CN107403296B - Transport capacity configuration method and device - Google Patents
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Abstract
The embodiment of the invention provides a transport capacity configuration method and device, and relates to the technical field of computer application. Determining at least one influence factor influencing the on-duty number of the distribution personnel; determining a predicted value of the at least one influencing factor based on historical statistical data of any service area; predicting the distribution personnel allocation quantity of any service area by utilizing a capacity prediction model based on the prediction value; the capacity prediction model is obtained by training based on the on-duty number of the distribution personnel in the historical statistical data and the historical numerical value of the at least one influence factor; and configuring any service area according to the configuration number of the distribution personnel. The technical scheme provided by the embodiment of the invention improves the accuracy of the transportation capacity configuration.
Description
Technical Field
The embodiment of the invention relates to the technical field of computer application, in particular to a region configuration method and device.
Background
The arrival of the electronic commerce era drives the rapid development of logistics service, the distribution orders are continuously increased, and the demand on distribution personnel is increasingly large.
In order to improve the delivery quality, the deliverable area is usually divided into a plurality of service areas, and the delivery personnel in each service area are responsible for delivering the delivery orders corresponding to the respective service area.
Therefore, reasonable capacity allocation needs to be performed for each service area, that is, allocation of a reasonable number of distribution personnel, and at present, capacity allocation is performed based on manual experience, which is not accurate enough, and thus capacity waste or capacity shortage can be caused.
Disclosure of Invention
The embodiment of the invention provides a transport capacity configuration method and a transport capacity configuration device, which are used for solving the technical problem of low transport capacity configuration accuracy in the prior art.
In a first aspect, an embodiment of the present invention provides a capacity configuration method, including:
determining at least one influence factor influencing the on-duty number of the distribution personnel;
determining a predicted value of the at least one influencing factor based on historical statistical data of any service area;
predicting the distribution personnel allocation quantity of any service area by utilizing a capacity prediction model based on the prediction value; the capacity prediction model is obtained by training based on the on-duty number of the distribution personnel in the historical statistical data and the historical numerical value of the at least one influence factor;
and configuring any service area according to the configuration number of the distribution personnel.
Optionally, the capacity prediction model is obtained by pre-training as follows:
constructing a transport capacity prediction model based on the at least one influence factor;
acquiring the on-duty quantity of a plurality of distribution personnel and the historical numerical value of the at least one influence factor corresponding to each distribution personnel from the historical statistical data to be used as a training sample;
and respectively taking the on-duty number of the plurality of the distribution personnel as result data of the transport capacity prediction model, and training to obtain a model coefficient of the transport capacity prediction model.
Optionally, the building a capacity prediction model based on the at least one influence factor includes:
and taking a weighted summation formula of the at least one influence factor as the capacity prediction model.
Optionally, the training of the model coefficients of the capacity prediction model using the on-duty number of the plurality of distribution staff as the result data of the capacity prediction model includes:
respectively taking the on-duty number of the plurality of the distribution personnel as result data of the transport capacity prediction model, and training to obtain an initial coefficient of the transport capacity prediction model;
calculating and obtaining the theoretical number of the distribution personnel by utilizing the transport capacity prediction model based on the historical numerical value of the at least one influence factor;
and adjusting the initial coefficient of the transport capacity prediction model according to the number of the on duty of the distribution personnel and the corresponding theoretical number of the distribution personnel until the theoretical number of the distribution personnel and the number of the distribution personnel on duty are within an error allowable range, and obtaining a model coefficient.
Optionally, the historical statistical data includes the number of on duty of the delivery personnel in each scheduling period and a historical value of the at least one influence factor;
the determining the predicted value of the at least one influencing factor based on the historical statistical data of any service area comprises:
based on the historical statistical data of any service area, taking the average value of the historical values of any influence factor in a plurality of scheduling periods before the period to be scheduled as the predicted value of any influence factor.
In a second aspect, an embodiment of the present invention provides a capacity allocation apparatus, including:
the factor determining module is used for determining at least one influence factor influencing the on-duty number of the distribution personnel;
a prediction module for determining a predicted value of the at least one influencing factor based on historical statistical data of any service area;
the calculation module is used for predicting the configuration quantity of the distribution personnel in any service area by utilizing a transport capacity prediction model based on the prediction value; the capacity prediction model is obtained by training based on the on-duty number of the distribution personnel in the historical statistical data and the historical numerical value of the at least one influence factor;
and the configuration module is used for configuring any service area according to the configuration quantity of the distribution personnel.
Optionally, the method further comprises:
the model construction module is used for constructing a transport capacity prediction model based on the at least one influence factor;
the sample determining module is used for acquiring the on-duty number of a plurality of distribution personnel and the historical numerical value of the at least one influence factor corresponding to each distribution personnel from the historical statistical data to serve as a training sample;
and the model training module is used for training the on-duty number of the plurality of the distribution personnel as result data of the transport capacity prediction model to obtain a model coefficient of the transport capacity prediction model.
Optionally, the model building module is specifically configured to use a weighted summation formula of the at least one influence factor as the capacity prediction model.
Optionally, the model training module comprises:
the first training unit is used for respectively taking the on-duty number of the plurality of the distribution personnel as result data of the transport capacity prediction model, and training to obtain an initial coefficient of the transport capacity prediction model;
the theoretical value calculating unit is used for calculating and obtaining the theoretical number of the distribution personnel by utilizing the transport capacity prediction model based on the historical numerical value of the at least one influence factor;
and the second training unit is used for adjusting the initial coefficient of the transport capacity prediction model according to the on-duty number of the distribution personnel and the corresponding theoretical number of the distribution personnel until the theoretical number of the distribution personnel and the on-duty number of the distribution personnel are within an error allowable range to obtain a model coefficient.
Optionally, the historical statistical data includes the number of on duty of the delivery personnel corresponding to each scheduling period and a historical numerical value of the at least one influence factor;
the prediction module is specifically configured to use an average value of historical values of any influence factor in a plurality of scheduling periods before a period to be scheduled as a prediction value of any influence factor based on historical statistical data of any service area.
In the embodiment of the invention, based on the historical numerical value of at least one influence factor influencing the on-duty number of the distribution personnel and the on-duty number of the distribution personnel, the transport capacity prediction model obtained by training and the historical statistical data of any service area, the prediction numerical value of any service area corresponding to the at least one influence factor is determined, and the distribution personnel configuration number can be calculated and obtained, so that the transport capacity configuration can be carried out on any service area according to the distribution personnel configuration number.
These and other aspects of the invention are apparent from and will be elucidated with reference to the embodiments described hereinafter.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a flow chart illustrating one embodiment of a capacity allocation method provided by the present invention;
FIG. 2 is a flow chart illustrating a capacity configuration method according to yet another embodiment of the present invention;
fig. 3 is a schematic structural diagram illustrating an embodiment of a capacity allocation apparatus provided in the present invention;
fig. 4 is a schematic structural diagram illustrating a further embodiment of a capacity allocation apparatus provided by the present invention;
fig. 5 shows a schematic structural diagram of an embodiment of an electronic device provided by the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention.
In some of the flows described in the present specification and claims and in the above figures, a number of operations are included that occur in a particular order, but it should be clearly understood that these operations may be performed out of order or in parallel as they occur herein, with the order of the operations being indicated as 101, 102, etc. merely to distinguish between the various operations, and the order of the operations by themselves does not represent any order of performance. Additionally, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first", "second", etc. in this document are used for distinguishing different messages, devices, modules, etc., and do not represent a sequential order, nor limit the types of "first" and "second" to be different.
The technical solution of the present invention is mainly applied to a logistics distribution scenario, as described in the background art, at present, distribution personnel in a service area are responsible for distributing distribution orders corresponding to respective service areas through division of the service areas. For example, in an online transaction scenario implemented based on O2O, a distributor is responsible for taking and distributing items from a merchant under the service area to which the distributor belongs to users in the service area to which the distributor belongs. The reasonable capacity allocation in the service area becomes a key factor affecting the quality of the distribution.
At present, the number of distribution personnel to be configured in each service area is determined based on manual experience, which is not accurate enough, and still causes the problem of transport tension or transport waste in one service area.
In the embodiment of the invention, a transport capacity prediction model can be trained and obtained according to the historical numerical value of at least one influence factor influencing the on-duty number of distribution personnel and the on-duty number of the distribution personnel, and the prediction numerical value of the at least one influence factor corresponding to any service area can be determined based on the historical statistical data of the service area; the allocation quantity of the distribution personnel can be calculated and obtained based on the prediction numerical value and the transportation capacity prediction model, so that the transportation capacity allocation can be carried out on any service area according to the allocation quantity of the distribution personnel. Thus improving the accuracy of the capacity configuration.
The number of the on Shift distribution personnel in any service area can refer to the number of the distribution personnel on Shift actually, and after the capacity of any service area is configured, all the distribution personnel which are not necessarily configured can arrive on Shift, and can not arrive on Shift possibly due to more influence factors, so that the number of the distribution personnel on Shift and the incidence relation between the influence factors can be counted to obtain a capacity prediction model, and the capacity configuration of the service area can be accurately predicted by using the capacity prediction model.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of an embodiment of a capacity allocation method provided in the present invention, which may include the following steps:
101: at least one influencing factor influencing the number of on-Shift positions of the dispatching personnel is determined.
Wherein, each service area corresponds to the number of on duty of the delivery personnel in each historical scheduling period. The scheduling period may be, for example, one day, and in practical applications, it is necessary to configure the capacity of the service area every day.
The invention is a technical scheme for carrying out capacity configuration aiming at the current cycle to be scheduled of any service area.
The number of delivery personnel on Shift may refer to delivery personnel who are on Shift for a time greater than 0. May be determined based on the time of the dispatch personnel's log-in to the dispatch system.
Optionally, the at least one influencing factor may include a total number of completed orders per scheduling period, a per-person number of completed orders, an average delivery duration, an average delivery on-time rate, and/or a total number of completed orders for a plurality of scheduling periods.
Each service area is configured with a plurality of delivery personnel in each scheduling period, and the total finished order quantity can refer to the quantity of delivery orders which are delivered and finished by all on duty delivery personnel; the per-person completion single quantity means the average completed delivery order quantity of each on Shift delivery person; the average distribution time length can be obtained by calculation according to the distribution time length of each distributor; the average delivery on-time rate may be calculated based on the delivery on-time rates of the respective delivery persons.
The plurality of scheduling periods may refer to a plurality of scheduling periods adjacent to each other before the to-be-scheduled period.
102: determining a predicted value of the at least one influencing factor based on historical statistics for any of the serving areas.
103: and predicting the distribution personnel allocation quantity of any service area by utilizing a capacity prediction model based on the prediction value.
And the capacity prediction model is obtained by training based on the on duty number of the distribution personnel in the historical statistical data of any service area and the historical numerical value of the at least one influence factor.
The capacity prediction model is obtained by training based on the number of the on duty of the dispatching personnel and the historical value of the at least one influence factor.
To determine the configured number of dispatchers for any service area, a predicted value for the at least one influencing factor may be determined based on historical statistics for that service area.
Optionally, the predicted value of each influencing factor may be a historical value of each influencing factor in any scheduling period before the period to be scheduled of any service area;
of course, the predicted value of each influencing factor may also be an average value of historical values of each influencing factor in a plurality of scheduling periods before the period to be scheduled.
And inputting the prediction value into the transport capacity prediction model, wherein the obtained result data is the allocation quantity of the distribution personnel in any service area.
104: and configuring any service area according to the configuration number of the distribution personnel.
That is, in the period to be scheduled, the corresponding number of the distribution personnel is configured for any service area.
In this embodiment, the allocation number of the distribution personnel in any service area is calculated by training the acquired transportation capacity prediction model according to the on-duty number of the distribution personnel in the historical statistical data and the influence factors influencing the on-duty number of the distribution personnel, so that the allocation number of the distribution personnel is more reasonable and accurate, and the accuracy of the allocation of the transportation capacity is improved.
Fig. 2 is a flowchart of another embodiment of a capacity allocation method provided in the present invention, which may include the following steps:
201: at least one influencing factor influencing the number of on-Shift positions of the dispatching personnel is determined.
202: and constructing a transport capacity prediction model based on the at least one influence factor.
Optionally, a weighted sum formula of the at least one influence factor may be used as the capacity prediction model.
For example, the capacity prediction model may be expressed as:
θ0χ0+θ1χ1+θ2χ2+...+βnχn
wherein, thetaiRepresenting the i-th model coefficient, χiIndicating the ith influencing factor. Where i ∈ (0, n).
203: and acquiring the on-duty quantity of a plurality of distribution personnel and the historical numerical value of the at least one influence factor corresponding to each distribution personnel from the historical statistical data of any service area, and taking the obtained historical numerical value as a training sample.
The on Shift quantity of the plurality of delivery personnel may refer to the on Shift quantity of the delivery personnel corresponding to the plurality of scheduling periods, and each scheduling period corresponds to the on Shift quantity of the delivery personnel and a historical value of at least one influence factor.
204: and respectively taking the on-duty number of the plurality of the distribution personnel as result data of the transport capacity prediction model, and training to obtain a model coefficient of the transport capacity prediction model.
The on-duty number of the distribution personnel and at least one influence factor can have the following relationship:
y=θ0χ0+β1χ11+θ2χ2+...+θnχn；
where y represents the number of dispatchers on Shift in each scheduling period.
The on-duty quantity of each distribution worker and the historical numerical value of at least one corresponding influence factor are used as a group of data; and solving to obtain model coefficients according to the multiple groups of data.
Optionally, in order to improve the calculation efficiency of the solution process and reduce the calculation difficulty, according to the relationship between the on-duty number of the distribution personnel and at least one influence factor, the following square error formula may be obtained:
wherein, χjHistorical values, y, representing influencing factors of group jjIndicating the number of on Shift dispatchers for the jth group.
By derivation of theta
θ＝(XTX)-1XTY。
Wherein Y represents a matrix formed by the on-duty number of the delivery personnel in each scheduling period, and X represents a matrix formed by historical numerical values of the influence factors. Theta is the model coefficient (theta)0 θ1 … θn-1 θn) Since Y and X are both known values, the model coefficients can be calculated.
It should be noted that the operations in steps 202 to 204 may be performed in advance, and are not limited to the execution order in this embodiment.
205: determining a predicted value of the at least one influencing factor based on the historical statistical data.
206: and predicting the distribution personnel allocation quantity of any service area by utilizing a capacity prediction model based on the prediction value.
207: and configuring any service area according to the configuration number of the distribution personnel.
According to the technical scheme of the embodiment, the allocation quantity of the distribution personnel in any service area is calculated by utilizing the on-duty quantity of the distribution personnel in the historical statistical data and the influence factors influencing the on-duty quantity of the distribution personnel, the transport capacity prediction model obtained by training is utilized, so that the allocation quantity of the distribution personnel obtained by calculation is more reasonable and accurate, the accuracy of transport capacity allocation is improved, and transport capacity shortage or transport capacity waste in the service area cannot be caused.
The configuration number of the distribution personnel can be calculated by using the transport capacity prediction model when the transport capacity prediction model is trained, the theoretical number of the distribution personnel can be calculated and obtained through the transport capacity prediction model for each historical scheduling period, errors may exist between the theoretical number of the distribution personnel and the number of the distribution personnel on duty, and in order to further improve the model training accuracy, the model coefficient of the transport capacity prediction model can be adjusted based on the errors so as to reduce the errors between the theoretical number of the distribution personnel and the number of the distribution personnel on duty, so that the transport capacity prediction model obtained through adjustment can be more accurate.
Therefore, in some embodiments, the training of the model coefficients for obtaining the capacity prediction model by using the on-duty number of the plurality of distribution personnel as the result data of the capacity prediction model may include:
respectively taking the on-duty number of the plurality of the distribution personnel as result data of the transport capacity prediction model, and training to obtain an initial coefficient of the transport capacity prediction model;
calculating and obtaining the theoretical number of the distribution personnel by utilizing the transport capacity prediction model based on the historical numerical value of the at least one influence factor;
and adjusting the initial coefficient of the transport capacity prediction model according to the number of the on duty of the distribution personnel and the corresponding theoretical number of the distribution personnel until the theoretical number of the distribution personnel and the number of the distribution personnel on duty are within an error allowable range, and obtaining a model coefficient.
And calculating the theoretical number of the distribution personnel based on a transport capacity prediction model corresponding to the initial coefficient.
As an alternative, in order to improve the calculation accuracy, as known from the foregoing, the capacity prediction model may be a linear equation, and thus the initial coefficients of the capacity prediction model may be adjusted by means of local weighted linear regression.
Specifically, the method comprises the following steps:
the above-described mean square error calculation formula
I.e. may be used to represent the error between the theoretical number of dispatchers and the number of dispatchers on duty.
However, since the traffic prediction model is a linear equation and cannot be estimated well for some deviation points, the model coefficient needs to be adjusted to reduce the error of the mean square error. Introducing weighting coefficients w for different analog coefficientsj. The error between the theoretical number of dispatchers and the number of dispatchers on duty can be expressed as:
when w isjThe larger the size of the tube is,
the greater the specific gravity, wjThe smaller the size of the tube is,
the resulting effect is negligible.
Thus, based on empirical formulas, w may be selectedjThe form of (A) is as follows:
wherein χ is an influencing factor χjThe parameter τ controls the variation of the weighting coefficients such that χjThe closer to χ, the larger the weighting factor, and the farther away from χ, the smaller the weighting factor.
Then the model coefficient solving formula can be concrete:
θ＝(XTWX)-1XTWY；
w represents a weight coefficient matrix of model coefficients.
And a certain weight is given to each model coefficient by a local weighted linear regression method, and the weight change is adjusted by the parameter tau, so that the on-duty number of distribution personnel and the mean square error of theoretical data of the distribution personnel are reduced, and the accuracy of the obtained transport capacity prediction model is improved.
Fig. 3 is a schematic structural diagram of an embodiment of a capacity allocation apparatus provided in the present invention, where the apparatus may include:
a factor determining module 301, configured to determine at least one influencing factor influencing the number of on-duty dispatchers.
Wherein, each service area corresponds to the number of on duty of the delivery personnel in each historical scheduling period.
Optionally, the at least one influencing factor may include a total number of completed orders per scheduling period, a per-person number of completed orders, an average delivery duration, an average delivery on-time rate, and/or a total number of completed orders for a plurality of scheduling periods.
A prediction module 302 configured to determine a predicted value of the at least one influencing factor based on historical statistical data of any service area.
The historical statistical data can comprise the on-duty number of the delivery personnel corresponding to each scheduling period and the historical numerical value of the at least one influence factor;
therefore, as an optional manner, the prediction module may be specifically configured to use a historical value of any influencing factor in any scheduling period before the period to be scheduled as the predicted value of any influencing factor based on historical statistical data of any service area.
As another alternative, the prediction module may be specifically configured to use, based on historical statistical data of any service area, an average value of historical values of any influencing factor in a plurality of scheduling periods before a period to be scheduled as a predicted value of the any influencing factor.
And the calculating module 303 is configured to predict the configured number of the distribution staff in any service area by using a capacity prediction model based on the predicted value.
Wherein the capacity prediction model is obtained based on the historical statistical data of the on duty number of the distribution personnel and the historical numerical training of the at least one influence factor.
A configuration module 304, configured to configure any of the service areas according to the configured number of the distribution staff. That is, in the period to be scheduled, the corresponding number of the distribution personnel is configured for any service area.
In this embodiment, the allocation number of the distribution personnel in any service area is calculated by training the acquired transportation capacity prediction model according to the on-duty number of the distribution personnel in the historical statistical data and the influence factors influencing the on-duty number of the distribution personnel, so that the allocation number of the distribution personnel is more reasonable and accurate, and the accuracy of the allocation of the transportation capacity is improved.
As another embodiment, as shown in fig. 4, the difference from fig. 3 is that the apparatus may further include:
a model building module 401, configured to build a transportation capability prediction model based on the at least one influence factor.
Optionally, the model building module is specifically configured to use a weighted summation formula of the at least one influence factor as the capacity prediction model.
A sample determining module 402, configured to obtain, from the historical statistical data, the on-duty number of the multiple distribution staff and the historical value of the at least one influence factor corresponding to each distribution staff, as a training sample.
The on Shift quantity of the plurality of delivery personnel may refer to the on Shift quantity of the delivery personnel corresponding to the plurality of scheduling periods, and each scheduling period corresponds to the on Shift quantity of the delivery personnel and a historical value of at least one influence factor.
And a model training module 403, configured to train the on-duty number of the multiple distribution staff as result data of the transportation capability prediction model, to obtain a model coefficient of the transportation capability prediction model.
According to the technical scheme of the embodiment, the allocation quantity of the distribution personnel in any service area is calculated by utilizing the on-duty quantity of the distribution personnel in the historical statistical data and the influence factors influencing the on-duty quantity of the distribution personnel, the transport capacity prediction model obtained by training is utilized, so that the allocation quantity of the distribution personnel obtained by calculation is more reasonable and accurate, the accuracy of transport capacity allocation is improved, and transport capacity shortage or transport capacity waste in the service area cannot be caused.
The configuration number of the distribution personnel can be calculated by using the transport capacity prediction model when the transport capacity prediction model is trained, the theoretical number of the distribution personnel can be calculated and obtained through the transport capacity prediction model for each historical scheduling period, errors may exist between the theoretical number of the distribution personnel and the number of the distribution personnel on duty, and in order to further improve the model training accuracy, the model coefficient of the transport capacity prediction model can be adjusted based on the errors so as to reduce the errors between the theoretical number of the distribution personnel and the number of the distribution personnel on duty, so that the transport capacity prediction model obtained through adjustment can be more accurate.
Thus, in some embodiments, the model training module may include:
and the first training unit is used for training to obtain an initial coefficient of the transport capacity prediction model by respectively taking the on-duty number of the plurality of the distribution personnel as result data of the transport capacity prediction model.
And the theoretical value calculating unit is used for calculating and obtaining the theoretical number of the distribution personnel by utilizing the transport capacity prediction model based on the historical numerical value of the at least one influence factor.
And calculating the theoretical number of the distribution personnel based on a transport capacity prediction model corresponding to the initial coefficient.
And the second training unit is used for adjusting the initial coefficient of the transport capacity prediction model according to the on-duty number of the distribution personnel and the corresponding theoretical number of the distribution personnel until the theoretical number of the distribution personnel and the on-duty number of the distribution personnel are within an error allowable range to obtain a model coefficient.
Optionally, a mean square error between the theoretical number of the delivery personnel and the number of the delivery personnel on duty can be used for representing an error between the theoretical number of the delivery personnel and the number of the delivery personnel on duty. And adjusting initial coefficients of the capacity prediction model by adopting a local weighted linear regression mode.
The model coefficient of the transport capacity prediction model is adjusted through the embodiment, so that the transport capacity prediction model obtained by training is more accurate, the accuracy of transport capacity configuration can be further improved, transport capacity waste or transport capacity tension cannot be caused, and efficient and time-saving distribution is realized.
In one possible design, the capacity configuration apparatus of the embodiment shown in fig. 3 or fig. 4 may be implemented as an electronic device, as shown in fig. 5, which may include one or more processors 501 and one or more memories 502;
the one or more memories 502 store one or more computer instructions that the one or more processors 501 invoke for execution;
the one or more processors 501 are configured to:
determining at least one influence factor influencing the on-duty number of the distribution personnel;
determining a predicted value of the at least one influencing factor based on historical statistical data of any service area;
predicting the distribution personnel allocation quantity of any service area by utilizing a capacity prediction model based on the prediction value; the capacity prediction model is obtained by training based on the on-duty number of the distribution personnel in the historical statistical data and the historical numerical value of the at least one influence factor;
and configuring any service area according to the configuration number of the distribution personnel.
The one or more processors may be further configured to perform the method for capacity allocation described in any of the above embodiments.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program;
the computer program enables a computer to implement the capacity allocation method according to any one of the above embodiments when executed.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
The invention discloses an A1 and a transport capacity configuration method, which comprises the following steps:
determining at least one influence factor influencing the on-duty number of the distribution personnel;
determining a predicted value of the at least one influencing factor based on historical statistical data of any service area;
predicting the distribution personnel allocation quantity of any service area by utilizing a capacity prediction model based on the prediction value; the capacity prediction model is obtained by training based on the on-duty number of the distribution personnel in the historical statistical data and the historical numerical value of the at least one influence factor;
and configuring any service area according to the configuration number of the distribution personnel.
A2, according to the method A1, the capacity prediction model is obtained by pre-training as follows:
constructing a transport capacity prediction model based on the at least one influence factor;
acquiring the on-duty quantity of a plurality of distribution personnel and the historical numerical value of the at least one influence factor corresponding to each distribution personnel from the historical statistical data to be used as a training sample;
and respectively taking the on-duty number of the plurality of the distribution personnel as result data of the transport capacity prediction model, and training to obtain a model coefficient of the transport capacity prediction model.
A3, the method of A1, wherein the building a capacity prediction model based on the at least one influencing factor comprises:
and taking a weighted summation formula of the at least one influence factor as the capacity prediction model.
A4, according to the method of A2 or A3, the training the model coefficients of the capacity prediction model by using the on duty number of the plurality of distribution personnel as the result data of the capacity prediction model comprises:
respectively taking the on-duty number of the plurality of the distribution personnel as result data of the transport capacity prediction model, and training to obtain an initial coefficient of the transport capacity prediction model;
calculating and obtaining the theoretical number of the distribution personnel by utilizing the transport capacity prediction model based on the historical numerical value of the at least one influence factor;
and adjusting the initial coefficient of the transport capacity prediction model according to the number of the on duty of the distribution personnel and the corresponding theoretical number of the distribution personnel until the theoretical number of the distribution personnel and the number of the distribution personnel on duty are within an error allowable range, and obtaining a model coefficient.
A5, according to the method of A1, the historical statistical data includes the on duty number of the delivery personnel and the historical value of the at least one influence factor in each scheduling period;
the determining the predicted value of the at least one influencing factor based on the historical statistical data of any service area comprises:
based on the historical statistical data of any service area, taking the average value of the historical values of any influence factor in a plurality of scheduling periods before the period to be scheduled as the predicted value of any influence factor.
A6, according to the method in A1, the historical statistical data includes the on duty number of the corresponding delivery personnel in each scheduling period and the historical value of the at least one influence factor;
the determining the predicted value of the at least one influencing factor based on the historical statistical data of any service area comprises:
and based on the historical statistical data of any service area, taking the historical numerical value of any influencing factor in any scheduling period before the period to be scheduled as the predicted numerical value of any influencing factor.
A7, the method of A1, the at least one influencing factor comprising total number of completed units per scheduling period, per-capita number of completed units, average delivery duration, average delivery on-time rate, and/or total number of completed units for a plurality of scheduling periods.
B8, a capacity allocation device, comprising:
the factor determining module is used for determining at least one influence factor influencing the on-duty number of the distribution personnel;
a prediction module for determining a predicted value of the at least one influencing factor based on historical statistical data of any service area;
the calculation module is used for predicting the configuration quantity of the distribution personnel in any service area by utilizing a transport capacity prediction model based on the prediction value; the capacity prediction model is obtained by training based on the on-duty number of the distribution personnel in the historical statistical data and the historical numerical value of the at least one influence factor;
and the configuration module is used for configuring any service area according to the configuration quantity of the distribution personnel.
B9, the apparatus according to B8, further comprising:
the model construction module is used for constructing a transport capacity prediction model based on the at least one influence factor;
the sample determining module is used for acquiring the on-duty number of a plurality of distribution personnel and the historical numerical value of the at least one influence factor corresponding to each distribution personnel from the historical statistical data to serve as a training sample;
and the model training module is used for training the on-duty number of the plurality of the distribution personnel as result data of the transport capacity prediction model to obtain a model coefficient of the transport capacity prediction model.
B10, the apparatus according to B9, the model construction module being specifically configured to use a weighted sum formula of the at least one influencing factor as the capacity prediction model.
B11, the apparatus of B9 or B10, the model training module comprising:
the first training unit is used for respectively taking the on-duty number of the plurality of the distribution personnel as result data of the transport capacity prediction model, and training to obtain an initial coefficient of the transport capacity prediction model;
the theoretical value calculating unit is used for calculating and obtaining the theoretical number of the distribution personnel by utilizing the transport capacity prediction model based on the historical numerical value of the at least one influence factor;
and the second training unit is used for adjusting the initial coefficient of the transport capacity prediction model according to the on-duty number of the distribution personnel and the corresponding theoretical number of the distribution personnel until the theoretical number of the distribution personnel and the on-duty number of the distribution personnel are within an error allowable range to obtain a model coefficient.
B12, according to the device in B8, the historical statistical data comprise the on duty number of the corresponding delivery personnel in each scheduling period and the historical value of the at least one influence factor;
the prediction module is specifically configured to use an average value of historical values of any influence factor in a plurality of scheduling periods before a period to be scheduled as a prediction value of any influence factor based on historical statistical data of any service area.
B13, according to the device in B8, the historical statistical data comprise the on duty number of the corresponding delivery personnel in each scheduling period and the historical value of the at least one influence factor;
the prediction module is specifically configured to use a historical numerical value of any influence factor in any scheduling period before a period to be scheduled as a prediction numerical value of any influence factor based on historical statistical data of any service area.
B14, the apparatus of B8, the at least one influencing factor comprising a total number of completed orders per scheduling period, a per-capita number of completed orders, an average delivery duration, an average delivery on-time rate, and/or a total number of completed orders for a plurality of scheduling periods.
C15, an electronic device comprising one or more processors and one or more memories;
the one or more memories store one or more computer instructions; the one or more computer instructions for execution by the one or more processor calls;
the one or more processors are to:
determining at least one influence factor influencing the on-duty number of the distribution personnel;
determining a predicted value of the at least one influencing factor based on historical statistical data of any service area;
predicting the distribution personnel allocation quantity of any service area by utilizing a capacity prediction model based on the prediction value; the capacity prediction model is obtained by training based on the on-duty number of the distribution personnel in the historical statistical data and the historical numerical value of the at least one influence factor;
and configuring any service area according to the configuration number of the distribution personnel.
C16, a computer readable storage medium storing a computer program;
the computer program causes a computer to implement a capacity allocation method as claimed in any one of claims 1 to 7 when executed.
Claims (12)
1. A capacity allocation method, comprising:
determining at least one influence factor influencing the on-duty number of the distribution personnel;
based on historical statistical data of any service area, taking an average value of historical values of any influence factor in a plurality of scheduling periods before a period to be scheduled as a prediction value of the any influence factor; the historical statistical data comprises the on-duty number of the delivery personnel in each scheduling period and the historical numerical value of the at least one influence factor;
predicting the distribution personnel allocation quantity of any service area by utilizing a capacity prediction model based on the prediction value; the capacity prediction model is obtained by training based on the on-duty number of the distribution personnel in the historical statistical data and the historical numerical value of the at least one influence factor;
and configuring any service area according to the configuration number of the distribution personnel.
2. The method of claim 1, wherein the capacity prediction model is pre-trained as follows:
constructing a transport capacity prediction model based on the at least one influence factor;
acquiring the on-duty quantity of a plurality of distribution personnel and the historical numerical value of the at least one influence factor corresponding to each distribution personnel from the historical statistical data to be used as a training sample;
and respectively taking the on-duty number of the plurality of the distribution personnel as result data of the transport capacity prediction model, and training to obtain a model coefficient of the transport capacity prediction model.
3. The method of claim 2, wherein constructing a capacity prediction model based on the at least one influence factor comprises:
and taking a weighted summation formula of the at least one influence factor as the capacity prediction model.
4. The method of claim 2 or 3, wherein the using the on-duty number of the plurality of distribution personnel as the result data of the capacity prediction model, and the training of the model coefficients for obtaining the capacity prediction model comprises:
respectively taking the on-duty number of the plurality of the distribution personnel as result data of the transport capacity prediction model, and training to obtain an initial coefficient of the transport capacity prediction model;
calculating and obtaining the theoretical number of the distribution personnel by utilizing the transport capacity prediction model based on the historical numerical value of the at least one influence factor;
and adjusting the initial coefficient of the transport capacity prediction model according to the number of the on duty of the distribution personnel and the corresponding theoretical number of the distribution personnel until the theoretical number of the distribution personnel and the number of the distribution personnel on duty are within an error allowable range, and obtaining a model coefficient.
5. The method of claim 1, the at least one influencing factor comprising a total number of completed orders per scheduling period, a per-person number of completed orders, an average delivery duration, an average delivery on-time rate, and/or a total number of completed orders for a plurality of scheduling periods.
6. A capacity allocation device, comprising:
the factor determining module is used for determining at least one influence factor influencing the on-duty number of the distribution personnel;
the prediction module is used for taking the average value of the historical values of any influence factor in a plurality of scheduling periods before a period to be scheduled as the prediction value of any influence factor based on the historical statistical data of any service area; the historical statistical data comprises the on-duty number of the delivery personnel corresponding to each scheduling period and the historical numerical value of the at least one influence factor;
the calculation module is used for predicting the configuration quantity of the distribution personnel in any service area by utilizing a transport capacity prediction model based on the prediction value; the capacity prediction model is obtained by training based on the on-duty number of the distribution personnel in the historical statistical data and the historical numerical value of the at least one influence factor;
and the configuration module is used for configuring any service area according to the configuration quantity of the distribution personnel.
7. The apparatus of claim 6, further comprising:
the model construction module is used for constructing a transport capacity prediction model based on the at least one influence factor;
the sample determining module is used for acquiring the on-duty number of a plurality of distribution personnel and the historical numerical value of the at least one influence factor corresponding to each distribution personnel from the historical statistical data to serve as a training sample;
and the model training module is used for training the on-duty number of the plurality of the distribution personnel as result data of the transport capacity prediction model to obtain a model coefficient of the transport capacity prediction model.
8. The apparatus of claim 7, wherein the model building module is specifically configured to use a weighted sum formula of the at least one influencing factor as the capacity prediction model.
9. The apparatus of claim 8 or 7, wherein the model training module comprises:
the first training unit is used for respectively taking the on-duty number of the plurality of the distribution personnel as result data of the transport capacity prediction model, and training to obtain an initial coefficient of the transport capacity prediction model;
the theoretical value calculating unit is used for calculating and obtaining the theoretical number of the distribution personnel by utilizing the transport capacity prediction model based on the historical numerical value of the at least one influence factor;
and the second training unit is used for adjusting the initial coefficient of the transport capacity prediction model according to the on-duty number of the distribution personnel and the corresponding theoretical number of the distribution personnel until the theoretical number of the distribution personnel and the on-duty number of the distribution personnel are within an error allowable range to obtain a model coefficient.
10. The apparatus of claim 6, the at least one influencing factor comprising a total number of completed orders per scheduling period, a per-person number of completed orders, an average delivery duration, an average delivery on-time rate, and/or a total number of completed orders for a plurality of scheduling periods.
11. An electronic device comprising one or more processors and one or more memories;
the one or more memories store one or more computer instructions; the one or more computer instructions for execution by the one or more processor calls;
the one or more processors are to:
determining at least one influence factor influencing the on-duty number of the distribution personnel;
based on historical statistical data of any service area, taking an average value of historical values of any influence factor in a plurality of scheduling periods before a period to be scheduled as a prediction value of the any influence factor; the historical statistical data comprises the on-duty number of the delivery personnel in each scheduling period and the historical numerical value of the at least one influence factor;
predicting the distribution personnel allocation quantity of any service area by utilizing a capacity prediction model based on the prediction value; the capacity prediction model is obtained by training based on the on-duty number of the distribution personnel in the historical statistical data and the historical numerical value of the at least one influence factor;
and configuring any service area according to the configuration number of the distribution personnel.
12. A computer-readable storage medium storing a computer program;
the computer program causes a computer to implement the capacity allocation method according to any one of claims 1 to 5 when executed.
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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 |
| 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 |
| CN108564269B (en) * | 2018-04-09 | 2021-07-30 | 北京星选科技有限公司 | Distribution task allocation method and device, electronic equipment and computer storage 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 | 国家邮政局邮政业安全中心 | Method, apparatus, electronic equipment and the storage medium of express delivery amount prediction |
| JP7126881B2 (en) * | 2018-07-02 | 2022-08-29 | 株式会社神戸製鋼所 | Human resource development support system and program |
| CN108985526B (en) * | 2018-08-21 | 2021-09-14 | 安吉汽车物流股份有限公司 | Transportation capacity prediction method and device, computer readable storage medium and terminal |
| CN109636276A (en) * | 2018-11-29 | 2019-04-16 | 拉扎斯网络科技（上海）有限公司 | Method and device for evaluating delivery resource capacity, allocating orders and scheduling delivery resources |
| 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 |
| 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 |
| 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 |
| KR102226115B1 (en) * | 2020-07-03 | 2021-03-11 | 쿠팡 주식회사 | Electronic apparatus and operation method thereof |
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