Order delivery method, device, equipment, computer readable medium and program product

Abstract

The embodiment of the invention discloses an order delivery method, device and equipment, a computer readable medium and a program product. A specific embodiment of the method comprises the steps of obtaining a target order set for a target region; according to the target order set, generating at least one piece of first delivery feature information and at least one piece of second delivery feature information; according to the at least one piece of first delivery feature information and the at least one piece of second delivery feature information, a delivery mode classification result is generated by using a target classification model, and the delivery mode classification result comprises a result representing that order delivery is performed by using the first delivery mode and a result representing that order delivery is performed by using the second delivery mode; and according to the distribution mode classification result, executing order distribution for the target order set. The implementation mode is related to artificial intelligence, and the order distribution of the target order set can be quickly and efficiently realized by utilizing the target classification model.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular to an order delivery method, apparatus, device, computer-readable medium, and program product. Background Art

[0002] At present, order sorting is an important part of the express delivery industry. An effective order sorting strategy can greatly improve express delivery efficiency. Order sorting is usually done by sorting orders based on product type or weight range.

[0003] However, the inventors have found that when the above method is used to divide orders into different networks, the following technical problems often occur:

[0004] As orders fluctuate, unreasonable network and order distribution based on order product type or order weight range often occurs, resulting in an explosion of orders and low delivery efficiency. In addition, a large amount of delivery resources will be wasted during the delivery process.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0006] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0007] Some embodiments of the present disclosure propose order delivery methods, devices, equipment, computer-readable media and program products to solve the technical problems mentioned in the above background technology section.

[0008] In a first aspect, some embodiments of the present disclosure provide an order delivery method, comprising: obtaining a target order set for a target area; generating at least one first delivery feature information and at least one second delivery feature information based on the target order set, wherein the at least one first delivery feature information is each feature information generated by order delivery based on a first delivery method, and the at least one second delivery feature information is each feature information generated by order delivery based on a second delivery method; generating a delivery method classification result using a target classification model based on the at least one first delivery feature information and the at least one second delivery feature information, wherein the delivery method classification result includes: a result characterizing the order delivery result using the first delivery method, and a result characterizing the order delivery result using the second delivery method; and executing order delivery for the target order set based on the delivery method classification result.

[0009] Optionally, the at least one first delivery characteristic information includes: a first single-vehicle delivery time and a first load coefficient; and the generating of at least one first delivery characteristic information and at least one second delivery characteristic information based on the target order set includes: obtaining a delivery vehicle information set for the first delivery method, and obtaining a target overall delivery time function and at least one corresponding constraint function; determining the order delivery related information corresponding to each target order in the target order set to obtain an order delivery related information set; generating the whole vehicle delivery time and the first load coefficient based on the delivery vehicle information set and the order delivery related information set, using the target overall delivery time function and the at least one constraint function; generating the first single-vehicle delivery time based on the whole vehicle delivery time and the delivery vehicle information set.

[0010] Optionally, the above-mentioned generating the whole vehicle delivery time and the above-mentioned first load factor according to the above-mentioned delivery vehicle information set and the above-mentioned order delivery related information set using the above-mentioned target overall delivery time function and the above-mentioned at least one constraint function includes: performing the following generating steps according to the delivery vehicle information set and the above-mentioned order delivery related information set: generating an initial whole vehicle delivery time according to the delivery vehicle information set and the above-mentioned order delivery related information set using the above-mentioned target overall delivery time function and the above-mentioned at least one constraint function; in response to not detecting vehicle overload information for the initial order allocation information, generating a load increment coefficient corresponding to the delivery vehicle information set as the first load coefficient, wherein the initial order allocation information is the order allocation information corresponding to the initial whole vehicle delivery time; determining the initial whole vehicle delivery time corresponding to the first load factor as the above-mentioned whole vehicle delivery time; in response to detecting vehicle overload information for the initial order allocation information, performing a load increment adjustment on the vehicle load corresponding to the delivery vehicle information set to obtain an adjusted delivery vehicle information set; using the adjusted delivery vehicle information set as the delivery vehicle information set, and continuing to perform the above-mentioned generating steps.

[0011] Optionally, the at least one second delivery characteristic information includes: the second bicycle delivery time and the second load coefficient; and the above-mentioned at least one first delivery characteristic information and the at least one second delivery characteristic information are used to generate a delivery method classification result by using the target classification model, including: generating a delivery method classification result by using the target classification model according to the first bicycle delivery time, the first load coefficient, the second bicycle delivery time and the second load coefficient.

[0012] Optionally, the at least one first delivery characteristic information further includes: the distance between the first order and the site, site efficiency information and the first single-vehicle delivery value consumption information, and the at least one second delivery characteristic information further includes: the distance between the second order and the site and the second single-vehicle delivery value consumption information; and

[0013] The above-mentioned target classification model is used to generate a delivery method classification result based on the at least one first delivery characteristic information and the at least one second delivery characteristic information, including: combining characteristic information of at least one of the first bicycle delivery time, the first load factor, the second bicycle delivery time, the second load factor, the distance between the first order and the site, the site efficiency information, the first bicycle delivery value consumption information, the distance between the second order and the site, and the second bicycle delivery value consumption information to generate combined characteristic information; and inputting the combined characteristic information into the target classification model to generate the above-mentioned delivery method classification result.

[0014] Optionally, the above-mentioned target overall delivery time function is a function based on the product of the delivery consumption time of the delivery point and the decision variable of the delivery point, wherein the above-mentioned at least one constraint function includes: a constraint condition that represents the number of delivery vehicles corresponding to the above-mentioned delivery vehicle information set is a predetermined number, a constraint condition that represents the delivery vehicle returns to the initial site after delivery, a constraint condition that represents that each order supports being delivered once, and a constraint condition that represents that the delivery vehicle is not supported to be overloaded with delivery order goods.

[0015] In a second aspect, some embodiments of the present disclosure provide an order delivery device, comprising: an acquisition unit, configured to acquire a target order set for a target area; a first generation unit, configured to generate at least one first delivery feature information and at least one second delivery feature information based on the target order set, wherein the at least one first delivery feature information is each feature information generated by order delivery based on a first delivery method, and the at least one second delivery feature information is each feature information generated by order delivery based on a second delivery method; the second generation unit, configured to generate a delivery method classification result based on the at least one first delivery feature information and the at least one second delivery feature information using a target classification model, wherein the delivery method classification result includes: a result characterizing the order delivery using the first delivery method, and a result characterizing the order delivery using the second delivery method; an execution unit, configured to execute order delivery for the target order set based on the delivery method classification result.

[0016] Optionally, the at least one first delivery characteristic information includes: a first single-vehicle delivery time and a first load coefficient; and the first generating unit can be configured to: obtain a delivery vehicle information set for the above-mentioned first delivery method, and obtain a target overall delivery time function and a corresponding at least one constraint function; determine the order delivery related information corresponding to each target order in the above-mentioned target order set, and obtain an order delivery related information set; based on the above-mentioned delivery vehicle information set and the above-mentioned order delivery related information set, using the above-mentioned target overall delivery time function and the above-mentioned at least one constraint function, generate the whole vehicle delivery time and the above-mentioned first load coefficient; based on the above-mentioned whole vehicle delivery time and the above-mentioned delivery vehicle information set, generate the above-mentioned first single-vehicle delivery time.

[0017] Optionally, the first generation unit can be configured to: perform the following generation steps based on the delivery vehicle information set and the above-mentioned order delivery related information set: generate an initial vehicle delivery time based on the delivery vehicle information set and the above-mentioned order delivery related information set using the above-mentioned target overall delivery time function and the above-mentioned at least one constraint function; in response to not detecting vehicle overload information for the initial order allocation information, generate a load increment coefficient corresponding to the delivery vehicle information set as a first load coefficient, wherein the initial order allocation information is the order allocation information corresponding to the initial vehicle delivery time; determine the initial vehicle delivery time corresponding to the first load coefficient as the above-mentioned vehicle delivery time; in response to detecting vehicle overload information for the initial order allocation information, perform a load increment adjustment on the vehicle load corresponding to the delivery vehicle information set to obtain an adjusted delivery vehicle information set; use the adjusted delivery vehicle information set as the delivery vehicle information set, and continue to execute the above-mentioned generation steps.

[0018] Optionally, the at least one second delivery characteristic information includes: a second bicycle delivery time and a second load coefficient; and the second generation unit can be configured to: generate a delivery method classification result based on the first bicycle delivery time, the first load coefficient, the second bicycle delivery time and the second load coefficient, using the target classification model.

[0019] Optionally, the at least one first delivery characteristic information also includes: the distance between the first order and the site, the site efficiency information and the first bicycle delivery value consumption information, and the at least one second delivery characteristic information also includes: the distance between the second order and the site and the second bicycle delivery value consumption information; and the second generation unit can be configured to: combine the characteristic information of at least one of the first bicycle delivery time, the first load factor, the second bicycle delivery time, the second load factor, the distance between the first order and the site, the site efficiency information, the first bicycle delivery value consumption information, the distance between the second order and the site and the second bicycle delivery value consumption information to generate combined characteristic information; input the combined characteristic information into the target classification model to generate the delivery method classification result.

[0020] Optionally, the above-mentioned target overall delivery time function is a function based on the product of the delivery consumption time of the delivery point and the decision variable of the delivery point, wherein the above-mentioned at least one constraint function includes: a constraint condition that represents the number of delivery vehicles corresponding to the above-mentioned delivery vehicle information set is a predetermined number, a constraint condition that represents the delivery vehicle returns to the initial site after delivery, a constraint condition that represents that each order supports being delivered once, and a constraint condition that represents that the delivery vehicle is not supported to be overloaded with delivery order goods.

[0021] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner in the first aspect.

[0022] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner in the first aspect is implemented.

[0023] In a fifth aspect, some embodiments of the present disclosure provide a computer program product, including a computer program, which implements the method described in any implementation manner in the above-mentioned first aspect when executed by a processor.

[0024] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the order delivery method of some embodiments of the present disclosure, the target order set can be delivered quickly and efficiently by using the target classification model. Specifically, the reason for the low efficiency of the relevant order delivery is that with the change of order fluctuations, unreasonable network division and order division often occur based on the order product type or order weight segment, resulting in an explosion order scenario, resulting in low delivery efficiency. In addition, a large amount of delivery resources will be wasted during the delivery process. Based on this, the order delivery method of some embodiments of the present disclosure first obtains the target order set for the target area, so as to facilitate the subsequent order delivery for the target order set. Then, according to the above-mentioned target order set, at least one first delivery feature information and at least one second delivery feature information can be accurately generated. Among them, the above-mentioned at least one first delivery feature information is each feature information generated by order delivery based on the first delivery method, and the above-mentioned at least one second delivery feature information is each feature information generated by order delivery based on the second delivery method. Here, by generating at least one first delivery feature information and at least one second delivery feature information, various feature conditions of using the first delivery method to deliver the target order set can be effectively obtained, as well as various feature conditions of using the second delivery method to deliver the target order set. This allows the subsequent target classification model to learn more feature information about the first delivery method and the second delivery method under the delivery target order set, so as to output accurate delivery method classification results in the future. Then, based on the above-mentioned at least one first delivery feature information and the above-mentioned at least one second delivery feature information, the target classification model can be used to accurately and efficiently generate a delivery method classification result, wherein the above-mentioned delivery method classification result includes: a result of characterizing the delivery of the order using the above-mentioned first delivery method, and a result of characterizing the delivery of the order using the above-mentioned second delivery method. Finally, according to the above-mentioned delivery method classification result, the order delivery for the above-mentioned target order set is executed to achieve efficient delivery of the target order set. In summary, through the generated at least one first delivery feature information and at least one second delivery feature information, the subsequent target classification model can learn more delivery feature information under the first delivery method and the second delivery method, so as to accurately output the delivery method classification result for the target order set, so as to achieve efficient allocation of orders in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0026] Figure 1is a schematic diagram of an application scenario of an order delivery method according to some embodiments of the present disclosure;

[0027] Figure 2 is a flow chart of some embodiments of the order delivery method according to the present disclosure;

[0028] Figure 3 are flow charts of other embodiments of the order delivery method according to the present disclosure;

[0029] Figure 4 is a schematic diagram of the structure of some embodiments of the order delivery device according to the present disclosure;

[0030] Figure 5 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0031] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0032] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0033] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0034] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0035] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0036] With regard to the collection, storage, and use of user personal information (such as the first delivery characteristic information) involved in this disclosure, before performing the corresponding operations, the relevant organizations or individuals shall fulfill their obligations, including conducting personal information security impact assessments, fulfilling the obligation to inform the personal information subject, and obtaining the authorization and consent of the personal information subject in advance.

[0037] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0038] Figure 1 It is a schematic diagram of an application scenario of the order delivery method according to some embodiments of the present disclosure.

[0039] exist Figure 1 In the application scenario, first, the electronic device 101 can obtain the target order set 102 for the target area. In this application scenario, the target order set 102 may include: order A1021, order B1022, and order C1023. Then, the electronic device 101 may generate at least one first delivery feature information 103 and at least one second delivery feature information 104 according to the target order set 102. Among them, the at least one first delivery feature information 103 is each feature information generated by delivering the order based on the first delivery method. The at least one second delivery feature information 104 is each feature information generated by delivering the order based on the second delivery method. In this application scenario, the at least one second delivery feature information 104 may include: second delivery feature information A1041, second delivery feature information B1042, and second delivery feature information C1043. The at least one first delivery feature information 103 may include: first delivery feature information A1031, first delivery feature information B1032, and first delivery feature information C1033. The first delivery method may be a delivery method based on a distribution station. The second delivery method may be a delivery method based on a city delivery fleet. Next, the electronic device 101 may generate a delivery method classification result 106 using the target classification model 105 based on the at least one first delivery feature information 103 and the at least one second delivery feature information 104. The delivery method classification result 106 includes: a result representing the order delivery using the first delivery method, and a result representing the order delivery using the second delivery method. Finally, the electronic device 101 may execute order delivery for the target order set 102 based on the delivery method classification result 106.

[0040] It should be noted that the electronic device 101 can be hardware or software. When the electronic device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or it can be implemented as a single server or a single terminal device. When the electronic device is embodied as software, it can be installed in the hardware devices listed above. It can be implemented as multiple software or software modules for providing distributed services, for example, or it can be implemented as a single software or software module. No specific limitation is made here.

[0041] It should be understood that Figure 1 The number of electronic devices in the embodiment is only for illustration. Any number of electronic devices may be provided according to implementation requirements.

[0042] Continue to refer Figure 2 , shows a process 200 of some embodiments of the order delivery method according to the present disclosure. The order delivery method comprises the following steps:

[0043] Step 201, obtaining a target order set for a target region.

[0044] In some embodiments, the execution entity of the above order delivery method (for example Figure 1 The electronic device 101 shown can obtain a target order set for a target region through a wired connection or a wireless connection. The target order set can be an order set corresponding to the target region and to be delivered.

[0045] Step 202: Generate at least one first delivery characteristic information and at least one second delivery characteristic information according to the target order set.

[0046] In some embodiments, the execution entity may generate at least one first delivery characteristic information and at least one second delivery characteristic information based on the target order set. The at least one first delivery characteristic information is characteristic information generated by delivering orders based on the first delivery method. That is, the at least one first delivery characteristic information may be characteristic information of at least one delivery characteristic generated during the process of delivering orders of the target order set using the first delivery method. The first delivery characteristic information may be an order delivery route based on the first delivery method, or may be the driving speed of an order delivery vehicle based on the first delivery method.

[0047] Similarly, the at least one second delivery characteristic information is the characteristic information generated by the order delivery based on the second delivery method. That is, the at least one second delivery characteristic information can be the characteristic information of at least one delivery characteristic generated in the process of delivering the target order set by the second delivery method. The second delivery characteristic information can be the order delivery route under the second delivery method, and can also be the driving speed of the order delivery vehicle under the second delivery method. In practice, both the first delivery method and the second delivery method can be express delivery methods. For example, the first delivery method can be a delivery method based on a distribution station. The second delivery method can be a delivery method based on a city distribution fleet.

[0048] As an example, first, the execution entity can determine the order address set based on the target order set. Then, based on the order address set, generate the first order delivery route under the first delivery method and the second order delivery route under the second delivery method. Then, based on the first order delivery route and the order timeliness corresponding to the target order, determine the driving speed of the first order delivery vehicle under the corresponding first delivery method. Then, based on the second order delivery route and the order timeliness corresponding to the target order, determine the driving speed of the second order delivery vehicle under the corresponding second delivery method. Determine the first order delivery route and the second order delivery route as at least one first delivery characteristic information. Determine the driving speed of the first order delivery vehicle and the driving speed of the second order delivery vehicle as at least one second delivery characteristic information.

[0049] Step 203: Generate a delivery method classification result by using a target classification model based on the at least one first delivery feature information and the at least one second delivery feature information.

[0050] In some embodiments, the execution subject may generate a delivery method classification result using a target classification model based on the at least one first delivery feature information and the at least one second delivery feature information. The target classification model may be a pre-trained network model for delivery method classification. In practice, the target classification model may be but is not limited to at least one of the following: K-Nearest Neighbor (KNN), Classification and Regression Tree (CART), Bayesian method, Support Vector Machine (SVM) based on kernel method, Neural Network. The delivery method classification result includes: a result of characterizing the order delivery using the first delivery method and a result of characterizing the order delivery using the second delivery method. For example, when the delivery method classification result is result 1 (for example, classification result identifier 1), it may represent that the first delivery method is used to perform order delivery processing on each order item in the target order set. When the delivery method classification result is result 2 (for example, classification result identifier 2), it may represent that the second delivery method is used to perform order delivery processing on each order item in the target order set. Result 1 is a result of delivering an order using the first delivery method. Result 2 is a result of delivering an order using the second delivery method.

[0051] As an example, the execution entity may directly input the at least one first delivery feature information and the at least one second delivery feature information into a target classification model to generate a delivery method classification result.

[0052] In some optional implementations of some embodiments, the at least one second delivery characteristic information includes: a second single-vehicle delivery time and a second load coefficient. The second single-vehicle delivery time can characterize the average order delivery time corresponding to each delivery vehicle under the second delivery method. The second load coefficient can characterize the load increment size of the delivery vehicle set under the second delivery method. That is, the second load coefficient can characterize the vehicle load amplification change of at least one delivery vehicle in the delivery vehicle set under the second delivery method. At the same time, the second load coefficient can characterize the explosion of the target order set under the second delivery method.

[0053] Optionally, the execution entity may generate a delivery method classification result by using the target classification model according to the first bicycle delivery time, the first load factor, the second bicycle delivery time and the second load factor.

[0054] As an example, the execution entity may input the first bicycle delivery time, the first load factor, the second bicycle delivery time and the second load factor into the target classification model to generate a delivery method classification result.

[0055] In some optional implementations of some embodiments, the at least one first delivery characteristic information further includes: the distance between the first order and the site, the site efficiency information, and the first single-vehicle delivery value consumption information. The at least one second delivery characteristic information further includes: the distance between the second order and the site, and the second single-vehicle delivery value consumption information. Among them, the distance between the first order and the site can be the distance between the order destination and the starting delivery site corresponding to the first delivery method. In practice, the starting delivery site corresponding to the first delivery method can be a distribution station. The site efficiency information can be the total delivery tonnage after the order is assigned to the distribution station / the site area of ​​the distribution station. The first single-vehicle delivery value consumption information can be the fuel replenishment cost of the delivery vehicle of the first delivery method. In practice, the first single-vehicle delivery value consumption information can be generated by the fuel replenishment cost per kilometer, the first weight coefficient, and the additional weight coefficient. The distance between the second order and the site can be the distance between the order destination and the starting delivery site corresponding to the second delivery method. The starting delivery site corresponding to the second delivery method can be the starting site corresponding to the city distribution fleet. The second single-vehicle delivery value consumption information can be the fuel replenishment cost of the delivery vehicle of the second delivery method. In practice, the second-bike delivery value consumption information can be generated through the per-kilometer fuel replenishment cost, the first weight coefficient and the additional weight coefficient.

[0056] Optionally, the generating of the delivery method classification result by using the target classification model according to the at least one first delivery feature information and the at least one second delivery feature information may include the following steps:

[0057] In the first step, the execution entity may combine characteristic information of at least one of the first bicycle delivery time, the first load factor, the second bicycle delivery time, the second load factor, the distance between the first order and the site, the site efficiency information, the first bicycle delivery value consumption information, the distance between the second order and the site and the second bicycle delivery value consumption information to generate combined characteristic information.

[0058] As an example, first, the execution entity may determine the duration type corresponding to the first bicycle delivery duration, the coefficient type corresponding to the first load factor, the duration type corresponding to the second bicycle delivery duration, and the coefficient type corresponding to the second load factor. Then, at least one of the duration type corresponding to the first bicycle delivery duration, the coefficient type corresponding to the first load factor, the duration type corresponding to the second bicycle delivery duration, the coefficient type corresponding to the second load factor, the distance between the first order and the site, the site efficiency information, the first bicycle delivery value consumption information, the distance between the second order and the site, and the second bicycle delivery value consumption information is combined to generate combined characteristic information.

[0059] In a second step, the execution entity may input the combined feature information into the target classification model to generate the delivery method classification result.

[0060] Step 204: Execute order delivery for the target order set according to the delivery method classification result.

[0061] In some embodiments, the execution entity may execute order delivery for the target order set based on the delivery method classification result.

[0062] As an example, in response to determining that the delivery method classification result is a result representing the use of the first delivery method for order delivery, the above-mentioned execution entity can adopt the first delivery method (for example, a delivery method based on a distribution station) to deliver each order item in the target order set.

[0063] As another example, in response to determining that the delivery method classification result is a result representing the use of the second delivery method for order delivery, the above-mentioned execution entity can adopt the second delivery method (for example, a delivery method based on a city distribution fleet) to deliver each order item in the target order set.

[0064] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the order delivery method of some embodiments of the present disclosure, the target order set can be delivered quickly and efficiently by using the target classification model. Specifically, the reason for the low efficiency of the relevant order delivery is that with the change of order fluctuations, unreasonable network division and order division often occur based on the order product type or order weight segment, resulting in an explosion order scenario, resulting in low delivery efficiency. In addition, a large amount of delivery resources will be wasted during the delivery process. Based on this, the order delivery method of some embodiments of the present disclosure first obtains the target order set for the target area, so as to facilitate the subsequent order delivery for the target order set. Then, according to the above-mentioned target order set, at least one first delivery feature information and at least one second delivery feature information can be accurately generated. Among them, the above-mentioned at least one first delivery feature information is each feature information generated by order delivery based on the first delivery method, and the above-mentioned at least one second delivery feature information is each feature information generated by order delivery based on the second delivery method. Here, by generating at least one first delivery feature information and at least one second delivery feature information, various feature conditions of using the first delivery method to deliver the target order set can be effectively obtained, as well as various feature conditions of using the second delivery method to deliver the target order set. This allows the subsequent target classification model to learn more feature information about the first delivery method and the second delivery method under the delivery target order set, so as to output accurate delivery method classification results in the future. Then, based on the above-mentioned at least one first delivery feature information and the above-mentioned at least one second delivery feature information, the target classification model can be used to accurately and efficiently generate a delivery method classification result, wherein the above-mentioned delivery method classification result includes: a result of characterizing the delivery of the order using the above-mentioned first delivery method, and a result of characterizing the delivery of the order using the above-mentioned second delivery method. Finally, according to the above-mentioned delivery method classification result, the order delivery for the above-mentioned target order set is executed to achieve efficient delivery of the target order set. In summary, through the generated at least one first delivery feature information and at least one second delivery feature information, the subsequent target classification model can learn more delivery feature information under the first delivery method and the second delivery method, so as to accurately output the delivery method classification result for the target order set, so as to achieve efficient allocation of orders in the future.

[0065] Further references Figure 3 , shows a process 300 of another embodiment of the order delivery method according to the present disclosure. The order delivery method comprises the following steps:

[0066] Step 301, obtaining a target order set for a target region.

[0067] Step 302: obtaining a delivery vehicle information set for the first delivery method, and obtaining a target overall delivery time function and at least one corresponding constraint function.

[0068] In some embodiments, an execution entity (e.g. Figure 1 The electronic device 101 shown) can obtain the delivery vehicle information set for the above-mentioned first delivery method by wired or wireless means, as well as the target overall delivery time function and the corresponding at least one constraint function. The delivery vehicle information set for the above-mentioned first delivery method can be the vehicle information of each vehicle that can provide order delivery under the first delivery method. In practice, the delivery vehicle information can include but is not limited to at least one of the following: delivery vehicle model, full load of delivery vehicle, and license plate information of delivery vehicle. The target overall delivery time function can be a function for outputting the overall delivery time. Among them, the overall delivery time can be the overall time consumed for order delivery under a certain delivery method. At least one constraint function can be a pre-set function. For example, the overall delivery time function can be a neural network model. At least one constraint function can include but is not limited to at least one of the following: a constraint function that order delivery is not allowed to be late, a constraint function that order goods are not allowed to be damaged, and a constraint function that the single delivery time must not exceed the target time.

[0069] Among them, the at least one first delivery characteristic information includes: the first single vehicle delivery time and the first load factor. The first single vehicle delivery time can represent the average order delivery time corresponding to each delivery vehicle under the first delivery mode. The first load factor can represent the load increment size of the delivery vehicle set under the first delivery mode. That is, the first load factor can represent the vehicle load amplification change of at least one delivery vehicle in the delivery vehicle set under the first delivery mode. At the same time, the first load factor can represent the explosion of the target order set under the first delivery mode. Specifically, the first load factor can be divided into three types, namely "satisfied load type", "exceeded load type" and "station explosion type". The first load factor corresponding to the "satisfied load type" is smaller than the first load factor corresponding to the "exceeded load type". The vehicle load amplification change corresponding to the "satisfied load type" is smaller than the vehicle load amplification change corresponding to the "exceeded load type". The vehicle load amplification change corresponding to the "exceeded load type" is smaller than the vehicle load amplification change corresponding to the "exceeded load type". The vehicle load amplification change corresponding to the "exceeded load type" is smaller than the vehicle load amplification change corresponding to the "station explosion type". The vehicle load amplification change interval corresponding to the "satisfied load type" can be [0, 1). The vehicle load amplification change interval corresponding to the "exceeded load type" can be [1, 1.5). The vehicle load expansion change interval corresponding to the "station explosion type" is [1.5, positive infinity). The first single-vehicle delivery time can be divided into three types, namely "satisfying the time limit type", "time limit delay type" and "failure span day type". The corresponding time interval for the "satisfying the time limit type" is [0, 8 hours]. The corresponding time interval for the "time limit delay type" is (8 hours, 12 hours). The corresponding time interval for the "failure span day type" is (12 hours, positive infinity).

[0070] In some optional implementations of some embodiments, the above-mentioned target overall delivery time function is a function based on the product of the delivery time consumption of the delivery point and the decision variable of the delivery point. Among them, the above-mentioned at least one constraint function includes: a constraint condition that the number of delivery vehicles corresponding to the above-mentioned delivery vehicle information set is a predetermined number, a constraint condition that the delivery vehicle returns to the initial station after delivery, a constraint condition that each order supports being delivered once, and a constraint condition that the delivery vehicle is not supported to be overloaded with the delivery order goods.

[0071] Specifically, the target overall delivery time function can be the following formula:

[0072]

[0073] Wherein, F can be the duration of vehicle delivery. i can be the i-th delivery point. j can be the j-th delivery point. k can be the k-th delivery point. N can be the number of delivery points. K can be the number of delivery vehicles corresponding to the delivery vehicle information set. It can be a decision variable that characterizes whether delivery vehicle k passes through delivery point i to delivery point j. The value of the decision variable is 0 or 1. ij It can be the transportation time of the delivery vehicle from delivery point i to delivery point j. j It can be the operation duration of delivery point j.

[0074] Step 303: determine the order delivery related information corresponding to each target order in the target order set to obtain an order delivery related information set.

[0075] In some embodiments, the execution entity may determine the order delivery related information corresponding to each target order in the target order set to obtain an order delivery related information set. The order delivery related information may be the relevant delivery information at the time of the target order. For example, the order delivery related information may include but is not limited to at least one of the following: the type of goods to be delivered, the total weight of the goods to be delivered, the total volume of the goods to be delivered, at least one order delivery route, at least one order delivery point (i.e., the order delivery location), and the order delivery time.

[0076] Step 304, based on the delivery vehicle information set and the order delivery related information set, using the target overall delivery time function and the at least one constraint function, generate the vehicle delivery time and the first load factor.

[0077] In some embodiments, the execution subject may generate the vehicle delivery time and the first load factor based on the delivery vehicle information set and the order delivery related information set, using the target overall delivery time function and the at least one constraint function. The vehicle delivery time may be the total vehicle delivery time corresponding to each delivery vehicle under the delivery of the target order set by the first delivery method.

[0078] As an example, the above-mentioned execution entity can directly input the delivery vehicle information set and the above-mentioned order delivery related information set into the above-mentioned target overall delivery time function to generate the whole vehicle delivery time and the above-mentioned first load coefficient under the constraints of the above-mentioned at least one constraint function.

[0079] In some optional implementations of some embodiments, the generating of the vehicle delivery time and the first load factor according to the delivery vehicle information set and the order delivery related information set, using the target overall delivery time function and the at least one constraint function, may include the following steps:

[0080] In the first step, according to the delivery vehicle information set and the above order delivery related information set, the following generation steps are performed:

[0081] Sub-step 1, based on the delivery vehicle information set and the above-mentioned order delivery related information set, using the above-mentioned target overall delivery time function and the above-mentioned at least one constraint function, generate an initial vehicle delivery time.

[0082] As an example, the above-mentioned execution entity can input the delivery vehicle information set and the order delivery related information set into the target overall delivery time function, and generate the initial vehicle delivery time with at least one constraint function as a constraint condition.

[0083] Sub-step 2, in response to the vehicle overload information for the initial order allocation information not being detected, generating a load increment coefficient corresponding to the delivery vehicle information set as a first load coefficient. The initial order allocation information is the order allocation information corresponding to the initial vehicle delivery duration. The load increment coefficient may be a coefficient that characterizes the incremental adjustment of the vehicle load capacity of the delivery vehicle. For example, the load increment coefficient may be a pre-set value, which may be dynamically adjusted according to the load difference between the vehicle and the order each time. In addition, the load increment coefficient may also be a fixed value, which may be adaptively selected according to the corresponding scenario. For example, the load increment coefficient may be 0.1. The vehicle overload information may characterize that there is an overload of goods corresponding to at least one delivery vehicle information in the delivery vehicle information set that carries the corresponding order. The load increment coefficient corresponding to the delivery vehicle information set may be a coefficient for carrying an incremental adjustment of each delivery vehicle information corresponding vehicle in the delivery vehicle information set, or may be a coefficient for carrying an incremental adjustment of at least one delivery vehicle information corresponding vehicle in the delivery vehicle information set. The at least one delivery vehicle is each vehicle that carries an overload of goods corresponding to the corresponding order.

[0084] Sub-step 3: determining the initial vehicle delivery time corresponding to the first load factor as the above vehicle delivery time. The initial vehicle delivery time corresponding to the first load factor may be the initial vehicle delivery time generated by the last execution of the generation step.

[0085] In the second step, in response to detecting vehicle overload information for the initial order allocation information, a load increment is adjusted for the vehicle load capacity corresponding to the delivery vehicle information set to obtain an adjusted delivery vehicle information set.

[0086] As an example, the execution entity may perform a load increment adjustment on the vehicle load capacity corresponding to each piece of delivery vehicle information in the delivery vehicle information set to obtain an adjusted delivery vehicle information set.

[0087] As another example, the execution entity may perform a load increment adjustment on the vehicle load capacity corresponding to at least one delivery vehicle information in the delivery vehicle information set to obtain an adjusted delivery vehicle information set.

[0088] The third step is to use the adjusted delivery vehicle information set as the delivery vehicle information set and continue to execute the above generation steps.

[0089] Step 305: Generate the first single-vehicle delivery time based on the full-vehicle delivery time and the delivery vehicle information set.

[0090] In some embodiments, the execution entity may generate the first single-vehicle delivery time based on the full-vehicle delivery time and the delivery vehicle information set.

[0091] As an example, first, the execution subject may determine the number of delivery vehicles corresponding to the delivery vehicle information set, and then divide the whole vehicle delivery time by the number of delivery vehicles to obtain the first single vehicle delivery time.

[0092] As another example, first, the above-mentioned execution entity can determine the number of delivery vehicles corresponding to the delivery vehicle information set and the number of orders corresponding to each delivery vehicle. Then, according to the number of orders corresponding to each delivery vehicle, determine the delivery weight corresponding to each delivery vehicle information. Next, divide the whole vehicle delivery time by the number of delivery vehicles to obtain the initial single vehicle delivery time. Finally, multiply the initial single vehicle delivery time by the delivery weight corresponding to each delivery vehicle information to generate the single vehicle delivery time corresponding to each delivery vehicle, and obtain the single vehicle delivery time set as the first single vehicle delivery time.

[0093] Step 306: Generate a delivery method classification result using a target classification model based on the at least one first delivery feature information and the at least one second delivery feature information.

[0094] Step 307: Execute order delivery for the target order set according to the delivery method classification result.

[0095] In some embodiments, the specific implementation of steps 301, 306-307 and the technical effects thereof can be referred to in Figure 2 The steps 201, 203-204 in the corresponding embodiment will not be described in detail here.

[0096] from Figure 3 It can be seen that Figure 2 Compared with the description of some corresponding embodiments, Figure 3 In the process 300 of the order delivery method in some corresponding embodiments, the first single-vehicle delivery time and the first load factor in at least one first delivery feature information can be accurately generated through the target overall delivery time function and the above-mentioned at least one constraint function. Based on this, under the premise of the first single-vehicle delivery time and the second load factor, the target classification model can be subsequently allowed to learn more time features and load features to accurately generate the delivery method classification results.

[0097] Further references Figure 4 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of an order delivery device. These device embodiments are similar to Figure 2 Corresponding to the method embodiments shown, the order delivery device can be specifically applied to various electronic devices.

[0098] like Figure 4 As shown, an order delivery device 400 includes: an acquisition unit 401, a first generation unit 402, a second generation unit 403 and an execution unit 404. The acquisition unit 401 is configured to acquire a target order set for a target region; the first generation unit 402 is configured to generate at least one first delivery feature information and at least one second delivery feature information according to the target order set, wherein the at least one first delivery feature information is each feature information generated by delivering an order based on a first delivery method, and the at least one second delivery feature information is each feature information generated by delivering an order based on a second delivery method; the second generation unit 403 is configured to generate a delivery method classification result using a target classification model according to the at least one first delivery feature information and the at least one second delivery feature information, wherein the delivery method classification result includes: a result representing the delivery of an order using the first delivery method and a result representing the delivery of an order using the second delivery method; the execution unit 404 is configured to execute order delivery for the target order set according to the delivery method classification result.

[0099] In some optional implementations of some embodiments, the at least one first delivery characteristic information includes: a first single-vehicle delivery time and a first load coefficient; and the first generation unit 402 can be further configured to: obtain a delivery vehicle information set for the above-mentioned first delivery method, and obtain a target overall delivery time function and a corresponding at least one constraint function; determine the order delivery related information corresponding to each target order in the above-mentioned target order set, and obtain an order delivery related information set; based on the above-mentioned delivery vehicle information set and the above-mentioned order delivery related information set, using the above-mentioned target overall delivery time function and the above-mentioned at least one constraint function, generate the whole vehicle delivery time and the above-mentioned first load coefficient; based on the above-mentioned whole vehicle delivery time and the above-mentioned delivery vehicle information set, generate the above-mentioned first single-vehicle delivery time.

[0100] In some optional implementations of some embodiments, the first generation unit 402 may be further configured to: perform the following generation steps based on the delivery vehicle information set and the above-mentioned order delivery related information set: generate an initial vehicle delivery time based on the delivery vehicle information set and the above-mentioned order delivery related information set using the above-mentioned target overall delivery time function and the above-mentioned at least one constraint function; in response to not detecting vehicle overload information for the initial order allocation information, generate a load increment coefficient corresponding to the delivery vehicle information set as a first load coefficient, wherein the initial order allocation information is the order allocation information corresponding to the initial vehicle delivery time; determine the initial vehicle delivery time corresponding to the first load coefficient as the above-mentioned vehicle delivery time; in response to detecting vehicle overload information for the initial order allocation information, perform a load increment adjustment on the vehicle load corresponding to the delivery vehicle information set to obtain an adjusted delivery vehicle information set; use the adjusted delivery vehicle information set as the delivery vehicle information set, and continue to perform the above generation steps.

[0101] In some optional implementations of some embodiments, the at least one second delivery characteristic information includes: a second bicycle delivery time and a second load coefficient; and the second generation unit 403 can be further configured to: generate a delivery method classification result based on the first bicycle delivery time, the first load coefficient, the second bicycle delivery time and the second load coefficient, using the target classification model.

[0102] In some optional implementations of some embodiments, the at least one first delivery characteristic information also includes: the distance between the first order and the site, the site efficiency information and the first bicycle delivery value consumption information, and the at least one second delivery characteristic information also includes: the distance between the second order and the site and the second bicycle delivery value consumption information; and the second generation unit 403 can be further configured to: combine the characteristic information of at least one of the first bicycle delivery time, the first load factor, the second bicycle delivery time, the second load factor, the distance between the first order and the site, the site efficiency information, the first bicycle delivery value consumption information, the distance between the second order and the site and the second bicycle delivery value consumption information to generate combined characteristic information; input the combined characteristic information into the target classification model to generate the delivery method classification result.

[0103] In some optional implementations of some embodiments, the above-mentioned target overall delivery time function is a function based on the product of the delivery consumption time of the delivery point and the decision variable of the delivery point, wherein the above-mentioned at least one constraint function includes: a constraint condition that represents the number of delivery vehicles corresponding to the above-mentioned delivery vehicle information set is a predetermined number, a constraint condition that represents the delivery vehicle returns to the initial site after delivery, a constraint condition that represents that each order supports being delivered once, and a constraint condition that represents that the delivery vehicle is not supported to be overloaded with delivery order goods.

[0104] It is understood that the units described in the order delivery device 400 are similar to those described in the reference Figure 2 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the order delivery device 400 and the units contained therein, and will not be repeated here.

[0105] Reference below Figure 5 , which shows an electronic device (eg, Figure 1 Schematic diagram of the structure of the electronic device 101)500. Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0106] like Figure 5 As shown, the electronic device 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory 502 or a program loaded from a storage device 508 to a random access memory 503. Various programs and data required for the operation of the electronic device 500 are also stored in the random access memory 503. The processing device 501, the read-only memory 502, and the random access memory 503 are connected to each other via a bus 504. An input / output interface 505 is also connected to the bus 504.

[0107] Typically, the following devices may be connected to the input / output interface 505: an input device 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 5 The electronic device 500 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 5Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0108] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 509, or installed from the storage device 508, or installed from the read-only memory 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0109] It should be noted that the computer-readable medium in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0110] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0111] The computer-readable medium may be included in the electronic device; or it may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains a target order set for a target area; generates at least one first delivery feature information and at least one second delivery feature information according to the target order set, wherein the at least one first delivery feature information is each feature information generated by delivering an order based on the first delivery method, and the at least one second delivery feature information is each feature information generated by delivering an order based on the second delivery method; generates a delivery method classification result based on the at least one first delivery feature information and the at least one second delivery feature information using a target classification model, wherein the delivery method classification result includes: a result representing the order delivery result using the first delivery method, and a result representing the order delivery result using the second delivery method; and executes order delivery for the target order set according to the delivery method classification result.

[0112] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0113] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0114] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be provided in a processor, for example, may be described as: a processor includes an acquisition unit, a first generation unit, a second generation unit, and an execution unit. The names of these units do not, in some cases, constitute limitations on the units themselves, for example, the acquisition unit may also be described as a "unit for acquiring a target order set for a target region".

[0115] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0116] Some embodiments of the present disclosure further provide a computer program product, including a computer program, which implements any of the above-mentioned order delivery methods when executed by a processor.

[0117] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. An order delivery method, comprising: Get a target order set for a target region; Generate at least one first delivery characteristic information and at least one second delivery characteristic information according to the target order set, wherein the at least one first delivery characteristic information is each characteristic information generated by delivering the order based on the first delivery method, and the at least one second delivery characteristic information is each characteristic information generated by delivering the order based on the second delivery method; Generate a delivery method classification result using a target classification model according to the at least one first delivery feature information and the at least one second delivery feature information, wherein the delivery method classification result includes: a result representing the order delivery using the first delivery method and a result representing the order delivery using the second delivery method; According to the delivery method classification result, the order delivery for the target order set is executed.

2. The method according to claim 1, wherein: The at least one first delivery characteristic information includes: a first single-vehicle delivery duration and a first load factor; and The generating at least one first delivery characteristic information and at least one second delivery characteristic information according to the target order set includes: Obtaining a delivery vehicle information set for the first delivery method, and obtaining a target overall delivery time function and at least one corresponding constraint function; Determine the order delivery related information corresponding to each target order in the target order set to obtain an order delivery related information set; Generate the vehicle delivery time and the first load factor according to the delivery vehicle information set and the order delivery related information set, using the target overall delivery time function and the at least one constraint function; The first single-vehicle delivery time is generated according to the full-vehicle delivery time and the delivery vehicle information set.

3. The method according to claim 2, wherein: The generating of the vehicle delivery duration and the first load factor according to the delivery vehicle information set and the order delivery related information set using the target overall delivery duration function and the at least one constraint function comprises: According to the delivery vehicle information set and the order delivery related information set, the following generation steps are performed: Generate an initial vehicle delivery duration according to the delivery vehicle information set and the order delivery related information set, using the target overall delivery duration function and the at least one constraint function; In response to not detecting vehicle overload information for initial order allocation information, generating a load increment coefficient corresponding to the delivery vehicle information set as a first load coefficient, wherein the initial order allocation information is order allocation information corresponding to an initial vehicle delivery duration; Determine the initial vehicle delivery duration corresponding to the first load factor as the vehicle delivery duration; In response to detecting vehicle overload information for the initial order allocation information, adjusting the vehicle carrying capacity corresponding to the delivery vehicle information set by a carrying increment to obtain an adjusted delivery vehicle information set; The adjusted delivery vehicle information set is used as the delivery vehicle information set, and the generation step is continued.

4. The method according to claim 2, wherein: The at least one second delivery characteristic information includes: a second single-vehicle delivery duration and a second load factor; and The step of generating a delivery method classification result by using a target classification model according to the at least one first delivery feature information and the at least one second delivery feature information includes: The target classification model is used to generate a delivery method classification result based on the first bicycle delivery time, the first load coefficient, the second bicycle delivery time and the second load coefficient.

5. The method according to claim 4, wherein: The at least one first delivery characteristic information further includes: the distance between the first order and the site, the site efficiency information and the first single-vehicle delivery value consumption information, and the at least one second delivery characteristic information further includes: the distance between the second order and the site and the second single-vehicle delivery value consumption information; and The step of generating a delivery method classification result by using a target classification model according to the at least one first delivery feature information and the at least one second delivery feature information includes: Combine at least one of the first bicycle delivery time, the first load factor, the second bicycle delivery time, the second load factor, the first order-to-site distance, the site efficiency information, the first bicycle delivery value consumption information, the second order-to-site distance, and the second bicycle delivery value consumption information to generate combined feature information; The combined feature information is input into the target classification model to generate the delivery method classification result.

6. The method according to claim 2, wherein: The target overall delivery time function is a function based on the product of the delivery consumption time of the delivery point and the decision variable of the delivery point, wherein the at least one constraint function includes: a constraint condition that the number of delivery vehicles corresponding to the delivery vehicle information set is a predetermined number, a constraint condition that the delivery vehicle returns to the initial site after delivery, a constraint condition that each order supports being delivered once, and a constraint condition that the delivery vehicle is not supported to be overloaded with delivery order goods.

7. An order delivery device, comprising: an acquisition unit configured to acquire a target order set for a target region; A first generating unit is configured to generate at least one first delivery characteristic information and at least one second delivery characteristic information according to the target order set, wherein the at least one first delivery characteristic information is each characteristic information generated by delivering the order based on the first delivery method, and the at least one second delivery characteristic information is each characteristic information generated by delivering the order based on the second delivery method; The second generating unit is configured to generate a delivery method classification result using a target classification model according to the at least one first delivery feature information and the at least one second delivery feature information, wherein the delivery method classification result includes: a result representing the order delivery using the first delivery method and a result representing the order delivery using the second delivery method; The execution unit is configured to execute order delivery for the target order set according to the delivery method classification result.

8. An electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

9. A computer readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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