CN116187675A - Task allocation method, device, equipment and storage medium - Google Patents
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Abstract
The application discloses a task allocation method, a device, equipment and a storage medium, which relate to the technical field of data processing and are used for improving allocation efficiency and accuracy of an issuing task and comprise the following steps: acquiring task information of a target task and basic information of a plurality of developers, wherein the task information of the target task comprises: the basic information of the task publisher, the task requirement and the task emergency degree comprises the following basic information of a plurality of developers: the existing workload, task completion quality and technical field; based on the matching degree between the task information of the target task and the basic information of a plurality of developers, respectively evaluating the plurality of developers to obtain evaluation parameters corresponding to each developer; a target developer performing the target task is determined based on the evaluation parameters corresponding to each of the plurality of developers. The method and the device are applied to the scene of distributing development tasks.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a task allocation method, device, apparatus, and storage medium.
Background
Currently, with the continuous development of science and technology, particularly the emergence of digital technologies such as big data, artificial intelligence, cloud computing, blockchain, the internet of things and the like, data become new production elements. In order to further deepen the data value mining, more flexible, agile and intelligent enabling digital operation and customer experience, the capability of the service platform needs to be continuously integrated and perfected. In the process of improving the capacity of the service platform, a business department needs to put forward development requirements on the service platform according to real-time requirements; the service platform needs to effectively manage and control the development requirements, and evaluates the suitability between the developer and the demand party according to the data characteristics between the developer and the demand party, so that reasonable assignment of the requirements is realized, and the effects of reducing human resource waste and ensuring the development requirement completion speed and quality are achieved.
Specifically, firstly, a demand party submits development demands to a service platform, and the service platform evaluates developers according to a certain demand distribution strategy for reference by the demand party. The demander comprehensively considers the evaluation result and the actual situation of the service platform, and designates the corresponding developer to finish the demand. The developer receiving the development requirement decides whether to accept the development task according to the actual situation and preference of the developer. In this process, the service platform allows developers to reject unsmooth development tasks due to different levels of preference of different developers for the development tasks. The distribution of the demand parties and developers also has spatiotemporal dynamics, which makes rational distribution of development demands challenging, due to the complexity of the situation itself. Therefore, the current development task is low in distribution efficiency and poor in accuracy.
Disclosure of Invention
The task allocation method, the device, the equipment and the storage medium can solve the problems of low allocation efficiency and poor accuracy of development tasks caused by the fact that the situation of a demand party and a developer has complexity and the distribution of the demand party and the developer also has space-time dynamic property, so that the allocation efficiency and accuracy of the development tasks can be improved.
In order to achieve the above purpose, the present application adopts the following technical scheme:
in a first aspect, a task allocation method is provided, the method including: acquiring task information of a target task and basic information of a plurality of developers, wherein the task information of the target task comprises: the basic information of the task publisher, the task requirement and the task emergency degree comprises the following basic information of a plurality of developers: the existing workload, task completion quality and technical field; based on the matching degree between the task information of the target task and the basic information of a plurality of developers, respectively evaluating the plurality of developers to obtain evaluation parameters corresponding to each developer; a target developer performing the target task is determined based on the evaluation parameters corresponding to each of the plurality of developers.
In one possible implementation, the method further includes: task information of a plurality of historical tasks is obtained, and analysis modeling processing is carried out on the task information of the plurality of historical tasks and basic information of a plurality of developers through a preset clustering algorithm to obtain a target model; based on the matching degree between the task information of the target task and the basic information of a plurality of developers, the plurality of developers are respectively evaluated to obtain evaluation parameters corresponding to each developer, and the evaluation parameters comprise: and evaluating the matching degree between the task information of the target task and the basic information of a plurality of developers based on the target model to obtain evaluation parameters corresponding to each developer.
In one possible implementation manner, performing analysis modeling processing on task information of a plurality of historical tasks and basic information of a plurality of developers through a preset clustering algorithm to obtain a target model, including: determining a first parameter corresponding to each historical task of the plurality of historical tasks and each developer of the plurality of developers based on task requirements included in the task information and technical fields included in basic information of the plurality of developers through a preset clustering algorithm; determining a second parameter corresponding to each of the plurality of historical tasks and each of the plurality of developers based on the task urgency degree included in the task information and the existing workload included in the basic information of the plurality of developers through a preset clustering algorithm; determining a third parameter corresponding to each developer in the plurality of developers based on task completion quality included in the basic information of the plurality of developers through a preset clustering algorithm; and constructing a target model based on the first parameter, the second parameter and the third parameter, wherein the first parameter, the second parameter and the third parameter are used for indicating the matching degree between the task and the developer.
In one possible implementation manner, based on the matching degree between the task information of the target task and the basic information of the plurality of developers, the plurality of developers are respectively evaluated to obtain an evaluation parameter corresponding to each developer, including: analyzing and processing task information of a target task and basic information of a plurality of developers based on a target model, and determining the matching degree between the task information of the target task and the basic information of the plurality of developers; determining a predicted time corresponding to completion of the target task by each of the plurality of developers based on the task urgency included in the task information of the target task and the existing workload included in the basic information of the plurality of developers; and based on the matching degree between the task information of the target task and the basic information of the plurality of developers and the expected time for each developer of the plurality of developers to complete the target task, respectively evaluating the plurality of developers to obtain the evaluation parameters corresponding to each developer.
In a second aspect, there is provided a task assigning apparatus including: an acquisition unit and a processing unit; the system comprises an acquisition unit, a storage unit and a storage unit, wherein the acquisition unit is used for acquiring task information of a target task and basic information of a plurality of developers, and the task information of the target task comprises: the basic information of the task publisher, the task requirement and the task emergency degree comprises the following basic information of a plurality of developers: the existing workload, task completion quality and technical field; the processing unit is used for respectively evaluating the plurality of developers based on the matching degree between the task information of the target task and the basic information of the plurality of developers to obtain an evaluation parameter corresponding to each developer; and the processing unit is used for determining a target developer for executing the target task based on the evaluation parameters corresponding to each developer in the plurality of developers.
In one possible implementation manner, the acquiring unit is configured to acquire task information of a plurality of historical tasks; the processing unit is used for analyzing and modeling task information of a plurality of historical tasks and basic information of a plurality of developers through a preset clustering algorithm to obtain a target model; and the processing unit is used for evaluating the matching degree between the task information of the target task and the basic information of a plurality of developers based on the target model to obtain evaluation parameters corresponding to each developer.
In a possible implementation manner, the processing unit is configured to determine, through a preset clustering algorithm, a first parameter corresponding to each of the plurality of historical tasks and each of the plurality of developers based on a task requirement included in the task information and a technical field included in the basic information of the plurality of developers; the processing unit is used for determining a second parameter corresponding to each historical task in the plurality of historical tasks and each developer in the plurality of developers based on the task emergency degree included in the task information and the existing workload included in the basic information of the plurality of developers through a preset clustering algorithm; the processing unit is used for determining a third parameter corresponding to each developer in the plurality of developers based on task completion quality included in the basic information of the plurality of developers through a preset clustering algorithm; and the processing unit is used for constructing a target model based on the first parameter, the second parameter and the third parameter, wherein the first parameter, the second parameter and the third parameter are used for indicating the matching degree between the task and the developer.
In one possible implementation manner, the processing unit is used for analyzing and processing the task information of the target task and the basic information of the plurality of developers based on the target model, and determining the matching degree between the task information of the target task and the basic information of the plurality of developers; the processing unit is used for determining the expected time for each developer of the plurality of developers to complete the target task based on the task emergency degree included in the task information of the target task and the existing workload included in the basic information of the plurality of developers; the processing unit is used for respectively evaluating the plurality of developers to obtain evaluation parameters corresponding to the plurality of developers based on the matching degree between the task information of the target task and the basic information of the plurality of developers and the expected time corresponding to the completion of the target task by each developer in the plurality of developers.
In a third aspect, an electronic device, comprising: a processor and a memory; wherein the memory is configured to store one or more programs, the one or more programs comprising computer-executable instructions that, when executed by the electronic device, cause the electronic device to perform a task allocation method as in the first aspect.
In a fourth aspect, there is provided a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computer, cause the computer to perform a task allocation method as in the first aspect.
The application provides a task allocation method, device, equipment and storage medium, which are applied to a scene of allocating development tasks. After the target task is released by the demand party, acquiring task information comprising task release agents, task demands and task emergency degrees corresponding to the target task, and acquiring basic information comprising the existing workload, task completion quality and technical fields corresponding to a plurality of developers; further, based on the matching degree between the task information of the target task and the basic information of the plurality of developers, the plurality of developers can be respectively evaluated to obtain the evaluation parameters corresponding to each developer; thus, the target developer performing the target task can be determined based on the evaluation parameters corresponding to each of the plurality of developers. According to the method and the system, the multiple developers are evaluated according to the matching degree between the task information of the target task and the basic information of the multiple developers, so that evaluation parameters corresponding to each developer are obtained; thus, a target developer suitable for executing the target task can be determined from the plurality of developers based on the evaluation parameters corresponding to each of the plurality of developers. Therefore, the distribution efficiency and accuracy of development tasks can be improved.
Drawings
Fig. 1 is a schematic structural diagram of a task allocation module according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a task allocation method according to an embodiment of the present application;
FIG. 3 is a schematic diagram illustrating steps of a task allocation method according to an embodiment of the present application;
fig. 4 is a schematic diagram of a task allocation method according to a second embodiment of the present application;
fig. 5 is a schematic diagram of a task allocation method according to an embodiment of the present application;
fig. 6 is a second schematic flow chart of a task allocation method according to an embodiment of the present application;
fig. 7 is a schematic flow chart III of a task allocation method according to an embodiment of the present application;
fig. 8 is a schematic diagram of a task allocation method according to a third embodiment of the present application;
fig. 9 is a schematic diagram of a task allocation method according to an embodiment of the present application;
fig. 10 is a schematic diagram fifth step of a task allocation method according to an embodiment of the present application;
fig. 11 is a schematic diagram sixth step of a task allocation method provided in an embodiment of the present application;
fig. 12 is a flowchart of a task allocation method according to an embodiment of the present application;
FIG. 13 is a schematic structural diagram of a task assigning device according to an embodiment of the present application;
Fig. 14 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
In the description of the present application, "/" means "or" unless otherwise indicated, for example, a/B may mean a or B. "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. Further, "at least one", "a plurality" means two or more. The terms "first," "second," and the like do not limit the number and order of execution, and the terms "first," "second," and the like do not necessarily differ.
The task allocation method provided by the embodiment of the application can be applied to a task allocation system. Fig. 1 shows a schematic diagram of a construction of the task distribution system. As shown in fig. 1, the task allocation system 20 includes: an electronic device 21 and a server 22.
The electronic device 21 is configured to execute a task allocation method, and specifically configured to acquire task information of a target task and basic information of a plurality of developers from the server 22, so as to evaluate the plurality of developers based on matching degrees between the task information of the target task and the basic information of the plurality of developers, to obtain evaluation parameters corresponding to each developer; thus, a target developer performing the target task is determined based on the evaluation parameters corresponding to each of the plurality of developers.
The server 22 is for storing task information of a target task, basic information of a plurality of developers, and task information of a history task, and performs data information interaction with the electronic device 21.
A task allocation method provided in the embodiments of the present application is described below with reference to the accompanying drawings.
As shown in fig. 2, a task allocation method provided in the embodiment of the present application includes S201 to S203:
s201, task information of a target task and basic information of a plurality of developers are acquired.
The task information of the target task comprises: the basic information of the task publisher, the task requirement and the task emergency degree comprises the following basic information of a plurality of developers: the existing workload, task completion quality and technical field.
Optionally, the task requirements may include: information such as task type, function requirement, task name, etc., the task completion quality may include: customer satisfaction rate, task online quality, task completion time and the like.
Optionally, the demander may issue the target task in the task allocation system and input the task demand and the task urgency, so that the task allocation system matches the corresponding developer for the target task.
Further, after the task distribution system acquires the task information of the target task, the task distribution system also needs to acquire basic information of a plurality of developers to determine target developers for executing the target task based on the matching degree between the task information of the target task and the basic information of the plurality of developers.
Optionally, the application discloses a reasonable assignment of development requirements (i.e. target tasks) issued by the demander based on the characteristics of the developer. Specifically, a matching scoring model (i.e., a target model described below) between the developer and the demander can be constructed based on deep learning, artificial intelligence, and other techniques.
For example, as shown in fig. 3, after the target task is issued by the demander, the task distribution system extracts features included in task information (i.e., development requirements) of the target task, and obtains features (i.e., basic information) of all currently available developers included in the task distribution system, so that data processing and feature construction are performed according to the development requirement features and the features of the developers, and a target developer for executing the target task is determined through a target model.
S202, based on the matching degree between the task information of the target task and the basic information of the plurality of developers, the plurality of developers are respectively evaluated to obtain evaluation parameters corresponding to each developer.
Optionally, the task allocation system may perform clustering processing on task information of the target task and basic information of multiple developers, and determine a matching degree between each developer and the target task, so as to evaluate the multiple developers to obtain an evaluation parameter corresponding to each developer.
For example, as shown in fig. 4, the task allocation system may evaluate each developer based on the target model, and estimate the completion time required by each developer to complete the target task, where the target model may assist the demander in selecting an appropriate developer to complete the target task, thereby shortening the completion time required to complete the target task and improving the working efficiency. Specifically, based on basic information of a plurality of developers, feature extraction can be performed to obtain capability features corresponding to the developers, and based on historical task completion information, portrait features corresponding to each developer are determined. And clustering the demand features extracted from the task information of the target task, and determining the corresponding clustering features and matching features between the task information of the target task and the basic information of a plurality of developers.
S203, determining a target developer for executing the target task based on the evaluation parameters corresponding to each developer in the plurality of developers.
Alternatively, the developer with the highest evaluation parameter may be determined from a plurality of developers as the target developer for executing the target task.
For example, as shown in fig. 5, the evaluation parameter corresponding to the developer a is 4.5, the evaluation parameter corresponding to the developer B is 4.3, the evaluation parameter corresponding to the developer C is 3.5, and the evaluation parameter corresponding to the developer D is 3.3, so that the developer a can be determined from the developers as a target developer for executing the target task.
In one design, as shown in fig. 6, the task allocation method provided in the embodiment of the present application may specifically further include step S301, and the method in step S202 may specifically include step S302:
s301, task information of a plurality of historical tasks is obtained, and analysis modeling processing is carried out on the task information of the historical tasks and basic information of a plurality of developers through a preset clustering algorithm to obtain a target model.
Optionally, when the target model is obtained, task information of a plurality of historical tasks may be specifically obtained, where the task information of each historical task mainly includes: task publishers (i.e., task demand team names), task demand sources, task demand names, task urgency, task demand types, and the like.
Furthermore, data information (i.e., basic information of the developer) of a plurality of developers needs to be collected, and image data of the developers is determined, wherein the image data mainly comprises: the developer corresponds to a developer, the existing development workload of the developer, the delivery time rate of the developer for completing development tasks, and the principal business of the developer (the principal business comprises more than one type, and the principal business of the same developer can comprise a development type, a configuration type, a query statistics type and the like).
Optionally, developer capability features may also be collected, where the capability features include: development workload, task satisfaction rate, task online quality, etc. Therefore, a series of new features such as matching features, clustering features, capability features and the like can be constructed according to task information of historical tasks and data information (features) of developers. And analyzing and modeling based on a series of new features such as the matching features, the clustering features, the capability features and the like to obtain a target model.
S302, evaluating the matching degree between the task information of the target task and the basic information of a plurality of developers based on the target model to obtain evaluation parameters corresponding to each developer.
Optionally, the task distribution system extracts task information of the target task and characteristics (basic information) of all currently available developers, and constructs multi-dimensional combination characteristics according to task demand characteristics, developer portraits and historical requirements of the developers; and then, the three parts of characteristics of the demand characteristics, the developer portraits, the state characteristics and the multidimensional combination characteristics are input into a matching model (namely a target model) of the demand and the developer for evaluation, so that evaluation parameters corresponding to each developer are obtained. Thus, the demander can specify the developer most suitable for the target task development based on the evaluation parameters of each developer.
In one design, as shown in fig. 7, in a task allocation method provided in the embodiment of the present application, the method in step S301 may specifically include steps S401 to S404:
s401, determining a first parameter corresponding to each historical task of the plurality of historical tasks and each developer of the plurality of developers based on task requirements included in task information and technical fields included in basic information of the plurality of developers through a preset clustering algorithm.
S402, determining a second parameter corresponding to each historical task of the plurality of historical tasks and each developer of the plurality of developers based on the task emergency degree included in the task information and the existing workload included in the basic information of the plurality of developers through a preset clustering algorithm.
S403, determining a third parameter corresponding to each developer in the plurality of developers based on task completion quality included in the basic information of the plurality of developers through a preset clustering algorithm.
S404, constructing a target model based on the first parameter, the second parameter and the third parameter.
The first parameter, the second parameter and the third parameter are all used for indicating the matching degree between the task and the developer.
Optionally, a series of matching features can be constructed according to the demand features obtained by analysis in the task information and the features in the basic information of the developer, matching is performed according to the task demands, the task types and the main features of the developer, and whether the task emergency degree is matched with the existing workload of the developer or not is determined. The matching features help the neural network model to further determine if the developer and task match.
It should be noted that, the purpose of the cluster analysis is to perform analysis modeling through a K-Means clustering algorithm according to the basic information and the statistically collected task information of the developer after the cleaning processing, so as to cluster the developer and the task demands, and the developer of the same class is better in handling similar demands. According to the method, the elbow method is selected in the clustering process to judge the number of categories of the best developer and task demands, the contour coefficient is used for verifying the clustering effect, and finally, the category matching analysis of the developer and the model is realized according to the clustering result, so that the reliability of the model on the evaluation of the developer is improved.
For example, as shown in fig. 8, by extracting the required feature data from the task information and extracting the feature data from the basic information of the developer, the data normalization (normalization) process is performed, so that the effect of excessive weight of a certain feature is avoided. Furthermore, the optimal developer group number and the demand group number are selected through an elbow method, the developers and the demand group number are clustered through K-Means clustering algorithm modeling, and the clustering effect is verified by combining the business scene and the contour coefficient, so that iterative optimization is achieved. And finally, according to the actual situation of the demand party, determining the matching relation between the demand category and the developer category.
For example, as shown in table one, the matching degree between three types of developers and three types of task demands is given, wherein the matching degree between the developer class a and the task demand a is 0.7, the matching degree between the developer class a and the task demand B is 0.6, the matching degree between the developer class a and the task demand C is 0.8, the matching degree between the developer class B and the task demand a is 0.3, the matching degree between the developer class B and the task demand B is 0.4, the matching degree between the developer class B and the task demand C is 0.3, the matching degree between the developer class C and the task demand a is 0.2, the matching degree between the developer class C and the task demand B is 0.9, and the matching degree between the developer class C and the task demand C is 0.6.
List one
| Degree of matching | Developer category A | Developer category B | Developer category C |
| Task demand a | 0.7 | 0.3 | 0.2 |
| Task demand b | 0.6 | 0.4 | 0.9 |
| Task demand c | 0.8 | 0.3 | 0.6 |
Optionally, according to the actual business situation and the historical demand information which are represented by the task information of the target task, the matching degree of the developer category and the demand side (or each task) category can be given, and the value range of the matching degree is between 0 and 1.
Thus, according to the completion time rate, development workload and task satisfaction rate of the historical tasks of the developerThe on-line quality of the task and the like are evaluated for the capability of the developer. X is X i C for the evaluation parameters of the developers in the project to exceed the proportion of other developers i Representing the importance of the target task, provided by the demander, based on X i And C i The product of (2) yields P i ，P i And representing the final evaluation parameters of the target task, so that all developers can perform standardized evaluation based on the evaluation parameters of the target task.
Optionally, after the task information of the historical task is acquired and constructed, five parts of features including developer portrait data features, developer historical demand features, current demand features (i.e. features extracted from the task information of the target task), developer and demand classification matching features, developer clustering features and demand clustering features can be obtained.
The developer portrait data features comprise the corresponding manufacturer name, timely delivery rate (namely task completion rate), development workload and other features of the developer, and have text data and continuous numerical data; the historical demand characteristics and the current demand characteristics of the developer comprise demand names, demand types and the like, and can be divided into data types such as text data, classification data and the like. The developer cluster feature and the demand cluster feature only comprise data types of different types.
For example, as shown in fig. 9, by extracting the required feature data from the task information and extracting the feature data from the basic information of the developer, further checking whether the data has a missing value, if the missing rate of a certain feature is higher (for example, greater than 30%), the data is directly deleted; if the loss rate is low (e.g., less than or equal to 30%), then the mode for that feature is filled. And, the non-numeric field, such as the classification characteristics of gender, demand type, manufacturer corresponding to developer, etc., is converted into numeric type. Discretizing and dimensionless processing are carried out on the continuous numerical type characteristics, and the continuous numerical type characteristics specifically comprise timely delivery rate, current development workload and the like. Therefore, the regularized data is finally obtained by means of normalization, normalization and the like.
It should be noted that, the present application processes the demand features and labels obtained by the task information of the historical task, however, in the process of learning the neural network model, in order to make the neural network evaluate the matching degree of the task demand of the target task and the developer, the demand features of the historical task need to be scored. Specifically, a manual scoring method can be adopted, and the manual scoring and the data characteristics are associated to obtain a training set for completing scoring based on historical task requirements.
For example, as shown in fig. 10, the scoring acquisition of the historical task demands is divided into two parts, firstly, the demand characteristics corresponding to the historical task and the characteristic data extracted from the basic information of the developer are sent to the demand department for review by the staff with deep qualification, and the corresponding evaluation is given to the developer. On the other hand, the task demand online effect is considered, the factors including demand development time, online effect, adjustment after online and the like are used for task completion effect evaluation, and the final evaluation is formed by two parts of linear weighting.
Optionally, as shown in fig. 11, a model base is constructed for subsequent iterative training based on the generated data feature model file and the predicted result file. The regularized series of feature data processed by the feature processing, the cluster analysis and the like and the evaluation of the personnel of the demand department are combined by collecting the feature data of the demand and the feature data of the developer and processing and warehousing, so that training data are generated and put into a model entry catalog. Further selecting a DNN model as an evaluation model to construct, selecting a set of initial neural network parameters, and performing gridding search on model parameters such as the number of neurons of each layer of neural network, the learning rate, the iteration number and the like. And starting model iterative training, and observing model index change conditions, wherein indexes comprise Loss rate Loss and Accuracy (ACC). Finally, model tuning is carried out by modifying model parameters, and a univariate tuning method is adopted, wherein the parameters comprise the number of layers of the neural network, the number of neurons at each layer, the iteration times, training batches and the learning rate until model indexes reach constraint values.
It should be noted that, the model adopts two layers of neural networks, and the last layer of activation function is SoftMax, which is used for controlling the final output of the evaluation model in a certain range, and meanwhile, dropout is set in the model to prevent the model from being over fitted. And selecting a mean square error (Mean Square Error, MSE) by using the model loss function, solving the MSE by using an Adam optimization algorithm in the model training process, minimizing until the ACC reaches a set threshold or reaches the iteration number, stopping iterative training, and deriving a model file.
In one design, as shown in fig. 12, the method in step S202 may specifically include steps S501 to S503:
s501, analyzing and processing task information of a target task and basic information of a plurality of developers based on a target model, and determining the matching degree between the task information of the target task and the basic information of the plurality of developers.
S502, determining the expected time for each developer of the plurality of developers to complete the target task based on the task emergency degree included in the task information of the target task and the existing workload included in the basic information of the plurality of developers.
S503, based on the matching degree between the task information of the target task and the basic information of the plurality of developers and the expected time for each developer of the plurality of developers to complete the target task, respectively evaluating the plurality of developers to obtain the evaluation parameters corresponding to each developer.
Specifically, an evaluation engine is used for starting an evaluation service, loading a deep learning model file, carrying out feature processing and cluster analysis, storing and warehousing feature data to be predicted, evaluating the matching degree of an developer and a target task according to the rule of the trained model, and finally obtaining the evaluation parameters of each developer.
Illustratively, a prediction client in the task distribution system initiates a prediction request according to a task issued by a demand party, so that a prediction engine starts a prediction service, the prediction service loads a model file, and data is acquired from a database. And the data are read in batches, quota data of the user for the next two hours are predicted, and the predicted result is written into the database in real time.
After a target task is issued by a demand party, task information comprising a task issuer, task demands and task emergency degrees corresponding to the target task can be obtained, and basic information comprising the existing workload, task completion quality and technical field corresponding to a plurality of developers can be obtained; further, based on the matching degree between the task information of the target task and the basic information of the plurality of developers, the plurality of developers can be respectively evaluated to obtain the evaluation parameters corresponding to each developer; thus, the target developer performing the target task can be determined based on the evaluation parameters corresponding to each of the plurality of developers. According to the method and the system, the multiple developers are evaluated according to the matching degree between the task information of the target task and the basic information of the multiple developers, so that evaluation parameters corresponding to each developer are obtained; thus, a target developer suitable for executing the target task can be determined from the plurality of developers based on the evaluation parameters corresponding to each of the plurality of developers. Therefore, the distribution efficiency and accuracy of development tasks can be improved.
The foregoing description of the solution provided in the embodiments of the present application has been mainly presented in terms of a method. To achieve the above functions, it includes corresponding hardware structures and/or software modules that perform the respective functions. Those of skill in the art will readily appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The embodiment of the application may divide functional modules of a task allocation device according to the above method example, for example, each functional module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The integrated modules may be implemented in hardware or in software functional modules. Optionally, the division of the modules in the embodiments of the present application is schematic, which is merely a logic function division, and other division manners may be actually implemented.
Fig. 13 is a schematic structural diagram of a task allocation device according to an embodiment of the present application. As shown in fig. 13, a task allocation device 40 is used to improve the allocation efficiency and accuracy of the task to be developed, for example, to perform a task allocation method shown in fig. 2. The task assigning apparatus 40 includes: an acquisition unit 401 and a processing unit 402;
an obtaining unit 401, configured to obtain task information of a target task and basic information of a plurality of developers, where the task information of the target task includes: the basic information of the task publisher, the task requirement and the task emergency degree comprises the following basic information of a plurality of developers: the existing workload, task completion quality and technical field;
the processing unit 402 is configured to evaluate the plurality of developers to obtain an evaluation parameter corresponding to each developer, based on a matching degree between task information of the target task and basic information of the plurality of developers;
the processing unit 402 is configured to determine a target developer that performs the target task based on the evaluation parameters corresponding to each of the plurality of developers.
In one possible implementation manner, in a task allocation device 40 provided in the embodiments of the present application, an obtaining unit 401 is configured to obtain task information of a plurality of historical tasks;
The processing unit 402 is configured to perform analysis modeling processing on task information of a plurality of historical tasks and basic information of a plurality of developers through a preset clustering algorithm, so as to obtain a target model;
and the processing unit 402 is configured to evaluate, based on the target model, matching degrees between task information of the target task and basic information of a plurality of developers, and obtain evaluation parameters corresponding to each developer.
In a possible implementation manner, in the task allocation device 40 provided in the embodiment of the present application, the processing unit 402 is configured to determine, by using a preset clustering algorithm, a first parameter corresponding between each of a plurality of historical tasks and each of a plurality of developers based on a task requirement included in task information and a technical field included in basic information of the plurality of developers;
a processing unit 402, configured to determine, by using a preset clustering algorithm, a second parameter corresponding between each of the plurality of historical tasks and each of the plurality of developers based on a task urgency level included in the task information and an existing workload included in the basic information of the plurality of developers;
A processing unit 402, configured to determine, by using a preset clustering algorithm, a third parameter corresponding to each developer of the plurality of developers based on task completion quality included in the basic information of the plurality of developers;
the processing unit 402 is configured to construct a target model based on a first parameter, a second parameter, and a third parameter, where the first parameter, the second parameter, and the third parameter are all used to indicate a matching degree between the task and the developer.
In a possible implementation manner, in a task allocation device 40 provided in an embodiment of the present application, a processing unit 402 is configured to analyze and process task information of a target task and basic information of a plurality of developers based on a target model, and determine a matching degree between the task information of the target task and the basic information of the plurality of developers;
a processing unit 402, configured to determine an estimated time for each of the plurality of developers to complete the target task, based on a task urgency level included in the task information of the target task and an existing workload included in the basic information of the plurality of developers;
the processing unit 402 is configured to evaluate the plurality of developers to obtain an evaluation parameter corresponding to each developer, based on a matching degree between task information of the target task and basic information of the plurality of developers, and an expected time for each developer of the plurality of developers to complete the target task.
In the case of implementing the functions of the integrated modules in the form of hardware, another possible structural schematic diagram of the electronic device involved in the foregoing embodiment is provided in the embodiments of the present application. As shown in fig. 14, an electronic device 60 is provided for improving the efficiency and accuracy of assignment of development tasks, such as for performing a task assignment method as shown in fig. 2. The electronic device 60 comprises a processor 601, a memory 602 and a bus 603. The processor 601 and the memory 602 may be connected by a bus 603.
The processor 601 is a control center of the communication device, and may be one processor or a collective term of a plurality of processing elements. For example, the processor 601 may be a general-purpose central processing unit (central processing unit, CPU), or may be another general-purpose processor. Wherein the general purpose processor may be a microprocessor or any conventional processor or the like.
As one example, processor 601 may include one or more CPUs, such as CPU 0 and CPU 1 shown in fig. 14.
The memory 602 may be, but is not limited to, a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (random access memory, RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), magnetic disk storage or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
As a possible implementation, the memory 602 may exist separately from the processor 601, and the memory 602 may be connected to the processor 601 through the bus 603 for storing instructions or program codes. The processor 601, when calling and executing instructions or program code stored in the memory 602, is capable of implementing a task allocation method provided in the embodiments of the present application.
In another possible implementation, the memory 602 may also be integrated with the processor 601.
It should be noted that the structure shown in fig. 14 does not constitute a limitation of the electronic device 60. The electronic device 60 may include more or fewer components than shown in fig. 14, or may combine certain components or a different arrangement of components.
As an example, in connection with fig. 13, the acquisition unit 401 and the processing unit 402 in the electronic device realize the same functions as those of the processor 601 in fig. 14.
Optionally, as shown in fig. 14, the electronic device 60 provided in the embodiment of the present application may further include a communication interface 604.
In one design, the electronic device provided in the embodiments of the present application may further include a communication interface integrated into the processor.
From the above description of embodiments, it will be apparent to those skilled in the art that the foregoing functional unit divisions are merely illustrative for convenience and brevity of description. In practical applications, the above-mentioned function allocation may be performed by different functional units, i.e. the internal structure of the device is divided into different functional units, as needed, to perform all or part of the functions described above. The specific working processes of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which are not described herein.
The embodiment of the application further provides a computer readable storage medium, in which instructions are stored, and when the computer executes the instructions, the computer executes each step in the method flow shown in the method embodiment.
Embodiments of the present application provide a computer program product comprising instructions which, when run on a computer, cause the computer to perform a task allocation method as in the method embodiments described above.
The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: electrical connections having one or more wires, portable computer diskette, hard disk. Random access Memory (Random Access Memory, RAM), read-Only Memory (ROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), registers, hard disk, optical fiber, portable compact disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any other form of computer-readable storage medium suitable for use by a person or persons of skill in the art.
An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuit, ASIC).
In the context of the present application, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
Since the electronic device, the computer readable storage medium, and the computer program product in the embodiments of the present application may be applied to the above-mentioned method, the technical effects that can be obtained by the electronic device, the computer readable storage medium, and the computer program product may also refer to the above-mentioned method embodiments, and the embodiments of the present application are not repeated herein.
The foregoing is merely a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should be covered in the protection scope of the present application.
Claims (10)
1. A method of task allocation, the method comprising:
Acquiring task information of a target task and basic information of a plurality of developers, wherein the task information of the target task comprises the following components: task publishers, task demands and task emergency degree, wherein the basic information of the plurality of developers comprises: the existing workload, task completion quality and technical field;
based on the matching degree between the task information of the target task and the basic information of the plurality of developers, the plurality of developers are respectively evaluated to obtain evaluation parameters corresponding to each developer;
and determining a target developer for executing the target task based on the evaluation parameters corresponding to each developer in the plurality of developers.
2. The method according to claim 1, wherein the method further comprises:
task information of a plurality of historical tasks is obtained, and analysis modeling processing is carried out on the task information of the historical tasks and basic information of a plurality of developers through a preset clustering algorithm to obtain a target model;
the step of respectively evaluating the plurality of developers based on the matching degree between the task information of the target task and the basic information of the plurality of developers to obtain the evaluation parameters corresponding to each developer comprises the following steps:
And evaluating the matching degree between the task information of the target task and the basic information of the plurality of developers based on the target model to obtain the evaluation parameters corresponding to each developer.
3. The method according to claim 2, wherein the analyzing and modeling the task information of the plurality of historical tasks and the basic information of the plurality of developers by using a preset clustering algorithm to obtain the target model includes:
determining a first parameter corresponding to each historical task of the plurality of historical tasks and each developer of the plurality of developers based on task requirements included in the task information and technical fields included in the basic information of the plurality of developers through the preset clustering algorithm;
determining, by the preset clustering algorithm, a second parameter corresponding to each of the plurality of historical tasks and each of the plurality of developers based on a task urgency degree included in the task information and an existing workload included in the basic information of the plurality of developers;
determining a third parameter corresponding to each developer of the plurality of developers based on task completion quality included in the basic information of the plurality of developers through the preset clustering algorithm;
And constructing the target model based on the first parameter, the second parameter and the third parameter, wherein the first parameter, the second parameter and the third parameter are used for indicating the matching degree between the task and the developer.
4. A method according to any one of claims 1 to 3, wherein the evaluating the plurality of developers to obtain the evaluation parameters corresponding to each developer based on the matching degree between the task information of the target task and the basic information of the plurality of developers, respectively, includes:
analyzing and processing the task information of the target task and the basic information of the plurality of developers based on a target model, and determining the matching degree between the task information of the target task and the basic information of the plurality of developers;
determining a predicted time for each developer of the plurality of developers to complete the target task based on the task urgency degree included in the task information of the target task and the existing workload included in the basic information of the plurality of developers;
and based on the matching degree between the task information of the target task and the basic information of the plurality of developers and the expected time for each developer in the plurality of developers to complete the target task, respectively evaluating the plurality of developers to obtain an evaluation parameter corresponding to each developer.
5. A task allocation device, characterized in that the task allocation device comprises: an acquisition unit and a processing unit;
the acquiring unit is configured to acquire task information of a target task and basic information of a plurality of developers, where the task information of the target task includes: task publishers, task demands and task emergency degree, wherein the basic information of the plurality of developers comprises: the existing workload, task completion quality and technical field;
the processing unit is used for respectively evaluating the plurality of developers based on the matching degree between the task information of the target task and the basic information of the plurality of developers to obtain an evaluation parameter corresponding to each developer;
the processing unit is used for determining a target developer for executing the target task based on the evaluation parameters corresponding to each developer in the plurality of developers.
6. The task allocation device according to claim 5, wherein the acquisition unit is configured to acquire task information of a plurality of history tasks;
the processing unit is used for analyzing and modeling the task information of the historical tasks and the basic information of the developers through a preset clustering algorithm to obtain a target model;
And the processing unit is used for evaluating the matching degree between the task information of the target task and the basic information of the plurality of developers based on the target model to obtain the evaluation parameters corresponding to each developer.
7. The task allocation device according to claim 6, wherein the processing unit is configured to determine, by using the preset clustering algorithm, a first parameter corresponding between each of the plurality of historical tasks and each of the plurality of developers based on task requirements included in the task information and technical fields included in the basic information of the plurality of developers;
the processing unit is used for determining a second parameter corresponding to each historical task of the plurality of historical tasks and each developer of the plurality of developers based on the task emergency degree included in the task information and the existing workload included in the basic information of the plurality of developers through the preset clustering algorithm;
the processing unit is used for determining a third parameter corresponding to each developer in the plurality of developers based on task completion quality included in the basic information of the plurality of developers through the preset clustering algorithm;
The processing unit is used for constructing the target model based on the first parameter, the second parameter and the third parameter, wherein the first parameter, the second parameter and the third parameter are used for indicating the matching degree between the task and the developer.
8. The task allocation device according to any one of claims 5 to 7, wherein the processing unit is configured to perform analysis processing on task information of the target task and basic information of the plurality of developers based on a target model, and determine a degree of matching between the task information of the target task and the basic information of the plurality of developers;
the processing unit is used for determining the expected time for each developer of the plurality of developers to complete the target task based on the task emergency degree included in the task information of the target task and the existing workload included in the basic information of the plurality of developers;
the processing unit is used for respectively evaluating the plurality of developers to obtain evaluation parameters corresponding to each developer based on the matching degree between the task information of the target task and the basic information of the plurality of developers and the expected time for each developer in the plurality of developers to complete the target task.
9. An electronic device, comprising: a processor and a memory; wherein the memory is configured to store one or more programs, the one or more programs comprising computer-executable instructions that, when executed by the electronic device, cause the electronic device to perform a task allocation method as claimed in any one of claims 1 to 4.
10. A computer readable storage medium storing one or more programs, wherein the one or more programs comprise instructions, which when executed by a computer, cause the computer to perform a task allocation method as claimed in any one of claims 1 to 4.
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| CN116579585A (en) * | 2023-07-12 | 2023-08-11 | 太平金融科技服务（上海）有限公司 | Resource allocation method, device, computer equipment and storage medium |
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| CN116579585A (en) * | 2023-07-12 | 2023-08-11 | 太平金融科技服务（上海）有限公司 | Resource allocation method, device, computer equipment and storage medium |
| CN116579585B (en) * | 2023-07-12 | 2023-10-03 | 太平金融科技服务（上海）有限公司 | Resource allocation method, device, computer equipment and storage medium |
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