CN107092974B - Distribution pressure prediction method and device - Google Patents

Distribution pressure prediction method and device Download PDF

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CN107092974B
CN107092974B CN201611077644.6A CN201611077644A CN107092974B CN 107092974 B CN107092974 B CN 107092974B CN 201611077644 A CN201611077644 A CN 201611077644A CN 107092974 B CN107092974 B CN 107092974B
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characteristic parameter
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delivery
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CN107092974A (en
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杨秋源
徐明泉
黄绍建
刘浪
咸珂
陈进清
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Beijing Xingxuan Technology Co Ltd
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Abstract

The embodiment of the application provides a distribution pressure prediction method and a distribution pressure prediction device. The delivery pressure prediction method comprises the following steps: acquiring at least one characteristic parameter of a distribution area, wherein the at least one characteristic parameter represents the distribution pressure of the distribution area in the current time period; predicting a delivery force parameter of the delivery area in a future time period according to at least one characteristic parameter; based on the dispensing force parameter, a dispensing pressure level of the dispensing area over a future time period is determined. By adopting the embodiment of the application, the distribution pressure can be predicted in advance, the coping strategies can be adopted conveniently in time, and the pressure of a distribution area and the dispatching pressure faced by a logistics dispatching system can be relieved in time.

Description

Distribution pressure prediction method and device
Technical Field
The application relates to the technical field of internet, in particular to a distribution pressure prediction method and a distribution pressure prediction device.
Background
With the rapid development of internet technology, internet-based applications are increasing, such as take-out applications and shopping applications. Based on the applications, the user can obtain the needed articles without going out. These applications are convenient for users and face the problem of goods delivery, so the logistics scheduling system comes up. The main task of the logistics scheduling system is to distribute orders to the dispatchers who deliver the items to the customers.
Under special conditions such as severe weather and marketing activities launched by merchants, the phenomena of sudden increase of order quantity and excessive order backlog are likely to occur in some distribution areas, so that the logistics scheduling system faces huge pressure. The main solution at present is to manually observe the transportation capacity supply and demand conditions in each distribution area and judge whether the single explosion condition occurs; when the condition of the order burst is judged, the order input amount in the distribution area is reduced by prolonging the distribution time length, canceling the counter-measures of commercial tenant activities and the like according to manual experience, the pressure of a logistics scheduling system is relieved, and the digestion of orders and the guarantee of user service under the condition of current logistics transport are guaranteed.
Disclosure of Invention
The inventor has tracked the effect of the existing solution and found that: the existing solution too depends on manual experience, has delay in judgment and low efficiency, and cannot timely reduce the pressure of a distribution area and the dispatching pressure faced by a logistics dispatching system.
Based on the foregoing, an embodiment of the present application provides a delivery pressure prediction method, including:
acquiring at least one characteristic parameter of a distribution area, wherein the at least one characteristic parameter represents distribution pressure of the distribution area in a current time period;
predicting a delivery force parameter of the delivery area in a future time period according to the at least one characteristic parameter;
determining a delivery pressure level of the delivery area over a future time period based on the delivery force parameter.
In an optional embodiment, the step of obtaining the at least one characteristic parameter includes: acquiring the distribution data of the distribution area in the current time period; extracting the at least one characteristic parameter from the delivery data.
In an alternative embodiment, the at least one characteristic parameter comprises a first characteristic parameter and a second characteristic parameter; accordingly, the step of extracting the at least one feature parameter includes: extracting the first characteristic parameter from the distribution data; extracting initial data corresponding to the second characteristic parameters from the distribution data; analyzing the initial data to obtain the second characteristic parameter.
In an alternative embodiment, the first characteristic parameter comprises at least one of: weather conditions, current pressure level; the second characteristic parameter includes at least one of: number of dispatchers, current pressure value, backlog order quantity, order concentration, order growth rate, order digestion rate.
In an alternative embodiment, the step of predicting the dispensing force parameter includes: and operating a prediction model corresponding to the future time period according to the at least one characteristic parameter to obtain the delivery force parameter.
In an optional embodiment, before running the predictive model corresponding to the future period, the method further comprises: acquiring characteristic parameters of the distribution area in a plurality of historical time periods; the plurality of historical periods and the future period belong to the same time period; and performing model training according to the characteristic parameters in the plurality of historical time periods to obtain the prediction model.
In an alternative embodiment, the dispensing force parameter comprises at least one of: the average distribution time length of the orders, the average distribution time length of the slowest N% orders, the distribution punctuality rate, the maximum distribution time length of the orders, the minimum distribution time length of the orders and the idle running distance; wherein N > 0.
In an optional embodiment, the method further comprises: determining a depressurization protocol matching the delivery pressure level; executing the decompression scheme or outputting prompt information for executing the decompression scheme.
In an alternative embodiment, the step of determining the depressurization protocol comprises: simulating to execute at least one candidate decompression scheme under the distribution environment corresponding to the distribution force level; selecting a candidate decompression scheme with a simulation result meeting preset requirements from the at least one candidate decompression scheme as the decompression scheme.
Correspondingly, the embodiment of the present application further provides a delivery pressure prediction apparatus, including:
the system comprises a first obtaining unit, a second obtaining unit and a control unit, wherein the first obtaining unit is used for obtaining at least one characteristic parameter of a distribution area, and the at least one characteristic parameter represents the distribution pressure of the distribution area in the current time period;
the prediction unit is used for predicting a distribution force parameter of the distribution area in a future time period according to the at least one characteristic parameter;
a determining unit for determining a delivery pressure level of the delivery area in a future time period according to the delivery force parameter.
In an optional embodiment, the first obtaining unit includes: an acquisition subunit and an extraction subunit; the acquisition subunit is configured to acquire the distribution data of the distribution area in the current time period; an extracting subunit, configured to extract the at least one feature parameter from the delivery data.
In an alternative embodiment, the at least one characteristic parameter comprises a first characteristic parameter and a second characteristic parameter; correspondingly, the extraction subunit is specifically configured to: extracting the first characteristic parameter from the distribution data; extracting initial data corresponding to the second characteristic parameters from the distribution data; analyzing the initial data to obtain the second characteristic parameter.
In an alternative embodiment, the first characteristic parameter comprises at least one of: weather conditions, current pressure level; the second characteristic parameter includes at least one of: number of dispatchers, current pressure value, backlog order quantity, order concentration, order growth rate, order digestion rate.
In an optional embodiment, the prediction unit is specifically configured to: and operating a prediction model corresponding to the future time period according to the at least one characteristic parameter to obtain the delivery force parameter.
In an alternative embodiment, the apparatus further comprises: a second obtaining unit and a model training unit; the second acquisition unit is used for acquiring characteristic parameters of the distribution area in a plurality of historical time periods; the plurality of historical periods and the future period belong to the same time period; and the model training unit is used for carrying out model training according to the characteristic parameters in the plurality of historical time periods so as to obtain the prediction model.
In an alternative embodiment, the dispensing force parameter comprises at least one of: the average distribution time length of the orders, the average distribution time length of the slowest N% orders, the distribution punctuality rate, the maximum distribution time length of the orders, the minimum distribution time length of the orders and the idle running distance; wherein N > 0.
In an alternative embodiment, the apparatus further comprises: a decompression processing unit; the determination unit is further configured to: determining a depressurization protocol matching the delivery pressure level; accordingly, the decompression processing unit is used for executing the decompression scheme or outputting the execution prompt information of the decompression scheme.
In an optional embodiment, the determining unit is specifically configured to: simulating to execute at least one candidate decompression scheme under the distribution environment corresponding to the distribution force level; selecting a candidate decompression scheme with a simulation result meeting preset requirements from the at least one candidate decompression scheme as the decompression scheme.
In the embodiment of the application, the distribution force parameter of the distribution area in the future time period is predicted according to the characteristic parameter representing the distribution pressure of the distribution area in the current time period; the distribution pressure grade of the distribution area in the future time period is determined based on the distribution force parameter of the distribution area in the future time period, manual experience is not relied on, the efficiency is high, in addition, the distribution pressure is predicted in advance before the single explosion situation occurs, the coping strategies are convenient to adopt in time, and the pressure of the distribution area and the dispatching pressure faced by a logistics dispatching system are favorably and timely reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flow chart of a delivery pressure prediction method according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart illustrating a delivery pressure prediction method according to another embodiment of the present disclosure;
FIG. 3 is a schematic flow chart illustrating a delivery pressure prediction method according to another embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a distribution pressure prediction apparatus according to another embodiment of the present application;
fig. 5 is a schematic structural diagram of a dispensing pressure predicting apparatus according to another embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, 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 application.
In a logistics distribution application scene, under special conditions such as severe weather and marketing activities launched by merchants, the phenomena of sudden increase of order quantity and excessive order backlog are likely to occur in some distribution areas, so that the distribution pressure of the distribution areas is too high, and a logistics scheduling system is also under great pressure. In the prior art, whether the single explosion condition occurs in the distribution area is judged based on manual experience, and when the single explosion condition is judged, a countermeasure is taken, so that the method has delay and low efficiency, and the pressure of the distribution area and the dispatching pressure faced by a logistics dispatching system cannot be timely reduced.
In view of the above problems, an embodiment of the present application provides a solution, and the main principle is: the distribution pressure level of the distribution area in the future time period is predicted in advance based on the distribution pressure condition of the distribution area in the current time period, manual experience is not relied on, and the prediction can be performed in advance, so that the handling measures can be taken conveniently and timely, the phenomena of sudden increase of the order quantity and excessive order overstock of the distribution area are avoided, and the digestion of the order and the guarantee of user service are ensured under the existing logistics and transportation conditions of the distribution area.
The technical solution of the present application will be described in detail with reference to the following specific examples.
Fig. 1 is a schematic flow chart of a delivery pressure prediction method according to an embodiment of the present disclosure. As shown in fig. 1, the method includes:
101. at least one characteristic parameter of the distribution area is obtained, and the at least one characteristic parameter represents the distribution pressure of the distribution area in the current time period.
102. And predicting a delivery force parameter of the delivery area in a future period according to the at least one characteristic parameter.
103. And determining the distribution pressure level of the distribution area in the future period according to the distribution force parameter of the distribution area in the future period.
In this embodiment, the range of the distribution area is not limited, and may be adaptively defined according to application requirements. For example, the delivery area may be a city-level area, or may be a provincial area, or may be a business district area, and so on. The business circles refer to commercial activity areas in cities.
In this embodiment, it is necessary to predict the delivery pressure level of the delivery area in the future period according to the delivery pressure condition of the delivery area in the current period. The distribution pressure condition of the distribution area in the current time period can be represented by at least one characteristic parameter of the distribution area in the current time period. Then, at least one characteristic parameter in the delivery area may be obtained, where the at least one characteristic parameter is a parameter that may represent the delivery pressure condition of the delivery area in the current period. By way of example, the at least one characteristic parameter may include: weather conditions, current pressure level, number of distributors, current pressure value, backlog order quantity, order concentration, order growth rate, order digestion rate and the like, wherein the parameters can reflect distribution pressure conditions of a distribution area in the current time period.
In this embodiment, the current time period refers to the current time and a period of time before the current time, and the specific time value may depend on the application requirement, for example, the current time period may be the last 2 days, 1 day, 1 hour, 2 hours, 3 hours, 50 minutes, 30 minutes, and the like. Accordingly, the future period may be a period of time after the current time, for example, may be 2 days, 1 day, 1 hour, 2 hours, 3 hours, 50 minutes, 40 minutes, 20 minutes after the current time. It should be noted that the time length of the current time interval may be the same as or different from the time length of the future time interval.
Based on the at least one characteristic parameter, a delivery force parameter of the delivery area within a future period of time may be predicted. The distribution force parameter is used for reflecting the distribution force (or logistics transportation force) of the distribution area in a future period. The present embodiment does not limit the distribution force parameter, and all the parameters that can represent the distribution force are applicable to the embodiments of the present application. Generally, the stronger the delivery force within the delivery area, the faster the delivery rate of orders within the delivery area, the fewer the number of overtime orders, the shorter the average delivery duration of orders, the shorter the total overtime duration, the greater the total number of orders delivered in the same time, the fewer the distance the deliverer has to empty, and so on. Based on this, the dispensing force parameter of the dispensing zone within the future period of time may be, but is not limited to, at least one of: total amount of orders in the delivery area in the future time period, average delivery duration of orders, number of overtime orders, delivery punctuality rate, total overtime duration, maximum delivery duration of orders, minimum delivery duration of orders, distance to empty, and the like.
The stronger the distribution force of the distribution area is, which means that the order backlog amount in the distribution area is less to a certain extent, and the capability of coping with sudden increase of the order amount is relatively stronger, the distribution pressure grade in the distribution area is relatively lower; conversely, a weaker distribution force in the distribution area means that the order backlog in the distribution area is large, and the relatively worse the ability to cope with a sudden increase in the order amount, the distribution pressure level in the distribution area increases. Further, the distribution force in the distribution area is embodied by combining the distribution force parameter, and then the distribution pressure level of the distribution area in the future period can be determined based on the distribution force parameter of the distribution area in the future period.
Furthermore, based on the distribution pressure level of the distribution area in the future time period, corresponding countermeasures can be adopted in the current time period, and the regulation is not required to be carried out after the phenomena of sudden increase of the order quantity, more order backlog quantity and the like occur, so that the distribution pressure of the distribution area in the future time period can be reduced, the distribution pressure of a logistics scheduling system is relieved, a virtuous circle can be formed, and the digestion of orders and the guarantee of user service can be guaranteed in any time period in the distribution area under the condition of the existing logistics transportation.
In the embodiment, the distribution force parameter of the distribution area in the future period is predicted according to the characteristic parameter representing the distribution pressure of the distribution area in the current period; the distribution pressure grade of the distribution area in the future period is determined based on the distribution force parameter of the distribution area in the future period, manual experience is not relied on, the efficiency of determining the distribution pressure grade is high, in addition, the advance prediction of the distribution pressure is realized before the single explosion situation occurs, the timely adoption of a coping strategy is convenient, and the pressure of the distribution area and the dispatching pressure faced by a logistics dispatching system are favorably and timely lightened.
In the foregoing embodiment or the following embodiments, the step of acquiring at least one characteristic parameter for reflecting the delivery pressure of the delivery area in the current time period may be: acquiring distribution data of a distribution area in a current time period; at least one characteristic parameter is extracted from the delivery data of the delivery area in the current time period. The delivery data refers to all data related to delivery and directly accessible to the delivery area in the current time period, and includes, for example, the status of the delivery person, the location of the delivery person, the starting location of the order, the destination location of the order, the delivery status of the order, the weather condition, the current pressure level, and the like. The current pressure level refers to a distribution pressure level of a distribution area in a current time period, and may be obtained by manual calculation, or may be obtained by prediction of a characteristic parameter in a previous time period by using the method provided in this embodiment.
Optionally, in an application example, the at least one characteristic parameter includes a first characteristic parameter and a second characteristic parameter. The first characteristic parameter refers to a characteristic parameter that can be directly obtained from distribution data of the distribution area in the current period. By way of example, the first characteristic parameter comprises at least one of: weather conditions, current pressure level, etc. The second characteristic parameter refers to a parameter that cannot be directly obtained from the distribution data of the distribution area in the current time period and needs to be analyzed or calculated again. By way of example, the second characteristic parameter comprises at least one of: number of dispatchers, current pressure value, backlog order quantity, order concentration, order growth rate, order digestion rate.
In the above application example, the step of obtaining at least one feature parameter may be: extracting a first characteristic parameter from distribution data of a distribution area in a current time period; extracting initial data corresponding to a second characteristic parameter from the distribution data; the initial data is analyzed to obtain a second characteristic parameter.
Optionally, in an application example, the at least one characteristic parameter may only include the first characteristic parameter. The step of obtaining at least one feature parameter may be: and extracting a first characteristic parameter from the distribution data of the distribution area in the current time period.
Optionally, in an application example, the at least one characteristic parameter may only include the second characteristic parameter. The step of obtaining at least one feature parameter may be: extracting initial data corresponding to the second characteristic parameter from distribution data of the distribution area in the current time period; the initial data is analyzed to obtain a second characteristic parameter.
In each of the above application examples, the step of analyzing the initial data to obtain the second characteristic parameter is illustrated as follows:
the number of the distributors can be obtained by counting according to the states and/or positions of the distributors in the distribution data; accordingly, the status and/or location of the dispatchers is the initial data corresponding to the number of dispatchers.
The current pressure value can be obtained by analyzing and calculating the quantity and the state of orders in the distribution data and the quantity and the state of distributors; correspondingly, the number and the state of the orders and the number and the state of the distributors are initial data corresponding to the current pressure value.
The backlog order quantity can be obtained by analyzing the distribution state and quantity of orders in the distribution data; correspondingly, the distribution state and the quantity of the order are initial data corresponding to the backlog order quantity.
The order concentration ratio can be obtained by analyzing the initial position of the order in the distribution data; correspondingly, the initial position of the order is the initial data corresponding to the order concentration.
The order growth speed can be obtained by analyzing the number of newly added orders in the distribution data within a certain time; correspondingly, the number of the newly added orders in the certain time is the initial data corresponding to the order growth speed.
The order digestion speed can be obtained by analyzing the number of orders which are delivered within a certain time in the delivery data; correspondingly, the number of the orders which are delivered within the certain time is the initial data corresponding to the order digestion speed.
Preferably, a plurality of characteristic parameters which represent the distribution pressure of the distribution area in the current time period can be adopted, so that the distribution pressure condition of the distribution area can be more comprehensively and effectively represented, and the accuracy of the prediction result is further improved.
In the foregoing embodiment or the following embodiments, the step of predicting the delivery force parameter of the delivery area in the future period may be: and operating a predictive model corresponding to the future time period according to at least one characteristic parameter representing the distribution pressure of the distribution area in the current time period to obtain a distribution force parameter of the distribution area in the future time period.
Based on the prediction model, in another embodiment of the application, a prediction model corresponding to a future time period can be obtained in advance based on the characteristic parameters of the distribution area in the historical time period, so as to provide conditions for the prediction process, and improve the prediction efficiency. As shown in fig. 2, another embodiment of the present application provides a delivery pressure prediction method, including:
201. acquiring characteristic parameters of a distribution area in a plurality of historical time periods; the plurality of historical periods belong to the same time period as the future period.
202. And performing model training according to the characteristic parameters in the plurality of historical time periods to obtain a prediction model corresponding to the future time period.
203. At least one characteristic parameter of the distribution area is obtained, and the at least one characteristic parameter represents the distribution pressure of the distribution area in the current time period.
204. And according to at least one characteristic parameter, operating a prediction model corresponding to the future time period to obtain a distribution force parameter of the distribution area in the future time period.
205. And determining the distribution pressure level of the distribution area in the future period according to the distribution force parameter of the distribution area in the future period.
In the present embodiment, the time is divided into different time periods. The time period may be divided according to application requirements. For example, the time may be divided into an early peak time period, a midday peak time period, a late peak time period, a flat peak time period, and the like in units of days, or may be divided into a first time period, a second time period, a third time period, a fourth time period, and the like. For example, the following steps are carried out: the early peak period or first period may be 7 o 'clock to 8 o' clock half, the mid-day peak period or second period may be 11 o 'clock to 1 o' clock half, the late peak period or third period may be 5 o 'clock half to eight o' clock, and the rest of the time belongs to the flat peak period or fourth period. In addition to this, the time may be divided into the first time period, the second time period, and the third time period in units of weeks. For example, the following steps are carried out: the first time period is monday to wednesday, the second time period is thursday to friday, and the third time period is saturday and sunday.
It is worth mentioning that the current period or the future period may belong to a certain time period, or may be a certain time period.
In this embodiment, the prediction model corresponding to each time period may be generated in advance based on the characteristic parameters of the delivery area in the history period corresponding to each time period. For each time segment, the generation process of the corresponding prediction model is the same, and this embodiment takes the time segment to which the future time segment belongs as an example to explain the generation process of the prediction model. Specifically, characteristic parameters of the distribution section in a plurality of historical time periods are obtained, and model training is performed according to the characteristic parameters of the distribution section in the plurality of historical time periods to obtain a prediction model corresponding to a future time period. The characteristic parameters of the distribution area in the plurality of historical periods represent the distribution pressure of the distribution area in the plurality of historical periods. Here, the plurality of history periods and the future period belong to the same time period, but the history periods and the future period are not required to be temporally completely corresponded.
For example, taking the above-mentioned early peak time period, the midday peak time period, the late peak time period and the peak-less time period as an example, assuming that the current time period is from 9 o ' clock to 10 o ' clock of 11 month and 20 days, and the future time period is from 11 o ' clock to 12 o ' clock of 11 month and 20 days, and the future time period belongs to the midday peak time period, the plurality of history time periods may be a time period between 11 o ' clock and 1 o ' clock in multiple days before 11 month and 20 days, and may include, for example, a history time period represented by 11 o ' clock to 12 o ' clock of 11 month and 19 days, a history time period represented by 12 o ' clock to 1 o ' clock of 11 month and 18 days, and a history time period represented by 11 o ' clock.
It should be noted that, the obtaining step of the characteristic parameters in the plurality of history periods may be: the characteristic parameters of the distribution areas in each historical period in the plurality of historical periods are respectively obtained from the distribution data of the distribution areas in each historical period in the plurality of historical periods. The characteristic parameter in each historical period is at least one. Optionally, the feature parameters in each historical period include a first feature parameter and a second feature parameter, and then the first feature parameter of the distribution area in each historical period may be extracted from the distribution data of the distribution area in each historical period respectively; extracting initial data associated with second characteristic data of the distribution region in each historical period from distribution data of the distribution region in each historical period; and analyzing initial data associated with the second characteristic data of the distribution area in each historical period to obtain a second characteristic parameter of the distribution area in each historical period.
In the model training process, the feature parameters in a plurality of historical periods may be used as training samples, the distribution force parameters of the distribution area in the time periods to which the plurality of historical periods belong may be used as training targets, and a machine learning algorithm, such as a regression method, is used to perform model training, so as to obtain prediction models corresponding to the time periods to which the plurality of historical periods belong.
In practical application, the time period to which the future time period belongs can be directly determined, and the prediction model corresponding to the time period to which the future time period belongs is obtained and used as the prediction model corresponding to the future time period; taking at least one characteristic parameter reflecting the distribution pressure of the distribution area in the current time period as a model parameter, operating a prediction model corresponding to the future time period, and obtaining the distribution force parameter of the distribution area in the future time period.
Optionally, the predicted delivery force parameter of the delivery area in the future time period in this embodiment includes at least one of the following: the average distribution time length of the orders, the average distribution time length of the slowest N% orders, the distribution punctuality rate, the maximum distribution time length of the orders, the minimum distribution time length of the orders and the idle running distance; wherein N > 0. The average order delivery time is the most direct parameter affecting the user experience, and is also the most direct parameter reflecting the delivery force condition of the delivery area. The average delivery duration of the slowest N% order is also a more direct parameter affecting the user experience, and may also reflect the delivery force situation in the delivery area.
Alternatively, one predictive model may be trained for all delivery force parameters, which may output all delivery force parameters simultaneously. For example, a predictive model may be trained on the average delivery duration of orders and the average delivery duration of the slowest N% order, and running the predictive model may output the average delivery duration of orders and the average delivery duration of the slowest N% order.
Alternatively, one predictive model may be trained for each delivery force parameter, with different predictive models outputting different delivery force parameters. For example, a prediction model may be trained for the average order distribution time length and the average distribution time length of the slowest N% order, respectively, the prediction model corresponding to the average order distribution time length may output the average order distribution time length, and the prediction model corresponding to the average distribution time length of the slowest N% order may output the average distribution time length of the slowest N% order.
For descriptions of other steps in this embodiment, reference may be made to the foregoing embodiments, which are not described herein again.
In the embodiment, model training is performed in advance based on the characteristic parameters of the distribution area in the historical period to obtain the prediction model, and the prediction model is directly operated in the actual prediction process, so that the prediction efficiency and the accuracy of the prediction result are improved.
In the foregoing embodiment or the following embodiments, after determining the distribution pressure level of the distribution area in the future period, a countermeasure may be flexibly and timely taken according to the distribution pressure level of the distribution area in the future period, so as to solve the problem of the distribution pressure that the distribution area is about to face. Based on this, a pressure solution is given in a further embodiment of the present application. This embodiment can be implemented based on the embodiment shown in fig. 1, and as shown in fig. 3, after step 103, further includes:
104. a reduced pressure schedule is determined that matches the delivery pressure level of the delivery area over the future time period.
105. Executing the decompression scheme or outputting prompt information for executing the decompression scheme.
Alternatively, the step of determining the decompression scheme may be: simulating to execute at least one candidate decompression scheme under the distribution environment corresponding to the distribution force level; and selecting a candidate decompression scheme with a simulation result meeting a preset requirement from at least one candidate decompression scheme as the decompression scheme matched with the distribution pressure grade. The at least one candidate reduced pressure protocol comprises: pressure reduction schemes of different gears. For example, the candidate depressurization protocol for the first gear is a dispensing length increased by 5 minutes, the candidate depressurization protocol for the second gear is a dispensing length increased by 10 minutes, the candidate depressurization protocol for the third gear is a dispensing length increased by 15 minutes, the candidate depressurization protocol for the fourth gear is a closing of a merchant's activities, and so on.
The above-mentioned preset requirements are used to select candidate pressure reduction schemes that can reduce the delivery pressure level of the delivery area in the future period, and may be adaptively set according to application requirements.
Optionally, the candidate decompression schemes may be subjected to simulation scheduling according to the delivery pressure levels and in combination with historical order conditions, and finally, a mapping relationship between the delivery pressure levels and the decompression schemes is obtained. Based on this, the determination step of the decompression scheme may be: and inquiring the mapping relation between the distribution pressure level and the decompression scheme according to the distribution pressure level of the distribution area in the future period, and determining the matched decompression scheme.
After obtaining the depressurization protocol, the depressurization protocol may be executed directly to reduce the distribution pressure of the distribution area, or an execution prompt of the depressurization protocol may be output to provide an operator to activate the depressurization protocol to reduce the distribution pressure of the distribution area.
It should be noted that the execution subjects of the steps of the methods provided in the above embodiments may be the same device, or different devices may be used as the execution subjects of the methods. For example, the execution subjects of step 201 to step 203 may be device a; for another example, the execution subject of steps 201 and 202 may be device a, and the execution subject of step 203 may be device B; and so on.
Fig. 4 is a schematic structural diagram of a dispensing pressure predicting apparatus according to another embodiment of the present disclosure. As shown in fig. 4, the apparatus includes: a first acquisition unit 41, a prediction unit 42, and a determination unit 43.
A first obtaining unit 41, configured to obtain at least one characteristic parameter of a distribution area, where the at least one characteristic parameter represents a distribution pressure of the distribution area in a current time period;
a prediction unit 42 for predicting a delivery force parameter of the delivery area in a future period of time based on the at least one characteristic parameter.
A determining unit 43 for determining a delivery pressure level of the delivery area in a future time period based on the delivery force parameter.
In an alternative embodiment, as shown in fig. 5, one implementation structure of the first obtaining unit 41 includes: an acquisition sub-unit 411 and an extraction sub-unit 412.
An obtaining subunit 411, configured to obtain the delivery data of the delivery area in the current time period.
An extracting subunit 412, configured to extract at least one feature parameter from the distribution data.
In an alternative embodiment, the at least one characteristic parameter includes a first characteristic parameter and a second characteristic parameter. Accordingly, the extraction subunit 412 is specifically configured to: extracting a first characteristic parameter from the distribution data; extracting initial data corresponding to the second characteristic parameters from the distribution data; the initial data is analyzed to obtain a second characteristic parameter.
In an alternative embodiment, the first characteristic parameter comprises at least one of: weather conditions, current pressure level.
In an alternative embodiment, the second characteristic parameter comprises at least one of: number of dispatchers, current pressure value, backlog order quantity, order concentration, order growth rate, order digestion rate.
In an alternative embodiment, the prediction unit 42 is specifically configured to: and operating a prediction model corresponding to the future time period according to the at least one characteristic parameter to obtain a distribution force parameter.
In an alternative embodiment, as shown in fig. 5, the apparatus further comprises: a second acquisition unit 44 and a model training unit 45.
A second obtaining unit 44, configured to obtain characteristic parameters of the distribution area in a plurality of historical time periods before the prediction unit 42 runs the prediction model corresponding to the future time period; the plurality of historical periods belong to the same time period as the future period.
And the model training unit 45 is used for performing model training according to the characteristic parameters in a plurality of historical time periods to obtain a prediction model.
In an alternative embodiment, the dispensing force parameter comprises at least one of: the average distribution time length of the orders, the average distribution time length of the slowest N% orders, the distribution punctuality rate, the maximum distribution time length of the orders, the minimum distribution time length of the orders and the idle running distance; wherein N > 0.
In an alternative embodiment, as shown in fig. 5, the apparatus further comprises: a decompression processing unit 46.
The determination unit 43 is further configured to: a reduced pressure protocol is determined that matches the dispense pressure rating. Accordingly, the decompression processing unit 46 is configured to execute the decompression scheme or output a prompt for execution of the decompression scheme.
Optionally, the determining unit 43 is specifically configured to, when determining the decompression scheme: simulating to execute at least one candidate decompression scheme under the distribution environment corresponding to the distribution force level; and selecting a candidate decompression scheme with a simulation result meeting preset requirements from at least one candidate decompression scheme as the decompression scheme.
The distribution pressure prediction apparatus provided in this embodiment may be used to perform the processes of the above method embodiments, and detailed descriptions thereof are omitted.
The distribution pressure prediction device provided in this embodiment predicts a distribution force parameter of a distribution area in a future period of time according to a characteristic parameter representing distribution pressure of the distribution area in a current period of time; the distribution pressure grade of the distribution area in the future time period is determined based on the distribution force parameter of the distribution area in the future time period, manual experience is not relied on, the efficiency is high, in addition, the distribution pressure is predicted in advance before the single explosion situation occurs, the coping strategies are convenient to adopt in time, and the pressure of the distribution area and the dispatching pressure faced by a logistics dispatching system are favorably and timely reduced.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (14)

1. A delivery pressure prediction method, comprising:
acquiring at least one characteristic parameter of a distribution area, wherein the at least one characteristic parameter represents distribution pressure of the distribution area in a current time period;
predicting a delivery force parameter of the delivery area in a future time period according to the at least one characteristic parameter; wherein the distribution force parameter is used for reflecting the logistics transportation capacity of the distribution area in the future time period;
determining a distribution pressure level of the distribution area in a future time period according to the distribution force parameter;
the step of predicting the dispensing force parameter comprises:
according to the at least one characteristic parameter, operating a prediction model corresponding to the future time period to obtain the distribution force parameter;
before the operation of the prediction model corresponding to the future period, the method further comprises the following steps:
acquiring characteristic parameters of the distribution area in a plurality of historical time periods; the plurality of historical periods and the future period belong to the same time period;
and performing model training according to the characteristic parameters in the plurality of historical time periods to obtain the prediction model.
2. The method according to claim 1, wherein the step of obtaining the at least one characteristic parameter comprises:
acquiring the distribution data of the distribution area in the current time period;
extracting the at least one characteristic parameter from the delivery data.
3. The method of claim 2, wherein the at least one characteristic parameter comprises a first characteristic parameter and a second characteristic parameter;
the step of extracting the at least one characteristic parameter comprises the following steps:
extracting the first characteristic parameter from the distribution data;
extracting initial data corresponding to the second characteristic parameters from the distribution data;
analyzing the initial data to obtain the second characteristic parameter.
4. The method of claim 3, wherein the first characteristic parameter comprises at least one of: weather conditions, current pressure level;
the second characteristic parameter includes at least one of: number of dispatchers, current pressure value, backlog order quantity, order concentration, order growth rate, order digestion rate.
5. The method of claim 1, wherein the dispensing force parameter comprises at least one of:
the average distribution time length of the orders, the average distribution time length of the slowest N% orders, the distribution punctuality rate, the maximum distribution time length of the orders, the minimum distribution time length of the orders and the idle running distance; wherein N > 0.
6. The method according to any one of claims 1-4, further comprising:
determining a depressurization protocol matching the delivery pressure level;
executing the decompression scheme or outputting prompt information for executing the decompression scheme.
7. The method of claim 6, wherein the step of determining the depressurization protocol comprises:
simulating to execute at least one candidate decompression scheme under the distribution environment corresponding to the distribution force level;
selecting a candidate decompression scheme with a simulation result meeting preset requirements from the at least one candidate decompression scheme as the decompression scheme.
8. A delivery pressure prediction apparatus, comprising:
the system comprises a first obtaining unit, a second obtaining unit and a control unit, wherein the first obtaining unit is used for obtaining at least one characteristic parameter of a distribution area, and the at least one characteristic parameter represents the distribution pressure of the distribution area in the current time period;
the prediction unit is used for predicting a distribution force parameter of the distribution area in a future time period according to the at least one characteristic parameter; wherein the distribution force parameter is used for reflecting the logistics transportation capacity of the distribution area in the future time period;
the determining unit is used for determining the distribution pressure level of the distribution area in the future time period according to the distribution force parameter;
the prediction unit is specifically configured to:
according to the at least one characteristic parameter, operating a prediction model corresponding to the future time period to obtain the distribution force parameter;
the device further comprises:
the second acquisition unit is used for acquiring the characteristic parameters of the distribution area in a plurality of historical time periods; the plurality of historical periods and the future period belong to the same time period;
and the model training unit is used for carrying out model training according to the characteristic parameters in the plurality of historical time periods so as to obtain the prediction model.
9. The apparatus of claim 8, wherein the first obtaining unit comprises:
the acquisition subunit is used for acquiring the distribution data of the distribution area in the current time period;
an extracting subunit, configured to extract the at least one feature parameter from the delivery data.
10. The apparatus of claim 9, wherein the at least one characteristic parameter comprises a first characteristic parameter and a second characteristic parameter;
the extraction subunit is specifically configured to:
extracting the first characteristic parameter from the distribution data;
extracting initial data corresponding to the second characteristic parameters from the distribution data;
analyzing the initial data to obtain the second characteristic parameter.
11. The apparatus according to any of the claims 8, wherein the determining unit is further configured to: determining a depressurization protocol matching the delivery pressure level;
the device further comprises:
and the decompression processing unit is used for executing the decompression scheme or outputting the execution prompt information of the decompression scheme.
12. The apparatus according to claim 11, wherein the determining unit is specifically configured to:
simulating to execute at least one candidate decompression scheme under the distribution environment corresponding to the distribution force level;
selecting a candidate decompression scheme with a simulation result meeting preset requirements from the at least one candidate decompression scheme as the decompression scheme.
13. A server, comprising: a memory, a processor, and a communications component, the memory to store a computer program;
the processor is coupled with the memory and the communication component for executing a computer program for performing the method of any of claims 1-7.
14. A computer-readable storage medium, in which a computer program is stored which, when being executed by a computer, is able to carry out the method of any one of claims 1 to 7.
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