A task data identification method and device based on pattern mining

Abstract

The present invention discloses a method and device for identifying task data based on pattern mining, the method comprising: obtaining a set of historical information on job assignments; the set of historical information on job assignments includes job demand information, human resource description information, and job manpower matching information; preprocessing the set of historical information on job assignments to obtain a set of information to be identified; and identifying the set of information to be identified to obtain job assignment model information. The present invention suppresses the noise and interference of collected data by performing systematic and automated comparative analysis on historical data on task assignments, while achieving the goal of optimizing task deployment information and improving task execution efficiency and success rate.

Description

Technical Field

[0001] The present invention relates to the fields of big data and artificial intelligence, and in particular to a task data identification method and device based on pattern mining. Background Art

[0002] Due to the development of modern technology, jobs are becoming increasingly diversified. For jobs in technology-intensive industries, task 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. Task deployment tables cover 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 has become diverse and complex.

[0003] Currently, there is a lack of systematic comparative analysis methods for a large amount of historical information data on job deployment. Traditional methods mainly rely on manual analysis, which is time-consuming, labor-intensive and error-prone, and cannot fully utilize these deployment information for experience sharing and optimization. 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 suppress the noise and interference of collected data by conducting systematic and automated comparative analysis of historical task allocation data, while achieving the goal of optimizing task deployment information and improving task execution efficiency and success rate, is a problem that needs to be solved urgently. Summary of the invention

[0005] The present invention mainly solves the problem of how to suppress the noise and interference of collected data by performing systematic and automated comparative analysis on historical task allocation data, while achieving the goal of optimizing task deployment information and improving task execution efficiency and success rate. The present invention discloses a task data identification method and device based on pattern mining.

[0006] In a first aspect of an embodiment of the present application, a task data identification method based on pattern mining 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, human resource description information, and job-manpower matching information;

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

[0009] S3, performing identification processing on the information set to be identified to obtain job allocation model information.

[0010] The preprocessing of the post allocation history information set to obtain the information set to be identified includes:

[0011] S21, performing outlier elimination processing on the job allocation history information set to obtain a first data set;

[0012] S22, performing boundary check processing on the first data set to obtain a set of information to be identified.

[0013] The performing boundary check processing on the first data set to obtain the information set to be identified includes:

[0014] 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;

[0015] 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;

[0016] S223, after completing S222 for all data attributes, a set of information to be identified is obtained.

[0017] The performing boundary check processing on the first data set to obtain the information set to be identified includes:

[0018] 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;

[0019] S2202: After completing S2201 for all data of the first data set, a set of information to be identified is obtained.

[0020] The step of performing identification processing on the information set to be identified to obtain the position allocation model information includes:

[0021] S31, constructing the job allocation model information to be solved by using the information set to be identified;

[0022] S32, solving the position allocation model information to be solved to obtain model parameter values;

[0023] S33, merging the to-be-solved job allocation model information and the model parameter values ​​to obtain the job allocation model information.

[0024] The job allocation model information to be solved includes:

[0025]

[0026] subject to D×D T =E,

[0027] Among them, fs() represents the accumulation and summation of the absolute value square of each element of the matrix, the matrix D is the model parameter value of the job allocation model information, which is the quantity to be solved, and the dimension of the matrix D is P×P; E is the unit matrix; the job demand information is represented by the job demand information matrix A; the dimension of the matrix A is T×N, and the element A of the matrix A is ij , represents the demand value of the i-th position for the j-th capability type; the human resource description information is represented by the human resource description matrix B; the dimension of the matrix B is P×N, and the element B of the matrix B is ij , represents the ability evaluation value of the i-th person in the j-th ability type; the position-manpower matching information is represented by the manpower allocation matrix C; the dimension of the matrix C is P×T, and the element C of the matrix C ij , which means the ratio of the workload of the jth position assigned to the ith person to the total workload of the jth position in the job task allocation; N, P and T respectively represent the number of capability types, the number of personnel and the number of positions.

[0028] The step of combining the to-be-solved job allocation model information and the model parameter values ​​to obtain the job allocation model information includes:

[0029] Substitute the model parameter values ​​into the job allocation model information to be solved to obtain a job allocation model; the expression of the job allocation model is:

[0030] C′=DB′A′ T ,

[0031] Among them, matrix C′ represents the output of the job allocation model, which represents the calculated job allocation matrix; the row vector of the job allocation matrix represents the proportion of the workload of a person participating in each job to the total workload of the job; matrix B′ and matrix A′ represent the obtained human resource description matrix and job demand information matrix respectively, and B′ and A′ are the inputs of the job allocation model.

[0032] In a second aspect of the embodiment of the present application, a task data identification device based on pattern mining is disclosed, the device comprising:

[0033] A memory storing executable program code;

[0034] a processor coupled to the memory;

[0035] The processor calls the executable program code stored in the memory to execute the task data identification method based on pattern mining.

[0036] 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 task data identification method based on pattern mining.

[0037] In a fourth aspect of the embodiments of the present application, an information data processing terminal is disclosed, wherein the information data processing terminal is used to implement the task data identification method based on pattern mining.

[0038] The beneficial effects of the present invention are:

[0039] The present invention suppresses the noise and interference of the collected data by performing systematic and automated comparative analysis on the historical data of task allocation, and at the same time achieves the goal of optimizing task deployment information and improving the efficiency and success rate of task execution. The present invention discovers and corrects the deficiencies in the task deployment table information through systematic comparative analysis, and improves the efficiency of task execution. The present invention can assist task planning decision makers to formulate more scientific and reasonable task deployment plans, reduce the workload of manual analysis, and improve the efficiency and accuracy of task data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flow chart for implementing the method of the present invention. DETAILED DESCRIPTION

[0041] In order to better understand the content of the present invention, an embodiment is given here.

[0042] Figure 1 It is a flow chart for implementing the method of the present invention.

[0043] In order to solve the problem of how to suppress the noise and interference of collected data by performing systematic and automated comparative analysis on historical task allocation data, and at the same time achieve the goal of optimizing task deployment information and improving task execution efficiency and success rate, the present invention discloses a task data identification method and device based on pattern mining.

[0044] In a first aspect of an embodiment of the present application, a task data identification method based on pattern mining is disclosed, comprising:

[0045] S1, obtaining a set of historical information on job assignments; the set of historical information on job assignments includes job requirement information, human resource description information, and job-manpower matching information;

[0046] S2, preprocessing the job assignment history information set to obtain a to-be-identified information set;

[0047] S3, performing identification processing on the information set to be identified to obtain job allocation model information.

[0048] The preprocessing of the post allocation history information set to obtain the information set to be identified includes:

[0049] S221, performing outlier elimination processing on the job allocation history information set to obtain a first data set;

[0050] S222: Perform boundary check processing on the first data set to obtain a set of information to be identified.

[0051] The performing boundary check processing on the first data set to obtain the information set to be identified includes:

[0052] 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;

[0053] 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;

[0054] S2223, after completing S2222 for all data attributes, a set of information to be identified is obtained.

[0055] The performing boundary check processing on the first data set to obtain the information set to be identified includes:

[0056] 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;

[0057] S2222, after completing S2221 for all data of the first data set, obtaining a set of information to be identified;

[0058] The step of performing identification processing on the information set to be identified to obtain the position allocation model information includes:

[0059] S31, constructing the job allocation model information to be solved by using the information set to be identified;

[0060] S32, solving the position allocation model information to be solved to obtain model parameter values;

[0061] S33, merging the to-be-solved job allocation model information and the model parameter values ​​to obtain the job allocation model information.

[0062] The job allocation model information to be solved includes:

[0063]

[0064] Among them, fs() represents the accumulation and summation of the absolute value square of each element of the matrix, the matrix D is the model parameter value of the job allocation model information, which is the quantity to be solved, and the dimension of the matrix D is P×P; E is the unit matrix; the job demand information is represented by the job demand information matrix A; the dimension of the matrix A is T×N, and the element A of the matrix A is ij , represents the demand value of the i-th position for the j-th capability type; the human resource description information is represented by the human resource description matrix B; the dimension of the matrix B is P×N, and the element B of the matrix B is ij , represents the ability evaluation value of the i-th person in the j-th ability type; the position-manpower matching information is represented by the manpower allocation matrix C; the dimension of the matrix C is P×T, and the element C of the matrix C 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;

[0065] The step of combining the to-be-solved job allocation model information and the model parameter values ​​to obtain the job allocation model information includes:

[0066] Substitute the model parameter values ​​into the job allocation model information to be solved to obtain a job allocation model; the expression of the job allocation model is:

[0067] C′=DB′A′ T ,

[0068] Among them, matrix C′ represents the output of the job allocation model, which represents the calculated job allocation matrix; the row vector of the job allocation matrix represents the proportion of the workload of a person participating in each job to the total workload of the job; matrix B′ and matrix A′ represent the obtained human resource description matrix and job demand information matrix respectively, and B′ and A′ are the inputs of the job allocation model.

[0069] In order to improve the accuracy of the calculation results of the matrix D and overcome the influence of various data collection noises, the A, B and C matrices are collected according to several historical moments, and the job allocation model information to be solved is constructed respectively, and the job allocation model information to be solved is solved to obtain the model parameter value corresponding to each historical moment; all model parameter values ​​are smoothed to obtain the final model parameter value; the final model parameter value is brought into the job allocation model information to be solved to obtain the job allocation model;

[0070] All model parameter values ​​are smoothed to obtain the final model parameter values, including:

[0071]

[0072] Among them, D i pq The element value of the pth row and qth column represents the model parameter value at the i-th moment, The element value of the p-th row and q-th column represents the final model parameter value, and m is the number of historical moments.

[0073] The meanings of matrices B′ and A′ are the same as those of matrices B and A.

[0074] Specifically, fs(DBA T -C) represents the matrix DBA T -The absolute value square of each element of C is accumulated and summed.

[0075] The solving process for the job allocation model information to be solved may adopt an iterative solving method, a least square solving method or a digital optimization solving method.

[0076] The iterative solution method comprises:

[0077] S321, initializing the values ​​of the matrix D;

[0078] S322, calculation is satisfied The value of D is D1;

[0079] S323, calculate whether |D1-D| is less than the set threshold. If not, set the value of D to D1 and execute S322. If it is less, complete the solution and determine that the value of D1 is the final solution of matrix D.

[0080] 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;

[0081] 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.

[0082] 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;

[0083] 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:

[0084] f(Ix)=α P1 (Ix) P1 +α P1-1 (Ix) P1-1 +…+α2(Ix) 2 +α1(Ix)+α0,

[0085] Wherein, P1 is the order of the characteristic polynomial f(Ix), α0, α1, α2, …, α P1are the coefficients of the characteristic polynomial f(Ix);

[0086] 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.

[0087] 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 task data identification method based on pattern mining, 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, human resource description information, and job-manpower matching information; S2, preprocessing the job assignment history information set to obtain a to-be-identified information set; S3, performing identification processing on the information set to be identified to obtain job allocation model information; The step of performing identification processing on the information set to be identified to obtain the position allocation model information includes: S31, constructing the job allocation model information to be solved by using the information set to be identified; S32, solving the position allocation model information to be solved to obtain model parameter values; S33, combining the to-be-solved job allocation model information and the model parameter values ​​to obtain the job allocation model information; The job allocation model information to be solved includes: subject to D×D T =E, Among them, fs() represents the accumulation and summation of the absolute value square of each element of the matrix, the matrix D is the model parameter value of the job allocation model information, which is the quantity to be solved, and the dimension of the matrix D is P×P; E is the unit matrix; the job demand information is represented by the job demand information matrix A; the dimension of the matrix A is T×N, and the element A of the matrix A is ij , represents the demand value of the i-th position for the j-th capability type; the human resource description information is represented by the human resource description matrix B; the dimension of the matrix B is P×N, and the element B of the matrix B is ij , represents the ability evaluation value of the i-th person in the j-th ability type; the position-manpower matching information is represented by the manpower allocation matrix C; the dimension of the matrix C is P×T, and the element C of the matrix C 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; The step of combining the to-be-solved job allocation model information and the model parameter values ​​to obtain the job allocation model information includes: Substitute the model parameter values ​​into the job allocation model information to be solved to obtain a job allocation model; the expression of the job allocation model is: C′=DB′A′ T , Among them, matrix C′ represents the output of the job allocation model, which represents the calculated job allocation matrix; the row vector of the job allocation matrix represents the proportion of the workload of a person participating in each job to the total workload of the job; matrix B′ and matrix A′ represent the obtained human resource description matrix and job demand information matrix respectively, and B′ and A′ are the inputs of the job allocation model.

2. The task data identification method based on pattern mining according to claim 1, characterized in that: 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.

3. The task data identification method based on pattern mining according to claim 2, characterized in that: 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.

4. The task data identification method based on pattern mining according to claim 2, 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.

5. A task data identification device based on pattern mining, 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 task data identification method based on pattern mining 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 task data identification method based on pattern mining as described in 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 task data identification method based on pattern mining as described in any one of claims 1 to 4.

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