CN113435746A - User workload scoring method and device, electronic equipment and storage medium - Google Patents
User workload scoring method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention relates to a big data technology, and discloses a user workload scoring method, which comprises the following steps: segmenting the workload data of the user based on the performance index to obtain a segmented data set; training a linear regression model by utilizing the segmented data set and the performance index, and normalizing the weight coefficient of the linear regression model to obtain a dimension score; merging the dimension scores to obtain the workload score of each user; classifying users into a category user set according to the workload scores and the performance indicators; and calculating the class scores of all classes in the class user set by using a linear regression model, and optimizing the workload scores of the users of all classes according to the class scores. Furthermore, the invention relates to a blockchain technique, and the workload data can be stored in the nodes of the blockchain. The invention also provides a scoring device, equipment and a storage medium for the user workload. The invention can solve the problems of unreasonable user workload scoring standard and low accuracy.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a user workload scoring method and device, electronic equipment and a computer readable storage medium.
Background
The work content of many employees is various, for example, the comprehensive extension manager is responsible for docking with the life insurance staff, and the work of the employees mainly comprises making a call to the life insurance staff, interviewing the life insurance staff, holding salons, sharing on line, following business opportunities and the like. Scoring the workload of the employee may provide better guidance and management of the employee's work. However, the existing scoring method is often to score according to a rule specified by people, and only to evaluate according to some result type index data, but no judgment standard exists when the numerical value is unreasonable, and various process index data in work are not included in the scoring standard, so that scoring of workers is not accurate enough, and accurate guidance can not be provided for the working behavior of the workers. For example, the work effect of a certain extension manager is not satisfactory, not necessarily due to insufficient workload, and if the extension manager only needs to increase workload, make calls and visit too many times, it may not be expected. Meanwhile, the existing scoring standard uses the same scoring standard for all employees, but the working capacity and the like of all employees are different, and the management by directly using the same scoring standard is not reasonable. Therefore, a more reasonable and accurate method for scoring employee users is needed.
Disclosure of Invention
The invention provides a method and a device for scoring user workload and a computer readable storage medium, and mainly aims to solve the problems of unreasonable scoring standard and low accuracy of user workload.
In order to achieve the above object, the present invention provides a method for scoring user workload, comprising:
acquiring workload data of a user, and performing segmented processing on data of each dimension in the workload data by using a linear algorithm based on a preset performance index to obtain a segmented data set;
training a pre-constructed linear regression model by using the segmented data set and the performance index, and adjusting a weight coefficient of the linear regression model according to a training result of the training until the training reaches a preset iteration number to obtain a trained linear regression model;
carrying out normalization processing on the weight coefficient of the trained linear regression model to obtain a dimension score;
merging the dimension scores of each dimension to obtain the workload score of each user;
classifying users according to the workload scores and the performance indexes to obtain a category user set;
and calculating the class scores of all classes in the class user set by using the trained linear regression model, and optimizing the workload scores of the users of all classes according to the class scores.
Optionally, the acquiring workload data of the user, and performing segmentation processing on data of each dimension in the workload data by using a linear algorithm based on a preset performance indicator to obtain a segmented data set includes:
acquiring workload data of a user within a preset time period from a preset database;
dividing the workload data according to a preset value of a performance index to obtain a plurality of sub-workload data sets;
calculating the average value and the mode of the data contained in each dimension in each sub-workload data set;
performing linear fitting on the average value and the mode and the performance index by using a linear algorithm to obtain a linear graph under each dimensionality of the workload data;
calculating the slope of each section of straight line in the linear graph, and combining adjacent straight lines with the slope difference smaller than a preset threshold value to obtain a line graph;
and segmenting the workload data according to the line segment in the line graph to obtain a segmented data set.
Optionally, the training a pre-constructed linear regression model by using the segmented data set and the performance indicator, and adjusting a weight coefficient of the linear regression model according to a training result of the training until a preset number of iterations is reached to obtain a trained linear regression model, including:
sequentially selecting the segment data of each user in the segment data set under one dimension to obtain an input data set, and taking performance index data corresponding to the user in the input data set as a corresponding label of the input data;
training the linear regression model based on the label and the input data set to obtain a training result;
and when the training result is different from the label, adjusting the weight coefficient of the linear regression model, and returning to the step of training the linear regression model based on the label and the input data set until reaching a preset iteration number to obtain the trained linear regression model.
Optionally, the merging the dimension scores of each dimension to obtain a workload score of each user includes:
calculating an average of data for each dimension in the workload data and a pearson correlation coefficient corresponding to the performance indicator;
and taking the average value as a weight coefficient of the dimension score of the corresponding dimension, and carrying out weighted average processing on the dimension scores of all the dimensions according to the weight coefficient to obtain the workload score of each user.
Optionally, the classifying the user according to the workload score and the performance indicator to obtain a category user set includes:
grading the workload scores according to ranking to obtain grade grades;
grading the performance indexes according to the ranking to obtain performance index grades;
and classifying the users in the workload data into corresponding categories in preset categories according to the grading grade and the performance index grade to obtain a category user set.
Optionally, the classifying the users in the workload data into corresponding categories in preset categories according to the rating level and the performance indicator level to obtain a category user set, including:
sequentially selecting one user in the workload data to obtain users to be classified;
when the rating level and the performance index level of the user to be classified are both first levels, the user to be classified is classified into a first type;
when the rating level of the user to be classified is a first level and the performance index level is a second level, the user to be classified is classified into a second type;
when the rating level and the performance index level of the user to be classified are both second levels, the user to be classified is classified into a third type;
when the rating level of the user to be classified is a second level and the performance index level is a third level, the user to be classified is classified into a fourth type;
when the rating level and the performance index level of the user to be classified are both the third level, the user to be classified is classified into a fifth type;
and collecting the users of the first type, the second type, the third type, the fourth type and the fifth type to obtain a category user set.
Optionally, the calculating, by using the trained linear regression model, a class score of each class in the class user set, and optimizing a workload score of each class of user according to the class score includes:
performing retraining on the trained linear regression model by using the user data set of each category in the category user set to obtain a category regression model;
acquiring a weight coefficient of the category regression model, and carrying out normalization processing on the weight coefficient to obtain a similar dimension score;
carrying out weighted average processing on the similar dimension scores to obtain class scores of users in all classes;
and optimizing the workload scores of the users of all categories according to the category scores.
In order to solve the above problem, the present invention further provides a user workload scoring apparatus, including:
the data acquisition module is used for acquiring workload data of a user and carrying out segmented processing on data of each dimension in the workload data by utilizing a linear algorithm based on a preset performance index to obtain a segmented data set;
the model training module is used for training a pre-constructed linear regression model by utilizing the segmented data set and the performance index, and adjusting the weight coefficient of the linear regression model according to the training result of the training until the training reaches the preset iteration number to obtain the trained linear regression model;
the dimension score calculation module is used for carrying out normalization processing on the weight coefficient of the trained linear regression model to obtain a dimension score;
the workload score calculation module is used for combining the dimension scores of each dimension to obtain the workload score of each user;
the user classification module is used for classifying users according to the workload scores and the performance indexes to obtain a category user set;
and the score optimization module is used for calculating the class scores of all classes in the class user set by using the trained linear regression model and optimizing the workload scores of the users of all classes according to the class scores.
In order to solve the above problem, the present invention also provides an electronic device, including:
a memory storing at least one instruction; and
and the processor executes the instructions stored in the memory to realize the user workload scoring method.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the above method for scoring a user workload.
According to the embodiment of the invention, when the workload data of the user is acquired, the workload data comprising a plurality of dimensions is acquired, the range of data used for grading is enlarged, the grading accuracy can be effectively improved, the workload data is subjected to segmented processing by using a linear algorithm, the processing data volume of a computer can be reduced, and the working efficiency is improved; the segmented data set and the performance indexes are used for carrying out data training on a pre-constructed linear regression model, multi-dimensional workload data can be effectively and uniformly processed, the relation among variable data is excavated, and the grading effectiveness is guaranteed; meanwhile, the users are classified according to the scores, the scores of the users in all categories are optimized, and a finer scoring method is provided for the users in different categories, so that the scores are more reasonable, and the subsequent iterative updating is facilitated. Therefore, the scoring method and device for the user workload, the electronic equipment and the computer readable storage medium provided by the invention can solve the problems of unreasonable scoring standard and low accuracy of the user workload.
Drawings
Fig. 1 is a schematic flow chart of a method for scoring a user workload according to an embodiment of the present invention;
FIG. 2 is a detailed flowchart illustrating a step of the method for scoring workload of a user provided in FIG. 1 according to a first embodiment of the present invention;
FIG. 3 is a functional block diagram of an apparatus for scoring workload of a user according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device for implementing the user workload scoring method according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a user workload scoring method. The execution subject of the user workload scoring method includes, but is not limited to, at least one of electronic devices such as a server and a terminal, which can be configured to execute the method provided by the embodiment of the present application. In other words, the scoring method for the user workload may be performed by software or hardware installed in the terminal device or the server device, and the software may be a block chain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Fig. 1 is a schematic flow chart of a user workload scoring method according to an embodiment of the present invention. In this embodiment, the method for scoring the workload of the user includes:
and S1, acquiring workload data of a user, and performing segmented processing on the data of each dimension in the workload data by using a linear algorithm based on a preset performance index to obtain a segmented data set.
In the embodiment of the present invention, the workload data of the user is service data processed by each user during work, and includes data of multiple dimensions, for example, in life insurance, the workload data of the comprehensive extension manager may include data of multiple dimensions such as interview, heavy guest salon, customer salon, SAT forwarding, heavy guest view viewing, and business opportunity following.
The preset performance index is a threshold value of result data of a specific service, such as passenger volume, which is set according to the actual service and needs to be reached.
In detail, referring to fig. 2, the acquiring workload data of a user and performing segmentation processing on the workload data by using a linear algorithm based on a preset performance indicator to obtain a segmented data set includes:
s21, acquiring workload data of the user in a preset time period from a preset database;
s22, dividing the workload data according to the numerical value of a preset performance index to obtain a plurality of sub-workload data sets;
s23, calculating the average value and the mode of data contained in each dimension in each sub-workload data set;
s24, performing linear fitting on the average value and the mode and the performance index by using a linear algorithm to obtain a linear graph under each dimensionality of the workload data;
s25, calculating the slope of each section of straight line in the linear graph, and combining the adjacent straight lines with the slope difference smaller than a preset threshold value to obtain a line graph;
s26, segmenting the workload data according to the line segments in the line graph to obtain a segmented data set.
The segmented data set is a set comprising a plurality of segmented data of each user in each dimension, for example, in the electric visit dimension, the number of electric visits per day can be divided into 0-10, 10-20, 20-25, 25-35 and 35-40 after segmentation processing.
Optionally, to further ensure the security and privacy of the workload data, the workload data may also be obtained from a node of a block chain.
S2, training the pre-constructed linear regression model by using the segmented data set and the performance index, and adjusting the weight coefficient of the linear regression model according to the training result until the training reaches the preset iteration number to obtain the trained linear regression model.
In the embodiment of the invention, the linear regression model is a mathematical model for quantitative description of statistical relationship. The linear regression model can be expressed as y w1 x1+ w2 x2+ … + wn xn, wherein y is a dependent variable and is a random variable; x is an independent variable, which may be a random variable or a non-random variable, and w1 and w2 … wn are called regression coefficients, i.e., weight coefficients, which indicate the degree of influence of the independent variable on the dependent variable.
In detail, the performing data training on a pre-constructed linear regression model by using the segmented data set and the performance indicator, optimizing and adjusting a weight coefficient of the linear regression model according to a training result, and performing normalization processing on the weight coefficient to obtain a dimension score includes:
sequentially selecting the segment data of each user in the segment data set under one dimension to obtain an input data set, and taking performance index data corresponding to the user in the input data set as a corresponding label of the input data;
training the linear regression model based on the label and the input data set to obtain a training result;
and when the training result is different from the label, adjusting the weight coefficient of the linear regression model, and returning to the step of training the linear regression model based on the label and the input data set until reaching a preset iteration number to obtain the trained linear regression model.
And S3, carrying out normalization processing on the weight coefficient of the trained linear regression model to obtain a dimension score.
According to the embodiment of the invention, the weight coefficient of the trained linear regression model is used as the initial score corresponding to each segment data in the input data, and the weight coefficient is subjected to normalization processing and converted into a percentage form, so that the dimension score corresponding to each dimension is obtained.
For example, using a regression model (y ═ w1 × 1+ w2 × 2+ … + wn × n), using as input the respective dimensional workloads after each integrated manager is segmented (i.e., x1, x2, …, xn, value 0 or 1), using as output the performance result indicators of the integrated managers (y is the amount of customers acquired by the banker), training the regression model, obtaining weight coefficients (w1, w2, …, wn) after the training is completed, and using the weight coefficients as the scores of the segmented workloads, for example, the coefficient before the number of electric visits per day of 0-10 is 0.01, and the coefficient before the number of electric visits per day of 10-20 is 0.05, and the like. And normalizing all the segmented workloads in each dimension, multiplying the normalized workloads by 100, and outputting the normalized workloads serving as actual workload scores in the corresponding dimension in a percentage system mode.
And S4, combining the dimension scores of each dimension to obtain the workload score of each user.
In detail, the merging the dimension scores of each dimension to obtain a workload score of each user includes:
calculating an average of data for each dimension in the workload data and a pearson correlation coefficient corresponding to the performance indicator;
and taking the average value as a weight coefficient of the dimension score of the corresponding dimension, and carrying out weighted average processing on the dimension scores of all the dimensions according to the weight coefficient to obtain the workload score of each user.
The embodiment of the invention scores the workload of each user according to the dimensionality, and integrates the dimensionality scores of multiple dimensionalities according to the weighted average to obtain the evaluation score output of the workload data of each user.
And S5, classifying the users according to the workload scores and the performance indexes to obtain a category user set.
In detail, the classifying the users according to the workload scores and the performance indicators to obtain a category user set includes:
grading the workload scores according to ranking to obtain grade grades;
grading the performance indexes according to the ranking to obtain performance index grades;
and classifying the users in the workload data into corresponding categories in preset categories according to the grading grade and the performance index grade to obtain a category user set.
In the embodiment of the invention, the grading grades are divided into a first grade, a second grade and a third grade, such as a good grade, a medium grade and a poor grade; the preset categories are classified into a first type, a second type, a third type, a fourth type and a fifth type, such as a performance type, an upgrading type, a medium type, an upgrading and upgrading amount type and a elimination type.
Further, the step of grading the workload scores according to ranking to obtain a grade comprises: rating the rating grade corresponding to the workload rating of the top 30% of the ranking as a first grade; scoring a rating level corresponding to the workload rating ranked between 30% and 70% as a second rating; and evaluating the grade corresponding to the workload grade after the ranking of 70% as a third grade.
Optionally, the ranking the performance indicators according to ranking to obtain a performance indicator level includes: evaluating the performance index grade corresponding to the performance index which is 30% of the top grade as a first grade; evaluating a performance index grade corresponding to the performance index ranked between 30% and 70% as a second grade; and evaluating the performance index grade corresponding to the performance index after 70% of the ranking as a third grade.
Further, the classifying the users in the workload data into corresponding categories in preset categories according to the rating level and the performance indicator level to obtain a category user set, including:
sequentially selecting one user in the workload data to obtain users to be classified;
when the rating level and the performance index level of the user to be classified are both first levels, the user to be classified is classified into a first type;
when the rating level of the user to be classified is a first level and the performance index level is a second level, the user to be classified is classified into a second type;
when the rating level and the performance index level of the user to be classified are both second levels, the user to be classified is classified into a third type;
when the rating level of the user to be classified is a second level and the performance index level is a third level, the user to be classified is classified into a fourth type;
when the rating level and the performance index level of the user to be classified are both the third level, the user to be classified is classified into a fifth type;
and collecting the users of the first type, the second type, the third type, the fourth type and the fifth type to obtain a category user set.
And S6, calculating the class scores of all classes in the class user set by using the trained linear regression model, and optimizing the workload scores of the users of all classes according to the class scores.
In detail, the calculating a class score of each class in the class user set by using the trained linear regression model, and optimizing a workload score of each class of user according to the class score includes:
performing retraining on the trained linear regression model by using the user data set of each category in the category user set to obtain a category regression model;
acquiring a weight coefficient of the category regression model, and carrying out normalization processing on the weight coefficient to obtain a similar dimension score;
carrying out weighted average processing on the similar dimension scores to obtain class scores of users in all classes;
and optimizing the workload scores of the users of all categories according to the category scores.
The embodiment of the invention not only calculates the score of the workload of each user according to the performance index of the user, but also can further make a more refined scoring method for the users of each category, thereby ensuring that the scoring of the users is more reasonable, facilitating the grouping management and the excitation for the users of different categories, and improving the working enthusiasm for the users of each category.
According to the embodiment of the invention, when the workload data of the user is acquired, the workload data comprising a plurality of dimensions is acquired, the range of data used for grading is enlarged, the grading accuracy can be effectively improved, the workload data is subjected to segmented processing by using a linear algorithm, the processing data volume of a computer can be reduced, and the working efficiency is improved; the segmented data set and the performance indexes are used for carrying out data training on a pre-constructed linear regression model, multi-dimensional workload data can be effectively and uniformly processed, the relation among variable data is excavated, and the grading effectiveness is guaranteed; meanwhile, the users are classified according to the scores, the scores of the users in all categories are optimized, and a finer scoring method is provided for the users in different categories, so that the scores are more reasonable, and the subsequent iterative updating is facilitated. Therefore, the scoring method and device for the user workload, the electronic equipment and the computer readable storage medium provided by the invention can solve the problems of unreasonable scoring standard and low accuracy of the user workload.
Fig. 3 is a functional block diagram of a user workload scoring apparatus according to an embodiment of the present invention.
The scoring apparatus 100 for user workload according to the present invention may be installed in an electronic device. According to the realized functions, the scoring device 100 for the user workload may include a data acquisition module 101, a model training module 102, a dimension score calculation module 103, a workload score calculation module 104, a user classification module 105, and a score optimization module 106. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the data acquisition module 101 is configured to acquire workload data of a user, and perform segmentation processing on data of each dimension in the workload data by using a linear algorithm based on a preset performance indicator to obtain a segmented data set.
In the embodiment of the present invention, the workload data of the user is service data processed by each user during work, and includes data of multiple dimensions, for example, in life insurance, the workload data of the comprehensive extension manager may include data of multiple dimensions such as interview, heavy guest salon, customer salon, SAT forwarding, heavy guest view viewing, and business opportunity following.
The preset performance index is a threshold value of result data of a specific service, such as passenger volume, which is set according to the actual service and needs to be reached.
In detail, the data obtaining module 101 is specifically configured to:
acquiring workload data of a user within a preset time period from a preset database;
dividing the workload data according to a preset value of a performance index to obtain a plurality of sub-workload data sets;
calculating the average value and the mode of the data contained in each dimension in each sub-workload data set;
performing linear fitting on the average value and the mode and the performance index by using a linear algorithm to obtain a linear graph under each dimensionality of the workload data;
calculating the slope of each section of straight line in the linear graph, and combining adjacent straight lines with the slope difference smaller than a preset threshold value to obtain a line graph;
and segmenting the workload data according to the line segment in the line graph to obtain a segmented data set.
The segmented data set is a set comprising a plurality of segmented data of each user in each dimension, for example, in the electric visit dimension, the number of electric visits per day can be divided into 0-10, 10-20, 20-25, 25-35 and 35-40 after segmentation processing.
The model training module 102 is configured to train a pre-constructed linear regression model by using the segmented data set and the performance indicator, and adjust a weight coefficient of the linear regression model according to a training result of the training until the training reaches a preset number of iterations, so as to obtain a trained linear regression model.
In the embodiment of the invention, the linear regression model is a mathematical model for quantitative description of statistical relationship.
In detail, the model training module 102 is specifically configured to:
sequentially selecting the segment data of each user in the segment data set under one dimension to obtain an input data set, and taking performance index data corresponding to the user in the input data set as a corresponding label of the input data;
training the linear regression model based on the label and the input data set to obtain a training result;
and when the training result is different from the label, adjusting the weight coefficient of the linear regression model, and returning to the step of training the linear regression model based on the label and the input data set until reaching a preset iteration number to obtain the trained linear regression model.
The dimension score calculating module 103 is configured to perform normalization processing on the weight coefficients of the trained linear regression model to obtain a dimension score.
According to the embodiment of the invention, the weight coefficient of the trained linear regression model is used as the initial score corresponding to each segment data in the input data, and the weight coefficient is subjected to normalization processing and converted into a percentage form, so that the dimension score corresponding to each dimension is obtained.
The workload score calculation module 104 combines the dimension scores of each dimension to obtain a workload score of each user.
In detail, the workload score calculating module 104 is specifically configured to:
calculating an average of data for each dimension in the workload data and a pearson correlation coefficient corresponding to the performance indicator;
and taking the average value as a weight coefficient of the dimension score of the corresponding dimension, and carrying out weighted average processing on the dimension scores of all the dimensions according to the weight coefficient to obtain the workload score of each user.
The user classification module 105 is configured to classify users according to the workload scores and the performance indicators to obtain a category user set.
In detail, the user classification module 105 is specifically configured to:
grading the workload scores according to ranking to obtain grade grades;
grading the performance indexes according to the ranking to obtain performance index grades;
and classifying the users in the workload data into corresponding categories in preset categories according to the grading grade and the performance index grade to obtain a category user set.
In the embodiment of the invention, the grading grades are divided into a first grade, a second grade and a third grade, such as a good grade, a medium grade and a poor grade; the preset categories are classified into a first type, a second type, a third type, a fourth type and a fifth type, such as a performance type, an upgrading type, a medium type, an upgrading and upgrading amount type and a elimination type.
Further, the step of grading the workload scores according to ranking to obtain a grade comprises: rating the rating grade corresponding to the workload rating of the top 30% of the ranking as a first grade; scoring a rating level corresponding to the workload rating ranked between 30% and 70% as a second rating; and evaluating the grade corresponding to the workload grade after the ranking of 70% as a third grade.
Optionally, the ranking the performance indicators according to ranking to obtain a performance indicator level includes: evaluating the performance index grade corresponding to the performance index which is 30% of the top grade as a first grade; evaluating a performance index grade corresponding to the performance index ranked between 30% and 70% as a second grade; and evaluating the performance index grade corresponding to the performance index after 70% of the ranking as a third grade.
Further, the classifying the users in the workload data into corresponding categories in preset categories according to the rating level and the performance indicator level to obtain a category user set, including:
sequentially selecting one user in the workload data to obtain users to be classified;
when the rating level and the performance index level of the user to be classified are both first levels, the user to be classified is classified into a first type;
when the rating level of the user to be classified is a first level and the performance index level is a second level, the user to be classified is classified into a second type;
when the rating level and the performance index level of the user to be classified are both second levels, the user to be classified is classified into a third type;
when the rating level of the user to be classified is a second level and the performance index level is a third level, the user to be classified is classified into a fourth type;
when the rating level and the performance index level of the user to be classified are both the third level, the user to be classified is classified into a fifth type;
and collecting the users of the first type, the second type, the third type, the fourth type and the fifth type to obtain a category user set.
The score optimization module 106 is configured to calculate a class score of each class in the class user set by using the trained linear regression model, and optimize the workload score of the user of each class according to the class score.
In detail, the calculating a class score of each class in the class user set by using the trained linear regression model, and optimizing a workload score of each class of user according to the class score includes:
performing retraining on the trained linear regression model by using the user data set of each category in the category user set to obtain a category regression model;
acquiring a weight coefficient of the category regression model, and carrying out normalization processing on the weight coefficient to obtain a similar dimension score;
carrying out weighted average processing on the similar dimension scores to obtain class scores of users in all classes;
and optimizing the workload scores of the users of all categories according to the category scores.
Fig. 4 is a schematic structural diagram of an electronic device implementing the method for scoring user workload according to the present invention.
The electronic device may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as a user workload scoring program, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (e.g., a scoring program for executing a user workload, etc.) stored in the memory 11 and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only to store application software installed in the electronic device and various types of data, such as codes of a rating program of a user's workload, etc., but also to temporarily store data that has been output or is to be output.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Fig. 4 shows only an electronic device having components, and those skilled in the art will appreciate that the structure shown in fig. 4 does not constitute a limitation of the electronic device, and may include fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The scoring program of the user workload stored in the memory 11 in the electronic device is a combination of a plurality of computer programs which, when run in the processor 10, may implement:
acquiring workload data of a user, and performing segmented processing on data of each dimension in the workload data by using a linear algorithm based on a preset performance index to obtain a segmented data set;
training a pre-constructed linear regression model by using the segmented data set and the performance index, and adjusting a weight coefficient of the linear regression model according to a training result of the training until the training reaches a preset iteration number to obtain a trained linear regression model;
carrying out normalization processing on the weight coefficient of the trained linear regression model to obtain a dimension score;
merging the dimension scores of each dimension to obtain the workload score of each user;
classifying users according to the workload scores and the performance indexes to obtain a category user set;
and calculating the class scores of all classes in the class user set by using the trained linear regression model, and optimizing the workload scores of the users of all classes according to the class scores.
Specifically, the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the computer program, which is not described herein again.
Further, the electronic device integrated module/unit, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a non-volatile computer-readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring workload data of a user, and performing segmented processing on data of each dimension in the workload data by using a linear algorithm based on a preset performance index to obtain a segmented data set;
training a pre-constructed linear regression model by using the segmented data set and the performance index, and adjusting a weight coefficient of the linear regression model according to a training result of the training until the training reaches a preset iteration number to obtain a trained linear regression model;
carrying out normalization processing on the weight coefficient of the trained linear regression model to obtain a dimension score;
merging the dimension scores of each dimension to obtain the workload score of each user;
classifying users according to the workload scores and the performance indexes to obtain a category user set;
and calculating the class scores of all classes in the class user set by using the trained linear regression model, and optimizing the workload scores of the users of all classes according to the class scores.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method for scoring a user workload, the method comprising:
acquiring workload data of a user, and performing segmented processing on data of each dimension in the workload data by using a linear algorithm based on a preset performance index to obtain a segmented data set;
training a pre-constructed linear regression model by using the segmented data set and the performance index, and adjusting a weight coefficient of the linear regression model according to a training result of the training until the training reaches a preset iteration number to obtain a trained linear regression model;
carrying out normalization processing on the weight coefficient of the trained linear regression model to obtain a dimension score;
merging the dimension scores of each dimension to obtain the workload score of each user;
classifying users according to the workload scores and the performance indexes to obtain a category user set;
and calculating the class scores of all classes in the class user set by using the trained linear regression model, and optimizing the workload scores of the users of all classes according to the class scores.
2. The method for scoring the workload of the user according to claim 1, wherein the step of acquiring the workload data of the user and performing segmentation processing on the data of each dimension in the workload data by using a linear algorithm based on a preset performance index to obtain a segmented data set comprises the following steps:
acquiring workload data of a user within a preset time period from a preset database;
dividing the workload data according to a preset value of a performance index to obtain a plurality of sub-workload data sets;
calculating the average value and the mode of the data contained in each dimension in each sub-workload data set;
performing linear fitting on the average value and the mode and the performance index by using a linear algorithm to obtain a linear graph under each dimensionality of the workload data;
calculating the slope of each section of straight line in the linear graph, and combining adjacent straight lines with the slope difference smaller than a preset threshold value to obtain a line graph;
and segmenting the workload data according to the line segment in the line graph to obtain a segmented data set.
3. The method for scoring workload of a user according to claim 1, wherein the training a pre-constructed linear regression model by using the segmented data set and the performance indicator, and adjusting the weight coefficient of the linear regression model according to the training result of the training until reaching a preset number of iterations to obtain a trained linear regression model comprises:
sequentially selecting the segment data of each user in the segment data set under one dimension to obtain an input data set, and taking performance index data corresponding to the user in the input data set as a corresponding label of the input data;
training the linear regression model based on the label and the input data set to obtain a training result;
and when the training result is different from the label, adjusting the weight coefficient of the linear regression model, and returning to the step of training the linear regression model based on the label and the input data set until reaching a preset iteration number to obtain the trained linear regression model.
4. A method for scoring a user workload according to claim 1, wherein said combining the dimension scores for each dimension to obtain a workload score for each user comprises:
calculating an average of data for each dimension in the workload data and a pearson correlation coefficient corresponding to the performance indicator;
and taking the average value as a weight coefficient of the dimension score of the corresponding dimension, and carrying out weighted average processing on the dimension scores of all the dimensions according to the weight coefficient to obtain the workload score of each user.
5. A method for scoring a user's workload as claimed in claim 1, wherein said classifying users according to said workload score and said performance indicator to obtain a set of category users comprises:
grading the workload scores according to ranking to obtain grade grades;
grading the performance indexes according to the ranking to obtain performance index grades;
and classifying the users in the workload data into corresponding categories in preset categories according to the grading grade and the performance index grade to obtain a category user set.
6. A method for scoring a user workload according to claim 5, wherein said categorizing users in said workload data into corresponding ones of preset categories according to said scoring level and said performance indicator level to obtain a set of category users comprises:
sequentially selecting one user in the workload data to obtain users to be classified;
when the rating level and the performance index level of the user to be classified are both first levels, the user to be classified is classified into a first type;
when the rating level of the user to be classified is a first level and the performance index level is a second level, the user to be classified is classified into a second type;
when the rating level and the performance index level of the user to be classified are both second levels, the user to be classified is classified into a third type;
when the rating level of the user to be classified is a second level and the performance index level is a third level, the user to be classified is classified into a fourth type;
when the rating level and the performance index level of the user to be classified are both the third level, the user to be classified is classified into a fifth type;
and collecting the users of the first type, the second type, the third type, the fourth type and the fifth type to obtain a category user set.
7. The method for scoring of user workload according to claim 6, wherein the calculating a class score for each class in the set of class users using the trained linear regression model and optimizing the workload scores for users of each class according to the class scores comprises:
performing retraining on the trained linear regression model by using the user data set of each category in the category user set to obtain a category regression model;
acquiring a weight coefficient of the category regression model, and carrying out normalization processing on the weight coefficient to obtain a similar dimension score;
carrying out weighted average processing on the similar dimension scores to obtain class scores of users in all classes;
and optimizing the workload scores of the users of all categories according to the category scores.
8. An apparatus for scoring a user workload, the apparatus comprising:
the data acquisition module is used for acquiring workload data of a user and carrying out segmented processing on data of each dimension in the workload data by utilizing a linear algorithm based on a preset performance index to obtain a segmented data set;
the model training module is used for training a pre-constructed linear regression model by utilizing the segmented data set and the performance index, and adjusting the weight coefficient of the linear regression model according to the training result of the training until the training reaches the preset iteration number to obtain the trained linear regression model;
the dimension score calculation module is used for carrying out normalization processing on the weight coefficient of the trained linear regression model to obtain a dimension score;
the workload score calculation module is used for combining the dimension scores of each dimension to obtain the workload score of each user;
the user classification module is used for classifying users according to the workload scores and the performance indexes to obtain a category user set;
and the score optimization module is used for calculating the class scores of all classes in the class user set by using the trained linear regression model and optimizing the workload scores of the users of all classes according to the class scores.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method of scoring a user workload according to any one of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out a method for scoring a user workload according to any one of claims 1 to 7.
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