CN117314121A - Information processing method, device, equipment and storage medium based on personnel allocation - Google Patents

Information processing method, device, equipment and storage medium based on personnel allocation Download PDF

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CN117314121A
CN117314121A CN202311502233.7A CN202311502233A CN117314121A CN 117314121 A CN117314121 A CN 117314121A CN 202311502233 A CN202311502233 A CN 202311502233A CN 117314121 A CN117314121 A CN 117314121A
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information
candidate
tester
parameter information
key parameter
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陈海锋
张世姣
钟忻
柴鹏
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China United Network Communications Group Co Ltd
Unicom Digital Technology Co Ltd
Unicom Cloud Data Co Ltd
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China United Network Communications Group Co Ltd
Unicom Digital Technology Co Ltd
Unicom Cloud Data Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G06Q10/063114—Status monitoring or status determination for a person or group
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis

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Abstract

The application provides an information processing method, device, equipment and storage medium based on personnel allocation, and relates to the technical field of data processing. The method comprises the following steps: acquiring key parameter information of each candidate tester; the key parameter information is information for indicating comprehensive working capacity, current workload and new task connectivity of candidate testers; inputting the key parameter information into a preset model to obtain scoring information of candidate testers; the method comprises the steps that a preset model is obtained by performing multiple linear regression analysis fitting on the basis of a sample data set, and scoring information is used for indicating the adaptation degree of candidate testers and a new task; determining target testers according to the grading information of each candidate tester; sending prompt information to a terminal of a target tester; the prompt information is used for indicating a target tester to complete a new task within a preset period. The method improves the overall efficiency of software function test.

Description

Information processing method, device, equipment and storage medium based on personnel allocation
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to an information processing method, apparatus, device, and storage medium based on personnel allocation.
Background
The software function test is an important work before software delivery, and plays a critical role in guaranteeing the quality of the software. However, in the course of testing according to the test schedule, there are often various kinds of temporary test tasks which are suddenly inserted and have different task sizes.
For these temporary test tasks, in the prior art, a manager usually evaluates the test state of the current tester manually according to a single or several limited factors, so as to allocate the suddenly inserted temporary test task. The personnel allocation efficiency of the mode is too low, the cost is high, the adaptation degree of personnel allocation is not high, and the allocated testers can hardly know the allocation condition of the respective tasks in time, so that the completion of the test tasks is not facilitated.
Therefore, there is a need for an information processing method based on personnel allocation to solve the above problems.
Disclosure of Invention
The application provides an information processing method, device, equipment and storage medium based on personnel allocation, which are used for solving the technical problems that in the prior art, the allocation efficiency of testers is low, the cost is high, the adaptation degree of the allocated testers is not high, and the allocated testers can hardly know respective task allocation conditions in time.
In a first aspect, the present application provides an information processing method based on personnel allocation, the method including:
acquiring key parameter information of each candidate tester; the key parameter information is information for indicating comprehensive working capacity, current workload and new task engagement of the candidate testers;
inputting the key parameter information into a preset model to obtain scoring information of the candidate test personnel; the preset model is obtained by performing multiple linear regression analysis fitting based on a sample data set, and the scoring information is used for indicating the suitability of the candidate test personnel to a new task;
determining target testers according to the grading information of each candidate tester;
sending prompt information to the terminal of the target tester; the prompt message is used for indicating the target tester to finish the new task within a preset period.
Optionally, the method as described above, determining the target tester according to the scoring information of each candidate tester includes:
and determining the candidate test person with the highest scoring information as a target test person according to the scoring information of each candidate test person.
Optionally, the method as described above, the method further comprises:
obtaining a sample data set from a server; each sample data in the sample data set comprises candidate parameter information and corresponding preset scoring information, wherein the candidate parameter information is all parameter information which can be used for indicating comprehensive working capacity, current workload and new task engagement of a tester, and the key parameter information is part of information in the candidate parameter information;
and performing multiple linear regression analysis processing on the sample data set to generate the preset model.
Optionally, in the method as described above, performing multiple linear regression analysis on the sample data set to generate the preset model, including:
carrying out regression analysis on the sample data set through a social science statistical software package tool, and screening out key parameter information in the candidate parameter information;
taking key parameter information in the candidate parameter information as an independent variable, taking the corresponding preset scoring information as a dependent variable, and fitting a multiple linear regression model;
and verifying the fitted multiple linear regression model to generate the preset model.
Optionally, the method includes verifying the fitted multiple linear regression model to generate the preset model, where the method includes:
if the determination coefficient and the significance information of the multiple linear regression model obtained by fitting are determined to meet the preset conditions, determining the multiple linear regression model obtained by fitting as the preset model; wherein the decision coefficient is used to indicate a goodness of fit of a model, and the saliency information is used to indicate a saliency degree of a linear relationship between the independent variable and the dependent variable.
Optionally, the method described above obtains key parameter information of each candidate tester, including:
sending an acquisition instruction to a server; the acquisition instruction is used for indicating to acquire key parameter information of candidate testers;
and receiving key parameter information of each candidate tester sent by the server.
Optionally, in the method as described above, before sending the prompt message to the terminal of the target tester, the method further includes:
displaying the target tester;
responding to a determining instruction of an administrator user, and generating the prompt information; wherein the determining instruction characterizes the manager confirming execution of the new task by the target tester.
In a second aspect, the present application provides an information processing apparatus based on personnel deployment, the apparatus comprising:
the acquisition unit is used for acquiring key parameter information of each candidate tester; the key parameter information is information for indicating comprehensive working capacity, current workload and new task engagement of the candidate testers;
the computing unit is used for inputting the key parameter information into a preset model to obtain the scoring information of the candidate testers; the preset model is obtained by performing multiple linear regression analysis fitting based on a sample data set, and the scoring information is used for indicating the suitability of the candidate test personnel to a new task;
the determining unit is used for determining target testers according to the scoring information of each candidate tester;
the prompting unit is used for sending prompting information to the terminal of the target tester; the prompt message is used for indicating the target tester to finish the new task within a preset period.
In a third aspect, the present application provides an electronic device, including: a processor, and a memory communicatively coupled to the processor;
The memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method as described above.
In a fourth aspect, the present application provides a computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method as described above.
In a fifth aspect, the present application provides a computer program product comprising a computer program for implementing the method as described above when being executed by a processor.
The information processing method, device, equipment and storage medium based on personnel allocation, wherein the method comprises the following steps: acquiring key parameter information of each candidate tester; the key parameter information is information for indicating comprehensive working capacity, current workload and new task engagement of the candidate testers; inputting the key parameter information into a preset model to obtain scoring information of the candidate test personnel; the preset model is obtained by performing multiple linear regression analysis fitting based on a sample data set, and the scoring information is used for indicating the suitability of the candidate test personnel to a new task; determining target testers according to the grading information of each candidate tester; sending prompt information to the terminal of the target tester; the prompt message is used for indicating the target tester to finish the new task within a preset period. According to the scheme, the grading information of each candidate tester is determined based on the key parameter information and the preset model, then the target tester is determined based on the grading information, and the allocation result is sent to the terminal of the target tester.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
Fig. 1 is a schematic flow chart of an information processing method based on personnel allocation according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an intelligent deployment system for a software functional tester according to an embodiment of the present application;
fig. 3 is a flowchart of a method for obtaining a preset model according to an embodiment of the present application;
FIG. 4 is a schematic diagram of actual values and predicted values of scoring information provided in an embodiment of the present application;
fig. 5 is a schematic structural diagram of an information processing device based on personnel allocation according to an embodiment of the present application;
FIG. 6 is a schematic structural diagram of another information processing apparatus based on personnel allocation according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Specific embodiments thereof have been shown by way of example in the drawings and will herein be described in more detail. These drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or fully authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards, and provide corresponding operation entries for the user to select authorization or rejection.
The software function test is an important work before software delivery, and plays a critical role in guaranteeing the quality of the software. Currently, the test status of current testers is often evaluated by a manager based on a single or several limited factors, and then a temporary test task inserted suddenly is allocated. For example, by evaluating the workload of the new task, the test deadline, and whether the tester is idle or in a less-tested state at the current time, it is determined whether the new task is assigned to the tester. Without the ability to evaluate a tester, the quality of the test of the tester on a new task will not be guaranteed. In addition, the efficiency of personnel allocation is too low, the cost is also high, the adaptation degree of personnel allocation is also not high, and the allocated testers can not know the respective task allocation conditions in time, so that the completion of test tasks is not facilitated.
In order to solve the problems, the application provides an information processing method based on personnel allocation, the application uses data analysis software SPSS (Solutions Statistical Package for the Social Sciences, social science statistics software package) to screen out a plurality of key parameter features which can represent comprehensive working capacity, comprehensive current workload and new task engagement, and carries out multiple linear regression analysis on the features to fit a regression model, when personnel allocation is carried out, scoring information of each candidate tester is calculated based on the model, then the candidate tester with the highest score is determined as a target tester, an allocation result is sent to a terminal of the target tester, personnel allocation is completed in an automatic allocation mode through an on-line system, the adaptation degree and efficiency are both higher, and the testers can know the allocation result as soon as possible, so that task testing can be carried out in time.
The following describes the technical solutions of the present application and how the technical solutions of the present application solve the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of an information processing method based on personnel allocation according to an embodiment of the present application. The execution subject of the embodiment of the application may be an information processing device based on personnel allocation, where the information processing device based on personnel allocation may be located on an electronic device, and the electronic device may be a mobile terminal, such as a mobile phone, a tablet, a computer, etc., which is not limited in the application. The embodiment of the present application will be described in detail taking an information processing apparatus in which an execution subject is personnel allocation.
As shown in fig. 1, the information processing method based on personnel allocation provided in this embodiment includes:
s101, acquiring key parameter information of each candidate tester; the key parameter information is information for indicating comprehensive working capacity, current workload and new task engagement of candidate testers.
Illustratively, the present application aims at allocating suitable testers for new test tasks to complete the test tasks faster and better, and accordingly, comprehensive consideration needs to be given to comprehensive working capacity, current workload and new task engagement of each candidate tester. And the comprehensive working capacity, the current workload and the new task engagement of the candidate testers are all related to a plurality of characteristic parameters.
For example, the comprehensive working capacity of the tester is mainly determined by the feature information such as the number of measurable modules, job level, working age, completed task scoring and the like. The number of the testable modules can reflect the understanding degree of the testers on the service, and some testers have wider understanding on the service, so that more service modules can be responsible for testing; the job level refers to the existing job level of the tester; the working life refers to the life of a tester for performing software function test tasks; scoring a completed task refers to scoring the complexity of the completed task.
For another example, the current workload of the tester can be determined by the feature information such as the number of the current test modules, the test schedule, the number of projects, the number of interfaces, the number of use cases, the number of associated systems, the test state and the like. The test schedule mainly refers to the determined test task schedule, the project number refers to the number of test projects contained in the current work, the interface number refers to the total number of test interfaces contained in the current work, the use case number refers to the number of test cases contained in the current work, the related system number refers to the system number related to the current work, and the test state refers to the current idle state or the task test.
For another example, the new task connectivity of the tester mainly refers to the correlation between the current test task and the new task of the tester, and can be mainly determined by the feature information such as the module to which the new task belongs, task content connection, task scheduling connection, and new task classification. The task content linking refers to whether a new task is linked with an existing task, the task scheduling linking refers to whether the new task is linked with the existing task scheduling, and the new task scoring refers to scoring the task complexity of the new task.
Among the above described parameters, the influence of the evaluation result of each parameter is different, part of the parameters are relatively important, the influence of part of the parameters on the result is not great, all the parameter information which can be used for indicating the comprehensive working capacity, the current workload and the new task engagement of the tester can be used as candidate parameter information, and then part of the information is screened out from the candidate parameter information to be used as key parameter information. In order to improve the accuracy of subsequent personnel allocation, the method only selects key parameter information in the candidate parameter information to determine the comprehensive working capacity, the current workload and the new task engagement of the candidate test personnel.
The present application does not limit how to obtain the key parameter information of each candidate tester. For example, it may be manually entered, may be invoked from another system, and so on. Fig. 2 is a schematic architecture diagram of an intelligent deployment system for a software functional tester according to an embodiment of the present application. As shown in fig. 2, the intelligent allocation system is different in corresponding authority for different users, and can perform user management, task management and intelligent allocation after an administrator registers a login system for the administrator user, and the intelligent allocation functional module uses the information processing method based on personnel allocation to perform intelligent allocation of test personnel; for the general user, after the general user registers the login system, personal information modification and task management, such as inputting respective key parameter information, can be performed through a personal center, task management, and the like.
Each functional module in the intelligent deployment system shown in fig. 2 is designed in detail from aspects of demand analysis, database design, front-end and back-end interaction and the like. In addition, the Django framework of the Python language has the advantages of good completeness and universality, and an intelligent allocation system of software function testers can be designed by adopting the Django framework, so that the system has good robustness and stability. In addition, to improve the efficiency of interaction with the database, the user can use the system more smoothly, and the ORM technique can also be used to optimize the functions of the database.
In one example, obtaining key parameter information of each candidate tester may include:
s1, sending an acquisition instruction to a server; the acquisition indication is used for indicating acquisition of key parameter information of candidate testers.
S2, key parameter information of each candidate tester sent by the server is received.
The server stores key parameter information of candidate testers in advance, and when the information processing device based on personnel allocation needs to acquire the key parameter information of each candidate tester, an acquisition instruction is sent to the server, and then the key parameter information of each candidate tester sent by the server is received.
S102, inputting key parameter information into a preset model to obtain scoring information of candidate testers; the preset model is obtained by performing multiple linear regression analysis fitting based on a sample data set, and the scoring information is used for indicating the adaptation degree of candidate testers to a new task.
For example, after obtaining the key parameter information of each candidate tester, the information processing device based on personnel allocation in the application respectively inputs the obtained key parameter information of each candidate tester into the preset model, so that scoring information for indicating the adaptation degree of each candidate tester to a new task can be obtained.
The preset model is obtained by performing multiple linear regression analysis fitting based on the sample data set. Fig. 3 is a schematic flow chart of a method for obtaining a preset model according to an embodiment of the present application. As shown in fig. 3, the process of obtaining the preset model may include:
s301, acquiring a sample data set from a server; each sample data in the sample data set comprises candidate parameter information and corresponding preset scoring information, wherein the candidate parameter information is all parameter information which can be used for indicating comprehensive working capacity, current workload and new task engagement of a tester, and the key parameter information is part of the candidate parameter information.
S302, performing multiple linear regression analysis processing on the sample data set to generate a preset model.
Illustratively, a sample data set is obtained from a server, and then a multiple linear regression analysis is performed on the sample data set to generate a preset model.
Illustratively, table 1 is one possible sample data set.
TABLE 1
As shown in table 1, each set of sample data includes candidate parameter information that can be used to indicate parameter information of comprehensive working capacity, current workload and new task engagement of a tester, including: the system comprises 15 characteristic parameters of measurable module number, job level, working period, completed task scoring, test module number, test schedule, project number, interface number, use case number, associated system number, test state, new task affiliated module, task content connection, task schedule connection and new task scoring, and each group of sample data also corresponds to a corresponding preset scoring information Y. The preset scoring information Y is represented by a number set of 1-10, and the larger the numerical value of the preset scoring information Y is, the higher the adaptation degree is, and the more meets the requirements.
And after the sample data set is obtained, performing multiple linear regression analysis on the sample data set to generate a preset model. In one example, step S302, performing multiple linear regression analysis on the sample data set to generate a preset model may include:
s3021, carrying out regression analysis on the sample data set through a social science statistical software package tool, and screening out key parameter information in the candidate parameter information.
S3022, fitting a multiple linear regression model by taking key parameter information in the candidate parameter information as an independent variable and corresponding preset scoring information as a dependent variable.
S3023, checking the multiple linear regression model obtained through fitting to generate a preset model.
Illustratively, for 15 characteristic parameters in the sample data set, the application carries out regression analysis on the sample data set through a social science statistical software package tool (Solutions Statistical Package for the Social Sciences, abbreviated as SPSS) to screen out key parameter information. For example, the key parameter information may include job level, working year, completed task scoring, number of items, number of interfaces, number of use cases, number of associated systems, test status, task content engagement, task scheduling engagement, new task scoring, etc. in the candidate parameter information. Then, key parameter information in the candidate parameter information is used as an independent variable, corresponding preset scoring information Y is used as an independent variable, fitting of the multiple linear regression model is carried out, the multiple linear regression model after fitting can be obtained, and the preset model can be obtained by checking the multiple linear regression model obtained by fitting.
Illustratively, since the fitted model main evaluation index mainly comprises a complex correlation coefficient R and a decision coefficient R 2 R after adjustment 2 And the like, wherein the complex correlation coefficient R reflects the linear correlation degree between all independent variables and dependent variables, and the larger the value is, the closer the linear correlation is; determining the coefficient R 2 The closer to 1, the more fit the model to the data; r after adjustment 2 The adjusted decision coefficients are also one of the important standard indicators for good model fitting. In addition, the overall regression effect F test is performed on the fitted multiple linear regression model, mainly to test the overall significance of the linear regression equation, which is used to determine whether the linear relationship between the explanatory variable and all explanatory variables is significant, and to determine whether it is appropriate to fit the relationship between these variables with the linear model.
Therefore, the checking of the multiple linear regression model obtained by fitting to generate a preset model may include: if the determination coefficient and the significance information of the multiple linear regression model obtained through fitting are determined to meet the preset conditions, determining the multiple linear regression model obtained through fitting as a preset model; wherein the decision coefficients are used to indicate the goodness of fit of the model and the saliency information is used to indicate the saliency of the linear relationship between the independent and dependent variables.
Illustratively, table 2 is the analysis results obtained by performing regression analysis based on the sample data set of table 1.
TABLE 2
As shown in table 2, the significance information P value based on the F test is 0.000, and the significance is displayed on the level, which indicates that there is a regression relationship between the independent variable and the dependent variable, and the significance degree is high, and at the same time, the coefficient R is determined 2 That is, the goodness of fit R2 of the model is 0.982, which indicates that the model performs better, so that the multiple linear regression model obtained by fitting can be determined as a preset model.
The preset conditions corresponding to the decision coefficients and the significance information are not limited, and the preset conditions can be set according to requirements. For example, the significance P of the model can be set only<0.05 and determining the coefficient R 2 The formula can pass the significance detection at > 0.95. If F-detection is not passed, then a non-linear regression need to be considered for re-fitting, which is not limiting in this application.
Fig. 4 is a schematic diagram of actual values and predicted values of scoring information according to an embodiment of the present application. As shown in fig. 4, a curve a is a true value of the scoring information, a curve B is a predicted value of the scoring information obtained based on a preset model, and the matching degree between the true value a and the predicted value B is relatively high, which indicates that the accuracy of the preset model is high.
Illustratively, the predetermined model that is ultimately determined may be:
y=7.291+0.189 Xjob level-0.076 Xworking years+0.149 Xcompleted task scoring-0.207 Xitem quantity-0.065 Xinterface quantity-0.033 Xuse case quantity-0.207 Xassociated System quantity+0.721 Xtest State+0.942 Xtask content engagement+1.755 Xtask scheduling engagement-0.499 XNew task scoring
Accordingly, the obtained key parameter information of each candidate tester is respectively input into the preset model, and the scoring information of each candidate tester can be obtained. The sample data set is subjected to regression analysis through the SPSS to screen candidate parameter information and fit a preset model meeting the verification requirement, and the obtained model is more accurate due to reasonable data analysis, so that more accurate grading information can be obtained, more accurate data reference is provided for personnel allocation, and the accuracy of subsequent personnel allocation is improved.
S103, determining target testers according to the grading information of each candidate tester.
Illustratively, after the scoring information of each candidate tester is obtained, the information processing device based on personnel allocation determines the target tester according to the scoring information of each candidate tester.
Illustratively, determining the target tester based on the scoring information of each candidate tester may include: and determining the candidate test person with the highest scoring information as a target test person according to the scoring information of each candidate test person.
Illustratively, the scoring information indicates the adaptation degree of the candidate testers to the new task, and the higher the scoring information is, the higher the adaptation degree is, so that the actual requirements are met.
S104, sending prompt information to a terminal of a target tester; the prompt information is used for indicating a target tester to complete a new task within a preset period.
In an exemplary embodiment, in order to enable the target tester to obtain the task allocation situation as soon as possible, the information processing device based on personnel allocation in the application further sends prompt information to the terminal of the target tester after determining the target tester, so that the target tester can complete a new task within a preset period.
In one possible example, before sending the prompt information to the terminal of the target tester, the method of the present application may further include:
s10, displaying a target tester;
s20, responding to a determination instruction of an administrator user, and generating prompt information; wherein the determining instruction characterizes the manager confirming execution of the new task by the target tester.
In some examples, the information processing device based on personnel allocation in the application further displays the target testers for the administrator to determine, generates prompt information after receiving the determination instruction of the administrator, and then sends the generated prompt information to the target testers determined by the administrator. Accordingly, personnel auditing is carried out by an administrator, and the accuracy of the allocation of the testers can be further improved.
The embodiment of the application provides an information processing method based on personnel allocation, which comprises the following steps: acquiring key parameter information of each candidate tester; the key parameter information is information for indicating comprehensive working capacity, current workload and new task connectivity of candidate testers; inputting the key parameter information into a preset model to obtain scoring information of candidate testers; the method comprises the steps that a preset model is obtained by performing multiple linear regression analysis fitting on the basis of a sample data set, and scoring information is used for indicating the adaptation degree of candidate testers and a new task; determining target testers according to the grading information of each candidate tester; sending prompt information to a terminal of a target tester; the prompt information is used for indicating a target tester to complete a new task within a preset period. According to the scheme, the grading information of each candidate tester is determined based on the key parameter information and the preset model, then the target tester is determined based on the grading information, and the allocation result is sent to the terminal of the target tester.
The following are device embodiments of the present application, which may be used to perform method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.
Fig. 5 is a schematic structural diagram of an information processing device based on personnel allocation according to an embodiment of the present application. As shown in fig. 5, the information processing apparatus 50 based on personnel allocation provided in the embodiment of the present application includes an acquisition unit 501, a calculation unit 502, a determination unit 503, and a presentation unit 504.
An obtaining unit 501, configured to obtain key parameter information of each candidate tester; the key parameter information is information for indicating comprehensive working capacity, current workload and new task engagement of candidate testers.
The calculating unit 502 is configured to input the key parameter information into a preset model, and obtain score information of candidate testers; the preset model is obtained by performing multiple linear regression analysis fitting based on a sample data set, and the scoring information is used for indicating the adaptation degree of candidate testers to a new task.
And a determining unit 503, configured to determine the target tester according to the scoring information of each candidate tester.
A prompt unit 504, configured to send prompt information to a terminal of a target tester; the prompt information is used for indicating a target tester to complete a new task within a preset period.
The device provided in this embodiment may be used to perform the method of the foregoing embodiment, and its implementation principle and technical effects are similar, and will not be described herein again.
Fig. 6 is a schematic structural diagram of another information processing apparatus based on personnel allocation according to an embodiment of the present application. As shown in fig. 6, the information processing apparatus 60 based on personnel allocation provided in the embodiment of the present application includes an acquisition unit 601, a calculation unit 602, a determination unit 603, and a prompt unit 604.
An acquiring unit 601, configured to acquire key parameter information of each candidate tester; the key parameter information is information for indicating comprehensive working capacity, current workload and new task engagement of candidate testers.
The computing unit 602 is configured to input the key parameter information into a preset model, so as to obtain scoring information of candidate testers; the preset model is obtained by performing multiple linear regression analysis fitting based on a sample data set, and the scoring information is used for indicating the adaptation degree of candidate testers to a new task.
And a determining unit 603, configured to determine a target tester according to the scoring information of each candidate tester.
The prompting unit 604 is configured to send prompting information to a terminal of a target tester; the prompt information is used for indicating a target tester to complete a new task within a preset period.
In one example, the determining unit 603 is specifically configured to determine, according to the scoring information of each candidate tester, that the candidate tester with the highest scoring information is the target tester.
In one example, the apparatus 60 further comprises a model unit 600, the model unit 600 comprising a retrieval module 6001 and a processing module 6002.
A retrieval module 6001 for obtaining a sample data set from a server; each sample data in the sample data set comprises candidate parameter information and corresponding preset scoring information, wherein the candidate parameter information is all parameter information which can be used for indicating comprehensive working capacity, current workload and new task engagement of a tester, and the key parameter information is part of the candidate parameter information.
The processing module 6002 is configured to perform multiple linear regression analysis on the sample data set to generate a preset model.
In one example, processing module 6002 includes a screening module 60021, a fitting module 60022, and a verification module 60023.
The screening module 60021 is configured to perform regression analysis on the sample data set through a social science statistical software package tool, and screen out key parameter information in the candidate parameter information.
The fitting module 60022 is configured to perform fitting of the multiple linear regression model with the key parameter information in the candidate parameter information as an independent variable and the corresponding preset score information as an independent variable.
The verification module 60023 is configured to verify the multiple linear regression model obtained by fitting, and generate a preset model.
In one example, the verification module 60023 is specifically configured to determine that the fitted multiple linear regression model is a preset model if it is determined that the decision coefficient and the significance information of the fitted multiple linear regression model both meet preset conditions; wherein the decision coefficients are used to indicate the goodness of fit of the model and the saliency information is used to indicate the saliency of the linear relationship between the independent and dependent variables.
In one example, the acquisition unit 601 includes a transmission module 6011 and a reception module 6012.
A sending module 6011, configured to send an acquisition instruction to a server; the acquisition instruction is used for indicating to acquire key parameter information of candidate testers.
And the receiving module 6012 is configured to receive key parameter information of each candidate tester sent by the server.
In one example, the prompt unit 604 includes a display module 6041 and a response module 6042.
And a display module 6041 for displaying the target tester.
A response module 6042 for generating prompt information in response to the determination instruction of the administrator user; wherein the determining instruction characterizes the manager confirming execution of the new task by the target tester.
The device provided in this embodiment may be used to perform the method of the foregoing embodiment, and its implementation principle and technical effects are similar, and will not be described herein again.
It should be noted that, it should be understood that the division of the modules of the above apparatus is merely a division of a logic function, and may be fully or partially integrated into a physical entity or may be physically separated. And these modules may all be implemented in software in the form of calls by the processing element; or can be realized in hardware; the method can also be realized in a form of calling software by a processing element, and the method can be realized in a form of hardware by a part of modules. The functions of the above data processing module may be called and executed by a processing element of the above apparatus, and may be stored in a memory of the above apparatus in the form of program codes. The implementation of the other modules is similar. In addition, all or part of the modules can be integrated together or can be independently implemented. The processing element here may be an integrated circuit with signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in a software form.
Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 7, the electronic device 70 includes: a processor 701, and a memory 702 communicatively coupled to the processor.
Wherein the memory 702 stores computer-executable instructions; the processor 701 executes computer-executable instructions stored in the memory 702 to implement a method as in any of the preceding claims.
In the specific implementation of the electronic device described above, it should be understood that the processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The method disclosed in connection with the embodiments of the present application may be embodied directly in hardware processor execution or in a combination of hardware and software modules in a processor.
Embodiments of the present application also provide a computer-readable storage medium having stored therein computer-executable instructions that, when executed by a processor, are configured to implement a method as in any of the preceding claims.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the method embodiments described above may be performed by computer instruction related hardware. The foregoing program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
Embodiments of the present application also provide a computer program product comprising a computer program for implementing a method as in any of the preceding claims when executed by a processor.
It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all alternative embodiments, and that the acts and modules referred to are not necessarily required in the present application.
It should be further noted that, although the steps in the flowchart are sequentially shown as indicated by arrows, the steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least a portion of the steps in the flowcharts may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order in which the sub-steps or stages are performed is not necessarily sequential, and may be performed in turn or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
It should be understood that the above-described device embodiments are merely illustrative, and that the device of the present application may be implemented in other ways. For example, the division of the units/modules in the above embodiments is merely a logic function division, and there may be another division manner in actual implementation. For example, multiple units, modules, or components may be combined, or may be integrated into another system, or some features may be omitted or not performed.
In addition, each functional unit/module in each embodiment of the present application may be integrated into one unit/module, or each unit/module may exist alone physically, or two or more units/modules may be integrated together, unless otherwise specified. The integrated units/modules described above may be implemented either in hardware or in software program modules.
The integrated units/modules, if implemented in hardware, may be digital circuits, analog circuits, etc. Physical implementations of hardware structures include, but are not limited to, transistors, memristors, and the like. The processor may be any suitable hardware processor, such as CPU, GPU, FPGA, DSP and ASIC, etc., unless otherwise specified. Unless otherwise indicated, the storage elements may be any suitable magnetic or magneto-optical storage medium, such as resistive Random Access Memory RRAM (Resistive Random Access Memory), dynamic Random Access Memory DRAM (Dynamic Random Access Memory), static Random Access Memory SRAM (Static Random-Access Memory), enhanced dynamic Random Access Memory EDRAM (Enhanced Dynamic Random Access Memory), high-Bandwidth Memory HBM (High-Bandwidth Memory), hybrid Memory cube HMC (Hybrid Memory Cube), etc.
The integrated units/modules may be stored in a computer readable memory if implemented in the form of software program modules and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, including several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned memory includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments. The technical features of the above embodiments may be combined in any way, and for brevity, all of the possible combinations of the technical features of the above embodiments are not described, but should be considered as the scope of the description
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (10)

1. An information processing method based on personnel allocation, which is characterized by comprising the following steps:
acquiring key parameter information of each candidate tester; the key parameter information is information for indicating comprehensive working capacity, current workload and new task engagement of the candidate testers;
inputting the key parameter information into a preset model to obtain scoring information of the candidate test personnel; the preset model is obtained by performing multiple linear regression analysis fitting based on a sample data set, and the scoring information is used for indicating the suitability of the candidate test personnel to a new task;
Determining target testers according to the grading information of each candidate tester;
sending prompt information to the terminal of the target tester; the prompt message is used for indicating the target tester to finish the new task within a preset period.
2. The method of claim 1, wherein determining the target tester based on the scoring information for each candidate tester comprises:
and determining the candidate test person with the highest scoring information as a target test person according to the scoring information of each candidate test person.
3. The method according to claim 1, wherein the method further comprises:
obtaining a sample data set from a server; each sample data in the sample data set comprises candidate parameter information and corresponding preset scoring information, wherein the candidate parameter information is all parameter information which can be used for indicating comprehensive working capacity, current workload and new task engagement of a tester, and the key parameter information is part of information in the candidate parameter information;
and performing multiple linear regression analysis processing on the sample data set to generate the preset model.
4. A method according to claim 3, wherein performing multiple linear regression analysis on the sample dataset to generate the predetermined model comprises:
carrying out regression analysis on the sample data set through a social science statistical software package tool, and screening out key parameter information in the candidate parameter information;
taking key parameter information in the candidate parameter information as an independent variable, taking the corresponding preset scoring information as a dependent variable, and fitting a multiple linear regression model;
and verifying the fitted multiple linear regression model to generate the preset model.
5. The method of claim 4, wherein verifying the fitted multiple linear regression model to generate the pre-set model comprises:
if the determination coefficient and the significance information of the multiple linear regression model obtained by fitting are determined to meet the preset conditions, determining the multiple linear regression model obtained by fitting as the preset model; wherein the decision coefficient is used to indicate a goodness of fit of a model, and the saliency information is used to indicate a saliency degree of a linear relationship between the independent variable and the dependent variable.
6. The method of any one of claims 1-5, wherein obtaining key parameter information for each candidate tester comprises:
sending an acquisition instruction to a server; the acquisition instruction is used for indicating to acquire key parameter information of candidate testers;
and receiving key parameter information of each candidate tester sent by the server.
7. The method of any of claims 1-5, wherein prior to sending the prompt message to the terminal of the target tester, the method further comprises:
displaying the target tester;
responding to a determining instruction of an administrator user, and generating the prompt information; wherein the determining instruction characterizes the manager confirming execution of the new task by the target tester.
8. An information processing apparatus based on personnel allocation, the apparatus comprising:
the acquisition unit is used for acquiring key parameter information of each candidate tester; the key parameter information is information for indicating comprehensive working capacity, current workload and new task engagement of the candidate testers;
the computing unit is used for inputting the key parameter information into a preset model to obtain the scoring information of the candidate testers; the preset model is obtained by performing multiple linear regression analysis fitting based on a sample data set, and the scoring information is used for indicating the suitability of the candidate test personnel to a new task;
The determining unit is used for determining target testers according to the scoring information of each candidate tester;
the prompting unit is used for sending prompting information to the terminal of the target tester; the prompt message is used for indicating the target tester to finish the new task within a preset period.
9. An electronic device, the electronic device comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1 to 7.
10. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1 to 7.
CN202311502233.7A 2023-11-10 2023-11-10 Information processing method, device, equipment and storage medium based on personnel allocation Pending CN117314121A (en)

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CN117314121A true CN117314121A (en) 2023-12-29

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