CN116362637A - Method and device for predicting dispatch time consumption, server and readable storage medium - Google Patents
Method and device for predicting dispatch time consumption, server and readable storage medium Download PDFInfo
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Abstract
The application provides a dispatch time consumption prediction method, a dispatch time consumption prediction device, a server and a readable storage medium, wherein the dispatch time consumption prediction method comprises the following steps: acquiring daily historical dispatch time consumption of the dispatch capacity in a target working area in a preset period; acquiring daily historical dispatch quantity of a target working area and area information of the target working area in a preset period; determining the time consumption of the daily ticket uniform dispatch of the capacity based on the daily historical dispatch time of the capacity, the daily historical dispatch amount of the target working area and the area information; based on the time spent for dispatch of the day ticket, the total time spent for dispatch of the day of the capacity in the target work area is predicted. According to the method and the device, a plurality of factors influencing the time consumption of dispatch are integrated, the calculation of the time consumption of refined dispatch is realized, and the accuracy of the total time consumption of dispatch in the future of transport capacity is improved.
Description
Technical Field
The application relates to the technical field of logistics, in particular to a dispatch time consumption prediction method, a dispatch time consumption prediction device, a server and a readable storage medium.
Background
With the rapid development of the logistics and express industry, the business is rapidly expanded, and the capacity efficiency is required to be evaluated in more and more scenes, and the most central evaluation of the efficiency is the time-consuming calculation of daily delivery operation of the capacity, namely delivery time. Dispatch time consuming may be applied as an input to an auxiliary decision or algorithm model in many scenarios, for example: dispatch path planning, capacity busy state prediction, dispatch difficulty assessment, and the like. The accurate calculation of historical dispatch time consumption can be used as a data basis for analysis of the capacity efficiency, and the prediction of daily dispatch time consumption can further intelligently plan the capacity work, and the calculation of the historical dispatch time consumption has great business value.
However, the calculation of the time consuming for the conventional dispatch is relatively rough, and the time for the dispatch to be completed by the capacity and the time for the delivery to be completed by the capacity are usually taken to be poor, and the difference is taken as the time consuming for the dispatch of the waybill. Such time-consuming computing schemes may incorporate the time of many non-dispatch related operations, resulting in inaccuracy of the time-consuming computation; moreover, the existing dispatch predictions are predictions of the waybill dimension, for example: the capacity estimates the dispatch volume today, but the same quantity of the freight bill is dispatched in different areas or different capacity dispatch, time consumption may have a certain difference, so that it is not accurate enough to evaluate the future task volume or efficiency only from the freight bill.
Therefore, how to realize the calculation of the refined dispatching time consumption and improve the accuracy of the total dispatching time consumption in the future of the transportation capacity is a technical problem to be solved in the current logistics technical field.
Disclosure of Invention
The application provides a dispatching time consumption prediction method, a dispatching time consumption prediction device, a server and a readable storage medium, and aims to solve the technical problem of how to achieve calculation of refined dispatching time consumption and improve accuracy of total dispatching time consumption in the future of transport capacity.
In one aspect, the present application provides a method for predicting dispatch time consumption, where the method includes:
Acquiring daily historical dispatch time consumption of the dispatch capacity in a target working area in a preset period;
acquiring daily historical dispatch quantity of a target working area and area information of the target working area in the preset period;
determining the time consumption of the current day ticket dispatch of the capacity based on the daily historical dispatch time consumption of the capacity, the daily historical dispatch amount of the target working area and the area information;
and predicting the total time consumption of dispatching the dispatch of the dispatch on the current date of the capacity in the target working area based on the time consumption of dispatching the dispatch on the current date ticket.
In one possible implementation manner of the present application, the obtaining the daily history of the delivery time of the delivery capacity in the target working area within the preset period includes:
acquiring a first operation code set corresponding to daily dispatch of the dispatch capacity in a target working area in a preset period;
deleting the non-dispatch operation codes in the first operation code set to obtain a second operation code set;
and determining daily historical dispatch time consumption of the capacity of dispatching the dispatch in the target working area in a preset period based on the second operation code set.
In one possible implementation manner of the present application, the determining, based on the second opcode set, a daily historical dispatch time for performing a dispatch in a target working area within a preset period includes:
Acquiring the total operation time consumption corresponding to the second operation code set;
and determining daily historical dispatch time consumption of the capacity of dispatching the dispatch in the target working area in a preset period based on the total operation time consumption.
In one possible implementation manner of the present application, the determining the time consuming for sending the current day ticket of the capacity based on the time consuming for sending the daily history of the capacity and the information of the daily history of the capacity and the area, includes:
performing feature preprocessing on the daily historical dispatch time of the capacity, the daily historical dispatch amount and the regional area information to obtain model input features;
and inputting the model input characteristics into a pre-trained dispatch time consumption prediction model, and generating the current day ticket dispatch time consumption of the capacity.
In one possible implementation manner of the present application, the predicting the total time-consuming for dispatching the dispatch in the target working area on the current day based on the current day ticket includes:
acquiring the estimated current day of the target working area;
and predicting the total time consumption of dispatching the dispatch of the dispatch capacity on the current day in the target working area according to the time consumption of dispatching the dispatch capacity on the current day ticket and the estimated quantity of the dispatch capacity on the current day in the target working area.
In one possible implementation manner of the present application, the obtaining the estimated current day of the target working area includes:
acquiring daily history estimated part quantity of the target working area in a preset period;
and inputting the daily historical estimated part quantity into a pre-trained part quantity prediction model to obtain the daily estimated part quantity of the target working area.
In one possible implementation manner of the present application, after predicting the total time consumption of dispatching the dispatch for the capacity on the day in the target working area based on the time ticket dispatching time, the method further includes:
determining an abnormal condition of the total time consumption of the dispatch in the target working area on the basis of the total time consumption of the dispatch in the target working area on the current date and a preset total time consumption threshold interval;
if the total consumption time of the delivery is higher than the maximum value of the preset daily total consumption time threshold interval, early warning is carried out on the overtime condition of the transportation;
and if the total consumption of the delivery is lower than the minimum value of the threshold interval of the preset total consumption of the day, carrying out early warning on the condition that the capacity is frequently insufficient.
In another aspect, the present application provides a dispatch time-consuming prediction apparatus, the apparatus including:
the first acquisition unit is used for acquiring daily historical dispatch time consumption of the dispatch capacity in the target working area in a preset period;
the second acquisition unit is used for acquiring daily historical dispatch quantity of the target working area and area information of the target working area in the preset period;
the first determining unit is used for determining the time consumption of the current day ticket uniform dispatch of the capacity based on the daily historical dispatch time consumption of the capacity and the daily historical dispatch amount and area information of the target working area;
the first prediction unit is used for predicting the total time consumption of dispatching the dispatch in the target working area on the current date of the capacity based on the time consumption of dispatching the dispatch in the current date ticket.
In one possible implementation manner of the present application, the first obtaining unit specifically includes:
the third acquisition unit is used for acquiring a first operation code set corresponding to daily dispatch of the dispatch capacity in the target working area in a preset period;
the first deleting unit is used for deleting the non-dispatch operation codes in the first operation code set to obtain a second operation code set;
And the second determining unit is used for determining daily historical dispatch time consumption of the capacity of dispatching the dispatch in the target working area in a preset period based on the second operation code set.
In a possible implementation manner of the present application, the second determining unit is specifically configured to:
acquiring the total operation time consumption corresponding to the second operation code set;
and determining daily historical dispatch time consumption of the capacity of dispatching the dispatch in the target working area in a preset period based on the total operation time consumption.
In one possible implementation manner of the present application, the first determining unit is specifically configured to:
performing feature preprocessing on the daily historical dispatch time of the capacity, the daily historical dispatch amount and the regional area information to obtain model input features;
and inputting the model input characteristics into a pre-trained dispatch time consumption prediction model, and generating the current day ticket dispatch time consumption of the capacity.
In one possible implementation manner of the present application, the first prediction unit specifically includes:
a fifth obtaining unit, configured to obtain a predicted piece quantity of the target working area on the same day;
and the second prediction unit is used for predicting the total time consumption of dispatching the workpieces in the target working area on the same day of the capacity according to the time consumption of dispatching the workpieces on the same day of the capacity and the estimated quantity of the workpieces on the same day in the target working area.
In one possible implementation manner of the present application, the fifth obtaining unit is specifically configured to:
acquiring daily history estimated part quantity of the target working area in a preset period;
and inputting the daily historical estimated part quantity into a pre-trained part quantity prediction model to obtain the daily estimated part quantity of the target working area.
In a possible implementation manner of the present application, the apparatus is further configured to:
determining an abnormal condition of the total time consumption of the dispatch in the target working area on the basis of the total time consumption of the dispatch in the target working area on the current date and a preset total time consumption threshold interval;
if the total consumption time of the delivery is higher than the maximum value of the preset daily total consumption time threshold interval, early warning is carried out on the overtime condition of the transportation;
and if the total consumption of the delivery is lower than the minimum value of the threshold interval of the preset total consumption of the day, carrying out early warning on the condition that the capacity is frequently insufficient.
In another aspect, the present application further provides a server, including:
one or more processors;
a memory; and
one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the dispatch time consuming prediction method.
In another aspect, the present application further provides a computer readable storage medium having stored thereon a computer program to be loaded by a processor to perform the steps of the dispatch time consuming prediction method.
In the method, daily history dispatch time for dispatching capacity is obtained in a target working area in a preset period; then acquiring daily historical dispatch quantity of a target working area and area information of the target working area in the preset period; determining the time consumption of the daily ticket uniform dispatch of the capacity based on the daily historical dispatch time consumption of the capacity, the daily historical dispatch amount of the target working area and the area information; finally, based on the time consumption of the dispatch of the present day ticket, the total time consumption of the dispatch of the present day in the target working area is predicted, and compared with the traditional method, the method and the device integrate a plurality of factors influencing the time consumption of the dispatch, realize the calculation of the refined time consumption of the dispatch, and improve the accuracy of the total time consumption of the dispatch of the future dispatch of the present day.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic view of a dispatch time-consuming prediction system according to an embodiment of the present disclosure;
FIG. 2 is a flowchart illustrating an embodiment of a method for dispatch time-consuming prediction provided in an embodiment of the present application;
FIG. 3 is a flow chart of one embodiment of step 201 provided in an embodiment of the present application;
FIG. 4 is a flow chart of one embodiment of step 204 provided in an embodiment of the present application;
FIG. 5 is a flowchart illustrating an embodiment of a dispatch time-consuming exception pre-warning provided in an embodiment of the present application
FIG. 6 is a schematic structural diagram of an embodiment of a dispatch time-consuming prediction apparatus according to the present disclosure;
fig. 7 is a schematic structural diagram of an embodiment of a server provided in an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
In the description of the present application, it should be understood that the terms "center," "longitudinal," "transverse," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate an orientation or positional relationship based on that shown in the drawings, merely for convenience of description and to simplify the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be configured and operated in a particular orientation, and thus should not be construed as limiting the present application. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In this application, the term "exemplary" is used to mean "serving as an example, instance, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the application. In the following description, details are set forth for purposes of explanation. It will be apparent to one of ordinary skill in the art that the present application may be practiced without these specific details. In other instances, well-known structures and processes have not been shown in detail to avoid obscuring the description of the present application with unnecessary detail. Thus, the present application is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.
The embodiment of the application provides a dispatch time consumption prediction method, a dispatch time consumption prediction device, a server and a readable storage medium, and the dispatch time consumption prediction method, the dispatch time consumption prediction device, the server and the readable storage medium are respectively described in detail below.
As shown in fig. 1, fig. 1 is a schematic view of a dispatch time-consuming prediction system provided in an embodiment of the present application, where the dispatch time-consuming prediction system may include a plurality of terminals 100 and a server 200, where the terminals 100 are connected to the server 200 by a network, and a dispatch time-consuming prediction device is integrated in the server 200, such as the server in fig. 1, and the terminals 100 may access the server 200.
The server 200 in the embodiment of the present application is mainly used for obtaining daily historical dispatch time consumption of the dispatch capacity in the target working area in the preset period; acquiring daily historical dispatch quantity of a target working area and area information of the target working area in a preset period; determining the time consumption of the daily ticket uniform dispatch of the capacity based on the daily historical dispatch time of the capacity, the daily historical dispatch amount of the target working area and the area information; based on the time spent for dispatch of the day ticket, the total time spent for dispatch of the day of the capacity in the target work area is predicted.
In this embodiment of the present application, the server 200 may be a stand-alone server, or may be a server network or a server cluster formed by servers, for example, the server 200 described in the embodiment of the present application includes, but is not limited to, a computer, a network terminal, a single network server, a plurality of network server sets, or a cloud server formed by a plurality of servers. Wherein the Cloud server is composed of a large number of computers or web servers based on Cloud Computing (Cloud Computing). In embodiments of the present application, communication between the server and the terminal may be achieved by any communication means, including, but not limited to, mobile communication based on the third generation partnership project (3rd Generation Partnership Project,3GPP), long term evolution (Long Term Evolution, LTE), worldwide interoperability for microwave access (Worldwide Interoperability for Microwave Access, wiMAX), or computer network communication based on the TCP/IP protocol family (TCP/IP Protocol Suite, TCP/IP), user datagram protocol (User Datagram Protocol, UDP), etc.
It is understood that the terminal 100 used in the embodiments of the present application may be a device including both receiving and transmitting hardware, i.e., a device having both receiving and transmitting hardware capable of performing two-way communication over a two-way communication link. Such a terminal may include: a cellular or other communication device having a single-line display or a multi-line display or a cellular or other communication device without a multi-line display. The terminal 100 may be a desktop terminal or a mobile terminal, and the terminal 100 may be one of a mobile phone, a tablet computer, a notebook computer, and the like.
Those skilled in the art will appreciate that the application environment shown in fig. 1 is merely one application scenario of the present application, and is not limited to the application scenario of the present application, and other application environments may also include more or fewer terminals than those shown in fig. 1, or a server network connection relationship, for example, only 1 server and 2 terminals are shown in fig. 1. It will be appreciated that the dispatch time-consuming prediction system may also include one or more other servers, or/and one or more terminals connected to a server network, and is not limited herein.
In addition, as shown in fig. 1, the dispatch time consuming prediction system may further include a memory 300 for storing data, such as daily historical dispatch time consuming data and dispatch time consuming prediction data, such as dispatch time consuming prediction data when the dispatch time consuming prediction system is running.
It should be noted that, the schematic view of the dispatch time-consuming prediction system shown in fig. 1 is only an example, and the dispatch time-consuming prediction system and the scene described in the embodiments of the present application are for more clearly describing the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application, and those skilled in the art can know that, with the evolution of the dispatch time-consuming prediction system and the appearance of a new service scenario, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.
Next, a method for predicting time consumption of dispatch provided in the embodiments of the present application will be described.
In the embodiments of the dispatch time-consuming prediction method of the present application, a dispatch time-consuming prediction device is used as an execution body, and for simplicity and convenience of description, in the subsequent method embodiments, the execution body is omitted, and the dispatch time-consuming prediction device is applied to a server, and the method includes: acquiring daily historical dispatch time consumption of the dispatch capacity in a target working area in a preset period; acquiring daily historical dispatch quantity of a target working area and area information of the target working area in a preset period; determining the time consumption of the daily ticket uniform dispatch of the capacity based on the daily historical dispatch time of the capacity, the daily historical dispatch amount of the target working area and the area information; based on the time spent for dispatch of the day ticket, the total time spent for dispatch of the day of the capacity in the target work area is predicted.
Referring to fig. 2 to fig. 7, fig. 2 is a flowchart illustrating an embodiment of a dispatch time-consuming prediction method according to an embodiment of the present application, where the dispatch time-consuming prediction method includes:
201. daily historical dispatch time for the capacity of dispatching the dispatch in the target working area in the preset period is obtained.
The dispatch refers to a process that an express company (logistics company) sends goods to a third party or a designated place through a logistics network according to the goods sent by a customer. And the capacity for dispatch is generally the courier of the courier company.
The target working area is divided into a plurality of target working areas, and corresponding capacity is generally set to be responsible for dispatch work of the target working areas, for example, a plurality of counties included in city A are divided into corresponding target working areas.
The preset period may be selected as one period of one week or one period of one month according to the requirement, and is specifically determined according to the requirement, if one month is selected as one period, then the preset period may be one month (or four weeks).
The time-consuming dispatch refers to the time-consuming dispatch of the capacity, and it should be noted that, obtaining the daily historical time-consuming dispatch of the capacity of the target work area for dispatch within the preset period is a relatively complex operation flow, because the capacity may take to process some non-dispatch operations, such as other operations, during the actual dispatch process, and if all the counted time-consuming dispatch includes the non-dispatch operation, the calculated actual dispatch time-consuming will have errors. Therefore, how to obtain the daily history dispatch time for the dispatch capacity of the target working area within the preset period can effectively reduce the error, and the following embodiments are omitted herein.
202. And acquiring daily historical dispatch quantity of the target working area and area information of the target working area in a preset period.
The preset period is the same as the preset period in step 201, and is specifically limited, please refer to step 201, and detailed description is omitted herein.
The area information of the target working area refers to the area size of the target working area, and because multiple factors are considered in the division of each target area, a certain difference exists in the specific area size of the target working area, and the difference in the area size of the target working area can affect the dispatch time of the capacity of the target working area, for example, the larger the area is, the more the dispatch nodes are corresponding in general, or the distance between the dispatch nodes is also the farther.
Specifically, the area information of the target working area stored in advance can be read by the server to obtain, or the area identification of the target working area can be obtained first, and then the area information of the target working area corresponding to the area identification configured in the background can be queried based on the area identification.
Daily historical dispatch volume refers to the actual daily historical dispatch volume. Specifically, the daily historical dispatch amount in the historical storage data can be directly read in the background of the system.
203. The time consumption of the daily ticket uniform dispatch of the capacity is determined based on the daily historical dispatch time consumption of the capacity, the daily historical dispatch amount of the target working area and the area information.
The day is the next day to the time of day, in particular 2021.11.30, assuming 2021.11.29 at this time, as compared to the history.
Ticket dispatch time refers to the average time taken to complete each ticket in all dispatches of the capacity for a full day. If the capacity is 3 times of dispatch operations today, the total number of tickets is 25 tickets, and the total time is 8 hours, then the time for day ticket dispatch is 8/25=0.32 hours.
In some embodiments of the present application, determining the current day ticket dispatch time of the capacity based on the daily historical dispatch time of the capacity, the daily historical dispatch amount and the area information may specifically include: and carrying out feature preprocessing on the daily historical dispatch time consumption of the transportation capacity, the daily historical dispatch quantity and the regional area information to obtain model input features. And inputting the model input characteristics into a pre-trained dispatch time consumption prediction model, and generating the current day ticket of the capacity to dispatch time consumption. Wherein the pre-trained dispatch time-consuming prediction model is a multi-time-step LSTM model, other time-series models or machine learning may be used, such as ARIMA, XGBoost.
Specifically, the preprocessing may include extracting static features of the above parameters (e.g., daily historic dispatch time of the capacity, and daily historic dispatch volume and area information of the target work area); by analysis, the piece quantity is periodically fluctuated in a unit of week, so that the sliding window thought is utilized, the periodicity of piece quantity fluctuation is combined, daily historical piece dispatching time consumption around the history is taken as an input characteristic of next day prediction, piece dispatching time consumption of the next day is predicted, and multi-time-step prediction is realized; specifically, the parameter input vector x can be converted into a vector with 28×4 dimensions (timetep=28), so as to predict the time spent in dispatching the tickets on the same day of the transport capacity.
In some embodiments of the present application, before using the dispatch-time-consuming prediction model, a model may be constructed, specifically, the input vector x dimension is 28×4, so that a 28×4-dimensional input layer is built, the hidden layer dimension is N (adjustable), the output layer is a numerical variable, and the dimension is 1.
The loss function uses MSE: wherein MSE (Mean Squared Error) mean square error is typically a loss function of regression prediction, representing the numerical difference between the predicted and actual values.
Wherein y is i It is time consuming to send out a piece for the real ticket,
dispatching a predicted ticket average time;
and the model solving target is to minimize MSE, and the SGD gradient descent algorithm is used for solving the optimal parameters of the model.
204. Based on the time spent for dispatch of the day ticket, the total time spent for dispatch of the day of the capacity in the target work area is predicted.
In the method, daily history dispatch time for dispatching capacity is obtained in a target working area in a preset period; then acquiring daily historical dispatch quantity of a target working area and area information of the target working area in a preset period; determining the time consumption of the dispatch of the current day ticket of the capacity based on the time consumption of the daily historical dispatch of the capacity, the daily historical dispatch amount of the target working area and the area information; finally, based on the time spent for dispatching the tickets on the same day, the total time spent for dispatching the tickets on the same day in the target working area is predicted, and compared with the traditional method, the method and the device integrate a plurality of factors influencing the time spent for dispatching the tickets, achieve the calculation of the time spent for dispatching the tickets on the same day in a refined mode, and improve the accuracy of the total time spent for dispatching the tickets in the future of the capacity.
In some embodiments of the present application, as shown in fig. 3, step 201, obtaining a daily history of delivery time of the delivery capacity in the target working area in the preset period includes:
301. And acquiring a first operation code set corresponding to daily dispatch of the dispatch capacity in the target working area in a preset period.
The operation code set corresponding to the dispatch refers to that the capacity can perform a code scanning operation on the ticket in the dispatch process, and each code scanning operation actually corresponds to a specific operation, so that the specific operation of the capacity can be obtained through the code scanning gun of the capacity. The first opcode set corresponding to the daily dispatch of capacity includes the opcodes corresponding to all operations performed by the day throughout the day. The operation code may be divided into a dispatch operation code and a non-dispatch operation code, where the non-dispatch operation code corresponds to an operation performed by the capacity not to be a dispatch operation, but to be other operations, and the other operations may be return operations of the receiving user.
302. And deleting the non-dispatch operation codes in the first operation code set to obtain a second operation code set.
It should be noted that, in general, the daily dispatch process of the capacity includes a dispatch operation and a non-dispatch operation, that is, the first opcode set includes a non-dispatch operation code, but only the dispatch operation exists in the dispatch process of a day including the capacity, and the non-dispatch operation is not included, that is, the first opcode set does not include a non-dispatch operation code, and step 302 may be omitted.
Therefore, when the condition that the first operation code set does not comprise the non-dispatch operation code is met, the calculated quantity of freight is n, differential calculation is carried out on the operation time set of freight list dispatch, the operation time consumption of each freight list is obtained, and the ticket average dispatch time consumption in each freight list set is obtained by summing and averaging:
wherein T is i Indicating that the ticket uniform dispatch in the ith batch operation set is time consuming,
and (3) representing the j-th dispatch operation time in the i-th set, wherein n is the quantity of the waybills in the waybill set.
Further, in a general case, the daily dispatch process of the capacity includes dispatch operations and non-dispatch operations, where k is the number of non-dispatch operations, the quantity of the calculated shipments is n-k-1, i.e. if there is one non-dispatch operation between two dispatch operations, no difference is calculated between the two dispatch operations. Obtaining the operation time consumption of each bill which is incorporated into the calculation, summing and averaging to obtain the ticket average dispatch time consumption T in each bill set i 。
The final ticket-average dispatch time in the target work area can be expressed as:
where m is the number of batch hand-over operation sets.
303. Based on the second set of opcodes, a daily historical dispatch time for the capacity of the target work area to dispatch within the preset period is determined.
In some embodiments of the present application, determining a daily historical dispatch time for the capacity of the target work area to dispatch within a preset period based on the second set of opcodes includes: acquiring the total operation time consumption corresponding to the second operation code set; based on the total time consumption of the operation, daily historical dispatch time consumption of the capacity of dispatching the dispatch in the target working area in the preset period is determined. And each operation code in the second operation code set corresponds to a time node, so that the time consumption corresponding to each operation in the second operation code set can be determined through the relation between the operation code and the time node, and then the time consumption corresponding to each operation is added to obtain the total time consumption of the operation corresponding to the second operation code set.
According to the method, a first operation code set corresponding to daily dispatch of the dispatch capacity is carried out in a target working area in a preset period is obtained; deleting the non-dispatch operation codes in the first operation code set to obtain a second operation code set; and determining daily historical dispatch time consumption of the capacity of dispatching the dispatch in the target working area in a preset period based on the second operation code set. The non-dispatch operation codes in the first operation code set are effectively eliminated, calculation errors of daily historical dispatch time are reduced, fine dispatch time calculation is achieved, and accuracy of total dispatch time in the future of carrying capacity is improved.
In some embodiments of the present application, as shown in fig. 4, step 204, predicting the total time-consuming for dispatching the dispatch in the target working area on the same day based on the same dispatch time of the day ticket includes:
401. and acquiring the estimated daily piece quantity of the target working area.
In some embodiments of the present application, obtaining a current day estimated piece count for a target work area includes:
and acquiring daily history estimated piece quantity of the target working area in a preset period.
And inputting the daily historical estimated part quantity into a pre-trained part quantity prediction model to obtain the daily estimated part quantity of the target working area.
In some embodiments of the present application, the estimated current day of the target working area may also be directly obtained through other interfaces of the server.
402. And predicting the total time consumption of dispatching the dispatch in the target working area on the same day of the transportation capacity according to the time consumption of dispatching the dispatch on the same day of the transportation capacity and the estimated amount of the dispatch on the same day in the target working area.
Specifically, the time consumed by the dispatch of the current day ticket of the capacity and the estimated current day of the target working area can be multiplied, so that the total time consumed by the dispatch of the capacity in the target working area on the current day is obtained.
In some embodiments of the present application, as shown in fig. 5, after the step 204 of predicting the total time consumption of dispatching the dispatch in the target working area on the day based on the day ticket, the method further includes:
501. And determining the abnormal condition of the total time consumption of the dispatch on the current date in the target working area based on the total time consumption of the dispatch on the current date in the target working area and a preset time consumption threshold interval.
The preset time threshold interval of total daily consumption can be adjusted according to actual requirements, such as [ 6-8 ]. Or [ 5-9 ].
502. And if the total consumption time of the dispatching is higher than the maximum value of the preset daily total consumption time threshold interval, carrying out early warning on the overtime condition of the operation of the transportation capacity.
According to step 501, the preset time-consuming threshold interval is [ 6-8 ], and when the total delivery consumption is higher than the maximum value of the preset time-consuming threshold interval, that is, the total delivery consumption is higher than 8 hours, the overtime condition of the operation is pre-warned. If overtime rewards can be carried out on the capacity, part of non-important time-effect express items can be adjusted to the dispatch work of the next day.
503. If the total time consumption of the dispatch is lower than the minimum value of the preset total time consumption threshold interval of the day, the frequent shortage condition of the transportation capacity is warned.
According to step 501, the preset time-consuming threshold interval of the day is taken as [ 6-8 ], and when the total time consumption of the dispatch is lower than the minimum value of the preset time-consuming threshold interval of the day, i.e. the total time consumption of the dispatch is lower than 6 hours, the insufficient condition of the transportation capacity can be warned. Such as punishing capacity or increasing the amount of dispatch tasks.
In order to better implement the dispatch time-consuming prediction method in the embodiment of the present application, based on the dispatch time-consuming prediction method, the embodiment of the present application further provides a dispatch time-consuming prediction apparatus, as shown in fig. 6, where the dispatch time-consuming prediction apparatus 600 includes a first obtaining unit 601, a second obtaining unit 602, a first determining unit 603, and a first predicting unit 604:
the first obtaining unit 601 is configured to obtain daily historic dispatch time consumption of a capacity of dispatching a dispatch in a target working area within a preset period.
The second obtaining unit 602 is configured to obtain the daily historical dispatch amount of the target working area and the area information of the target working area in a preset period.
The first determining unit 603 is configured to determine the time spent for dispatch on the current day ticket of the capacity based on the time spent for dispatch on the daily history of the capacity, the daily history of the target work area, and the area information.
The first prediction unit 604 is configured to predict a total time consumption of dispatching the dispatch in the target working area on the same day based on the time ticket.
In some embodiments of the present application, the first obtaining unit 601 specifically includes:
the third acquisition unit is used for acquiring a first operation code set corresponding to daily dispatch of the dispatch capacity in the target working area in a preset period.
The first deleting unit is used for deleting the non-dispatch operation codes in the first operation code set to obtain a second operation code set.
And the second determining unit is used for determining daily historical dispatch time consumption of the capacity of dispatching the dispatch in the target working area in a preset period based on the second operation code set.
In some embodiments of the present application, the second determining unit is specifically configured to:
acquiring the total operation time consumption corresponding to the second operation code set;
based on the total time consumption of the operation, daily historical dispatch time consumption of the capacity of dispatching the dispatch in the target working area in the preset period is determined.
In some embodiments of the present application, the first determining unit 603 is specifically configured to:
and carrying out feature preprocessing on the daily historical dispatch time consumption of the transportation capacity, the daily historical dispatch quantity and the regional area information to obtain model input features.
And inputting the model input characteristics into a pre-trained dispatch time consumption prediction model, and generating the current day ticket of the capacity to dispatch time consumption.
In some embodiments of the present application, the first prediction unit 604 specifically includes:
and the fifth acquisition unit is used for acquiring the estimated current day piece quantity of the target working area.
The second prediction unit is used for predicting the total time consumption of dispatching the workpieces in the target working area on the same day according to the time consumption of dispatching the workpieces on the same day of the capacity ticket and the estimated quantity of the workpieces on the same day in the target working area.
In some embodiments of the present application, the fifth obtaining unit is specifically configured to:
and acquiring daily history estimated piece quantity of the target working area in a preset period.
And inputting the daily historical estimated part quantity into a pre-trained part quantity prediction model to obtain the daily estimated part quantity of the target working area.
In some embodiments of the present application, the apparatus is further to:
and determining the abnormal condition of the total time consumption of the dispatch on the current date in the target working area based on the total time consumption of the dispatch on the current date in the target working area and a preset time consumption threshold interval.
And if the total consumption time of the dispatching is higher than the maximum value of the preset daily total consumption time threshold interval, carrying out early warning on the overtime condition of the operation of the transportation capacity.
If the total time consumption of the dispatch is lower than the minimum value of the preset total time consumption threshold interval of the day, the frequent shortage condition of the transportation capacity is warned.
According to the dispatch time-consuming prediction device 600, the daily historical dispatch time consumption of the dispatch capacity in the target working area in the preset period is obtained through the first obtaining unit 601; a second obtaining unit 602, configured to obtain daily historical dispatch amounts of the target working area and area information of the target working area in a preset period; the first determining unit 603 determines the time spent for dispatch on the current day ticket of the capacity based on the daily historical dispatch time spent for dispatch of the capacity and the daily historical dispatch amount and area information of the target work area; the first prediction unit 604 predicts the total time consumption of dispatching the dispatching of the dispatching in the target working area on the same day based on the time of dispatching the dispatching in the same day ticket, and compared with the traditional method, the method integrates a plurality of factors influencing the time consumption of dispatching, realizes the calculation of the refined dispatching time consumption, and improves the accuracy of the total time consumption of dispatching the dispatching in the future of the dispatching.
In addition to the above description of the method and apparatus for dispatch time consumption prediction, the embodiments of the present application further provide a server, which integrates any of the dispatch time consumption prediction apparatuses provided in the embodiments of the present application, where the server includes:
one or more processors;
a memory; and
one or more applications, wherein the one or more applications are stored in memory and configured to perform the operations of any of the method embodiments of any of the dispatch time-consuming prediction method embodiments described above by a processor.
The embodiment of the application also provides a server which integrates any dispatch time consumption prediction device provided by the embodiment of the application. Referring to fig. 7, fig. 7 is a schematic structural diagram of an embodiment of a server according to an embodiment of the present application.
Fig. 7 is a schematic structural diagram of a dispatch time-consuming prediction device according to an embodiment of the present application, specifically:
the dispatch time consuming prediction device may include one or more processors 701 of a processing core, one or more storage units 702 of a computer readable storage medium, a power supply 703, and an input unit 704. Those skilled in the art will appreciate that the dispatch time consuming predictive device configuration shown in FIG. 7 is not limiting of the dispatch time consuming predictive device and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components. Wherein:
The processor 701 is a control center of the dispatch time consuming apparatus, and connects various parts of the entire dispatch time consuming apparatus using various interfaces and lines, and performs various functions and processes of the dispatch time consuming apparatus by running or executing software programs and/or modules stored in the storage unit 702 and calling data stored in the storage unit 702, thereby performing overall monitoring of the dispatch time consuming apparatus. Optionally, processor 701 may include one or more processing cores; preferably, the processor 701 may integrate an application processor and a modem processor, wherein the application processor primarily handles operating systems, user interfaces, applications, etc., and the modem processor primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 701.
The storage unit 702 may be used to store software programs and modules, and the processor 701 performs various functional applications and data processing by executing the software programs and modules stored in the storage unit 702. The storage unit 702 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data created from the use of the dispatch time consuming prediction device, and the like. In addition, the storage unit 702 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory unit 702 may also include a memory controller to provide access to the memory unit 702 by the processor 701.
The dispatch time-consuming prediction apparatus further includes a power supply 703 for supplying power to each component, and preferably, the power supply 703 may be logically connected to the processor 701 through a power management system, so as to implement functions of managing charging, discharging, and power consumption management through the power management system. The power supply 703 may also include one or more of any component, such as a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, etc.
The dispatch time consuming prediction device may further include an input unit 704, wherein the input unit 704 may be configured to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.
Although not shown, the dispatch time-consuming prediction apparatus may further include a display unit, etc., which will not be described herein. In particular, in the embodiment of the present application, the processor 701 in the dispatch time-consuming prediction apparatus loads executable files corresponding to the processes of one or more application programs into the storage unit 702 according to the following instructions, and the processor 701 executes the application programs stored in the storage unit 702, so as to implement various functions as follows:
Acquiring daily historical dispatch time consumption of the dispatch capacity in a target working area in a preset period; acquiring daily historical dispatch quantity of a target working area and area information of the target working area in a preset period; determining the time consumption of the daily ticket uniform dispatch of the capacity based on the daily historical dispatch time of the capacity, the daily historical dispatch amount of the target working area and the area information; based on the time spent for dispatch of the day ticket, the total time spent for dispatch of the day of the capacity in the target work area is predicted.
In the method, daily history dispatch time for dispatching capacity is obtained in a target working area in a preset period; then acquiring daily historical dispatch quantity of a target working area and area information of the target working area in a preset period; determining the time consumption of the dispatch of the current day ticket of the capacity based on the time consumption of the daily historical dispatch of the capacity, the daily historical dispatch amount of the target working area and the area information; finally, based on the time spent for dispatching the tickets on the same day, the total time spent for dispatching the tickets on the same day in the target working area is predicted, and compared with the traditional method, the method and the device integrate a plurality of factors influencing the time spent for dispatching the tickets, achieve the calculation of the time spent for dispatching the tickets on the same day in a refined mode, and improve the accuracy of the total time spent for dispatching the tickets in the future of the capacity.
To this end, embodiments of the present application provide a computer-readable storage medium, which may include: read Only Memory (ROM), random access Memory (RAM, random Access Memory), magnetic or optical disk, and the like. The computer readable storage medium has stored therein a plurality of instructions capable of being loaded by a processor to perform the steps of any of the dispatch time consuming prediction methods provided by embodiments of the present application. For example, the instructions may perform the steps of:
acquiring daily historical dispatch time consumption of the dispatch capacity in a target working area in a preset period; acquiring daily historical dispatch quantity of a target working area and area information of the target working area in a preset period; determining the time consumption of the daily ticket uniform dispatch of the capacity based on the daily historical dispatch time of the capacity, the daily historical dispatch amount of the target working area and the area information; based on the time spent for dispatch of the day ticket, the total time spent for dispatch of the day of the capacity in the target work area is predicted.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
The foregoing describes in detail a method, an apparatus, a server and a readable storage medium for predicting dispatch time consumption provided in the embodiments of the present application, and specific examples are applied to illustrate principles and implementations of the present application, where the foregoing description of the embodiments is only for helping to understand the method and core ideas of the present application; meanwhile, as those skilled in the art will vary in the specific embodiments and application scope according to the ideas of the present application, the contents of the present specification should not be construed as limiting the present application in summary.
Claims (10)
1. A dispatch time consumption prediction method is characterized by comprising the following steps:
acquiring daily historical dispatch time consumption of the dispatch capacity in a target working area in a preset period;
acquiring daily historical dispatch quantity of a target working area and area information of the target working area in the preset period;
determining the time consumption of the current day ticket dispatch of the capacity based on the daily historical dispatch time consumption of the capacity, the daily historical dispatch amount of the target working area and the area information;
and predicting the total time consumption of dispatching the dispatch of the dispatch on the current date of the capacity in the target working area based on the time consumption of dispatching the dispatch on the current date ticket.
2. The method for predicting dispatch time consumption according to claim 1, wherein the obtaining daily historical dispatch time consumption of the capacity of dispatching in the target working area in the preset period includes:
acquiring a first operation code set corresponding to daily dispatch of the dispatch capacity in a target working area in a preset period;
deleting the non-dispatch operation codes in the first operation code set to obtain a second operation code set;
and determining daily historical dispatch time consumption of the capacity of dispatching the dispatch in the target working area in a preset period based on the second operation code set.
3. The method for predicting dispatch time consumption according to claim 2, wherein determining a daily historical dispatch time consumption of the capacity of dispatching the dispatch in the target working area within the preset period based on the second opcode set comprises:
acquiring the total operation time consumption corresponding to the second operation code set;
and determining daily historical dispatch time consumption of the capacity of dispatching the dispatch in the target working area in a preset period based on the total operation time consumption.
4. The dispatch time-consuming prediction method according to claim 1, wherein the determining the current day ticket dispatch time-consuming of the capacity based on the daily historical dispatch time-consuming of the capacity, the daily historical dispatch volume and the area information comprises:
Performing feature preprocessing on the daily historical dispatch time of the capacity, the daily historical dispatch amount and the regional area information to obtain model input features;
and inputting the model input characteristics into a pre-trained dispatch time consumption prediction model, and generating the current day ticket dispatch time consumption of the capacity.
5. The method for predicting the time consumption of dispatch for a dispatch according to claim 1, wherein predicting the total time consumption of dispatch for a dispatch for the capacity on the current day in the target work area based on the time ticket comprises:
acquiring the estimated current day of the target working area;
and predicting the total time consumption of dispatching the dispatch of the dispatch capacity on the current day in the target working area according to the time consumption of dispatching the dispatch capacity on the current day ticket and the estimated quantity of the dispatch capacity on the current day in the target working area.
6. The method for predicting time consumption of dispatch in accordance with claim 5, wherein said obtaining the estimated amount of dispatch on the day of the target work area comprises:
acquiring daily history estimated part quantity of the target working area in a preset period;
and inputting the daily historical estimated part quantity into a pre-trained part quantity prediction model to obtain the daily estimated part quantity of the target working area.
7. The dispatch time-consuming prediction method of claim 1, wherein after predicting the total dispatch time consumption for dispatch on the target work area for the capacity day based on the day ticket dispatch time consumption, the method further comprises:
determining an abnormal condition of the total time consumption of the dispatch in the target working area on the basis of the total time consumption of the dispatch in the target working area on the current date and a preset total time consumption threshold interval;
if the total consumption time of the delivery is higher than the maximum value of the preset daily total consumption time threshold interval, early warning is carried out on the overtime condition of the transportation;
and if the total consumption of the delivery is lower than the minimum value of the threshold interval of the preset total consumption of the day, carrying out early warning on the condition that the capacity is frequently insufficient.
8. A dispatch time consuming prediction apparatus, the apparatus comprising:
the first acquisition unit is used for acquiring daily historical dispatch time consumption of the dispatch capacity in the target working area in a preset period;
the second acquisition unit is used for acquiring daily historical dispatch quantity of the target working area and area information of the target working area in the preset period;
The first determining unit is used for determining the time consumption of the current day ticket uniform dispatch of the capacity based on the daily historical dispatch time consumption of the capacity and the daily historical dispatch amount and area information of the target working area;
the first prediction unit is used for predicting the total time consumption of dispatching the dispatch in the target working area on the current date of the capacity based on the time consumption of dispatching the dispatch in the current date ticket.
9. A server, the server comprising:
one or more processors;
a memory; and
one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the dispatch time consuming prediction method of any one of claims 1 to 7.
10. A computer readable storage medium having stored thereon a computer program, the computer program being loaded by a processor to perform the steps of the dispatch time consuming prediction method of any one of claims 1 to 7.
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