A quantitative evaluation method and device for task allocation

Abstract

The present invention discloses a quantitative evaluation method and device for task allocation, the method comprising: obtaining task requirement information and task allocation information; the task requirement information comprises a task requirement matrix; the task allocation information comprises a task completion performance matrix and a task realization order sequence of the task allocation; the row vector of the task completion performance matrix is ​​the evaluation value of each performance index after a task is completed; the task realization order sequence is used to represent the order of realization of each task to be allocated; pre-processing the task requirement information and the task allocation information to obtain a set of information to be evaluated; performing quantitative evaluation processing on the set of information to be evaluated to obtain a quantitative evaluation result of the task allocation; the quantitative evaluation result of the task allocation is used to represent the matching degree between the incumbent and the position after the task allocation.

Description

Technical Field

[0001] The invention relates to the field of task optimization and human resources, and in particular to a quantitative evaluation method and device for task allocation. Background Art

[0002] With the development of science and technology, labor positions are becoming more and more diversified, and at the same time, the relevant human resource data of the incumbent's job skills, work experience, ability and quality are also becoming more and more diversified. Each job position has different requirements for the incumbent's quality, ability, experience, etc. Only when the incumbent has more qualities than these requirements can he be better qualified for this job, thus achieving a win-win situation for the unit and the incumbent. For the matching of existing incumbents and positions, it is necessary to match them according to the similarity between the position where the incumbent has worked and the position to be matched. There is a lot of subjectivity, and it cannot achieve the best match between the incumbent and the position, thereby reducing the unit's operating capacity. Therefore, how to quickly quantify and accurately evaluate the allocation of task positions is of great significance to achieve the optimal job matching. Summary of the invention

[0003] The present invention mainly solves the problem of how to quickly and accurately evaluate the assignment of tasks and positions and achieve optimal position matching. The present invention discloses a quantitative evaluation method and device for task assignment.

[0004] In a first aspect of an embodiment of the present application, a quantitative evaluation method for task allocation is disclosed, comprising:

[0005] S1, obtaining task requirement information and task allocation information; the task requirement information includes a task requirement matrix; the task allocation information includes a task completion performance matrix and a task implementation order sequence of the task allocation; the row vector of the task completion performance matrix is ​​the evaluation value of each performance indicator after a task is completed; the task implementation order sequence is used to indicate the order of implementation of each task to be allocated;

[0006] S2, preprocessing the task requirement information and task assignment information to obtain a set of information to be evaluated;

[0007] S3, performing quantitative evaluation processing on the information set to be evaluated to obtain a quantitative evaluation result of the task allocation; the quantitative evaluation result of the task allocation is used to indicate the matching degree between the incumbent and the position after the task allocation.

[0008] The preprocessing of the task requirement information and the task assignment information to obtain the information set to be evaluated includes:

[0009] S21, performing outlier elimination and normalization processing on the task requirement information and the task assignment information, respectively, to obtain a normalized information set; the normalized information set includes the task requirement information and the task assignment information after outlier elimination and normalization processing;

[0010] S22, performing category discrimination processing on the normalized information set to obtain a first information set;

[0011] S23: Perform boundary check processing on the first information set to obtain an information set to be evaluated.

[0012] The performing boundary check processing on the first information set to obtain the information set to be evaluated includes:

[0013] S231, for each data attribute of the first information 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;

[0014] S232, for each data of each data attribute of the first information 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;

[0015] S233, after completing S232 for the data of all data attributes, a set of information to be evaluated is obtained.

[0016] The step of performing quantitative evaluation processing on the information set to be evaluated to obtain a quantitative evaluation result of the task assignment includes:

[0017] S31, performing an implementation order evaluation process on the task implementation order sequence and task requirement information in the task allocation information in the information set to be evaluated, and obtaining a first evaluation result; the task requirement information includes the priority, task type value and business volume requirement value of each task;

[0018] S32, performing performance evaluation processing on the task completion performance matrix in the task allocation information in the information set to be evaluated to obtain a second evaluation result;

[0019] S33, performing weighted sum processing on the first evaluation result and the second evaluation result to obtain a quantitative evaluation result of the task allocation.

[0020] The step of performing an implementation sequence evaluation process on the task implementation sequence sequence and the task requirement information in the task allocation information in the information set to be evaluated to obtain a first evaluation result includes:

[0021] S311, obtaining the priority, task type value and business volume demand value information of N tasks in the task demand information; for the task demand information of the i-th task, it is expressed as the priority P i 、Task type value T i and business volume demand value B i ;

[0022] S312, normalize the priority value of each task; i , and its normalized priority is denoted as ρ i =P i / (P max -P min ), where P max and P min They represent the upper and lower limits of the priority value range respectively;

[0023] S313, classify the task types of all tasks into N t Class, each task type has a corresponding task type coefficient, the nth t The task type coefficient of each task type is recorded as Among them, n t =1,2,...,N t , According to the task type value T of the i-th task i , from N t Select the matching task type coefficient τ from the task types i ;

[0024] S314, divide the task's business volume demand value into N from high to low b Each level has a corresponding business demand value coefficient. b The business volume demand value coefficient of each level is recorded as where n b =1,2,...,N b , According to the business volume demand value B of the i-th task i , determine the grade corresponding to the business demand value, which is the business demand value coefficient ω i ;

[0025] S315, for each task, calculate its urgency value; the calculation expression of the urgency value is:

[0026] η i =w1ρ i +w2τ i +w3ω i ,

[0027] Among them, w1, w2, and w3 are the weights of priority, task type, and business volume demand value, respectively, and η i is the urgency value of the i-th task;

[0028] S316, sorting the serial numbers of all tasks from low to high according to the urgency values ​​to obtain an urgency sorting vector;

[0029] S317, performing relevant calculation processing on the urgency ranking vector and the task implementation order sequence to obtain a first evaluation result.

[0030] The performing performance evaluation processing on the task completion performance matrix in the task allocation information in the information set to be evaluated to obtain a second evaluation result includes:

[0031] S321, perform time-frequency transformation on each row vector in the task completion performance matrix to obtain a corresponding spectrum sequence; use all spectrum sequences to construct a spectrum sequence set; the spectrum sequence set includes several spectrum sequences; the spectrum sequence set is expressed as [E1, E2, …, E N ], where E i represents the i-th spectrum sequence;

[0032] S322, performing a first fusion process on the spectrum sequence set to obtain a first fusion signal;

[0033] S323, performing inverse time-frequency transform processing on the first fusion signal to obtain an evaluation sequence;

[0034] S324, performing averaging processing on the evaluation sequence to obtain a second evaluation result.

[0035] The performing a first fusion process on the spectrum sequence set to obtain a first fusion signal includes:

[0036] S3221, performing correlation calculation on the spectrum sequences in the spectrum sequence set to obtain a mutual correlation matrix R of the spectrum sequence set; i,j represents the i-th row and j-th element of the cross-correlation matrix R, r i,j The calculation expression is:

[0037] r i,j =E(e i e j ),

[0038] Among them, E() means finding the mean;

[0039] S3222, for each spectrum sequence, respectively perform normalization calculation on its mutual correlation value to obtain a normalized correlation value sequence of each spectrum sequence; the normalization calculation expression of the mutual correlation value is:

[0040]

[0041] in, represents the jth value of the normalized correlation value sequence of the i-th spectrum sequence;

[0042] S3223, performing logarithmic summation processing on the normalized correlation value sequence of each spectrum sequence to obtain a spectrum feature sequence;

[0043] The calculation expression of the logarithmic summation process is:

[0044]

[0045] Among them, H i is the i-th value of the spectral feature sequence;

[0046] S3224, performing feature extraction processing on the task completion performance matrix to obtain a feature vector;

[0047] S3225, using the feature vector, perform weighted summation on the spectral feature sequence to obtain a first fusion signal.

[0048] The feature extraction process is performed on the task completion performance matrix to obtain a feature vector, including:

[0049] S341, using all row vectors in the task completion performance matrix, calculate a correlation coefficient matrix; the element in the i-th row and j-th column in the correlation coefficient matrix is ​​the cross-correlation value between the i-th row vector and the j-th row vector in the task completion performance matrix;

[0050] S342, performing eigenvalue calculation on the correlation coefficient matrix to obtain an eigenvalue vector;

[0051] S343, performing cumulative normalization processing on the eigenvalue vector to obtain a cumulative normalized vector;

[0052] S344, for each row vector in the correlation coefficient matrix, calculate its range value; and construct a characteristic vector using the range values ​​of all row vectors of the correlation coefficient matrix.

[0053] According to a second aspect of an embodiment of the present invention, a quantitative evaluation device for task allocation is disclosed, the device comprising:

[0054] A memory storing executable program code;

[0055] a processor coupled to the memory;

[0056] The processor calls the executable program code stored in the memory to execute the quantitative evaluation method of task allocation.

[0057] 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 quantitative evaluation method of task allocation.

[0058] The beneficial effects of the present invention are:

[0059] The present invention solves the problem of how to quickly quantify and accurately evaluate the assignment of task positions and achieve optimal position matching. The present invention effectively suppresses the influence of noise by performing noise reduction processing on various types of collected task data.

[0060] In the quantitative evaluation process, the present invention obtains the task urgency value by calculation, and performs evaluation calculation with the task realization order sequence, thereby effectively realizing accurate evaluation of the efficiency of task allocation. The present invention effectively suppresses the influence of various non-related factors by performing spectral analysis and calculation processing on the task completion index matrix, thereby ensuring the accuracy of the evaluation result. The present invention can provide an effective reference for task allocation. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0062] In order to better understand the content of the present invention, an embodiment is given here.

[0063] Figure 1 It is a flow chart for implementing the method of the present invention.

[0064] In a first aspect of an embodiment of the present application, a quantitative evaluation method for task allocation is disclosed, comprising:

[0065] S1, obtaining task requirement information and task allocation information; the task requirement information includes a task requirement matrix; the task allocation information includes a task completion performance matrix and a task implementation order sequence of the task allocation; the row vector of the task completion performance matrix is ​​the evaluation value of each performance indicator after a task is completed; the task implementation order sequence is used to indicate the order of implementation of each task to be allocated;

[0066] S2, preprocessing the task requirement information and task assignment information to obtain a set of information to be evaluated;

[0067] S3, performing quantitative evaluation processing on the information set to be evaluated to obtain a quantitative evaluation result of the task allocation; the quantitative evaluation result of the task allocation is used to indicate the matching degree between the incumbent and the position after the task allocation.

[0068] The preprocessing of the task requirement information and the task assignment information to obtain the information set to be evaluated includes:

[0069] S21, performing outlier elimination and normalization processing on the task requirement information and the task assignment information, respectively, to obtain a normalized information set; the normalized information set includes the task requirement information and the task assignment information after outlier elimination and normalization processing;

[0070] S22, performing category discrimination processing on the normalized information set to obtain a first information set;

[0071] S23: Perform boundary check processing on the first information set to obtain an information set to be evaluated.

[0072] The performing boundary check processing on the first information set to obtain the information set to be evaluated includes:

[0073] S231, for each data attribute of the first information 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] S232, for each data of each data attribute of the first information 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] S233, after completing S232 for the data of all data attributes, a set of information to be evaluated is obtained.

[0076] The step of performing quantitative evaluation processing on the information set to be evaluated to obtain a quantitative evaluation result of the task assignment includes:

[0077] S31, performing an implementation order evaluation process on the task implementation order sequence and task requirement information in the task allocation information in the information set to be evaluated, and obtaining a first evaluation result; the task requirement information includes the priority, task type value and business volume requirement value of each task;

[0078] S32, performing performance evaluation processing on the task completion performance matrix in the task allocation information in the information set to be evaluated to obtain a second evaluation result;

[0079] S33, performing weighted sum processing on the first evaluation result and the second evaluation result to obtain a quantitative evaluation result of the task allocation.

[0080] The step of performing an implementation sequence evaluation process on the task implementation sequence sequence and the task requirement information in the task allocation information in the information set to be evaluated to obtain a first evaluation result includes:

[0081] S311, obtaining the priority, task type value and business volume demand value information of N tasks in the task demand information; for the task demand information of the i-th task, it is expressed as the priority P i 、Task type value T i and business volume demand value B i ;

[0082] S312, normalize the priority value of each task. i , and its normalized priority is denoted as ρ i =P i / (P max -P min ), where P max and P min Respectively represent the upper and lower limits of the priority value range;

[0083] S313, classify the task types of all tasks into N t Class, each task type has a corresponding task type coefficient, the nth t The task type coefficient of each task type is recorded as Among them, n t =1,2,...,N t , According to the task type value T of the i-th task i , from N t The coefficient of the task type that matches the task type is selected from the task types, denoted as τ i ;

[0084] S314, divide the task's business volume demand value into N from high to low b Each level has a corresponding business demand value coefficient. b The business volume demand value coefficient of each level is recorded as where n b =1,2,...,N b , According to the business volume demand value B of the i-th task i , determine the grade corresponding to the business demand value, and then obtain its business demand value coefficient, recorded as ω i ;

[0085] S315, for each task, calculate its urgency value; the calculation expression of the urgency value is:

[0086] η i=w1ρ i +w2τ i +w3ω i ,

[0087] Among them, w1, w2, and w3 are the weights of priority, task type, and business volume demand value, respectively, and η i is the urgency value of the i-th task;

[0088] S316, sorting the serial numbers of all tasks from low to high according to the urgency, and obtaining an urgency sorting vector;

[0089] S317, performing relevant calculation processing on the urgency ranking vector and the task realization order sequence to obtain a first evaluation result;

[0090] The performing performance evaluation processing on the task completion performance matrix in the task allocation information in the information set to be evaluated to obtain a second evaluation result includes:

[0091] For each row vector in the task completion performance matrix, time-frequency transformation is performed to obtain the corresponding spectrum sequence; using all spectrum sequences, a spectrum sequence set is constructed; the spectrum sequence set includes several spectrum sequences; the spectrum sequence set is expressed as [E1, E2, …, E N ], where E i represents the i-th spectrum sequence;

[0092] Performing a first fusion process on the spectrum sequence set to obtain a first fusion signal;

[0093] Performing inverse time-frequency transform processing on the first fusion signal to obtain an evaluation sequence;

[0094] The mean of the evaluation sequence is calculated and determined as the second evaluation result.

[0095] The performing a first fusion process on the spectrum sequence set to obtain a first fusion signal includes:

[0096] S31, performing correlation calculation on the spectrum sequences in the spectrum sequence set to obtain a mutual correlation matrix R of the spectrum sequence set; i,j represents the i-th row and j-th element of the cross-correlation matrix R, r i,j The calculation expression is:

[0097] r i,j =E(e i e j ),

[0098] Among them, E() means finding the mean;

[0099] S32, for each spectrum sequence, respectively perform normalization calculation on its mutual correlation value to obtain a normalized correlation value sequence of each spectrum sequence; the normalization calculation expression of the mutual correlation value is:

[0100]

[0101] in, represents the jth value of the normalized correlation value sequence of the i-th spectrum sequence;

[0102] S33, performing logarithmic summation processing on the normalized correlation value sequence of each spectrum sequence to obtain a spectrum feature sequence;

[0103] The calculation expression of the logarithmic summation process is:

[0104]

[0105] Among them, H i is the i-th value of the spectral feature sequence;

[0106] S34, performing feature extraction processing on the task completion performance matrix to obtain a feature vector;

[0107] S35, using the feature vector, performing weighted summation on the spectral feature sequence to obtain a first fusion signal.

[0108] The feature extraction process is performed on the task completion performance matrix to obtain a feature vector, including:

[0109] S341, using all row vectors in the task completion performance matrix, calculate a correlation coefficient matrix; the element in the i-th row and j-th column in the correlation coefficient matrix is ​​the cross-correlation value between the i-th row vector and the j-th row vector in the task completion performance matrix;

[0110] S342, performing eigenvalue calculation on the correlation coefficient matrix to obtain an eigenvalue vector;

[0111] S343, performing cumulative normalization processing on the eigenvalue vector to obtain a cumulative normalized vector;

[0112] S344, for each row vector in the correlation coefficient matrix, calculate its range value; and construct a characteristic vector using the range values ​​of all row vectors of the correlation coefficient matrix.

[0113] The task requirement information and task allocation information can be obtained from various data sensors set in the task scene.

[0114] The task completion performance matrix of the task allocation is an evaluation result of each performance index of the completed task after each task in the task requirement information is completed according to the task allocation plan.

[0115] The performance indicators include reliability indicators, task completion cost indicators, task completion efficiency indicators, task completion time indicators, etc.

[0116] The feature extraction process is performed on the task completion performance matrix to obtain a feature vector, including:

[0117] S201, decomposing the task completion performance matrix to obtain a left decomposition matrix, a characteristic matrix and a right decomposition matrix of the task completion performance matrix;

[0118] The decomposition process is calculated as follows:

[0119] Y=UAV,

[0120] Among them, U is the left decomposition matrix, A is the characteristic matrix, V is the right decomposition matrix, U and V are both orthogonal matrices, and A is a diagonal matrix;

[0121] The decomposition process can be implemented by using a matrix singular value decomposition algorithm.

[0122] S202, extracting the diagonal elements of the feature matrix to obtain a characterization vector; the characterization vector is represented by I a , I a =λ1,λ2,,λ N1 , N1 is the number of elements contained in the characterization vector;

[0123] S203, performing linear fitting processing on the elements and element sequence values ​​of the characterization vector to obtain the best consistent approximation polynomial;

[0124] The linear fitting process is to use the value Ix representing the sequence number of the vector element as a known independent variable and the value of the vector element as a known dependent variable, and to construct the curve to be approximated using the known independent variable and the known dependent variable, and to perform curve fitting on the curve to be approximated using the function approximation method to obtain the best consistent approximation polynomial f(Ix).

[0125] S204, using the best consistent approximation polynomial, calculating and processing the characterization vector to obtain a feature vector.

[0126] The S204 includes: taking each element of the characterization vector and subjecting it to the best consistent approximation polynomial to obtain the output, and using it as the corresponding element in the feature vector, to construct the feature vector.

[0127] The range value is the difference between the maximum value and the minimum value;

[0128] The cumulative normalization process is to divide each element of the eigenvalue vector by the sum of the absolute values ​​of the elements of the eigenvalue vector to obtain a normalized element value; and to construct a cumulative normalized vector using all the normalized element values.

[0129] The related calculation process is:

[0130]

[0131] Among them, aa is the first evaluation result, κ i The i-th element of the task implementation order sequence;

[0132] The first element in the urgency sorting vector is the serial number of the element with the smallest urgency value, and so on. The last element in the urgency sorting vector is the serial number of the element with the largest urgency value.

[0133] The performing boundary check processing on the first information set to obtain the information set to be evaluated includes:

[0134] S231, for each type of data attribute of the first information set, the data collection information of the data is used as a known independent variable, and 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 the best consistent approximation polynomial f(Ix) of the type of data attribute; the best consistent approximation 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 second regression discrimination threshold; if it is greater than the second regression discrimination threshold, the data is deleted from the first information set; if it is less than the second regression discrimination threshold, the data is not processed;

[0135] S232, executing S231 for the data of all data attributes of the first information set to obtain the information set to be evaluated;

[0136] 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:

[0137] f(Ix)=α P1 (Ix) P1 +α P1-1 (Ix) P1-1 +…+α2(Ix) 2 +α1(Ix)+α0,

[0138] 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);

[0139] The data collection information includes collection time information, collection space information, etc.

[0140] 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.

[0141] The normalization process is to map data in different value ranges to a specified value range, and its mathematical expression is:

[0142]

[0143] Among them, t is a data value in the data set, t max is the maximum value of all data in the data set, t min is the minimum value of all data in the data set, t 归一化 is the value of data t after normalization;

[0144] The category discrimination process is to discriminate the attributes of each data in each category of information in the normalized information set to see whether it is within the preset attribute range of the information, and to remove data that is not within the attribute range from the information.

[0145] According to a second aspect of an embodiment of the present invention, a quantitative evaluation device for task allocation is disclosed, the device comprising:

[0146] A memory storing executable program code;

[0147] a processor coupled to the memory;

[0148] The processor calls the executable program code stored in the memory to execute the quantitative evaluation method of task allocation.

[0149] 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 quantitative evaluation method of task allocation.

[0150] 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 quantitative evaluation method for task assignment, characterized in that: include: S1, obtain task requirement information and task assignment information; The task requirement information includes a task requirement matrix; the task allocation information includes a task completion performance matrix and a task realization order sequence of the task allocation; the row vector of the task completion performance matrix is ​​the evaluation value of each performance indicator after a task is completed; the task realization order sequence is used to indicate the order of realization of each task to be allocated; S2, preprocessing the task requirement information and task assignment information to obtain a set of information to be evaluated; S3, performing quantitative evaluation processing on the information set to be evaluated to obtain a quantitative evaluation result of the task assignment; the quantitative evaluation result of the task assignment is used to indicate the matching degree between the incumbent and the position after the task assignment; The step of performing quantitative evaluation processing on the information set to be evaluated to obtain a quantitative evaluation result of the task assignment includes: S31, performing an implementation order evaluation process on the task implementation order sequence and task requirement information in the task allocation information in the information set to be evaluated, and obtaining a first evaluation result; the task requirement information includes the priority, task type value and business volume requirement value of each task; S32, performing performance evaluation processing on the task completion performance matrix in the task allocation information in the information set to be evaluated to obtain a second evaluation result; S33, performing weighted sum processing on the first evaluation result and the second evaluation result to obtain a quantitative evaluation result of the task allocation; The step of performing an implementation sequence evaluation process on the task implementation sequence sequence and the task requirement information in the task allocation information in the information set to be evaluated to obtain a first evaluation result includes: S311, obtaining the priority, task type value and business volume demand value information of N tasks in the task demand information; for the task demand information of the i-th task, it is expressed as the priority P i 、Task type value T i and business volume demand value B i ; S312, normalize the priority value of each task; i , and its normalized priority is denoted as ρ i =P i / P max -P min ), where P max and P min Respectively represent the upper and lower limits of the priority value range; S313, classify the task types of all tasks into N t Class, each task type has a corresponding task type coefficient, the nth t The task type coefficient of each task type is recorded as Among them, n t =1,2,…,N t , According to the task type value T of the i-th task i , from N t Select the matching task type coefficient τ from the task types i ; S314, divide the task's business volume demand value into N from high to low b Each level has a corresponding business demand value coefficient. b The business volume demand value coefficient of each level is recorded as where n b =1,2,...,N b , According to the business volume demand value B of the i-th task i , determine the grade corresponding to the business demand value, which is the business demand value coefficient ω i ; S315, for each task, calculate its urgency value; the calculation expression of the urgency value is: or i =w1ρ i +w2τ i +w3ω i , Among them, w1, w2, and w3 are the weights of priority, task type, and business volume demand value, respectively, and η i is the urgency value of the i-th task; S316, sorting the serial numbers of all tasks from low to high according to the urgency values ​​to obtain an urgency sorting vector; S317, performing relevant calculation processing on the urgency ranking vector and the task realization order sequence to obtain a first evaluation result; The performing performance evaluation processing on the task completion performance matrix in the task allocation information in the information set to be evaluated to obtain a second evaluation result includes: S321, perform time-frequency transformation on each row vector in the task completion performance matrix to obtain a corresponding spectrum sequence; use all spectrum sequences to construct a spectrum sequence set; the spectrum sequence set includes several spectrum sequences; the spectrum sequence set is expressed as [E1, E2, …, E N ], where E i represents the i-th spectral sequence; S322, performing a first fusion process on the spectrum sequence set to obtain a first fusion signal; S323, performing inverse time-frequency transform processing on the first fusion signal to obtain an evaluation sequence; S324, performing averaging processing on the evaluation sequence to obtain a second evaluation result.

2. The quantitative evaluation method for task allocation according to claim 1, characterized in that: The preprocessing of the task requirement information and the task assignment information to obtain the information set to be evaluated includes: S21, performing outlier elimination and normalization processing on the task requirement information and the task assignment information, respectively, to obtain a normalized information set; the normalized information set includes the task requirement information and the task assignment information after outlier elimination and normalization processing; S22, performing category discrimination processing on the normalized information set to obtain a first information set; S23: Perform boundary check processing on the first information set to obtain an information set to be evaluated.

3. The quantitative evaluation method for task allocation according to claim 2, characterized in that: The performing boundary check processing on the first information set to obtain the information set to be evaluated includes: S231, for each data attribute of the first information 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; S232, for each data of each data attribute of the first information 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; S233, after completing S232 for the data of all data attributes, a set of information to be evaluated is obtained.

4. The quantitative evaluation method for task allocation according to claim 3, characterized in that: The performing a first fusion process on the spectrum sequence set to obtain a first fusion signal includes: S3221, performing correlation calculation on the spectrum sequences in the spectrum sequence set to obtain a mutual correlation matrix R of the spectrum sequence set; i,j represents the i-th row and j-th element of the cross-correlation matrix R, r i,j The calculation expression is: r i,j =And(and i And j ), Among them, E() means finding the mean; S3222, for each spectrum sequence, respectively perform normalization calculation on its mutual correlation value to obtain a normalized correlation value sequence of each spectrum sequence; the normalization calculation expression of the mutual correlation value is: in, represents the jth value of the normalized correlation value sequence of the i-th spectrum sequence; S3223, performing logarithmic summation processing on the normalized correlation value sequence of each spectrum sequence to obtain a spectrum feature sequence; The calculation expression of the logarithmic summation process is: Among them, H i is the i-th value of the spectral feature sequence; S3224, performing feature extraction processing on the task completion performance matrix to obtain a feature vector; S3225, using the feature vector, perform weighted summation on the spectral feature sequence to obtain a first fusion signal.

5. The quantitative evaluation method for task allocation according to claim 4, characterized in that: The task completion performance matrix is ​​subjected to feature extraction processing, Get the feature vector, including: S341, using all row vectors in the task completion performance matrix, calculate a correlation coefficient matrix; the element in the i-th row and j-th column in the correlation coefficient matrix is ​​the cross-correlation value between the i-th row vector and the j-th row vector in the task completion performance matrix; S342, performing eigenvalue calculation on the correlation coefficient matrix to obtain an eigenvalue vector; S343, performing cumulative normalization processing on the eigenvalue vector to obtain a cumulative normalized vector; S344, for each row vector in the correlation coefficient matrix, calculate its range value; and construct a characteristic vector using the range values ​​of all row vectors of the correlation coefficient matrix.

6. A quantitative evaluation device for task allocation, 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 quantitative evaluation method for task allocation according to any one of claims 1 to 5.

7. 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 quantitative evaluation method for task allocation according to any one of claims 1 to 5.

Images