CN109872036A

CN109872036A - Method for allocating tasks, device and computer equipment based on sorting algorithm

Info

Publication number: CN109872036A
Application number: CN201910024184.8A
Authority: CN
Inventors: 刘建华; 周剀
Original assignee: Ping An Technology Shenzhen Co Ltd
Current assignee: Ping An Technology Shenzhen Co Ltd
Priority date: 2019-01-10
Filing date: 2019-01-10
Publication date: 2019-06-11
Anticipated expiration: 2039-01-10
Also published as: CN109872036B

Abstract

This application discloses method for allocating tasks, device and computer equipments based on sorting algorithm, wherein based on the method for allocating tasks of sorting algorithm, it include: task processing capacity grade classification to be carried out according to preset rules to all distribution objects in specified database, and mark ability label respectively；According to the ATTRIBUTE INDEX of task allocation model, from all distribution objects Jing Guo task processing capacity grade classification, candidate allocation groups of objects corresponding with current task allocation model is filtered out by specified sorting algorithm；According to the corresponding allocation rule of the current task allocation model, in candidate allocation groups of objects corresponding with current task allocation model, the corresponding Target Assignment object of task to be allocated is determined；The task to be allocated is distributed into the Target Assignment object with predetermined manner.The formation reasonability of candidate allocation groups of objects has been fully considered by sorting algorithm, is improved precision, the reasonability of task distribution, is improved the efficiency of processing task.

Description

Method for allocating tasks, device and computer equipment based on sorting algorithm

Technical field

This application involves computer field is arrived, especially relate to method for allocating tasks based on sorting algorithm, device and Computer equipment.

Background technique

In the prior art, task distribution often distributes personnel by special task, according to the process object of specific tasks Situation carries out task distribution manually, but allocative efficiency is low and task matching is not accurate.It is existing to distribute mould by matching multiple-task Formula, and the personnel of existing distribution task are subjected to mechanical division group according to different task allocation models, to realize difference The corresponding distribution group not being overlapped of task allocation model, pass through by way of multiple-task allocation model carries out simultaneously and improve distribution Efficiency, but waiting task and the matching of task processing people are poor in task distribution, it cannot be more accurately according to different task The particular attribute of processing carry out pointedly, rapidly distribute waiting task.

Summary of the invention

The main purpose of the application is to provide a kind of method for allocating tasks based on sorting algorithm, it is intended to solve existing task The not high technical problem of the precision of distribution.

This application provides a kind of method for allocating tasks based on sorting algorithm, comprising:

Task processing capacity grade classification is carried out according to preset rules to all distribution objects in specified database, and is divided It Biao Zhu not ability label；

According to the ATTRIBUTE INDEX of task allocation model, from all distribution pair by task processing capacity grade classification As in, candidate allocation groups of objects corresponding with current task allocation model is filtered out by specified sorting algorithm；

According to the corresponding allocation rule of the current task allocation model, corresponding with the current task allocation model In candidate allocation groups of objects, the corresponding Target Assignment object of task to be allocated is determined；

The task to be allocated is distributed into the Target Assignment object with predetermined manner.

Preferably, the ATTRIBUTE INDEX according to task allocation model, from the institute Jing Guo task processing capacity grade classification Have in the distribution object, candidate allocation groups of objects corresponding with current task allocation model is filtered out by specified sorting algorithm The step of, comprising:

Corresponding first ATTRIBUTE INDEX of first task allocation model is obtained, wherein first ATTRIBUTE INDEX and the ability At least one in label is corresponding；

All distribution objects of the ability label will be marked, has been input to the clustering algorithm based on support vector machines In；

Obtain corresponding first clustering cluster of the first task allocation model of the clustering algorithm output；

It is corresponding as the first task allocation model by the corresponding first distribution object population of first clustering cluster Candidate allocation groups of objects.

It preferably, include multiple attributes in corresponding second ATTRIBUTE INDEX of the second task allocation model, it is described according to task The ATTRIBUTE INDEX of allocation model passes through specified point from all distribution objects Jing Guo task processing capacity grade classification Class algorithm filters out the step of candidate allocation groups of objects corresponding with current task allocation model, comprising:

In corresponding second ATTRIBUTE INDEX of the second task allocation model, obtain to the second task allocation model Influence maximum first attribute；

According to the classified zoning of corresponding first specified quantity of first attribute, all distribution objects are divided into First subset of the first specified quantity；

Screening meets the first subset of target of the first preset condition from each first subset, wherein the target the One subset is one or more；

In all second ATTRIBUTE INDEXs that first subset of target includes, the letter to first subset of target is obtained Breath amount influences maximum second attribute, and second attribute is different from first attribute；

According to the classified zoning of corresponding second specified quantity of second attribute, it is by first subset division of target The second subset of second specified quantity；

In the second subset for judging second specified quantity, if there is the second son of target for meeting the second preset condition Collection；

If it exists, then stop the division to the target second subset, and determine the mesh for meeting second preset condition Second subset is marked, is the corresponding candidate allocation groups of objects of the second task allocation model.

Preferably, described in corresponding second ATTRIBUTE INDEX of the second task allocation model, it obtains to described second Task allocation model influences the step of maximum first attribute, comprising:

Each attribute is obtained in second ATTRIBUTE INDEX respectively to the influence value of the second task allocation model；

Descending arrangement is carried out to each attribute according to the size of each influence value；

The arrangement order in the descending arrangement is set near the preceding corresponding attribute of the first influence value, is belonged to for described first Property.

Preferably, all distribution objects in specified database carry out task processing capacity etc. according to preset rules Grade divides, and the step of marking ability label respectively, comprising:

The corresponding historic task processing data of all distribution objects are obtained, wherein the historic task handles number According to history comprehensive score is included at least, the history comprehensive score is obtained by summarizing the score of each professional ability；

The task processing capacity grade of each distribution object is divided according to the history comprehensive score respectively；

Obtain the highest first specified services ability that scores in the history comprehensive score；

It is labeled using the corresponding first specified services ability of each distribution object as the ability label.

Preferably, the history comprehensive score includes the corresponding score of certain capabilities, and the acquisition history synthesis is commented After the step of highest first specified services ability that scores in point, comprising:

Judge whether the first specified services ability is the certain capabilities；

If it is not, then by the corresponding first specified services ability of each distribution object and certain capabilities, while conduct The ability label is labeled.

Preferably, the current task allocation model includes at least two kinds of task allocation models, described according to described current The corresponding allocation rule of task allocation model, in candidate allocation groups of objects corresponding with the current task allocation model, really The step of fixed task to be allocated corresponding Target Assignment object, comprising:

It is sorted according to the pre-set priority of each task allocation model, obtains each institute in the current task allocation model State the corresponding candidate allocation groups of objects sequence of task allocation model；

The first candidate allocation groups of objects of highest priority is determined from candidate allocation groups of objects sequence；

Using the assignment of allocation object in the first candidate allocation groups of objects as the Target Assignment object.

Present invention also provides a kind of task allocation apparatus based on sorting algorithm, comprising:

Labeling module, for carrying out task processing capacity according to preset rules to all distribution objects in specified database Grade classification, and ability label is marked respectively；

Screening module, for the ATTRIBUTE INDEX according to task allocation model, from by task processing capacity grade classification In all distribution objects, candidate allocation object corresponding with current task allocation model is filtered out by specified sorting algorithm Group；

Determining module, for according to the corresponding allocation rule of the current task allocation model, with the current task In the corresponding candidate allocation groups of objects of allocation model, the corresponding Target Assignment object of task to be allocated is determined；

Distribution module, for the task to be allocated to be distributed to the Target Assignment object with predetermined manner.

Present invention also provides a kind of computer equipment, including memory and processor, the memory is stored with calculating The step of machine program, the processor realizes the above method when executing the computer program.

Present invention also provides a kind of computer readable storage mediums, are stored thereon with computer program, the computer The step of above method is realized when program is executed by processor.

The application has fully considered the formation reasonability of candidate allocation groups of objects by sorting algorithm, to ensure for each The candidate allocation groups of objects for allocation model of being engaged in has best match, and the candidate allocation groups of objects of each task allocation model is It screens and obtains from all distribution objects, it is corresponding different candidate points completely unduplicated without limiting different task allocation models With object, allow the candidate allocation pair of each task allocation model under the premise of meeting the ATTRIBUTE INDEX of each task allocation model As group allows to improve the precision and reasonability of candidate allocation object in the presence of the distribution object to partially overlap.And work as having determined After preceding task allocation model and the corresponding optimal candidate distribution object group of current task allocation model, in combination be randomly assigned, The mode of distribution is freely claimed, task distribution is carried out, while improving task allocative efficiency, improves the accurate of task distribution Degree, reasonability improve the efficiency of processing task.

Detailed description of the invention

The method for allocating tasks flow diagram based on sorting algorithm of one embodiment of Fig. 1 the application；

The task allocation apparatus structural schematic diagram based on sorting algorithm of one embodiment of Fig. 2 the application；

The computer equipment schematic diagram of internal structure of one embodiment of Fig. 3 the application.

Specific embodiment

It should be appreciated that specific embodiment described herein is only used to explain the application, it is not used to limit the application.

Referring to Fig.1, the method for allocating tasks based on sorting algorithm of one embodiment of the application, comprising:

S1: task processing capacity grade classification is carried out according to preset rules to all distribution objects in specified database.

The specified database of the present embodiment is the linked database of manpower management platform, includes each task in specified database It handles people and the corresponding historic task of each task processing people handles data.The distribution object of the present embodiment is specified database In all tasks to be allocated task handle people.Preset rules include handling the comprehensive of data according to the historic task of task processing people It closes scoring and evaluates each task processing people, and divide the task processing capacity grade of each task processing people according to history comprehensive score； Or the speciality technical ability of people is handled according to each task, to each task, processing people carries out certain capabilities grade distinction.The present embodiment passes through To each task, processing people carries out task processing capacity grade classification, to mark corresponding ability label to each task processing people, Aforementioned capabilities label includes the various parameters data of mark task processing people's task processing capacity, to optimize the needle of task distribution To property and overall effect, the precision of task distribution is improved.

S2: according to the ATTRIBUTE INDEX of different task allocation model, from all institutes Jing Guo task processing capacity grade classification It states in distribution object, candidate allocation groups of objects corresponding with current task allocation model is filtered out by specified sorting algorithm.

The present embodiment is by the ATTRIBUTE INDEX of acquisition different task allocation model, and ATTRIBUTE INDEX is including at least an index letter Breath, and indication information is corresponding with some supplemental characteristic in the ability label of task processing people.The task of the present embodiment is distributed Mode includes history assignment record mode, claims mode, is randomly assigned mode, customized allocation model and assignment of allocation mode Deng the corresponding ATTRIBUTE INDEX difference of different task allocation models.Citing, in the ATTRIBUTE INDEX of history assignment record mode extremely Less include the frequency indication information that history handles the task, is fitted in the ATTRIBUTE INDEX of assignment of allocation mode including at least certain capabilities Right indication information.The present embodiment determines the ability mark of task processing people according to the ATTRIBUTE INDEX of current task allocation model The first design parameter data in label, the first design parameter data include multiple, and by above-mentioned multiple first design parameter data It is input in the clustering algorithm based on support vector machines simultaneously, to get the first design parameter data pair of clustering algorithm output The clustering cluster answered, and candidate allocation corresponding with current task allocation model is filtered out from specified database according to the clustering cluster Groups of objects.The application other embodiments determine the ability mark of task processing people according to the ATTRIBUTE INDEX of current task allocation model The second design parameter data in label, the second design parameter data include multiple, and according to decision-tree model to above-mentioned second tool Body supplemental characteristic carry out importance descending sort, so as to according to importance descending sort successively to the task in specified database at Reason people carries out classification division, until filtering out the candidate allocation for meeting task processing people's quantitative requirement and task processing capacity and requiring Groups of objects.

S3: according to the corresponding allocation rule of the current task allocation model, corresponding with current task allocation model The corresponding Target Assignment object of task to be allocated is determined in candidate allocation groups of objects.

The present embodiment from specified database by the way that it is optimal to filter out matching degree previously according to current task allocation model Candidate allocation groups of objects, to improve task allocative efficiency under the premise of meeting task distribution precision.In the present embodiment not It with the candidate allocation groups of objects under task allocation model, is divided, is allowed different according to the design parameter data of different attribute Candidate allocation groups of objects under task allocation model has the phenomenon that partial intersection, will be at the task in database compared to mechanically Reason people is distinguished in a manner of being absolutely grouped, and showing as different groups, utterly to correspond to different candidate populations more reasonable.When not When having part repetition with the design parameter data of attribute, what the task assigner of each candidate allocation groups of objects kind overlapped shows As which candidate allocation groups of objects can be preferentially adjusted by the task allocation model decision preferentially selected, to solve in task distribution Collision problem.

S4: the task to be allocated is distributed into the Target Assignment object with predetermined manner.

The present embodiment is determining current task allocation model and the corresponding optimal candidate point of current task allocation model After groups of objects, the combinable mode for being randomly assigned, freely claiming distribution carries out task distribution, is improving task allocative efficiency While, precision, the reasonability of task distribution are improved, the efficiency of task processing is improved.

Further, the ATTRIBUTE INDEX according to task allocation model, from by task processing capacity grade classification In all distribution objects, candidate allocation object corresponding with current task allocation model is filtered out by specified sorting algorithm The step S2 of group, comprising:

S21: obtaining corresponding first ATTRIBUTE INDEX of first task allocation model, wherein first ATTRIBUTE INDEX with it is described At least one in ability label is corresponding.

First ATTRIBUTE INDEX of the present embodiment, working condition, age, gender, history length of service including distribution object, The business information such as schooling, professional grade.For example, working condition include task processing in, task to be allocated and distributed appoint It is engaged in be processed.The personal information that first ATTRIBUTE INDEX of the present embodiment inputs when buying health insurance product from user, or comment The personal information of typing when estimating Pre purchase health insurance product.

S22: all distribution objects of the ability label will have been marked, has been input to the cluster based on support vector machines In algorithm.

The clustering algorithm of the present embodiment be based on support vector machines, support vector machines (Support Vector Machine, SVM peculiar advantage) is shown in solution small sample, the identification of non-linear and high dimensional pattern, analysis data can be efficiently carried out, know Other mode, classification and regression analysis.Due to support vector machines can according to limited sample information model complexity (i.e. to spy Determine the study precision of training sample) and learning ability (i.e. without error identify arbitrary sample ability) between seek most preferably to roll over In to get to the lower model of complexity with certain learning ability, in the hope of obtaining best Generalization Ability, so that this implementation Example has the more efficient of processing compared to traditional clustering algorithm based on the clustering algorithm of support vector machines, so that point of output Class data obtain maximized minimum interval, are more advantageous to the accurate picture for establishing the corresponding distribution object of first task allocation model Picture improves the matching precision of first task allocation model and distribution object.

S23: corresponding first clustering cluster of the first task allocation model of the clustering algorithm output is obtained.

S24: by the corresponding first distribution object population of first clustering cluster, as the first task allocation model pair The candidate allocation groups of objects answered.

The present embodiment is calculated by will mark all distribution objects of ability label as the cluster based on support vector machines The input of method, will pass through corresponding first clustering cluster of clustering algorithm output first task allocation model, will pass through to each The corresponding distribution object of business allocation model carries out classification sub-clustering, reaches distribution object population and specific tasks allocation model carries out more Accurately match.

It further, include multiple attributes in corresponding second ATTRIBUTE INDEX of the second task allocation model, the basis is appointed The ATTRIBUTE INDEX for allocation model of being engaged in, from all distribution objects Jing Guo task processing capacity grade classification, by specified Sorting algorithm filters out the step S2 of candidate allocation groups of objects corresponding with current task allocation model, comprising:

S201: it in corresponding second ATTRIBUTE INDEX of the second task allocation model, obtains to second task point With maximum first attribute of mode influences.

The present embodiment influences maximum first attribute to the second task allocation model, to influence the second task allocation model The maximum feature of fluctuation, it is first that the second task allocation model is corresponding for example, the second task allocation model is assignment of allocation mode The second ATTRIBUTE INDEX in the attributes of 10 dimensions attribute is classified as according to one respectively according to decision tree calculation method, one is classified as The presence or absence of second task allocation model history distribution object carrier state value carries out data assignment, according to the corresponding data of each attribute Arrangement calculates separately the corresponding matching rate of each attribute, arranges 10 attributes according to descending according to the size of matching rate value, selects Select descending arrangement in sequence front end 3 attributes to all distribution objects Jing Guo task processing capacity grade classification into Row divides, and wherein sorts in descending arrangement in the attribute of front end, for the influence maximum first to the second task allocation model Attribute.

S202: according to the classified zoning of corresponding first specified quantity of first attribute, by all distribution objects It is divided into the first subset of the first specified quantity.

The classified zoning quantity of above-mentioned first specified quantity and the first attribute corresponds.Such as first attribute sign be work State, working condition include in task processing, task to be allocated and have distributed task three classified zonings to be processed, then first refers to Fixed number amount is three, can be divided into three for respectively corresponding to all distribution objects Jing Guo task processing capacity grade classification First subset.

S203: screening meets the first subset of target of the first preset condition from each first subset, wherein described The first subset of target is one or more.

First preset condition of the present embodiment can have for the distribution object that the first subset includes can receive new task Remaining time, the first subset for meeting the first preset condition is the first subset of target, to enter in the echelon that second divides.

S204: it in all second ATTRIBUTE INDEXs that first subset of target includes, obtains to first son of target The information content of collection influences maximum second attribute, and second attribute is different from first attribute.

The present embodiment is found according to decision tree calculation method to the first subset of target by taking the first subset of task to be allocated as an example Maximum second attribute is influenced, for example is the age.Because by using " working condition " as the first attribute to all distribution objects After division, the obtained working condition in the first subset of the same target is task to be allocated, again to Importance of Attributes into When row sequence, " working condition " this feature will be no longer participate in sequence, and in feature descending sort at this time, making number one is " age ", then the second attribute is " age ".

S205: according to the classified zoning of corresponding second specified quantity of second attribute, by first subset of target It is divided into the second subset of the second specified quantity.

The present embodiment by by the age it is discrete for [20,30), [30,40), [40,50), [and 50,60) four sections, by mesh It marks the first subset correspondence and is divided into corresponding four second subset in four sections.It is equivalent to the first subset of task to be allocated Further refinement is carried out, to find and the more matched Target Assignment group of the second task allocation model.

S206: in the second subset for judging second specified quantity, if there is the target for meeting the second preset condition Second subset.

Second preset condition of the present embodiment is that the total amount for the distribution object that the first subset includes is less than or equal to default threshold It is worth and is greater than zero.Second preset condition of the present embodiment is the newly-increased qualifications met after the first preset condition, logical to realize It crosses and is stepped up qualifications subset range is gradually reduced, meet the corresponding candidate allocation pair of the second task allocation model to find As group.

S207: meeting the target second subset of the second preset condition if it exists, then stops to the target second subset It divides, and determines the target second subset for meeting second preset condition, be the corresponding time of the second task allocation model Select distribution object group.

Meet the target second subset of the second preset condition in the present embodiment if it does not exist, then again to target second subset Increase new qualifications, into the third preset condition of third time subset division, until being quickly found out satisfaction in a manner of gathering It is required that candidate allocation groups of objects, realize more accurately task distribute.

Further, described in corresponding second ATTRIBUTE INDEX of the second task allocation model, it obtains to described the Two task allocation models influence the step S201 of maximum first attribute, comprising:

S2011: each attribute is respectively to the influence value of the second task allocation model in acquisition second ATTRIBUTE INDEX.

S2012: descending arrangement is carried out to each attribute according to the size of each influence value.

S2013: the arrangement order in the descending arrangement is set near the preceding corresponding attribute of the first influence value, is described First attribute.

The present embodiment is by information gain algorithm, after individually calculating each attribute addition calculating, to the second task point The influence amplitude of whole entropy with mode, to obtain each influence value.Information gain algorithm calculates as follows: g (D, A)=H (D)-H (D | A), wherein g (D, A) indicates influence amplitude of the A attribute to whole entropy, and H (D) indicates the entropy of all distribution objects, H (D Ι A) table Show the entropy according to the subset after A Attribute transposition.The numerical value of influence value is bigger, illustrates, the work of respective attributes bigger to entire effect It is stronger with ability, it is more important to the effect for finding candidate allocation groups of objects.

Further, all distribution objects in specified database carry out task processing capacity according to preset rules Grade classification, and the step S1 of ability label is marked respectively, comprising:

S11: it obtains the corresponding historic task of all distribution objects and handles data, wherein at the historic task It manages data and includes at least history comprehensive score, the history comprehensive score is obtained by summarizing the score of each professional ability.

S12: the task processing capacity grade of each distribution object is divided according to the history comprehensive score respectively.

S13: the highest first specified services ability that scores in the history comprehensive score is obtained.

S14: it is marked using the corresponding first specified services ability of each distribution object as the ability label Note.

The present embodiment evaluates the task processing capacity of each distribution object by history comprehensive score, and history comprehensive score is got over Height shows that the task processing capacity of distribution object is stronger.Each professional ability can set identical or different weight proportion, so as to The history comprehensive score arrived has more reference value.The present embodiment highest first is specified by will score in history comprehensive score Professional ability is labeled as ability label, is filtered out and current task allocation model pair with will pass through specified sorting algorithm When the candidate allocation groups of objects answered, quickly positioning or classification can be realized according to ability label, and increase ability label pair can be passed through The weight proportion answered makes the Target Assignment object found be more suitable for task to be allocated.

Further, the history comprehensive score includes the corresponding score of certain capabilities, described to obtain the history synthesis It is scored after the step S13 of highest first specified services ability in scoring, further includes:

S15: judge whether the first specified services ability is the certain capabilities.

S16: if the first specified services ability is not the certain capabilities, each distribution object is respectively corresponded The first specified services ability and certain capabilities, while being labeled as the ability label.

It not only include the highest first specified services energy that scores in history comprehensive score in ability label in the present embodiment Power further includes certain capabilities.Above-mentioned certain capabilities can be regarded as particular professional technology, particular job experience etc., than if any joining the army It undergoes or experience etc. of going to sea, finds the accuracy of Target Assignment group, matching degree to meet for special duty.

Further, the current task allocation model includes at least two kinds of task allocation models, described to work as according to described The corresponding allocation rule of preceding task allocation model, in candidate allocation groups of objects corresponding with the current task allocation model, Determine the step S3 of the corresponding Target Assignment object of task to be allocated, comprising:

S31: it is sorted, is obtained in the current task allocation model according to the pre-set priority of each task allocation model The corresponding candidate allocation groups of objects sequence of each task allocation model.

S32: the first candidate allocation groups of objects of highest priority is determined from candidate allocation groups of objects sequence.

S33: using the assignment of allocation object in the first candidate allocation groups of objects as the Target Assignment object.

The task allocation model of the present embodiment include historical record allocation model, claim allocation model, be randomly assigned mode, Customized allocation model and assignment of allocation mode.Pre-set priority sequence is followed successively by assignment of allocation mode, customized from high to low Allocation model, claims allocation model, is randomly assigned mode at historical record allocation model.According to above-mentioned from all distribution objects The method for screening the corresponding candidate allocation groups of objects of appointed task allocation model obtains history note from all distribution objects respectively Record allocation model claims allocation model, is randomly assigned the time that mode, customized allocation model and assignment of allocation mode are corresponding in turn to Distribution object group is selected, with the corresponding candidate allocation groups of objects of the highest assignment of allocation mode of priority ranking, as the first candidate Distribution object group.The present embodiment can be by directly from all distribution objects, obtaining obtaining highest specified point of priority ranking With the corresponding candidate allocation groups of objects of mode, the corresponding candidate allocation groups of objects of other task allocation models is classified without calculating, To save program, accelerate treatment effeciency.The application other embodiments also obtain each task distribution from all distribution objects respectively The corresponding candidate allocation groups of objects of mode, then two or more candidate allocations forward by priority ranking selected and sorted Groups of objects, and analyzing duplicate distribution object in above-mentioned two or more than two candidate allocation groups of objects is new candidate allocation object Group, to ensure that the Target Assignment object selected from new candidate allocation groups of objects is more bonded current task requirement.The present embodiment is true Determine the corresponding candidate allocation groups of objects of current task allocation model, is randomly assigned or claims the shapes such as distribution by group member in group Formula realizes task distribution.

The present embodiment has fully considered the formation reasonability of candidate allocation groups of objects by sorting algorithm, to ensure for each The candidate allocation groups of objects of task allocation model has best match, and the candidate allocation groups of objects of each task allocation model is equal It is obtained to be screened from all distribution objects, it is corresponding completely unduplicated different candidate without limiting different task allocation models Distribution object allows the candidate allocation of each task allocation model under the premise of meeting the ATTRIBUTE INDEX of each task allocation model Groups of objects allows to have the distribution object to partially overlap, improves the precision and reasonability of candidate allocation object.And it is determining After current task allocation model and the corresponding optimal candidate distribution object group of current task allocation model, in combination with random point Match, freely claim the mode of distribution, carry out task distribution, while improving task allocative efficiency, improves the essence that task is distributed Accuracy, reasonability improve the efficiency of processing task.

Referring to Fig. 2, the task allocation apparatus based on sorting algorithm of one embodiment of the application, comprising:

Labeling module 1 handles energy for carrying out task according to preset rules to all distribution objects in specified database Power grade classification.

The specified database of the present embodiment is the linked database of manpower management platform, includes each task in specified database It handles people and the corresponding historic task of each task processing people handles data.The distribution object of the present embodiment refers to specified database In all tasks to be allocated task handle people.Preset rules include handling the comprehensive of data according to the historic task of task processing people It closes scoring and evaluates each task processing people, and divide the task processing capacity grade of each task processing people according to history comprehensive score； Or the speciality technical ability of people is handled according to each task, to each task, processing people carries out certain capabilities grade distinction.The present embodiment passes through To each task, processing people carries out task processing capacity grade classification, to mark corresponding ability label to each task processing people, Aforementioned capabilities label includes the various parameters data of mark task processing people's task processing capacity, to optimize the needle of task distribution To property and overall effect, the precision of task distribution is improved.

Screening module 2 is drawn for the ATTRIBUTE INDEX according to different task allocation model from by task processing capacity grade In all distribution objects divided, candidate allocation corresponding with current task allocation model is filtered out by specified sorting algorithm Groups of objects.

Determining module 3, for being distributed with current task according to the corresponding allocation rule of the current task allocation model The corresponding Target Assignment object of task to be allocated is determined in the corresponding candidate allocation groups of objects of mode.

The present embodiment from specified database by the way that it is optimal to filter out matching degree previously according to current task allocation model Candidate allocation groups of objects, to improve task allocative efficiency under the premise of meeting task distribution precision.In the present embodiment not It with the candidate allocation groups of objects under task allocation model, is divided, is allowed different according to the design parameter data of different attribute Candidate allocation groups of objects under task allocation model has the phenomenon that partial intersection, will be at the task in database compared to mechanically Reason people distinguishes in an absolute way, and showing as different groups, to correspond to different candidate populations more reasonable.When the tool of different attribute When body supplemental characteristic has part repetition, the phenomenon that task assigner of each candidate allocation groups of objects kind overlaps, it can pass through Which candidate allocation groups of objects the task allocation model decision preferentially selected preferentially adjusts, and is asked with the conflict in the distribution of solution task Topic.

Distribution module 4, for the task to be allocated to be distributed to the Target Assignment object with predetermined manner.

Further, the screening module 2 of the present embodiment, comprising:

First acquisition unit, for obtaining corresponding first ATTRIBUTE INDEX of first task allocation model, wherein described first ATTRIBUTE INDEX is corresponding at least one in the ability label.

First ATTRIBUTE INDEX of the present embodiment, working condition, age, gender, history length of service including distribution object, The business information such as schooling, professional grade.For example, working condition include task processing in, task to be allocated and distributed appoint It is engaged in be processed.The personal information of input when first ATTRIBUTE INDEX of the present embodiment buys health insurance product from user, or The personal information of typing when assessing Pre purchase health insurance product.

Input unit, for all distribution objects of the ability label will to have been marked, be input to based on support to In the clustering algorithm of amount machine.

Second acquisition unit, for obtaining the first task allocation model corresponding first of the clustering algorithm output Clustering cluster.

First is used as unit, is used for by the corresponding first distribution object population of first clustering cluster, as described first The corresponding candidate allocation groups of objects of task allocation model.

It further, include multiple attributes in corresponding second ATTRIBUTE INDEX of the second task allocation model, the application is another The screening module 2 of embodiment, comprising:

Third acquiring unit, for obtaining to institute in corresponding second ATTRIBUTE INDEX of the second task allocation model Stating the second task allocation model influences maximum first attribute.

First division unit will own for the classified zoning according to corresponding first specified quantity of first attribute The distribution object is divided into the first subset of the first specified quantity.

Screening unit, for screening the first subset of target for meeting the first preset condition from each first subset, Wherein first subset of target is one or more.

4th acquiring unit, for obtaining to institute in all second ATTRIBUTE INDEXs that first subset of target includes The information content for stating the first subset of target influences maximum second attribute, and second attribute is different from first attribute.

Second division unit will be described for the classified zoning according to corresponding second specified quantity of second attribute The first subset division of target is the second subset of the second specified quantity.

First judging unit, in the second subset for judging second specified quantity, if it is pre- to there is satisfaction second If the target second subset of condition.

Judging unit then stops for meeting the target second subset of the second preset condition if it exists to the target the The division of two subsets, and determine the target second subset for meeting second preset condition, it is the second task allocation model Corresponding candidate allocation groups of objects.

In the present embodiment, meet the target second subset of the second preset condition if it does not exist, then to target second subset weight New qualifications are newly increased, into the third preset condition of third time subset division, until being quickly found out in a manner of gathering full The candidate allocation groups of objects required enough realizes that more accurately task is distributed.

Further, the third acquiring unit, comprising:

Subelement is obtained, for obtaining in second ATTRIBUTE INDEX each attribute respectively to the second task allocation model Influence value.

Subelement is arranged, for carrying out descending arrangement to each attribute according to the size of each influence value.

Subelement is set, for setting the arrangement order in the descending arrangement near the preceding corresponding category of the first influence value Property, it is first attribute.

Further, the labeling module 1 of the present embodiment, comprising:

5th acquiring unit, for obtaining the corresponding historic task processing data of all distribution objects, wherein The historic task processing data include at least history comprehensive score, and the history comprehensive score is by summarizing each professional ability Score obtains.

Third division unit divides the task processing capacity of each distribution object according to the history comprehensive score respectively Grade.

6th acquiring unit, for obtaining the highest first specified services ability that scores in the history comprehensive score.

Second is used as unit, for using the corresponding first specified services ability of each distribution object as the energy Power label is labeled.

Further, the labeling module 1, further includes:

Second judgment unit, for judging whether the first specified services ability is the certain capabilities.

Third is as unit, if not being the certain capabilities for the first specified services ability, by each described point It is labeled with the corresponding first specified services ability of object and certain capabilities, while as the ability label.

Further, the current task allocation model includes at least two kinds of task allocation models, the determination of the present embodiment Module 3, comprising:

7th acquiring unit obtains described current for being sorted according to the pre-set priority of each task allocation model The corresponding candidate allocation groups of objects sequence of each task allocation model in task allocation model.

Determination unit, for determining the first candidate allocation pair of highest priority from candidate allocation groups of objects sequence As group.

4th is used as unit, for using the assignment of allocation object in the first candidate allocation groups of objects as the target Distribution object.

Referring to Fig. 3, a kind of computer equipment is also provided in the embodiment of the present application, which can be server, Its internal structure can be as shown in Figure 3.The computer equipment includes processor, the memory, network connected by system bus Interface and database.Wherein, the processor of the Computer Design is for providing calculating and control ability.The computer equipment is deposited Reservoir includes non-volatile memory medium, built-in storage.The non-volatile memory medium is stored with operating system, computer program And database.The internal memory provides environment for the operation of operating system and computer program in non-volatile memory medium.It should The database of computer equipment is used to store all data that the task assignment procedure based on sorting algorithm needs.The computer is set Standby network interface is used to communicate with external end by network connection.To realize base when the computer program is executed by processor In the method for allocating tasks of sorting algorithm.

Above-mentioned processor executes the above-mentioned method for allocating tasks based on sorting algorithm, comprising: to the institute in specified database There is distribution object to carry out task processing capacity grade classification according to preset rules, and marks ability label respectively；According to task point ATTRIBUTE INDEX with mode passes through specified classification from all distribution objects Jing Guo task processing capacity grade classification Algorithm filters out candidate allocation groups of objects corresponding with current task allocation model；It is corresponding according to the current task allocation model Allocation rule determine that task to be allocated is corresponding in candidate allocation groups of objects corresponding with the current task allocation model Target Assignment object；The task to be allocated is distributed into the Target Assignment object with predetermined manner.

Above-mentioned computer equipment has fully considered the formation reasonability of candidate allocation groups of objects, by sorting algorithm with true The candidate allocation groups of objects protected for each task allocation model has best match, and the candidate allocation of each task allocation model Groups of objects is to screen to obtain from all distribution objects, corresponding completely unduplicated without limiting different task allocation models Different candidate allocation objects allow under the premise of meeting the ATTRIBUTE INDEX of each task allocation model, each task allocation model Candidate allocation groups of objects allows to have the distribution object to partially overlap, improves the precision and reasonability of candidate allocation object.And It is combinable after current task allocation model and the corresponding optimal candidate distribution object group of current task allocation model has been determined It is randomly assigned, freely claims the mode of distribution, carry out task distribution, while improving task allocative efficiency, improve task and divide The precision matched, reasonability improve the efficiency of processing task.

In one embodiment, above-mentioned processor handles energy from by task according to the ATTRIBUTE INDEX of task allocation model In all distribution objects of power grade classification, filtered out by specified sorting algorithm corresponding with current task allocation model The step of candidate allocation groups of objects, comprising: corresponding first ATTRIBUTE INDEX of first task allocation model is obtained, wherein described first ATTRIBUTE INDEX is corresponding at least one in the ability label；All distribution pair of the ability label will have been marked As being input in the clustering algorithm based on support vector machines；Obtain the first task distribution mould of the clustering algorithm output Corresponding first clustering cluster of formula；By the corresponding first distribution object population of first clustering cluster, as the first task point With the corresponding candidate allocation groups of objects of mode.

It in one embodiment, include multiple attributes in corresponding second ATTRIBUTE INDEX of the second task allocation model, it is above-mentioned Processor is according to the ATTRIBUTE INDEX of task allocation model, from all distribution objects Jing Guo task processing capacity grade classification In, pass through the step of specifying sorting algorithm to filter out candidate allocation groups of objects corresponding with current task allocation model, comprising: In corresponding second ATTRIBUTE INDEX of the second task allocation model, obtain the second task allocation model is influenced it is maximum First attribute；According to the classified zoning of corresponding first specified quantity of first attribute, all distribution objects are divided For the first subset of the first specified quantity；Screening meets the first son of target of the first preset condition from each first subset Collection, wherein the first subset of the target is one or more；In all second ATTRIBUTE INDEXs that first subset of target includes In, obtaining influences maximum second attribute to the information content of first subset of target, and second attribute and described first belong to Property it is different；According to the classified zoning of corresponding second specified quantity of second attribute, it is by first subset division of target The second subset of second specified quantity；In the second subset for judging second specified quantity, if it is default to there is satisfaction second The target second subset of condition；If it exists, then stop the division to the target second subset, and determine to meet described second in advance It is the corresponding candidate allocation groups of objects of the second task allocation model if the target second subset of condition.

In one embodiment, above-mentioned processor is in corresponding second ATTRIBUTE INDEX of the second task allocation model, Obtain the step of maximum first attribute is influenced on the second task allocation model, comprising: obtain second ATTRIBUTE INDEX In each attribute respectively to the influence value of the second task allocation model；According to the size of each influence value to each attribute Carry out descending arrangement；The arrangement order in the descending arrangement is set near the preceding corresponding attribute of the first influence value, is described First attribute.

In one embodiment, above-mentioned processor carries out all distribution objects in specified database according to preset rules Task processing capacity grade classification, and the step of marking ability label respectively, comprising: it is right respectively to obtain all distribution objects The historic task processing data answered, wherein historic task processing data include at least history comprehensive score, the history is comprehensive Scoring is closed to obtain by summarizing the score of each professional ability；Each distribution object is divided according to the history comprehensive score respectively Task processing capacity grade；Obtain the highest first specified services ability that scores in the history comprehensive score；It will be each described The corresponding first specified services ability of distribution object is labeled as the ability label.

In one embodiment, above-mentioned processor history comprehensive score includes the corresponding score of certain capabilities, the acquisition After the step of highest first specified services ability that scores in the history comprehensive score, comprising: judge that described first is specified Whether professional ability is the certain capabilities；If it is not, then by the corresponding first specified services ability of each distribution object And certain capabilities, while being labeled as the ability label.

In one embodiment, the current task allocation model includes at least two kinds of task allocation models, above-mentioned processing Device is according to the corresponding allocation rule of the current task allocation model, at candidate point corresponding with the current task allocation model With the step of in groups of objects, determining task to be allocated corresponding Target Assignment object, comprising: according to each task allocation model Pre-set priority sequence, obtain the corresponding candidate allocation pair of each task allocation model in the current task allocation model As group sorts；The first candidate allocation groups of objects of highest priority is determined from candidate allocation groups of objects sequence；It will be described Assignment of allocation object in first candidate allocation groups of objects is as the Target Assignment object.

It will be understood by those skilled in the art that structure shown in Fig. 3, only part relevant to application scheme is tied The block diagram of structure does not constitute the restriction for the computer equipment being applied thereon to application scheme.

One embodiment of the application also provides a kind of computer readable storage medium, is stored thereon with computer program, calculates The method for allocating tasks based on sorting algorithm is realized when machine program is executed by processor, comprising: to all in specified database Distribution object carries out task processing capacity grade classification according to preset rules, and marks ability label respectively；It is distributed according to task The ATTRIBUTE INDEX of mode is calculated from all distribution objects Jing Guo task processing capacity grade classification by specified classification Method filters out candidate allocation groups of objects corresponding with current task allocation model；It is corresponding according to the current task allocation model Allocation rule determines that task to be allocated is corresponding in candidate allocation groups of objects corresponding with the current task allocation model Target Assignment object；The task to be allocated is distributed into the Target Assignment object with predetermined manner.

Above-mentioned computer readable storage medium has fully considered that the formation of candidate allocation groups of objects is reasonable by sorting algorithm Property, to ensure that the candidate allocation groups of objects for each task allocation model has best match, and each task allocation model Candidate allocation groups of objects is to screen to obtain from all distribution objects, corresponding complete without limiting different task allocation models Unduplicated difference candidate allocation object, allows under the premise of meeting the ATTRIBUTE INDEX of each task allocation model, each task point Candidate allocation groups of objects with mode allows to have the distribution object to partially overlap, improves precision and the conjunction of candidate allocation object Rationality.And current task allocation model and the corresponding optimal candidate distribution object group of current task allocation model is being determined Afterwards, the combinable mode for being randomly assigned, freely claiming distribution carries out task distribution and mentions while improving task allocative efficiency The precision of high task distribution, reasonability, improve the efficiency of processing task.

Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with Relevant hardware is instructed to complete by computer program, above-mentioned computer program can be stored in a non-volatile computer In read/write memory medium, the computer program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein, Any reference used in provided herein and embodiment to memory, storage, database or other media, Including non-volatile and/or volatile memory.Nonvolatile memory may include read-only memory (ROM), programming ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM) or flash memory.Volatile memory may include Random access memory (RAM) or external cache.By way of illustration and not limitation, RAM is available in many forms, Such as static state RAM (SRAM), dynamic ram (DRAM), synchronous dram (SDRAM), double speed are according to rate SDRAM (SSRSDRAM), enhancing Type SDRAM (ESDRAM), synchronization link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic ram (DRDRAM) and memory bus dynamic ram (RDRAM) etc..

It should be noted that, in this document, the terms "include", "comprise" or its any other variant are intended to non-row His property includes, so that the process, device, article or the method that include a series of elements not only include those elements, and And further include other elements that are not explicitly listed, or further include for this process, device, article or method institute it is intrinsic Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including being somebody's turn to do There is also other identical elements in the process, device of element, article or method.

The foregoing is merely preferred embodiment of the present application, are not intended to limit the scope of the patents of the application, all utilizations Equivalent structure or equivalent flow shift made by present specification and accompanying drawing content is applied directly or indirectly in other correlations Technical field, similarly include in the scope of patent protection of the application.

Claims

1. a kind of method for allocating tasks based on sorting algorithm characterized by comprising

Task processing capacity grade classification is carried out according to preset rules to all distribution objects in specified database, and is marked respectively Note ability label；

According to the ATTRIBUTE INDEX of task allocation model, from all distribution objects Jing Guo task processing capacity grade classification In, candidate allocation groups of objects corresponding with current task allocation model is filtered out by specified sorting algorithm；

According to the corresponding allocation rule of the current task allocation model, in candidate corresponding with the current task allocation model In distribution object group, the corresponding Target Assignment object of task to be allocated is determined；

2. the method for allocating tasks according to claim 1 based on sorting algorithm, which is characterized in that described according to task point ATTRIBUTE INDEX with mode passes through specified classification from all distribution objects Jing Guo task processing capacity grade classification Algorithm filters out the step of candidate allocation groups of objects corresponding with current task allocation model, comprising:

Corresponding first ATTRIBUTE INDEX of first task allocation model is obtained, wherein first ATTRIBUTE INDEX and the ability label In at least one of it is corresponding；

All distribution objects of the ability label will be marked, has been input in the clustering algorithm based on support vector machines；

By the corresponding first distribution object population of first clustering cluster, as the corresponding candidate of the first task allocation model Distribution object group.

3. the method for allocating tasks according to claim 1 based on sorting algorithm, which is characterized in that the second task distributes mould It include multiple attributes, the ATTRIBUTE INDEX according to task allocation model, from by task in corresponding second ATTRIBUTE INDEX of formula In all distribution objects of processing capacity grade classification, filtered out and current task allocation model by specified sorting algorithm The step of corresponding candidate allocation groups of objects, comprising:

In corresponding second ATTRIBUTE INDEX of the second task allocation model, obtaining influences the second task allocation model Maximum first attribute；

According to the classified zoning of corresponding first specified quantity of first attribute, all distribution objects are divided into first First subset of specified quantity；

Screening meets the first subset of target of the first preset condition from each first subset, wherein the first son of the target Collection is one or more；

In all second ATTRIBUTE INDEXs that first subset of target includes, the information content to first subset of target is obtained Maximum second attribute is influenced, second attribute is different from first attribute；

It is second by first subset division of target according to the classified zoning of corresponding second specified quantity of second attribute The second subset of specified quantity；

In the second subset for judging second specified quantity, if there is the target second subset for meeting the second preset condition；

If it exists, then stop division to the target second subset, and determine to meet the target the of second preset condition Two subsets are the corresponding candidate allocation groups of objects of the second task allocation model.

4. the method for allocating tasks according to claim 3 based on sorting algorithm, which is characterized in that described described second In corresponding second ATTRIBUTE INDEX of task allocation model, obtaining influences maximum first attribute to the second task allocation model The step of, comprising:

The arrangement order in the descending arrangement is set near the preceding corresponding attribute of the first influence value, is first attribute.

5. the method for allocating tasks according to claim 1 based on sorting algorithm, which is characterized in that described pair of specified data All distribution objects in library carry out task processing capacity grade classification according to preset rules, and mark the step of ability label respectively Suddenly, comprising:

The corresponding historic task processing data of all distribution objects are obtained, wherein historic task processing data are extremely It less include history comprehensive score, the history comprehensive score is obtained by summarizing the score of each professional ability；

6. the method for allocating tasks according to claim 5 based on sorting algorithm, which is characterized in that the history synthesis is commented Dividing includes the corresponding score of certain capabilities, described to obtain the highest first specified services ability that scores in the history comprehensive score The step of after, comprising:

If it is not, then by the corresponding first specified services ability of each distribution object and certain capabilities, while described in conduct Ability label is labeled.

7. the method for allocating tasks according to claim 6 based on sorting algorithm, which is characterized in that the current task point Two kinds of task allocation models are included at least with mode, it is described according to the corresponding allocation rule of the current task allocation model, In candidate allocation groups of objects corresponding with the current task allocation model, the corresponding Target Assignment object of task to be allocated is determined The step of, comprising:

It is sorted according to the pre-set priority of each task allocation model, obtains in the current task allocation model each described The corresponding candidate allocation groups of objects sequence of allocation model of being engaged in；

8. a kind of task allocation apparatus based on sorting algorithm characterized by comprising

Labeling module, for carrying out task processing capacity grade according to preset rules to all distribution objects in specified database It divides, and marks ability label respectively；

Screening module, for the ATTRIBUTE INDEX according to task allocation model, from by all of task processing capacity grade classification In the distribution object, candidate allocation groups of objects corresponding with current task allocation model is filtered out by specified sorting algorithm；

Determining module, for being distributed with the current task according to the corresponding allocation rule of the current task allocation model In the corresponding candidate allocation groups of objects of mode, the corresponding Target Assignment object of task to be allocated is determined；

9. a kind of computer equipment, including memory and processor, the memory are stored with computer program, feature exists In the step of processor realizes any one of claims 1 to 7 the method when executing the computer program.

10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program The step of method described in any one of claims 1 to 7 is realized when being executed by processor.