times information is lost within the statistical period; when the number of times the information of the current enterprise employees is lost is 5 When the number of times the information is lost is greater than or equal to the threshold value, the current cluster feature data set is adjusted until the number of times the information of the current enterprise employees is lost is equal to or greater than the threshold value. 5 When the number of information loss times is less than the threshold, the current final cluster feature data set is obtained; otherwise, the current cluster feature data set does not need to be adjusted, and the current cluster feature data set is used as the current final cluster feature data set.

[0059] In S42, the current cluster feature data set is adjusted until the current enterprise employee information loss times data e 5When the number of information loss times is less than the threshold, obtaining the current final cluster feature data set includes the following steps: S421. Constructing the second dragonfly population , represents the i-th dragonfly in the second dragonfly population, represents the size of the second dragonfly population; sets the maximum number of iterations of the second dragonfly population to And the current number of iterations is , respectively recorded as the second maximum number of iterations and the second current number of iterations; the search space dimension of the second dragonfly population is .

[0060] S422, setting the value interval of the data corresponding to each employee information storage cluster feature type in the employee information storage cluster feature type set to obtain a cluster feature value interval set d 3 ;as follows, ; in, , respectively represent the lower limit and upper limit of the data corresponding to the i-th employee information storage cluster feature type in the employee information storage cluster feature type set; The initial position of each dragonfly in the second dragonfly population is set according to the cluster feature value interval set to obtain a second initial position matrix ;as follows, ; in, represents the component of the initial position of the kth dragonfly in the second dragonfly population in the i-th dimension; the calculation formula is as follows, ; In the formula, rand 2ki Indicates for Generates a random number between 0 and 1.

[0061] S423, according to the current enterprise employee information loss times data e 5 Construct the fitness function of the second dragonfly population ;as follows,

[0062] S424, start iteration, before iteration, set the second current iteration number Set to 1; the fitness function of the second dragonfly population is used in the first round of iteration The fitness value of the initial position of each dragonfly in the second initial position matrix is ​​calculated in conjunction with the final employee information loss times mapping model to obtain a third fitness value set; the maximum fitness value in the third fitness value set and the corresponding initial position of the dragonfly are respectively used as the third global optimal fitness and the third global optimal position; the initial position of each dragonfly in the second initial position matrix is ​​updated according to the third global optimal fitness and the third global optimal position; after the update is completed, the second current iteration number is Add 1 and enter the next iteration; The fitness function of the second dragonfly population is used in each other iteration process And cooperate with the final employee information loss times mapping model to calculate the fitness value of the position of each dragonfly in the second dragonfly population updated in the previous iteration process to obtain a fourth fitness value set; the maximum fitness value in the fourth fitness value set and the corresponding dragonfly position are used as the fourth global optimal fitness and the fourth global optimal position respectively; according to the fourth global optimal fitness and the fourth global optimal position, the position of each dragonfly in the second dragonfly population updated in the previous iteration process is updated again; after the update is completed, the second current iteration number Add 1 and enter the next iteration.

[0063] S425, when When , stop the iteration and get the second final global optimal position; otherwise, continue to iterate until until the second final global optimal position is used as the optimized current cluster feature data set; the optimized current cluster feature data set is input into the final employee information loss times mapping model for mapping, and the optimized current enterprise employee information loss times data is obtained; When the optimized current enterprise employee information loss times data is less than the information loss times threshold, the optimized current cluster feature data set is used as the current final cluster feature data set; otherwise, return to S424 to continue iterating until the optimized current enterprise employee information loss times data is less than the information loss times threshold.

[0064] Embodiment 2 This embodiment discloses a human resource management intelligent optimization system based on data mining, and the system can implement the method of the above embodiment, including an employee information type setting module, an enterprise employee information storage mode data collection module, an enterprise employee information storage mode data collection module, a storage mode mapping equation construction module, an employee information loss times mapping model construction module, a current enterprise employee storage data collection module, a first mapping module, a second mapping module, a storage mode management module and a cluster feature optimization module; The employee information type setting module is used to set common information types and sensitive information types of multiple employees, and obtain an employee common information type set and an employee sensitive information type set; The enterprise employee information storage method data collection module is used to cooperate with the employee general information type set and the employee sensitive information type set to collect employee data, storage method data and storage method influencing factor data stored by multiple companies, and obtain an enterprise employee data matrix, an enterprise storage method data set and an enterprise storage method influencing factor data matrix; The enterprise employee information storage mode data collection module is used to collect characteristic data of employee information clusters stored by multiple enterprises and the number of times employee information is lost during a statistical period, and obtain an enterprise cluster characteristic data matrix and an enterprise employee information loss number data set; The storage mode mapping equation construction module is used to construct the final storage mode mapping equation using the enterprise employee data matrix, the enterprise storage mode data set and the enterprise storage mode influencing factor data matrix; The employee information loss frequency mapping model construction module is used to construct a final employee information loss frequency mapping model using the enterprise cluster feature data matrix and the enterprise employee information loss frequency data set; The current enterprise employee storage data collection module is used to collect employee data, storage method influencing factor data and characteristic data of the stored employee information cluster of the enterprise that currently needs to optimize human resource management, and obtain the current employee data set, the current storage method influencing factor data set and the current cluster characteristic data set; The first mapping module is used to map the current employee data set and the current storage mode influencing factor data set using the final storage mode mapping equation to obtain the current storage mode data; The second mapping module is used to map the current cluster feature data set using the final employee information loss frequency mapping model to obtain the current enterprise employee information loss frequency data; The storage mode management module is used to store the employee information of the current enterprise according to the current storage mode data; The cluster feature optimization module is used to optimize the current cluster feature data set according to the current enterprise employee information loss frequency data to obtain the current final cluster feature data set.

[0065] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0066] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the invention, so that those skilled in the art can understand and use the invention well.

Claims

1. A human resource management intelligent optimization method based on data mining, characterized in that: The following steps are involved: S1. Collect employee data, storage method data, storage method influencing factor data, characteristic data of stored employee information clusters, and the number of times employee information is lost during the statistical period saved by multiple companies, and obtain enterprise employee data matrix, enterprise storage method data set, enterprise storage method influencing factor data matrix, enterprise cluster characteristic data matrix, and enterprise employee information loss number data set; S2, using the enterprise employee data matrix, enterprise storage method data set and enterprise storage method influencing factor data matrix to construct the final storage method mapping equation; The enterprise cluster characteristic data matrix and the enterprise employee information loss frequency data set are used to build the final employee information loss frequency mapping model; S3, using the final storage mode mapping equation to map the current enterprise's employee data and storage mode influencing factor data to obtain current storage mode data; The final employee information loss frequency mapping model is used to map the characteristic data of the current enterprise stored employee information cluster to obtain the current enterprise employee information loss frequency data; S4, storing the employee information of the current enterprise according to the current storage mode data; The current cluster feature data set is optimized according to the current number of times the enterprise employee information is lost, and the current final cluster feature data set is obtained.

2. According to claim 1, a method for intelligent optimization of human resource management based on data mining is characterized in that: Set common information types and sensitive information types of multiple employees to obtain common information type sets and sensitive information type sets of employees; set employee information collection type sets, employee information storage method sets, and storage method influencing factor type sets; S12, collecting employee data, storage method data, and storage method influencing factor data stored by multiple companies according to the employee general information type set and the employee sensitive information type set and the employee information collection type set, the employee information storage method set, and the storage method influencing factor type set, to obtain an enterprise employee data matrix, an enterprise storage method data set, and an enterprise storage method influencing factor data matrix; S13. Set a characteristic type set of an employee information storage cluster; the characteristic type set of the employee information storage cluster includes the number of servers, performance level, cost-effectiveness level, scalability level, availability level, load balancing capability level, error recovery capability level, transparency level, manageability level and programmability level of the employee information storage cluster; set a statistical period; collect characteristic data of employee information storage clusters of multiple enterprises and the number of times employee information is lost during the statistical period according to the characteristic type set of the employee information storage cluster, and obtain an enterprise cluster characteristic data matrix and an enterprise employee information loss frequency data set.

3. The method for intelligent optimization of human resource management based on data mining according to claim 2 is characterized in that: The S2 comprises the following steps: S21, constructing an initial storage mode mapping equation; S22, optimizing the initial storage mode mapping equation using the enterprise employee data matrix, the enterprise storage mode data set, and the enterprise storage mode influencing factor data matrix to obtain a final storage mode mapping equation; S23, constructing an initial employee information loss frequency mapping model; using the enterprise cluster feature data matrix and the enterprise employee information loss frequency data set to train and test the initial employee information loss frequency mapping model; after the training and testing are completed, obtaining the final employee information loss frequency mapping model.

4. The method for intelligent optimization of human resource management based on data mining according to claim 3 is characterized in that: The S22 comprises the following steps: S221, inputting each row of data in the enterprise employee data matrix and the enterprise storage mode influencing factor data matrix into the initial storage mode mapping equation for mapping, to obtain an initial storage mode mapping data set; S222. When the initial storage mode mapping data set contains storage mode mapping data that is different from the corresponding enterprise storage mode data in the enterprise storage mode data set, the initial storage mode mapping equation is optimized until the initial storage mode mapping data set contains storage mode mapping data that is different from the corresponding enterprise storage mode data in the enterprise storage mode data set, and a final storage mode mapping equation is obtained; otherwise, there is no need to optimize the initial storage mode mapping equation, and the initial storage mode mapping equation is used as the final storage mode mapping equation.

5. The method for intelligent optimization of human resource management based on data mining according to claim 4 is characterized by: In S222, the initial storage mode mapping equation is optimized using a dragonfly optimization algorithm.

6. The method for intelligent optimization of human resource management based on data mining according to claim 3 is characterized by: The initial employee information loss times mapping model described in S23 adopts the SVM model.

7. The method for intelligent optimization of human resource management based on data mining according to claim 6 is characterized in that: The S3 comprises the following steps: S31, collecting employee data and storage method influencing factor data of the enterprise currently requiring human resource management optimization according to the employee general information type set and the employee sensitive information type set and according to the employee information collection type set and the storage method influencing factor type set, to obtain a current employee data set and a current storage method influencing factor data set; S32, collecting characteristic data of the employee information storage cluster of the enterprise that currently needs to optimize human resource management according to the employee information storage cluster characteristic type set, to obtain a current cluster characteristic data set; S33, input the current employee data set, the current storage mode data and the current storage mode influencing factor data set into the final storage mode mapping equation for mapping, and obtain the current storage mode data; input the current cluster feature data set into the final employee information loss times mapping model for mapping, and obtain the current enterprise employee information loss times data.

8. The method for intelligent optimization of human resource management based on data mining according to claim 7 is characterized in that: The S4 comprises the following steps: S41, storing the employee information of the current enterprise according to the current storage mode data; S42. Set a threshold value for the number of information losses within the statistical period; when the current number of times the enterprise employee information is lost is greater than or equal to the threshold value for the number of information losses, adjust the current cluster feature data set until the current number of times the enterprise employee information is lost is less than the threshold value for the number of information losses, and obtain the current final cluster feature data set; otherwise, there is no need to adjust the current cluster feature data set, and the current cluster feature data set is used as the current final cluster feature data set.

9. The method for intelligent optimization of human resource management based on data mining according to claim 8 is characterized in that: In S42, the current cluster feature data set is adjusted until the number of times the current enterprise employee information is lost is less than the information loss number threshold, and obtaining the current final cluster feature data set includes the following steps: S421, construct a second dragonfly population; set the maximum number of iterations of the second dragonfly population to And the current number of iterations is , respectively recorded as the second maximum number of iterations and the second current number of iterations; S422, setting a value interval of data corresponding to each employee information storage cluster feature type in the employee information storage cluster feature type set to obtain a cluster feature value interval set; setting an initial position of each dragonfly in the second dragonfly population according to the cluster feature value interval set to obtain a second initial position matrix; S423, constructing a fitness function of the second dragonfly population according to the current enterprise employee information loss frequency data; S424, start iteration, set the second current iteration number to 1 before iteration; in each round of iteration, use the fitness function of the second dragonfly population and cooperate with the final employee information loss number mapping model to calculate the fitness value of the position of each dragonfly in the second dragonfly population updated in the previous round of iteration, and update the position of each dragonfly in the second dragonfly population updated in the previous round of iteration; after the update is completed, add 1 to the second current iteration number and enter the next round of iteration; S425, when When , stop the iteration and get the second final global optimal position; otherwise, continue to iterate until until the second final global optimal position is used as the optimized current cluster feature data set; the optimized current cluster feature data set is input into the final employee information loss times mapping model for mapping, and the optimized current enterprise employee information loss times data is obtained; When the optimized current enterprise employee information loss times data is less than the information loss times threshold, the optimized current cluster feature data set is used as the current final cluster feature data set; otherwise, return to S424 to continue iterating until the optimized current enterprise employee information loss times data is less than the information loss times threshold.

10. A system for implementing the intelligent optimization method for human resource management based on data mining as described in any one of claims 1 to 9.

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