CN107403296A - Conveyance equilibrium method and device - Google Patents
Conveyance equilibrium method and device Download PDFInfo
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Abstract
The embodiment of the present invention provides a kind of conveyance equilibrium method and device, is related to Computer Applied Technology field.Wherein it is determined that influence at least one influence factor of dispatching personnel quantity on duty；Based on the historical statistical data of any coverage, the prediction numerical value of at least one influence factor is determined；Based on the prediction numerical value, the dispatching personnel depaly quantity of transport power forecast model prediction any coverage is utilized；Wherein, the transport power forecast model is trained and obtained based on dispatching personnel's quantity on duty in the historical statistical data and the historical values of at least one influence factor；According to the dispatching personnel depaly quantity, any coverage is configured.Technical scheme provided in an embodiment of the present invention improves the degree of accuracy of conveyance equilibrium.
Description
Technical field
The present embodiments relate to Computer Applied Technology field, more particularly to a kind of region collocation method and device.
Background technology
The arrival of electronic commerce times, has driven the rapid development of logistics service, and dispatching order constantly increases, to making a gift to someone
The demand of member is increasing.
And in order to improve dispatching quality, it will usually which it is multiple coverages that can dispense region division, due to each service
Dispatching personnel in region are responsible for dispensing dispatching order corresponding to respective coverage.
Therefore this needs to carry out each coverage rational conveyance equilibrium, namely matching somebody with somebody for configuration fair amount makes a gift to someone
Member, and be typically based on artificial experience at present and carry out conveyance equilibrium, it is not accurate enough, transport power waste or transport power can be caused tight
.
The content of the invention
The embodiment of the present invention provides a kind of conveyance equilibrium method and device, accurate to solve conveyance equilibrium in the prior art
Spend low technical problem.
In a first aspect, a kind of conveyance equilibrium method is provided in the embodiment of the present invention, including：
It is determined that influence at least one influence factor of dispatching personnel quantity on duty；
Based on the historical statistical data of any coverage, the prediction numerical value of at least one influence factor is determined；
Based on the prediction numerical value, the dispatching personnel depaly number of transport power forecast model prediction any coverage is utilized
Amount；Wherein, the transport power forecast model based on the dispatching personnel quantity on duty in the historical statistical data and it is described at least
The historical values training of one influence factor obtains；
According to the dispatching personnel depaly quantity, any coverage is configured.
Alternatively, training in advance obtains the transport power forecast model as follows：
Based at least one influence factor, transport power forecast model is built；
From the historical statistical data, multiple dispatching personnel quantity on duty and each self-corresponding described at least one is obtained
The historical values of influence factor, as training sample；
Obtained using the multiple dispatching personnel quantity on duty as the result data of the transport power forecast model, training
The model coefficient of the transport power forecast model.
Alternatively, described to be based at least one influence factor, structure transport power forecast model includes：
Using the weighted sum formula of at least one influence factor as the transport power forecast model.
Alternatively, the result data using the multiple dispatching personnel quantity on duty as the transport power forecast model,
The model coefficient that training obtains the transport power forecast model includes：
Obtained using the multiple dispatching personnel quantity on duty as the result data of the transport power forecast model, training
The initial coefficients of the transport power forecast model；
Based on the historical values of at least one influence factor, calculate to obtain to match somebody with somebody using the transport power forecast model and make a gift to someone
The theoretical quantity of member；
According to dispatching personnel quantity on duty and its corresponding dispatching personnel theory quantity, the transport power prediction mould is adjusted
The initial coefficients of type are until the dispatching personnel theory quantity and the dispatching personnel quantity on duty obtain in error allowed band
Obtain model coefficient.
Alternatively, the historical statistical data includes dispensing personnel's quantity on duty and described at least one in each dispatching cycle
The historical values of individual influence factor；
The historical statistical data based on any coverage, determine the prediction numerical value of at least one influence factor
Including：
Based on the historical statistical data of any coverage, by any shadow in multiple dispatching cycles before treating dispatching cycle
Prediction numerical value of the mean values of the historical values of the factor of sound as any influence factor.
Second aspect, the embodiments of the invention provide a kind of conveyance equilibrium device, including：
Factor determining module, for determining at least one influence factor of influence dispatching personnel quantity on duty；
Prediction module, for the historical statistical data based on any coverage, determine at least one influence factor
Prediction numerical value；
Computing module, for based on the prediction numerical value, any coverage to be predicted using transport power forecast model
Dispense personnel depaly quantity；Wherein, the transport power forecast model is based on the dispatching personnel number on duty in the historical statistical data
The training of the historical values of amount and at least one influence factor obtains；
Configuration module, for according to the dispatching personnel depaly quantity, configuring any coverage.
Alternatively, in addition to：
Model construction module, for based at least one influence factor, building transport power forecast model；
Sample determining module, for from the historical statistical data, obtain multiple dispatching personnel quantity on duty and each
The historical values of corresponding at least one influence factor, as training sample；
Model training module, for using the multiple dispatching personnel quantity on duty as the transport power forecast model
Result data, training obtain the model coefficient of the transport power forecast model.
Alternatively, the model construction module is specifically used for making the weighted sum formula of at least one influence factor
For the transport power forecast model.
Alternatively, the model training module includes：
First training unit, for using the multiple dispatching personnel quantity on duty as the transport power forecast model
Result data, training obtain the initial coefficients of the transport power forecast model；
Theoretical value computing unit, it is pre- using the transport power for the historical values based at least one influence factor
Survey model and calculate acquisition dispatching personnel's theory quantity；
Second training unit, for according to dispatching personnel quantity on duty and its corresponding dispatching personnel theory quantity,
The initial coefficients of the transport power forecast model are adjusted until the dispatching personnel theory quantity and the dispatching personnel quantity on duty
In error allowed band, model coefficient is obtained.
Alternatively, the historical statistical data includes dispatching personnel quantity on duty corresponding to each dispatching cycle and described
The historical values of at least one influence factor；
The prediction module is specifically used for the historical statistical data based on any coverage, before treating dispatching cycle
Prediction numerical value of the mean values of the historical values of any influence factor as any influence factor in multiple dispatching cycles.
In the embodiment of the present invention, based on the history number according at least one influence factor for influenceing dispatching personnel quantity on duty
Value and dispatching personnel's quantity on duty, train the transport power forecast model of acquisition, and the historical statistics based on any coverage
Data, it is determined that any coverage correspond to the prediction numerical value of at least one influence factor, can calculate and be dispensed
Personnel depaly quantity, so as to which conveyance equilibrium can be carried out to any coverage according to the dispatching personnel depaly quantity, this
Inventive embodiments, which are based on the transport power forecast model, accurately and effectively to be predicted dispatching personnel depaly quantity, therefore improve
The degree of accuracy of conveyance equilibrium.
The aspects of the invention or other aspects can more straightforwards in the following description.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the required accompanying drawing used in technology description to be briefly described, it should be apparent that, drawings in the following description are this hairs
Some bright embodiments, for those of ordinary skill in the art, on the premise of not paying creative work, can be with root
Other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 shows a kind of conveyance equilibrium method one embodiment flow chart provided by the invention；
Fig. 2 shows a kind of flow chart of another embodiment of conveyance equilibrium method provided by the invention；
Fig. 3 shows a kind of structural representation of conveyance equilibrium device one embodiment provided by the invention；
Fig. 4 shows a kind of structural representation of another embodiment of conveyance equilibrium device provided by the invention；
Fig. 5 shows the structural representation of a kind of electronic equipment one embodiment provided by the invention.
Embodiment
In order that those skilled in the art more fully understand the present invention program, below in conjunction with the embodiment of the present invention
Accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described.
In some flows of description in description and claims of this specification and above-mentioned accompanying drawing, contain according to
Particular order occur multiple operations, but it should be clearly understood that these operation can not occur herein according to it is suitable
Sequence is performed or performed parallel, the sequence number such as 101,102 etc. of operation, is only used for distinguishing each different operation, sequence number
Any execution sequence is not represented for itself.In addition, these flows can include more or less operations, and these operations can
To perform or perform parallel in order.It should be noted that the description such as " first " herein, " second ", is to be used to distinguish not
Message together, equipment, module etc., do not represent sequencing, it is different types also not limit " first " and " second ".
Technical scheme is mainly used in logistics distribution scene, as described in the background art, at present generally
By the division of coverage, dispense and order as corresponding to the dispatching personnel in a coverage are responsible for dispensing respective coverage
It is single.Such as in the online transaction scene realized based on O2O, dispatching personnel are responsible for from its affiliated submedial trade company in coverage
Pickup, and it is distributed to the user of its affiliated coverage.Therefore rational conveyance equilibrium turns into influence dispatching matter in coverage
The key factor of amount.
And the dispatching personnel amount that artificial experience determines to configure in each coverage is all based at present, it is not accurate enough
Really, still result in a coverage and the problem of transport power is nervous or transport power wastes occur.
In order to improve the conveyance equilibrium degree of accuracy, inventor has researched and proposed technical scheme by a series of,
In the embodiment of the present invention, according to the historical values at least one influence factor for influenceing dispatching personnel quantity on duty and with making a gift to someone
Member's quantity on duty, can train and obtain transport power forecast model, and based on the historical statistical data of any coverage, it may be determined that
Any coverage corresponds to the prediction numerical value of at least one influence factor；So as to pre- based on prediction numerical value and the transport power
Acquisition dispatching personnel depaly quantity can be calculated by surveying model, so that can be according to the dispatching personnel depaly quantity, to this
One coverage progress conveyance equilibrium, dispatching personnel on duty quantity of the embodiment of the present invention in historical statistical data, and
The influence factor of dispatching personnel quantity on duty is influenceed, can train obtaining transport power forecast model, therefore predict based on the transport power
Model can be accurately and effectively predicted dispatching personnel depaly quantity.Therefore the degree of accuracy of conveyance equilibrium is improved.
Wherein, the dispatching personnel quantity on duty of any coverage can refer to actual dispatching personnel amount on duty, by
After to any coverage conveyance equilibrium, whole dispatching personnel Jun Huidao hilllocks for not necessarily configuring may be due to more shadow
The factor of sound and can not be on duty, therefore the incidence relation between dispatching personnel quantity on duty and influence factor can be counted, obtained
Transport power forecast model so that can carry out Accurate Prediction to the conveyance equilibrium of coverage using the transport power forecast model.
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, rather than whole embodiments.It is based on
Embodiment in the present invention, the every other implementation that those skilled in the art are obtained under the premise of creative work is not made
Example, belongs to the scope of protection of the invention.
Fig. 1 is a kind of flow chart of conveyance equilibrium method one embodiment provided by the invention, this method can include with
Under several steps：
101：It is determined that influence at least one influence factor of dispatching personnel quantity on duty.
Wherein, each coverage can correspond to dispatching personnel quantity on duty in the dispatching cycle of each history.The tune
It for example can be one day to spend the cycle, need to carry out conveyance equilibrium to coverage daily in actual applications.
The present invention is for the current technical scheme for treating progress dispatching cycle conveyance equilibrium in any one coverage.
Dispatching personnel quantity on duty can refer to the dispatching personnel for the time on duty being more than 0.It can be existed according to dispatching personnel
The login time of delivery system determines.
Alternatively, the completion that at least one influence factor can be included in each dispatching cycle is always singly measured, completed per capita
Dan Liang, averagely dispense duration, averagely dispense punctual rate and/or the completion of multiple dispatching cycles is always singly measured.
Each coverage can be configured multiple dispatching personnel in each dispatching cycle, and always singly amount can refer to for the completion
All dispatching personnel on duty dispense the dispatching quantity on order completed；Single amount is completed per capita refers to that each on duty matching somebody with somebody is made a gift to someone
The dispatching quantity on order of the average completion of member；It can be obtained according to the dispatching duration calculation of each dispatching personnel averagely to dispense duration
；It can be calculated to obtain according to the punctual rate of dispatching of each dispatching personnel averagely to dispense punctual rate.
Multiple dispatching cycles adjacent before treating dispatching cycle can be referred to the plurality of dispatching cycle.
102：Based on the historical statistical data of any coverage, the prediction number of at least one influence factor is determined
Value.
103：Based on the prediction numerical value, the dispatching personnel for predicting any coverage using transport power forecast model match somebody with somebody
Put quantity.
Wherein, the dispatching personnel in the historical statistical data of the transport power forecast model based on any coverage are on duty
The training of the historical values of quantity and at least one influence factor obtains.
Because transport power forecast model is going through based on dispatching personnel quantity on duty and at least one influence factor
The training of history numerical value obtains.
, can going through based on any coverage in order to determine that personnel depaly quantity is dispensed corresponding to any coverage
History statistics, determine the prediction numerical value of at least one influence factor.
Alternatively, the prediction numerical value of each influence factor, can be any coverage treat dispatching cycle before
Any one dispatching cycle in each influence factor historical values；
Certainly, the prediction numerical value of each influence factor, or in multiple dispatching cycles before treating dispatching cycle
The mean values of the historical values of each influence factor.
Prediction numerical value is inputted into the transport power forecast model, the result data of acquisition is to make a gift to someone matching somebody with somebody for any coverage
Member's configuration quantity.
104：According to the dispatching personnel depaly quantity, any coverage is configured.
Namely dispatching cycle is treated at this, to match somebody with somebody corresponding to any coverage configuration dispatching personnel depaly quantity
The person of making a gift to someone.
In the present embodiment, using the dispatching personnel quantity on duty in historical statistical data, and the dispatching is influenceed
The influence factor of personnel's quantity on duty, the transport power forecast model of acquisition is trained, calculate the dispatching personnel depaly of any coverage
Quantity so that dispatching personnel depaly quantity is more rationally accurate, therefore improves the degree of accuracy of conveyance equilibrium.
Fig. 2 is a kind of flow chart of another embodiment of conveyance equilibrium method provided by the invention, and this method can include
Following steps：
201：It is determined that influence at least one influence factor of dispatching personnel quantity on duty.
202：Based at least one influence factor, transport power forecast model is built.
It is alternatively possible to mould will be predicted using the weighted sum formula of at least one influence factor as the transport power
Type.
For example, the transport power forecast model can be expressed as：
θ0χ0+θ1χ1+θ2χ2+...+βnχn
Wherein, θiRepresent i-th of model coefficient, χiRepresent i-th of influence factor.Wherein i ∈ (0, n).
203：From the historical statistical data of any coverage, obtain multiple dispatching personnel quantity on duty and each correspond to
At least one influence factor historical values, as training sample.
Wherein, multiple dispatching personnel quantity on duty can refer to dispatching personnel number on duty corresponding to multiple difference dispatching cycles
Amount, each dispatching cycle corresponding one dispense the historical values of personnel's quantity on duty and at least one influence factor.
204：Using the multiple dispatching personnel quantity on duty as the result data of the transport power forecast model, training
Obtain the model coefficient of the transport power forecast model.
Wherein, personnel's quantity on duty and at least one influence factor are dispensed, there can be following relation：
y=θ0χ0+β1χ11+θ2χ2+...+θnχn；
Wherein, y represents the dispatching personnel quantity on duty in each dispatching cycle.
The historical values of each dispatching personnel quantity on duty and its corresponding at least one influence factor are as one group of data；
Acquisition model coefficient can be solved according to multi-group data.
Alternatively, in order to improve the computational efficiency of solution procedure, reduce difficulty in computation, according to dispatching personnel's quantity on duty with
The relation of at least one influence factor, following squared difference formula can be obtained：
Wherein, χjRepresent the historical values of the influence factor of jth group, yjRepresent the dispatching personnel quantity on duty of jth group.
Derivation is carried out to θ, you can to draw
θ=(XTX)-1XTY。
Wherein, Y represents the matrix that the dispatching personnel quantity on duty of each dispatching cycle is formed, and X represents going through for influence factor
The matrix that history numerical value is formed.θ is model coefficient (θ0 θ1 … θn-1 θn), because Y and X is known numeric value, so as to i.e.
Acquisition model coefficient can be calculated.
Need what is illustrated, the operation of step 202~step 204 can first carry out in advance, be not limited in the present embodiment
Execution sequence.
205：Based on the historical statistical data, the prediction numerical value of at least one influence factor is determined.
206：Based on the prediction numerical value, the dispatching personnel for predicting any coverage using transport power forecast model match somebody with somebody
Put quantity.
207：According to the dispatching personnel depaly quantity, any coverage is configured.
The technical scheme of the present embodiment, using the dispatching personnel quantity on duty in historical statistical data, and influence
The influence factor of dispatching personnel quantity on duty, the transport power forecast model of acquisition is trained, calculate and make a gift to someone matching somebody with somebody for any coverage
Member's configuration quantity so that calculate the dispatching personnel depaly quantity of acquisition more rationally accurately, therefore improve the standard of conveyance equilibrium
Exactness, the transport power anxiety or transport power that will not cause coverage waste.
Due to when carrying out the training of transport power forecast model, dispatching personnel depaly number can be calculated using transport power forecast model
Amount, for each dispatching cycle of history, it can also be calculated by transport power forecast model and obtain dispatching personnel's theory quantity, and matched somebody with somebody
The person's of making a gift to someone theory quantity there may be error with dispatching personnel quantity on duty, can in order to further improve the model training degree of accuracy
To be adjusted based on the error to the model coefficient of transport power forecast model, to reduce dispatching personnel's theory quantity and dispatching personnel
The error of quantity on duty so that the transport power forecast model for adjusting acquisition can be more accurate.
Therefore in certain embodiments, it is described using the multiple dispatching personnel quantity on duty as the transport power forecast model
Result data, the model coefficient that training obtains the transport power forecast model can include：
Obtained using the multiple dispatching personnel quantity on duty as the result data of the transport power forecast model, training
The initial coefficients of the transport power forecast model；
Based on the historical values of at least one influence factor, calculate to obtain to match somebody with somebody using the transport power forecast model and make a gift to someone
The theoretical quantity of member；
According to dispatching personnel quantity on duty and its corresponding dispatching personnel theory quantity, the transport power prediction mould is adjusted
The initial coefficients of type are until the dispatching personnel theory quantity and the dispatching personnel quantity on duty obtain in error allowed band
Obtain model coefficient.
Wherein, dispatching personnel theory quantity is to be based on transport power forecast model corresponding to initial coefficients to calculate acquisition.
Alternatively, in order to improve accuracy in computation, from the foregoing, it can be understood that transport power forecast model can be linear
Equation, therefore the initial coefficients of the transport power forecast model can be adjusted by way of local weighted linear regression.
Specifically：
Above-described mean square deviation calculation formulaExpression can be used for and state dispatching personnel's theory quantity
With the error of the dispatching personnel quantity on duty.
But it because transport power forecast model is a linear equation, can not well estimate for some deviation points, therefore need
Model coefficient is adjusted to reduce the error of mean square deviation.Weight coefficient w is introduced for different simulation coefficientsj.Then match somebody with somebody
The error of the person's of making a gift to someone theory quantity and the dispatching personnel quantity on duty can be expressed as：
Work as wjIt is bigger,The proportion accounted for is bigger, wjIt is smaller,Caused influence can be ignored.
Therefore, rule of thumb formula, can select wjForm it is as follows：
Wherein, χ is influence factor χjPrediction a sample values, parameter τ controls the change of weight coefficient so that
χjIt is bigger from the nearlyer weight coefficients of χ, it is smaller from the more remote weight coefficients of χ.
Then model coefficient solution formula can be specific：
θ=(XTWX)-1XTWY；
W represents the weight coefficient matrix of model coefficient.
By the method for local weighted linear regression, certain weight is assigned for each model coefficient, is adjusted by parameter τ
Weight change is saved, so as to reduce dispatching personnel quantity on duty and dispense the mean square deviation of personnel's gross data, improves acquisition
The degree of accuracy of transport power forecast model.
Fig. 3 is a kind of structural representation of conveyance equilibrium device one embodiment provided by the invention, and the device can wrap
Include：
Factor determining module 301, for determining at least one influence factor of influence dispatching personnel quantity on duty.
Wherein, each coverage can correspond to dispatching personnel quantity on duty in the dispatching cycle of each history.
Alternatively, the completion that at least one influence factor can be included in each dispatching cycle is always singly measured, completed per capita
Dan Liang, averagely dispense duration, averagely dispense punctual rate and/or the completion of multiple dispatching cycles is always singly measured.
Prediction module 302, for the historical statistical data based on any coverage, determine at least one influence because
The prediction numerical value of element.
Wherein, the historical statistical data can include dispatching personnel quantity on duty and institute corresponding to each dispatching cycle
State the historical values of at least one influence factor；
Therefore, alternatively, the prediction module can be specifically used for the history based on any coverage
Statistics, using the historical values of any influence factor in any dispatching cycle before treating dispatching cycle as any shadow
The prediction numerical value of the factor of sound.
As another optional mode, the prediction module can be specifically used for the historical statistics number based on any coverage
According to using the mean values of the historical values of any influence factor in multiple dispatching cycles before treating dispatching cycle as described
The prediction numerical value of one influence factor.
Computing module 303, for based on the prediction numerical value, any coverage to be predicted using transport power forecast model
Dispatching personnel depaly quantity.
Wherein, the transport power forecast model is based on the dispatching personnel quantity on duty in the historical statistical data and described
The historical values training of at least one influence factor obtains.
Configuration module 304, for according to the dispatching personnel depaly quantity, configuring any coverage.Namely
This treats dispatching cycle, for dispatching personnel corresponding to any coverage configuration dispatching personnel depaly quantity.
In the present embodiment, using the dispatching personnel quantity on duty in historical statistical data, and the dispatching is influenceed
The influence factor of personnel's quantity on duty, the transport power forecast model of acquisition is trained, calculate the dispatching personnel depaly of any coverage
Quantity so that dispatching personnel depaly quantity is more rationally accurate, therefore improves the degree of accuracy of conveyance equilibrium.
As another embodiment, as shown in figure 4, being with difference shown in Fig. 3, the device can also include：
Model construction module 401, for based at least one influence factor, building transport power forecast model.
Alternatively, the model construction module be specifically used for using the weighted sum formula of at least one influence factor as
The transport power forecast model.
Sample determining module 402, for from the historical statistical data, obtaining multiple dispatching personnel quantity on duty and each
The historical values of self-corresponding at least one influence factor, as training sample.
Wherein, multiple dispatching personnel quantity on duty can refer to dispatching personnel number on duty corresponding to multiple difference dispatching cycles
Amount, each dispatching cycle corresponding one dispense the historical values of personnel's quantity on duty and at least one influence factor.
Model training module 403, for predicting mould using the multiple dispatching personnel quantity on duty as the transport power
The result data of type, training obtain the model coefficient of the transport power forecast model.
The technical scheme of the present embodiment, using the dispatching personnel quantity on duty in historical statistical data, and influence
The influence factor of dispatching personnel quantity on duty, the transport power forecast model of acquisition is trained, calculate and make a gift to someone matching somebody with somebody for any coverage
Member's configuration quantity so that calculate the dispatching personnel depaly quantity of acquisition more rationally accurately, therefore improve the standard of conveyance equilibrium
Exactness, the transport power anxiety or transport power that will not cause coverage waste.
Due to when carrying out the training of transport power forecast model, dispatching personnel depaly number can be calculated using transport power forecast model
Amount, for each dispatching cycle of history, it can also be calculated by transport power forecast model and obtain dispatching personnel's theory quantity, and matched somebody with somebody
The person's of making a gift to someone theory quantity there may be error with dispatching personnel quantity on duty, can in order to further improve the model training degree of accuracy
To be adjusted based on the error to the model coefficient of transport power forecast model, to reduce dispatching personnel's theory quantity and dispatching personnel
The error of quantity on duty so that the transport power forecast model for adjusting acquisition can be more accurate.
Therefore in certain embodiments, the model training module can include：
First training unit, for using the multiple dispatching personnel quantity on duty as the transport power forecast model
Result data, training obtain the initial coefficients of the transport power forecast model.
Theoretical value computing unit, it is pre- using the transport power for the historical values based at least one influence factor
Survey model and calculate acquisition dispatching personnel's theory quantity.
Wherein, dispatching personnel theory quantity is to be based on transport power forecast model corresponding to initial coefficients to calculate acquisition.
Second training unit, for according to dispatching personnel quantity on duty and its corresponding dispatching personnel theory quantity,
The initial coefficients of the transport power forecast model are adjusted until the dispatching personnel theory quantity and the dispatching personnel quantity on duty
In error allowed band, model coefficient is obtained.
Wherein, it is alternatively possible to which the mean square deviation for dispensing personnel's theory quantity and the dispatching personnel quantity on duty is represented
The error of dispatching personnel's theory quantity and the dispatching personnel quantity on duty.Adjusted by the way of local weighted linear regression
The initial coefficients of the transport power forecast model.
The model coefficient of transport power forecast model is adjusted by the present embodiment so that train the transport power of acquisition to predict mould
Type is more accurate, so as to further improve the degree of accuracy of conveyance equilibrium, transport power waste or transport power will not be caused nervous, real
The dispatching of existing high efficiency and time conservation.
In a possible design, the conveyance equilibrium device of Fig. 3 or embodiment illustrated in fig. 4 can be implemented as electronics and set
It is standby, as shown in figure 5, the electronic equipment can include one or more processors 501 and one or more memories 502；
One or more of memories 502 store one or more computer instruction, one or more computer
Instruction is called for one or more of processors 501 and performed；
One or more of processors 501 are used for：
It is determined that influence at least one influence factor of dispatching personnel quantity on duty；
Based on the historical statistical data of any coverage, the prediction numerical value of at least one influence factor is determined；
Based on the prediction numerical value, the dispatching personnel depaly number of transport power forecast model prediction any coverage is utilized
Amount；Wherein, the transport power forecast model based on the dispatching personnel quantity on duty in the historical statistical data and it is described at least
The historical values training of one influence factor obtains；
According to the dispatching personnel depaly quantity, any coverage is configured.
In addition, the one or more processors can be also used for performing the letter conveyance equilibrium side described in any of the above-described embodiment
Method.
The embodiment of the present invention additionally provides a kind of computer-readable recording medium, the computer-readable recording medium storage
There is computer program；
The computer program makes computer realize the conveyance equilibrium method described in any of the above-described embodiment when performing.
It is apparent to those skilled in the art that for convenience and simplicity of description, the system of foregoing description,
The specific work process of device and unit, the corresponding process in preceding method embodiment is may be referred to, will not be repeated here.
Device embodiment described above is only schematical, wherein the unit illustrated as separating component can
To be or may not be physically separate, it can be as the part that unit is shown or may not be physics list
Member, you can with positioned at a place, or can also be distributed on multiple NEs.It can be selected according to the actual needs
In some or all of module realize the purpose of this embodiment scheme.Those of ordinary skill in the art are not paying creativeness
Work in the case of, you can to understand and implement.
Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can
Realized by the mode of software plus required general hardware platform, naturally it is also possible to pass through hardware.Based on such understanding, on
The part that technical scheme substantially in other words contributes to prior art is stated to embody in the form of software product, should
Computer software product can store in a computer-readable storage medium, such as ROM/RAM, magnetic disc, CD, including some fingers
Make to cause a computer equipment (can be personal computer, server, or network equipment etc.) to perform each implementation
Method described in some parts of example or embodiment.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
The present invention is described in detail with reference to the foregoing embodiments, it will be understood by those within the art that：It still may be used
To be modified to the technical scheme described in foregoing embodiments, or equivalent substitution is carried out to which part technical characteristic；
And these modification or replace, do not make appropriate technical solution essence depart from various embodiments of the present invention technical scheme spirit and
Scope.
The invention discloses A1, a kind of conveyance equilibrium method, including：
It is determined that influence at least one influence factor of dispatching personnel quantity on duty；
Based on the historical statistical data of any coverage, the prediction numerical value of at least one influence factor is determined；
Based on the prediction numerical value, the dispatching personnel depaly number of transport power forecast model prediction any coverage is utilized
Amount；Wherein, the transport power forecast model based on the dispatching personnel quantity on duty in the historical statistical data and it is described at least
The historical values training of one influence factor obtains；
According to the dispatching personnel depaly quantity, any coverage is configured.
A2, the method according to A1, training in advance obtains the transport power forecast model as follows：
Based at least one influence factor, transport power forecast model is built；
From the historical statistical data, multiple dispatching personnel quantity on duty and each self-corresponding described at least one is obtained
The historical values of influence factor, as training sample；
Obtained using the multiple dispatching personnel quantity on duty as the result data of the transport power forecast model, training
The model coefficient of the transport power forecast model.
A3, the method according to A1, it is described to be based at least one influence factor, build transport power forecast model bag
Include：
Using the weighted sum formula of at least one influence factor as the transport power forecast model.
A4, the method according to A2 or A3, it is described that the multiple dispatching personnel quantity on duty is pre- as the transport power
The result data of model is surveyed, the model coefficient that training obtains the transport power forecast model includes：
Obtained using the multiple dispatching personnel quantity on duty as the result data of the transport power forecast model, training
The initial coefficients of the transport power forecast model；
Based on the historical values of at least one influence factor, calculate to obtain to match somebody with somebody using the transport power forecast model and make a gift to someone
The theoretical quantity of member；
According to dispatching personnel quantity on duty and its corresponding dispatching personnel theory quantity, the transport power prediction mould is adjusted
The initial coefficients of type are until the dispatching personnel theory quantity and the dispatching personnel quantity on duty obtain in error allowed band
Obtain model coefficient.
A5, the method according to A1, the historical statistical data include dispensing personnel's number on duty in each dispatching cycle
The historical values of amount and at least one influence factor；
The historical statistical data based on any coverage, determine the prediction numerical value of at least one influence factor
Including：
Based on the historical statistical data of any coverage, by any shadow in multiple dispatching cycles before treating dispatching cycle
Prediction numerical value of the mean values of the historical values of the factor of sound as any influence factor.
A6, the method according to A1, the historical statistical data exist including dispatching personnel corresponding to each dispatching cycle
The historical values of hilllock quantity and at least one influence factor；
The historical statistical data based on any coverage, determine the prediction numerical value of at least one influence factor
Including：
Based on the historical statistical data of any coverage, by any shadow in any dispatching cycle before treating dispatching cycle
Prediction numerical value of the historical values of the factor of sound as any influence factor.
A7, the method according to A1, the completion that at least one influence factor was included in each dispatching cycle are always single
Amount, single amount is completed per capita, averagely dispenses duration, averagely dispenses punctual rate and/or the completion of multiple dispatching cycles is always singly measured.
B8, a kind of conveyance equilibrium device, including：
Factor determining module, for determining at least one influence factor of influence dispatching personnel quantity on duty；
Prediction module, for the historical statistical data based on any coverage, determine at least one influence factor
Prediction numerical value；
Computing module, for based on the prediction numerical value, any coverage to be predicted using transport power forecast model
Dispense personnel depaly quantity；Wherein, the transport power forecast model is based on the dispatching personnel number on duty in the historical statistical data
The training of the historical values of amount and at least one influence factor obtains；
Configuration module, for according to the dispatching personnel depaly quantity, configuring any coverage.
B9, the device according to B8, in addition to：
Model construction module, for based at least one influence factor, building transport power forecast model；
Sample determining module, for from the historical statistical data, obtain multiple dispatching personnel quantity on duty and each
The historical values of corresponding at least one influence factor, as training sample；
Model training module, for using the multiple dispatching personnel quantity on duty as the transport power forecast model
Result data, training obtain the model coefficient of the transport power forecast model.
B10, the device according to B9, the model construction module are specifically used at least one influence factor
Weighted sum formula is as the transport power forecast model.
B11, the device according to B9 or B10, the model training module include：
First training unit, for using the multiple dispatching personnel quantity on duty as the transport power forecast model
Result data, training obtain the initial coefficients of the transport power forecast model；
Theoretical value computing unit, it is pre- using the transport power for the historical values based at least one influence factor
Survey model and calculate acquisition dispatching personnel's theory quantity；
Second training unit, for according to dispatching personnel quantity on duty and its corresponding dispatching personnel theory quantity,
The initial coefficients of the transport power forecast model are adjusted until the dispatching personnel theory quantity and the dispatching personnel quantity on duty
In error allowed band, model coefficient is obtained.
B12, the device according to B8, the historical statistical data exist including dispatching personnel corresponding to each dispatching cycle
The historical values of hilllock quantity and at least one influence factor；
The prediction module is specifically used for the historical statistical data based on any coverage, before treating dispatching cycle
Prediction numerical value of the mean values of the historical values of any influence factor as any influence factor in multiple dispatching cycles.
B13, the device according to B8, the historical statistical data exist including dispatching personnel corresponding to each dispatching cycle
The historical values of hilllock quantity and at least one influence factor；
The prediction module is specifically used for the historical statistical data based on any coverage, before treating dispatching cycle
Prediction numerical value of the historical values of any influence factor as any influence factor in any dispatching cycle.
B14, the device according to B8, the completion that at least one influence factor was included in each dispatching cycle are always single
Amount, single amount is completed per capita, averagely dispenses duration, averagely dispenses punctual rate and/or the completion of multiple dispatching cycles is always singly measured.
C15, a kind of electronic equipment, including one or more processors and one or more memories；
One or more of computer instructions of memory storage one or more；One or more computer instruction
Call and perform for one or more of processors；
One or more of processors are used for：
It is determined that influence at least one influence factor of dispatching personnel quantity on duty；
Based on the historical statistical data of any coverage, the prediction numerical value of at least one influence factor is determined；
Based on the prediction numerical value, the dispatching personnel depaly number of transport power forecast model prediction any coverage is utilized
Amount；Wherein, the transport power forecast model based on the dispatching personnel quantity on duty in the historical statistical data and it is described at least
The historical values training of one influence factor obtains；
According to the dispatching personnel depaly quantity, any coverage is configured.
C16, a kind of computer-readable recording medium, the computer-readable recording medium storage have computer program；
The computer program makes computer realize the conveyance equilibrium side as described in any one of claim 1~7 when performing
Method.
Claims (10)
- A kind of 1. conveyance equilibrium method, it is characterised in that including：It is determined that influence at least one influence factor of dispatching personnel quantity on duty；Based on the historical statistical data of any coverage, the prediction numerical value of at least one influence factor is determined；Based on the prediction numerical value, the dispatching personnel depaly quantity of transport power forecast model prediction any coverage is utilized； Wherein, the transport power forecast model is based on the dispatching personnel quantity on duty in the historical statistical data and described at least one The historical values training of influence factor obtains；According to the dispatching personnel depaly quantity, any coverage is configured.
- 2. according to the method for claim 1, it is characterised in that transport power forecast model training in advance as follows Obtain：Based at least one influence factor, transport power forecast model is built；From the historical statistical data, multiple dispatching personnel quantity on duty and each self-corresponding at least one influence are obtained The historical values of factor, as training sample；Using the multiple dispatching personnel quantity on duty as the result data of the transport power forecast model, train described in obtaining The model coefficient of transport power forecast model.
- 3. according to the method for claim 1, it is characterised in that described to be based at least one influence factor, structure fortune Power forecast model includes：Using the weighted sum formula of at least one influence factor as the transport power forecast model.
- 4. according to the method in claim 2 or 3, it is characterised in that described to make the multiple dispatching personnel quantity on duty For the result data of the transport power forecast model, the model coefficient that training obtains the transport power forecast model includes：Using the multiple dispatching personnel quantity on duty as the result data of the transport power forecast model, train described in obtaining The initial coefficients of transport power forecast model；Based on the historical values of at least one influence factor, calculated using the transport power forecast model and obtain dispatching personnel reason By quantity；According to dispatching personnel quantity on duty and its corresponding dispatching personnel theory quantity, the transport power forecast model is adjusted Initial coefficients are until the dispatching personnel theory quantity and the dispatching personnel quantity on duty in error allowed band, obtain mould Type coefficient.
- 5. according to the method for claim 1, it is characterised in that the historical statistical data includes matching somebody with somebody in each dispatching cycle The historical values of the person's of making a gift to someone quantity on duty and at least one influence factor；The historical statistical data based on any coverage, determine the prediction numerical value bag of at least one influence factor Include：Based on the historical statistical data of any coverage, by any influence in multiple dispatching cycles before treating dispatching cycle because Prediction numerical value of the mean values of the historical values of element as any influence factor.
- A kind of 6. conveyance equilibrium device, it is characterised in that including：Factor determining module, for determining at least one influence factor of influence dispatching personnel quantity on duty；Prediction module, for the historical statistical data based on any coverage, determine the pre- of at least one influence factor Survey numerical value；Computing module, for based on the prediction numerical value, the dispatching of any coverage to be predicted using transport power forecast model Personnel depaly quantity；Wherein, the transport power forecast model based on the dispatching personnel quantity on duty in the historical statistical data with And the historical values training of at least one influence factor obtains；Configuration module, for according to the dispatching personnel depaly quantity, configuring any coverage.
- 7. device according to claim 6, it is characterised in that also include：Model construction module, for based at least one influence factor, building transport power forecast model；Sample determining module, for from the historical statistical data, obtaining multiple dispatching personnel quantity on duty and each corresponding to At least one influence factor historical values, as training sample；Model training module, for the result using the multiple dispatching personnel quantity on duty as the transport power forecast model Data, training obtain the model coefficient of the transport power forecast model.
- 8. device according to claim 7, it is characterised in that the model construction module is specifically used at least one by described in The weighted sum formula of individual influence factor is as the transport power forecast model.
- 9. the device according to claim 7 or 8, it is characterised in that the model training module includes：First training unit, for the result using the multiple dispatching personnel quantity on duty as the transport power forecast model Data, training obtain the initial coefficients of the transport power forecast model；Theoretical value computing unit, for the historical values based at least one influence factor, mould is predicted using the transport power Type, which calculates, obtains dispatching personnel's theory quantity；Second training unit, for according to dispatching personnel quantity on duty and its corresponding dispatching personnel theory quantity, adjustment The initial coefficients of the transport power forecast model are until the dispatching personnel theory quantity is being missed with the dispatching personnel quantity on duty In poor allowed band, model coefficient is obtained.
- 10. device according to claim 6, it is characterised in that the historical statistical data includes each dispatching cycle pair The dispatching personnel quantity on duty and the historical values of at least one influence factor answered；The prediction module is specifically used for the historical statistical data based on any coverage, will be multiple before treating dispatching cycle Prediction numerical value of the mean values of the historical values of any influence factor as any influence factor in dispatching cycle.
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108564269A (en) * | 2018-04-09 | 2018-09-21 | 北京小度信息科技有限公司 | Dispense method for allocating tasks, device, electronic equipment and computer storage media |
| CN108564247A (en) * | 2018-03-02 | 2018-09-21 | 阿里巴巴集团控股有限公司 | Data adjustment method, device and server |
| CN108596399A (en) * | 2018-05-04 | 2018-09-28 | 国家***邮政业安全中心 | Method, apparatus, electronic equipment and the storage medium of express delivery amount prediction |
| CN108985526A (en) * | 2018-08-21 | 2018-12-11 | 安吉汽车物流股份有限公司 | Transport power prediction technique and device, computer readable storage medium, terminal |
| CN109636276A (en) * | 2018-11-29 | 2019-04-16 | 拉扎斯网络科技（上海）有限公司 | Assessment dispatching resource capability, Order splitting, dispatching resource regulating method and device |
| CN109886489A (en) * | 2019-02-21 | 2019-06-14 | 上海德启信息科技有限公司 | Configuration system and method applied to transfer resource |
| CN109961203A (en) * | 2017-12-26 | 2019-07-02 | 顺丰科技有限公司 | It is a kind of to receive dispatch officers personnel's method of adjustment and device, equipment, storage medium |
| CN110163707A (en) * | 2018-02-13 | 2019-08-23 | 北京嘀嘀无限科技发展有限公司 | Net about vehicle method for processing business, terminal device and server |
| CN110414875A (en) * | 2018-04-26 | 2019-11-05 | 北京京东振世信息技术有限公司 | Capacity data processing method, device, electronic equipment and computer-readable medium |
| CN111324829A (en) * | 2020-03-06 | 2020-06-23 | 拉扎斯网络科技（上海）有限公司 | Transportation capacity resource recommendation method and device, server and storage medium |
| CN111553530A (en) * | 2020-04-27 | 2020-08-18 | 华侨大学 | Inter-city network car booking and packing travel capacity prediction and travel recommendation method and system |
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105719008A (en) * | 2015-05-22 | 2016-06-29 | 北京小度信息科技有限公司 | Method and device for performing optimization on delivery system |
| CN105719111A (en) * | 2015-05-22 | 2016-06-29 | 北京小度信息科技有限公司 | Method and device for dynamically adjusting transport capacity |
-
2017
- 2017-06-29 CN CN201710517909.8A patent/CN107403296B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105719008A (en) * | 2015-05-22 | 2016-06-29 | 北京小度信息科技有限公司 | Method and device for performing optimization on delivery system |
| CN105719111A (en) * | 2015-05-22 | 2016-06-29 | 北京小度信息科技有限公司 | Method and device for dynamically adjusting transport capacity |
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| CN109961203A (en) * | 2017-12-26 | 2019-07-02 | 顺丰科技有限公司 | It is a kind of to receive dispatch officers personnel's method of adjustment and device, equipment, storage medium |
| CN110163707B (en) * | 2018-02-13 | 2020-08-25 | 北京嘀嘀无限科技发展有限公司 | Network car booking service processing method, terminal equipment and server |
| CN110163707A (en) * | 2018-02-13 | 2019-08-23 | 北京嘀嘀无限科技发展有限公司 | Net about vehicle method for processing business, terminal device and server |
| CN108564247A (en) * | 2018-03-02 | 2018-09-21 | 阿里巴巴集团控股有限公司 | Data adjustment method, device and server |
| CN108564269A (en) * | 2018-04-09 | 2018-09-21 | 北京小度信息科技有限公司 | Dispense method for allocating tasks, device, electronic equipment and computer storage media |
| CN110414875A (en) * | 2018-04-26 | 2019-11-05 | 北京京东振世信息技术有限公司 | Capacity data processing method, device, electronic equipment and computer-readable medium |
| CN110414875B (en) * | 2018-04-26 | 2022-09-06 | 北京京东振世信息技术有限公司 | Capacity data processing method and device, electronic equipment and computer readable medium |
| CN108596399A (en) * | 2018-05-04 | 2018-09-28 | 国家***邮政业安全中心 | Method, apparatus, electronic equipment and the storage medium of express delivery amount prediction |
| CN112262400A (en) * | 2018-07-02 | 2021-01-22 | 株式会社神户制钢所 | Talent training support system and storage medium |
| CN108985526A (en) * | 2018-08-21 | 2018-12-11 | 安吉汽车物流股份有限公司 | Transport power prediction technique and device, computer readable storage medium, terminal |
| CN109636276A (en) * | 2018-11-29 | 2019-04-16 | 拉扎斯网络科技（上海）有限公司 | Assessment dispatching resource capability, Order splitting, dispatching resource regulating method and device |
| CN109886489A (en) * | 2019-02-21 | 2019-06-14 | 上海德启信息科技有限公司 | Configuration system and method applied to transfer resource |
| CN111324829A (en) * | 2020-03-06 | 2020-06-23 | 拉扎斯网络科技（上海）有限公司 | Transportation capacity resource recommendation method and device, server and storage medium |
| CN111324829B (en) * | 2020-03-06 | 2023-08-25 | 拉扎斯网络科技（上海）有限公司 | Method, device, server and storage medium for recommending capacity resources |
| CN111553530A (en) * | 2020-04-27 | 2020-08-18 | 华侨大学 | Inter-city network car booking and packing travel capacity prediction and travel recommendation method and system |
| CN111553530B (en) * | 2020-04-27 | 2022-08-02 | 华侨大学 | Inter-city network car booking and packing travel capacity prediction and travel recommendation method and system |
| US20220004987A1 (en) * | 2020-07-03 | 2022-01-06 | Coupang Corp. | Electronic apparatus and operation method thereof |
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