CN110766269A - Task allocation method and device, readable storage medium and terminal equipment - Google Patents
Task allocation method and device, readable storage medium and terminal equipment Download PDFInfo
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Abstract
The present invention relates to the field of data processing technologies, and in particular, to a method and an apparatus for task allocation, a storage medium, and a terminal device. The task allocation method comprises the following steps: acquiring each first attribute characteristic corresponding to a task to be distributed; inputting each first attribute characteristic into a preset decision tree model to obtain a first task grade corresponding to a task to be distributed and output by the preset decision tree model, wherein the preset decision tree model is a model taking the attribute characteristics as nodes and the task grade as a decision result; determining a candidate employee group corresponding to the first task level according to a preset corresponding relation between the task level and the employee group; and selecting corresponding target employees from the candidate employee group based on a preset selection mode, sending the tasks to be distributed to terminals corresponding to the target employees, and comprehensively analyzing each first attribute characteristic of the tasks to be distributed through a preset decision tree model to accurately determine the task grade of the tasks to be distributed, so that reasonable and accurate task distribution can be performed according to the task grade.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method and an apparatus for task allocation, a computer-readable storage medium, and a terminal device.
Background
In the current task or job allocation method, the allocation is usually performed directly according to the task or job type, that is, the allocation is performed only according to the corresponding relationship between the task or job type and the staff, and when the determination of a certain task or job type is inaccurate or unreasonable, the task allocation method simply performed according to the corresponding relationship between the task or job type and the staff is easy to cause task allocation errors or unreasonable task allocation, and the task allocation efficiency is greatly affected.
In conclusion, how to improve the task allocation efficiency and rationality becomes a problem to be solved urgently by those skilled in the art.
Disclosure of Invention
The embodiment of the invention provides a task allocation method and device, a computer readable storage medium and terminal equipment, which can reasonably and accurately allocate tasks and improve the allocation efficiency and the allocation rationality of the tasks.
In a first aspect of the embodiments of the present invention, a task allocation method is provided, including:
acquiring each first attribute characteristic corresponding to a task to be distributed;
inputting each first attribute characteristic into a preset decision tree model to obtain a first task grade corresponding to the task to be distributed and output by the preset decision tree model, wherein the preset decision tree model takes the attribute characteristics as nodes and the task grade as a decision result;
determining a candidate employee group corresponding to the first task level according to a preset corresponding relation between the task level and the employee group;
and selecting corresponding target employees from the candidate employee group based on a preset selection mode, and sending the tasks to be distributed to terminals corresponding to the target employees so as to prompt the target employees to process the tasks to be distributed.
In a second aspect of the embodiments of the present invention, there is provided a task allocation apparatus, including:
the attribute feature acquisition module is used for acquiring each first attribute feature corresponding to the task to be distributed;
the task level determination module is used for inputting each first attribute characteristic into a preset decision tree model to obtain a first task level corresponding to the task to be distributed and output by the preset decision tree model, wherein the preset decision tree model takes the attribute characteristics as nodes and the task level as a decision result;
the candidate staff group determining module is used for determining a candidate staff group corresponding to the first task level according to a preset corresponding relation between the task level and the staff group;
and the task allocation module is used for selecting corresponding target employees from the candidate employee group based on a preset selection mode and sending the tasks to be allocated to the terminals corresponding to the target employees so as to prompt the target employees to process the tasks to be allocated.
In a third aspect of the embodiments of the present invention, a computer-readable storage medium is provided, where computer-readable instructions are stored, and the computer-readable instructions, when executed by a processor, implement the steps of the task allocation method according to the first aspect.
In a fourth aspect of the embodiments of the present invention, there is provided a terminal device, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, where the processor executes the computer-readable instructions to implement the following steps:
acquiring each first attribute characteristic corresponding to a task to be distributed;
inputting each first attribute characteristic into a preset decision tree model to obtain a first task grade corresponding to the task to be distributed and output by the preset decision tree model, wherein the preset decision tree model takes the attribute characteristics as nodes and the task grade as a decision result;
determining a candidate employee group corresponding to the first task level according to a preset corresponding relation between the task level and the employee group;
and selecting corresponding target employees from the candidate employee group based on a preset selection mode, and sending the tasks to be distributed to terminals corresponding to the target employees so as to prompt the target employees to process the tasks to be distributed.
According to the technical scheme, the embodiment of the invention has the following advantages:
in the embodiment of the invention, after the task to be distributed is obtained, each first attribute characteristic corresponding to the task to be distributed can be obtained, and then each first attribute characteristic can be input into the preset decision tree model to obtain the first task grade corresponding to the task to be distributed, further, a candidate employee group corresponding to the first task level can be determined according to the preset corresponding relation, and corresponding target employees are selected from the candidate employee group based on a preset selection mode to automatically distribute tasks to be distributed, the task grade of the task to be distributed is accurately determined by comprehensively analyzing each first attribute characteristic of the task to be distributed through a preset decision tree model, therefore, reasonable and accurate task allocation can be performed according to the task level, automation and standardization of task allocation are achieved, labor cost in task allocation is reduced, and task allocation efficiency and allocation rationality are improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a flowchart of an embodiment of a task allocation method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a task allocation method for constructing a preset decision tree model in an application scenario according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of task allocation performed in an application scenario by a task allocation method according to an embodiment of the present invention;
fig. 4 is a schematic flowchart of task allocation performed by a task allocation method in another application scenario in an embodiment of the present invention;
FIG. 5 is a block diagram of an embodiment of a task assigning apparatus according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a task allocation method and device, a computer readable storage medium and terminal equipment, which are used for reasonably and accurately allocating tasks and improving the task allocation efficiency and the allocation rationality.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention. Furthermore, the terms "first," "second," and "third," etc. are used to distinguish between different objects and are not used to describe a particular order.
As shown in fig. 1, an embodiment of the present invention provides a task allocation method, where the task allocation method includes:
s101, acquiring each first attribute characteristic corresponding to a task to be distributed;
the execution subject of the embodiment of the invention is terminal equipment, and the terminal equipment comprises but is not limited to: desktop computers, notebooks, palm computers, cloud servers, and other computing devices. Here, when the user needs to perform task allocation, the user may upload or send the task to be allocated to the terminal device, where the uploaded or sent task to be allocated may include introduction content and the like of the task to be allocated. When the terminal device receives the task to be distributed, the terminal device can acquire each first attribute feature corresponding to the task to be distributed from the corresponding introduction content, and if a corresponding keyword can be set for each first attribute feature in advance, the terminal device can acquire each first attribute feature by performing keyword retrieval on the introduction content.
For example, in an application scenario in which a task to be assigned is a sales list, the sales list may be stored in the form of an Excel file, and the like, where the sales list may be a list of people who have a possibility of purchasing a related product or service, and the Excel file in which the sales list is stored may be uploaded or sent to the terminal device, and the terminal device may extract first attribute features, such as a category, an area, a source, an age, and an occupation, corresponding to the sales list from the Excel file.
Step S102, inputting each first attribute characteristic into a preset decision tree model to obtain a first task grade output by the preset decision tree model and corresponding to the task to be distributed, wherein the preset decision tree model takes the attribute characteristics as nodes and the task grade as a decision result;
it can be understood that, after acquiring each first attribute feature corresponding to the task to be allocated, the terminal device may input each first attribute feature to a preset decision tree model, where the preset decision tree model takes each attribute feature as a node and takes the corresponding task level as a decision result, and the preset decision tree model may match each first attribute feature with each node in a decision tree, so as to determine the first task level corresponding to the task to be allocated.
Here, the first task level is used to indicate task quality of the task to be allocated, where the task quality may be determined based on a predicted allocation result corresponding to the task to be allocated, and the predicted allocation result refers to a predicted allocation success rate (or sales success rate) of the task to be allocated, for example, the task quality of the sales list may be evaluated based on the predicted sales result of the sales list to construct the first task level corresponding to the sales list. For example, a first task level corresponding to a sales list with a predicted sales result (e.g., a predicted sales success rate) higher than 90% may be determined as a good level, indicating that the quality of the sales list is good; the first task grade corresponding to the sales list with the predicted sales result between 75% and 90% can be determined as a good grade, which indicates that the quality of the sales list is good; the first task grade corresponding to the sales list with the predicted sales result between 60% and 75% can be determined as a medium grade, which indicates that the quality of the sales list is general; and determining the first task grade corresponding to the sales list with the predicted sales result lower than 60% as a poor grade, indicating that the quality of the sales list is poor, and the like.
Further, as shown in fig. 2, in a specific application scenario, the preset decision tree model is constructed through the following steps:
step S201, obtaining a preset amount of historical distribution data, wherein the historical distribution data comprise second attribute characteristics and second distribution results corresponding to each historical task;
step S202, determining attribute weights corresponding to the second attribute characteristics according to the second distribution results, and determining second distribution grades corresponding to the historical tasks according to the attribute weights and the second distribution results;
in this scenario, when the preset decision tree model is constructed, historical allocation data may be first obtained, and each obtained historical allocation data may include a second attribute feature and a second allocation result corresponding to each historical task, where the second allocation result may include allocation success and allocation failure; secondly, big data analysis can be carried out on all the second distribution results to determine the distribution success rate of the historical tasks corresponding to the second attribute characteristics, and determining an attribute weight corresponding to each second attribute feature according to the allocation success rate of each second attribute feature, for example, a big data analysis may be performed on all the second allocation results to determine the allocation success rate of the second attribute feature (e.g., source), the number of the history tasks which contain the second attribute characteristics (sources) and are successfully distributed can be obtained, and the total number of the number/historical tasks is used to obtain the allocation success rate containing the second attribute feature (source), the higher the assigned composition power, the greater the attribute weight corresponding to the second attribute feature (source), otherwise, the lower the allocation score, the less attribute weight the second attribute feature (source) corresponds to.
Further, after obtaining the attribute weight corresponding to each second attribute feature, the second task level corresponding to each historical task may be obtained according to the attribute weight corresponding to the second attribute feature, for example, according to:
therefore, the second task grade of each historical task can be determined according to the corresponding final distribution success rate, the task grade of each historical task is accurately analyzed by comprehensively considering the plurality of attribute characteristics and the attribute weights corresponding to the plurality of attribute characteristics, and the reasonability and the effectiveness of the task grade determination are ensured, wherein SuccRaterThe final distribution success rate, FeaValue, corresponding to the r-th historical taskrtThe power, Weight, is allocated to the t second attribute feature of the r historical taskrtThe property weight corresponding to the tth second property characteristic of the tth historical task, the quotarAnd the weighting coefficient is corresponding to the r-th historical task.
Step S203, calculating information gain of each second attribute feature based on each attribute weight and each second task level, and constructing nodes of a decision tree according to each information gain and each second attribute feature;
and S204, constructing the preset decision tree model according to the constructed nodes and the second task levels.
Preferably, in this scenario, the calculating an information gain of each second attribute feature based on each attribute weight and each second task level may include:
calculating the attribute entropy (S) of the data set formed by the historical allocation data according to the following formula:
wherein the content of the first and second substances,s is a data set formed by historical distribution data, n is the total number of second task levels contained in the data set, and piThe ratio of the ith second task grade in the data set;
and calculating the information gain of each second attribute characteristic according to the attribute entropy and the attribute weight according to the following formula:
wherein Gain (S, d) is an information Gain of the second attribute feature d, m is the number of data subsets obtained by dividing the data set according to the second attribute feature d, | S | is the number of data in the data set, | SjI is the number of data in the jth data subset, and Wegiht (d) is the attribute weight corresponding to the second attribute feature d.
Here, each level of non-leaf nodes in the decision tree may be constructed according to the information gain of each second attribute feature, where the greater the information gain, the higher the level number of the node corresponding to the second attribute feature in the decision tree, for example, the second attribute features may be sorted according to the information gain first, and then the determination condition may be determined according to each historical allocation data, so that each level of non-leaf nodes in the decision tree may be constructed according to the sorted second attribute features and the determination condition, and the decision result corresponding to each leaf node in the constructed decision tree may be determined according to the second task level corresponding to each historical allocation task. Each non-leaf node is a second attribute feature, a connecting line between nodes can be a judgment condition required to be met from a previous-level node to a next-level node, each leaf node is a decision result, and the decision result is a second task level meeting all judgment conditions from a root node to the leaf node. After a decision tree which takes the second attribute characteristics as nodes and the second task level as a decision result is obtained, the decision tree can be determined as the preset decision tree model, the node positions of the attribute characteristics in the decision tree are accurately determined through the calculation of the attribute entropy and the information gain, the judgment conditions in the decision process are accurately determined, the accuracy of the construction of the preset decision tree model is ensured, and the accuracy of the task level determination in the task to be distributed is improved.
In the scene, the attribute weight of each attribute feature is determined by performing big data analysis on a large amount of historical distribution data, and the information gain of each historical distribution data is obtained by calculation, so that the node position of each attribute feature in the decision tree is accurately determined according to the information gain and the attribute weight, the accuracy of construction of a preset decision tree model can be ensured, and the accuracy of task level determination in subsequent tasks to be distributed is improved.
Optionally, in this scenario, after sending the task to be allocated to the terminal corresponding to the target employee, the method may include:
and obtaining a first distribution result corresponding to the task to be distributed, and updating the historical distribution data based on the first distribution result corresponding to the task to be distributed and the first attribute characteristic so as to update the preset decision tree model according to the updated historical distribution data.
After the distribution of the tasks to be distributed is completed and the corresponding first distribution results are obtained, the distribution data (including the first distribution results and the first attribute characteristics) corresponding to the tasks to be distributed can be used as data samples to update the selected historical distribution data in the decision tree, so that the decision tree is updated according to the updated historical distribution data, that is, a more reasonable and more accurate preset decision tree model for determining the task level is obtained by updating and increasing the data samples in the decision tree construction in real time.
S103, determining a candidate employee group corresponding to the first task level according to a preset corresponding relation between the task level and the employee group;
in the embodiment of the present invention, a preset corresponding relationship between a task level and a staff group may also be preset, where the number of the staff group is the same as the number of the task level, and the staff group may be divided according to the working capacity of each staff, for example, each staff may be scored according to the working capacity of each staff, and each staff may be divided into groups according to a score interval in which the score is located, so as to obtain a plurality of staff groups, for example, staff whose score is located in a first score interval (for example, more than 90 points) may be divided into the first staff group, staff whose score is located in a second score interval (for example, 80 to 90 points) may be divided into the second staff group, staff whose score is located in a third score interval (for example, 70 to 80 points) may be divided into the third staff group, and staff whose score is located in a fourth score interval (for example, may be lower than 70 points) may be divided into the fourth staff group. Here, the preset corresponding relationship between the preset task level and the employee group may be: the first crew corresponds to the high-grade, the second crew corresponds to the good-grade, the third crew corresponds to the medium-grade, and the fourth crew corresponds to the poor-grade.
Therefore, after the first task level corresponding to the task to be distributed is determined by using the preset decision tree model, and the first task level is a specific level in the task levels, a candidate staff group corresponding to the first task level can be determined according to the preset corresponding relationship, wherein the candidate staff group is a specific staff group in the staff group. If the first task grade corresponding to the task to be distributed is determined to be the superior grade, determining that the corresponding candidate staff group is the first staff group; and when the first task grade corresponding to the task to be distributed is determined to be the middle grade, determining that the corresponding candidate employee group is a third employee group, and the like.
And S104, selecting corresponding target employees from the candidate employee group based on a preset selection mode, and sending the tasks to be distributed to terminals corresponding to the target employees so as to prompt the target employees to process the tasks to be distributed.
It can be understood that after the candidate employee group corresponding to the task to be distributed is determined, the target employee can be determined from the candidate employee group to perform automatic distribution of the task. In the embodiment of the invention, the preset selection mode corresponding to each employee group can be preset, so that the target employee can be selected from the employee group through the preset selection mode corresponding to the employee group. For example, the preset selection mode corresponding to the first crew group may be set as a random selection mode, the preset selection mode corresponding to the second crew group may be set as a selection mode based on processing capability (i.e., the more easily the employees with stronger processing capability are selected), the preset selection mode corresponding to the third crew group may be set as a selection mode based on backlog task volume (i.e., the less easily the employees with less backlog task volume are selected), and so on. Therefore, after the candidate employee group corresponding to the task to be distributed is determined, the preset selection mode corresponding to the candidate employee group can be obtained, so that the corresponding target employee can be selected from the candidate employee group based on the preset selection mode, and the task to be distributed can be automatically sent to the terminal corresponding to the selected target employee, so as to prompt the target employee to process the task to be distributed.
Preferably, in a specific application scenario, the same preset selection mode may be set for all the employee groups, for example, the preset selection modes of all the employee groups may be set as selection modes based on the processing capacity and the backlog task amount. Therefore, as shown in fig. 3, the selecting, based on a preset selection manner, a corresponding target employee from the candidate employee group and sending the task to be assigned to the terminal corresponding to the target employee may include:
s301, acquiring a task state of the candidate staff group, and determining idle staff in the candidate staff group according to the task state;
step S302, determining the task processing capacity of each idle employee according to the distribution record of each idle employee, and performing descending order arrangement on each idle employee according to the task processing capacity to obtain an arrangement group;
step S303, selecting the idle staff with the first sequence in the arrangement group as the target staff corresponding to the task to be distributed, and sending the task to be distributed to the terminal corresponding to the target staff.
As for the above steps S301 to S303, it can be understood that after the candidate employee group corresponding to the task to be allocated is determined, the task state of the candidate employee group may be obtained, for example, the backlog task amount of each employee in the candidate employee group is obtained, so that the current task state of each employee in the candidate employee group may be determined according to the task state, and thus, the employee whose task state is in the idle state is determined, that is, an idle employee without task processing at present is found out from the candidate employee group, and when one idle employee is found, the task to be allocated may be directly sent to the terminal corresponding to the idle employee.
When a plurality of idle employees are found, the allocation records of the idle employees can be obtained, namely the task processing records of the historical tasks which are processed by the idle employees are obtained, so that the task processing capacity of the idle employees is determined according to the task records of the historical tasks, wherein the task processing capacity can be measured by the task processing success rate of the idle employees, and the higher the task processing success rate is, the stronger the corresponding task processing capacity is. After the task processing capacity of each idle employee is determined, each idle employee can be subjected to descending order arrangement according to the task processing capacity to obtain an arrangement number group in descending order arrangement, in the arrangement number group, the idle employee with the stronger task processing capacity is ranked more forward, the idle employee with the first ranking in the arrangement number group is selected as the target employee corresponding to the task to be distributed, namely, the idle employee with the strongest task processing capacity in each idle employee is determined as the target employee corresponding to the task to be distributed, and the task to be distributed can be sent to the terminal corresponding to the target employee, so that the accuracy and the efficiency of task distribution are improved.
Preferably, as shown in fig. 4, the selecting the idle employee with the first rank in the rank group as the target employee corresponding to the task to be distributed, and sending the task to be distributed to the terminal corresponding to the target employee may include:
step S401, selecting the first idle employee in the ranking group as the target employee corresponding to the task to be distributed, sending a task distribution request to the terminal corresponding to the target employee, and receiving the reply information returned by the terminal;
step S402, judging whether the reply message confirms to receive the task to be distributed;
step S403, when the reply message confirms that the task to be distributed is received, sending the task to be distributed to a terminal corresponding to the target staff;
step S404, when the reply message is that the task to be distributed is refused to be received, moving the first-ranked idle staff to the last position of the ranking group to update the ranking group, returning to execute the step of selecting the first-ranked idle staff in the ranking group as the target staff corresponding to the task to be distributed, and sending a task distribution request to the terminal corresponding to the target staff, and the subsequent steps.
For the above steps S401 to S404, in the embodiment of the present invention, after the target employee corresponding to the task to be allocated is determined, a task allocation request may be first sent to a terminal corresponding to the target employee, so as to determine whether the target employee can receive the task to be allocated according to the reply information of the target employee to the task allocation request, thereby ensuring the correctness and the validity of task allocation. When the reply information returned by the target staff indicates that the target staff confirms to receive the task to be distributed, the task to be distributed is sent to a terminal corresponding to the target staff; and when the reply information returned by the target staff indicates that the target staff cannot receive the tasks to be distributed, the next idle staff can be selected from the arrangement number group to execute the distribution operation of the tasks, namely, the first idle staff in the sequence can be moved to the last position of the arrangement group to update the arrangement group, the first idle staff in the sequence in the updated arrangement number group is reselected as the target staff corresponding to the tasks to be distributed, then the task distribution request is sent until the idle staff receives the tasks to be distributed, so that the tasks are timely and effectively distributed by sending the task distribution request before the task distribution, the return redistribution after the distribution is avoided, and the like, and the distribution efficiency of the tasks is improved.
In the embodiment of the invention, after the task to be distributed is obtained, each first attribute characteristic corresponding to the task to be distributed can be obtained, then each first attribute characteristic can be input into the preset decision tree model, the first task grade corresponding to the task to be distributed is obtained, the candidate staff group corresponding to the first task grade can be further determined according to the preset corresponding relation, the corresponding target staff is selected from the candidate staff group based on the preset selection mode to carry out automatic distribution of the task to be distributed, and the task grade of the task to be distributed is accurately determined by comprehensively analyzing each attribute characteristic of the task to be distributed through the preset decision tree model, so that reasonable and accurate task distribution can be carried out according to the task grade, automation and standardization of the task distribution are realized, the labor cost in the task distribution is reduced, and the task distribution efficiency and distribution rationality are improved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The above mainly describes a task assigning method, and a task assigning apparatus will be described in detail below.
As shown in fig. 5, an embodiment of the present invention provides a task assigning apparatus, where the task assigning apparatus includes:
an attribute feature obtaining module 501, configured to obtain each first attribute feature corresponding to a task to be allocated;
a task level determining module 502, configured to input each first attribute feature into a preset decision tree model, and obtain a first task level corresponding to the task to be allocated, where the first task level is output by the preset decision tree model, and the preset decision tree model is a model with attribute features as nodes and task levels as decision results;
a candidate employee group determining module 503, configured to determine a candidate employee group corresponding to the first task level according to a preset correspondence between task levels and employee groups;
and the task allocation module 504 is configured to select a corresponding target employee from the candidate employee group based on a preset selection manner, and send the task to be allocated to a terminal corresponding to the target employee, so as to prompt the target employee to process the task to be allocated.
Further, the task assigning apparatus may further include:
the historical distribution data acquisition module is used for acquiring a preset amount of historical distribution data, wherein the historical distribution data comprises second attribute characteristics and second distribution results corresponding to the historical tasks;
an attribute weight determining module, configured to determine, according to each second allocation result, an attribute weight corresponding to each second attribute feature, and determine, according to each attribute weight and each second allocation result, a second task level corresponding to each historical task;
an information gain calculation module, configured to calculate an information gain of each second attribute feature based on each attribute weight and each second task level, and construct a node of a decision tree according to each information gain and each second attribute feature;
and the decision tree model building module is used for building the preset decision tree model according to the built nodes and the second task grades.
Preferably, the information gain calculating module may include:
an attribute entropy calculation unit, configured to calculate an attribute entropy(s) of a data set formed by the historical allocation data according to the following formula:
wherein S is a data set formed by historical allocation data, n is the total number of second task levels contained in the data set, and piThe ratio of the ith second task grade in the data set;
an information gain calculation unit, configured to calculate an information gain of each second attribute feature according to the attribute entropy and the attribute weight according to the following formula:
wherein Gain (S, d) is an information Gain of the second attribute feature d, m is the number of data subsets obtained by dividing the data set according to the second attribute feature d, | S | is the number of data in the data set, | SjI is the number of data in the jth data subset, and Wegiht (d) is the attribute weight corresponding to the second attribute feature d.
Optionally, the task allocation apparatus may further include:
and the model updating module is used for acquiring a first distribution result corresponding to the task to be distributed, updating the historical distribution data based on the first distribution result corresponding to the task to be distributed and the first attribute characteristics, and updating the preset decision tree model according to the updated historical distribution data.
Further, the task assigning module 504 may include:
the idle staff determining unit is used for acquiring the task state of the candidate staff group and determining idle staff in the candidate staff group according to the task state;
the arrangement group acquisition unit is used for determining the task processing capacity of each idle employee according to the distribution record of each idle employee and performing descending order arrangement on each idle employee according to the task processing capacity to obtain an arrangement group;
and the task allocation unit is used for selecting the first-ranked idle staff in the ranking group as the target staff corresponding to the tasks to be allocated and sending the tasks to be allocated to the terminals corresponding to the target staff.
Preferably, the task allocation unit may include:
the allocation request sending subunit is used for selecting the first idle employee in the arrangement group as the target employee corresponding to the task to be allocated, sending a task allocation request to the terminal corresponding to the target employee, and receiving the reply information returned by the terminal;
the task allocation sub-unit is used for sending the task to be allocated to the terminal corresponding to the target staff when the reply message confirms that the task to be allocated is received;
and the array updating unit is used for moving the first-ordered idle staff to the last position of the array group to update the array group when the reply message is that the reply message refuses to receive the task to be distributed, returning to execute the step of selecting the first-ordered idle staff in the array group as the target staff corresponding to the task to be distributed and sending a task distribution request to the terminal corresponding to the target staff, and the subsequent steps.
Fig. 6 is a schematic diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 6, the terminal device 6 of this embodiment includes: a processor 60, a memory 61, and computer readable instructions 62, such as a task allocation program, stored in the memory 61 and executable on the processor 60. The processor 60, when executing the computer readable instructions 62, implements the steps in the various task allocation method embodiments described above, such as steps S101-S104 shown in fig. 1. Alternatively, the processor 60, when executing the computer readable instructions 62, implements the functions of the modules/units in the device embodiments, such as the functions of the modules 501 to 504 shown in fig. 5.
Illustratively, the computer readable instructions 62 may be partitioned into one or more modules/units that are stored in the memory 61 and executed by the processor 60 to implement the present invention. The one or more modules/units may be a series of computer-readable instruction segments capable of performing specific functions, which are used to describe the execution process of the computer-readable instructions 62 in the terminal device 6.
The terminal device 6 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 60, a memory 61. Those skilled in the art will appreciate that fig. 6 is merely an example of a terminal device 6 and does not constitute a limitation of terminal device 6 and may include more or less components than those shown, or some components in combination, or different components, for example, the terminal device may also include input output devices, network access devices, buses, etc.
The Processor 60 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 61 may be an internal storage unit of the terminal device 6, such as a hard disk or a memory of the terminal device 6. The memory 61 may also be an external storage device of the terminal device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 6. Further, the memory 61 may also include both an internal storage unit and an external storage device of the terminal device 6. The memory 61 is used for storing the computer readable instructions and other programs and data required by the terminal device. The memory 61 may also be used to temporarily store data that has been output or is to be output.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A task allocation method, comprising:
acquiring each first attribute characteristic corresponding to a task to be distributed;
inputting each first attribute characteristic into a preset decision tree model to obtain a first task grade corresponding to the task to be distributed and output by the preset decision tree model, wherein the preset decision tree model takes the attribute characteristics as nodes and the task grade as a decision result;
determining a candidate employee group corresponding to the first task level according to a preset corresponding relation between the task level and the employee group;
and selecting corresponding target employees from the candidate employee group based on a preset selection mode, and sending the tasks to be distributed to terminals corresponding to the target employees so as to prompt the target employees to process the tasks to be distributed.
2. The task allocation method according to claim 1, wherein the predetermined decision tree model is constructed by:
acquiring a preset amount of historical distribution data, wherein the historical distribution data comprises second attribute characteristics and second distribution results corresponding to the historical tasks;
determining attribute weights corresponding to the second attribute features according to the second distribution results, and determining second task grades corresponding to the historical tasks according to the attribute weights and the second distribution results;
calculating information gain of each second attribute feature based on each attribute weight and each second task level, and constructing nodes of a decision tree according to each information gain and each second attribute feature;
and constructing the preset decision tree model according to the constructed nodes and the second task levels.
3. The task assignment method according to claim 2, wherein the calculating an information gain for each of the second attribute features based on each of the attribute weights and each of the second task levels comprises:
calculating the attribute entropy (S) of the data set formed by the historical allocation data according to the following formula:
wherein S is a data set formed by historical allocation data, n is the total number of second task levels contained in the data set, and piThe ratio of the ith second task grade in the data set;
and calculating the information gain of each second attribute characteristic according to the attribute entropy and the attribute weight according to the following formula:
wherein Gain (S, d) is the information Gain of the second attribute feature d, and m is obtained by dividing the data set according to the second attribute feature dIs the number of data subsets, | S | is the number of data in the data set, | SjI is the number of data in the jth data subset, and Wegiht (d) is the attribute weight corresponding to the second attribute feature d.
4. The task allocation method according to claim 2, wherein after the task to be allocated is sent to the terminal corresponding to the target employee, the method comprises the following steps:
and obtaining a first distribution result corresponding to the task to be distributed, and updating the historical distribution data based on the first distribution result corresponding to the task to be distributed and the first attribute characteristic so as to update the preset decision tree model according to the updated historical distribution data.
5. The task allocation method according to any one of claims 1 to 4, wherein the selecting a corresponding target employee from the candidate employee group based on a preset selection mode and sending the task to be allocated to a terminal corresponding to the target employee comprises:
acquiring a task state of the candidate staff group, and determining idle staff in the candidate staff group according to the task state;
determining the task processing capacity of each idle employee according to the distribution record of each idle employee, and performing descending order arrangement on each idle employee according to the task processing capacity to obtain an arrangement group;
and selecting the first idle employee in the ranking group as a target employee corresponding to the task to be distributed, and sending the task to be distributed to a terminal corresponding to the target employee.
6. The task allocation method according to claim 5, wherein the step of selecting the idle employee with the first rank in the rank group as the target employee corresponding to the task to be allocated and sending the task to be allocated to the terminal corresponding to the target employee comprises the steps of:
selecting the first idle employee in the ranking group as a target employee corresponding to the task to be distributed, sending a task distribution request to a terminal corresponding to the target employee, and receiving reply information returned by the terminal;
when the reply message confirms that the task to be distributed is received, the task to be distributed is sent to a terminal corresponding to the target staff;
and when the reply message is that the task to be distributed is refused to be received, moving the first-ranked idle staff to the last position of the ranking group to update the ranking group, returning to execute the step of selecting the first-ranked idle staff in the ranking group as the target staff corresponding to the task to be distributed, and sending a task distribution request to the terminal corresponding to the target staff.
7. A task assigning apparatus, comprising:
the attribute feature acquisition module is used for acquiring each first attribute feature corresponding to the task to be distributed;
the task level determination module is used for inputting each first attribute characteristic into a preset decision tree model to obtain a first task level corresponding to the task to be distributed and output by the preset decision tree model, wherein the preset decision tree model takes the attribute characteristics as nodes and the task level as a decision result;
the candidate staff group determining module is used for determining a candidate staff group corresponding to the first task level according to a preset corresponding relation between the task level and the staff group;
and the task allocation module is used for selecting corresponding target employees from the candidate employee group based on a preset selection mode and sending the tasks to be allocated to the terminals corresponding to the target employees so as to prompt the target employees to process the tasks to be allocated.
8. A computer readable storage medium storing computer readable instructions, which when executed by a processor implement the steps of the task assignment method of any one of claims 1 to 6.
9. A terminal device comprising a memory, a processor, and computer readable instructions stored in the memory and executable on the processor, wherein the processor when executing the computer readable instructions performs the steps of:
acquiring each first attribute characteristic corresponding to a task to be distributed;
inputting each first attribute characteristic into a preset decision tree model to obtain a first task grade corresponding to the task to be distributed and output by the preset decision tree model, wherein the preset decision tree model takes the attribute characteristics as nodes and the task grade as a decision result;
determining a candidate employee group corresponding to the first task level according to a preset corresponding relation between the task level and the employee group;
and selecting corresponding target employees from the candidate employee group based on a preset selection mode, and sending the tasks to be distributed to terminals corresponding to the target employees so as to prompt the target employees to process the tasks to be distributed.
10. The terminal device of claim 9, wherein the predetermined decision tree model is constructed by:
acquiring a preset amount of historical distribution data, wherein the historical distribution data comprises second attribute characteristics and second distribution results corresponding to the historical tasks;
determining attribute weights corresponding to the second attribute features according to the second distribution results, and determining second task grades corresponding to the historical tasks according to the attribute weights and the second distribution results;
calculating information gain of each second attribute feature based on each attribute weight and each second task level, and constructing nodes of a decision tree according to each information gain and each second attribute feature;
and constructing the preset decision tree model according to the constructed nodes and the second task levels.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020087499A1 (en) * | 2001-01-03 | 2002-07-04 | Stockfisch Thomas P. | Methods and systems of classifying multiple properties simultaneously using a decision tree |
| CN109376759A (en) * | 2018-09-10 | 2019-02-22 | 平安科技（深圳）有限公司 | User information classification method, device, computer equipment and storage medium |
| CN109872036A (en) * | 2019-01-10 | 2019-06-11 | 平安科技（深圳）有限公司 | Method for allocating tasks, device and computer equipment based on sorting algorithm |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104346576A (en) * | 2014-12-01 | 2015-02-11 | 北京奇虎科技有限公司 | Methods for feedback and matching of privilege escalation configuration information as well as corresponding devices |
| CN106155807A (en) * | 2015-04-15 | 2016-11-23 | 阿里巴巴集团控股有限公司 | A kind of method and apparatus realizing scheduling of resource |
| CN106888237B (en) * | 2015-12-15 | 2020-01-07 | 中国移动通信集团公司 | Data scheduling method and system |
| CN110163474A (en) * | 2019-04-12 | 2019-08-23 | 平安普惠企业管理有限公司 | A kind of method and apparatus of task distribution |
-
2019
- 2019-09-02 CN CN201910824906.8A patent/CN110766269A/en active Pending
- 2019-11-08 WO PCT/CN2019/116633 patent/WO2021042510A1/en active Application Filing
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020087499A1 (en) * | 2001-01-03 | 2002-07-04 | Stockfisch Thomas P. | Methods and systems of classifying multiple properties simultaneously using a decision tree |
| CN109376759A (en) * | 2018-09-10 | 2019-02-22 | 平安科技（深圳）有限公司 | User information classification method, device, computer equipment and storage medium |
| CN109872036A (en) * | 2019-01-10 | 2019-06-11 | 平安科技（深圳）有限公司 | Method for allocating tasks, device and computer equipment based on sorting algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 康琦等: "《机器学习中的不平衡分类方法》", 同济大学出版社 * |
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