CN111798075A

CN111798075A - Transport capacity object scheduling method, device, server and storage medium

Info

Publication number: CN111798075A
Application number: CN201910276826.3A
Authority: CN
Inventors: 王圣尧
Original assignee: Beijing Sankuai Online Technology Co Ltd
Current assignee: Beijing Sankuai Online Technology Co Ltd
Priority date: 2019-04-08
Filing date: 2019-04-08
Publication date: 2020-10-20

Abstract

The invention discloses a capacity object scheduling method, a capacity object scheduling device, a capacity object scheduling server and a storage medium, and belongs to the technical field of computer application. According to the embodiment of the invention, the estimated loss value of the first capacity object of at least one capacity object type for processing the to-be-processed order can be obtained, the overtime risk and the detour degree of the first capacity object caused by processing the to-be-processed order can be estimated, so that the second capacity object which is best matched can be dispatched to process the to-be-processed order according to the estimated loss value, the difficulty of the second capacity object for processing the to-be-processed order is reduced, a user can receive ordered articles quickly, and the operation efficiency of the whole capacity object team is greatly improved.

Description

Transport capacity object scheduling method, device, server and storage medium

Technical Field

The invention relates to the technical field of computer application, in particular to a capacity object scheduling method, a capacity object scheduling device, a capacity object scheduling server and a storage medium.

Background

With the development of the internet, more and more applications, such as take-out applications, that can improve convenience of life have appeared. The user can release an order through the takeaway application, the server can schedule a corresponding capacity object for the order through a capacity object scheduling method, and the ordered commodity is sent to the user through the capacity object.

Currently, a commonly used capacity object scheduling method is as follows: obtaining order parameters such as a user address and a merchant address of the order, and obtaining the scheduling possibility of each capacity object according to the order parameters and the total amount of different capacity objects, thereby determining the capacity object with the highest scheduling possibility to process the current order according to the scheduling possibility of each resource.

Based on the capacity object scheduling method, capacity objects with longer distances are easy to schedule to process the current orders, the difficulty of processing the orders by the capacity objects is increased, and users cannot receive ordered articles in time easily, so that the operation efficiency of the whole capacity object team is greatly reduced.

Disclosure of Invention

The embodiment of the invention provides a capacity object scheduling method, a capacity object scheduling device, a server and a storage medium, which can solve the problems that a capacity object has high difficulty in order processing, a user cannot receive ordered articles in time easily, and the operation efficiency of a whole capacity object team is greatly reduced. The technical scheme is as follows:

in one aspect, a capacity object scheduling method is provided, and the method includes:

acquiring an order to be processed;

acquiring the expected loss value of a first capacity object of at least one capacity object type for processing the to-be-processed order, wherein each expected loss value is used for representing the overtime risk and the detour degree of one first capacity object;

determining a second transport capacity object with an estimated loss value meeting a preset condition from the first transport capacity object of the at least one transport capacity object type;

and distributing the to-be-processed order to the second capacity object.

In one possible embodiment, the obtaining the expected loss value of the first capacity object of the at least one capacity object type for processing the pending order comprises:

acquiring the predicted detour distance and the predicted overtime length of each first capacity object for processing the to-be-processed order and each received order;

and acquiring the expected loss value of each first capacity object based on the expected detour distance and the expected timeout duration.

In one possible embodiment, the determining, from among the first capacity objects of the at least one capacity object type, the second capacity object whose expected loss value meets a preset condition includes:

and according to the priority of the at least one capacity object type, determining a second capacity object with an estimated loss value meeting the preset condition from the first capacity object of the capacity object type corresponding to the highest priority.

In one possible embodiment, after obtaining the expected loss value of the first capacity object of the at least one capacity object type for processing the pending order, the method further comprises:

and if the second capacity object does not exist in the first capacity object of the capacity object type corresponding to the highest priority, determining the second capacity object in the first capacity objects corresponding to the capacity object types corresponding to the remaining priorities according to the sequence of the priority of the at least one capacity object type from high to low.

if the second capacity object does not exist in the first capacity object of the at least one capacity object type, determining a third capacity object with the minimum estimated loss value from the first capacity object of the capacity object type corresponding to the highest priority;

and distributing the to-be-processed order to the third capacity object.

and acquiring the priority of each transporting capacity object type according to the relation between the proportion of the finished order quantity of the first transporting capacity object of each transporting capacity object type in the total finished order quantity and a preset proportion.

In a possible embodiment, the obtaining the priority of each capacity object type according to the relation between the ratio of the amount of completed orders of the first capacity object of each capacity object type to the total amount of completed orders and a preset ratio comprises at least one of the following items:

when the proportion of the finished order quantity of a first capacity object of a capacity object type to the total finished order quantity is smaller than the preset proportion by a first difference value, according to the first difference value, the initial priority of the capacity object type is increased by a first level, and the priority of the capacity object type is obtained;

when the proportion of the finished order quantity of the first capacity object of the capacity object type to the total finished order quantity is larger than the preset proportion by a second difference value, reducing the initial priority of the capacity object type by a second level according to the second difference value to obtain the priority of the capacity object type;

when the proportion of the finished order quantity of the first capacity object of the capacity object type to the total finished order quantity is equal to the preset proportion, taking the initial priority of the capacity object type as the priority of the capacity object type.

In one possible embodiment, before the obtaining the expected loss value of the first capacity object of the at least one capacity object type for processing the pending order, the method further comprises:

acquiring a filtering condition corresponding to the order to be processed based on the order to be processed;

and screening each transport capacity object according to the filtering condition to obtain a first transport capacity object of the at least one transport capacity object type.

In one possible embodiment, the obtaining, based on the to-be-processed order, a filtering condition corresponding to the to-be-processed order includes:

acquiring the arrival time required by the merchant in the order to be processed based on the order to be processed;

and taking the predicted time of arrival of each capacity object at the merchant to be greater than the arrival time as the filtering condition.

In a possible implementation manner, the filtering, according to the filtering condition, each of the transportation capacity objects to obtain the first transportation capacity object of the at least one transportation capacity object type includes:

acquiring the estimated time according to the current position of each capacity object, the position of the merchant and the current time;

and deleting the transportation objects with the estimated time larger than the arrival time from the various transportation objects to obtain the first transportation object of the at least one transportation object type.

In one aspect, a capacity object scheduling apparatus is provided, the apparatus including:

the first acquisition module is used for acquiring the order to be processed;

the second acquisition module is used for acquiring the expected loss values of the first capacity object of at least one capacity object type for processing the to-be-processed order, wherein each expected loss value is used for representing the overtime risk and the detour degree of one first capacity object;

the determining module is used for determining a second transport capacity object with an estimated loss value meeting a preset condition from the first transport capacity object of the at least one transport capacity object type;

and the distribution module is used for distributing the to-be-processed order to the second capacity object.

In one possible implementation, the second obtaining module is configured to:

In one possible embodiment, the determining module is configured to:

In one possible embodiment, the apparatus further comprises:

the determining module is further configured to determine, if the second capacity object does not exist in the first capacity object of the capacity object type corresponding to the highest priority, the second capacity object in the first capacity object corresponding to the capacity object type corresponding to the remaining priorities according to a sequence from high to low of the priority of the at least one capacity object type.

In one possible embodiment, the apparatus further comprises:

the determining module is further configured to determine, if the second capacity object does not exist in the first capacity object of the at least one capacity object type, a third capacity object with a minimum expected loss value from the first capacity object of the capacity object type corresponding to the highest priority;

the allocation module is further configured to allocate the to-be-processed order to the third capacity object.

In one possible embodiment, the apparatus further comprises:

and the third acquisition module is used for acquiring the priority of each transporting object type according to the relation between the proportion of the finished order quantity of the first transporting object of each transporting object type to the total finished order quantity and a preset proportion.

In one possible implementation, the third obtaining module is configured to:

In one possible embodiment, the apparatus further comprises:

a fourth obtaining module, configured to obtain, based on the to-be-processed order, a filtering condition corresponding to the to-be-processed order;

and the screening module is used for screening each transport capacity object according to the filtering condition to obtain the first transport capacity object of the at least one transport capacity object type.

In one possible implementation, the fourth obtaining module is configured to:

In one possible embodiment, the screening module is configured to:

In one aspect, a server is provided that includes one or more processors and one or more memories having stored therein at least one instruction that is loaded and executed by the one or more processors to perform an operation performed by a capacity object scheduling method according to any one of the possible implementations described above.

In one aspect, a computer-readable storage medium is provided, in which at least one instruction is stored, and the instruction is loaded and executed by a processor to implement the operations performed by the capacity object scheduling method according to any one of the above possible implementation manners.

According to the embodiment of the invention, the estimated loss value of the first capacity object of at least one capacity object type for processing the to-be-processed order can be obtained, the overtime risk and the detour degree of the first capacity object caused by processing the to-be-processed order can be estimated, so that the second capacity object which is best matched can be dispatched to process the to-be-processed order according to the estimated loss value, the difficulty of the second capacity object for processing the to-be-processed order is reduced, a user can receive ordered articles quickly, and the operation efficiency of the whole capacity object team is greatly improved.

Drawings

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

Fig. 1 is a schematic diagram of an implementation environment of a capacity object scheduling method according to an embodiment of the present invention;

fig. 2 is a flowchart of a capacity object scheduling method according to an embodiment of the present invention;

fig. 3 is a flowchart of a capacity object scheduling method according to an embodiment of the present invention;

fig. 4 is a schematic structural diagram of a capacity object scheduling apparatus according to an embodiment of the present invention;

fig. 5 is a block diagram of a terminal 500 according to an embodiment of the present invention;

fig. 6 is a schematic structural diagram of a server 600 according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Fig. 1 is a schematic diagram of an implementation environment of a capacity object scheduling method according to an embodiment of the present invention. Referring to fig. 1, at least one terminal 101 and a server 102 are included in the implementation environment:

the at least one terminal 102 may be an electronic device capable of connecting with the server 102, the electronic device may be a computer, a smartphone, a tablet computer, or other electronic device, an application client may be installed on the at least one terminal 101, and the application client may be any client capable of providing an ordering service, for example, the application client may be an online shopping client, a takeaway client, or the like. The server 102 is configured to provide order processing services, the server 102 may generate an order or receive an order sent by the at least one terminal 102, the server 102 may schedule an appropriate capacity object to process the order based on the obtained order, and the server 102 may further have at least one database for storing user information, business information, and item information, etc. related to the order.

Fig. 2 is a flowchart of a capacity object scheduling method according to an embodiment of the present invention. Referring to fig. 2, the embodiment may be applied to a server, and includes:

201. and acquiring the order to be processed.

202. And acquiring the expected loss value of the first capacity object of at least one capacity object type for processing the order to be processed, wherein each expected loss value is used for representing the overtime risk and the detour degree of one first capacity object.

203. From the first capacity object of the at least one capacity object type, a second capacity object is determined for which the expected loss value meets a preset condition.

204. The pending order is assigned to the second capacity object.

In one possible embodiment, the obtaining the projected loss value of the first capacity object of the at least one capacity object type for processing the pending order comprises:

acquiring the predicted detour distance and the predicted overtime length of each first capacity object for processing the order to be processed and each received order;

and acquiring the predicted loss value of each first capacity object based on the predicted detour distance and the predicted time-out duration.

In one possible embodiment, the determining, from among the first capacity objects of the at least one capacity object type, the second capacity object for which the expected loss value meets a preset condition includes:

and according to the priority of the at least one capacity object type, determining a second capacity object with an expected loss value meeting the preset condition from the first capacity object of the capacity object type corresponding to the highest priority.

In one possible embodiment, after the obtaining of the expected loss value of the first capacity object of the at least one capacity object type for processing the pending order, the method further comprises:

the pending order is assigned to the third capacity object.

In one possible embodiment, the obtaining the priority of each capacity object type according to the relation between the proportion of the finished order quantity of the first capacity object of each capacity object type to the total finished order quantity and the preset proportion comprises at least one of the following items:

when the proportion of the finished order quantity of a first transport capacity object of one transport capacity object type in the total finished order quantity is smaller than the preset proportion by a first difference value, the initial priority of the transport capacity object type is increased by a first level according to the first difference value, and the priority of the transport capacity object type is obtained;

when the proportion of the finished order quantity of the first transporting capacity object of the transporting capacity object type to the total finished order quantity is larger than the preset proportion by a second difference value, the initial priority of the transporting capacity object type is reduced by a second level according to the second difference value, and the priority of the transporting capacity object type is obtained;

when the proportion of the completed order amount of the first capacity object of the one capacity object type to the total completed order amount is equal to the preset proportion, the initial priority of the one capacity object type is taken as the priority of the one capacity object type.

In one possible embodiment, before the obtaining the projected loss value of the first capacity object of the at least one capacity object type for processing the pending order, the method further comprises:

based on the order to be processed, obtaining a filtering condition corresponding to the order to be processed;

In one possible embodiment, the obtaining, based on the pending order, a filtering condition corresponding to the pending order includes:

and taking the predicted time of arrival of each capacity object at the merchant greater than the arrival time as the filtering condition.

In a possible embodiment, the filtering, according to the filtering condition, each of the transportation capacity objects to obtain the first transportation capacity object of the at least one transportation capacity object type includes:

and deleting the transportation capacity objects with the estimated time larger than the arrival time from the various transportation capacity objects to obtain the first transportation capacity object of the at least one transportation capacity object type.

All the above optional technical solutions may be combined arbitrarily to form the optional embodiments of the present disclosure, and are not described herein again.

Fig. 3 is a flowchart of a capacity object scheduling method according to an embodiment of the present invention. Referring to fig. 3, the embodiment includes:

301. the server obtains the order to be processed.

In an embodiment of the present invention, the pending order may include details of the item ordered by the user via the internet. As an example, the pending order may include a time when the user places an order, a time when the merchant receives an order, a user name, a user contact address, a location of the user, a merchant name, a merchant contact address, a location of the merchant, a name of an ordered item, and the like.

For example, the process of the server acquiring the pending order may include the following steps 301A to 301C:

301A: the terminal obtains an order generation request.

The order generation request may carry a user identifier, a merchant identifier, a name of an ordered item, an order placing time, and the like.

In some embodiments, the terminal may obtain an order placing page of the ordered item from the server based on the takeaway application, and when the terminal detects that the user triggers an operation of submitting an option of the order placing page, the terminal may trigger an order generation request for the takeaway item corresponding to the order placing page. The ordering page may display information such as the item ordered by the user, the name of the merchant, the location of the merchant, and the like.

301B: and the terminal sends the order generation request to the server.

301C: the server generates a to-be-processed order corresponding to the order generation request based on the order generation request.

In some embodiments, after receiving the order generation request, the server may extract a user identifier, a merchant identifier, a name of an ordered item, an order placing time, and the like carried by the order generation request, and the server may obtain, according to the user identifier and the merchant identifier, a user name, a user contact address, a user location, and the like associated with the user identifier, obtain a merchant name, a merchant contact address, a merchant location, and the like associated with the merchant identifier, and further, the server may generate the order to be processed based on the user name, the user contact address, the user location, the merchant name, the merchant contact address, the merchant location, the takeaway item name, the order placing time, and the like.

In some embodiments, the server may further obtain a required store-to-store time length associated with the merchant identifier according to the merchant identifier, determine a store-to-store time required by the merchant according to the order placing time and the required store-to-store time length, and generate the to-be-processed order according to the store-to-store time required by the merchant. The required to-store time length may be used to indicate a time length required by the merchant from the ordering time to the arrival of the capacity object at the location of the corresponding merchant, and the required to-store time of the merchant is the time when the capacity object required by the merchant arrives at the location of the corresponding merchant. In other embodiments, the server may further generate the to-be-processed order according to other information, which is not limited herein in the embodiments of the present invention.

The above steps 301A to 301C are described with reference to the process of the server obtaining the to-be-processed order by taking the server generating the to-be-processed order as an example, but in other embodiments, the server may also obtain the to-be-processed order by other manners, for example, the terminal may generate the to-be-processed order and then send the to-be-processed order to the server, which is not limited herein in the embodiment of the present invention.

302. And the server acquires the store arrival time required by the merchant in the pending order based on the pending order.

In the embodiment of the present invention, the server may extract, based on the pending order, the store arrival time required by the merchant recorded in the pending order.

In the above steps 301 to 302, the server generates the to-be-processed order first, and then obtains the store-to-store time required by the merchant from the to-be-processed order, in some embodiments, the server may further determine the store-to-store time required by the merchant directly according to the order placing time and the store-to-store time duration associated with the merchant identifier before generating the to-be-processed order, which is not limited herein.

303. And the server acquires the expected time when each capacity object reaches the merchant according to the current position, the position and the current time of each capacity object, the position and the current time of the merchant.

In this embodiment of the present invention, each of the capacity objects is used for the server to schedule and process the to-be-processed order, each of the capacity objects may be a capacity object of multiple capacity object types, and the multiple capacity object types may be used to represent a working mode of each of the capacity objects.

In some embodiments, the process of the server obtaining the expected time may include the following steps 303A to 303C:

303A: and the server acquires the current position of each capacity object, the position of the merchant and the current moment in real time.

In some embodiments, each transportation capacity object may obtain the current location of the transportation capacity object in real time through a positioning function, each transportation capacity object may upload the current location to the server in real time, the server may obtain the current location of each transportation capacity object in real time, the server may further extract the location of the merchant from the order to be processed, and the server may further obtain the current time in real time through a timing function.

303B: and the server acquires the expected time length required by each capacity object to reach the merchant according to the preset speed, the current position of each capacity object and the position of the merchant.

The preset speed can be used for representing the average speed of each capacity object in the moving process, the preset speed can be any value prestored by the server, and the expected time length is the time length that each capacity object needs to spend from the current position to the position of the merchant.

In some embodiments, the server may determine, from a preset map, a distance between the current location of each capacity object and the location of the merchant according to the current location of each capacity object and the location of the merchant, and the server may use a ratio of the distance to the preset speed as the estimated duration. In other embodiments, the server may further obtain the predicted duration in other manners, for example, the server may further directly obtain a displacement between the current location of each capacity object and the location of the merchant, and the server may use a ratio of the displacement to the preset speed as the predicted duration.

303C: and the server acquires the expected time of each capacity object reaching the merchant according to the current time and the expected duration.

In some embodiments, the server uses the sum of the current time and the projected duration as the projected time of arrival of each capacity object at the merchant.

Through the steps 303A to 303C, the server may obtain the estimated time when each capacity object reaches the merchant according to the current location of each capacity object, the location of the merchant, the current time, and the preset speed, and in other embodiments, the server may also obtain the estimated time when each capacity object reaches the merchant through other manners, which is not limited in the embodiment of the present invention. Through the process, the server can predict the predicted time when each capacity object reaches the merchant of the order to be processed, so that the server can preliminarily screen each capacity object according to the predicted time, and the subsequent burden of the server is reduced.

It should be noted that, in the above steps 302 to 303, the server first obtains the store arrival time required by the merchant in the pending order, and then obtains the estimated time of each capacity object arriving at the merchant, in other embodiments, the server may also first obtain the estimated time of each capacity object arriving at the merchant, and obtain the store arrival time required by the merchant in the pending order, which is not limited herein in the embodiments of the present invention.

304. The server deletes the transportation capacity objects with the estimated time larger than the store arrival time from the transportation capacity objects to obtain a first transportation capacity object of at least one transportation capacity object type.

In the embodiment of the present invention, the first transportation capacity object is a transportation capacity object obtained by filtering each transportation capacity object.

The foregoing steps 303 to 304 are a process in which the server filters each transportation capability object of the at least one transportation capability object type according to the filtering condition to obtain a first transportation capability object of the at least one transportation capability object type, and the process is described by taking the filtering condition as an example that the expected time of each transportation capability object arriving at the merchant is greater than the arrival time of the transportation capability object. In other embodiments, the server may further filter the transportation capacity object according to other filtering conditions, for example, the server may obtain information such as the weight of the ordered item according to the to-be-processed order, and the server may delete the transportation capacity object whose weight limit value is smaller than the weight of the ordered item from the transportation capacity objects according to the weight limit value of each transportation capacity object, to obtain the first transportation capacity object of the at least one transportation capacity object type, where the weight limit value is used to indicate the maximum weight value of each transportation capacity object. As another example, the server may obtain, according to the merchant identifier, a target capacity object type associated with the merchant identifier, and the server may regard, from among the capacity objects, a capacity object of the target capacity object type as the first capacity object according to the capacity object types of the capacity objects. In other embodiments, the server may further obtain the first capacity object of the at least one capacity object type according to other filtering conditions, which is not limited herein in this embodiment of the present invention.

Through steps 303 to 304, the server can perform preliminary screening on each capacity object according to the to-be-processed order, and delete the capacity objects meeting the filtering condition, so that the server does not need to allocate the to-be-processed order to the capacity objects meeting the filtering condition subsequently, the range of the capacity objects for processing the to-be-processed order determined by the server is reduced, and the subsequent burden of the server is reduced.

305. The server obtains the predicted detour distance and the predicted time-out duration of each first capacity object for processing the to-be-processed order and each received order.

In an embodiment of the present invention, the respective received order is an order that has been currently allocated to each first capacity object by the server, and the predicted detour path may be used to indicate a detour degree of each first capacity object for processing the to-be-processed order and the respective received order. The expected timeout duration may be used to indicate a risk of the each first capacity object overtaking the pending order and the respective received order, and for example, the expected timeout duration may be an average timeout duration of all orders of the each first capacity object due to the pending order being processed, which is estimated by the server for the each first capacity object.

In some embodiments, the process of the server obtaining the predicted detour and the predicted timeout period for each first capacity object to process the pending order and the respective orders taken may include the following steps 305A to 305D:

305A: the server may determine a projected optimal processing order for each first capacity object to process the pending order and the respective received order.

The predicted optimal processing order may be a processing order corresponding to a minimum total route for each first capacity object to predict processing of the to-be-processed order and each received order, where the processing order is a sequence of the to-be-processed order and each received order predicted to be processed by each first capacity object.

In some embodiments, the server may determine, according to the to-be-processed order and the each received order, a merchant position in the to-be-processed order and a merchant position of the each received order, perform a path planning algorithm such as environment modeling, path search, and path smoothing according to the merchant position in the to-be-processed order and the merchant position of the each received order, to obtain total routes corresponding to the plurality of processing sequences, respectively, and the server may determine a processing sequence corresponding to the minimum total route as the predicted optimal processing sequence. In other embodiments, the server may also determine the predicted optimal processing order in other manners, which is not limited herein.

305B: the server may determine, according to the optimal processing order, the merchant location in the order to be processed, the merchant location in each received order, and the current location of each first capacity object, a first estimated route for each first capacity object to process the order to be processed, a second estimated route for each first capacity object to process each received order, a first estimated time at which each first capacity object has finished processing the order to be processed, and a second estimated time at which each first capacity object has finished processing each received order.

The first estimated route may be a route from the current location of each first transportation object to the merchant location in the order to be processed according to the optimal processing order, each second estimated route may be a route from the current location of each first transportation object to the merchant location in each received order according to the optimal processing order, the first estimated time may be an estimated time when each first transportation object finishes processing the order to be processed according to the optimal processing order, and each second estimated time may be an estimated time when each first transportation object finishes processing the order to be received according to the optimal processing order.

305C: the server obtains a first estimated detour route for each first capacity object to process the to-be-processed order, a second estimated detour route for each first capacity object to process each received order, a first estimated timeout duration for each first capacity object to process the to-be-processed order and a second estimated timeout duration for each first capacity object to process each received order based on the first estimated route, the second estimated routes, the first estimated time instant and the second estimated time instant.

In some embodiments, the server may determine a first initial range from the current location of each first capacity object to the merchant location in the pending order when each first capacity object is only processing the pending order, the server may determine a second initial range from the current location of each first capacity object to the merchant location in each received order when each first capacity object is not processing the pending order, the server may further determine a first initial time when each first capacity object is only processing the pending order and is expected to have finished processing the pending order, and the server may further determine a second initial time when each first capacity object is not processing the pending order and is expected to have finished processing each received order. The server may use a difference between the first predicted route and the first initial route as the first predicted detour route, may use a difference between each second predicted route and the corresponding second initial route as each second predicted detour route, may use a difference between each first predicted time and each first initial time as the first predicted timeout period, and may use a difference between each second predicted time and each corresponding second initial time as each second predicted timeout period.

305D: and the server acquires the predicted detour routes and the predicted overtime duration for processing the to-be-processed orders and the received orders by each first capacity object based on the first predicted detour routes, the second predicted detour routes, the first predicted overtime duration and the second predicted overtime durations.

In some embodiments, the server may use a weighted average of the first predicted detour routes and the respective second predicted detour routes as the predicted detour routes for the each first capacity object to process the pending order and the respective received order, and the server may use a weighted average of the first predicted timeout duration and the respective second predicted timeout duration as the predicted timeout duration for the each first capacity object to process the pending order and the respective received order. In other embodiments, the server may further obtain, by other means, an expected detour and an expected timeout duration for each first capacity object to process the to-be-processed order and each received order, which is not limited herein.

The steps 305A to 305D are a process in which the server obtains the predicted detour distance and the predicted timeout duration for each first transportation object to process the to-be-processed order and each received order, and through the process, the server can predict the overall timeout risk and the overall detour degree of each first transportation object due to the processing of the to-be-processed order, so that the server can preferentially select a first transportation object from each first transportation object to process the to-be-processed order according to the overall timeout risk and the overall detour degree of each first transportation object. In other embodiments, the server may further obtain, by other means, an expected detour and an expected timeout duration for each first capacity object to process the pending order and each taken order, which is not limited herein.

306. The server obtains an expected loss value for each first capacity object based on the expected detour and the expected timeout duration.

The expected loss value of each first capacity object may be used to represent the timeout risk and detour degree of each first capacity object, that is, the expected loss value of each first capacity object may be used to represent the expected cost of each first capacity object due to processing the pending order.

After some embodiments, the server may take an average of the predicted detour and the predicted timeout period for each first capacity object as the predicted loss value for that first capacity object. In other embodiments, the server may also obtain the expected loss value of each first capacity object by other manners, which is not limited herein.

The above steps 305 to 306 are a process in which the server obtains the expected loss value of each first capacity object for processing the to-be-processed order, and based on this process, the server may estimate the risk of timeout and the detour degree of all orders of each first capacity object due to the processing of the to-be-processed order by each first capacity object, so that the server may select the first capacity object with a relatively low expected loss value to process the to-be-processed order, thereby reducing the overall cost of processing the order by each first capacity object. Of course, in other embodiments, the server may also obtain the expected loss value of each first capacity object for processing the pending order in other manners, which is not limited herein.

307. When the proportion of the finished order quantity of a first transport capacity object of one transport capacity object type to the total finished order quantity is smaller than the preset proportion by a first difference value, the server increases the initial priority of the transport capacity object type by a first level according to the first difference value to obtain the priority of the transport capacity object type.

In this embodiment of the present invention, the completed order quantity of the first capacity object of one capacity object type may be a total order quantity completed by each first capacity object of the one capacity object type within a preset time duration, the total completed order quantity may be a total order quantity completed by each capacity object of each capacity object type within the preset time duration, the preset proportion may be any value preset by the server, the initial priority may be a fixed priority preset by the server for the one capacity object type, different capacity object types may correspond to different initial priorities, the first level may be a level value preset by the server and corresponding to the first difference, and as an example, when the first difference is 1%, the first level may be one level. In some embodiments, the process of the server obtaining the priority of the one capacity object type according to the first difference value may include the following steps 307A to 307D:

307A: the server may determine a completed order amount for each first capacity object of a capacity object type for a preset duration and a total completed order amount for each capacity object of each capacity object type for the preset duration.

As an example, the server may determine a first completed order amount for each first capacity object of a capacity object type from the time of day to the current time, the server may determine a sum of the respective first completed order amounts from the time of day to the current time as a completed order amount for each capacity object of a capacity object type from the time of day to the current time, and similarly, the server may determine a total completed order amount for each capacity object of each capacity object type from the time of day to the current time. The above process is described by taking a preset time length as an example of a time length from the time of the day zero to the current time, in other embodiments, the server may also set the preset time length as other time lengths, and the embodiment of the present invention is not limited herein.

307B: the server obtains the proportion of the finished order quantity corresponding to one capacity object type to the total finished order quantity.

307C: and determining the size relation between the proportion and a preset proportion, and determining a first difference value of the proportion smaller than the preset proportion when the proportion is smaller than the preset proportion.

307D: and based on the first difference, the initial priority of one capacity object type is increased by a first level, and the priority of the capacity object type is obtained.

As an example, the server may divide the first difference by 1% to obtain a first quotient of the first difference and 1%, the server may round the first quotient to obtain a first integer of the first quotient, and the server may increase the initial priority of one capacity object type by a first integer number of levels based on the initial priority of the one capacity object type and the first integer to obtain the priority of the one capacity object type. In other embodiments, the server may also obtain the priority of the one capacity object type in other ways.

The above-mentioned steps 307A to 307D are processes of obtaining the priority of one transporting object type by the server when the ratio of the completed order amount of the first transporting object of the one transporting object type to the total completed order amount is smaller than the preset ratio by the first difference value. Similarly, when the ratio of the completed order amount of the first capacity object of the capacity object type to the total completed order amount is greater than the preset ratio by a second difference value, the server may decrease the initial priority of the capacity object type by a second level according to the second difference value to obtain the priority of the capacity object type. Similarly, when the ratio of the completed order amount of the first capacity object of the one capacity object type to the total completed order amount is equal to the preset ratio, the server may use the initial priority of the one capacity object type as the priority of the one capacity object type.

Through step 307, the server may obtain the priority of one capacity object type based on the initial priority of one capacity object type according to the size relationship between the ratio of the completed order amount of the first capacity object of one capacity object type to the total completed order amount and the preset ratio, and similarly, the server may obtain the priority of each capacity object type, which is not described in detail herein. Through the process, the priority of each capacity object type can be adjusted to different levels by the server according to the size relationship between the finished order quantity of one capacity object type and the total finished order quantity of each capacity object type, and the capacity object capable of processing the order to be processed can be determined from the first capacity object of the capacity object corresponding to the higher priority by the subsequent server according to the priority of each capacity object type, so that the order quantity processed by each capacity object type is relatively balanced, the situation that the order quantity processed by each capacity object type has a large difference is avoided, and the total benefit value of each capacity object type is relatively balanced.

It should be noted that, in the above steps 305 to 307, the server first obtains the expected loss value of each first capacity object, and then obtains the priority of at least one capacity object type. In other embodiments, the server may further obtain the priority of the at least one capacity object type first and then obtain the expected loss value of each first capacity object, or the server may obtain the expected loss value of each first capacity object simultaneously in the process of obtaining the priority of the at least one capacity object type, which is not limited herein.

308. And the server determines a second capacity object with an estimated loss value meeting a preset condition from the first capacity object of the capacity object type corresponding to the highest priority according to the priority of the at least one capacity object type.

In this embodiment of the present invention, the second capacity object is a capacity object determined by the server that can process the pending order.

In some embodiments, the server may determine a target capacity object type corresponding to a highest priority from among the at least one capacity object type, compare the expected loss value of each first capacity object of the target capacity object type with a loss value threshold, and when at least one target first capacity object having an expected loss value smaller than the loss value threshold exists in each first capacity object of the target capacity object type, the server may determine a target first capacity object corresponding to a smallest expected loss value from among the at least one target first capacity object as the second capacity object. Wherein the loss value threshold may be a smaller cost value preset by the server.

In other embodiments, when the server determines that at least one target first capacity object with an expected loss value smaller than the loss value threshold does not exist in the first capacity objects of the target capacity object types, the server may determine the second capacity object in the first capacity objects of the capacity object types corresponding to the remaining priorities in the order from high to low according to the priority of the at least one capacity object type. As an example, the server may compare the expected loss value of each first capacity object of the capacity object type corresponding to the next highest priority with the loss value threshold, and when there is at least one candidate first capacity object whose expected loss value is smaller than the loss value threshold in each first capacity object of the capacity object type corresponding to the next highest priority, the server may determine, as the second capacity object, the candidate first capacity object corresponding to the smallest expected loss value from the at least one candidate first capacity object, and so on until the server determines the second capacity object. Wherein the remaining priority is a priority other than the highest priority.

The server may determine a second capacity object capable of processing the order to be processed according to the priority of the at least one capacity object type and the relationship between the expected loss value of each first capacity object and the loss value threshold, through step 308, and the server may determine the second capacity object from the first capacity objects of the capacity object type corresponding to the highest possible priority in the case that the expected loss value is less than the loss value threshold, so that the server may determine the second capacity object from both the amount of orders to be processed of the at least one capacity object type and the expected loss value of each first capacity object during the process of allocating the order to be processed, so that the expected loss value of the order to be processed by the second capacity object is low, and the amount of orders to be processed by the second capacity object is not large, the condition of uneven income distribution is avoided.

The above steps 307 to 308 are a process of determining, by the server, a second transportation object with an expected loss value meeting a preset condition from among the first transportation objects of at least one transportation object type, by which the server may determine, from each first transportation object, the second transportation object with a lower expected loss value, so that after the server allocates the to-be-processed order to the second transportation object, average gains of the second transportation object and other first transportation objects reach higher values, thereby being beneficial to both the second transportation object and other first transportation objects.

309. The server assigns the pending order to the second capacity object.

It should be noted that, in the above steps 308 to 309, the server determines a second capacity object with an expected loss value meeting a preset condition from the first capacity object of at least one capacity object type, so as to allocate the pending order to the second capacity object. In some embodiments, when there is no second capacity object with an expected loss value meeting a preset condition in each first capacity object of the at least one capacity object type, the server may determine a third capacity object with a minimum expected loss value from the first capacity objects of the capacity object type corresponding to the highest priority, and the server may assign the to-be-processed order to the third capacity object. And the third capacity object is the capacity object which is determined by the server and can process the to-be-processed order.

In other embodiments, when there is no second capacity object with an expected loss value meeting a preset condition in each first capacity object of the at least one capacity object type, the server may further determine a third capacity object in other manners, which is not limited herein.

All the above-mentioned optional technical solutions can be combined arbitrarily to form the optional embodiments of the present invention, and are not described herein again.

According to the embodiment of the invention, the estimated loss value of the first capacity object of at least one capacity object type for processing the to-be-processed order can be obtained, the overtime risk and the detour degree of the first capacity object caused by processing the to-be-processed order can be estimated, so that the second capacity object which is best matched can be dispatched to process the to-be-processed order according to the estimated loss value, the difficulty of the second capacity object for processing the to-be-processed order is reduced, a user can receive ordered articles quickly, and the operation efficiency of the whole capacity object team is greatly improved. Furthermore, the optimal matching of the to-be-processed orders and the capacity objects can be carried out from the global perspective by combining the priority of each capacity object type, and the relative fracture of the to-be-processed orders and the capacity objects in the matching process is avoided, so that the matching efficiency of the to-be-processed orders and the capacity objects is improved, and the user experience is improved.

Fig. 4 is a schematic structural diagram of a capacity object scheduling apparatus according to an embodiment of the present invention. Referring to fig. 4, the embodiment includes: a first obtaining module 401, a second obtaining module 402, a determining module 403 and an assigning module 404.

A first obtaining module 401, configured to obtain an order to be processed;

a second obtaining module 402, configured to obtain expected loss values of a first capacity object of at least one capacity object type for processing the to-be-processed order, where each expected loss value is used to represent a timeout risk and a detour degree of the first capacity object;

a determining module 403, configured to determine, from the first capacity object of the at least one capacity object type, a second capacity object whose expected loss value meets a preset condition;

an assigning module 404 for assigning the pending order to the second capacity object.

In a possible implementation, the second obtaining module 402 is configured to:

In one possible implementation, the determining module 403 is configured to:

In one possible embodiment, the apparatus further comprises:

the determining module is further configured to determine, if the second capacity object does not exist in the first capacity object of the capacity object type corresponding to the highest priority, the second capacity object in the first capacity object corresponding to the capacity object type corresponding to the remaining priority according to a sequence from high to low of the priority of the at least one capacity object type.

In one possible embodiment, the apparatus further comprises:

the allocation module is further configured to allocate the pending order to the third capacity object.

In one possible embodiment, the apparatus further comprises:

In one possible implementation, the third obtaining module is configured to:

In one possible embodiment, the apparatus further comprises:

the fourth obtaining module is used for obtaining the filtering condition corresponding to the order to be processed based on the order to be processed;

and the screening module is used for screening each transport capacity object according to the filtering condition to obtain a first transport capacity object of the at least one transport capacity object type.

In one possible implementation, the fourth obtaining module is configured to:

In one possible embodiment, the screening module is configured to:

It should be noted that: in the capacity object scheduling apparatus provided in the above embodiment, only the division of the above functional modules is used for illustration when capacity object scheduling is performed, and in practical applications, the above function allocation may be completed by different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the above described functions. In addition, the embodiment of the capacity object scheduling apparatus and the embodiment of the capacity object scheduling method provided by the above embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments, and are not described herein again.

Fig. 5 is a block diagram of a terminal 500 according to an embodiment of the present invention. The terminal 500 may be: a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III, motion video Experts compression standard Audio Layer 3), an MP4 player (Moving Picture Experts Group Audio Layer IV, motion video Experts compression standard Audio Layer 4), a notebook computer, or a desktop computer. Terminal 500 may also be referred to by other names such as user equipment, portable terminal, laptop terminal, desktop terminal, and the like.

In general, the terminal 500 includes: a processor 501 and a memory 502.

The processor 501 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and so on. The processor 501 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 501 may also include a main processor and a coprocessor, where the main processor is a processor for processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 501 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the display screen. In some embodiments, processor 501 may also include an AI (Artificial Intelligence) processor for processing computational operations related to machine learning.

Memory 502 may include one or more computer-readable storage media, which may be non-transitory. Memory 502 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 502 is used to store at least one instruction for execution by processor 501 to implement the capacity object scheduling method provided by method embodiments of the present invention.

In some embodiments, the terminal 500 may further optionally include: a peripheral interface 503 and at least one peripheral. The processor 501, memory 502 and peripheral interface 503 may be connected by a bus or signal lines. Each peripheral may be connected to the peripheral interface 503 by a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of radio frequency circuitry 504, display screen 505, camera 506, audio circuitry 507, positioning components 508, and power supply 509.

The peripheral interface 503 may be used to connect at least one peripheral related to I/O (Input/Output) to the processor 501 and the memory 502. In some embodiments, the processor 501, memory 502, and peripheral interface 503 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 501, the memory 502, and the peripheral interface 503 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

The Radio Frequency circuit 504 is used for receiving and transmitting RF (Radio Frequency) signals, also called electromagnetic signals. The radio frequency circuitry 504 communicates with communication networks and other communication devices via electromagnetic signals. The rf circuit 504 converts an electrical signal into an electromagnetic signal to transmit, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 504 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and so forth. The radio frequency circuitry 504 may communicate with other terminals via at least one wireless communication protocol. The wireless communication protocols include, but are not limited to: metropolitan area networks, various generation mobile communication networks (2G, 3G, 4G, and 5G), Wireless local area networks, and/or WiFi (Wireless Fidelity) networks. In some embodiments, the rf circuit 504 may further include NFC (Near Field Communication) related circuits, which are not limited in the present disclosure.

The display screen 505 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. When the display screen 505 is a touch display screen, the display screen 505 also has the ability to capture touch signals on or over the surface of the display screen 505. The touch signal may be input to the processor 501 as a control signal for processing. At this point, the display screen 505 may also be used to provide virtual buttons and/or a virtual keyboard, also referred to as soft buttons and/or a soft keyboard. In some embodiments, the display screen 505 may be one, providing the front panel of the terminal 500; in other embodiments, the display screens 505 may be at least two, respectively disposed on different surfaces of the terminal 500 or in a folded design; in still other embodiments, the display 505 may be a flexible display disposed on a curved surface or on a folded surface of the terminal 500. Even more, the display screen 505 can be arranged in a non-rectangular irregular figure, i.e. a shaped screen. The Display screen 505 may be made of LCD (liquid crystal Display), OLED (Organic Light-Emitting Diode), and the like.

The camera assembly 506 is used to capture images or video. Optionally, camera assembly 506 includes a front camera and a rear camera. Generally, a front camera is disposed at a front panel of the terminal, and a rear camera is disposed at a rear surface of the terminal. In some embodiments, the number of the rear cameras is at least two, and each rear camera is any one of a main camera, a depth-of-field camera, a wide-angle camera and a telephoto camera, so that the main camera and the depth-of-field camera are fused to realize a background blurring function, and the main camera and the wide-angle camera are fused to realize panoramic shooting and VR (Virtual Reality) shooting functions or other fusion shooting functions. In some embodiments, camera assembly 506 may also include a flash. The flash lamp can be a monochrome temperature flash lamp or a bicolor temperature flash lamp. The double-color-temperature flash lamp is a combination of a warm-light flash lamp and a cold-light flash lamp, and can be used for light compensation at different color temperatures.

Audio circuitry 507 may include a microphone and a speaker. The microphone is used for collecting sound waves of a user and the environment, converting the sound waves into electric signals, and inputting the electric signals to the processor 501 for processing, or inputting the electric signals to the radio frequency circuit 504 to realize voice communication. For the purpose of stereo sound collection or noise reduction, a plurality of microphones may be provided at different portions of the terminal 500. The microphone may also be an array microphone or an omni-directional pick-up microphone. The speaker is used to convert electrical signals from the processor 501 or the radio frequency circuit 504 into sound waves. The loudspeaker can be a traditional film loudspeaker or a piezoelectric ceramic loudspeaker. When the speaker is a piezoelectric ceramic speaker, the speaker can be used for purposes such as converting an electric signal into a sound wave audible to a human being, or converting an electric signal into a sound wave inaudible to a human being to measure a distance. In some embodiments, audio circuitry 507 may also include a headphone jack.

The positioning component 508 is used to locate the current geographic position of the terminal 500 for navigation or LBS (location based Service). The positioning component 508 may be a positioning component based on the GPS (global positioning System) in the united states, the beidou System in china, the graves System in russia, or the galileo System in the european union.

Power supply 509 is used to power the various components in terminal 500. The power source 509 may be alternating current, direct current, disposable or rechargeable. When power supply 509 includes a rechargeable battery, the rechargeable battery may support wired or wireless charging. The rechargeable battery may also be used to support fast charge technology.

In some embodiments, terminal 500 also includes one or more sensors 510. The one or more sensors 510 include, but are not limited to: acceleration sensor 511, gyro sensor 512, pressure sensor 513, fingerprint sensor 514, optical sensor 515, and proximity sensor 516.

The acceleration sensor 511 may detect the magnitude of acceleration on three coordinate axes of the coordinate system established with the terminal 500. For example, the acceleration sensor 511 may be used to detect components of the gravitational acceleration in three coordinate axes. The processor 501 may control the display screen 505 to display the user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 511. The acceleration sensor 511 may also be used for acquisition of motion data of a game or a user.

The gyro sensor 512 may detect a body direction and a rotation angle of the terminal 500, and the gyro sensor 512 may cooperate with the acceleration sensor 511 to acquire a 3D motion of the user on the terminal 500. The processor 501 may implement the following functions according to the data collected by the gyro sensor 512: motion sensing (such as changing the UI according to a user's tilting operation), image stabilization at the time of photographing, game control, and inertial navigation.

The pressure sensor 513 may be disposed on a side frame of the terminal 500 and/or underneath the display screen 505. When the pressure sensor 513 is disposed on the side frame of the terminal 500, a user's holding signal of the terminal 500 may be detected, and the processor 501 performs left-right hand recognition or shortcut operation according to the holding signal collected by the pressure sensor 513. When the pressure sensor 513 is disposed at the lower layer of the display screen 505, the processor 501 controls the operability control on the UI interface according to the pressure operation of the user on the display screen 505. The operability control comprises at least one of a button control, a scroll bar control, an icon control and a menu control.

The fingerprint sensor 514 is used for collecting a fingerprint of the user, and the processor 501 identifies the identity of the user according to the fingerprint collected by the fingerprint sensor 514, or the fingerprint sensor 514 identifies the identity of the user according to the collected fingerprint. Upon recognizing that the user's identity is a trusted identity, the processor 501 authorizes the user to perform relevant sensitive operations including unlocking the screen, viewing encrypted information, downloading software, paying, and changing settings, etc. The fingerprint sensor 514 may be provided on the front, back, or side of the terminal 500. When a physical button or a vendor Logo is provided on the terminal 500, the fingerprint sensor 514 may be integrated with the physical button or the vendor Logo.

The optical sensor 515 is used to collect the ambient light intensity. In one embodiment, the processor 501 may control the display brightness of the display screen 505 based on the ambient light intensity collected by the optical sensor 515. Specifically, when the ambient light intensity is high, the display brightness of the display screen 505 is increased; when the ambient light intensity is low, the display brightness of the display screen 505 is reduced. In another embodiment, processor 501 may also dynamically adjust the shooting parameters of camera head assembly 506 based on the ambient light intensity collected by optical sensor 515.

A proximity sensor 516, also referred to as a distance sensor, is typically disposed on the front panel of the terminal 500. The proximity sensor 516 is used to collect the distance between the user and the front surface of the terminal 500. In one embodiment, when the proximity sensor 516 detects that the distance between the user and the front surface of the terminal 500 gradually decreases, the processor 501 controls the display screen 505 to switch from the bright screen state to the dark screen state; when the proximity sensor 516 detects that the distance between the user and the front surface of the terminal 500 becomes gradually larger, the display screen 505 is controlled by the processor 501 to switch from the breath screen state to the bright screen state.

Those skilled in the art will appreciate that the configuration shown in fig. 5 is not intended to be limiting of terminal 500 and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components may be used.

Fig. 6 is a schematic structural diagram of a server 600 according to an embodiment of the present invention, where the server 600 may generate relatively large differences due to different configurations or performances, and may include one or more CPUs (central processing units) 601 and one or more memories 602, where the memory 602 stores at least one instruction, and the at least one instruction is loaded and executed by the processor 601 to implement the capacity object scheduling method provided by the foregoing method embodiments. Of course, the server may also have components such as a wired or wireless network interface, a keyboard, and an input/output interface, so as to perform input/output, and the server may also include other components for implementing the functions of the device, which are not described herein again.

In an exemplary embodiment, a computer-readable storage medium, such as a memory, is also provided that includes instructions executable by a processor in a terminal to perform the capacity object scheduling method of the above embodiments. For example, the computer-readable storage medium may be a ROM (Read-Only Memory), a RAM (Random access Memory), a CD-ROM (Compact Disc Read-Only Memory), a magnetic tape, a floppy disk, an optical data storage device, and the like.

It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

The above description is only exemplary of the present invention and should not be taken as limiting the invention, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A capacity object scheduling method, the method comprising:

acquiring an order to be processed;

and distributing the to-be-processed order to the second capacity object.

2. The method of claim 1, wherein said obtaining a projected loss value for a first capacity object of at least one capacity object type to process the pending order comprises:

3. The method of claim 1, wherein determining, from a first capacity object of the at least one capacity object type, a second capacity object having an expected loss value meeting a predetermined condition comprises:

4. The method of claim 1, wherein after obtaining the projected loss value for the first capacity object of the at least one capacity object type to process the pending order, the method further comprises:

5. The method of claim 1, wherein after obtaining the projected loss value for the first capacity object of the at least one capacity object type to process the pending order, the method further comprises:

and distributing the to-be-processed order to the third capacity object.

6. Any of the methods of claims 3-5, wherein after said obtaining a projected loss value for a first capacity object of at least one capacity object type to process the pending order, the method further comprises:

7. The method of claim 6, wherein the obtaining the priority of each capacity object type according to the relation between the ratio of the amount of completed orders of the first capacity object of each capacity object type to the total amount of completed orders and a preset ratio comprises at least one of the following:

8. The method of claim 1, wherein prior to said obtaining a projected loss value for a first capacity object of at least one capacity object type to process the pending order, the method further comprises:

9. The method of claim 8, wherein obtaining the filter condition corresponding to the pending order based on the pending order comprises:

10. The method of claim 9, wherein the screening the respective capacity objects according to the filtering condition to obtain the first capacity object of the at least one capacity object type comprises:

11. A capacity object scheduling apparatus, comprising:

the acquisition module is used for acquiring the order to be processed;

the obtaining module is further configured to obtain expected loss values of the first capacity object of at least one capacity object type for processing the to-be-processed order, where each expected loss value is used to represent a timeout risk and a detour degree of one first capacity object;

12. A server, comprising one or more processors and one or more memories having stored therein at least one instruction, the instruction being loaded and executed by the one or more processors to perform an operation performed by the capacity object scheduling method of any one of claims 1 to 10.

13. A computer-readable storage medium having stored therein at least one instruction which is loaded and executed by a processor to perform operations performed by the capacity object scheduling method of any one of claims 1 to 10.