CN114139907A - Transport capacity evaluation method, computer-readable storage medium, and computer device - Google Patents

Transport capacity evaluation method, computer-readable storage medium, and computer device Download PDF

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CN114139907A
CN114139907A CN202111397348.5A CN202111397348A CN114139907A CN 114139907 A CN114139907 A CN 114139907A CN 202111397348 A CN202111397348 A CN 202111397348A CN 114139907 A CN114139907 A CN 114139907A
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order
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任子杰
周宇
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Shenzhen Yishi Huolala Technology Co Ltd
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Abstract

The invention is suitable for the field of logistics, and provides a capacity evaluation method, a computer-readable storage medium and computer equipment, wherein the capacity evaluation method comprises the following steps: screening orders according to the order characteristics and the user characteristics, and taking the screened orders as target orders; acquiring the total order amount, the total order amount and the total order distance of each daily target order, and predicting the total order amount, the total order amount and the total order distance of each day in the future by using a time series model; screening drivers according to the characteristics of the drivers, and taking the screened drivers as target drivers; acquiring the total daily order amount, the total order distance and the driver number of a target driver to calculate the transport capacity daily parameter of the target driver; and calculating the quantity of the drivers required in the future day according to the transport capacity daily parameters of the target drivers and the total daily order amount, the total daily order amount and the total daily order distance of the predicted future day. The driver is mobilized through the transport capacity assessment, and transport capacity waste is reduced.

Description

Transport capacity evaluation method, computer-readable storage medium, and computer device
Technical Field
The invention belongs to the field of logistics, and particularly relates to a transportation capacity evaluation method, a computer-readable storage medium and computer equipment.
Background
Most of the traffic of the current order distribution system is the scheme of order taking hall, that is, after the order is generated, the order is configured according to cities along with time and distance, for example, the configuration (10s, 1km) represents (the 0 th pushes a driver in 1km, the 10 th pushes a driver in 2km, the 20 th pushes a driver in 3km, and the like until the order is responded); the order receiving distance is short, and cancellation caused by too far distance or restriction of a loading point and the like can be avoided to a certain extent.
But the scheme does not consider wagon matching, vehicle type matching, whether drivers are high in quality or not, environmental characteristics and the like, so that the cancellation rate of certain pairs is high; when the driver density is too high, the driver experience may be influenced by too many drivers in each carousel, otherwise, when the driver density is too low, the response rate may be reduced due to too few drivers in each carousel, and the transport capacity is wasted.
Disclosure of Invention
The invention aims to provide a transport capacity assessment method, a computer readable storage medium and computer equipment, aiming at solving the problem of transport capacity waste caused by incapability of fully utilizing the transport capacity of a driver.
In a first aspect, the present invention provides a capacity assessment method, including:
screening orders according to the order characteristics and the user characteristics, and taking the screened orders as target orders;
acquiring the total order amount, the total order amount and the total order distance of each daily target order, and predicting the total order amount, the total order amount and the total order distance of each day in the future by using a time series model;
screening drivers according to the characteristics of the drivers, and taking the screened drivers as target drivers;
acquiring the total daily order amount, the total order distance and the driver number of a target driver to calculate the transport capacity daily parameter of the target driver;
and calculating the quantity of the drivers required in the future day according to the transport capacity daily parameters of the target drivers and the total daily order amount, the total daily order amount and the total daily order distance of the predicted future day.
Further, the capacity day parameters comprise daily order capacity, daily order amount capacity and daily order distance capacity.
Further, the driver characteristics include: driver water flow, total transport distance, total pick-up amount, spatial features and temporal features.
Further, the step of obtaining the total daily order amount, the total order distance and the driver number of the target driver to calculate the daily parameters of the transport capacity of the target driver specifically comprises the following steps:
dividing the total order amount, the total order amount and the total order distance by the number of drivers to obtain the daily capacity of the target driver set, namely:
Figure BDA0003370369450000021
Figure BDA0003370369450000022
Figure BDA0003370369450000023
wherein, date represents the date of the day,
Figure BDA0003370369450000024
for the order quantity parameter for date the day,
Figure BDA0003370369450000025
for the order amount parameter for date that day,
Figure BDA0003370369450000026
the order distance parameter of date on the day;
calculating the daily average transport capacity parameter corresponding to the target driver set according to the daily transport capacity parameter, namely:
Figure BDA0003370369450000027
Figure BDA0003370369450000028
Figure BDA0003370369450000029
wherein n is the number of days of statistics,
Figure BDA00033703694500000210
the daily average order volume capacity parameter,
Figure BDA00033703694500000211
is the average daily order amount parameter,
Figure BDA0003370369450000031
is the average daily order distance parameter.
Further, the capacity evaluation method further includes:
acquiring the total order amount, the total order amount and the total order distance of each target order, and predicting the total order amount, the total order amount and the total order distance of one hour in the future by using a time series model respectively;
acquiring the total order amount, the total order distance and the driver number of a target driver at each time, and calculating the transport capacity hour parameter of the target driver;
and calculating the quantity of drivers required for one hour in the future according to the target driver transport capacity hour parameter, the total predicted order amount of one hour in the future, the total predicted order amount of the one hour in the future and the total predicted order distance.
Further, the time series model is an ARMA model.
Further, the order features include: vehicle type, transport distance, order amount, vehicle following and carrying.
Further, the user characteristics include a user active cancellation ratio.
In a second aspect, the invention provides a computer-readable storage medium, in which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the capacity assessment method.
In a third aspect, the present invention provides a computer device comprising: one or more processors, a memory, and one or more computer programs, the processors and the memory being connected by a bus, wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, which when executing the computer programs implement the steps of the capacity assessment method.
In the invention, the order range and the statistical historical data and the driver group parameters of the driver group are determined, the future order data are predicted through a time sequence, the driver quantity of the driver group is estimated according to the future order data and the driver group parameters, the drivers can be dispatched according to the estimated driver quantity in the actual order dispatching, the transport capacity waste is reduced, the transport capacity level of the drivers can be divided according to the driver parameters, and the drivers are selected according to the preference.
Drawings
Fig. 1 is a flowchart of a method for estimating capacity according to an embodiment of the present invention.
Fig. 2 is a block diagram of a specific structure of a computer device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clearly apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Referring to fig. 1, a method for estimating a transport capacity according to an embodiment of the present invention includes the following steps: it should be noted that the method for estimating the capacity of the present invention is not limited to the flow sequence shown in fig. 1 if the results are substantially the same.
S1, screening orders according to the order characteristics and the user characteristics, and taking the screened orders as target orders;
s2, acquiring the total order amount, the total order amount and the total order distance of each daily target order, and predicting the total order amount, the total order amount and the total order distance of each day in the future by using a time series model;
s3, screening drivers according to the characteristics of the drivers, and taking the screened drivers as target drivers;
s4, acquiring the total daily order amount, total order distance and driver number of the target driver to calculate the daily capacity parameter of the target driver;
and S5, calculating the quantity of drivers required in the future day according to the capacity day parameters of the target driver, the total daily order amount of the predicted future day, the total daily order amount and the total daily order distance.
In an embodiment of the present invention, the capacity day parameters include a daily order capacity, a daily order amount capacity, and a daily order distance capacity.
In one embodiment of the present invention, the driver characteristics include: driver water flow, total transport distance, total pick-up amount, spatial features and temporal features.
In an embodiment of the present invention, the obtaining of the total daily order amount, the total order distance, and the driver number of the target driver to calculate the daily parameters of the transport capacity of the target driver specifically includes:
dividing the total order amount, the total order amount and the total order distance by the number of drivers to obtain the daily capacity of the target driver set, namely:
Figure BDA0003370369450000051
Figure BDA0003370369450000052
Figure BDA0003370369450000053
wherein, date represents the date of the day,
Figure BDA0003370369450000054
for the order quantity parameter for date the day,
Figure BDA0003370369450000055
for the order amount parameter for date that day,
Figure BDA0003370369450000056
the order distance parameter of date on the day;
calculating the daily average transport capacity parameter corresponding to the target driver set according to the daily transport capacity parameter, namely:
Figure BDA0003370369450000057
Figure BDA0003370369450000058
Figure BDA0003370369450000059
wherein n is the number of days of statistics,
Figure BDA00033703694500000510
the daily average order volume capacity parameter,
Figure BDA00033703694500000511
is the average daily order amount parameter,
Figure BDA00033703694500000512
is the average daily order distance parameter.
Calculating the quantity of drivers required in the future day according to the transport capacity daily parameters of the target drivers, the total daily order amount and the total daily order distance predicted in the future day, namely:
Figure BDA00033703694500000513
wherein the content of the first and second substances,
Figure BDA00033703694500000514
for the number of drivers required for a future day,
Figure BDA00033703694500000515
to predict the total daily order for the future day.
In an embodiment of the present invention, the capacity evaluation method further includes:
acquiring the total order amount, the total order amount and the total order distance of each target order, and predicting the total order amount, the total order amount and the total order distance of one hour in the future by using a time series model respectively;
acquiring the total order amount, the total order distance and the driver number of a target driver at each time, and calculating the transport capacity hour parameter of the target driver;
and calculating the quantity of drivers required for one hour in the future according to the target driver transport capacity hour parameter, the total predicted order amount of one hour in the future, the total predicted order amount of the one hour in the future and the total predicted order distance.
Obtaining the order quantity parameter, the order amount parameter and the order distance parameter of the target driver in each time of the target order, namely:
Figure BDA0003370369450000061
Figure BDA0003370369450000062
Figure BDA0003370369450000063
wherein date represents date, hour represents hour, DNdate，hourIs the order quantity parameter at date hour, DMdate，hourOrder amount parameter for date time of hour，DDdate,hourAn order distance parameter when date hour;
at the hourly node, counting the hourly order total quantity parameter, the hourly order amount parameter and the hourly order distance parameter of n days, namely:
Figure BDA0003370369450000064
Figure BDA0003370369450000065
Figure BDA0003370369450000066
wherein n is the number of days of statistics,
Figure BDA0003370369450000067
for the average order volume capacity parameter at date hour,
Figure BDA0003370369450000068
the average order amount parameter at date hour,
Figure BDA0003370369450000069
average order distance parameter at date hour;
predicting the total number of drivers per hour in the future day as:
Figure BDA00033703694500000610
wherein the content of the first and second substances,
Figure BDA0003370369450000071
for the number of drivers required at hour in the future,
Figure BDA0003370369450000072
hou for predicting a future dayThe total amount of the order in hours at r,
Figure BDA0003370369450000073
the transport capacity hour average parameter.
In an embodiment of the invention, the time series model is an ARMA model.
The method comprises the steps of obtaining the total order amount, the total order amount and the total order distance of each daily target order, and predicting the total order amount, the total order amount and the total order distance of each day in the future by using a time series model:
and respectively carrying out difference twice after taking the logarithm of the total order amount, the total order amount and the total order distance, inputting the time sequence after difference into an ARMA model, and predicting the total order amount, the total order amount and the total order distance of a day in the future.
Taking the order quantity as an example, when n is 7:
taking logarithm of the order quantity, carrying out 7-order difference on the data obtained after the logarithm is taken, then carrying out the first-order difference, inputting the obtained time sequence into an ARMA (7, 1) model, and predicting the order quantity of one day in the future.
In an embodiment of the present invention, the order features include: vehicle type, transport distance, order amount, vehicle following and carrying.
In an embodiment of the invention, the user characteristic comprises a user active cancellation ratio.
An embodiment of the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the capacity assessment method provided by an embodiment of the present invention.
Fig. 2 is a block diagram showing a specific structure of a computer device according to an embodiment of the present invention, where the computer device 100 includes: one or more processors 101, a memory 102, and one or more computer programs, wherein the processors 101 and the memory 102 are connected by a bus, the one or more computer programs being stored in the memory 102 and configured to be executed by the one or more processors 101, the processor 101 implementing the steps of the capacity assessment method as provided by an embodiment of the invention when executing the computer programs.
The computer equipment comprises a server, a terminal and the like. The computer device may be a desktop computer, a mobile terminal or a vehicle-mounted device, and the mobile terminal includes at least one of a mobile phone, a tablet computer, a personal digital assistant or a wearable device.
In the embodiment of the invention, the order range, the statistical historical data of the driver group and the parameters of the driver group are determined, the future order data are predicted through time series, the number of drivers of the driver group is estimated according to the future order data and the parameters of the driver group, the drivers can be dispatched according to the estimated number of the drivers in the actual order dispatching, the transport capacity waste is reduced, the transport capacity level of the drivers can be divided according to the parameters of the drivers, and the drivers are selected according to the preference.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (10)

1. A capacity assessment method, comprising:
screening orders according to the order characteristics and the user characteristics, and taking the screened orders as target orders;
acquiring the total order amount, the total order amount and the total order distance of each daily target order, and predicting the total order amount, the total order amount and the total order distance of each day in the future by using a time series model;
screening drivers according to the characteristics of the drivers, and taking the screened drivers as target drivers;
acquiring the total daily order amount, the total order distance and the driver number of a target driver to calculate the transport capacity daily parameter of the target driver;
and calculating the quantity of the drivers required in the future day according to the transport capacity daily parameters of the target drivers and the total daily order amount, the total daily order amount and the total daily order distance of the predicted future day.
2. The capacity assessment method of claim 1, wherein the capacity day parameters include daily order volume capacity, daily order amount capacity, and daily order distance capacity.
3. The capacity assessment method according to claim 1, wherein the driver characteristics include: driver water flow, total transport distance, total pick-up amount, spatial features and temporal features.
4. The capacity assessment method according to claim 1, wherein the step of obtaining the total daily order amount, total order distance and driver number of the target driver to calculate the target driver capacity day parameters specifically comprises:
dividing the total order amount, the total order amount and the total order distance by the number of drivers to obtain the daily capacity of the target driver set, namely:
Figure FDA0003370369440000011
Figure FDA0003370369440000012
Figure FDA0003370369440000021
wherein, date represents the date of the day,
Figure FDA0003370369440000022
for the order quantity parameter for date the day,
Figure FDA0003370369440000023
for the order amount parameter for date that day,
Figure FDA0003370369440000024
the order distance parameter of date on the day;
calculating the daily average transport capacity parameter corresponding to the target driver set according to the daily transport capacity parameter, namely:
Figure FDA0003370369440000025
Figure FDA0003370369440000026
Figure FDA0003370369440000027
wherein n is the number of days of statistics,
Figure FDA0003370369440000028
the daily average order volume capacity parameter,
Figure FDA0003370369440000029
is the average daily order amount parameter,
Figure FDA00033703694400000210
is the average daily order distance parameter.
5. The capacity assessment method according to claim 1, further comprising:
acquiring the total order amount, the total order amount and the total order distance of each target order, and predicting the total order amount, the total order amount and the total order distance of one hour in the future by using a time series model respectively;
acquiring the total order amount, the total order distance and the driver number of a target driver at each time, and calculating the transport capacity hour parameter of the target driver;
and calculating the quantity of drivers required for one hour in the future according to the target driver transport capacity hour parameter, the total predicted order amount of one hour in the future, the total predicted order amount of the one hour in the future and the total predicted order distance.
6. The capacity assessment method according to claim 1, wherein the time series model is an ARMA model.
7. The capacity assessment method of claim 1, wherein said order characteristics comprise: vehicle type, transport distance, order amount, vehicle following and carrying.
8. The capacity assessment method of claim 1, wherein the user characteristic comprises a user active cancellation rate.
9. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the capacity assessment method according to any one of claims 1 to 8.
10. A computer device, comprising: one or more processors, a memory and one or more computer programs, the processors and the memory being connected by a bus, wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, characterized in that the steps of the capacity assessment method according to any one of claims 1 to 8 are implemented when the computer programs are executed by the processors.
CN202111397348.5A 2021-11-23 2021-11-23 Transport capacity evaluation method, computer-readable storage medium, and computer device Pending CN114139907A (en)

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