A classification and identification method and device for professional position deployment

Abstract

The present invention discloses a classification and identification method and device for professional job deployment, the method comprising: obtaining a job allocation history information set; the job allocation history information set includes job demand information and human resource description information; preprocessing the job allocation history information set to obtain a to-be-identified information set; performing classification and evaluation processing based on the to-be-identified information set to obtain an evaluation information set; and constructing job allocation model information based on the evaluation information set. The present invention analyzes and processes the task allocation history data, accurately evaluates and processes the job demand information and human energy evaluation information, and establishes an accurate and effective job allocation model based on the evaluation results.

Description

Technical Field

[0001] The present invention relates to the field of big data and industrial data processing, and in particular to a classification and identification method and device for professional job deployment. Background Art

[0002] Due to the development of modern science and technology, various types of jobs are becoming increasingly diversified. Especially for jobs in technology-intensive industries, task deployment plans are more diversified and load-oriented. Accurate job deployment plans are crucial to the successful completion of work tasks. In traditional methods, task deployment tables are constantly modified and improved based on experience for different tasks, different working conditions, and personnel allocation. The historical deployment table of task positions covers multiple aspects such as personnel allocation, task position requirements, and specific deployment content. With the continuous increase in the types of work, the task deployment information collected in different tasks and environments shows diversity and complexity.

[0003] At present, there is a lack of systematic analysis methods for a large amount of historical information data on job deployment, and there is a lack of effective evaluation of the personnel capability detection information and job demand information in the historical information data on job deployment. This makes it impossible to effectively use a large amount of historical information data on job deployment for job allocation processing. At the same time, there is a lot of noise and interference in the historical information data on job deployment, which affects the accuracy of the job allocation analysis results.

[0004] How to analyze and process historical task allocation data, accurately evaluate and process job demand information and human energy evaluation information, and establish an accurate and effective job allocation model based on the evaluation results is a problem that needs to be solved urgently. Summary of the invention

[0005] The present invention mainly solves the problem of how to accurately evaluate and process job requirement information and human energy evaluation information by analyzing and processing historical task allocation data, and establish an accurate and effective job allocation model based on the evaluation results. The present invention discloses a classification and identification method and device for professional job deployment.

[0006] In a first aspect of the embodiment of the present application, a classification and identification method for professional position deployment is disclosed, comprising:

[0007] S1, obtaining a set of historical information on job assignments; the set of historical information on job assignments includes job requirement information and human resource description information;

[0008] S2, preprocessing the job assignment history information set to obtain a to-be-identified information set;

[0009] S3, performing classification and evaluation processing based on the information set to be identified to obtain an evaluation information set;

[0010] S4: constructing job allocation model information based on the evaluation information set.

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

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

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

[0014] The performing boundary check processing on the first data set to obtain the information set to be identified 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 information to be identified is obtained.

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

[0019] S2201, for each type of data attribute of the first data set, using the data collection information of the data as a known independent variable and the data value of the data as a known dependent variable, constructing a curve to be approximated using the known independent variable and the known dependent variable, performing curve fitting on the curve to be approximated using a function approximation method to obtain a characteristic polynomial of the type of data attribute; using the characteristic polynomial, calculating and processing the known independent variable to obtain an approximate dependent variable; determining whether the absolute value of the difference between the approximate dependent variable and the corresponding known dependent variable is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, deleting the data from the first data set; if it is less than the first regression discrimination threshold, not processing the data;

[0020] S2202: After completing S2201 for all data of the first data set, a set of information to be identified is obtained.

[0021] The classification and evaluation process is performed based on the information set to be identified to obtain an evaluation information set, including:

[0022] S31, using the human resource evaluation sub-model, processing the human resource description information in the to-be-identified information set to obtain human resource evaluation result information;

[0023] S32, using the job requirement assessment sub-model, processing the job requirement information in the to-be-identified information set to obtain job requirement assessment result information;

[0024] S33, merging the human resource assessment result information and the job requirement assessment result information to obtain an assessment information set.

[0025] The human resource assessment sub-model includes:

[0026] The human resource description information of each person in the information set to be identified is represented as a matrix A; the i-th row vector of the matrix A represents the sampling value of the i-th capability type of the person at all sampling times;

[0027] Decomposing the matrix A to obtain a feature matrix Y;

[0028] The decomposition process is calculated as follows:

[0029] A=UYV,

[0030] 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;

[0031] Extract the diagonal elements of the feature matrix to obtain a feature vector; the feature vector is represented by r;

[0032] Performing time-frequency transformation on each row vector of the matrix A to obtain a corresponding time-frequency vector; constructing a time-frequency matrix S using all the time-frequency vectors;

[0033] The time-frequency matrix S and the eigenvector r are subjected to correlation calculation processing to obtain a correlation matrix V; the calculation expression of the correlation calculation processing is:

[0034]

[0035] Among them, v kj represents the element in the kth row and jth column of the incidence matrix. The dimension of the incidence matrix V is m×n, r k represents the kth element of the eigenvector r, s kj is the element in the kth row and jth column of the time-frequency matrix S, and ρ is the correlation calculation factor;

[0036] Perform logarithmic weighting processing on each row element of the correlation matrix V to obtain a human resource evaluation result vector; the human resource evaluation result vector is human resource evaluation result information;

[0037] The calculation expression of the logarithmic weighting process is:

[0038]

[0039] Among them, H k is the kth element value of the human resources evaluation result vector, k = 1, 2,…, m.

[0040] The job demand assessment sub-model includes:

[0041] The job requirement information of each job in the information set to be identified is represented as a matrix Q; the i-th row vector of the matrix Q represents the demand value of the i-th capability type of the job at all sampling times;

[0042] The matrix Q is subjected to deviation quantization calculation processing to obtain a deviation matrix H; the calculation expression of the deviation quantization calculation processing is:

[0043]

[0044] In the formula, h ki represents the element that deviates from the kth row and ith column of the matrix H, q ki represents the i-th element of the k-th row of the matrix q, and the dimension of the matrix H is m×n;

[0045] Using a preset weight vector, diagonally weighting the deviation matrix H to obtain a first discriminant matrix F;

[0046] The expression of the diagonal weighted processing is:

[0047] F=HB,

[0048] Where B is a matrix of [w1,w2,…,w n ] is the weighted matrix constructed on the diagonal, [w1,w2,…,w n ] is the preset weight vector, w j is the jth element in the weight vector, f ij Represents the element in the i-th row and j-th column of the first discriminant matrix F, i = 1, 2, ..., m, j = 1, 2, ..., n.

[0049] Performing classification extreme value extraction processing on each column vector of the first discriminant matrix F to obtain corresponding first solution information and second solution information;

[0050] The expression of the classification extreme value extraction process is:

[0051]

[0052]

[0053] in, is the first solution information obtained using the i-th row vector of the first discriminant matrix F, f i ′ is the second solution information obtained using the i-th row vector of the first discriminant matrix F; 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 Q;

[0054] The Euclidean distance calculation is performed on each column vector of the first discriminant matrix F and the corresponding first solution information and second solution information to obtain a first Euclidean distance vector and a second Euclidean distance vector respectively; the expression of the Euclidean distance calculation is:

[0055]

[0056]

[0057] 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;

[0058] Perform relative sequence calculation on the first Euclidean distance vector and the second Euclidean distance vector to obtain a job demand assessment result vector;

[0059] The expression for relative sequence calculation is:

[0060]

[0061] in, is the i-th element of the job requirement assessment result vector; the job requirement assessment result vector represents the job requirement assessment result information.

[0062] In a second aspect of an embodiment of the present invention, a classification and identification device for professional post deployment is disclosed, the device comprising:

[0063] A memory storing executable program code;

[0064] a processor coupled to the memory;

[0065] The processor calls the executable program code stored in the memory to execute the classification and identification method for professional position deployment.

[0066] According to a third aspect of an embodiment of the present invention, 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 classification and identification method for professional position deployment.

[0067] According to a fourth aspect of an embodiment of the present invention, an information data processing terminal is disclosed, and the information data processing terminal is used to implement the classification and identification method of professional position deployment.

[0068] The beneficial effects of the present invention are:

[0069] The present invention analyzes and processes historical task allocation data, thereby achieving accurate evaluation and processing of job demand information and human energy evaluation information, and establishes an accurate and effective job allocation model based on the evaluation results.

[0070] The present invention discovers and corrects deficiencies in historical deployment data through systematic comparative analysis of historical task allocation data, thereby improving task execution efficiency.

[0071] The present invention can accurately evaluate based on task requirements and human resource collection data, obtain effective evaluation data, and provide detailed job allocation plans based on the evaluation data, assisting decision makers in formulating more scientific and reasonable deployment plans, reducing the workload of manual analysis, and improving the efficiency and accuracy of job and human resource data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a flow chart for implementing the method of the present invention. DETAILED DESCRIPTION

[0073] In order to better understand the content of the present invention, an embodiment is given here.

[0074] Figure 1 It is a flow chart for implementing the method of the present invention.

[0075] In order to solve the problem of how to analyze and process historical task allocation data, accurately evaluate and process job requirement information and human energy evaluation information, and establish an accurate and effective job allocation model based on the evaluation results, the present invention discloses a classification and identification method and device for professional job deployment.

[0076] In a first aspect of the embodiment of the present application, a classification and identification method for professional position deployment is disclosed, comprising:

[0077] S1, obtaining a set of historical information on job assignments; the set of historical information on job assignments includes job requirement information and human resource description information;

[0078] S2, preprocessing the job assignment history information set to obtain a to-be-identified information set;

[0079] S3, performing classification and evaluation processing based on the information set to be identified to obtain an evaluation information set;

[0080] S4: constructing job allocation model information based on the evaluation information set.

[0081] The preprocessing of the post allocation history information set to obtain the information set to be identified includes:

[0082] S221, performing outlier elimination processing on the job allocation history information set to obtain a first data set;

[0083] S222: Perform boundary check processing on the first data set to obtain a set of information to be identified.

[0084] The performing boundary check processing on the first data set to obtain the information set to be identified includes:

[0085] S2221, 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;

[0086] S2222, 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;

[0087] S2223, after completing S2222 for all data attributes, a set of information to be identified is obtained.

[0088] The performing boundary check processing on the first data set to obtain the information set to be identified includes:

[0089] S2221, for each type of data attribute of the first data set, the data collection information of the data is used as a known independent variable, the data value of the data is used as a known dependent variable, the known independent variable and the known dependent variable are used to construct a curve to be approximated, and the function approximation method is used to perform curve fitting on the curve to be approximated to obtain a characteristic polynomial f(Ix) of the type of data attribute; the characteristic polynomial f(Ix) is used to calculate and process the known independent variables to obtain an approximate dependent variable; it is determined whether the absolute value of the difference between the approximate dependent variable and the corresponding known dependent variable is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, the data is deleted from the first data set; if it is less than the first regression discrimination threshold, the data is not processed;

[0090] S2222, after completing S2221 for all data of the first data set, obtaining a set of information to be identified;

[0091] The classification and evaluation process is performed based on the information set to be identified to obtain an evaluation information set, including:

[0092] S31, using the human resource evaluation sub-model, processing the human resource description information in the to-be-identified information set to obtain human resource evaluation result information;

[0093] S32, using the job requirement assessment sub-model, processing the job requirement information in the to-be-identified information set to obtain job requirement assessment result information;

[0094] S33, merging the human resource assessment result information and the job requirement assessment result information to obtain an assessment information set.

[0095] The human resource assessment sub-model includes:

[0096] The human resource description information of each person in the information set to be identified is represented as a matrix A; the i-th row vector of the matrix A represents the sampling value of the i-th capability type of the person at all sampling times;

[0097] Decomposing the matrix A to obtain a feature matrix Y;

[0098] The decomposition process is calculated as follows:

[0099] A=UYV,

[0100] 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;

[0101] Extract the diagonal elements of the feature matrix to obtain a feature vector; the feature vector is represented by r;

[0102] Performing time-frequency transformation on each row vector of the matrix A to obtain a corresponding time-frequency vector; constructing a time-frequency matrix S using all the time-frequency vectors;

[0103] The i-th row vector in the matrix S is the time-frequency vector of the i-th row vector of the matrix A.

[0104] The time-frequency matrix S and the eigenvector r are subjected to correlation calculation processing to obtain a correlation matrix V; the calculation expression of the correlation calculation processing is:

[0105]

[0106] Among them, v kj represents the element in the kth row and jth column of the incidence matrix. The dimension of the incidence matrix V is m×n, r k represents the kth element of the eigenvector r, s kj is the element in the kth row and jth column in the time-frequency matrix S, ρ is the correlation calculation factor, and its value can be 0.5.

[0107] Perform logarithmic weighting processing on each row element of the correlation matrix V to obtain a human resource evaluation result vector; the human resource evaluation result vector is human resource evaluation result information;

[0108] The calculation expression of the logarithmic weighting process is:

[0109]

[0110] Among them, H k is the kth element value of the human resources evaluation result vector, k = 1, 2,…, m.

[0111] The job demand assessment sub-model includes:

[0112] The job requirement information of each job in the information set to be identified is represented as a matrix Q; the i-th row vector of the matrix Q represents the demand value of the i-th capability type of the job at all sampling times;

[0113] The matrix Q is subjected to deviation quantization calculation processing to obtain a deviation matrix H; the calculation expression of the deviation quantization calculation processing is:

[0114]

[0115] In the formula, h ki represents the element that deviates from the kth row and ith column of the matrix H, q ki represents the i-th element of the k-th row of the matrix q, and the dimension of the matrix H is m×n;

[0116] Using a preset weight vector, diagonally weighting the deviation matrix H to obtain a first discriminant matrix F;

[0117] The expression of the diagonal weighted processing is:

[0118] F=HB,

[0119] Where B is a matrix of [w1,w2,…,w n ] is the weighted matrix constructed on the diagonal, [w1,w2,…,w n ] is the preset weight vector, w j is the jth element in the weight vector, f ij Represents the element in the i-th row and j-th column of the first discriminant matrix F, i=1, 2…m, j=1, 2…n.

[0120] Performing classification extreme value extraction processing on each column vector of the first discriminant matrix F to obtain corresponding first solution information and second solution information;

[0121] The expression of the classification extreme value extraction process is:

[0122]

[0123]

[0124] Among them, f i * is the first solution information obtained using the i-th row vector of the first discriminant matrix F, f i ′ is the second solution information obtained using the i-th row vector of the first discriminant matrix F; 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 Q;

[0125] The Euclidean distance calculation is performed on each column vector of the first discriminant matrix F and the corresponding first solution information and second solution information to obtain a first Euclidean distance vector and a second Euclidean distance vector respectively; the expression of the Euclidean distance calculation is:

[0126]

[0127]

[0128] 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;

[0129] Perform relative sequence calculation on the first Euclidean distance vector and the second Euclidean distance vector to obtain a job demand assessment result vector;

[0130] The expression for relative sequence calculation is:

[0131]

[0132] in, is the i-th element of the job demand assessment result vector; the job demand assessment result vector represents job demand assessment result information;

[0133] The step of constructing the job allocation model information based on the evaluation information set includes:

[0134] Based on the evaluation information set, a job allocation sub-model is constructed;

[0135] The post allocation sub-model includes:

[0136] S331, initializing an allocation result vector; each element of the allocation result vector represents the number of the person allocated to each position;

[0137] S332, for each job demand assessment result vector in the assessment information set, sequentially calculate the Euclidean distance between the result vector and all human resource assessment result vectors in the assessment information set; determine the person corresponding to the human resource assessment result vector corresponding to the minimum value among all Euclidean distances as the person assigned to the job corresponding to the job demand assessment result vector, use the serial number of the assigned person as the element corresponding to the job corresponding to the job demand assessment result vector in the assignment result vector, and the serial number of the element in the assignment result vector is the serial number of the job corresponding to the job demand assessment result vector;

[0138] S333, executing S332 on each job requirement evaluation result vector in the evaluation information set to obtain an allocation result vector.

[0139] The post allocation sub-model, the post demand assessment sub-model and the human resource assessment sub-model are combined to obtain post allocation model information;

[0140] In the job allocation model information, the output ends of the job demand assessment sub-model and the human resource assessment sub-model are respectively connected to the input end of the job allocation sub-model;

[0141] The position allocation model information is used to process the position requirement information and the human resource description information to obtain the position allocation result vector. The position requirement information and the human resource description information are the outputs of the position requirement evaluation sub-model and the human resource evaluation sub-model respectively.

[0142] For example, the allocation result vector is [3,5,2], which means that the first position is completed by the person with serial number 3, and the second task is completed by the person with serial number 5. The position number is the number of the human resource evaluation result information in the evaluation information set, which starts from 1; the person serial number is the number of the human resource evaluation result information in the evaluation information set, which starts from 1.

[0143] The job manpower matching information is represented by a manpower allocation matrix C; the dimension of the matrix C is P×T, and the element C of the matrix C is ij , represents the ratio of the workload of the jth position assigned to the i-th person to the total workload of the j-th position in the job task allocation; N, P and T represent the number of capability types, the number of personnel and the number of positions respectively;

[0144] The time-frequency transform may adopt short-time Fourier transform, wavelet transform or Hilbert-Huang transform.

[0145] The decomposition process can be implemented by using a matrix singular value decomposition algorithm.

[0146] The job allocation model information is the classification and identification result of industry job deployment, and is used to realize the allocation of human resources under various jobs according to job requirement information and human resource description information. Its input variables are job requirement information and human resource description information, and its output variable is job allocation information, that is, the proportion of the workload allocated to each person in each job; the proportion value is the proportion of the workload undertaken by the person in the job to the total workload of the job.

[0147] The job requirement information includes the job requirement information in terms of various capability types; the human resource description information includes the evaluation values ​​of the personnel in terms of various capability types;

[0148] 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 filtering 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.

[0149] 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;

[0150] The efficiency indicators include electrical professional ability, electronic information professional ability, control professional ability, and work efficiency.

[0151] The cost-based indicators include stress resistance, work error rate, word processing ability, and structural design ability.

[0152] 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 characteristic polynomial f(Ix) is expressed as:

[0153] f(Ix)=α P1 (Ix) P1 +α P1-1 (Ix) P1-1 +…+α2(Ix) 2 +α1(Ix)+α0,

[0154] Wherein, P1 is the order of the characteristic polynomial f(Ix), α0, α1, α2, …, α P1 are the coefficients of the characteristic polynomial f(Ix);

[0155] 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.

[0156] 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 classification and identification method for professional post deployment, characterized in that: include: S1, obtaining a set of historical information on job assignments; the set of historical information on job assignments includes job requirement information and human resource description information; S2, preprocessing the job assignment history information set to obtain a to-be-identified information set; S3, performing classification and evaluation processing based on the information set to be identified to obtain an evaluation information set; S4, constructing job allocation model information based on the evaluation information set; The preprocessing of the post allocation history information set to obtain the information set to be identified includes: S21, performing outlier elimination processing on the job allocation history information set to obtain a first data set; S22, performing boundary check processing on the first data set to obtain a set of information to be identified; The performing boundary check processing on the first data set to obtain the information set to be identified 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 information to be identified is obtained; The classification and evaluation process is performed based on the information set to be identified to obtain an evaluation information set, including: S31, using the human resource evaluation sub-model, processing the human resource description information in the to-be-identified information set to obtain human resource evaluation result information; S32, using the job requirement assessment sub-model, processing the job requirement information in the to-be-identified information set to obtain job requirement assessment result information; S33, combining the human resource assessment result information and the job requirement assessment result information to obtain an assessment information set; The human resource assessment sub-model includes: The human resource description information of each person in the information set to be identified is represented as a matrix A; the i-th row vector of the matrix A represents the sampling value of the i-th capability type of the person at all sampling times; Decomposing the matrix A to obtain a feature matrix Y; The decomposition process is calculated as follows: A=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; Extract the diagonal elements of the feature matrix to obtain a feature vector; the feature vector is represented by r; Performing time-frequency transformation on each row vector of the matrix A to obtain a corresponding time-frequency vector; constructing a time-frequency matrix S using all the time-frequency vectors; The time-frequency matrix S and the eigenvector r are subjected to correlation calculation processing to obtain a correlation matrix V; the calculation expression of the correlation calculation processing is: Among them, v kj represents the element in the kth row and jth column of the incidence matrix. The dimension of the incidence matrix V is m×n, r k represents the kth element of the eigenvector r, s kj is the element in the kth row and jth column of the time-frequency matrix S, and ρ is the correlation calculation factor; Perform logarithmic weighting processing on each row element of the correlation matrix V to obtain a human resource evaluation result vector; the human resource evaluation result vector is human resource evaluation result information; The calculation expression of the logarithmic weighting process is: Among them, H k is the kth element value of the human resources evaluation result vector, k = 1, 2, ..., m; The job demand assessment sub-model includes: The job requirement information of each job in the information set to be identified is represented as a matrix Q; the i-th row vector of the matrix Q represents the demand value of the i-th capability type of the job at all sampling times; The matrix Q is subjected to deviation quantization calculation processing to obtain a deviation matrix H; the calculation expression of the deviation quantization calculation processing is: In the formula, h ki represents the element that deviates from the kth row and ith column of the matrix H, q ki represents the i-th element of the k-th row of the matrix q, and the dimension of the matrix H is m×n; Using a preset weight vector, diagonally weighting the deviation matrix H to obtain a first discriminant matrix F; The expression of the diagonal weighted processing is: F=HB, Where B is a matrix of [w1,w2,…,w n ] is the weighted matrix constructed on the diagonal, [w1,w2,…,w n ] is the preset weight vector, w j is the jth element in the weight vector, f ij represents the element in the i-th row and j-th column of the first discriminant matrix F, i = 1, 2, ..., m, j = 1, 2, ..., n; Performing classification extreme value extraction processing on each column vector of the first discriminant matrix F to obtain corresponding first solution information and second solution information; The expression of the classification extreme value extraction process is: Among them, f i * is the first solution information obtained using the i-th row vector of the first discriminant matrix F, f i ′ is the second solution information obtained using the i-th row vector of the first discriminant matrix F; 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 Q; The Euclidean distance calculation is performed on each column vector of the first discriminant matrix F and the corresponding first solution information and second solution information to obtain a first Euclidean distance vector and a second Euclidean distance vector respectively; 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; Perform relative sequence calculation on the first Euclidean distance vector and the second Euclidean distance vector to obtain a job demand assessment result vector; The expression for relative sequence calculation is: in, is the i-th element of the job requirement assessment result vector; the job requirement assessment result vector represents the job requirement assessment result information.

2. The classification and identification method for professional post deployment according to claim 1, characterized in that: The performing boundary check processing on the first data set to obtain the information set to be identified includes: S2201, for each type of data attribute of the first data set, using the data collection information of the data as a known independent variable and the data value of the data as a known dependent variable, constructing a curve to be approximated using the known independent variable and the known dependent variable, performing curve fitting on the curve to be approximated using a function approximation method to obtain a characteristic polynomial of the type of data attribute; using the characteristic polynomial, calculating and processing the known independent variable to obtain an approximate dependent variable; determining whether the absolute value of the difference between the approximate dependent variable and the corresponding known dependent variable is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, deleting the data from the first data set; if it is less than the first regression discrimination threshold, not processing the data; S2202: After completing S2201 for all data of the first data set, a set of information to be identified is obtained.

3. A classification and identification device for professional post deployment, 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 classification and identification method for professional position deployment as described in any one of claims 1 to 2.

4. 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 classification and identification method for professional position deployment as described in any one of claims 1 to 2.

5. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the classification and identification method for professional position deployment as described in any one of claims 1 to 2.

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