CN108537619A - A kind of method for allocating tasks, device and equipment based on maximum-flow algorithm - Google Patents
A kind of method for allocating tasks, device and equipment based on maximum-flow algorithm Download PDFInfo
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Abstract
This application discloses a kind of method for allocating tasks, device and equipment based on maximum-flow algorithm.Wherein, which specifically includes, and obtains target information and mission bit stream to be allocated；Capacity-network is built according to target information and mission bit stream to be allocated, wherein the weight on each side handles the treatment effeciency determination of different classes of task according to target in the Capacity-network；It is solved in the Capacity-network using default maximum-flow algorithm, all task distribution paths from source node to sink nodes；The task to be allocated is distributed according to the task distribution path.The application is reference with the value that the treatment effeciency of task to be allocated and/or target generate, configuration obtains the weight of each edge in Capacity-network, make in final result, the value for the treatment of effeciency or the target generation of task to be allocated reaches the state of total optimization.
Description
Technical field
This application involves Internet technical field more particularly to a kind of method for allocating tasks, dresses based on maximum-flow algorithm
It sets and equipment.
Background technology
With a large amount of service for life demands generated in daily life, O2O, that is, online to offline (arrive line on line
Under) service sexual norm development it is swift and violent, will subscribe on line, serviced under line, payment etc. flows be transferred in service for life scene,
Such as：Supplier cleans household electrical appliances, the services such as household installation by providing on-site maintenance household electrical appliances on line.
In order to improve occupation rate of market, necessity that efficiency of service and service quality become major supplier considers a problem institute
Therefore, how efficiently to assign order just to become an important problem for multipotency work.
In the prior art, region worksheet processing pattern is mostly used；That is, current most suitable multipotency work is assigned when order reaches,
This worksheet processing pattern substantially uses greedy algorithm, does not consider globally optimal solution performance.
Invention content
This specification embodiment provides a kind of method for allocating tasks, device and equipment based on maximum-flow algorithm, to realize
The allocation strategy integrally controlled in assignment operation.
A kind of method for allocating tasks based on maximum-flow algorithm that this specification embodiment provides, specifically includes：
Obtain target information and mission bit stream to be allocated；
Capacity-network is built according to target information and mission bit stream to be allocated, wherein each side in the Capacity-network
Weight according to target handle different classes of task treatment effeciency determine；
It is solved in the Capacity-network using default maximum-flow algorithm, all tasks from source node to sink nodes distribute road
Diameter；
The task to be allocated is distributed according to the task distribution path.
In addition, the present embodiment additionally provides a kind of task allocation apparatus based on maximum-flow algorithm, specifically include：
Data obtaining module, for obtaining target information and mission bit stream to be allocated；
Module is built, for building Capacity-network according to target information and mission bit stream to be allocated, wherein the appearance
The weight on each side handles the treatment effeciency determination of different classes of task according to target in amount network；
Path solves module, for being solved in the Capacity-network using presetting maximum-flow algorithm, it is all from source node to
The task distribution path of sink nodes；
Task allocating module, for distributing the task to be allocated according to the task distribution path.
In addition, the present embodiment additionally provides a kind of task distributing equipment based on maximum-flow algorithm, specifically include：
Memory stores the task distribution program based on maximum-flow algorithm；
Communication interface receives information acquisition request；
Processor calls what is stored in memory to be calculated based on max-flow after communication interface receives information extraction request
The task distribution program of method, and execute：
Obtain target information and mission bit stream to be allocated；
Capacity-network is built according to target information and mission bit stream to be allocated, wherein each side in the Capacity-network
Weight according to target handle different classes of task treatment effeciency determine；
It is solved in the Capacity-network using default maximum-flow algorithm, all tasks from source node to sink nodes distribute road
Diameter；
The task to be allocated is distributed according to the task distribution path.
This specification embodiment can reach following advantageous effect using above-mentioned at least one technical solution：
Capacity-network is built according to target information and mission bit stream to be allocated, with the treatment effeciency for task to be allocated
And/or the value that target generates is reference, configuration obtains the weight of each edge in Capacity-network, makes in final result, wait for
The value that the treatment effeciency or target of distribution task generate, reaches the state of total optimization.
Description of the drawings
Attached drawing described herein is used for providing further understanding of the present application, constitutes part of this application, this Shen
Illustrative embodiments and their description please do not constitute the improper restriction to the application for explaining the application.In the accompanying drawings：
Fig. 1 is the method for allocating tasks flow chart based on maximum-flow algorithm that this specification embodiment provides；
Fig. 2 is another method for allocating tasks flow chart based on maximum-flow algorithm that this specification embodiment provides；
Fig. 3 is another method for allocating tasks flow chart based on maximum-flow algorithm that this specification embodiment provides；
Fig. 4 is the Capacity-network structural schematic diagram that this specification embodiment provides；
Fig. 5 is another Capacity-network structural schematic diagram that this specification embodiment provides；
Fig. 6 is the offer of this specification embodiment for the task allocation apparatus structural schematic diagram based on maximum-flow algorithm.
Specific implementation mode
To keep the purpose, technical scheme and advantage of the application clearer, below in conjunction with the application specific embodiment and
Technical scheme is clearly and completely described in corresponding attached drawing.Obviously, described embodiment is only the application one
Section Example, instead of all the embodiments.Based on the embodiment in the application, those of ordinary skill in the art are not doing
Go out the every other embodiment obtained under the premise of creative work, shall fall in the protection scope of this application.
In one or more embodiments in the description, method for allocating tasks can be applied to multiple tasks to be allocated
Reasonable distribution gives multiple targets, to improve the efficiency of task processing, generates the value of bigger.
Wherein, technical ability difference is good at due to target, the efficiency for handling different types of task is also different, therefore needs
It will be according to technical ability and task type distribution task be good at, so that the whole efficiency that target handles task to be allocated reaches most
Greatly.Meanwhile method for allocating tasks can also be by the value of task to be allocated as reference, so that target handles to be allocated
Maximum value is generated when business in same time.
Specifically, in the embodiment of the method for allocating tasks of the application, need to obtain the work pair being in idle condition
As with task to be allocated, using each target and task to be allocated as node, and according to the workmanship of target and waiting for
The task type of distribution task is established each node and is connected；Meanwhile increasing the connection of source node and target node, sink nodes
With the connection of task node to be allocated, Capacity-network is formed, and appointing from source node to sink nodes is solved based on the Capacity-network
Business distribution path, and then the task to be allocated is distributed according to the task distribution path, make the treatment effeciency of task to be allocated
Reach maximum or target generation value and reaches maximum.
The method for allocating tasks based on maximum-flow algorithm provided in this specification embodiment, as shown in Figure 1, specifically including
With lower part：
Step S100, target information and mission bit stream to be allocated are obtained.
In the present embodiment, target information includes all working object being in idle condition, the target information
It can be obtained from target pond.Mission bit stream to be allocated includes all tasks to be treated, the task to be allocated
Information can be obtained from task pool to be allocated.Illustratively, it is waited for point from user (for example, passing through client) receiving
When with task (for example, energy services demand class order), the information of these tasks to be allocated can be obtained and be stored, is largely waited for
Distribution mission bit stream forms task pool to be allocated.
Specifically, the target described in the present embodiment can be any type of work pair that can provide various services
As, for example, it may be have the robot of certain skills, for distribute and the process of scheduling resource, for for client provide altogether
Enjoy service shared bicycle or shared automobile, at least two technical ability multipotency work (multipotency work may include with operate it is more
The operating personnel of kind machinery equipment ability).Corresponding with target, mission bit stream to be allocated can be according to customer demand
The various types of tasks generated, for example, it may be when being handled by robot in plant produced line for task, application program are run
Special services that are that memory and resource, shared automobile or the order of shared automobile, user needed is sent out and needing multipotency work to handle
Demand.
According to the preferred embodiment of the application, target is preferred real with the worker of energy services and/or service for life
Apply example, usually can they be referred to as multipotency work, multipotency work can have multinomial technical ability (for example, repair gas pipeline, repair water
Pipe, installation gas meter, flow meter, replacement gas-cooker etc.).Accordingly, mission bit stream to be allocated can be that energy services demand class order is appointed
Business, for example, gas pipeline maintenance order, gas meter, flow meter that gas user is submitted by client install order etc..It is led in the energy
The most distribution that worksheet processing is carried out using the technical ability for being based only on worker or the position for being based only on worker of service in domain, multipotency work are advised
Then, more technical ability value of multipotency work is ignored, and does not consider total optimization state, therefore, the embodiment of the present application can solve
The problem present in energy field.
Specifically, in the acquisition process of target information and mission bit stream to be allocated, can first judge target and
The state of task to be allocated, and then satisfactory target and task to be allocated are stored in the form of array or queue
In storage unit.
Step S120, Capacity-network is built according to target information and mission bit stream to be allocated.
Wherein, in the Capacity-network weight on each side handled according to target different classes of task treatment effeciency it is true
It is fixed.
In the present embodiment, the target information includes the working condition of target, can handle task category, difference
The treatment effeciency of category task.Mission bit stream to be allocated includes the information such as the working condition of task to be allocated, task generic.
Capacity-network includes several target nodes, several task node, a source node and a remittance section to be allocated
Point.It can be attached by directed line segment between each node, to indicate the direction of service logic stream in Capacity-network.Specifically
, source node is connect with each target node respectively, and target node is directed toward in the direction of service logic stream by source node.It converges
Node is connect with each task node to be allocated respectively, and sink nodes are directed toward in the direction of service logic stream by task node to be allocated.Work
The connection made between Object node and task node to be allocated can handle task category and to be allocated according to target
The task generic of business is established, and task node to be allocated is directed toward in the direction of service logic stream by target node.
Specifically, it can be method for allocating tasks that each edge, which is required for configuration weighted value, the weighted value, in Capacity-network
As a result guiding function is provided, i.e., in same Capacity-network, according to the weight configured without factor, finally solve obtained optimal road
Diameter is different, and obtained final result is also different.Therefore, with the treatment effeciency of task to be allocated and/or work pair in the present embodiment
As the value of generation to refer to, configures and obtain the weight of each edge in Capacity-network, make in final result, the place of task to be allocated
Reason efficiency reaches maximum or target generation value and reaches maximum.
Further, the not necessary being of the Capacity-network described in the present embodiment, but for more accurate description thing
Particular kind of relationship between object has this relationship with node on behalf things between indicating corresponding two things with 2 points of line of connection.
Step S130, it is solved in the Capacity-network using default maximum-flow algorithm, it is all from source node to sink nodes
Task distribution path.
Wherein, the task distribution path is distributed by the task based on the optimal solution obtained in above-mentioned Capacity-network
The final result that path obtains can make the overall treatment efficiency of task to be allocated reach maximum or target generation valence
Value reaches maximum.
In the present embodiment, the default maximum-flow algorithm includes Edmond-Karp algorithms, Ford-Fulkerson algorithms
With Dinic algorithms.Wherein, Edmond-Karp algorithms are most short augmenting path algorithm, and the principle of the algorithm is, constantly from
Source node begins look for augmenting path, every time to the further augmentation of the augmenting path searched out, until source node and sink nodes
It is not connected to, that is, extra augmenting path is not present in the Capacity-network, at this time in current task point in current capacities network
Optimal solution with path.
The also known as continuous most short augmenting path algorithm of Dinic algorithms, the principle of the algorithm is to increase in Capacity-network by stages
Extensively.It is a difference in that with Edmond-Karp algorithms：Edmond-Karp algorithms have executed primary most short increasing in each stage
After wide, to restart from source node and begin look for another augmenting path；And in Dinic algorithms, only needing once can be real
Now multiple augmentation.
Step S140, the task to be allocated is distributed according to the task distribution path.
Specifically, in some embodiments, the task distribution path is the optimal solution in the Capacity-network.Pass through task
Distribution path distributes the task to be allocated, maximally utilizing for target whole work efficiency may be implemented, and then make reality
The maximization of existing target work values.
In some embodiments, the target information and mission bit stream to be allocated meeting real-time update, a subtask point
Method of completing the square can also execute in real time, execution each time during, directed line segment, Yi Jiyou between the node of Capacity-network, node
It can all change to the weight of line segment.
In some embodiments, another method for allocating tasks based on maximum-flow algorithm is additionally provided.Such as Fig. 2 institutes
Show, which specifically includes：
Step S201, target information and mission bit stream to be allocated are obtained.
The target information includes the workmanship and skill proficiency of multiple targets；The task to be allocated
Information includes the task category of multiple tasks to be allocated.
Step S203, according to the task category structure of the workmanship of the multiple target and multiple tasks to be allocated
Build Capacity-network.
Wherein, specific workmanship can handle specific task to be allocated, i.e., the workmanship of target and wait for
The task category of distribution task will produce correlation, and correlation can be prestored in database.And then it is executed in method
During, the association between target node and task node to be allocated can be established by the correlation.
Processing when step S205, being calculated task to be allocated described in any operative object handles using skill proficiency is imitated
Rate.
In some embodiments, the mission bit stream to be allocated further includes the workload of task to be allocated；Specifically, utilizing
Treatment effeciency described in skill proficiency evaluation work object handles when task to be allocated, specifically includes：It is ripe using the technical ability
White silk degree and workload computing target handle the treatment effeciency when task to be allocated.
Step S207, the weight on side in the Capacity-network is configured according to the treatment effeciency.
Specifically, the weight can provide guiding function for the result of method for allocating tasks, i.e., in same Capacity-network, root
According to the weight configured without factor, obtained optimal path difference is finally solved, obtained final result is also different.Therefore, originally
The value generated with the treatment effeciency of task to be allocated and/or target in embodiment is reference, and configuration obtains Capacity-network
The weight of middle each edge, makes in final result, and the treatment effeciency of task to be allocated reaches maximum.
Step S209, it is solved in the Capacity-network using default maximum-flow algorithm, it is all from source node to sink nodes
Task distribution path.
Step S211, the task to be allocated is distributed according to the task distribution path.
Task distribution path includes a plurality of path from source node to sink nodes, and each path representation one is to be allocated
The allocation result of task.For example, when A targets are connected with B tasks to be allocated, B tasks to be allocated can be distributed to A by expression
Target.
In some other embodiments, another method for allocating tasks based on maximum-flow algorithm is additionally provided, it should
Method can be used for matching for multipotency work point in the usage scenario of order, as shown in figure 3, the method for allocating tasks specifically includes：
Step S300, multipotency work information and order information to be allocated are obtained.
In the present embodiment, multipotency work information includes the information for the whole multipotency works being in idle condition in multipotency work pond, often
One multipotency work all grasps at least two workmanship, but due to individual difference, multipotency work is for different operating technical ability
It is skilled different.It makes house calls specifically, the workmanship can be to provide repair household electrical appliances, clean household electrical appliances service or household installation
Service etc..
Order to be allocated is sent out by user, can be various types of orders, such as repair household electrical appliances make house calls, clean house
Electricity service or household installation service etc..It can be classified according to order to be allocated and workmanship in the present embodiment, and to belonging to
Same category of order to be allocated is associated with workmanship foundation, and the incidence relation is stored in database.
Step S302, it is built according to the workmanship of the multiple multipotency work and the order classification of multiple orders to be allocated
Capacity-network.
Wherein, the weight on each side of the Capacity-network handles value caused by different classes of order really according to multipotency work
It is fixed.Optionally, the order value can be indicated by the order amount of money that user is paid.
Specifically, as shown in figure 4, the Capacity-network structural schematic diagram that this specification embodiment provides.Wherein, work1,
Work2, work3, work4 indicate four multipotency work nodes, can be acquired from multipotency work pond, order1, order2,
Order3, order4 indicate four order nodes to be allocated, can be obtained from order pond to be allocated.It, can basis in this implementation
The workmanship of multipotency work and the order type of order establish connection to work nodes and order nodes.
In Capacity-network structure shown in Fig. 4, work1 connect respectively with order1, order2, order3 to be formed it is oriented
Line segment indicates that work1 has the workmanship of processing order1, order2 and order3, but the weighted of directed line segment
(not shown), the weight is related to the had qualification of different operating technical ability of multipotency work, and qualification is higher, corresponding power
It is again bigger.
Further specifically, in Capacity-network, the side between multipotency work node and order node to be matched can be by E1={ e
(1,1), e (1,2) ... e (n, k) } it indicates, wherein the n indicates that the number of multipotency work, k indicate the number of order to be matched,
E (n, k) indicates that the side between n-th of multipotency work and k-th of order to be matched, the weight on side can be indicated by We (n, k), further
, We (n, k)=B+V (wn,ok), wnIndicate n-th of worker, okIndicate k-th of order to be matched；V(wn,ok), it indicates more
It can work wnServe okThe value that can be generated, B are a customized parameters, optionally, B ＞ max (V (wn,ok))。
Side between source node and multipotency work node can be indicated that s is indicated by E2={ e (s, 1), e (s, 2) ... e (s, n) }
Source node.Side between order node and sink nodes to be allocated can be indicated, t by E3={ e (1, t), e (2, t) ... e (k, t) }
Indicate sink nodes.
Step S304, the salvage value when order to be allocated is handled using the order value calculation multipotency work.
Wherein, the salvage value can be payment amount of the user to order to be allocated, can also be according to multipotency work
The efficiency of the order to be allocated is handled and a reference data that user obtains the payment amount of order to be allocated, with for task
The overall value that distribution method obtains provides reference.For example, for same task order, the higher multipotency work of working efficiency is produced
Raw value is higher
In another example it is the income that Order splitting platform generates that salvage value, which can also be multipotency work,.Specifically, salvage value
The time consumed according to multipotency work order-processing and order value are calculated, and order is worth higher and/or order-processing institute
The time of consumption is shorter, and corresponding salvage value is higher；The time that the lower and/or order-processing of order value is consumed is longer,
Corresponding salvage value is higher.
Step S306, the weight on side in the Capacity-network is configured according to the salvage value.
Compared to above-described embodiment, the weight configured using salvage value in the present embodiment is more accurate, because of place
Managing can be by the treatment effeciency of multipotency work order-processing as reference, for the side of Capacity-network configuration in the calculating process of value
Weight and business demand more close to.
The configuration thinking of weight is same as the previously described embodiments in the present embodiment, here no longer narration in detail.
Step S308, it is solved in the Capacity-network using default maximum-flow algorithm, it is all from source node to sink nodes
Order splitting path.
The default maximum-flow algorithm includes：Edmond-Karp algorithms, Ford-Fulkerson algorithms and Dinic are calculated
Method.
Specifically, it is the order that directed line segment, which is formed by path, in Capacity-network as shown in Figure 5, between each node
Distribution path can obtain Capacity-network total optimization solution according to the Order splitting path.
Step S310, according to order to be allocated described in the Order splitting path allocation.
In addition, additionally providing a kind of task allocation apparatus based on maximum-flow algorithm, as described in Figure 6, institute in this specification
Task allocation apparatus is stated to specifically include：
Data obtaining module 601, for obtaining target information and mission bit stream to be allocated；
Module 602 is built, for building Capacity-network according to target information and mission bit stream to be allocated；Wherein, institute
The weight for stating each side in Capacity-network handles the treatment effeciency determination of different classes of task according to target；
Path solves module 603, all from source node for being solved in the Capacity-network using default maximum-flow algorithm
To the task distribution path of sink nodes；
Task allocating module 604, for distributing the task to be allocated according to the task distribution path.
On the basis of the above embodiments, the target information includes the workmanship and technical ability of multiple targets
Proficiency；The mission bit stream to be allocated includes the task category of several tasks to be allocated；Build module 602, it may also be used for,
Capacity-network is built according to the task category of the workmanship of the multiple target and multiple tasks to be allocated；Utilize skill
Treatment effeciency described in energy proficiency evaluation work object handles when task to be allocated；The appearance is configured according to the treatment effeciency
Measure the weight on side in network.
On the basis of the above embodiments, the mission bit stream to be allocated further includes the workload of task to be allocated；Structure
Module 602, it may also be used for when handling the task to be allocated using the skill proficiency and workload computing target
Treatment effeciency.
On the basis of the above embodiments, the weight on each side of the Capacity-network handles different classes of according to target
Value caused by business determines.
On the basis of the above embodiments, the default maximum-flow algorithm includes：Edmond-Karp algorithms, Ford-
Fulkerson algorithms and Dinic algorithms.
On the basis of the above embodiments, target information include multiple multipotency works workmanship and technical ability it is skilled
Degree；Order information to be allocated includes the task category and order value of multiple orders to be allocated；Structure module 602 can be used for, root
Capacity-network is built according to the workmanship of the multiple multipotency work and the order classification of multiple orders to be allocated；Utilize price-purchase order
Value calculates the salvage value when order to be allocated is managed in multipotency work point other places；The capacity net is configured according to the salvage value
The weight on side in network.
In addition, additionally providing a kind of task distributing equipment based on maximum-flow algorithm in this specification, task distribution is set
It is standby to specifically include：
Memory stores the task distribution program based on maximum-flow algorithm；
Communication interface receives information acquisition request；
Processor calls what is stored in memory to be calculated based on max-flow after communication interface receives information extraction request
The task distribution program of method, and execute：
Obtain target information and mission bit stream to be allocated；
Capacity-network is built according to target information and mission bit stream to be allocated；Wherein, each side in the Capacity-network
Weight according to target handle different classes of task treatment effeciency determine；
It is solved in the Capacity-network using default maximum-flow algorithm, all tasks from source node to sink nodes distribute road
Diameter；
The task to be allocated is distributed according to the task distribution path.
Each embodiment in this specification is described in a progressive manner, identical similar portion between each embodiment
Point just to refer each other, and each embodiment focuses on the differences from other embodiments.Especially for device,
For equipment and medium class embodiment, just no longer repeat one by one here.
It is above-mentioned that this specification specific embodiment is described.Other embodiments are in the scope of the appended claims
It is interior.In some cases, the action recorded in detail in the claims or step or module can be according to different from embodiments
Sequence executes and desired result still may be implemented.In addition, the process described in the accompanying drawings is not necessarily required and is shown
Particular order or consecutive order could realize desired result.In some embodiments, multitasking and parallel processing
It is also possible or it may be advantageous.
In the 1990s, the improvement of a technology can be distinguished clearly be on hardware improvement (for example,
Improvement to circuit structures such as diode, transistor, switches) or software on improvement (improvement for method flow).So
And with the development of technology, the improvement of current many method flows can be considered as directly improving for hardware circuit.
Designer nearly all obtains corresponding hardware circuit by the way that improved method flow to be programmed into hardware circuit.Cause
This, it cannot be said that the improvement of a method flow cannot be realized with hardware entities module.For example, programmable logic device
(Programmable Logic Device, PLD) (such as field programmable gate array (Field Programmable Gate
Array, FPGA)) it is exactly such a integrated circuit, logic function determines device programming by user.By designer
Voluntarily programming comes a digital display circuit " integrated " on a piece of PLD, designs and makes without asking chip maker
Dedicated IC chip.Moreover, nowadays, substitution manually makes IC chip, this programming is also used instead mostly " patrols
Volume compiler (logic compiler) " software realizes that software compiler used is similar when it writes with program development,
And the source code before compiling also write by handy specific programming language, this is referred to as hardware description language
(Hardware Description Language, HDL), and HDL is also not only a kind of, but there are many kind, such as ABEL
(Advanced Boolean Expression Language)、AHDL(Altera Hardware Description
Language)、Confluence、CUPL(Cornell University Programming Language)、HDCal、JHDL
(Java Hardware Description Language)、Lava、Lola、MyHDL、PALASM、RHDL(Ruby
Hardware Description Language) etc., VHDL (Very-High-Speed are most generally used at present
Integrated Circuit Hardware Description Language) and Verilog.Those skilled in the art also answer
This understands, it is only necessary to method flow slightly programming in logic and is programmed into integrated circuit with above-mentioned several hardware description languages,
The hardware circuit for realizing the logical method flow can be readily available.
Controller can be implemented in any suitable manner, for example, controller can take such as microprocessor or processing
The computer for the computer readable program code (such as software or firmware) that device and storage can be executed by (micro-) processor can
Read medium, logic gate, switch, application-specific integrated circuit (Application Specific Integrated Circuit,
ASIC), the form of programmable logic controller (PLC) and embedded microcontroller, the example of controller includes but not limited to following microcontroller
Device：ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320, are deposited
Memory controller is also implemented as a part for the control logic of memory.It is also known in the art that in addition to
Pure computer readable program code mode is realized other than controller, can be made completely by the way that method and step is carried out programming in logic
Controller is obtained in the form of logic gate, switch, application-specific integrated circuit, programmable logic controller (PLC) and embedded microcontroller etc. to come in fact
Existing identical function.Therefore this controller is considered a kind of hardware component, and to including for realizing various in it
The device of function can also be considered as the structure in hardware component.Or even, it can will be regarded for realizing the device of various functions
For either the software module of implementation method can be the structure in hardware component again.
System, device, module or the unit that above-described embodiment illustrates can specifically realize by computer chip or entity,
Or it is realized by the product with certain function.It is a kind of typically to realize that equipment is computer.Specifically, computer for example may be used
Think personal computer, laptop computer, cellular phone, camera phone, smart phone, personal digital assistant, media play
It is any in device, navigation equipment, electronic mail equipment, game console, tablet computer, wearable device or these equipment
The combination of equipment.
For convenience of description, it is divided into various units when description apparatus above with function to describe respectively.Certainly, implementing this
The function of each unit is realized can in the same or multiple software and or hardware when application.
It should be understood by those skilled in the art that, the embodiment of the present invention can be provided as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the present invention
Apply the form of example.Moreover, the present invention can be used in one or more wherein include computer usable program code computer
The computer program production implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.)
The form of product.
The present invention be with reference to according to the method for the embodiment of the present invention, the flow of equipment (system) and computer program product
Figure and/or block diagram describe.It should be understood that can be realized by computer program instructions every first-class in flowchart and/or the block diagram
The combination of flow and/or box in journey and/or box and flowchart and/or the block diagram.These computer programs can be provided
Instruct the processor of all-purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine so that the instruction executed by computer or the processor of other programmable data processing devices is generated for real
The device for the function of being specified in present one flow of flow chart or one box of multiple flows and/or block diagram or multiple boxes.
These computer program instructions, which may also be stored in, can guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works so that instruction generation stored in the computer readable memory includes referring to
Enable the manufacture of device, the command device realize in one flow of flow chart or multiple flows and/or one box of block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device so that count
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, in computer or
The instruction executed on other programmable devices is provided for realizing in one flow of flow chart or multiple flows and/or block diagram one
The step of function of being specified in a box or multiple boxes.
In a typical configuration, computing device includes one or more processors (CPU), input/output interface, net
Network interface and memory.
Memory may include computer-readable medium in volatile memory, random access memory (RAM) and/or
The forms such as Nonvolatile memory, such as read-only memory (ROM) or flash memory (flash RAM).Memory is computer-readable medium
Example.
Computer-readable medium includes permanent and non-permanent, removable and non-removable media can be by any method
Or technology realizes information storage.Information can be computer-readable instruction, data structure, the module of program or other data.
The example of the storage medium of computer includes, but are not limited to phase transition internal memory (PRAM), static RAM (SRAM), moves
State random access memory (DRAM), other kinds of random access memory (RAM), read-only memory (ROM), electric erasable
Programmable read only memory (EEPROM), fast flash memory bank or other memory techniques, read-only disc read only memory (CD-ROM) (CD-ROM),
Digital versatile disc (DVD) or other optical storages, magnetic tape cassette, tape magnetic disk storage or other magnetic storage apparatus
Or any other non-transmission medium, it can be used for storage and can be accessed by a computing device information.As defined in this article, it calculates
Machine readable medium does not include temporary computer readable media (transitory media), the data letter number and carrier wave of such as modulation.
It should also be noted that, the terms "include", "comprise" or its any other variant are intended to nonexcludability
Including so that process, method, commodity or equipment including a series of elements include not only those elements, but also wrap
Include other elements that are not explicitly listed, or further include for this process, method, commodity or equipment intrinsic want
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that wanted including described
There is also other identical elements in the process of element, method, commodity or equipment.
It will be understood by those skilled in the art that embodiments herein can be provided as method, system or computer program product.
Therefore, complete hardware embodiment, complete software embodiment or embodiment combining software and hardware aspects can be used in the application
Form.It is deposited moreover, the application can be used to can be used in the computer that one or more wherein includes computer usable program code
The shape for the computer program product implemented on storage media (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.)
Formula.
The application can describe in the general context of computer-executable instructions executed by a computer, such as program
Module.Usually, program module includes routine, program, object, the group for executing particular transaction or realizing particular abstract data type
Part, data structure etc..The application can also be put into practice in a distributed computing environment, in these distributed computing environments, by
Affairs are executed by the connected remote processing devices of communication network.In a distributed computing environment, program module can be with
In the local and remote computer storage media including storage device.
Each embodiment in this specification is described in a progressive manner, identical similar portion between each embodiment
Point just to refer each other, and each embodiment focuses on the differences from other embodiments.Especially for system reality
For applying example, since it is substantially similar to the method embodiment, so description is fairly simple, related place is referring to embodiment of the method
Part explanation.
Above is only an example of the present application, it is not intended to limit this application.For those skilled in the art
For, the application can have various modifications and variations.It is all within spirit herein and principle made by any modification, equivalent
Replace, improve etc., it should be included among the interest field of the application.
Claims (10)
1. a kind of method for allocating tasks based on maximum-flow algorithm, which is characterized in that including：
Obtain target information and mission bit stream to be allocated；
Capacity-network is built according to target information and mission bit stream to be allocated, wherein the power on each side in the Capacity-network
The treatment effeciency that repeated root handles different classes of task according to target determines；
It is solved in the Capacity-network using default maximum-flow algorithm, all task distribution paths from source node to sink nodes；
The task to be allocated is distributed according to the task distribution path.
2. according to the method described in claim 1, it is characterized in that, the target information includes the work of multiple targets
Make technical ability and skill proficiency；The mission bit stream to be allocated includes the task category of multiple tasks to be allocated；
Capacity-network is built according to target information and mission bit stream to be allocated, wherein the power on each side in the Capacity-network
The treatment effeciency that repeated root handles different classes of task according to target determines, specifically includes：
Capacity-network is built according to the task category of the workmanship of the multiple target and multiple tasks to be allocated；
Treatment effeciency when task to be allocated described in any operative object handles is calculated using skill proficiency；
The weight on side corresponding with the target in the Capacity-network is determined according to the treatment effeciency.
3. according to the method described in claim 2, it is characterized in that, the mission bit stream to be allocated further includes task to be allocated
Workload；
Treatment effeciency when task to be allocated described in any operative object handles is calculated using skill proficiency, is specifically included：
It is handled using target described in the skill proficiency and the workload computing of the task to be allocated described to be allocated
Treatment effeciency when task.
4. according to the method described in claim 1, it is characterized in that：
The weight on each side of Capacity-network is handled to be worth caused by different classes of task according to target and be determined.
5. according to the method described in claim 1, it is characterized in that,
The default maximum-flow algorithm includes：Edmond-Karp algorithms, Ford-Fulkerson algorithms and Dinic algorithms.
6. according to the method described in claim 1, it is characterized in that：
Target information includes the workmanship and skill proficiency of multiple multipotency works；Order information to be allocated includes multiple waits for
Distribute the task category and order value of order；
Capacity-network is built according to target information and mission bit stream to be allocated, wherein the power on each side in the Capacity-network
The treatment effeciency that repeated root handles different classes of task according to target determines, specifically includes：
Capacity-network is built according to the workmanship of the multiple multipotency work and the order classification of multiple orders to be allocated；
The salvage value when order to be allocated is handled using order value calculation multipotency work；
The weight on side in the Capacity-network is configured according to the salvage value.
7. a kind of task allocation apparatus based on maximum-flow algorithm, which is characterized in that including：
Data obtaining module, for obtaining target information and mission bit stream to be allocated；
Module is built, for building Capacity-network according to target information and mission bit stream to be allocated, wherein the capacity net
The weight on each side handles the treatment effeciency determination of different classes of task according to target in network；
Path solution module, it is all to be saved from source node to remittance for being solved in the Capacity-network using default maximum-flow algorithm
The task distribution path of point；
Task allocating module, for distributing the task to be allocated according to the task distribution path.
8. device according to claim 7, which is characterized in that the target information includes the work of multiple targets
Make technical ability and skill proficiency；The mission bit stream to be allocated includes the task category of multiple tasks to be allocated；
Build module, it may also be used for according to the task class of the workmanship of the multiple target and multiple tasks to be allocated
Capacity-network is not built；
Treatment effeciency when task to be allocated described in any operative object handles is calculated using skill proficiency；
The weight on side corresponding with the target in the Capacity-network is determined according to the treatment effeciency.
9. device according to claim 7, which is characterized in that the mission bit stream to be allocated further includes task to be allocated
Workload；
Build module, it may also be used for utilize to be allocated described in the skill proficiency and workload computing any operative object handles
Treatment effeciency when task.
10. a kind of task distributing equipment based on maximum-flow algorithm, which is characterized in that including：
Memory stores the task distribution program based on maximum-flow algorithm；
Communication interface receives information acquisition request；
Processor, after communication interface receives information extraction request, call stored in memory based on maximum-flow algorithm
Task distribution program, and execute：
Obtain target information and mission bit stream to be allocated；
Capacity-network is built according to target information and mission bit stream to be allocated, wherein the power on each side in the Capacity-network
The treatment effeciency that repeated root handles different classes of task according to target determines；
It is solved in the Capacity-network using default maximum-flow algorithm, all task distribution paths from source node to sink nodes；
The task to be allocated is distributed according to the task distribution path.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110033246A (en) * | 2018-12-07 | 2019-07-19 | 阿里巴巴集团控股有限公司 | Network payment Current limited Control method and apparatus |
| CN110119876A (en) * | 2019-04-03 | 2019-08-13 | 口碑(上海)信息技术有限公司 | Worksheet method and device |
| CN110334436A (en) * | 2019-07-03 | 2019-10-15 | 腾讯科技（深圳）有限公司 | A kind of data processing method and equipment |
| CN110618855A (en) * | 2018-12-25 | 2019-12-27 | 北京时光荏苒科技有限公司 | Task allocation method and device, electronic equipment and storage medium |
| CN110866687A (en) * | 2019-11-07 | 2020-03-06 | 中盈优创资讯科技有限公司 | Task allocation method and device |
| CN110991808A (en) * | 2019-11-06 | 2020-04-10 | 中国建设银行股份有限公司 | Task allocation method and device |
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| CN111309821A (en) * | 2020-01-20 | 2020-06-19 | 上海依图网络科技有限公司 | Graph database-based task scheduling method and device and electronic equipment |
| CN111652552A (en) * | 2020-05-29 | 2020-09-11 | 优信数享（北京）信息技术有限公司 | Logistics order transportation planning method, system and device |
| CN111882152A (en) * | 2020-06-18 | 2020-11-03 | 科大讯飞股份有限公司 | Task allocation method and related device |
| CN113159628A (en) * | 2021-05-13 | 2021-07-23 | 中国建设银行股份有限公司 | Task allocation method and device |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102904750A (en) * | 2012-09-24 | 2013-01-30 | 曙光信息产业(北京)有限公司 | Activity allocation method based on network topology |
| CN103338228A (en) * | 2013-05-30 | 2013-10-02 | 江苏大学 | Cloud calculating load balancing scheduling algorithm based on double-weighted least-connection algorithm |
| CN104599168A (en) * | 2015-02-02 | 2015-05-06 | 北京嘀嘀无限科技发展有限公司 | Method and device for allocating taxi-calling orders |
| US20150188844A1 (en) * | 2011-02-01 | 2015-07-02 | Google Inc. | System to Share Network Bandwidth Among Competing Applications |
| CN106203893A (en) * | 2016-09-09 | 2016-12-07 | 扬州大学 | A kind of method for allocating tasks of based on genetic algorithm under mass-rent environment |
| CN106982139A (en) * | 2017-03-21 | 2017-07-25 | 深圳先进技术研究院 | A kind of method and device of the Assignment Problems based on multirobot multitask, user equipment |
| CN107705000A (en) * | 2017-09-18 | 2018-02-16 | 平安科技（深圳）有限公司 | Choosing method, device, storage medium and the computer equipment of scanning device |
-
2018
- 2018-03-05 CN CN201810178166.0A patent/CN108537619A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150188844A1 (en) * | 2011-02-01 | 2015-07-02 | Google Inc. | System to Share Network Bandwidth Among Competing Applications |
| CN102904750A (en) * | 2012-09-24 | 2013-01-30 | 曙光信息产业(北京)有限公司 | Activity allocation method based on network topology |
| CN103338228A (en) * | 2013-05-30 | 2013-10-02 | 江苏大学 | Cloud calculating load balancing scheduling algorithm based on double-weighted least-connection algorithm |
| CN104599168A (en) * | 2015-02-02 | 2015-05-06 | 北京嘀嘀无限科技发展有限公司 | Method and device for allocating taxi-calling orders |
| CN106203893A (en) * | 2016-09-09 | 2016-12-07 | 扬州大学 | A kind of method for allocating tasks of based on genetic algorithm under mass-rent environment |
| CN106982139A (en) * | 2017-03-21 | 2017-07-25 | 深圳先进技术研究院 | A kind of method and device of the Assignment Problems based on multirobot multitask, user equipment |
| CN107705000A (en) * | 2017-09-18 | 2018-02-16 | 平安科技（深圳）有限公司 | Choosing method, device, storage medium and the computer equipment of scanning device |
Non-Patent Citations (2)
| Title |
|---|
| 姚慧峰: "移动云计算环境下任务分配问题的研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 王正元: "基于作战能力的部队作战任务分配方法", 《指挥控制与仿真》 * |
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| CN110033246B (en) * | 2018-12-07 | 2024-01-12 | 创新先进技术有限公司 | Network payment current limiting control method and device |
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| CN110991808A (en) * | 2019-11-06 | 2020-04-10 | 中国建设银行股份有限公司 | Task allocation method and device |
| CN110991808B (en) * | 2019-11-06 | 2022-06-28 | 中国建设银行股份有限公司 | Task allocation method and device |
| CN110866687A (en) * | 2019-11-07 | 2020-03-06 | 中盈优创资讯科技有限公司 | Task allocation method and device |
| CN110991846B (en) * | 2019-11-25 | 2023-06-20 | 深圳市北斗智能科技有限公司 | Service personnel task allocation method, device, equipment and storage medium |
| CN110991846A (en) * | 2019-11-25 | 2020-04-10 | 深圳市北斗智能科技有限公司 | Service personnel task allocation method, device, equipment and storage medium |
| CN111309821B (en) * | 2020-01-20 | 2023-07-14 | 上海依图网络科技有限公司 | Task scheduling method and device based on graph database and electronic equipment |
| CN111309821A (en) * | 2020-01-20 | 2020-06-19 | 上海依图网络科技有限公司 | Graph database-based task scheduling method and device and electronic equipment |
| CN111652552A (en) * | 2020-05-29 | 2020-09-11 | 优信数享（北京）信息技术有限公司 | Logistics order transportation planning method, system and device |
| CN111652552B (en) * | 2020-05-29 | 2024-07-12 | 优信数享（北京）信息技术有限公司 | Logistics order transportation planning method, system and device |
| CN111882152A (en) * | 2020-06-18 | 2020-11-03 | 科大讯飞股份有限公司 | Task allocation method and related device |
| CN111882152B (en) * | 2020-06-18 | 2024-07-05 | 科大讯飞股份有限公司 | Task allocation method and related device |
| CN113159628A (en) * | 2021-05-13 | 2021-07-23 | 中国建设银行股份有限公司 | Task allocation method and device |
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