CN113222308A

CN113222308A - Order allocation method and device, electronic equipment and storage medium

Info

Publication number: CN113222308A
Application number: CN202010072549.7A
Authority: CN
Inventors: 谭佳楠; 邹鹏; 张国伟; 张涛; 孔兵
Original assignee: Beijing Sankuai Online Technology Co Ltd
Current assignee: Beijing Sankuai Online Technology Co Ltd
Priority date: 2020-01-21
Filing date: 2020-01-21
Publication date: 2021-08-06
Anticipated expiration: 2040-01-21
Also published as: CN113222308B

Abstract

The embodiment of the application discloses an order distribution method and device. The method comprises the following steps: determining the target transport capacity of the target type to be subjected to the missed order according to the transport capacity supply and demand tension degree of the target area and the estimated timeout duration of the missed order in the target area; and under the condition that the supply and demand tension degree of the transport capacity meets a first preset condition, determining whether to distribute the missed order to the target transport capacity according to the predicted income and cost of the target transport capacity for receiving the missed order. The invention can reduce the rate of the tail orders in the target area.

Description

Order allocation method and device, electronic equipment and storage medium

Technical Field

Embodiments of the present application relate to the field of computer technologies, and in particular, to an order allocation method and apparatus, an electronic device, and a computer-readable storage medium.

Background

The capacity of the distribution field can be divided into two categories, crowdsourcing and exclusive distribution. In the crowdsourcing mode, due to lack of strong control over the capacity, there may be situations where the rider chooses to order and is unwilling to take over some of the hard-to-deliver orders, resulting in frequent generation of a trail order. The order form generated by the rider is mainly divided into two types, namely an order form cancelled by a user/a merchant due to unmanned order taking, and an order form with a serious delivery overtime due to too long order taking time.

In order to reduce the generation of the tail order, in the related art, a model is mainly used for predicting that the order which is likely to become the tail order exists in the orders, or the orders which are not taken by people for a long time are determined through manual rules, and the distribution fee is added for the two orders which are likely to become the tail order, so that the orders are attracted to be popular and taken by riders.

However, the problem of outstanding tailnotes in the crowdsourcing mode cannot be fundamentally solved only by attracting and guiding crowdsourcing riders to accept the tailnotes in the pricing level.

Disclosure of Invention

The embodiment of the application provides an order distribution method to solve the problem of outstanding tail orders in the related art.

In order to solve the above problem, in a first aspect, an embodiment of the present application provides an order allocation method, including:

determining the target transport capacity of the target type to be subjected to the missed order according to the transport capacity supply and demand tension degree of the target area and the estimated timeout duration of the missed order in the target area;

and under the condition that the supply and demand tension degree of the transport capacity meets a first preset condition, determining whether to distribute the missed order to the target transport capacity according to the predicted income and cost of the target transport capacity for receiving the missed order.

Optionally, under the condition that the target capacity is switched from accepting a new order to accepting a trailer, performing a first preset weighting operation on a first timeout probability of the trailer, a second probability of damage to a real object corresponding to the trailer, and an order price of the trailer to generate a predicted profit of the target capacity accepting the missed order;

optionally, in a case where the target capacity is switched from accepting a new order to accepting a trailer and the new order is accepted by a first capacity, calculating a second timeout probability that the new order is increased by the target capacity accepting to being switched from the first capacity accepting, calculating route information that the new order is increased by the target capacity accepting to being switched from the first capacity accepting, and calculating a dispatch efficiency loss in the target area that the new order is switched from the target capacity accepting to being switched from the first capacity accepting; and performing second preset weighting operation on the distance information and the order dispatching efficiency loss of the second overtime probability to generate the estimated cost of the target capacity for bearing the missed order.

In a second aspect, an embodiment of the present application provides an order distribution apparatus, including:

the first determination module is used for determining the target transport capacity of the target type to be subjected to the missed order according to the transport capacity supply and demand tension degree of the target area and the estimated timeout duration of the missed order in the target area;

and the second determining module is used for determining whether the missed orders are allocated to the target transport capacity according to the predicted income and cost of the target transport capacity for receiving the missed orders under the condition that the supply and demand tension degree of the transport capacity meets a first preset condition.

Optionally, the apparatus further comprises:

the first generation module is used for performing first preset weighting operation on a first overtime probability of the tail order, a second probability of damage of a real object corresponding to the tail order and the order price of the tail order to generate predicted income of the target transport capacity after receiving the missed order under the condition that the target transport capacity is switched from receiving a new order to receiving the tail order;

optionally, the apparatus further comprises:

a second generation module, configured to calculate a second timeout probability that the new order is increased due to the target capacity being accepted to be switched from the target capacity to the first capacity when the target capacity is accepted to accept the tail sheet and the new order is accepted by the first capacity, calculate route information that the new order is increased due to the target capacity being accepted to be switched from the target capacity to the first capacity, and calculate a dispatch efficiency loss in the target area due to the new order being switched from the target capacity to the first capacity; and performing second preset weighting operation on the distance information and the order dispatching efficiency loss of the second overtime probability to generate the estimated cost of the target capacity for bearing the missed order.

In a third aspect, an embodiment of the present application further discloses an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the order allocation method according to the embodiment of the present application when executing the computer program.

In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, performs the steps of the order distribution method disclosed in the present application.

In the embodiment of the invention, the target capacity of the target type for intervening and accepting the missed orders which may become the tail orders can be determined according to the overall capacity supply and demand tension degree of the target area and the estimated timeout duration of the missed orders in the target area, whether the missed orders are allocated to the target capacity or not is determined according to the estimated profit and cost of accepting the missed orders which may become the tail orders by the target capacity under the condition that the capacity supply and demand tension degree meets a first preset condition, the tail order rate in the target area can be reduced by means of the capacity of the target type, the problem of reduction of the order taking rate in the target area due to reduction of the tail order rate in the target area can be avoided, and a certain order taking rate can be maintained while the tail order rate is ensured to be reduced.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.

FIG. 1 is a flow chart of the steps of an order distribution method according to one embodiment of the present application;

FIG. 2 is a flow chart of steps of an order distribution method according to another embodiment of the present application;

FIG. 3 is a first schematic diagram of a coordinate system of an order allocation method according to an embodiment of the present application;

FIG. 4 is a second schematic coordinate system diagram of an order allocation method according to an embodiment of the present application;

FIG. 5 is a block diagram of an order distribution apparatus according to an embodiment of the present application;

FIG. 6 schematically shows a block diagram of a computing processing device for performing a method according to the present disclosure; and

fig. 7 schematically shows a storage unit for holding or carrying program code implementing a method according to the present disclosure.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. 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.

At present, the crowdsourcing transport capacity is easy to generate a tail list, so the crowdsourcing tail experience problem is still outstanding, and the crowdsourcing transport capacity is still far away from the strongly controlled special delivery transport capacity.

In the related art, crowdsourced capacity is attracted to accept orders which may become tail orders mainly through price strategies, but the tail experience is not optimized at the level of capacity management and scheduling. On the contrary, the price strategy can only solve a part of the problems, and the overall tail order experience of crowdsourcing still has a large gap with the exclusive delivery. In order to optimize the user experience of tail orders in the crowdsourcing mode, the delivery platform corresponding to the method in the embodiments of the present invention builds a set of strongly controlled capacity for delivery services, that is, the capacity of a target type, which may be named as a running rider (also not belonging to a dedicated delivery) in an example, for convenience of description, the capacity of the target type is expressed by the running rider in each of the following embodiments. The delivery platform can give the running rider certain degree of slope in the dispatch, guarantees their certain salary advantage, lets them help the platform digest the order that probably fell into the tail sheet.

Under the condition that the running rider strongly controls the transport capacity, the method provided by the embodiment of the invention can distribute the orders which can become the tail order to the running rider through a technical means, so that the optimal solution of the whole experience is realized, and the tail order in a crowdsourcing mode is reduced.

Specifically, the embodiment of the present application discloses an order allocation method, as shown in fig. 1, the method may include steps 100 and 105:

step 100, determining target transport capacity of a target type to be subjected to the missed order according to the transport capacity supply and demand tension degree of a target area and the estimated timeout duration of the missed order in the target area;

the difference in the exercise capacity supply and demand tension of the target area and the difference in the estimated timeout duration of the missed orders in the target area cause the target exercise capacity (specifically, which racing rider) of the target type (such as the racing rider) to be subjected to the missed orders to be different, and the difference can be reflected in the difference between the time when the missed orders are allocated to the racing riders and the difference between the time when the missed orders are allocated to the racing riders.

Further, the target area is a geographical area. For example, a city may be divided into geographic regions, where the target region is the geographic region where a trail list needs to be reduced by dispatching a racing rider.

A geographical area may have an indication of how tight the capacity is in supply and demand.

When determining the degree of stress of the capacity supply and demand in a geographic area, the stress determination can be realized by a pre-trained neural network model, and the input characteristics of the model comprise: missed orders rate over a preset duration (e.g., 10 minutes) within the geographic area, capacity load within the geographic area (e.g., the sum of the number of running and crowdsourcing riders within the geographic area); the output result of the model is the value of the transport capacity supply and demand tension degree or the level of the transport capacity supply and demand tension degree (each level may correspond to a numerical range of the transport capacity supply and demand tension degree, for example, 5 levels including 1 to 5 levels).

Wherein, the lower the numerical value, the lower the grade, the less tense the supply and demand of the transport capacity, that is, the transport capacity in the geographic area is enough; the higher the value, the higher the grade, the more tense the capacity supply and demand, i.e. the capacity in the geographical area is insufficient.

Then in this step, the model may be used to obtain the level of the transportation capacity supply and demand tension of the target area, for example, the level of the transportation capacity supply and demand tension.

And 105, under the condition that the supply and demand tension degree of the transport capacity meets a first preset condition, determining whether to distribute the missed order to the target transport capacity according to the predicted income and cost of the target transport capacity for receiving the missed order.

That is, in the case of the capacity supply and demand in the target area being not tense, after the target capacity to which the missed order M is to be allocated is determined, and before the missed order is allocated to the target capacity, for example, the running rider S1, that is, each time after a scheduling result for a missed order that may become a trailer is obtained, the profit reward obtained by the running rider S1 for allocating the trailer and the cost for allocating the trailer (both the profit reward and the cost for payment are dimensionless units) need to be estimated. Thereby determining whether to distribute the order M to the running rider S1.

Optionally, on the basis of the above embodiment, in an embodiment, when the above step 100 is executed, the following steps may be implemented by steps 401 and 402:

step 401, determining an order allocation strategy corresponding to the transport capacity of the target type according to the transport capacity supply and demand tension degree of the target area;

in this step, the order allocation strategy corresponding to the target type of the exercise capacity (i.e., the running rider) may be reasonably determined by combining the difference in the exercise capacity supply and demand tension degree of the target area, i.e., the difference in the exercise capacity supply and demand tension degree, so that the order allocation strategy for allocating missed orders to the running rider is different.

The specific difference in order allocation strategy may be reflected in the difference between the morning and evening when the running rider takes over and stops taking over missed orders that may become trailers, and the difference in the degree of following the road between the running rider and the missed orders to be taken over.

For example, the more strenuous the capacity is, the later the time it takes to control the running rider to intervene in taking over missed orders that may become trailers.

Step 402, if the estimated timeout duration of the missed order in the target area is matched with the order distribution strategy, determining the target transport capacity of the target type to be subjected to the missed order according to the order distribution strategy.

For the order distribution strategy configured for the target type of the capacity (such as the running rider), information about the overtime length of the order and the screening condition of the target type of the capacity can be carried in the order distribution strategy, and in the order distribution strategy, the difference of the overtime lengths can enable the screening condition of the running rider to be different.

For example, in this step, when the estimated timeout period of the missed order matches the timeout period in the order allocation policy, the specific rider of the running rider to receive the missed order may be determined based on the screening condition for, for example, the running rider corresponding to the estimated timeout period in the order allocation policy.

In the embodiment of the invention, the order distribution strategy corresponding to the transport capacity of the target type can be reasonably determined according to the transport capacity supply and demand tension degree of the target area, then, the order distribution strategy matched with the estimated timeout duration of the missed order in the target area is determined, so that the target transport capacity of the target type for bearing the missed order is determined according to the order distribution strategy, the target transport capacity for solving the tail order can be flexibly and accurately positioned by setting the order distribution strategy, the certainty of digesting the tail order can be ensured by utilizing the transport capacity of the target type which is strongly controlled, and the problem of tail projection is solved.

Optionally, on the basis of the above embodiment, the present application also discloses another embodiment of an order allocation method, as shown in fig. 2, the method may include step 101, step 102, step 103, step 104, and the above step 105:

with reference to the above embodiment, in the present embodiment, when the above step 401 is executed, it can be implemented by the following step 101; when the above step 402 is executed, the following

steps

102 and 103 may be implemented; in addition, step 104 in this embodiment is an optional step.

The order allocation method according to the embodiment of the present invention is described in detail below with reference to fig. 2:

step 101, determining a target timeout interval corresponding to the transport capacity of a target type and a target corresponding relation between a timeout duration corresponding to the transport capacity of the target type and a dispatching threshold according to the transport capacity supply and demand tension degree of a target area, wherein the target timeout interval comprises the timeout duration;

since the core idea of the embodiment of the present invention is to digest orders that may become orders in the target area by the running rider, and the orders to be distributed (i.e. missed orders) are mainly due to the fact that no person takes the order for a long time or the order taking time is too long, it is necessary to determine the target timeout interval for taking the possible orders (i.e. the missed orders are the orders that may become orders) by the running rider, i.e. when the estimated timeout time of the missed orders is within what value range, the running rider takes the missed orders;

further, since the number of running riders is large, it is necessary to determine which running rider received the missed order, and particularly which running rider received the missed order is related to the order dispatching condition of the running rider, it is necessary to determine the target correspondence between the timeout period and the order dispatching threshold. Wherein the target correspondence is associated with a capacity of the target type. In addition, the timeout durations in the target correspondence are all taken from the target timeout periods, and the timeout durations may be one or more of the target timeout periods, and are preferably each timeout duration. In addition, different values of the dispatching threshold correspond to different dispatching conditions.

In one example, the order threshold may be a forward threshold, a higher forward threshold representing a lower forward demand on the ordered rider, and a lower forward threshold representing a higher forward demand on the ordered rider.

In order to reasonably distribute the orders which are likely to be reduced into the tail sheet to the running rider under the condition that the degrees of the stress of the transportation power supply and demand are different, a target timeout interval corresponding to the orders to be distributed by the running rider needs to be determined according to the degree of the stress of the transportation power supply and demand in the target area, and a target corresponding relation between the timeout duration in the target timeout interval and the order dispatching threshold value. Because, there is a difference in the timing at which the racing rider intervenes and receives the ride that may become the tail sheet in both the case of a tense supply and demand and the case of no tense supply and demand.

Optionally, in an embodiment, when step 101 is executed, in a case that the level of the capacity supply and demand tension satisfies a second preset condition, a first preset timeout interval may be determined as a target timeout interval, and a first correspondence between a first timeout duration and a first order dispatching threshold may be determined as the target correspondence, where each timeout duration in the first preset timeout interval is the first timeout duration;

optionally, in an embodiment, in step 101, when the level of the transport capacity supply and demand tension meets a first preset condition, a duration threshold may be determined according to the level of the transport capacity supply and demand tension; adding the time threshold value to each timeout time in the first preset timeout interval to generate the target timeout interval; adding the time threshold to each first timeout duration in the first corresponding relationship to generate a second corresponding relationship between a second timeout duration and the first order dispatching threshold; and determining the second corresponding relation as the target corresponding relation.

Specifically, the level of the transport capacity supply and demand tension satisfying the second preset condition may be understood as that the transport capacity supply and demand tension is not tense (for example, the level of the transport capacity supply and demand tension is less than or equal to a preset level (for example, 3 levels)); the degree of the transport capacity supply and demand tension satisfying the first preset condition may be understood as the transport capacity supply and demand tension (for example, the level of the transport capacity supply and demand tension is greater than a preset level (for example, 3 levels)).

In an example, as shown in fig. 3, the method of the embodiment of the present invention may generate a curve 1a in advance, in the coordinate system of fig. 3, a horizontal axis x represents an estimated timeout duration of an order to be allocated, and a unit is a minute, that is, each number of the horizontal axis represents a corresponding minute; the vertical axis Y represents the forward threshold.

As can be seen from fig. 3, the estimated timeout period x ∈ [8,18] in the curve 1a, and the curve 1a reflects the functional correspondence between the forward path threshold Y and the estimated timeout period x. The curve 1a represents the correspondence between the forward threshold and the estimated timeout duration when the transport capacity supply and demand is not tense, that is, the transport capacity supply and demand tense degree meets the second preset condition.

Therefore, according to the pre-generated curve 1a, a first preset timeout period (here, x ∈ [8,18], and the unit is minute) may be determined as a target timeout period, and a first correspondence between each first timeout period (i.e., a value of x) in the first preset timeout period (x ∈ [8,18]) and a first order-dispatching threshold (here, an on-road threshold Y) may be determined as the target correspondence;

that is, the range of the horizontal axis in the curve 1a is a target timeout period corresponding to an order to be assigned by the running rider without tension in the supply and demand of the transportation capacity, and the functional relationship represented by the curve 1a is a target correspondence relationship between the target timeout period corresponding to the order to be assigned by the running rider and the order assignment threshold value without tension in the supply and demand of the transportation capacity.

As can be seen from the curve 1a in fig. 3, the target correspondence expresses the time when the running rider intervenes to take over an order to be distributed which may become a trailer (i.e. the order estimation timeout is 8 minutes), the time when the expanded on-road threshold is triggered to dispatch the order (i.e. the order estimation timeout is 12 minutes), and the time when the expanded on-road threshold is stopped to dispatch the order (i.e. the order estimation timeout is 18 minutes).

For convenience of understanding, the method of the embodiment of the present invention is described in detail with a specific example, for example, if the level of the capacity supply and demand tension in a certain geographic area is level 1, the target timeout interval expressed by the curve 1a and the functional relationship between the estimated timeout duration in the target timeout interval and the forward route threshold may be obtained.

For example, if an order M in the geographic area is not received by people at present, and the estimated timeout duration of the order M can be determined to be 8 minutes through prediction, the forward threshold y1 corresponding to the 8 minutes can be queried according to the functional relationship of the curve 1 a; then, a running rider whose order condition satisfies the target order condition corresponding to the on-road threshold y1 can be found in the running riders in the geographic area, for example, the running rider S1 is found, and the order M which is not taken by the person is distributed to the running rider S1, so that the effect that the running rider digests the order M which may become a tail order is achieved.

The foregoing example is illustrated with an embodiment of the capacity supply and demand tension, and when the capacity supply and demand tension in the target area is in tension, for example, the capacity supply and demand tension level is 5, it is necessary to determine a duration threshold according to the capacity supply and demand tension level, and the duration threshold is used to make an overall offset on the horizontal axis x with respect to the curve 1a in fig. 3. In a case where the transport capacity supply and demand tension degree satisfies the first preset condition, the higher the transport capacity supply and demand tension degree is, the more tense the transport capacity supply and demand tension degree is, the larger the duration threshold value is (for example, a correspondence between the transport capacity supply and demand tension degree and the duration threshold value is preset, and/or a correspondence between the transport capacity supply and demand tension level and the duration is preset), that is, the more the offset amount of the overall offset of the curve 1a on the horizontal axis x is. Of course, the offset also has a maximum value, i.e. the duration threshold is at most 5 (minutes). In this example, since the level of the capacity supply and demand tension is the maximum level 5, the time period threshold here is the maximum time period threshold of 5 minutes.

Therefore, in this embodiment, the target timeout period x ∈ [13,23] can be obtained by adding 5 minutes to the first preset timeout period (x ∈ [8,18]), and the curve 1b in the case where the capacity supply and demand is tight can be obtained by shifting the curve 1a in fig. 3 by 5 units to the right on the horizontal axis.

A process of obtaining a curve 1b based on the curve 1a and a duration threshold (5 minutes), substantially adding the duration threshold to each first timeout duration in the first corresponding relationship, and generating a second corresponding relationship between a second timeout duration and the first order-assigning threshold (that is, the on-road threshold changing from the curve 1a to the ordinate Y of the curve 1b in fig. 3 does not change, but the value of x corresponding to each on-road threshold is added by 5 minutes); and determining the second corresponding relation as the target corresponding relation.

Therefore, the range [13,23] of the horizontal axis in the curve 1b is a target timeout period corresponding to an order to be assigned to the running rider in the case of a tension in the supply and demand of the transportation capacity, and the functional relationship represented by the curve 1b is a target correspondence relationship between the target timeout period corresponding to the order to be assigned to the running rider in the case of a tension in the supply and demand of the transportation capacity and the dispatch threshold value (here, the on-road threshold value).

As can be seen from the curve 1b in fig. 3, the target correspondence expresses the time when the running rider intervenes to take over the order to be distributed which may become the trailer (i.e. the order estimation timeout 13 minutes), the time when the expanded on-road threshold is triggered to dispatch the order (i.e. the order estimation timeout 17 minutes), and the time when the expanded on-road threshold is stopped to dispatch the order (i.e. the order estimation timeout 23 minutes).

Curve 1b expresses that the capacity supply and demand is tense compared to curve 1a, the moment when the running rider intervenes to take over an order to be allocated that may become a tail order is delayed by 5 minutes, the moment when the enlargement of the on-road threshold is triggered to dispatch an order is delayed by 5 minutes, and the moment when the enlargement of the on-road threshold is stopped to dispatch an order is delayed by 5 minutes. That is, the curve 1b for making an order when the capacity supply and demand are in tension is shifted to the right as a whole compared to the estimated timeout time in the curve 1a, and the time for the running rider to intervene to take over an order that may become a tail order is delayed.

Because the transport capacity supply and demand tension degree in the target area can be changed continuously, the method of the embodiment of the invention can flexibly adjust the intervention time of dispatching the running rider to take a missed order which can become a tail order, the time of triggering the enlargement of the on-road threshold value to dispatch the order and the time of stopping the enlargement of the on-road threshold value to dispatch the order according to the change of the transport capacity supply and demand tension degree, and determines the latest on-road threshold value corresponding to the moment according to the determined latest on-road threshold value, thereby searching the running rider meeting the order dispatching condition by means of the latest on-road threshold value.

Thus, in the embodiment of the present invention, when the level of the tense of the transportation power supply and demand satisfies the first preset condition, it indicates that the level of the tense of the transportation power supply and demand is relatively tense, for example, it indicates that the transportation power (the sum of the number of running and crowdsourcing riders) in the target area is relatively small, and the missed order rate is relatively high. Since the supply and demand of the transportation capacity of the target area are in tension, the order taking rate in the target area needs to be ensured, so that compared with the situation that the supply and demand tension degree of the transportation capacity meets the second preset condition (the supply and demand degree of the transportation capacity is not in tension), the overtime time in the target corresponding relation can be prolonged, the time for taking predicted overtime missed orders by the intervention of the transportation capacity of the target type is delayed, and the order taking rate of the target area in which the supply and demand are in tension is reduced because the transportation capacity of the target type is controlled to take orders which may become tail orders too early. Therefore, the method provided by the embodiment of the invention can avoid the problem that the order taking rate in the target area is reduced and the user experience is further influenced because the transport capacity of the scheduling target type is used for taking over orders which may become the tail orders under the condition that the supply and demand of the transport capacity in the target area are in tension.

It should be noted that the target correspondence relationship in the embodiment of the present invention is not limited to the correspondence relationship shown by the curve 1a or the curve 1b in fig. 3, which includes two straight lines and a linearly increasing line, and may also be a correspondence relationship expressed by a curve in which the order threshold value linearly increases along with the estimated timeout duration within the target timeout interval, or other curves that are not listed.

Optionally, the target correspondence between the timeout duration corresponding to the capacity of the target type and the dispatch threshold may include:

when the timeout duration in the target timeout interval is within a first time interval [ t1, t2], the timeout duration corresponds to a first threshold;

in one example, the target correspondence is as shown by curve 1a in fig. 3, or the target correspondence is as shown by curve 1 b.

In curve 1a, where t1 is 8 minutes, where t2 is 12 minutes, and the first threshold is y 1.

In curve 1b, where t1 is 13 minutes, where t2 is 17 minutes, and the first threshold is y 1.

When the timeout duration in the target timeout interval is within a second duration interval [ t2, t3], the dispatch threshold value and the timeout duration are in a linear increasing relationship, and the dispatch threshold value is linearly increased from the first threshold value to a second threshold value;

in one example, in curve 1a, where t3 is 15 minutes, the second threshold is y 2.

In curve 1b, where t3 is 20 minutes, the second threshold is y 2.

Taking the dispatch threshold as the forward path threshold in fig. 3 as an example, for example, in the target corresponding relationship represented by the curve 1a, when the estimated timeout duration of the order is within the range of 12-15 minutes, the forward path threshold and the estimated timeout duration are in a linear functional relationship as shown by the curve 1a, and as the estimated timeout duration increases, the forward path threshold also increases, so that the forward path threshold is in a linear increasing functional relationship.

When the timeout duration in the target timeout interval is within a third duration interval [ t3, t4], the timeout duration corresponds to the second threshold;

in one example, in curve 1a, t4 here is 18 minutes.

In curve 1b, t4 here is 23 minutes.

Wherein the first threshold is less than the second threshold, t1 < t2 < t3 < t 4.

Thus, in the embodiment of the present invention, in the defined order-target correspondence relationship that may be a trailer for the capacity dispatch of the target type, when the timeout duration is short, the same dispatch threshold is used to search for the target capacity meeting the target dispatch condition corresponding to the dispatch threshold; in the process that the overtime length is increased from the shorter time length to the longer overtime length, the order dispatching threshold value is also continuously adjusted along with the continuous increase of the overtime length, and the longer the overtime length is, the larger the order dispatching threshold value is. That is, because the timeout duration of the estimated overtime missed order is continuously increased, if a stricter order dispatching condition corresponding to a smaller order dispatching threshold is used to search the capacity of the target type meeting the demand, it is difficult to find a proper target capacity, and the probability of the missed order becoming the tail order is increased, so that the order dispatching threshold can be moderately increased along with the increase of the estimated timeout duration, the severity of the order dispatching condition is reduced, the target capacity capable of receiving orders can be found in a range not smooth enough, and the tail order rate is reduced; finally, when the estimated timeout duration of the missed order is already very long, if the target capacity meeting the dispatching condition is not found under the least severe dispatching condition corresponding to the second threshold corresponding to the estimated timeout duration, the attempt to dispatch the capacity of the target type to take over the missed order is stopped because it is stated that the order may be an order exceeding the dispatching range, and there is no need to waste capacity to take over the order. The method of the embodiment of the invention can adjust the used order dispatching threshold value along with the change of the estimated overtime time of the missed order, thereby reducing the target order dispatching condition along with the increase of the estimated overtime time, finding the target transport capacity meeting the target order dispatching condition as much as possible and optimizing the tail order.

102, if the estimated timeout duration of the missed order in the target area is matched with the target timeout interval, acquiring a target order dispatching threshold matched with the estimated timeout duration according to the target corresponding relation;

the missed order may be each missed order in the target area, or a partially designated missed order.

The method of the embodiment of the invention can predict the timeout duration of any missed order, namely the predicted timeout duration.

The estimated timeout duration of a missed order may change continuously with the lapse of time, i.e. it is a changing value, so this step may query the latest target order dispatching threshold value matched with the estimated timeout duration from the target corresponding relationship according to the latest value of the estimated timeout duration.

Alternatively, when determining the estimated timeout duration of the missed orders in the target area in step 102, the determining may be implemented by the following S201 to S203:

s201, for the missed orders in the target area, estimating the time duration consumed by delivery of the missed orders according to the path planning information of the missed orders and historical order data between buyers and sellers corresponding to the missed orders;

specifically, for a missed order, the order may have a buyer address and a seller address, and thus, a path plan (ETR) may be obtained from the seller address to the buyer address; and obtaining historical order data between the buyer and seller (e.g., elapsed delivery time for a historical order, i.e., the length of time between the time of order placement and the time of delivery); according to the path plan and the historical order data, the time length from the order placing time to the delivery time of the missed order, namely the time duration of the delivery time is estimated.

For a missed order, the estimated delivery elapsed time is not dynamically variable, but is a fixed value.

S202, calculating the sum of system time and the time duration of the delivery time to obtain first expected delivery time;

the system time is the time used by the system of the embodiment of the invention, for example, the beijing time, and the sum of the system time and the time duration of the delivery time can be calculated to obtain a first expected delivery time.

Wherein, the system time is continuously changed, so the first estimated delivery time is also continuously changed.

S203, calculating the difference between the first expected delivery time and the second expected delivery time of the missed order output by the client side to obtain the estimated timeout duration of the missed order.

After an order is submitted by the client side, the system may automatically generate a second estimated delivery time of the order, where the second estimated delivery time (i.e., ETA) is an estimated delivery time of the order output by the client side for the user to view, and the calculation method of the second estimated delivery time may adopt various methods in the conventional technology, which is not described herein again.

The first estimated time corresponds to the actual time of delivery of the missed order estimated by the method of the embodiment of the present invention, and ETA is the estimated time of delivery that can be seen by the user outputted from the client side. Therefore, the estimated timeout duration is used to query the target correspondence, for example, a curve 1a or a curve 1b shown in fig. 3 or other curves not shown, in which the offset of the horizontal axis is greater than zero and less than 5, to obtain a target forward road threshold for finding the running rider, so that the target running capacity, i.e., a certain running rider, is scheduled by using the target forward road threshold.

In the embodiment of the invention, the time-consuming delivery time of the missed order can be estimated by utilizing the path planning of the missed order and the historical order data between the buyer and the seller, so that the estimated time-consuming delivery time has higher accuracy, the actual delivery time of the missed order, namely the first estimated delivery time, is estimated by utilizing the time-consuming delivery time, and the first estimated delivery time and the second estimated delivery time of the missed order output by the client side are calculated, so that the overtime time of the missed order is estimated; the estimated overtime duration is accurate, whether the missed order is an order possibly reduced to the tail order can be determined based on the estimated overtime duration and the target corresponding relation, namely whether the estimated overtime duration is within a target overtime interval (yes, the order possibly reduced to the tail order) can be timely and accurately positioned to the missed order possibly reduced to the tail order, the target transport capacity of the target type is scheduled to receive the missed order by utilizing the determined target order dispatching threshold value, and the tail order rate of the target area is reduced.

It should be noted that the missed orders described in the various embodiments described throughout this disclosure represent orders that have been picked by the seller, but that have not yet been picked up by the capacity (i.e., the rider).

Alternatively, based on the first estimated time of delivery of the missed order, the first estimated time of delivery is the estimated actual time of delivery of the embodiment of the present invention, and the first estimated time of delivery is continuously changed, so in the embodiment of the present invention, the curves 1a and 1b shown in fig. 3 can be obtained from the

curves

2a and 2b shown in fig. 4.

In other words, the target correspondence relationship according to the embodiment of the present invention may also be a correspondence relationship between the first expected delivery time of the missed order and the order dispatch threshold, and when the target correspondence relationship is used, the abscissa value in the curve 2a or the curve 2b is located by judging that the specific time of the estimated first expected delivery time is ETA plus several minutes.

The curve 2a in fig. 4 corresponds to the curve 1a in fig. 3, and the curve 2b in fig. 4 corresponds to the curve 1b in fig. 3, which have similar principles and are not repeated here, and the description referring to fig. 3 may be only needed.

Whereas 8 in ETA +8 on the horizontal axis in fig. 4 is 8 minutes, for example, indicating that the estimated actual delivery time of the missed order is 8 minutes later than the estimated delivery time ETA displayed on the client side. I.e., the order forecast time-out is 8 minutes.

103, determining that the order dispatching condition in the target area meets the target type target transport capacity of the target order dispatching condition corresponding to the target order dispatching threshold;

the order-sending threshold is taken as an example to explain, for the capacity of each target type in the target area, for example, a running rider, a corresponding order-sending condition, for example, a running value, may be calculated, and it may be determined whether there is a running rider whose difference between the running value and the target running threshold is smaller than a preset threshold, if there is a running rider whose difference is smaller than the preset threshold, it may be determined that the order-sending condition satisfies the target capacity of the target order-sending condition, and if there is no running rider whose difference is smaller than the preset threshold, it may be determined that the order-sending condition does not exist in the target area, and the target capacity of the target order-sending condition is satisfied.

It should be noted that the number of the target capacity is one, and when there are a plurality of running riders whose menu conditions satisfy the target menu condition in the target area, the running rider whose following value is closest to the target following threshold value among the plurality of running riders is determined as the target capacity.

Of course, when the target forward route threshold is determined according to the estimated timeout duration of a missed order M and the target capacity of the target type is obtained by using the target forward route threshold, the target capacity meeting the target dispatch condition may not be found. However, the estimated timeout duration of the missed order is constantly changing, so that the target correspondence can be queried in real time according to the latest estimated timeout duration to find whether the target capacity exists in the target area by using the latest target order dispatching threshold, such as the target forward route threshold.

The description is continued with reference to the curve 1a in fig. 3 as an example of the target correspondence.

The longer the estimated overtime duration of the missed order M is, for example, when the estimated overtime is 8 minutes, no running rider meeting the target order dispatching condition is found based on the forward threshold y1, and in the process of continuously increasing the estimated overtime (for example, the estimated overtime duration is in the interval of 8 minutes to 12 minutes), the method of the embodiment of the invention can find whether a running rider meeting the target order dispatching condition exists in the geographic area based on the forward threshold y1 in real time; if the target forward route is not found, in the process that the time-out duration to be estimated exceeds 12 minutes and continuously increases to 15 minutes, the corresponding target forward route threshold is inquired according to the forward route threshold (in y 1-y 2) corresponding to the estimated time-out time in the curve 1a, in the process, the target forward route threshold is continuously increased, namely the forward route requirement for searching the target transport capacity is lower and lower, and the target order dispatching condition is lower and lower; if the running rider meeting the latest target road following threshold value is not found, the running rider can be found according to the target road following threshold value y2 in the process of continuously increasing the estimated overtime (for example, the estimated overtime duration is in the interval of 15-18 minutes); when the estimated timeout duration reaches 18 minutes, the track rider stops being searched.

That is to say, in step 103 of the embodiment of the present invention, when the target transportation capacity is determined, the latest target dispatch threshold may be searched according to the estimated timeout duration that changes in real time, so as to find whether the target transportation capacity of the target type for which the dispatch condition satisfies the target dispatch condition exists in the target area according to the latest target dispatch condition.

When the missed order M is estimated to be 8 minutes out of time, attempting to dispatch the order to the running rider within a range where the forward road requirement of the forward road threshold y1 is high; if the estimated overtime time reaches 12 minutes and no running rider with the following road performance meeting the following road performance requirement of the following road threshold y1 is found, the action of expanding the following road threshold to dispatch the order is triggered (namely, the following road threshold is improved along with the increase of the estimated overtime time, namely, the smoothness requirement is reduced, and the running rider meeting the smoothness requirement is searched in a larger geographical range), wherein the following road threshold at the stage is linearly increased along with the system time or the estimated overtime time; when the estimated timeout period reaches 15 minutes, then the in-route threshold for the order no longer increases and remains unchanged (because the timeout is too long, it is no longer necessary to dispatch the order from a more out-of-route racing rider); after the estimated timeout period exceeds 18 minutes, if a jogger meeting the ride comfort requirements has not been found, the dispatch of the jogger to the missed order is abandoned (because the order has not to be dispatched again because the timeout is too long).

Wherein a higher on-road threshold represents a lower on-road demand on the dispatched rider, e.g., a less on-road may also be picked up.

In addition, when the target transport capacity is searched and determined in step 103 of the embodiment of the present invention, the used target corresponding relationship may also be adjusted in real time according to the change of the transport capacity supply and demand tension degree in the target area, and then the latest target dispatch threshold is searched according to the latest estimated timeout duration, so as to search whether the target transport capacity of the target type for which the dispatch condition meets the target dispatch condition exists in the target area or not according to the latest target dispatch condition.

Alternatively, when step 103 is executed, it can be realized by S301 to S302:

s301, obtaining an order dispatching condition of each transport capacity of the target type according to the positioning information of each transport capacity of the target type in the target area, the order data of the distributed and unfinished transport capacity and the order data of the missed orders;

wherein the location information of each of the running riders in the target area, and the order data of each of the running riders which has been assigned but not yet completed (i.e. not delivered to the buyer), and the order data such as the missed order M (e.g. including buyer location, seller location, etc.) can be obtained;

then, a dispatch piece, such as a road grade, for each running rider is calculated from the data.

In one example, the distance between the running rider and both the buyer and the buyer of the missed order M may be determined from the location information of the running rider; and calculating the following parameters of the running rider to the missed order M according to the data of the orders which have been accepted by the running rider but not completed delivery; then, the following road parameter, the distance between the running rider and the buyer and the distance between the running rider and the seller are weighted to obtain a following road value. In this way, the menu conditions for each running rider in the target area can be obtained.

Of course, the method of calculating the conditions for the running rider to make a menu is not limited to the above example, and may be implemented by other known or future-developed methods.

S302, determining the delivery condition to meet the transportation capacity of the target delivery condition corresponding to the target delivery threshold value as the target transportation capacity.

Specifically, if there is a candidate capacity in which the difference between the value (for example, the road grade value) of the order-dispatching condition and the target order-dispatching threshold is smaller than a preset threshold in the running rider in the target area, if the candidate capacity is one, the candidate capacity is determined as the target capacity; if the number of the candidate transport capacity is multiple, determining the transport capacity corresponding to the minimum difference value in the multiple candidate transport capacities as the target transport capacity;

if the difference between the value (such as a forward value) of the order sending condition and the target order sending threshold value is not smaller than the candidate capacity of the preset threshold value in the running rider in the target area, determining the target order sending condition corresponding to the latest target forward threshold value according to the changed estimated timeout time of the latest missed order M, and searching whether the order sending condition meets the target capacity of the latest target order sending condition again.

In the embodiment of the present invention, a dispatch condition of each capacity of the target type may be obtained according to the location information of each capacity of the target type in the target area, the order data of the allocated and unfinished capacity, and the order data of the missed order, so that the determined dispatch condition of each capacity combines the order data of the missed order, and the current location of the transport capacity, and the situation of the undelivered order which needs to be delivered by the transport capacity, the determined delivery condition can more accurately express the actual order taking capability of the corresponding transport capacity to the undelivered order, so that the target capacity of the target type with the strongest actual order taking capacity for the missed order can be found in the target area to take over the missed order, the method reduces the influence on the distribution efficiency of new orders in the target area while reducing the rate of orders.

The new order may be an order for which the difference in time between the time of placing the order and the system time is less than a threshold, such as 5 minutes.

Optionally, in step 104, in a case that the supply and demand tension degree of the transportation capacity meets a second preset condition, the missed order is allocated to the target transportation capacity;

that is, in the case where the capacity supply and demand of the target area is not tense, the missed order M, for example, may be directly assigned to the target capacity, for example, the running rider S1 for distribution.

Since the supply and demand of the transportation capacity in the target area are not tense, that is, the transportation capacity is sufficient, the order can be directly dispatched without considering the income and cost corresponding to the order M distributed to the running rider S1, the tail order in the target area can be quickly reduced, the running rider meeting the conditions of the target order dispatching tends to directly dispatch the running rider to take over the order M which can become the tail order, and the tail order rate is reduced.

Specifically, the description of step 105 in the embodiment of fig. 1 is omitted here for brevity.

In the embodiment of the invention, a target timeout interval corresponding to the target type of capacity intervention receiving missed orders which may become tail orders is determined according to the overall capacity supply and demand tension degree of a target area, and a target corresponding relation between the timeout duration in the target timeout interval and an order dispatching threshold is determined, so that the target corresponding relation can be inquired according to the estimated timeout duration of the missed orders, and the time of the target type of capacity receiving the missed orders and the target order dispatching threshold are further determined; the method can determine the target transport capacity of the target type of the corresponding target order sending condition according to the target order sending threshold value in time under the condition that the missed order is not seriously overtime or cancelled, optimize the tail experience of the target area in the transport capacity control and scheduling aspect, ensure the certainty of digesting the tail order by using the transport capacity of the target type which is strongly controlled, and solve the problem of outstanding tail orders. In addition, under the condition that the transport capacity supply and demand tension degree meets a second preset condition, the missed order is directly distributed to the target transport capacity, and the rate of the orders in the target area can be timely reduced under the condition that the transport capacity is not tensioned enough; and under the condition that the transport capacity supply and demand tension degree meets a first preset condition, determining whether to distribute the missed orders to the target transport capacity according to the predicted income and cost of the target transport capacity for receiving the missed orders which can become the tail orders, so that the problem of reduction of the order receiving rate in the target area due to reduction of the tail order rate in the target area is avoided.

Alternatively, when step 105 is performed, this may be achieved by: if the target transport capacity receives the missed orders and the predicted income is larger than or equal to the cost, distributing the missed orders to the target transport capacity; and if the target capacity accepts the missed order and the predicted income is less than the cost, refusing to distribute the missed order to the target capacity.

In this step, the profit and the cost of the missed order M are estimated mainly by estimating the target capacity if the missed order M is received.

And when the profit and the cost are estimated, estimating the profit and the cost of the target capacity carrying the missed order M which can become the order based on the relevant data of the generated order.

Specifically, the following two types of orders are first defined:

and (4) new order: the time difference between the client side time of placing the order from the system time is less than a threshold (e.g., 5 minutes).

Tail sheet: including orders cancelled by the user/merchant due to unattended orders taking, orders taken too long resulting in delivery of heavily timed-out orders (e.g., actual timeout period greater than 15 minutes).

For example, in the case of a shortage of capacity, supply and demand, the determined target capacity is the running rider S1, and the running rider S1 is the running rider who looks up the dispatch conditions corresponding to the target forward route threshold y1 (see curve 1a in fig. 3) corresponding to the estimated timeout period 13 minutes for a missed order M

Specifically, in order pool 1 of new orders for the target area, the new orders in the order pool are assigned to crowd-sourced riders and joggers in the target area according to a greedy algorithm, thereby determining which new orders, such as order B and order C (both new orders), are assigned to the jogger S1.

In addition, add the end orders (definition refers to above) in the target area into the order pool to form a new order pool 2 (wherein, a large proportion, for example, more than 80% of the order pools 2 are new orders); when a greedy algorithm is used for dispatching orders for riders in a target area, new orders in the order pool 2 are ignored firstly, tail orders in the order pool 2 are preferentially distributed to the running riders according to the greedy algorithm, and after all the tail orders in the order pool 2 are distributed, the new orders in the order pool 2 are distributed to the riders according to the greedy algorithm (the order pool can be a crowdsourcing rider or the running riders). For example, via assignment, the Racing rider S1 is assigned the trailer A, and the rider S2 (without limitation, the Racing rider or the crowd-sourced rider) is assigned the order B and the order C (both new orders).

That is, it is predicted that the running rider S1 would have been required to deliver the new order B and the new order C, but it did not deliver the two new orders, but delivered the tail order a.

It is therefore necessary to estimate the cost of delivering the missed order M, which is caused by the running rider S1 not delivering the new order (here, the new order B and the new order C are taken as an example) because the missed order M is delivered, on the basis of the delivery tail sheet (here, the tail sheet a); and predicting the profit reward the running rider S1 obtained because of delivery of the missed order M, based on the delivery trailer (here, trailer a).

Then the method of the embodiment of the invention will distribute the missed order M to the Racing rider S1 for distribution when the reward is larger than or equal to cost;

on the contrary, if reward < cost, the scheduling of assigning the missed order M to the running rider S1 is abandoned, that is, when the estimated timeout duration of the missed order M is 13 minutes as listed above, the scheduling result is the running rider S1, but the scheduling is abandoned, and the missed order M is not assigned to the running rider S1 for delivery; and when the target order dispatching threshold determined according to the target corresponding relation changes due to the change of the estimated overtime duration of the missed order M or the change of the transport capacity supply and demand tension degree of the target area during the next dispatching, recalculating the cost and the profit.

Further, when reward < cost, the music runner S1 may continue to be assigned new orders.

Optionally, in an embodiment, when the revenue is estimated, when the target capacity is switched from accepting a new order to accepting a tail order, a first preset weighting operation may be performed on a first timeout probability of the tail order, a second probability of damage to a real object corresponding to the tail order, and an order price of the tail order, so as to generate the revenue estimated when the target capacity accepts the missed order;

continuing with the above example:

for example, tail order A was assigned to the running rider S1, resulting in new order B and new order C being the most on the way with rider S1 to the next on rider S2:

the return obtained by the running rider S1 distributing the delivery order a is calculated by equation 1 as the predicted return obtained by the running rider S1 distributing the missed order M:

reward α risk1(a, t) + β price (a) risk2(a, t), formula 1;

wherein risk1(a, t) represents the risk of timeout of the trailer a at the scheduling time t;

risk2(a, t) represents the risk of the trailer a generating a meal loss at the scheduling time t;

price (A) represents the user in order A to make the payment.

The risk1(a, t) and the risk2(a, t) are predicted by using models which have already been used.

Optionally, in an embodiment, in a case where the target capacity is switched from accepting a new order to accepting a trailer and the new order is accepted by a first capacity, calculating a second timeout probability that the new order is increased from the target capacity acceptance to being accepted by the first capacity acceptance, calculating route information that the new order is increased from the target capacity acceptance to being accepted by the first capacity acceptance, and calculating a dispatch efficiency loss in the target area that the new order is switched from the target capacity acceptance to being accepted by the first capacity acceptance; and performing second preset weighting operation on the distance information and the order dispatching efficiency loss of the second overtime probability to generate the estimated cost of the target capacity for bearing the missed order.

Continuing with the above example:

the cost paid by the running rider S1 for delivery of the trailer a is calculated by equation 2 as the estimated cost of the running rider S1 for delivering the missed order M:

cost ═ γ [ Δ risk1(B, t) + Δ risk1(C, t) ] + δ [ ross (B, s1, s2) + loss (C, s1, s2) ] + θ f (t), formula 2;

wherein Δ risk1(B, t) represents the increased risk of timeout for new order B to be dispatched to the next rider S2;

Δ risk1(C, t) represents the increased risk of timeout for new order C to be dispatched to the next rider S2;

loss (B, S1, S2) represents the incremental increase in distance that new order B was changed from the most on-road rider S1 to the next-on-road rider S2;

loss (C, S1, S2) represents the incremental distance that the new order C has added to change from the most on-road rider S1 to the next on-road rider S2;

f (t) represents the scheduling time t because the lost new order B and new order C of the Racing rider S1 resulted in the subsequent potential loss of overall order efficiency within the target area.

In one example, f (t) is a constant related to the target region and the scheduling time t, that is, the value of the constant is different for different target regions and different scheduling times.

In summary, the method of the present embodiment utilizes the scheduling efficiency and order value of the orders to evaluate the above rewarded and cost.

It should be noted that the scheduling time t described by the above formula of the present invention is the time for determining the running rider to accept the missed order M in the embodiments of the present invention, that is, the methods of the embodiments of the present invention are all executed at the scheduling time t, and include an operation of selecting the order-accepting rider for the missed order M and a series of operations of evaluating the above reward and cost.

In the embodiment of the invention, under the condition of tense capacity supply and demand, in order to avoid the reduction of order taking rate in a target area caused by reducing the tail order, the method of the embodiment of the invention takes the digestion of orders as a main target, namely only when the predicted income of the target capacity receiving the tail order is greater than the cost of receiving the tail order, the target capacity which is originally allocated with the new order is changed into the allocation of the missed order which can become the tail order, and the scheduling capacity is ensured to preferentially receive the new order under the condition of tense capacity, thereby ensuring the order taking efficiency. In addition, the benefits and the cost brought by the transport capacity receiving end orders of the transfer target type can be comprehensively evaluated from the aspects of scheduling efficiency and order value, so that the estimated benefits and cost are accurate, the final decision of scheduling the missed orders each time is determined based on the benefits and the cost, and the optimal overall experience in the target area can be realized.

The inventor considers that the attraction and guidance of receiving the tail form by crowdsourcing riders are only carried out on the pricing level, and the problem of outstanding tail form in the crowdsourcing mode cannot be fundamentally solved, but by means of the technical scheme of the embodiments of the invention, the problem of tail form experience can be solved by scheduling the transport capacity, specifically, the crowdsourced tail experience is optimized on the transport capacity control and scheduling level, and the strongly controlled running riders are used for ensuring digestion of the tail form, so that the certainty of tail form digestion is ensured; meanwhile, the time for the running rider to intervene to take the tail order and the order dispatching threshold (such as the on-road threshold) are adjusted according to the current overall transport capacity supply and demand tension degree; under the situation that the supply and demand of the transport capacity are in tension, the cost and the profit of the tail monotonicity decision-making each time are fully considered, and only when the profit is lost, the scheduling decision of the missed orders which can become the tail monotone is executed, so that the tail monotony decision-making which injures the large-scale experience is reduced, and the optimal overall experience is realized.

The present embodiment discloses an order distribution apparatus, as shown in fig. 5, the apparatus includes:

the first determining module 51 is configured to determine a target capacity of a target type to receive a missed order according to a capacity supply and demand tension degree of a target area and an estimated timeout duration of the missed order in the target area;

a second determining module 52, configured to determine whether to allocate the missed order to the target capacity according to the predicted profit and cost of the target capacity receiving the missed order when the capacity supply and demand tension degree meets a first preset condition.

Optionally, the first determining module 51 includes:

the first determining submodule is used for determining an order distribution strategy corresponding to the transport capacity of the target type according to the transport capacity supply and demand tension degree of the target area;

and the second determining submodule is used for determining the target transport capacity of the target type to be subjected to the missed order according to the order distribution strategy if the estimated timeout duration of the missed order in the target area is matched with the order distribution strategy.

Optionally, the first determining sub-module includes:

the first determining unit is used for determining a target timeout interval corresponding to the transport capacity of a target type and a target corresponding relation between the timeout duration corresponding to the transport capacity of the target type and a dispatching threshold according to the transport capacity supply and demand tension degree of a target area, wherein the target timeout interval comprises the timeout duration;

optionally, the second determining sub-module includes:

the obtaining unit is used for obtaining a target order dispatching threshold matched with the estimated overtime duration according to the target corresponding relation if the estimated overtime duration of the missed order in the target area is matched with the target overtime interval;

a second determining unit, configured to determine that a dispatch condition in the target area meets a target capacity of the target type of a target dispatch condition corresponding to the target dispatch threshold.

In the embodiment of the invention, a target timeout interval corresponding to the target type of capacity intervention receiving missed orders which may become tail orders is determined according to the overall capacity supply and demand tension degree of a target area, and a target corresponding relation between the timeout duration in the target timeout interval and an order dispatching threshold is determined, so that the target corresponding relation can be inquired according to the estimated timeout duration of the missed orders, and the time of the target type of capacity receiving the missed orders and the target order dispatching threshold are further determined; the method can determine the target transport capacity of the target type of the corresponding target order sending condition according to the target order sending threshold value in time under the condition that the missed order is not seriously overtime or cancelled, optimize the tail experience of the target area in the transport capacity control and scheduling aspect, ensure the certainty of digesting the tail order by using the transport capacity of the target type which is strongly controlled, and solve the problem of outstanding tail orders. In addition, under the condition that the supply and demand tension degree of the transport capacity meets a first preset condition, whether the missed orders are distributed to the target transport capacity is determined according to the predicted income and cost of the target transport capacity for receiving the missed orders which can become the tail orders, and the problem that the order receiving rate in the target area is reduced due to the fact that the tail order rate in the target area is reduced is solved.

Optionally, the first determining unit includes:

the first determining subunit is configured to determine, when the capacity supply and demand tension degree meets a second preset condition, a first preset timeout interval as a target timeout interval, and determine a first correspondence between a first timeout duration and a first order dispatching threshold as the target correspondence, where each timeout duration in the first preset timeout interval is the first timeout duration;

the second determining subunit is used for determining a duration threshold according to the transport capacity supply and demand tension degree under the condition that the transport capacity supply and demand tension degree meets a first preset condition; adding the time threshold value to each timeout time in the first preset timeout interval to generate the target timeout interval; adding the time threshold to each first timeout duration in the first corresponding relationship to generate a second corresponding relationship between a second timeout duration and the first order dispatching threshold; and determining the second corresponding relation as the target corresponding relation.

Optionally, the target correspondence between the timeout duration corresponding to the capacity of the target type and the dispatch threshold includes:

Optionally, the obtaining unit includes:

the pre-estimation subunit is used for pre-estimating the time consumed for delivering the missed order according to the path planning information of the missed order and the historical order data between the buyer and the seller corresponding to the missed order for the missed order in the target area;

the first calculating subunit is used for calculating the sum of the system time and the time duration consumed by delivery to obtain a first expected delivery time;

the second calculating subunit is configured to calculate a difference between the first expected delivery time and a second expected delivery time of the missed order output by the client side, so as to obtain an estimated timeout duration of the missed order;

and the first obtaining subunit is configured to, if the estimated timeout period matches the target timeout interval, obtain a target order dispatching threshold matching the estimated timeout period according to the target correspondence.

Optionally, the second determining module 52 includes:

the distribution submodule is used for distributing the missed orders to the target transport capacity if the predicted income of the target transport capacity for receiving the missed orders is larger than or equal to the cost;

and the rejection submodule is used for rejecting the allocation of the missed order to the target transport capacity if the target transport capacity receives the missed order and the predicted income is less than the cost.

Optionally, the apparatus further comprises:

optionally, the apparatus further comprises:

Optionally, the second determining unit includes:

the second obtaining subunit is configured to obtain an order dispatching condition of each transportation capacity of the target type according to the positioning information of each transportation capacity of the target type in the target area, the order data of the allocated and unfinished transportation capacity, and the order data of the missed order;

and the third determining subunit is used for determining the transportation capacity of the target dispatch condition, which meets the target dispatch condition corresponding to the target dispatch threshold value, as the target transportation capacity.

Optionally, the apparatus further comprises:

and the allocation module is used for allocating the missed orders to the target transport capacity under the condition that the transport capacity supply and demand tension degree meets a second preset condition.

The embodiment of the invention can directly distribute the missed order to the target transport capacity under the condition that the transport capacity supply and demand tension degree meets the second preset condition, and can timely reduce the rate of the orders in the target area under the condition that the transport capacity is not tensioned enough.

The order distribution device disclosed in the embodiments of the present application is configured to implement each step of the order distribution method described in each embodiment of the present application, and for specific implementation of each module of the device, reference is made to the corresponding step, which is not described herein again.

Correspondingly, the application also discloses an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to implement the order allocation method according to any one of the above embodiments of the application. The electronic device can be a PC, a mobile terminal, a personal digital assistant, a tablet computer and the like.

The present application also discloses a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the order distribution method according to any of the above embodiments of the present application.

The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.

The order allocation method and apparatus provided by the present application are introduced in detail, and a specific example is applied to illustrate the principle and the implementation manner of the present application, and the description of the above embodiment is only used to help understanding the method and the core idea of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.

Various component embodiments of the disclosure may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components in a computing processing device according to embodiments of the present disclosure. The present disclosure may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present disclosure may be stored on a computer-readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.

For example, FIG. 6 illustrates a computing processing device that may implement methods in accordance with the present disclosure. The computing processing device conventionally includes a processor 1010 and a computer program product or computer-readable medium in the form of a memory 1020. The memory 1020 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. The memory 1020 has a storage space 1030 for program code 1031 for performing any of the method steps of the above-described method. For example, the storage space 1030 for program code may include respective program code 1031 for implementing various steps in the above method, respectively. The program code can be read from or written to one or more computer program products. These computer program products comprise a program code carrier such as a hard disk, a Compact Disc (CD), a memory card or a floppy disk. Such a computer program product is typically a portable or fixed storage unit as described with reference to fig. 7. The memory unit may have memory segments, memory spaces, etc. arranged similarly to memory 1020 in the computing processing device of fig. 6. The program code may be compressed, for example, in a suitable form. Typically, the memory unit comprises computer readable code 1031', i.e. code that can be read by a processor, such as 1010, for example, which when executed by a computing processing device causes the computing processing device to perform the steps of the method described above.

Reference herein to "one embodiment," "an embodiment," or "one or more embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Moreover, it is noted that instances of the word "in one embodiment" are not necessarily all referring to the same embodiment.

In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the disclosure may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The disclosure may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.

Finally, it should be noted that: the above examples are only intended to illustrate the technical solutions of the present disclosure, not to limit them; although the present disclosure has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present disclosure.

Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.

Claims

1. An order allocation method, comprising:

2. The method according to claim 1, wherein the determining the target capacity of the target type to be subjected to the missed order according to the supply and demand tension degree of the capacity in the target area and the estimated timeout duration of the missed order in the target area comprises:

determining an order allocation strategy corresponding to the transport capacity of the target type according to the transport capacity supply and demand tension degree of the target area;

and if the estimated timeout duration of the missed orders in the target area is matched with the order distribution strategy, determining the target transport capacity of the target type to be subjected to the missed orders according to the order distribution strategy.

3. The method of claim 2,

the step of determining an order allocation strategy corresponding to the transport capacity of the target type according to the transport capacity supply and demand tension degree of the target area comprises the following steps:

determining a target timeout interval corresponding to the transport capacity of a target type and a target corresponding relation between a timeout duration corresponding to the transport capacity of the target type and a menu dispatching threshold according to the transport capacity supply and demand tension degree of a target area, wherein the target timeout interval comprises the timeout duration;

if the estimated timeout duration of the missed order in the target area is matched with the order distribution strategy, determining the target transport capacity of the target type to be subjected to the missed order according to the order distribution strategy, wherein the determining comprises the following steps:

if the estimated timeout duration of the missed order in the target area is matched with the target timeout interval, acquiring a target order dispatching threshold matched with the estimated timeout duration according to the target corresponding relation;

determining a target capacity of the target type for which a dispatch condition within the target area satisfies a target dispatch condition corresponding to the target dispatch threshold.

4. The method according to claim 3, wherein the determining a target timeout interval corresponding to the capacity of the target type and a target correspondence between a timeout duration corresponding to the capacity of the target type and a dispatching threshold according to the capacity supply and demand tension degree of the target area comprises:

under the condition that the transport capacity supply and demand tension degree meets a second preset condition, determining a first preset timeout interval as a target timeout interval, and determining a first corresponding relation between a first timeout duration and a first order dispatching threshold as the target corresponding relation, wherein each timeout duration in the first preset timeout interval is the first timeout duration;

under the condition that the transport capacity supply and demand tension degree meets a first preset condition, determining a duration threshold according to the transport capacity supply and demand tension degree; adding the time threshold value to each timeout time in the first preset timeout interval to generate the target timeout interval; adding the time threshold to each first timeout duration in the first corresponding relationship to generate a second corresponding relationship between a second timeout duration and the first order dispatching threshold; and determining the second corresponding relation as the target corresponding relation.

5. The method of claim 3, wherein the target correspondence between the timeout period corresponding to the capacity of the target type and the dispatch threshold comprises:

6. The method according to claim 3, wherein if the estimated timeout period of the missed orders in the target area matches the target timeout period, obtaining a target order dispatching threshold matching the estimated timeout period according to the target correspondence comprises:

for the missed orders in the target area, estimating the time duration consumed by delivery of the missed orders according to the path planning information of the missed orders and historical order data between buyers and sellers corresponding to the missed orders;

calculating the sum of the system time and the time duration of the delivery time to obtain a first expected delivery time;

calculating the difference between the first expected delivery time and a second expected delivery time of the missed order output by the client side to obtain the estimated timeout duration of the missed order;

and if the estimated timeout duration is matched with the target timeout interval, acquiring a target order dispatching threshold matched with the estimated timeout duration according to the target corresponding relation.

7. The method of claim 1, wherein determining whether to allocate the missed order for the target capacity based on revenue and costs forecasted for the target capacity to take over the missed order comprises:

if the target transport capacity receives the missed orders and the predicted income is larger than or equal to the cost, distributing the missed orders to the target transport capacity;

and if the target capacity accepts the missed order and the predicted income is less than the cost, refusing to distribute the missed order to the target capacity.

8. An order distribution apparatus, comprising:

9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the order allocation method of any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the order allocation method according to any one of claims 1 to 7.