CN106203750A - A kind of method and device of resource distribution - Google Patents
A kind of method and device of resource distribution Download PDFInfo
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Abstract
The method and apparatus that this application discloses a kind of resource distribution, in order to solve the problem that the utilization rate of artificial customer service resource in prior art is the highest, server burden is bigger.The method comprises determining that the degree of association of the currently received data of server and each default category information；Inquire about current information and the historical information of each resource group corresponding to each default category information；According to data and the degree of association of each default category information and the current information of each resource group and historical information, each resource group is carried out resource distribution.
Description
Technical field
The application relates to field of computer technology, particularly relates to the method and device of a kind of resource distribution.
Background technology
Along with development and the raising of network popularity of Internet technology, increasing user selects to use
The mode of on-line consulting, obtains desired service.
At present, for this on-line consulting business, Internet firm is mainly provided with two kinds of methods of service: one
Zhong Shi customer service robot, its resources costs is low and within 24 hours, can automatically reply user's consulting incessantly；
But the inferior position of customer service robot is also evident from, limited by Artificial intellectual technology, going out of its response
Error rate is of a relatively high, especially when having the most ageing problem for some, can not provide fine
Consultancy service.The mode of another kind of artificial online service then can make up above-mentioned customer service robot well
Deficiency；For different consultation services, artificial customer service resource can provide more to have and service targetedly,
It is greatly improved the satisfaction of user.
But, due to the multiformity of user's consultation service, it usually needs difference is entered in artificial customer service resource distribution
Resource group, targetedly user is serviced, improve service efficiency.
In prior art, generally by the way of manual intervention configures, by artificial customer service resource by the ratio preset
Example configures into different resource groups.This mode has a problem in that: user consulting every day the most in the same time
Having different types to stress, the artificial customer service resource of preset ratio configuration can not meet actual
Demand for services.A lot of time periods of one day, all can occur that the artificial customer service resource of some resource groups is idle,
Other resource groups have then overstock the situation of substantial amounts of queuing user, and this result also in artificial customer service resource
Utilization rate the highest；Further, the queuing user overstock also occupies substantial amounts of server resource.
Summary of the invention
The embodiment of the present application provides the method and device of a kind of resource distribution, artificial in order to solve in prior art
The problem that the utilization rate of customer service resource is the highest, server burden is bigger.
The method of a kind of resource distribution that the embodiment of the present application provides, including:
Determine the degree of association of the currently received data of server and each default category information；
Inquire about current information and the historical information of each resource group corresponding to described each default category information；
According to described data and the degree of association of each default category information and the current information of described each resource group
And historical information, described each resource group is carried out resource distribution.
The method of a kind of resource distribution that the another embodiment of the application provides, including:
Determine the degree of association of the currently received data of server and each default category information；
Inquire about current information and the historical information of each resource group corresponding with described each default category information；
Estimate the data volume of next per time instance of described data each resource group；
According to described data and the degree of association of each default category information, the current information of described each resource group with go through
History information, and described in the data volume of each resource group estimated described each resource group is carried out resource distribution.
The device of a kind of resource distribution that the embodiment of the present application provides, including:
Data analysis module, for determining associating of the currently received data of server and each default category information
Degree；
Enquiry module, for inquiring about the current information of each resource group corresponding to described each default category information and going through
History information；
Configuration module, for the degree of association according to described data and each default category information and described each money
The current information of source group and historical information, carry out resource distribution to described each resource group.
The device of a kind of resource distribution that the another embodiment of the application provides, including:
Data analysis module, for determining associating of the currently received data of server and each default category information
Degree；
Enquiry module, for the inquiry each resource group corresponding with described each default category information current information and
Historical information；
Prediction module, for estimating the data volume of next per time instance of described data each resource group；
Configuration module, for according to described data and the degree of association of each default category information, described each resource group
Current information and historical information, and described in the data volume of each resource group estimated described each resource group is entered
Row resource distribution.
The embodiment of the present application provides the method and device of a kind of resource distribution, and the method currently connects according to server
Each resource group that data and the degree of association of each default category information received, each default category information are corresponding current
Information and historical information carry out resource distribution to each resource group, and without being configured by manual intervention, and respectively provide
The resource distribution amount of source group dynamically adjusts according to the change of above-mentioned data, improves the profit of artificial customer service resource
By rate, alleviate the burden of server.
Accompanying drawing explanation
Accompanying drawing described herein is used for providing further understanding of the present application, constitutes of the application
Point, the schematic description and description of the application is used for explaining the application, is not intended that to the application not
Work as restriction.In the accompanying drawings:
The schematic flow sheet of the resource distribution that Fig. 1 provides for the embodiment of the present application；
The degree of association according to current data and each default category information that Fig. 2 provides for the embodiment of the present application, with
And the current information of each resource group and historical information carry out the schematic flow sheet of resource distribution；
The schematic flow sheet of the resource distribution that Fig. 3 provides for the another embodiment of the application；
The flow process signal estimating each resource group data volume that Fig. 4 to Fig. 6 provides for the another embodiment of the application
Figure；
The associating according to current data and each default category information that Fig. 7 provides for the another embodiment of the application
Degree, the current information of each resource group and historical information and each resource group data volume estimated carry out resource and join
The schematic flow sheet put；
The scene schematic diagram of the resource distribution that Fig. 8 provides for the embodiment of the present application；
The module diagram of the device of the resource distribution that Fig. 9 provides for the embodiment of the present application；
The module diagram of the device of the resource distribution that Figure 10 provides for the another embodiment of the application.
Detailed description of the invention
For making the purpose of the application, technical scheme and advantage clearer, specifically real below in conjunction with the application
Execute example and technical scheme is clearly and completely described by corresponding accompanying drawing.Obviously, described
Embodiment is only some embodiments of the present application rather than whole embodiments.Based on the enforcement in the application
Example, the every other enforcement that those of ordinary skill in the art are obtained under not making creative work premise
Example, broadly falls into the scope of the application protection.
The process of the resource distribution that Fig. 1 provides for the embodiment of the present application, specifically includes following steps:
S11: determine the degree of association of the currently received data of server and each default category information.
In the present embodiment, server can carry out data analysis for the data received, and according to data analysis
Result determine the degree of association of these data and each default category information.
Coordinate ginseng Fig. 8, such as, after user encounters problems, access cschannel (unified portal page face,
I.e. it is supplied to the online question and answer page of user) submit to and put question to request: " what if payment cipher have forgotten？",
Server, after receiving this enquirement request, can call the unified Conversation Model of robot platform csrobot,
And analyze alita by text semantic, determine this enquirement request and the pass of each default problem classification in server
Connection degree.Exemplarily, it is determined that this enquirement request and the pass that the degree of association is 50% and B classification of A classification
Connection degree be the degree of association of 30% and C classification be 70% etc..
S12: inquire about current information and the historical information of each resource group corresponding to each default category information.
Server is configured with corresponding resource group, different resource groups for each default category information correspondence respectively
In be configured with the customer service resource data of respective numbers, and on backstage, each customer service resource data is to there being people
Work customer service.Described resource group is equivalent to the collection in server with the customer service resource data of identical " label "
Close, the allocation process of customer service resource data namely be equivalent to is changed " label " of this customer service resource data
Process.
In the present embodiment, for the current information of each resource group: first inquire about the current data of each resource group
Amount and Current resource amount, then the ratio by the current data amount of each resource group Yu Current resource amount, be defined as each
The current information of resource group.The current data amount of each resource group and Current resource amount can such as pass through server
Call cloud customer service system clive to inquire about.
Continue to use the example above, in the server, it is assumed that A, B, C classification respectively corresponding A ', B ', C ' money
Source group, at current time, wait in line distribute to A ', B ', user's number of requests of C ' resource group namely A ',
B ', C ' resource group respective current data amount, the customer service resource data that A ', B ', C ' resource group each include
Amount namely A ', B ', C ' resource group respective Current resource amount.It is clear that the current letter of each resource group
Breath reflects each resource group desirability currently to customer service resource.
Similarly, for the historical information of each resource group: first inquire about each resource group in default history length,
Historical data amount during per time instance corresponding with current data and history stock number, then the history by each resource group
Data volume and the ratio of history stock number, be defined as the historical information of each resource group.The history number of each resource group
All it is stored among server according to amount and history stock number.
When inquiring about historical data amount and the history stock number of correspondence per time instance in default history length, permissible
Set multiple inquiry mode.Such as, the beginning of the month of every month is the peak period that user pays telephone expenses, then in the moon
At the beginning, can be in units of the moon, the history of correspondence per time instance during beginning of the month every month in the default history length of inquiry
Data volume and history stock number；So, it is ensured that the historical information of each resource group inquired more has reference
It is worth.
S13: according to these data and the degree of association of each default category information and the current information of each resource group
And historical information, each resource group is carried out resource distribution.
Ginseng Fig. 2, introduces the concrete steps that each resource group carries out in step S13 resource distribution, including:
S131: preset weight according to the degree of association and first of these data Yu each default category information, it is thus achieved that respectively provide
First reference value of source group.
Current data and the degree of association of each default category information, reflect each resource group to a certain extent not
Carry out the trend of time data amount.
S132: current information and second according to each resource group preset weight, it is thus achieved that the second ginseng of each resource group
Examine value.
S133: preset weight according to the historical information and the 3rd of each resource group, it is thus achieved that the 3rd ginseng of each resource group
Examine value.
S134: first, second, and third reference value based on each resource group obtained, is carried out each resource group
Resource distribution；Wherein, first, second, and third to preset weight sum be 1.
In the present embodiment, resource group need the stock number of configuration by current data and each default category information
The degree of association and the current information of each resource group and the impact of three factors of historical information.And according to each
The difference of power of influence, server is respectively it and configures respective weight.The stock number of each resource group configuration is with each
First, second, and third reference value positive correlation of resource group, that is, the resource group the most relevant to current data
More tend to configure more resource；The current data amount of resource group and the ratio of stock number are the biggest, the most more incline
To in configuring more resource；The historical data amount of resource group and the ratio of stock number are the biggest, the most more tend to
Configure more resource.
Specifically, such as server includes algorithm platform Agap, Agap is preset with and each resource group
The relevant model formation of first, second, and third reference value, such as w=x1+x2+x3, wherein x1, x2,
X3 is respectively first, second, and third reference value.According to what the w value of calculating gained, with Agap were preset
Resource distribution table is compared, and concrete interval in the resource distribution table fallen into according to w value, determines each
The resource allocation proposal of resource group.
The process of the resource distribution that Fig. 3 provides for the another embodiment of the application, specifically includes following steps:
S21: determine the degree of association of the currently received data of server and each default category information.
S22: inquire about current information and the historical information of each resource group corresponding to each default category information.
Step S21 in the present embodiment and step S22 and step S11 in a upper embodiment and step S12
Similar, and therefore not to repeat here, and in a upper embodiment, the explanation for step S11 and step S12 can be with
The mode being fully incorporated is applied in the present embodiment.
S23: estimate the data volume of next per time instance of these data each resource group.
Ginseng Fig. 4, introduces the concrete steps estimating each resource group data volume in step S23, including:
S231: the data volume of moment each resource group before inquiring about these data.
S232: set up the forecast model of each resource group according to the data volume of moment each resource group before these data.
S233: estimate the data of next per time instance of these data each resource group according to the forecast model of each resource group
Amount.
Specifically, such as Agap is also pre-configured with forecast model formula y=ax2+ bx+c, when wherein x is
Between (unit is s), y is the data volume of resource group.The data volume of 3s each resource group before inquiring about these data,
And substitute into this forecast model formula, the value of a, b, c, Jin Erjian in this forecast model formula can be calculated
The forecast model of vertical each resource group data volume.And then, by substituting into the time value of next per time instance, permissible
Estimate out the data volume of each resource group during next per time instance.
Ginseng Fig. 5, so that this forecast model formula can estimate each resource group the most accurately
Data volume, need in time this forecast model to be adjusted.Specifically:
S301: at next per time instance of these data, inquire about the data volume of current each resource group.
S302: by this current data volume and the current data volume estimated according to the forecast model of each resource group
Compare.
S303: judge that whether the comparison result of each resource group is more than predetermined threshold value.
S304: be more than the part resource group of predetermined threshold value for comparison result in each resource group, then in these data
Next per time instance continues to estimate the data volume of this part resource group according to the forecast model of this part resource group.
At next per time instance of current data, occur that comparison result is more than the situation of predetermined threshold value, then show this
Time there occurs that data volume is suddenlyd change.Such as, when the payment of certain bank goes wrong, server receives
User about payment funding puts question to request to uprush.But at this moment, server can not determine now
Data volume uprushes whether have practical significance, namely data volume now uprush be also likely to be by other accidental
Factor is caused, and the data volume caused for accidentalia is uprushed, and the most obviously can not use this data volume
Build forecast model.
Due to above-mentioned, calculating comparison result next per time instance more than predetermined threshold value, this portion
Point resource group still uses the forecast model set up before to carry out the prediction of data volume.Certainly, it is to be understood that
It is in various embodiments, different time delays can be determined according to different situations, calculate comparison
Result is more than in this time delay after predetermined threshold value, and the forecast model set up before all using carries out the pre-of data volume
Estimate.
S305: be not more than the part resource group of predetermined threshold value for comparison result in each resource group, then at this number
According to next per time instance, this part resource group is re-established forecast model.
The purpose of step S305 is the data volume of the resource group arrived according to actual queries, finely tunes prediction in real time
Model (i.e. fine setting forecast model formula y=ax2The value of a, b, c in+bx+c), improve forecast model to respectively
The precision that resource group data volume is estimated.
Ginseng Fig. 6, and in order to tackle the data volume sudden change not being to be caused by factor once in a while, the present embodiment also wraps
Include:
S401: after these data in N number of per time instance, inquires about the data volume of each resource group, Qi Zhongsuo
Stating N is predetermined number.
Here N number of per time instance namely the above " time delay ", its occurrence can be according to demand
Configure.
S402: by the data volume of each resource group inquired and the number estimated according to the forecast model of each resource group
Compare according to amount.
S403: be both greater than the portion of predetermined threshold value for comparison result in each resource group in N number of per time instance
Divide resource group, then according to the data volume of this part resource group in N number of per time instance, to this part resource group
Re-establish forecast model.
If in N number of per time instance, the comparison result of some resource group is both greater than predetermined threshold value, then may be used
It not to be caused by accidentalia to determine that the data volume of now these resource groups is suddenlyd change.Accordingly, it would be desirable to according to this
These resource groups are re-established prediction by the data volume in N number of per time instance that segment data amount is undergone mutation
Model.
Or as a example by the payment of certain bank goes wrong, it is assumed that with " payment funding " classification in server
Information corresponding for resource group A, then can monitor resource group A by the monitor monitor in server
Within the ensuing time, data volume can be uprushed.And determine that the data volume of resource group A is dashed forward at server
Increase be not caused by accidentalia after, the pre-of Agap can be redefined according to data volume data of 3s after prominent
Survey model formation y=ax2The value of a, b, c in+bx+c, and then re-establish forecast model.Correspondingly,
After the payment problem of this bank is repaired, the data volume of resource group A can fall after rise accordingly, based on phase
Same principle, it was predicted that model formation y=ax2In+bx+c, the value of a, b, c also can be re-determined, thus
Ensure the accuracy that each moment resource group data volume is estimated.
S24: according to these data and the degree of association of each default category information, the current information of each resource group with go through
History information, and the data volume of each resource group estimated carries out resource distribution to each resource group.
Ginseng Fig. 7, introduces the concrete steps that each resource group carries out in step S24 resource distribution, including:
S241: preset weight according to the degree of association and first of these data Yu each default category information, it is thus achieved that respectively provide
First reference value of source group.
S242: current information and second according to each resource group preset weight, it is thus achieved that the second ginseng of each resource group
Examine value.
S243: preset weight according to the historical information and the 3rd of each resource group, it is thus achieved that the 3rd ginseng of each resource group
Examine value.
S244: preset weight according to the data volume and the 4th of each resource group estimated, it is thus achieved that the of each resource group
Four reference values.
S245: first, second, third and fourth reference value based on each resource group obtained, to each resource
Group carries out resource distribution；Wherein, first, second, third and fourth presets weight sum is 1.
Unlike a upper embodiment, in the present embodiment, resource group need the stock number of configuration by working as
Front data and the degree of association of each default category information, the current information of each resource group and historical information and each
The impact of four factors of estimated data amount of resource group.And according to the difference of respective power of influence, server is respectively
Respective weight is configured for it.The stock number of each resource group configuration and the first, second, third of each resource group
With the 4th reference value positive correlation, that is, the resource group the most relevant to current data more to tend to configuration more
Resource；The current data amount of resource group and the ratio of stock number are the biggest, the most more tend to configure more resource；
The historical data amount of resource group and the ratio of stock number are the biggest, the most more tend to configure more resource；Estimate
The resource group data volume when next per time instance is the biggest, the most more tends to configure more resource.
Specifically, such as the Agap in server is preset with and the first, second, third of each resource group
The model formation relevant with the 4th reference value, such as w=x1+x2+x3+x4, wherein x1, x2, x3, x4
It is respectively first, second, third and fourth reference value.According to pre-in the w value of calculating gained, with Agap
If resource distribution table compare, and concrete interval, really in the resource distribution table fallen into according to w value
The resource allocation proposal of fixed each resource group.
In the above-described embodiment, for the currently received data of server, also can be according to itself and each default class
The current information of each resource group that the degree of association of mesh information, each default category information are corresponding and historical information, will
This data distribution is ranked in optimal resource group.Generally, the principle of this data distribution is: tendency
In branching to resource group corresponding to the higher category information of the degree of association, tending to branch to current data amount and work as
Resource group that front stock number ratio is less, tend to branch to historical data amount and history stock number ratio is less
Resource group, and the Agap of server is also each configured with corresponding weight for above-mentioned three, to combine
Group photo rings the shunting result of these data.
The module diagram of the device of the resource distribution that Fig. 9 provides for the embodiment of the present application, specifically includes:
Data analysis module 51, for determining the currently received data of server and each default category information
The degree of association；
Enquiry module 52, for inquiring about the current information of each resource group corresponding to described each default category information
And historical information；
Configuration module 53, for according to described data and the degree of association of each default category information and described
The current information of each resource group and historical information, carry out resource distribution to described each resource group.
In the present embodiment, described configuration module 53 specifically for, according to described data and each default classification
The degree of association of information and first presets weight, it is thus achieved that the first reference value of described each resource group；According to described respectively
The current information of resource group and second presets weight, it is thus achieved that the second reference value of described each resource group；According to institute
The historical information and the 3rd stating each resource group presets weight, it is thus achieved that the 3rd reference value of described each resource group；Base
In first, second, and third reference value of the described each resource group obtained, described each resource group is carried out resource
Configuration；Wherein, described first, second, and third to preset weight sum be 1.
In the present embodiment, the stock number of described each resource group configuration and the first, second of described each resource group
With the 3rd reference value positive correlation.
The module diagram of the device of the resource distribution that Figure 10 provides for the another embodiment of the application, specifically wraps
Include:
Data analysis module 61, for determining the currently received data of server and each default category information
The degree of association；
Enquiry module 62, each resource group current corresponding with described each default category information for inquiry is believed
Breath and historical information；
Prediction module 63, for estimating the data volume of next per time instance of described data each resource group；
Configuration module 65, for according to described data and the degree of association of each default category information, described each money
The current information of source group and historical information, and described in the data volume of each resource group estimated to described each resource
Group carries out resource distribution.
In the present embodiment, described configuration module 65 specifically for, according to described data and each default classification
The degree of association of information and first presets weight, it is thus achieved that the first reference value of described each resource group；According to described respectively
The current information of resource group and second presets weight, it is thus achieved that the second reference value of described each resource group；According to institute
The historical information and the 3rd stating each resource group presets weight, it is thus achieved that the 3rd reference value of described each resource group；Root
Weight is preset, it is thus achieved that the 4th ginseng of described each resource group according to the data volume and the 4th of the described each resource group estimated
Examine value；Based on the first, second, third and fourth reference value of described each resource group obtained, to described respectively
Resource group carries out resource distribution；Wherein, described first, second, third and fourth presets weight sum is 1.
In the present embodiment, the stock number of described each resource group configuration and described each resource group first, second,
Third and fourth reference value positive correlation.
In the present embodiment, moment each resource group before described enquiry module 62 is additionally operable to inquire about described data
Data volume；
Described prediction module 63 specifically for, build according to the data volume of moment each resource group before described data
The forecast model of vertical described each resource group；Forecast model according to described each resource group estimate described data next
The data volume of per time instance each resource group.
In the present embodiment, described enquiry module 62 is additionally operable to, and at next per time instance of described data, looks into
Ask the data volume of presently described each resource group；
Described device also includes comparing module 64, for by described current data volume and according to described each money
The current data volume that the forecast model of source group is estimated is compared；
Described prediction module 63 is additionally operable to, for comparison result in described each resource group more than predetermined threshold value
Part resource group, then continue pre-according to the forecast model of this part resource group at next per time instance of described data
Estimate the data volume of this part resource group；It is not more than the portion of predetermined threshold value for comparison result in described each resource group
Divide resource group, then at next per time instance of described data, this part resource group is re-established forecast model.
In the present embodiment, described enquiry module 62 is additionally operable to, N number of per time instance after described data
In, inquiring about the data volume of described each resource group, wherein said N is predetermined number；
Described comparing module 64 is additionally operable to, by the data volume of described each resource group that inquires with according to described
The data volume that the forecast model of each resource group is estimated is compared；
Described prediction module 63 is additionally operable to, for comparison result in described each resource group in described N number of unit
It is both greater than the part resource group of predetermined threshold value in moment, then provides according to this part in described N number of per time instance
The data volume of source group, re-establishes forecast model to this part resource group.
In the present embodiment, described enquiry module 62 specifically for, inquire about the current number of described each resource group
According to amount and Current resource amount；By the ratio of the current data amount of described each resource group Yu Current resource amount, determine
Current information for described each resource group.
In the present embodiment, described enquiry module 62 specifically for, inquire about described each resource group preset go through
In history length, historical data amount during per time instance corresponding with described data and history stock number；By described respectively
The historical data amount of resource group and the ratio of history stock number, be defined as the historical information of described each resource group.
The embodiment of the present application provides the method and device of resource distribution, and the method currently receives according to server
Data and the degree of association of each default category information, the current letter of each resource group corresponding to each default category information
Breath and historical information carry out resource distribution to each resource group, and without being configured by manual intervention, and each resource
The resource distribution amount of group dynamically adjusts according to the change of above-mentioned data, improves the utilization of artificial customer service resource
Rate, alleviates the burden of server.
Those skilled in the art are it should be appreciated that embodiments of the invention can be provided as method, system or meter
Calculation machine program product.Therefore, the present invention can use complete hardware embodiment, complete software implementation or knot
The form of the embodiment in terms of conjunction software and hardware.And, the present invention can use and wherein wrap one or more
Computer-usable storage medium containing computer usable program code (include but not limited to disk memory,
CD-ROM, optical memory etc.) form of the upper computer program implemented.
The present invention is with reference to method, equipment (system) and computer program product according to embodiments of the present invention
The flow chart of product and/or block diagram describe.It should be understood that can by computer program instructions flowchart and
/ or block diagram in each flow process and/or flow process in square frame and flow chart and/or block diagram and/
Or the combination of square frame.These computer program instructions can be provided to general purpose computer, special-purpose computer, embedding
The processor of formula datatron or other programmable data processing device is to produce a machine so that by calculating
The instruction that the processor of machine or other programmable data processing device performs produces for realizing at flow chart one
The device of the function specified in individual flow process or multiple flow process and/or one square frame of block diagram or multiple square frame.
These computer program instructions may be alternatively stored in and computer or the process of other programmable datas can be guided to set
In the standby computer-readable memory worked in a specific way so that be stored in this computer-readable memory
Instruction produce and include the manufacture of command device, this command device realizes in one flow process or multiple of flow chart
The function specified in flow process and/or one square frame of block diagram or multiple square frame.
These computer program instructions also can be loaded in computer or other programmable data processing device, makes
Sequence of operations step must be performed to produce computer implemented place on computer or other programmable devices
Reason, thus the instruction performed on computer or other programmable devices provides for realizing flow chart one
The step of the function specified in flow process or multiple flow process and/or one square frame of block diagram or multiple square frame.
In a typical configuration, calculating equipment includes one or more processor (CPU), input/defeated
Outgoing interface, network interface and internal memory.
Internal memory potentially includes the volatile memory in computer-readable medium, random access memory
(RAM) and/or the form such as Nonvolatile memory, such as read only memory (ROM) or flash memory (flash
RAM).Internal memory is the example of computer-readable medium.
Computer-readable medium includes that removable media permanent and non-permanent, removable and non-can be by appointing
Where method or technology realize information storage.Information can be computer-readable instruction, data structure, program
Module 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), dynamic random access memory (DRAM), its
The random access memory (RAM) of his type, read only memory (ROM), electrically erasable are read-only
Memorizer (EEPROM), fast flash memory bank or other memory techniques, read-only optical disc read only memory
(CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tape, tape magnetic
Disk storage or other magnetic storage apparatus or any other non-transmission medium, can be used for storage can be calculated
The information that equipment accesses.According to defining herein, computer-readable medium does not include temporary computer-readable matchmaker
Body (transitory media), such as data signal and the carrier wave of modulation.
Also, it should be noted term " includes ", " comprising " or its any other variant are intended to non-
Comprising of exclusiveness, so that include that the process of a series of key element, method, commodity or equipment not only wrap
Include those key elements, but also include other key elements being not expressly set out, or also include for this process,
The key element that method, commodity or equipment are intrinsic.In the case of there is no more restriction, statement " include
One ... " key element that limits, it is not excluded that including the process of described key element, method, commodity or setting
Other identical element is there is also in Bei.
It will be understood by those skilled in the art that embodiments herein can be provided as method, system or computer journey
Sequence product.Therefore, the application can use complete hardware embodiment, complete software implementation or combine software and
The form of the embodiment of hardware aspect.And, the application can use and wherein include calculating one or more
The computer-usable storage medium of machine usable program code (include but not limited to disk memory, CD-ROM,
Optical memory etc.) form of the upper computer program implemented.
The foregoing is only embodiments herein, be not limited to the application.For this area skill
For art personnel, the application can have various modifications and variations.All institutes within spirit herein and principle
Any modification, equivalent substitution and improvement etc. made, within the scope of should be included in claims hereof.
Claims (24)
1. the method for a resource distribution, it is characterised in that including:
Determine the degree of association of the currently received data of server and each default category information；
Inquire about current information and the historical information of each resource group corresponding to described each default category information；
According to described data and the degree of association of each default category information and the current information of described each resource group
And historical information, described each resource group is carried out resource distribution.
2. the method for claim 1, it is characterised in that according to described data and each default classification
The degree of association of information and the current information of described each resource group and historical information, enter described each resource group
Row resource distribution, specifically includes:
The degree of association and first according to described data Yu each default category information presets weight, it is thus achieved that described each money
First reference value of source group；
Current information and second according to described each resource group presets weight, it is thus achieved that the second of described each resource group
Reference value；
Historical information and the 3rd according to described each resource group presets weight, it is thus achieved that the 3rd of described each resource group
Reference value；
First, second, and third reference value based on the described each resource group obtained, enters described each resource group
Row resource distribution；Wherein, described first, second, and third to preset weight sum be 1.
3. method as claimed in claim 2, it is characterised in that the stock number of described each resource group configuration
The first, second, and third reference value positive correlation with described each resource group.
4. the method for a resource distribution, it is characterised in that including:
Determine the degree of association of the currently received data of server and each default category information；
Inquire about current information and the historical information of each resource group corresponding with described each default category information；
Estimate the data volume of next per time instance of described data each resource group；
According to described data and the degree of association of each default category information, the current information of described each resource group with go through
History information, and described in the data volume of each resource group estimated described each resource group is carried out resource distribution.
5. method as claimed in claim 4, it is characterised in that according to described data and each default classification
The degree of association of information, the current information of described each resource group and historical information, and described in each resource of estimating
The data volume of group carries out resource distribution to described each resource group, specifically includes:
The degree of association and first according to described data Yu each default category information presets weight, it is thus achieved that described each money
First reference value of source group；
Current information and second according to described each resource group presets weight, it is thus achieved that the second of described each resource group
Reference value；
Historical information and the 3rd according to described each resource group presets weight, it is thus achieved that the 3rd of described each resource group
Reference value；
Data volume and the 4th according to the described each resource group estimated presets weight, it is thus achieved that described each resource group
4th reference value；
First, second, third and fourth reference value based on the described each resource group obtained, to described each money
Source group carries out resource distribution；Wherein, described first, second, third and fourth presets weight sum is 1.
6. method as claimed in claim 5, it is characterised in that the stock number of described each resource group configuration
First, second, third and fourth reference value positive correlation with described each resource group.
7. method as claimed in claim 4, it is characterised in that estimate next per time instance of described data
The data volume of each resource group, specifically includes:
The data volume of moment each resource group before inquiring about described data；
The forecast model of described each resource group is set up according to the data volume of moment each resource group before described data；
Forecast model according to described each resource group estimates the number of next per time instance of described data each resource group
According to amount.
8. method as claimed in claim 7, it is characterised in that described method also includes:
At next per time instance of described data, inquire about the data volume of presently described each resource group；
By described current data volume and the current data estimated according to the forecast model of described each resource group
Amount is compared；
The part resource group of predetermined threshold value it is more than, then in described data for comparison result in described each resource group
Next per time instance continues to estimate the data volume of this part resource group according to the forecast model of this part resource group；
It is not more than the part resource group of predetermined threshold value, then at described number for comparison result in described each resource group
According to next per time instance, this part resource group is re-established forecast model.
Method the most according to claim 7, it is characterised in that described method also includes:
After described data in N number of per time instance, inquire about the data volume of described each resource group, Qi Zhongsuo
Stating N is predetermined number；
The data volume of the described each resource group inquired is estimated with according to the forecast model of described each resource group
Data volume compare；
In described N number of per time instance, predetermined threshold value it is both greater than for comparison result in described each resource group
Part resource group, then according to the data volume of this part resource group in described N number of per time instance, to this part
Resource group re-establishes forecast model.
10. the method as described in any one of claim 1 to 9, it is characterised in that inquiry is with described
The current information of each resource group that each default category information is corresponding, specifically includes:
Inquire about current data amount and the Current resource amount of described each resource group；
By the ratio of the current data amount of described each resource group Yu Current resource amount, it is defined as described each resource group
Current information.
11. methods as described in any one of claim 1 to 9, it is characterised in that inquiry is with described
The historical information of each resource group that each default category information is corresponding, specifically includes:
Inquire about described each resource group history in default history length, during per time instance corresponding with described data
Data volume and history stock number；
By the ratio of the historical data amount of described each resource group Yu history stock number, it is defined as described each resource group
Historical information.
12. methods as described in any one of claim 1 to 9, it is characterised in that
Described data are the enquirement request of user；
Described resource is the customer service resource data being correspondingly arranged for the artificial customer service in backstage in server；
Described resource group is the set of the customer service resource data of configuration in server.
The device of 13. 1 kinds of resource distributions, it is characterised in that including:
Data analysis module, for determining associating of the currently received data of server and each default category information
Degree；
Enquiry module, for inquiring about the current information of each resource group corresponding to described each default category information and going through
History information；
Configuration module, for the degree of association according to described data and each default category information and described each money
The current information of source group and historical information, carry out resource distribution to described each resource group.
14. devices as claimed in claim 13, it is characterised in that described configuration module specifically for,
The degree of association and first according to described data Yu each default category information presets weight, it is thus achieved that described each resource group
The first reference value；Current information and second according to described each resource group presets weight, it is thus achieved that described each money
Second reference value of source group；Historical information and the 3rd according to described each resource group presets weight, it is thus achieved that described
3rd reference value of each resource group；First, second, and third reference value based on the described each resource group obtained,
Described each resource group is carried out resource distribution；Wherein, described first, second, and third preset weight sum and be
1。
15. devices as claimed in claim 14, it is characterised in that the resource of described each resource group configuration
The first, second, and third reference value positive correlation with described each resource group.
The device of 16. 1 kinds of resource distributions, it is characterised in that including:
Data analysis module, for determining associating of the currently received data of server and each default category information
Degree；
Enquiry module, for the inquiry each resource group corresponding with described each default category information current information and
Historical information；
Prediction module, for estimating the data volume of next per time instance of described data each resource group；
Configuration module, for according to described data and the degree of association of each default category information, described each resource group
Current information and historical information, and described in the data volume of each resource group estimated described each resource group is entered
Row resource distribution.
17. devices as claimed in claim 16, it is characterised in that described configuration module specifically for,
The degree of association and first according to described data Yu each default category information presets weight, it is thus achieved that described each resource group
The first reference value；Current information and second according to described each resource group presets weight, it is thus achieved that described each money
Second reference value of source group；Historical information and the 3rd according to described each resource group presets weight, it is thus achieved that described
3rd reference value of each resource group；Data volume and the 4th according to the described each resource group estimated presets weight,
Obtain the 4th reference value of described each resource group；Based on the described each resource group obtained first, second, the
Three and the 4th reference value, carries out resource distribution to described each resource group；Wherein, described first, second,
Three and the 4th to preset weight sum be 1.
18. devices as claimed in claim 17, it is characterised in that the resource of described each resource group configuration
First, second, third and fourth reference value positive correlation with described each resource group.
19. devices as claimed in claim 16, it is characterised in that described enquiry module is additionally operable to inquiry
The data volume of moment each resource group before described data；
Described prediction module specifically for, set up institute according to the data volume of moment each resource group before described data
State the forecast model of each resource group；Forecast model according to described each resource group estimates next unit of described data
The data volume of moment each resource group.
20. devices as claimed in claim 19, it is characterised in that described enquiry module is additionally operable to,
Next per time instance of described data, inquires about the data volume of presently described each resource group；
Described device also includes comparing module, for by described current data volume and according to described each resource group
The current data volume estimated of forecast model compare；
Described prediction module is additionally operable to, and is more than the part of predetermined threshold value for comparison result in described each resource group
Resource group, then continue to estimate this according to the forecast model of this part resource group at next per time instance of described data
The data volume of part resource group；The part money of predetermined threshold value it is not more than for comparison result in described each resource group
Source group, then re-establish forecast model at next per time instance of described data to this part resource group.
21. devices according to claim 19, it is characterised in that described enquiry module is additionally operable to,
After described data in N number of per time instance, inquire about the data volume of described each resource group, wherein said N
For predetermined number；
Described comparing module is additionally operable to, by the data volume of described each resource group that inquires with according to described each money
The data volume that the forecast model of source group is estimated is compared；
Described prediction module is additionally operable to, for comparison result in described each resource group when described N number of unit
The part resource group of predetermined threshold value it is both greater than, then according to this part resource in described N number of per time instance in carving
The data volume of group, re-establishes forecast model to this part resource group.
22. devices as described in any one of claim 13 to 21, it is characterised in that described inquiry
Module specifically for, inquire about current data amount and the Current resource amount of described each resource group；By described each resource
The current data amount of group and the ratio of Current resource amount, be defined as the current information of described each resource group.
23. devices as described in any one of claim 13 to 21, it is characterised in that described inquiry
Module specifically for, inquire about described each resource group in default history length, during unit corresponding with described data
Historical data amount during quarter and history stock number；By the historical data amount of described each resource group and history stock number
Ratio, be defined as the historical information of described each resource group.
24. devices as described in any one of claim 13 to 21, it is characterised in that
Described data are the enquirement request of user；
Described resource is the customer service resource data being correspondingly arranged for the artificial customer service in backstage in server；
Described resource group is the set of the customer service resource data of configuration in server.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201510213804.4A CN106203750A (en) | 2015-04-29 | 2015-04-29 | A kind of method and device of resource distribution |
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| CN201510213804.4A CN106203750A (en) | 2015-04-29 | 2015-04-29 | A kind of method and device of resource distribution |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108632871A (en) * | 2018-05-07 | 2018-10-09 | 成都西加云杉科技有限公司 | Flow status analysis method, device and analysis system |
| CN108958939A (en) * | 2018-07-06 | 2018-12-07 | 阿里巴巴集团控股有限公司 | Distribution method, device and the server of Service Source |
| CN109002973A (en) * | 2018-06-29 | 2018-12-14 | 口碑(上海)信息技术有限公司 | The distribution of customer service resource, customer service resource data methods of exhibiting and device |
| CN111552569A (en) * | 2020-04-28 | 2020-08-18 | 咪咕文化科技有限公司 | System resource scheduling method, device and storage medium |
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| CN109002973A (en) * | 2018-06-29 | 2018-12-14 | 口碑(上海)信息技术有限公司 | The distribution of customer service resource, customer service resource data methods of exhibiting and device |
| CN108958939A (en) * | 2018-07-06 | 2018-12-07 | 阿里巴巴集团控股有限公司 | Distribution method, device and the server of Service Source |
| CN111552569A (en) * | 2020-04-28 | 2020-08-18 | 咪咕文化科技有限公司 | System resource scheduling method, device and storage medium |
| CN111552569B (en) * | 2020-04-28 | 2023-10-20 | 咪咕文化科技有限公司 | System resource scheduling method, device and storage medium |
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