A method and device for dynamic iterative configuration of personnel positions

Abstract

The present invention discloses a method and device for dynamic iterative configuration of personnel positions, the method comprising: obtaining a position allocation information set; the position allocation information set comprises position demand information, human resource description information and position allocation information; in the position allocation information set, the position demand information and human resource description information correspond to the position allocation information, and the position allocation information is used to indicate the personnel assigned to each position demand. The position allocation information set is preprocessed to obtain a position information set; the human resource description information of each person is evaluated and processed to obtain the human resource evaluation information of the person; a position allocation optimization model is constructed; and the human resource evaluation information and the position information set are solved and processed using the position allocation optimization model to obtain position allocation result information.

Description

Technical Field

[0001] The present invention relates to the fields of task allocation and artificial intelligence, and in particular to a method and device for dynamically iterating configuration of personnel positions. Background Art

[0002] In modern technology-intensive industries, efficient operation and task execution of the industrial chain are crucial. However, traditional personnel position allocation methods usually rely on manual experience and historical data, lack of systematic and intelligent analysis tools, resulting in many problems in actual operations. For example, due to insufficient understanding of the complexity of different tasks and actual working environments, it is often difficult to accurately allocate personnel, resulting in low efficiency in task execution. In addition, traditional allocation methods are difficult to respond quickly to task requirements and environmental changes, and cannot adjust personnel positions in real time, increasing the risk and cost of task execution.

[0003] In job allocation in technology-intensive industries, how to quickly respond to task requirements and environmental changes, adjust personnel positions in real time, reduce the risks and costs of task execution, and improve the efficiency and accuracy of job allocation is an issue that needs to be addressed urgently. Summary of the invention

[0004] The present invention mainly solves the problem of how to quickly respond to task requirements and environmental changes, adjust personnel positions in real time, reduce the risks and costs of task execution, and improve the efficiency and accuracy of position allocation in position allocation in technology-intensive industries. The present invention discloses a method and device for dynamic iterative configuration of personnel positions.

[0005] In a first aspect of an embodiment of the present application, a method for dynamically iterating configuration of personnel positions is disclosed, comprising:

[0006] S1, obtaining a post allocation information set; the post allocation information set includes post requirement information, human resource description information and post allocation information; in the post allocation information set, the post requirement information and human resource description information correspond to the post allocation information, and the post allocation information is used to indicate the personnel assigned to each post requirement;

[0007] S2, preprocessing the position allocation information set to obtain a position information set;

[0008] S3, evaluating and processing the human resource description information of any person to obtain the human resource evaluation information of the person;

[0009] S4, build the job allocation optimization model;

[0010] S5, using the job allocation optimization model to solve the human resource evaluation information and job allocation information to obtain job allocation result information.

[0011] The preprocessing of the post allocation information set to obtain the post information set includes:

[0012] S21, performing outlier elimination processing on the job allocation information set to obtain a first data set;

[0013] S22, performing boundary check processing on the first data set to obtain a position information set.

[0014] The performing boundary check processing on the first data set to obtain a position information set includes:

[0015] S221, for each data attribute of the first data set, preset a corresponding value range; the value range includes an upper limit value of the value range and a lower limit value of the value range;

[0016] S222, for each data of each data attribute of the first data set, judging whether the value of the data is within the value range according to the value range of the data attribute; if the data is within the value range, not processing the data; if the data is not within the value range, setting the value of the data to the upper limit value or the lower limit value of the value range that is closest to the data;

[0017] S223, after completing S222 for all data attributes, a set of position information is obtained.

[0018] The performing boundary check processing on the first data set to obtain a position information set includes:

[0019] S2201, for each type of data attribute of the first data set, using the data collection information of the data as an independent variable and the data value of the data as a dependent variable, perform autoregression-sliding average modeling to obtain regression models of the data attributes of the class; using the regression model, perform calculation processing on the independent variable to obtain regression data values; determine whether the absolute value of the difference between the regression data value and the corresponding dependent variable value is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, delete the data from the first data set; if it is less than the first regression discrimination threshold, do not process the data;

[0020] S2202, after completing S2201 for all data attributes, a set of position information is obtained.

[0021] The human resource description information of each person is evaluated and processed, and the human resource evaluation information of the person includes:

[0022] The human resource description information of each person is represented as a matrix W; the i-th row vector of the matrix W represents the sampling value of the i-th capability type of the person at all sampling times;

[0023] Performing time-frequency transformation on each row vector of the matrix W to obtain a corresponding time-frequency vector; constructing a time-frequency matrix P using all the time-frequency vectors;

[0024] Performing classification extreme value extraction processing on each column vector of the time-frequency matrix P to obtain corresponding first solution information and second solution information;

[0025] The expression of the classification extreme value extraction process is:

[0026]

[0027]

[0028] in, is the first solution information obtained using the i-th row vector of the time-frequency matrix P, p i is the second solution information obtained using the i-th row vector of the time-frequency matrix P; i∈J * indicates that the i-th capability type belongs to the benefit-type indicator, i∈J′ indicates that the i-th capability type belongs to the cost-type indicator, and J * is a benefit indicator, J' is a cost indicator; the category number of the capability type is determined according to the row number of the matrix W; m and n are the row dimension and column dimension of the time-frequency matrix P respectively;

[0029] The Euclidean distance calculation is performed on each column vector of the time-frequency matrix P and the corresponding first solution information and the second solution information, respectively, to obtain a first Euclidean distance vector and a second Euclidean distance vector; the expression of the Euclidean distance calculation is:

[0030]

[0031]

[0032] in, is the i-th element of the first Euclidean distance vector, S i ′ is the i-th element of the second Euclidean distance vector;

[0033] Decomposing the time-frequency matrix P to obtain a feature matrix Y;

[0034] The decomposition process is calculated as follows:

[0035] P=UYV,

[0036] Among them, U is the left decomposition matrix, Y is the characteristic matrix, V is the right decomposition matrix, and both U and V are orthogonal matrices;

[0037] Extracting the diagonal elements of the characteristic matrix, and constructing a diagonal vector using all the extracted diagonal elements;

[0038] The first Euclidean distance vector, the second Euclidean distance vector and the diagonal vector are fused and weighted to obtain a human resource evaluation vector; and the human resource evaluation vector is used to represent human resource evaluation information.

[0039] The calculation expression of the fusion weighted processing is:

[0040]

[0041] Among them, v i is the i-th element value of the human resource evaluation vector, i=1,2,…,m,u i is the i-th element of the diagonal vector.

[0042] The construction obtains a job allocation optimization model, including:

[0043] The job demand information is represented as a matrix A; the element A in the i-th row and j-th column of the matrix A ij , represents the urgency value of the demand for the j-th capability type for the i-th position;

[0044] The human resource evaluation vectors of all personnel are represented as a matrix B; the i-th row vector of the matrix B represents the human resource evaluation vector of the i-th personnel;

[0045] Initialize the post allocation vector; the i-th element Ti of the post allocation vector T represents the number of the person to whom the i-th post is assigned;

[0046] Establish a job allocation optimization model, and its expression is:

[0047]

[0048] Where N is the number of positions, B Ti,j Represents the element in the Ti-th row and j-th column of matrix B, T0 is the preset allocation threshold, and the job allocation vector T is the vector to be solved.

[0049] A second aspect of an embodiment of the present invention discloses a device for optimizing personnel position configuration based on a network model, the device comprising:

[0050] A memory storing executable program code;

[0051] a processor coupled to the memory;

[0052] The processor calls the executable program code stored in the memory to execute the dynamic iterative configuration method of personnel positions.

[0053] A third aspect of an embodiment of the present invention discloses a computer-storable medium storing computer instructions, which, when called by a computer, are used to execute the method for dynamic iterative configuration of personnel positions.

[0054] According to a fourth aspect of the embodiments of the present invention, an information data processing terminal is disclosed, and the information data processing terminal is used to implement the dynamic iterative configuration method of personnel positions.

[0055] The beneficial effects of the present invention are:

[0056] The present invention can quickly respond to task requirements and environmental changes, adjust personnel positions in real time, reduce the risk and cost of task execution, and improve the efficiency and accuracy of position allocation. The present invention automatically generates the optimal personnel position deployment plan through an intelligent optimization model, greatly reduces manual intervention, and improves the efficiency and accuracy of task personnel deployment.

[0057] The present invention enhances the reliability of task execution. The present invention timely analyzes task requirement data and human resource data, adjusts personnel positions according to changes in job requirements, ensures the continuity and effectiveness of task execution, and reduces the risk of task failure.

[0058] The present invention optimizes the personnel post deployment, avoids personnel redundancy and resource waste, and reduces the overall cost of post operation. The present invention improves the emergency response capability of post operation, and the present invention can quickly respond to emergencies during task execution, thereby ensuring the safety of personnel and equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a flow chart for implementing the method of the present invention. DETAILED DESCRIPTION

[0060] In order to better understand the content of the present invention, an embodiment is given here.

[0061] Figure 1 It is a flow chart for implementing the method of the present invention.

[0062] In order to quickly respond to task requirements and environmental changes, adjust personnel positions in real time, reduce the risk and cost of task execution, and improve the efficiency and accuracy of position allocation, the present invention discloses a method and device for dynamic iterative configuration of personnel positions.

[0063] In a first aspect of an embodiment of the present application, a method for dynamically iterating configuration of personnel positions is disclosed, comprising:

[0064] S1, obtain a job allocation information set; the job allocation information set includes job requirement information, human resource description information and job allocation information; in the job allocation information set, the job requirement information and human resource description information correspond to the job allocation information, and the job allocation information is used to indicate the personnel assigned to each job requirement.

[0065] S2, preprocessing the position allocation information set to obtain a position information set;

[0066] S3, evaluating and processing the human resource description information of each person, and the human resource evaluation information of the person;

[0067] S4, build the job allocation optimization model;

[0068] S5, using the job allocation optimization model to solve the human resource description information and job allocation information to obtain job allocation result information;

[0069] The preprocessing of the post allocation information set to obtain the post information set includes:

[0070] S21, performing outlier elimination processing on the job allocation information set to obtain a first data set;

[0071] S22, performing boundary check processing on the first data set to obtain a position information set.

[0072] The performing boundary check processing on the first data set to obtain a position information set includes:

[0073] S221, for each data attribute of the first data set, preset a corresponding value range; the value range includes an upper limit value of the value range and a lower limit value of the value range;

[0074] S222, for each data of each data attribute of the first data set, judging whether the value of the data is within the value range according to the value range of the data attribute; if the data is within the value range, not processing the data; if the data is not within the value range, setting the value of the data to the upper limit value or the lower limit value of the value range that is closest to the data;

[0075] S223, after completing S222 for all data attributes, a set of position information is obtained.

[0076] The performing boundary check processing on the first data set to obtain a position information set includes:

[0077] S2201, for each type of data attribute of the first data set, using the data collection information of the data as an independent variable and the data value of the data as a dependent variable, perform autoregression-sliding average modeling to obtain regression models of the data attributes of the class; using the regression model, perform calculation processing on the independent variable to obtain regression data values; determine whether the absolute value of the difference between the regression data value and the corresponding dependent variable value is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, delete the data from the first data set; if it is less than the first regression discrimination threshold, do not process the data;

[0078] S2202, after completing S2201 for all data attributes, a set of position information is obtained.

[0079] The human resource description information of each person is evaluated and processed, and the human resource evaluation information of the person includes:

[0080] The human resource description information of each person is represented as a matrix W; the i-th row vector of the matrix W represents the sampling value of the i-th capability type of the person at all sampling times;

[0081] Performing time-frequency transformation on each row vector of the matrix W to obtain a corresponding time-frequency vector; constructing a time-frequency matrix P using all the time-frequency vectors;

[0082] Performing classification extreme value extraction processing on each column vector of the time-frequency matrix P to obtain corresponding first solution information and second solution information;

[0083] The expression of the classification extreme value extraction process is:

[0084]

[0085]

[0086] in, is the first solution information obtained using the i-th row vector of the time-frequency matrix P, p i ′ is the second solution information obtained using the i-th row vector of the time-frequency matrix P; i∈J * indicates that the i-th capability type belongs to the benefit-type indicator, i∈J′ indicates that the i-th capability type belongs to the cost-type indicator, and J * is a benefit indicator, J' is a cost indicator; the category number of the capability type is determined according to the row number of the matrix W; m and n are the row dimension and column dimension of the time-frequency matrix P respectively;

[0087] The Euclidean distance calculation is performed on each column vector of the time-frequency matrix P and the corresponding first solution information and the second solution information, respectively, to obtain a first Euclidean distance vector and a second Euclidean distance vector; the expression of the Euclidean distance calculation is:

[0088]

[0089]

[0090] in, is the i-th element of the first Euclidean distance vector, S i ′ is the i-th element of the second Euclidean distance vector;

[0091] Decomposing the time-frequency matrix P to obtain a feature matrix Y;

[0092] The decomposition process is calculated as follows:

[0093] P=UYV,

[0094] Among them, U is the left decomposition matrix, Y is the characteristic matrix, V is the right decomposition matrix, and both U and V are orthogonal matrices;

[0095] Extracting the diagonal elements of the characteristic matrix, and constructing a diagonal vector using all the extracted diagonal elements;

[0096] The first Euclidean distance vector, the second Euclidean distance vector and the diagonal vector are fused and weighted to obtain a human resource evaluation vector; and the human resource evaluation vector is used to represent human resource evaluation information.

[0097] The calculation expression of the fusion weighted processing is:

[0098]

[0099] Among them, v i is the i-th element value of the human resource evaluation vector, i=1,2,…,m,u i is the i-th element of the diagonal vector.

[0100] The construction obtains a job allocation optimization model, including:

[0101] The job demand information is represented as a matrix A; the element A in the i-th row and j-th column of the matrix A ij , represents the urgency value of the demand for the j-th capability type for the i-th position;

[0102] The human resource evaluation vectors of all personnel are represented as a matrix B; the i-th row vector of the matrix B represents the human resource evaluation vector of the i-th personnel;

[0103] Initialize the post allocation vector; the i-th element Ti of the post allocation vector T represents the number of the person to whom the i-th post is assigned;

[0104] Establish a job allocation optimization model, and its expression is:

[0105]

[0106] Where N is the number of positions, B Ti,j Represents the element in the Ti-th row and j-th column of matrix B, T0 is the preset allocation threshold, and the job allocation vector T is the vector to be solved.

[0107] The method of using the job allocation optimization model to solve the human resource description information and job allocation information to obtain job allocation result information includes:

[0108] The job allocation optimization model is converted into a matrix form, and its expression is:

[0109] Objective function: maxC(T) = AB,

[0110] Constraints: B≤T0,

[0111] The job allocation optimization model is solved using an iterative method to obtain job allocation result information.

[0112] Optionally, solving the job allocation optimization model by using an iterative method includes:

[0113] S401, initializing the optimal objective function value;

[0114] S402, solving and obtaining the relaxed solution of the job allocation optimization model. If the relaxed solution is an integer solution, it is considered that the relaxed solution is the optimal solution of the job allocation optimization model, thereby obtaining a job allocation vector as job allocation result information; if the relaxed solution is not an integer solution, a non-integer component x0 is selected from the relaxed solution for branching, and the solution space is divided into x i ≤x0 and x i >x0 two solution spaces.

[0115] S403, for each solution space obtained, find its relaxed solution:

[0116] S404, if there is no relaxation solution in the current solution space, continue to branch the current solution space to obtain a pruned solution, and return to step S402; if there is a relaxation solution in the current solution space, proceed to step S405;

[0117] S405, if the relaxed solution is an integer solution, branching the current solution space to obtain a pruned solution, substituting the pruned solution into the job allocation optimization model, and calculating the objective function value of the pruned job allocation optimization model;

[0118] If the objective function value of the pruned job allocation optimization model is better than the current optimal objective function value, the optimal objective function value and its integer solution are updated, and the process goes to step S401;

[0119] If the relaxed solution is a non-integer solution and its objective function value is less than or equal to the current optimal objective function value, the current solution space is pruned to obtain a pruned solution, and the process proceeds to step S401;

[0120] If the relaxed solution is a non-integer solution and its objective function value is greater than the current optimal objective function value, then select a non-integer component from the relaxed solution, branch the current solution space, and proceed to step S401; if the current solution space cannot be branched or has been traversed, proceed to step S406;

[0121] S406, taking the obtained integer solution as the optimal solution of the job allocation optimization model, thereby obtaining a job allocation vector as job allocation result information.

[0122] exist In , i ranges from 1 to N, j ranges from 1 to m, and In the above example, the value of i ranges from 1 to N.

[0123] The job allocation information set may be obtained by averaging historical data or by means of a questionnaire survey.

[0124] The decomposition process can be implemented by using a matrix singular value decomposition algorithm.

[0125] The autoregressive-moving average modeling can be implemented using the ARMA method;

[0126] The job requirement information includes the urgency value of the job requirement in each capability type; the human resource description information includes the sample values ​​of the evaluation values ​​of the personnel in each capability type;

[0127] The outlier elimination includes filling missing values, smoothing noise data, smoothing or deleting outlier points; the smoothed noise data is first determined to obtain noise data, and then the noise data is smoothed according to the data before and after the noise data; the noise data is a value whose value is less than the detection sensitivity of the sensor of the observed data, or greater than the measurement upper limit of the sensor of the observed data. The outlier point can be determined by the Kalman filter method. The filling value of the missing value can be determined by averaging the measured values ​​within a certain sampling interval before and after the missing value.

[0128] The capability types include electrical professional capability, electronic information professional capability, control professional capability, word processing capability, structural design capability, etc. The requirement information can be strong, medium, general, or weak; the evaluation value can be leading, excellent, proficient, general, understood, or not possessed;

[0129] The capability types include electrical professional capability, electronic information professional capability, control professional capability, word processing capability, structural design capability, work efficiency, stress resistance, work error rate, etc. The requirement information can be strong, medium, general, or weak; the evaluation value can be leading, excellent, proficient, general, understood, or not possessed;

[0130] The efficiency indicators include electrical professional ability, electronic information professional ability, control professional ability, and work efficiency.

[0131] The cost-based indicators include stress resistance, work error rate, word processing ability, and structural design ability.

[0132] The curve fitting of the curve to be approximated by the function approximation method may be performed by using the best consistent linear approximation method. The best consistent approximation polynomial f(Ix) is expressed as:

[0133] f(Ix)=α P1 (Ix) P1 +α P1-1 (Ix) P1-1 +…+α2(Ix) 2 +α1(Ix)+α0,

[0134] Where P1 is the order of the best consistent approximation polynomial f(Ix), α0, α1, α2, …, α P1 are the coefficients of the optimal consistent approximation polynomial f(Ix);

[0135] The data collection information includes time information, space information or capability type information corresponding to the data collection; when performing data fitting, any of the above information can be used as an independent variable, and the corresponding data value can be used as a dependent variable.

[0136] The time-frequency transform may adopt short-time Fourier transform, wavelet transform or Hilbert-Huang transform.

[0137] The i-th row vector in the matrix P is the time-frequency vector of the i-th row vector in the matrix W.

[0138] In a second aspect of the embodiment of the present application, a device for optimizing personnel position configuration based on a network model is disclosed, the device comprising:

[0139] A memory storing executable program code;

[0140] a processor coupled to the memory;

[0141] The processor calls the executable program code stored in the memory to execute the dynamic iterative configuration method of personnel positions.

[0142] In a third aspect of an embodiment of the present application, a computer-storable medium is disclosed, wherein the computer-storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the method for dynamic iterative configuration of personnel positions.

[0143] In a fourth aspect of the embodiments of the present application, an information data processing terminal is disclosed, and the information data processing terminal is used to implement the dynamic iterative configuration method of personnel positions.

[0144] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for dynamic iterative configuration of personnel positions, characterized in that: include: S1, obtaining a job allocation information set; the job allocation information set includes job requirement information, human resource description information and job allocation information; The job requirement information and human resource description information in the job allocation information set correspond to the job allocation information, and the job allocation information is used to indicate the personnel assigned to each job requirement; S2, preprocessing the position allocation information set to obtain a position information set; S3, evaluating and processing the human resource description information of any person to obtain the human resource evaluation information of the person; S4, build the job allocation optimization model; S5, using the job allocation optimization model to solve the human resource evaluation information and job allocation information to obtain job allocation result information; The preprocessing of the post allocation information set to obtain the post information set includes: S21, performing outlier elimination processing on the job allocation information set to obtain a first data set; S22, performing boundary check processing on the first data set to obtain a position information set; The human resource description information of any person is evaluated and processed, and the human resource evaluation information of the person includes: The human resource description information of each person is represented as a matrix W; the i-th row vector of the matrix W represents the sampling value of the i-th capability type of the person at all sampling times; Performing time-frequency transformation on each row vector of the matrix W to obtain a corresponding time-frequency vector; constructing a time-frequency matrix P using all the time-frequency vectors; Performing classification extreme value extraction processing on each column vector of the time-frequency matrix P to obtain corresponding first solution information and second solution information; The expression of the classification extreme value extraction process is: in, is the first solution information obtained using the i-th row vector of the time-frequency matrix P, p i ′ is the second solution information obtained using the i-th row vector of the time-frequency matrix P; i∈J * indicates that the i-th capability type belongs to the benefit-type indicator, i∈J′ indicates that the i-th capability type belongs to the cost-type indicator, and J * is a benefit indicator, J' is a cost indicator; the category number of the capability type is determined according to the row number of the matrix W; m and n are the row dimension and column dimension of the time-frequency matrix P respectively; The Euclidean distance calculation is performed on each column vector of the time-frequency matrix P and the corresponding first solution information and the second solution information, respectively, to obtain a first Euclidean distance vector and a second Euclidean distance vector; the expression of the Euclidean distance calculation is: in, is the i-th element of the first Euclidean distance vector, S′ i is the i-th element of the second Euclidean distance vector; Decomposing the time-frequency matrix P to obtain a feature matrix Y; The decomposition process is calculated as follows: P=UYV, Among them, U is the left decomposition matrix, Y is the characteristic matrix, V is the right decomposition matrix, and both U and V are orthogonal matrices; Extracting the diagonal elements of the characteristic matrix, and constructing a diagonal vector using all the extracted diagonal elements; Performing weighted fusion processing on the first Euclidean distance vector, the second Euclidean distance vector and the diagonal vector to obtain a human resource evaluation vector; using the human resource evaluation vector to represent human resource evaluation information; The construction obtains a job allocation optimization model, including: The job demand information is represented as a matrix A; the element A in the i-th row and j-th column of the matrix A ij , represents the urgency value of the demand for the j-th capability type for the i-th position; The human resource evaluation vectors of all personnel are represented as a matrix B; the i-th row vector of the matrix B represents the human resource evaluation vector of the i-th personnel; Initialize the post allocation vector; the i-th element Ti of the post allocation vector T represents the number of the person to whom the i-th post is assigned; Establish a job allocation optimization model, and its expression is: Ti≠0,i=1,2,…,N, Where N is the number of positions, B Ti,j Represents the element in the Ti-th row and j-th column of matrix B, T0 is the preset allocation threshold, and the job allocation vector T is the vector to be solved.

2. The method for dynamic iterative configuration of personnel positions according to claim 1, characterized in that: The performing boundary check processing on the first data set to obtain a position information set includes: S221, for each data attribute of the first data set, preset a corresponding value range; the value range includes an upper limit value of the value range and a lower limit value of the value range; S222, for each data of each data attribute of the first data set, judging whether the value of the data is within the value range according to the value range of the data attribute; if the data is within the value range, not processing the data; if the data is not within the value range, setting the value of the data to the upper limit value or the lower limit value of the value range that is closest to the data; S223, after completing S222 for all data attributes, a set of position information is obtained.

3. The method for dynamic iterative configuration of personnel positions according to claim 1, characterized in that: The performing boundary check processing on the first data set to obtain a position information set includes: S2201, for each type of data attribute of the first data set, using the data collection information of the data as an independent variable and the data value of the data as a dependent variable, perform autoregression-sliding average modeling to obtain regression models of the data attributes of the class; using the regression model, perform calculation processing on the independent variable to obtain regression data values; determine whether the absolute value of the difference between the regression data value and the corresponding dependent variable value is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, delete the data from the first data set; if it is less than the first regression discrimination threshold, do not process the data; S2202, after completing S2201 for all data attributes, a set of position information is obtained.

4. The method for dynamic iterative configuration of personnel positions according to claim 1, characterized in that: The calculation expression of the fusion weighted processing is: Among them, v i is the i-th element value of the human resource evaluation vector, i=1,2,…,m,u i is the i-th element of the diagonal vector.

5. A device for optimizing personnel positions based on a network model, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the dynamic iterative configuration method for personnel positions according to any one of claims 1 to 4.

6. A computer storable medium, characterized in that: The computer storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the dynamic iterative configuration method for personnel positions according to any one of claims 1 to 4.

7. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the dynamic iterative configuration method of personnel positions as described in any one of claims 1 to 4.

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