CN117094536A - Order data analysis method and system - Google Patents
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Abstract
The invention discloses a method and a system for analyzing order data, which belong to the technical field of data analysis, and the method comprises the following steps: s1, acquiring distribution information of each order to be analyzed in an order set to be analyzed; s2, determining distribution consumption indexes of all orders to be analyzed according to distribution information of all orders to be analyzed; s3, splitting the to-be-analyzed order set into a plurality of common distribution order subsets according to the distribution consumption indexes of each to-be-analyzed order, and completing order data analysis. According to the order data analysis method, the distribution consumption index of each order is determined according to the distribution starting point position, the distribution ending point position and the distribution route of each order, and in the process of determining the distribution consumption index, the influence of the number of intersections of the distribution route on the distribution time length is fully considered; and finally, reasonably arranging all the delivery orders according to the delivery consumption indexes of the orders, so that resource waste is avoided.
Description
Technical Field
The invention belongs to the technical field of data analysis, and particularly relates to an order data analysis method and system.
Background
With the rapid development of internet technology, the scale of various online distribution is also expanding, and more orders are generated online. And whether reasonable distribution arrangement can be carried out on all orders according to the distribution starting point and the distribution ending point of the orders becomes a problem to be solved urgently.
In the prior art, for the delivery of multiple orders, the delivery is generally carried out one by one according to the order placing sequence of users. However, a plurality of orders often have the condition that the distribution starting point position and the distribution ending point position are not far apart, so that reasonable scheduling is required to be carried out on all the orders, and resource waste is avoided.
Disclosure of Invention
In order to solve the problems, the invention provides an order data analysis method and an order data analysis system.
The technical scheme of the invention is as follows: an order data analysis method includes the steps of:
s1, acquiring distribution information of each order to be analyzed in an order set to be analyzed;
s2, determining distribution consumption indexes of all orders to be analyzed according to distribution information of all orders to be analyzed;
s3, splitting the to-be-analyzed order set into a plurality of common distribution order subsets according to the distribution consumption indexes of each to-be-analyzed order, and completing order data analysis.
Further, in S1, the delivery information of the order to be analyzed includes a delivery start position, a delivery end position, a delivery route, a delivery duration corresponding to the delivery route, and a longest waiting delivery duration.
Further, S2 comprises the following sub-steps:
s21, acquiring a linear distance between a delivery starting point position and a delivery ending point position of an order to be analyzed; acquiring the driving distance of a distribution route of an order to be analyzed;
s22, judging whether the straight line distance and the driving distance of the order to be analyzed meet the distance constraint condition, if so, entering S23, otherwise, entering S24;
s23, taking an average value of the straight line distance and the driving distance of the order to be analyzed as a distribution distance weight value of the order to be analyzed, and entering S25;
s24, extracting the number of the road junctions in the delivery route, calculating a delivery distance weight value of the order to be analyzed according to the number of the road junctions, and entering S25;
s25, calculating a distribution time weight value according to the distribution time length and the longest waiting distribution time length corresponding to the distribution route;
s26, taking the ratio of the distribution distance weight value and the distribution time weight value of the order to be analyzed as a distribution consumption index.
Further, in S22, the expression of the distance constraint F isThe method comprises the steps of carrying out a first treatment on the surface of the Where L represents a straight line distance between the delivery start position and the delivery end position, and S represents a travel distance of the delivery route.
Further, in S24, the calculation formula of the distribution distance weight value σ of the order to be analyzed is:the method comprises the steps of carrying out a first treatment on the surface of the Wherein, I n+1_n Represents the straight line distance from the nth intersection to the (n+1) th intersection, l n_0 Representing the straight line distance s from the nth intersection to the delivery start position n+1_n Representing the travel distance from the nth intersection to the (n+1) th intersection, s n_0 The travel distance from the nth intersection to the delivery start position is represented, and N represents the number of intersections of the delivery route.
Further, in S25, an order to be analyzedThe calculation formula of the distribution time weight value rho is as follows:the method comprises the steps of carrying out a first treatment on the surface of the In the formula, T represents the corresponding delivery time length of the delivery route, L represents the straight line distance between the delivery start position and the delivery end position, S represents the running distance of the delivery route, e represents the index, and T represents the longest waiting delivery time length.
Further, S3 comprises the following sub-steps:
s31, sorting the distribution consumption indexes of all orders to be analyzed from small to large to generate a distribution consumption index sequence, and extracting the average value, the median and the standard deviation of the distribution consumption index sequence to be respectively used as a first characterization parameter, a second characterization parameter and a third characterization parameter of the distribution consumption index sequence;
s32, preprocessing each distribution consumption index of the distribution consumption index sequence according to the first characterization parameter, the second characterization parameter and the third characterization parameter to generate corresponding standard distribution consumption indexes, and sequencing all the standard distribution consumption indexes from small to large to generate a standard distribution consumption index sequence;
s33, splitting the to-be-analyzed order set into a plurality of common distribution order subsets according to the standard distribution consumption index sequence, and completing order data analysis.
Further, in S32, the calculation formula of the standard distribution consumption index C is:
；
wherein b is 1 Representing the first characterizing parameter, b 2 Representing a second characterization parameter, b 3 Representing a third characterization parameter, c max Representing the maximum value of the distribution consumption index sequence, c min Representing the minimum value of the distribution consumption index sequence, c ave The average value of the distribution consumption index sequence is represented, and c represents the distribution consumption index.
Further, in S33, the specific method for splitting the to-be-analyzed order set into a plurality of common distribution order subsets is as follows: consumption of standard deliveryFront in exponential sequenceAll to-be-analyzed order sets corresponding to the standard distribution consumption indexes are used as a first common distribution order subset, and the post +.>All to-be-analyzed order sets corresponding to the standard distribution consumption indexes are used as a second common distribution order subset, and all to-be-analyzed order sets corresponding to the rest standard distribution consumption indexes in the standard distribution consumption index sequence are used as a third common distribution order subset, so that order data analysis is completed; wherein (1)>Representing an upward rounding.
The beneficial effects of the invention are as follows: according to the order data analysis method, the distribution consumption index of each order is determined according to the distribution starting point position, the distribution ending point position and the distribution route of each order, and in the process of determining the distribution consumption index, the influence of the number of intersections of the distribution route on the distribution time length is fully considered; and finally, reasonably arranging all the delivery orders according to the delivery consumption indexes of the orders, ensuring that the orders with the adjacent delivery start points and delivery end points are used as a common delivery order subset, and avoiding resource waste.
Based on the method, the invention also provides an order data analysis system which comprises a delivery information generation unit, a delivery consumption index generation unit and a delivery order subset generation unit;
the distribution information generating unit is used for acquiring the distribution information of each order to be analyzed in the order set to be analyzed;
the distribution consumption index generation unit is used for determining the distribution consumption index of each order to be analyzed according to the distribution information of each order to be analyzed;
the distribution order subset generating unit is used for splitting the to-be-analyzed order set into a plurality of common distribution order subsets according to the distribution consumption indexes of the to-be-analyzed orders to complete the order data analysis.
The beneficial effects of the invention are as follows: the order data analysis system considers the distribution distance of all orders, ensures that all orders are reasonably arranged, and avoids resource waste.
Drawings
FIG. 1 is a flow chart of an order data analysis method;
FIG. 2 is a schematic diagram of an order data analysis system.
Detailed Description
Embodiments of the present invention are further described below with reference to the accompanying drawings.
As shown in fig. 1, the present invention provides an order data analysis method, which includes the following steps:
s1, acquiring distribution information of each order to be analyzed in an order set to be analyzed;
s2, determining distribution consumption indexes of all orders to be analyzed according to distribution information of all orders to be analyzed;
s3, splitting the to-be-analyzed order set into a plurality of common distribution order subsets according to the distribution consumption indexes of each to-be-analyzed order, and completing order data analysis.
In the embodiment of the present invention, in S1, the delivery information of the order to be analyzed includes a delivery start position, a delivery end position, a delivery route, a delivery duration corresponding to the delivery route, and a longest waiting delivery duration.
In an embodiment of the present invention, S2 comprises the following sub-steps:
s21, acquiring a linear distance between a delivery starting point position and a delivery ending point position of an order to be analyzed; acquiring the driving distance of a distribution route of an order to be analyzed;
s22, judging whether the straight line distance and the driving distance of the order to be analyzed meet the distance constraint condition, if so, entering S23, otherwise, entering S24;
s23, taking an average value of the straight line distance and the driving distance of the order to be analyzed as a distribution distance weight value of the order to be analyzed, and entering S25;
s24, extracting the number of the road junctions in the delivery route, calculating a delivery distance weight value of the order to be analyzed according to the number of the road junctions, and entering S25;
s25, calculating a distribution time weight value according to the distribution time length and the longest waiting distribution time length corresponding to the distribution route;
s26, taking the ratio of the distribution distance weight value and the distribution time weight value of the order to be analyzed as a distribution consumption index.
In the invention, if the straight line distance and the running distance of the order to be analyzed meet the distance constraint condition, the running distance of the order to be analyzed is smaller than the deviation of the straight line distance, the average value of the straight line distance and the running distance of the order to be analyzed can be directly used as the distribution distance weight value of the order to be analyzed, otherwise, the curve of the running distance of the order to be analyzed is more, the number of crossing passes is more, and the influence of each crossing on the distribution consumption index is fully considered.
In the embodiment of the present invention, in S22, the distance constraint F has the expression ofThe method comprises the steps of carrying out a first treatment on the surface of the Where L represents a straight line distance between the delivery start position and the delivery end position, and S represents a travel distance of the delivery route.
In the embodiment of the present invention, in S24, the calculation formula of the distribution distance weight value σ of the order to be analyzed is:the method comprises the steps of carrying out a first treatment on the surface of the Wherein, I n+1_n Represents the straight line distance from the nth intersection to the (n+1) th intersection, l n_0 Representing the straight line distance s from the nth intersection to the delivery start position n+1_n Representing the travel distance from the nth intersection to the (n+1) th intersection, s n_0 The travel distance from the nth intersection to the delivery start position is represented, and N represents the number of intersections of the delivery route.
In the embodiment of the present invention, in S25, the calculation formula of the distribution time weight value ρ of the order to be analyzed is:the method comprises the steps of carrying out a first treatment on the surface of the Wherein T represents the delivery time length corresponding to the delivery route, L represents the delivery start point position and the delivery end pointThe straight line distance between the positions, S represents the travel distance of the delivery route, e represents an index, and t represents the longest waiting delivery duration.
In an embodiment of the present invention, S3 comprises the following sub-steps:
s31, sorting the distribution consumption indexes of all orders to be analyzed from small to large to generate a distribution consumption index sequence, and extracting the average value, the median and the standard deviation of the distribution consumption index sequence to be respectively used as a first characterization parameter, a second characterization parameter and a third characterization parameter of the distribution consumption index sequence;
s32, preprocessing each distribution consumption index of the distribution consumption index sequence according to the first characterization parameter, the second characterization parameter and the third characterization parameter to generate corresponding standard distribution consumption indexes, and sequencing all the standard distribution consumption indexes from small to large to generate a standard distribution consumption index sequence;
s33, splitting the to-be-analyzed order set into a plurality of common distribution order subsets according to the standard distribution consumption index sequence, and completing order data analysis.
In the invention, the average value, the median and the standard deviation of the distribution consumption index sequence are all characteristic parameters capable of representing the distribution consumption index, and can further influence the distribution consumption index, so that the distribution consumption index sequence can be used for generating a standard distribution consumption index and further used for splitting the standard distribution consumption index sequence.
In the embodiment of the present invention, in S32, the calculation formula of the standard distribution consumption index C is:the method comprises the steps of carrying out a first treatment on the surface of the Wherein b is 1 Representing the first characterizing parameter, b 2 Representing a second characterization parameter, b 3 Representing a third characterization parameter, c max Representing the maximum value of the distribution consumption index sequence, c min Representing the minimum value of the distribution consumption index sequence, c ave The average value of the distribution consumption index sequence is represented, and c represents the distribution consumption index.
In the embodiment of the present invention, in S33, a specific method for splitting an order set to be analyzed into a plurality of common distribution order subsetsThe method comprises the following steps: leading in a standard delivery consumption index sequenceAll to-be-analyzed order sets corresponding to the standard distribution consumption indexes are used as a first common distribution order subset, and the post +.>All to-be-analyzed order sets corresponding to the standard distribution consumption indexes are used as a second common distribution order subset, and all to-be-analyzed order sets corresponding to the rest standard distribution consumption indexes in the standard distribution consumption index sequence are used as a third common distribution order subset, so that order data analysis is completed; wherein (1)>Representing an upward rounding.
Based on the method, the invention also provides an order data analysis system which comprises a delivery information generation unit, a delivery consumption index generation unit and a delivery order subset generation unit;
the distribution information generating unit is used for acquiring the distribution information of each order to be analyzed in the order set to be analyzed;
the distribution consumption index generation unit is used for determining the distribution consumption index of each order to be analyzed according to the distribution information of each order to be analyzed;
the distribution order subset generating unit is used for splitting the to-be-analyzed order set into a plurality of common distribution order subsets according to the distribution consumption indexes of the to-be-analyzed orders to complete the order data analysis.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (10)
1. An order data analysis method, comprising the steps of:
s1, acquiring distribution information of each order to be analyzed in an order set to be analyzed;
s2, determining distribution consumption indexes of all orders to be analyzed according to distribution information of all orders to be analyzed;
s3, splitting the to-be-analyzed order set into a plurality of common distribution order subsets according to the distribution consumption indexes of each to-be-analyzed order, and completing order data analysis.
2. The method according to claim 1, wherein in S1, the delivery information of the order to be analyzed includes a delivery start position, a delivery end position, a delivery route, a delivery duration corresponding to the delivery route, and a longest waiting delivery duration.
3. The order data analysis method according to claim 1, wherein S2 comprises the sub-steps of:
s21, acquiring a linear distance between a delivery starting point position and a delivery ending point position of an order to be analyzed; acquiring the driving distance of a distribution route of an order to be analyzed;
s22, judging whether the straight line distance and the driving distance of the order to be analyzed meet the distance constraint condition, if so, entering S23, otherwise, entering S24;
s23, taking an average value of the straight line distance and the driving distance of the order to be analyzed as a distribution distance weight value of the order to be analyzed, and entering S25;
s24, extracting the number of the road junctions in the delivery route, calculating a delivery distance weight value of the order to be analyzed according to the number of the road junctions, and entering S25;
s25, calculating a distribution time weight value according to the distribution time length and the longest waiting distribution time length corresponding to the distribution route;
s26, taking the ratio of the distribution distance weight value and the distribution time weight value of the order to be analyzed as a distribution consumption index.
4. The order data analysis method according to claim 3, wherein in S22, the expression of the distance constraint F isThe method comprises the steps of carrying out a first treatment on the surface of the Where L represents a straight line distance between the delivery start position and the delivery end position, and S represents a travel distance of the delivery route.
5. The method for analyzing order data according to claim 3, wherein in S24, the calculation formula of the distribution distance weight value σ of the order to be analyzed is:the method comprises the steps of carrying out a first treatment on the surface of the Wherein, I n+1_n Represents the straight line distance from the nth intersection to the (n+1) th intersection, l n_0 Representing the straight line distance s from the nth intersection to the delivery start position n+1_n Representing the travel distance from the nth intersection to the (n+1) th intersection, s n_0 The travel distance from the nth intersection to the delivery start position is represented, and N represents the number of intersections of the delivery route.
6. The method for analyzing order data according to claim 3, wherein in S25, the calculation formula of the distribution time weight value ρ of the order to be analyzed is:the method comprises the steps of carrying out a first treatment on the surface of the In the formula, T represents the corresponding delivery time length of the delivery route, L represents the straight line distance between the delivery start position and the delivery end position, S represents the running distance of the delivery route, e represents the index, and T represents the longest waiting delivery time length.
7. The order data analysis method according to claim 1, wherein S3 comprises the sub-steps of:
s31, sorting the distribution consumption indexes of all orders to be analyzed from small to large to generate a distribution consumption index sequence, and extracting the average value, the median and the standard deviation of the distribution consumption index sequence to be respectively used as a first characterization parameter, a second characterization parameter and a third characterization parameter of the distribution consumption index sequence;
s32, preprocessing each distribution consumption index of the distribution consumption index sequence according to the first characterization parameter, the second characterization parameter and the third characterization parameter to generate corresponding standard distribution consumption indexes, and sequencing all the standard distribution consumption indexes from small to large to generate a standard distribution consumption index sequence;
s33, splitting the to-be-analyzed order set into a plurality of common distribution order subsets according to the standard distribution consumption index sequence, and completing order data analysis.
8. The order data analysis method according to claim 7, wherein in S32, the standard distribution consumption index C is calculated by the formula:the method comprises the steps of carrying out a first treatment on the surface of the Wherein b is 1 Representing the first characterizing parameter, b 2 Representing a second characterization parameter, b 3 Representing a third characterization parameter, c max Representing the maximum value of the distribution consumption index sequence, c min Representing the minimum value of the distribution consumption index sequence, c ave The average value of the distribution consumption index sequence is represented, and c represents the distribution consumption index.
9. The method for analyzing the order data according to claim 7, wherein in S33, the specific method for splitting the to-be-analyzed order set into a plurality of common distribution order subsets is as follows: leading in a standard delivery consumption index sequenceAll to-be-analyzed order sets corresponding to the standard distribution consumption indexes are used as a first common distribution order subset, and the post +.>All to-be-analyzed order sets corresponding to the standard distribution consumption indexes are used as a second common distribution order subset, and all to-be-analyzed order sets corresponding to the rest standard distribution consumption indexes in the standard distribution consumption index sequence are used as a third common distribution order subset, so that order data analysis is completed; wherein (1)>Representing an upward rounding.
10. An order data analysis system is characterized by comprising a delivery information generation unit, a delivery consumption index generation unit and a delivery order subset generation unit;
the distribution information generating unit is used for acquiring the distribution information of each order to be analyzed in the order set to be analyzed;
the distribution consumption index generation unit is used for determining distribution consumption indexes of all orders to be analyzed according to distribution information of all orders to be analyzed;
the distribution order subset generating unit is used for splitting the to-be-analyzed order set into a plurality of common distribution order subsets according to distribution consumption indexes of all to-be-analyzed orders to complete order data analysis.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107844933A (en) * | 2017-09-21 | 2018-03-27 | 北京小度信息科技有限公司 | order processing method and device |
| CN109002902A (en) * | 2018-06-11 | 2018-12-14 | 华东理工大学 | Subregion multistage fresh agricultural products dynamic vehicle method for optimizing route |
| CN109685609A (en) * | 2018-12-14 | 2019-04-26 | 拉扎斯网络科技（上海）有限公司 | Order allocation method, device, electronic equipment and storage medium |
| CN110310058A (en) * | 2019-04-17 | 2019-10-08 | 郑州时空隧道信息技术有限公司 | Item dispenser fee calculation procedure, device and terminal |
| CN111860914A (en) * | 2020-07-31 | 2020-10-30 | 安徽梦馨信息技术有限公司 | Flower appointment order distribution method and system |
| CN112365109A (en) * | 2021-01-14 | 2021-02-12 | 北京每日优鲜电子商务有限公司 | Distribution path information generation method and device, electronic equipment and readable medium |
| CN112907177A (en) * | 2021-03-06 | 2021-06-04 | 盐城志娟网络科技有限公司 | Direct delivery order real-time online management method based on big data tracking and analysis |
| WO2021147353A1 (en) * | 2020-01-21 | 2021-07-29 | 北京三快在线科技有限公司 | Order dispatch |
| CN113420928A (en) * | 2021-06-30 | 2021-09-21 | 上海东普信息科技有限公司 | Order scheduling method, device, equipment and storage medium |
| CN113723676A (en) * | 2021-08-23 | 2021-11-30 | 郑州时空隧道信息技术有限公司 | One-path and multiple-path line planning method and device |
| CN113919781A (en) * | 2021-10-19 | 2022-01-11 | 北京沃东天骏信息技术有限公司 | Distribution route determining method and device and electronic equipment |
| WO2022206323A1 (en) * | 2021-03-29 | 2022-10-06 | 北京沃东天骏信息技术有限公司 | Scheduling method and apparatus, and electronic device and storage medium |
| CN115293841A (en) * | 2022-07-20 | 2022-11-04 | 北京三快在线科技有限公司 | Order scheduling method, device, equipment and storage medium |
-
2023
- 2023-10-19 CN CN202311353607.3A patent/CN117094536B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107844933A (en) * | 2017-09-21 | 2018-03-27 | 北京小度信息科技有限公司 | order processing method and device |
| CN109002902A (en) * | 2018-06-11 | 2018-12-14 | 华东理工大学 | Subregion multistage fresh agricultural products dynamic vehicle method for optimizing route |
| CN109685609A (en) * | 2018-12-14 | 2019-04-26 | 拉扎斯网络科技（上海）有限公司 | Order allocation method, device, electronic equipment and storage medium |
| CN110310058A (en) * | 2019-04-17 | 2019-10-08 | 郑州时空隧道信息技术有限公司 | Item dispenser fee calculation procedure, device and terminal |
| WO2021147353A1 (en) * | 2020-01-21 | 2021-07-29 | 北京三快在线科技有限公司 | Order dispatch |
| CN111860914A (en) * | 2020-07-31 | 2020-10-30 | 安徽梦馨信息技术有限公司 | Flower appointment order distribution method and system |
| CN112365109A (en) * | 2021-01-14 | 2021-02-12 | 北京每日优鲜电子商务有限公司 | Distribution path information generation method and device, electronic equipment and readable medium |
| CN112907177A (en) * | 2021-03-06 | 2021-06-04 | 盐城志娟网络科技有限公司 | Direct delivery order real-time online management method based on big data tracking and analysis |
| WO2022206323A1 (en) * | 2021-03-29 | 2022-10-06 | 北京沃东天骏信息技术有限公司 | Scheduling method and apparatus, and electronic device and storage medium |
| CN113420928A (en) * | 2021-06-30 | 2021-09-21 | 上海东普信息科技有限公司 | Order scheduling method, device, equipment and storage medium |
| CN113723676A (en) * | 2021-08-23 | 2021-11-30 | 郑州时空隧道信息技术有限公司 | One-path and multiple-path line planning method and device |
| CN113919781A (en) * | 2021-10-19 | 2022-01-11 | 北京沃东天骏信息技术有限公司 | Distribution route determining method and device and electronic equipment |
| CN115293841A (en) * | 2022-07-20 | 2022-11-04 | 北京三快在线科技有限公司 | Order scheduling method, device, equipment and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| ZHU, S等: "Optimization of product category allocation in multiple warehouses to minimize splitting of online supermarket customer orders", 《EUROPEAN JOURNAL OF OPERATIONAL RESEARCH》, pages 556 - 571 * |
| 潘晓 等: "基于订单拆分的容量限制商超配送路径规划", 《华东师范大学学报》, pages 147 - 164 * |
| 程月娇;施炯;黄彬;: "众包物流环境下订单合并及配送路径优化方法研究", 浙江万里学院学报, no. 04, pages 33 - 39 * |
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