Order distribution method according to right

Abstract

The invention discloses a method for distributing orders according to the right, and belongs to the field of logistics distribution, and the method comprises the following steps: 1, a dispatching center collects to-be-distributed orders of a whole platform, and prepares to distribute distribution orders to distribution merchants; 2, the dispatching center sends the order to be delivered to the big data center, and the big data center carries out information extraction on the order to be delivered; 3, the big data center obtains basic information of delivery merchants; 4, the big data center confirms the payment conditions of the delivery merchants, screens different merchants through a built-in distribution module, a range determination module, a redistribution module and an autonomous learning module, and integrates an order distribution method; the logistics transportation cost is reduced, the historical order information and the existing order information are subjected to comprehensive weight judgment, the weight information of the order is organically combined, and the technical problem of unreasonable weight order distribution in the prior art is solved.

Description

Technical Field

[0001] The invention belongs to the technical field of logistics algorithms, and in particular relates to a method for allocating orders by weight. Background Art

[0002] As the logistics industry develops towards informatization, the transmission and management of logistics information are becoming more and more informatized. By adopting high-tech technologies such as instant order processing, instant merchant information analysis, and computer background comprehensive evaluation, modern logistics systems have basically possessed the technological characteristics of digitalization, agility, automation, networking, integration, informatization, and intelligence, so that goods can be efficiently delivered from suppliers to demanders, ultimately enabling suppliers to maximize profits, demanders to enjoy the best services, natural and social resource consumption to be greatly reduced, and the natural ecological environment to be protected to the maximum extent.

[0003] However, the transportation resources required for goods of different weights are different. If high transportation resources are allocated to lighter goods, it will cause a waste of transportation capacity. If low transportation resources are allocated to heavier goods, timeliness cannot be guaranteed.

[0004] In particular, the transportation of items such as mineral water is a difficult problem that needs to be solved. This type of goods is heavy and requires a lot of transportation power. Under the existing order delivery system, there is no emphasis on weight distinction, nor is there any optimization of the delivery route for this type of goods. It is not suitable for the delivery of this type of heavy goods.

[0005] Therefore, the market urgently needs a method of splitting orders that can reasonably allocate transportation resources according to the weight of the goods. Summary of the invention

[0006] In view of the problems in the prior art of unreasonable order allocation and ineffective delivery of heavier items, the present invention proposes a method for allocating orders by weight, the purpose of which is to increase the number of orders received by new merchants and improve the delivery efficiency of heavier items.

[0007] To achieve the above purpose, the technical solution adopted by the present invention is to provide an order allocation method according to weights. The following steps are involved: Step 1: The dispatch center collects all the orders to be delivered on the platform and prepares to distribute the delivery orders to the delivery merchants. The entire platform includes but is not limited to e-commerce platforms, telephone platforms, and online platforms; Step 2: The dispatch center sends the order to be delivered to the big data center, which extracts information about the order to be delivered and obtains the distance information, weight information, and delivery cost information of the order to be delivered; Step 3: The big data center obtains basic information about the delivery merchant, including but not limited to: the total number of orders delivered by the delivery merchant, the delivery merchant's location, the delivery merchant's transportation capacity, order fulfillment status, and payment status; Step 4: The big data center confirms the payment status of the delivery merchants, collects the information obtained in steps 2 and 3, and screens different merchants through the built-in allocation module, scope determination module, redistribution module, and big data self-learning module to integrate the order allocation method: the formula is as follows: weight = cos(π*accumulate*proportion1)*proportion2+proportion3 / accumulate+distance+promise+paid+artificial+add_step_weight; Among them, weight is the total weight, accumulate is the total number of orders that the merchant has delivered, proportion represents the number of orders, distance represents distance, promise represents fulfillment, paid represents payment, artificial represents artificial, and add_step_weight represents multi-parameter steps.

[0008] Preferably, there are multiple delivery merchants in step one, and the geographical location of each delivery merchant is different.

[0009] Preferably, the order fulfillment in step three is determined based on the fulfillment quality score of the delivery merchant obtained by the big data center in the background; after the big data center has accumulated the order volume before selecting the delivery merchant, it will give priority to the delivery merchant with a smaller number of orders when other conditions are the same.

[0010] Preferably, in step 2, the big data center monitors in real time the delivery merchant's in-transit rider trajectory information, road condition change information, weather change information, delivery merchant's delivery situation information, and multi-dimensional feature information; the multi-dimensional feature information refers to the impact of the in-transit rider trajectory information, road condition change information, weather change information, and delivery merchant's delivery situation information on the order delivery process.

[0011] Preferably, in step 3, after obtaining the payment status of the delivery merchants, the big data center allocates the delivery orders to the paying merchants and the non-paying merchants according to a custom ratio.

[0012] Preferably, the dispatch center can manually adjust and control abnormal merchants, including merchants with abnormal delivery, merchants with low evaluation scores, and merchants with low fulfillment rates.

[0013] Preferably, the formula in step 4 contains proportion1, proportion2, and proportion3; Among them, proportion1 controls the fluctuation period of the impact of order volume on weight; proportion2 controls the impact of order volume on the overall weight; Proportion3 controls the additional weight given to new merchants.

[0014] Preferably, the add_step_weight multi-parameter ladder in step 4 includes the following parameters: inventory at the distribution site, in-transit delivery volume, number of idle delivery personnel, business hours, cumulative delivery time, order delivery fee amount, number of goods, total weight of order goods, and customer level.

[0015] Preferably, the dispatch center includes the following modules: Allocation module: Calculate the location information of each order to be allocated received by the dispatch center and multiple alternative delivery paths preset by the delivery merchant to determine the delivery merchant and delivery path corresponding to the shortest distance; use the alternative service range of the location of the order to be allocated as the initial service range, and the initial service range is a circular area or other shaped area with the location of the order to be allocated as the center and the preset delivery distance as the radius; allocate the order to be allocated to the delivery merchant according to the initial service range of the delivery merchant: calculate the distance between the order location of the order to be allocated received within a distribution cycle and the delivery merchant within the initial service range, and allocate the order closest to the center of the initial service range as the first order to the delivery merchant closest to the center of the initial service range within the corresponding initial service range; Range determination module: used to determine the commuting time required for the delivery merchant of the first order that has been assigned to arrive at the service location included in the first order, and determine the shape of the newly added service range according to the commuting time; the newly added service range is a circular area or a regular polygonal area with the order location of the first order as the center and a preset length as the radius, and after obtaining the shape of the newly added service range, the radius of the newly added service range can be adjusted according to the order information included in the subsequent order; Reassignment module: used to determine whether to assign the second order to the delivery merchant according to the area of ​​the newly added service range and the initial service range; expand the initial service range to the sum of the initial service range and the newly added service range, and the newly added service range and the initial service range intersect or are tangent; calculate the distance between the location included in the order to be assigned in the dispatch center and the location included in the first order, and take the remaining order to be assigned corresponding to the shortest distance as the second order and assign it to the delivery merchant; Self-learning module: Based on the information provided by the big data center, the module can understand the transportation capacity and delivery completion status of each delivery merchant at the global level, and achieve the best match between orders and delivery units by analyzing the driving trajectory data, warehouse inventory data, user geographic location data, and road condition data in the entire region included in the orders completed every day; The better, self-learning module estimates the order placement time, the estimated order completion time, the order delivery route, and allocates transportation resources according to the weight of the order delivery items. After deep learning the user's location information, traffic speed, traffic light time and average congestion time, it affects the decision of order delivery, and can realize automatic planning of order delivery routes and automatic reassignment of orders.

[0016] Preferably, the allocation module, scope determination module, redistribution module, and autonomous learning module can be manually replaced according to different order requirements, and can complete the addition and deletion of different modules.

[0017] Preferably, the self-learning module estimates the order placement time, the estimated order completion time, and the order delivery route, and allocates transportation resources according to the weight of the order delivery items. After deep learning the user's location information, traffic speed, traffic light time, and average congestion time, it affects the decision of order delivery, and can realize automatic planning of order delivery routes and automatic reassignment of orders.

[0018] Compared with the prior art, the technical solution of the present invention has the following advantages / benefits: 1. By capturing information on different types of merchants, we obtained basic information about various merchants and assigned different weights to each type of information. In the order splitting process, we appropriately increased the order volume of new merchants and reduced the order volume of old merchants, so that the transportation capacity is maintained at an appropriate level and the waste of transportation capacity is reduced.

[0019] 2. The distribution weights of delivery merchants can be manually adjusted, and customized distribution can be made for any delivery merchant, which reduces order delays caused by bad merchants’ failure to deliver and slow delivery, and improves delivery efficiency.

[0020] 3. The present invention focuses on optimizing the delivery of larger and heavier goods. By comprehensively considering the weight of the goods and the delivery distance, the most suitable merchant is selected for delivery, thereby alleviating the pressure of heavy goods delivery.

[0021] 4. Through the self-learning function of the autonomous learning module, the status of in-transit orders, the carrying capacity of the delivery merchants, the difficulty of the delivery route, weather conditions, etc. can be continuously analyzed and judged. The order distribution plan can be continuously optimized during use, making the order distribution more reasonable and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 It is a schematic diagram of a method for allocating orders by weight according to the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present invention. Therefore, the detailed description of the embodiments of the present invention provided below is not intended to limit the scope of the claimed invention, but merely represents the selected embodiments of the present invention.

[0025] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and thus, once an item is defined in one drawing, it may not be further defined or explained in the subsequent drawings.

[0026] Embodiment 1: For current order delivery platforms, delivery timeliness and accuracy are important completion indicators. How to achieve order delivery timeliness and accuracy through reasonable order allocation is the core competitiveness of order delivery platforms. Existing order delivery technologies are all based on simple methods, such as allocating more delivery personnel and expanding transportation capacity, but this will lead to increased transportation costs and economic efficiency cannot be guaranteed.

[0027] Furthermore, when distributing orders in a region, there are multiple different merchants that can deliver the orders. The present invention selects one to obtain the order based on factors such as the distance between the merchant and the delivery location, the merchant's previous fulfillment of orders, the cumulative number of orders received, and whether payment has been made.

[0028] In actual operations, priority is given to merchants with a smaller number of orders, short first-level order delivery distances and good fulfillment quality, so that the number of orders obtained by each type of merchant is roughly the same, and the ratio of orders obtained by paying merchants and non-paying merchants is controlled. This ratio can be manually changed according to actual conditions.

[0029] The ratio of the number of orders obtained by paying merchants and non-paying merchants is generally similar in terms of initial orders, and subsequent orders are in a state of reservation ratio.

[0030] like Figure 1 As shown, the present invention proposes a method for allocating orders by weight. The following steps are involved: Step 1: The dispatch center collects all the orders to be delivered on the platform and prepares to distribute the delivery orders to the delivery merchants; Step 2: The dispatch center sends the order to be delivered to the big data center, which extracts information about the order to be delivered and obtains the distance information, weight information, and delivery cost information of the order to be delivered; Step 3: The big data center obtains basic information about the delivery merchant, including but not limited to: the total number of orders delivered by the delivery merchant, the delivery merchant's location, the delivery merchant's transportation capacity, order fulfillment status, and payment status; Step 4: The big data center confirms the payment status of the delivery merchants, collects the information obtained in steps 2 and 3, and screens different merchants through the built-in allocation module, scope determination module, redistribution module, and big data self-learning module to integrate the order allocation method: the formula is as follows: weight = cos(π*accumulate*proportion1)*proportion2+proportion3 / accumulate+distance+promise+paid+artificial+add_step_weight; Among them, weight is the total weight, accumulate is the total number of orders that the merchant has delivered, proportion represents the number of orders, distance represents distance, promise represents fulfillment, paid represents payment, artificial represents artificial, and add_step_weight represents multi-parameter steps.

[0031] In step 1, there are multiple delivery merchants, and the geographical location of each delivery merchant is different.

[0032] The order fulfillment status in step three is determined based on the fulfillment quality score of the delivery merchant obtained by the big data center in the background; after the big data center has accumulated the order volume before selecting the delivery merchant, it will give priority to the delivery merchant with a smaller number of orders when other conditions are the same.

[0033] In step 2, the big data center monitors in real time the delivery merchant's in-transit rider trajectory information, road condition change information, weather change information, delivery merchant's delivery situation information, and multi-dimensional feature information; the multi-dimensional feature information refers to the impact of the in-transit rider trajectory information, road condition change information, weather change information, and delivery merchant's delivery situation information on the order delivery process.

[0034] In step 3, after obtaining the payment status of the delivery merchants, the big data center allocates the delivery orders to paying merchants and non-paying merchants according to the custom ratio.

[0035] The dispatch center can manually adjust and control abnormal merchants, including merchants with abnormal delivery, merchants with low ratings, and merchants with low fulfillment rates.

[0036] The formula in step 4 contains proportion1, proportion2, and proportion3; Among them, proportion1 controls the fluctuation period of the impact of order volume on weight; proportion2 controls the impact of order volume on the overall weight; Proportion3 controls the additional weight given to new merchants.

[0037] The add_step_weight multi-parameter ladder in step 4 contains the following parameters: inventory at the distribution site, delivery volume in transit, number of idle delivery personnel, business hours, cumulative delivery time, order delivery fee amount, number of goods, total weight of order goods, and customer level.

[0038] Among them, the total weight of the ordered goods is the main consideration in the order allocation process.

[0039] This method can manually assign merchants with larger transportation capacity for orders that exceed normal weight, and will not deliver new orders to this merchant before delivery is completed, so as not to reduce the merchant's transportation capacity and reduce the timeliness of delivery.

[0040] Example 2: In daily life, how to reasonably split cargo orders according to different situations has always been a difficult problem that the modern logistics industry needs to solve. At present, whether it is Taobao, JD.com, Meituan, or Ele.me, they all encounter the same problem: the number of orders received by new merchants is always too small compared to old merchants, the transportation capacity is not fully utilized, the merchants who deliver orders are sometimes not of high quality, and they cannot adjust the delivery merchants according to different situations according to their own preferences.

[0041] Especially when it comes to goods such as bottled water, it is difficult to assign them to the right merchants. The normal distribution method does not organically combine the weight of the goods with the distribution distance, which greatly affects the efficiency of the distribution process.

[0042] The method of allocating orders by weight provided by the present invention can solve the above technical problems. This method deeply analyzes the merchant information, and affects the final order distribution results by weighting different information. It can quickly and accurately distribute orders according to different situations, especially the weight of the goods and the length of the delivery route.

[0043] At this time, accumulate is X, which is the total number of orders that the merchant has delivered; proportion1 is 1, which controls the fluctuation cycle of the order volume on the weight; proportion2 is 30, which controls the impact of the order volume on the overall weight; proportion3 is 500, which controls the additional weight of new merchants; distance is 50, which is the distance weight, obtained according to the interval of the straight-line distance from the merchant's store location to the order location. Promise is 15, which means the fulfillment weight is obtained according to the interval of the score range of the merchant's evaluated fulfillment quality score. Paid is 1, which is the paid weight. When each order is split, the program will give the order to the paying merchant or the non-paying merchant according to a certain ratio. The ratio here is 1:1. Artificial is 8, which means the weight is manually adjusted.

[0044] add_step_weight contains the following coefficients: The inventory of the distribution site is 25 in this embodiment; Total weight of goods: in this embodiment: 150; The amount of delivery in transit, in this embodiment, is: 12.5; Idle delivery personnel, in this embodiment: 0.5; The cumulative delivery time, in this embodiment, is: 12; The order delivery amount, in this embodiment: 15; The number of products, in this embodiment, is: 5.

[0045] From this we can clearly know that after paying special consideration to the weight of the goods, the efficiency of the distribution of goods such as bottled water can be greatly improved. Not only will more transportation capacity be allocated to this type of goods, but the distribution routes of these goods will also be more concentrated, reducing the waste of transportation capacity.

[0046] Embodiment 3: In daily life, there are already many apps that provide different types of services to users. Different apps can generate different types of orders, such as takeout orders, door-to-door pickup orders, cargo transportation orders, housekeeping and cleaning orders, etc. However, no matter which of the above orders are allocated, the service quality is a factor that needs to be guaranteed. Among them, the rationality of order allocation will directly affect whether the service provider can provide services to users within the time agreed by the user, which will indirectly affect the service quality of the order.

[0047] In this case, in order to improve the rationality of order allocation, the order allocation method provided in the following embodiments of the present invention can be used.

[0048] Connect the allocation module, scope determination module, redistribution module, and self-learning module through the dispatch center; Allocation module: Calculate the location information of each order to be allocated received by the dispatch center and multiple alternative delivery paths preset by the delivery merchant to determine the delivery merchant and delivery path corresponding to the shortest distance; use the alternative service range of the location of the order to be allocated as the initial service range, and the initial service range is a circular area or other shaped area with the location of the order to be allocated as the center and the preset delivery distance as the radius; allocate the order to be allocated to the delivery merchant according to the initial service range of the delivery merchant: calculate the distance between the order location of the order to be allocated received within a distribution cycle and the delivery merchant within the initial service range, and allocate the order closest to the center of the initial service range as the first order to the delivery merchant closest to the center of the initial service range within the corresponding initial service range; Range determination module: used to determine the commuting time required for the delivery merchant of the first order that has been assigned to arrive at the service location included in the first order, and determine the shape of the newly added service range according to the commuting time; the newly added service range is a circular area or a regular polygonal area with the order location of the first order as the center and a preset length as the radius, and after obtaining the shape of the newly added service range, the radius of the newly added service range can be adjusted according to the order information included in the subsequent order; Reassignment module: used to determine whether to assign the second order to the delivery merchant according to the area of ​​the newly added service range and the initial service range; expand the initial service range to the sum of the initial service range and the newly added service range, and the newly added service range and the initial service range intersect or are tangent; calculate the distance between the location included in the order to be assigned in the dispatch center and the location included in the first order, and take the remaining order to be assigned corresponding to the shortest distance as the second order and assign it to the delivery merchant; Self-learning module: Based on the information provided by the big data center, the module can understand the transportation capacity and delivery completion status of each delivery merchant at the global level, and achieve the best match between orders and delivery units by analyzing the driving trajectory data, warehouse inventory data, user geographic location data, and road condition data in the entire region included in the orders completed every day; Through the above modules, we can effectively solve the common order allocation problems and alleviate the current unreasonable order allocation problem to a great extent.

[0049] The above are only preferred embodiments of the present invention. It should be noted that the above preferred embodiments should not be regarded as limiting the present invention, and the protection scope of the present invention should be based on the scope defined by the claims. For ordinary technicians in this technical field, several improvements and modifications can be made without departing from the spirit and scope of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A method for allocating orders by weight, characterized in that: The following steps are involved: Step 1: The dispatch center collects all the orders to be delivered on the platform and prepares to distribute the delivery orders to the delivery merchants; Step 2: The dispatch center sends the order to be delivered to the big data center, which extracts information about the order to be delivered and obtains the distance information, weight information, and delivery cost information of the order to be delivered; Step 3: The big data center obtains basic information about the delivery merchant, including but not limited to: the total number of orders delivered by the delivery merchant, the delivery merchant's location, the delivery merchant's transportation capacity, order fulfillment status, and payment status; Step 4: The big data center confirms the payment status of the delivery merchants, collects the information obtained in steps 2 and 3, and screens different merchants through the built-in allocation module, scope determination module, redistribution module, and big data self-learning module to integrate the order allocation method: the formula is as follows: weight = cos(π*accumulate*proportion1)*proportion2+ proportion3 / accumulate+distance+promise+paid+artificial+add_step_weight; Among them, weight is the total weight, accumulate is the total number of orders that the merchant has delivered, proportion represents the number of orders, distance represents distance, promise represents fulfillment, paid represents payment, artificial represents artificial, and add_step_weight represents multi-parameter steps.

2. The method for allocating orders by weight according to claim 1, characterized in that: In the step 1, there are multiple delivery merchants, and the geographical location of each delivery merchant is different.

3. The method for allocating orders by weight according to claim 1, characterized in that: The order fulfillment status in the step three is determined based on the fulfillment quality score of the delivery merchant obtained by the big data center in the background; after the big data center has accumulated the order volume before drawing the delivery merchant, it will give priority to the delivery merchant with a smaller number of orders when other conditions are the same.

4. The method for allocating orders by weight according to claim 1, characterized in that: In the step 2, the big data center monitors the on-the-way rider trajectory information, road condition change information, weather change information, the delivery situation information of the delivery merchant, and multi-dimensional feature information in real time; Multi-dimensional feature information refers to the impact of on-the-way rider trajectory information, road condition change information, weather change information, and delivery merchant delivery information on the order delivery process.

5. The method for allocating orders by weight according to claim 1, characterized in that: In the step 3, after obtaining the payment status of the delivery merchants, the big data center allocates the delivery orders to the paying merchants and the non-paying merchants according to the custom ratio.

6. The method for allocating orders by weight according to claim 1, characterized in that: The dispatch center can manually adjust and control abnormal merchants, including merchants with abnormal delivery, merchants with low ratings, and merchants with low fulfillment rates.

7. The method for allocating orders by weight according to claim 1, characterized in that: The formula in step 4 contains proportion1, proportion2, and proportion3; Among them, proportion1 controls the fluctuation period of the impact of order volume on weight; proportion2 controls the impact of order volume on the overall weight; Proportion3 controls the additional weight given to new merchants.

8. The method for allocating orders by weight according to claim 1, characterized in that: The add_step_weight multi-parameter ladder in step 4 includes the following parameters: inventory at the distribution site, in-transit distribution volume, number of idle delivery personnel, business hours, cumulative distribution time, order delivery fee amount, number of goods, total weight of order goods, and customer level.

9. The method for allocating orders by weight according to claim 1, characterized in that: The dispatch center includes the following modules: Allocation module: Calculate the location information of each order to be allocated received by the dispatch center and multiple alternative delivery paths preset by the delivery merchant to determine the delivery merchant and delivery path corresponding to the shortest distance; use the alternative service range of the location of the order to be allocated as the initial service range, and the initial service range is a circular area or other shaped area with the location of the order to be allocated as the center and the preset delivery distance as the radius; allocate the order to be allocated to the delivery merchant according to the initial service range of the delivery merchant: calculate the distance between the order location of the order to be allocated received within a distribution cycle and the delivery merchant within the initial service range, and allocate the order closest to the center of the initial service range as the first order to the delivery merchant closest to the center of the initial service range within the corresponding initial service range; Range determination module: used to determine the commuting time required for the delivery merchant of the first order that has been assigned to arrive at the service location included in the first order, and determine the shape of the newly added service range according to the commuting time; the newly added service range is a circular area or a regular polygonal area with the order location of the first order as the center and a preset length as the radius, and after obtaining the shape of the newly added service range, the radius of the newly added service range can be adjusted according to the order information included in the subsequent order; Reassignment module: used to determine whether to assign the second order to the delivery merchant according to the area of ​​the newly added service range and the initial service range; expand the initial service range to the sum of the initial service range and the newly added service range, and the newly added service range and the initial service range intersect or are tangent; calculate the distance between the location included in the order to be assigned in the dispatch center and the location included in the first order, and take the remaining order to be assigned corresponding to the shortest distance as the second order and assign it to the delivery merchant; Self-learning module: Based on the information provided by the big data center, the module can understand the transportation capacity and delivery completion status of each delivery merchant at the global level, and achieve the best match between orders and delivery units by analyzing the driving trajectory data, warehouse inventory data, user geographic location data, and road condition data in the entire area included in the orders completed every day.

10. The method for allocating orders by weight according to claim 9, characterized in that: The autonomous learning module estimates the order placement time, the estimated order completion time, and the order delivery route, and allocates transportation resources according to the weight of the order delivery items. After deep learning the user's location information, traffic speed, traffic light time, and average congestion time, it affects the decision-making of order delivery, and can realize automatic planning of order delivery routes and automatic reassignment of orders.

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