CN109583806B - Vehicle dispatching and pickup method and system based on intelligent adjustment of order weight - Google Patents
Vehicle dispatching and pickup method and system based on intelligent adjustment of order weight Download PDFInfo
- Publication number
- CN109583806B CN109583806B CN201811271119.7A CN201811271119A CN109583806B CN 109583806 B CN109583806 B CN 109583806B CN 201811271119 A CN201811271119 A CN 201811271119A CN 109583806 B CN109583806 B CN 109583806B
- Authority
- CN
- China
- Prior art keywords
- weight
- goods
- customer
- average weight
- order
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a vehicle dispatching and pickup method and a system based on intelligent adjustment of order weight, wherein the method comprises the following steps: acquiring current order information; whether a target client matched with the client information exists in the historical client order data is matched; if so, acquiring the average weight benchmarking value and the range of the average weight benchmarking value of the target client single goods from the historical client order data; judging whether the average weight of the unit goods in the current order information is within the range of the average weight benchmark value; if not, the average weight of the goods in the current order information is adjusted to be an average weight benchmark value, and the adjusted total weight of the goods is obtained through calculation; and dispatching a proper vehicle to go to the pickup according to the total weight of the goods. The invention judges whether the total weight of the goods currently placed by the client is accurate or not by analyzing the historical order data of the client, and if not, the total weight is estimated again, thereby avoiding the problems of resource waste and long time consumption of the goods taking operation caused by inaccurate weight of the placed goods.
Description
Technical Field
The invention relates to the technical field of logistics scheduling, in particular to a scheduling method and system for a logistics vehicle interval.
Background
In the existing logistics industry, in order to more reasonably schedule pickup personnel or vehicles for pickup, when a client places an order, the client is generally required to provide the total weight of the placed goods, and then the pickup personnel or the vehicles are scheduled to go to pickup according to the total weight of the goods provided by the client. However, when a customer orders a delivery, the total weight of the goods provided by the customer is often inaccurate, and the inaccuracy of the weight of the goods may affect the pre-judgment of the dispatching center on the vehicle allocation load, whether a tailboard is needed, the expected operation time consumption and the like, for example, when the vehicle goes to the customer site to find that the goods are not loaded, the vehicle needs to be changed, the expected operation time consumption has a large deviation, which affects the next delivery timeliness of the customer, and the like, and may cause resource waste or affect timeliness.
Disclosure of Invention
The invention provides a dispatching method and a dispatching system for a logistics vehicle interval, which aim to solve the problem that in the existing logistics industry, the total weight of goods provided by a client when placing an order is often inaccurate, so that pickup personnel and vehicles cannot be reasonably dispatched to pick up the goods.
In order to solve the problems, the invention provides a vehicle dispatching and pickup method based on intelligent adjustment of order weight, which comprises the following steps:
acquiring current order information, wherein the current order information comprises customer information, the quantity of goods and the total weight of the goods;
matching whether historical customer order data has a target customer matched with the customer information;
when a target client exists, acquiring a mean weight benchmarking value and a mean weight benchmarking value interval range of a target client single piece of goods from historical client order data;
judging whether the average weight of the single cargos in the current order information is within the range of the average weight benchmarking value, wherein the average weight of the single cargos is equal to the total weight of the cargos/the quantity of the cargos;
if yes, the total weight of the goods in the current order information is not adjusted; if not, the average weight of the single goods in the current order information is adjusted to be the average weight benchmark value, and the adjusted total weight of the goods is obtained through calculation;
and dispatching a proper vehicle to go to the pickup according to the total weight of the goods.
As a further improvement of the present invention, obtaining an average weight benchmarking value and an average weight benchmarking value interval range of target customer single goods from historical customer order data comprises:
acquiring historical order cargo weight data of a target client from historical client order data to obtain a cargo weight data set;
calculating the average weight benchmark value mu and the standard deviation sigma of the single piece of goods according to the goods weight data set, wherein,
miactual average weight of each single piece of goods in the historical orders, wherein n is the corresponding historical order number;
and setting the range of the average weight benchmark value according to the average weight benchmark value mu and the standard deviation sigma.
As a further improvement of the invention, before the average weight benchmark value mu and the standard deviation sigma of the single piece of goods are calculated according to the goods weight data set, historical order goods weight data of which the actual average weight of the single piece of goods is in abnormal fluctuation are removed from the goods weight data set.
As a further improvement of the present invention, after the step of matching whether there is a target customer matching the customer information with the historical customer order data, the method further comprises:
and when the target customer does not exist, dispatching the vehicle to go to the pickup according to the total weight of the goods, and creating and storing a customer file according to the customer information.
As a further improvement of the present invention, the method further comprises:
and after the piece taking is finished, acquiring the actual average weight of the single piece goods of the current order and storing the actual average weight as new historical customer order data.
In order to solve the above problem, the present invention further provides a vehicle dispatching pickup system based on intelligent adjustment of order weight, which comprises:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring current order information, and the current order information comprises customer information, the quantity of goods and the total weight of the goods;
the customer order storage module is used for extracting the customer information, the quantity of the goods and the total weight of the current goods from the order information to form historical customer order data and storing the historical customer order data; the matching module is used for matching whether a target client matched with the client information exists in the client order storage module or not;
the second acquisition module is used for acquiring the average weight benchmark value and the range of the average weight benchmark value interval of the target customer single goods from the customer order storage module according to the result fed back by the matching module when the target customer exists;
the judging module is used for judging whether the average weight of the single goods in the current order information is within the range of the average weight benchmark value interval; the average weight of the single cargos is equal to the total weight of the cargos/the number of the cargos;
the weight adjusting module is used for adjusting the average weight of the single cargos in the current order information into the average weight benchmark value and calculating to obtain the adjusted total weight of the cargos if the average weight of the single cargos in the current order information is not within the range of the average weight benchmark value interval according to the result fed back by the judging module;
and the first scheduling module is used for scheduling a proper vehicle to go to the pickup according to the total weight of the goods.
As a further improvement of the present invention, it further comprises:
a third obtaining module, configured to obtain historical order cargo weight data of the target customer from the customer order storage module, so as to obtain a cargo weight data set;
a calculation module for calculating the average weight benchmark value mu and the standard deviation sigma of the single piece of cargo according to the cargo weight data set, wherein,
miactual average weight of each single piece of goods in the historical orders, wherein n is the corresponding historical order number;
and the setting module is used for setting the range of the average weight benchmark value according to the average weight benchmark value mu and the standard deviation sigma.
As a further improvement of the present invention, it further comprises:
and the removing module is used for removing the historical order goods weight data of which the actual average weight of the single piece of goods belongs to abnormal fluctuation from the goods weight data set after obtaining the goods weight data set.
As a further improvement of the present invention, it further comprises:
the second scheduling module is used for scheduling the vehicle to go to the pickup according to the total weight of the goods when the target customer does not exist according to the result fed back by the matching module;
and the customer order storage module is also used for creating and storing a customer file according to the customer information when no target customer exists according to the result fed back by the matching module.
As a further improvement of the present invention, it further comprises:
the actual weight obtaining module is used for obtaining the actual average weight of the single goods of the current order after the goods taking is finished;
and the customer order storage module is also used for storing the actual average weight of the single goods.
Compared with the prior art, the invention obtains the total weight of the goods from the order information and obtains the average weight of the single goods when the customer places an order, compares the average weight of the single goods with the range of the average weight benchmarking value obtained by statistics in the historical customer order data, if the average weight of the single goods of the current order is within the range of the average weight benchmarking value, the average weight data of the single goods provided at this time is considered to be accurate, the adjustment is not needed, the vehicle can be directly scheduled to get the goods according to the weight data, if the weight of the goods of the current order is not within the range of the average weight benchmarking value, the total weight of the goods provided at this time is considered to be inaccurate, the total weight of the goods needs to be adjusted according to the average weight benchmarking value obtained by statistics according to the historical customer order data, and the vehicle is scheduled to get the goods according to the adjusted total weight of the goods, thereby effectively avoiding the problem of resource waste caused by the inaccurate total weight of the goods provided by the customer, the time consumption of the prediction operation is reduced due to the fact that the total weight of the goods is inaccurate when orders are placed, and the risk of overtime taking is reduced.
Drawings
FIG. 1 is a schematic flow chart illustrating an embodiment of a vehicle dispatch pickup method based on intelligently adjusting the weight of a vehicle placed in a sheet;
FIG. 2 is a schematic flow chart illustrating an embodiment of obtaining a mean weight benchmark value and a range of the mean weight benchmark value in the vehicle dispatching pickup method based on intelligently adjusted weight orders according to the present invention;
FIG. 3 is a schematic flow chart illustrating another embodiment of obtaining a mean weight benchmark value and a range of the mean weight benchmark value in the vehicle dispatching pickup method based on intelligently adjusted weight orders;
FIG. 4 is a schematic flow chart illustrating a vehicle dispatch pickup method based on intelligent adjustment of the weight of the lower sheet in accordance with another embodiment of the present invention;
FIG. 5 is a schematic flow chart illustrating a vehicle dispatch pickup method based on intelligent adjustment of the weight of the lower sheet in accordance with another embodiment of the present invention;
FIG. 6 is a functional block diagram of an embodiment of a vehicle dispatch pickup system based on intelligent adjustment of the weight of a vehicle drop;
FIG. 7 is a functional block diagram of another embodiment of a vehicle dispatch pickup system based on intelligent adjustment of weight of a vehicle after a weight is placed;
FIG. 8 is a functional block diagram of another embodiment of a vehicle dispatch pickup system based on intelligent adjustment of weight of a vehicle after a weight is placed;
FIG. 9 is a functional block diagram of another embodiment of a vehicle dispatch pickup system based on intelligent adjustment of weight of a vehicle after a weight is placed;
FIG. 10 is a functional block diagram of another embodiment of a vehicle dispatch pickup system based on intelligent adjustment of weight of a vehicle.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Fig. 1 illustrates an embodiment of the vehicle dispatching pickup method based on intelligent adjustment of the order weight. In this embodiment, as shown in fig. 1, the method for vehicle dispatch pickup based on intelligently adjusting the order weight includes the following steps:
step S1, obtaining current order information, where the current order information includes customer information, quantity of goods, and total weight of goods.
Specifically, after receiving an order of a client, acquiring client information, a goods number and a goods total weight according to the order information, wherein the client information comprises a client name, a client code number, a client address and the like, and the goods number and the goods total weight are information which is filled or reported to a waiter when the client places the order.
Step S2, whether there is a target customer matching the customer information in the matching historical customer order data. When the target client exists, step S3 is performed.
Specifically, the historical customer order data includes information for all customers for which business transactions occurred. And after the current order information is obtained, obtaining the client information from the current order information, and matching the client information with the historical client order data to confirm whether a target client matched with the client information exists in the historical client order data.
Step S3, obtaining the average weight benchmarking value and the range of the average weight benchmarking value of the target customer single goods from the historical customer order data.
Specifically, the average weight of the goods of the customer historical order, namely the average weight benchmarking value, can be obtained by analyzing the historical order data of the customer, and then an average weight benchmarking value interval range can be set according to the average weight benchmarking value. In the embodiment, the average weight benchmarking value and the range of the average weight benchmarking value of the single goods of the target customer can be obtained by analyzing the historical order data of the target customer.
And step S4, judging whether the average weight of the single goods in the current order information is within the range of the average weight benchmarking value. If yes, go to step S5; if not, step S6 is executed.
It should be noted that the weight of each cargo is equal to the total weight of the cargo/the number of the cargo.
Specifically, the weight of the piece goods in the current order information is within the range of the average weight benchmarking value, that is, the total weight of the goods provided by the customer at this time is determined to be accurate, and step S5 is executed; if the weight of each piece of goods is within the range of the average weight benchmarking value, that is, the total weight of the goods provided by the customer at this time is determined to be inaccurate, step S6 is executed.
In step S5, the total weight of the goods in the current order information is not adjusted.
And step S6, the average weight of the goods in the current order information is adjusted to be an average weight marker post value, and the adjusted total weight of the goods is obtained through calculation.
It should be noted that the adjusted total weight of the cargo is the average weight benchmarking value per quantity of the cargo
And step S7, dispatching a proper vehicle to get to the pickup according to the total weight of the goods.
Specifically, if the weights of the single cargos in the current order information are within the range of the weight-average benchmarking value, the total weight of the cargos in the current order information is not adjusted, and a proper vehicle is dispatched to get the single cargos according to the total weight of the cargos; and if the average weight of the single goods in the current order information is not within the range of the average weight benchmark value, adjusting the average weight of the single goods in the current order information to be the average weight benchmark value, calculating to obtain the adjusted total weight of the goods, and scheduling a proper vehicle to go to pick up the goods according to the adjusted total weight of the goods.
For example, table 1 shows the average weight benchmarking value and the range of the average weight benchmarking value intervals set for company a.
TABLE 1 company average weight benchmarking value and range information of set average weight benchmarking value interval
| Company(s) | Average weight marker post value | Range of average weight benchmarking value |
| Company A | 12.99889 | [11.42511，14.57267] |
If the order of the company is received at the moment, the quantity of the ordered goods is 10, the total weight of the goods is 100, and the average weight of the single goods is calculated;
the average weight of the single goods is 100/10-10;
at the moment, the average weight of the single piece goods does not fall within the range [11.42511, 14.57267] of the average weight benchmarking value, so that the total weight of the goods provided by the company is inaccurate, the total weight of the goods of the current order needs to be adjusted and estimated, and the estimated total weight of the goods after adjustment can be calculated according to the average weight benchmarking value;
the adjusted estimated total weight of the cargo is 12.99889 × 10 — 129.9889;
and dispatching vehicles to go to the company for taking the goods according to the adjusted and estimated total weight of the goods.
Table 2 below shows the comparison of the actual weight data with the adjusted estimated weights for a plurality of orders placed in the historical order of company A.
TABLE 2A company order record
| Order placing company | Ordering coding | Date of placing order | Number of lower sheets | Weight of lower sheet | Actual weight | Pre-estimated weight | Whether or not to adjust | |
| Company A | Code 1 | 2018/4/10 | 6 | 30 | 105 | 76 | Is that | |
| Company A | Code 2 | 2018/5/12 | 15 | 30 | 164 | 191 | Is that | |
| Company A | Code 3 | 2018/5/14 | 25 | 30 | 410 | 318 | Is that | |
| Company A | Code 4 | 2018/5/25 | 5 | 30 | 73 | 64 | Is that | |
| Company A | Coding 5 | 2018/5/25 | 2 | 20 | 23 | 25 | Is that | |
| | Code | 6 | 2018/6/9 | 13 | 30 | 154 | 165 | Is that |
| Company A | Code 7 | 2018/6/22 | 39 | 200 | 367 | 496 | Is that | |
| Company A | Code 8 | 2018/6/30 | 21 | 200 | 173 | 267 | Is that | |
| Company A | Code 9 | 2018/7/12 | 16 | 200 | 235 | 200 | Whether or not |
In this embodiment, when a customer places an order, the total weight of goods is obtained from order information, the average weight of single goods is obtained, the average weight of single goods is compared with an average weight benchmarking value interval range obtained by statistics in historical customer order data, if the average weight of single goods of a current order is within the average weight benchmarking value interval range, the average weight data of single goods provided at this time is considered to be accurate, adjustment is not needed, a vehicle can be directly scheduled to get a piece according to the weight data, if the weight of the current order is not within the average weight benchmarking value interval range, the total weight of goods provided at this time is considered to be inaccurate, the total weight of goods needs to be adjusted and estimated according to the average weight benchmarking value obtained by statistics according to historical customer order data, and the vehicle is scheduled to get the piece according to the adjusted and estimated total weight of goods. As can be seen from table 2, by adopting the embodiment, the difference between the estimated ordering cargo weight and the actual weight is obviously reduced by adjusting the ordering weight, the problem of resource waste caused by inaccurate cargo total weight provided by a client is effectively avoided, the time-consuming inaccuracy of the prediction operation caused by inaccurate cargo total weight during ordering is reduced, and the risk of overtime pickup is reduced.
Further, as shown in fig. 2, the step of obtaining the average weight benchmarking value and the average weight benchmarking value interval range of the target customer single piece goods from the historical customer order data specifically includes the following steps:
step S10, obtaining historical order goods weight data of the target customer from the historical customer order data, and obtaining a goods weight data set.
And step S11, calculating the average weight benchmark value mu and the standard deviation sigma of the single piece of goods according to the goods weight data set.
It should be noted that, in the following description,
mithe actual average weight of each single goods in the historical orders is calculated, and n is the corresponding historical order number.
In step S12, a weight average target value range is set based on the weight average target value μ and the standard deviation σ.
Specifically, after the weight-average benchmark value μ is obtained through calculation, the standard deviation σ is obtained through calculation according to the weight-average benchmark value μ, and the range of the weight-average benchmark value interval is confirmed according to the weight-average benchmark value μ and the standard deviation σ. In this embodiment, the range of the average weight scale value is preferably set to [ μ - σ, μ + σ ]. For example, table 3 below shows the weight data for the 9 orders from company a:
table 3 cleaned cargo weight data
| 11.88 | 13.5 | 13.43 | 11.5 | 16 | 12.75 | 11.5 | 11.72 | 14.71 |
The average weight benchmarking value μ can be calculated according to table 3 above, wherein,
further, the standard deviation sigma is calculated according to the formula for calculating the standard deviation, wherein the standard deviation sigma is 1.573781;
thus, the upper limit value μ + σ of the average weight scale value interval range is calculated to be 14.57267 and the lower limit value μ - σ is calculated to be 11.42511, i.e., the average weight scale value interval range is 11.42511, 14.57267.
It should be noted that the calculation of the average weight benchmarking value and the interval range of the average weight benchmarking value of the target client single goods is carried out in real time according to the update of the historical order data; when a new order comes in, the calculated average weight benchmarking value and the range of the average weight benchmarking value of the single-piece goods of the target customer can be directly called.
Further, in order to reduce the influence of the abnormal historical order goods weight data in the goods weight data set on the calculation of the average weight benchmark value μ and the standard deviation σ, as shown in fig. 3, before step S11, the method further includes:
and step S20, removing historical order goods weight data of which the actual average weight of the single goods belongs to abnormal fluctuation from the goods weight data set.
Specifically, in the cargo weight data set of the target customer, the historical order cargo weight data of the target customer is unstable and fluctuates in a certain range, and an abnormal data value may occur, for example, a certain historical order cargo weight data is much smaller than the rest of the historical order cargo weight data, so that the abnormal data value is removed by cleaning a plurality of historical order cargo weight data of the target customer, and the influence of the abnormal data value on the calculation of the average weight benchmarking value μ and the standard deviation σ is reduced.
In this embodiment, after obtaining a plurality of historical order goods weight data of a target customer, first, a lower quartile Q1 and an upper quartile Q2 in the plurality of historical order goods weight data are confirmed, and a quartile range IQR which is Q2-Q1 is obtained through calculation, where it should be noted that the quartile refers to a lower quartile located at a position of 25% and an upper quartile located at a position of 75% which are arranged from small to large in statistics and divided into equal parts; next, a normal interval range is confirmed, preferably, the normal interval range is set to [ Q1-1.5 × IQR, Q2+1.5 × IQR ]; and finally, removing the historical order goods weight data which do not fall into the normal interval range in the goods weight data set, thereby obtaining the cleaned goods weight data set. For example, table 4 below shows the actual weight data for company a for the last 10 orders:
TABLE 4 actual weight data for individual goods from company A
| 11.88 | 13.5 | 13.43 | 11.5 | 16 | 12.75 | 11.5 | 11.72 | 14.71 | 21.82 |
As can be seen from table 4 above, the lower quartile Q1, the upper quartile Q2 and the quartile range IQR are respectively:
Q1＝11.72，Q2＝14.71，IQR＝Q2-Q1＝2.65；
calculating an upper limit value and a lower limit value of a normal interval range according to Q1, Q2 and IQR:
the lower limit value is Q1-1.5 — IQR 7.745, and the upper limit value is Q2+1.5 — IQR 18.685, that is, the normal range is [7.745, 18.685 ]. As can be seen from comparison of table 4, the data of 21.82 does not fall within the normal range, and therefore, the data is removed to obtain a cleaned cargo weight data set.
It should be noted that, in this embodiment, a simple statistic analysis method, a 3 σ criterion, a cluster analysis method, and the like may also be used to identify the historical order cargo weight data in the cargo weight data set that belongs to the abnormal fluctuation, so as to achieve the purpose of removing the abnormal data in the cargo weight data set, which all fall within the protection scope of the present invention.
In the embodiment, the total weight of the goods of the weight data of the goods of a plurality of historical orders of the target customer is cleaned, so that abnormal data values are eliminated, and the average weight benchmarking value and the range of the average weight benchmarking value interval obtained by calculation are more accurate.
In order to facilitate the service of the client, in another embodiment, as shown in fig. 4, when there is no target client, the following steps are further included after step S2:
and step S30, dispatching the vehicle to get to the pickup according to the total weight of the goods, and creating and storing a customer file according to the customer information.
Specifically, when there is no target customer matching with the customer information in the historical customer order data, it may be determined that the customer is a new customer, and the current order is the first order placed by the customer, at this time, the historical order cargo weight data of the customer is lacked, that is, a vehicle is dispatched to get a pickup according to the total cargo weight provided by the customer, and a customer file is newly created and stored, and the subsequent order placing data of the customer may gradually form rich historical customer order data, and the subsequent cargo weight data when the customer places an order may be evaluated or adjusted by analyzing and counting the historical order cargo weight data of the customer.
According to the method and the system, when the customer is confirmed to be a new customer, the customer file is established and stored in the historical customer order data, so that historical customer order data resources are enriched, and the historical order data of the customer can be conveniently subjected to subsequent statistical analysis to evaluate or adjust the weight of the goods.
In order to further enrich the historical database of the client and make the result obtained by analyzing the historical database of the client more accurate, in another embodiment based on the above embodiment, as shown in fig. 5, after the pickup is completed, the method for picking up the vehicle schedule based on the intelligent adjustment of the singleweight further includes:
and step S40, after the pickup is finished, acquiring the actual average weight of the single goods of the current order and storing the actual average weight as new historical customer order data.
Specifically, after the pickup personnel picks up the piece, the goods of the current order are weighed again, the actual average weight of the single goods of the goods is recorded, and the actual average weight of the single goods is associated with the target customer and then stored.
Further, if the customer of the current order information is a new customer, after the customer profile of the customer is established, the actual average weight of the single goods is associated with the customer profile of the customer and then stored.
The goods are actually weighed after the goods are taken, the actual average weight of the single goods of the goods is stored, the evaluation on the weight adjustment and evaluation effect of the order can be achieved, the optimization on subsequent adjustment and evaluation is facilitated, and the result is more accurate when the weight of the goods placed by the customer is evaluated or adjusted by using the historical database of the customer subsequently.
Fig. 6 shows a first embodiment of the vehicle dispatch pickup system based on intelligent adjustment of the weight of the lower sheet. As shown in fig. 6, the vehicle dispatching pickup system based on intelligent adjustment of order placement comprises a first obtaining module 10, a customer order storage module 11, a matching module 12, a second obtaining module 13, a judging module 14, a weight adjusting module 15 and a first dispatching module 16.
The first obtaining module 10 is configured to obtain current order information, where the current order information includes customer information, a quantity of goods, and a total weight of the goods; the customer order storage module 11 is used for extracting the customer information, the quantity of the goods and the total weight of the goods from the order information to obtain historical customer order data and storing the historical customer order data, wherein the customer order storage module 11 stores all the historical customer order data of all the customers; a matching module 12, configured to match whether there is a target customer matching the customer information in the customer order storage module 11; a second obtaining module 13, configured to obtain, according to a result fed back by the matching module 12, an average weight benchmarking value and an average weight benchmarking value interval range of a target customer single piece of goods from the customer order storage module 11 when the target customer exists; the judging module 14 is used for judging whether the average weight of the single-piece goods in the current order information is within the range of the average weight benchmarking value; the average weight of single cargo is the total weight of the cargo/the number of the cargo; the weight adjusting module 15 is configured to, according to the result fed back by the determining module 14, adjust the average weight of the unit goods in the current order information to the average weight benchmarking value if the average weight of the unit goods in the current order information is not within the range of the average weight benchmarking value interval, and calculate to obtain the adjusted total weight of the goods; and the first dispatching module 16 is used for dispatching a proper vehicle to get the goods according to the total weight of the goods.
Specifically, the matching module 12 matches the current order information acquired by the first acquiring module 10 with the customer order data in the customer order storage module 11 to determine whether there is a target customer matching the customer information, when there is a matched target customer, the second acquiring module 13 acquires the average weight of the goods in the customer's historical order from the historical order data of the customer, that is, the average weight benchmarking value and the average weight benchmarking value interval range, and then determines whether the average weight of the goods in the current order information is within the average weight benchmarking value interval range through the determining module 14 to determine whether the total weight of the goods provided by the customer is accurate, if so, the first scheduling module 16 schedules the vehicle to go to pick up the goods according to the total weight of the goods provided by the customer, if not, the weight adjusting module 15 adjusts the average weight of the goods in the current order information to the average weight benchmarking value, the adjusted total weight of the goods is obtained through calculation, and the first scheduling module 16 schedules a proper vehicle to go to the part taking according to the adjusted total weight of the goods, so that the problem of resource waste caused by inaccurate total weight of the goods provided by a client is effectively solved, the time consumption of prediction operation caused by inaccurate total weight of the goods when the order is placed is reduced, and the risk of overtime part taking is reduced.
Based on the above embodiment, in other embodiments, as shown in fig. 7, the vehicle dispatching and pickup system after the weight of the vehicle is settled based on the intelligent adjustment further includes a third obtaining module 20, a calculating module 21 and a setting module 22.
The third obtaining module 20 is configured to obtain historical order cargo weight data of the target customer from the customer order storage module 11, so as to obtain a cargo weight data set; the calculation module 21 is used for calculating the average weight benchmark value mu and the standard deviation sigma of the single piece of goods according to the goods weight data set; and the setting module 22 is configured to set a range of the average weight benchmark value interval according to the average weight benchmark value μ and the standard deviation σ.
Specifically, after acquiring the historical order goods weight data of the target customer from the customer order storage module 11, the third acquiring module 20 calculates the historical order goods weight data through the calculating module 21 to obtain a mean weight benchmarking value μ and a standard deviation σ, and then sets a mean weight benchmarking value interval range according to the mean weight benchmarking value μ and the standard deviation σ through the setting module 22.
It should be noted that, in the following description,
mithe actual average weight of each single goods in the historical orders is calculated, and n is the corresponding historical order number.
It should be noted that the calculation of the average weight benchmarking value and the interval range of the average weight benchmarking value of the target client single goods is carried out in real time according to the update of the historical order data; when a new order comes in, the calculated average weight benchmarking value and the range of the average weight benchmarking value of the single-piece goods of the target customer can be directly called.
Based on the foregoing embodiment, in another embodiment, as shown in fig. 8, the vehicle dispatching pickup system after placing the weight of the single piece based on the intelligent adjustment further includes a removing module 30, configured to remove historical order goods weight data, in which the actual average weight of the single piece goods is abnormally fluctuated, from the goods weight data set after obtaining the goods weight data set.
Specifically, in the cargo weight data set of the target customer, the historical order cargo weight data of the target customer is unstable and fluctuates in a certain range, and an abnormal data value may occur, for example, a certain historical order cargo weight data is much smaller than the rest of the historical order cargo weight data, so that the plurality of historical order cargo weight data of the target customer are cleaned by the removing module 30 to remove the abnormal data value, thereby reducing the influence of the abnormal data value on the calculation of the average weight benchmarking value μ and the standard deviation σ.
It should be noted that, in this embodiment, a box line diagram analysis method, a simple statistics analysis method, a 3 σ criterion, a cluster analysis method, and the like may be used to identify historical order cargo weight data belonging to abnormal fluctuation in the cargo weight data set, so as to achieve the purpose of removing abnormal data in the cargo weight data set, which is specifically described in the embodiment of the vehicle scheduling pickup method based on intelligently adjusting the order weight, and is not described here again.
On the basis of the above embodiment, in another embodiment, as shown in fig. 9, the vehicle dispatching and pickup system after placing the order based on the intelligent adjustment further includes a second dispatching module 30, configured to dispatch the vehicle to the pickup according to the total weight of the goods when there is no target customer according to the result fed back by the matching module 12, and meanwhile, the customer order storage module 11 creates and stores a customer profile according to the customer information when there is no target customer according to the result fed back by the matching module 12.
Specifically, when there is no target customer matching with the customer information in the historical customer order data, it may be determined that the customer is a new customer, and the current order is the first order placed by the customer, and at this time, the historical order cargo weight data of the customer is lacked, that is, the vehicle is scheduled to go to pick up the parts according to the total weight of the cargos provided by the customer. Meanwhile, when the client who places the order currently is determined to be a new client, the client order storage module 11 creates and stores a client file according to the client information, so that the order information of the client who places the order subsequently is conveniently recorded.
Based on the foregoing embodiment, in another embodiment, as shown in fig. 10, the vehicle dispatching and pickup system after placing the piece weight based on intelligent adjustment further includes an actual weight obtaining module 40, configured to obtain an actual average weight of the piece goods of the current order after pickup is completed. Meanwhile, the customer order storage module 11 associates and stores the actual average weight of the single piece of goods with the customer information corresponding to the current order information, and if the current customer is a new user, associates and stores the actual average weight of the single piece of goods after the customer file of the new user is established.
Specifically, after the pickup personnel finishes the pickup task, the pickup personnel can weigh the goods again to obtain the actual average weight of the single goods, and the actual weight obtaining module 40 stores the actual average weight of the single goods into the customer order storage module 11, so that the actual average weight of the single goods can be used for evaluating the weight adjustment and evaluation effect of the current order, and the optimization of subsequent adjustment and evaluation is facilitated, so that the result is more accurate when the historical database of the customer is used for evaluating or adjusting the weight of the goods placed by the customer subsequently.
For other details of the technical solution implemented by each module in the vehicle dispatching pickup system after the weight of the order is intelligently adjusted in the above five embodiments, reference may be made to the description of the vehicle dispatching pickup method after the weight of the order is intelligently adjusted in the above embodiments, and details are not described here again.
It should be noted that, in the present specification, the embodiments are all described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other. For the system-class embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The embodiments of the present invention have been described in detail, but the present invention is only exemplary and is not limited to the embodiments described above. It will be apparent to those skilled in the art that any equivalent modifications or substitutions can be made within the scope of the present invention, and thus, equivalent changes and modifications, improvements, etc. made without departing from the spirit and scope of the present invention should be included in the scope of the present invention.
Claims (8)
1. A vehicle dispatching and pickup method based on intelligent adjustment of order weight is characterized by comprising the following steps:
acquiring current order information, wherein the current order information comprises customer information, the quantity of goods and the total weight of the goods;
matching whether historical customer order data has a target customer matched with the customer information;
when a target customer exists, acquiring a mean weight benchmarking value and a mean weight benchmarking value interval range of the target customer single goods from the historical customer order data;
judging whether the average weight of the single cargos in the current order information is within the range of the average weight benchmarking value, wherein the average weight of the single cargos is equal to the total weight of the cargos/the quantity of the cargos;
if yes, the total weight of the goods in the current order information is not adjusted; if not, the average weight of the goods in the current order information is adjusted to the average weight marker post value, and the adjusted total weight of the goods is obtained through calculation;
dispatching a proper vehicle to go to pick up the goods according to the total weight of the goods;
the obtaining of the average weight benchmarking value and the average weight benchmarking value interval range of the target customer single piece goods from the historical customer order data comprises:
obtaining historical order cargo weight data of the target customer from the historical customer order data to obtain a cargo weight data set;
calculating the average weight benchmark value mu and the standard deviation sigma of the single piece of goods according to the goods weight data set, wherein,
miactual average weight of each single piece of goods in the historical orders, wherein n is the corresponding historical order number;
and setting the range of the average weight benchmark value according to the average weight benchmark value mu and the standard deviation sigma.
2. The method as claimed in claim 1, wherein before the average weight benchmark μ and the standard deviation σ of the individual cargo are calculated according to the cargo weight data set, historical cargo weight data of the order with the actual average weight of the individual cargo being in abnormal fluctuation is removed from the cargo weight data set.
3. The method for vehicle dispatch pickup after placing an order based on intelligent adjustment of claim 1, wherein the step of matching historical customer order data for the target customer matching the customer information is followed by further comprising:
and when the target customer does not exist, dispatching the vehicle to go to the pickup according to the total weight of the goods, and creating and storing a customer file according to the customer information.
4. The method for vehicle dispatch taking of a single weight based on intelligent adjustment of claims 1 or 3, further comprising:
and after the piece taking is finished, acquiring the actual average weight of the single piece goods of the current order and storing the actual average weight as new historical customer order data.
5. The utility model provides a vehicle dispatch system of getting after single weight based on intelligent adjustment which characterized in that it includes:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring current order information, and the current order information comprises customer information, the quantity of goods and the total weight of the goods;
the customer order storage module is used for extracting the customer information, the goods quantity and the goods total weight from the order information to form historical customer order data and storing the historical customer order data;
the matching module is used for matching whether a target client matched with the client information exists in the client order storage module or not;
the second acquisition module is used for acquiring the average weight benchmark value and the range of the average weight benchmark value interval of the target customer single goods from the customer order storage module according to the result fed back by the matching module when the target customer exists;
the judging module is used for judging whether the average weight of the single goods in the current order information is within the range of the average weight benchmark value interval; the average weight of the single cargos is equal to the total weight of the cargos/the number of the cargos;
the weight adjusting module is used for adjusting the average weight of the single cargos in the current order information into the average weight benchmark value and calculating to obtain the adjusted total weight of the cargos if the average weight of the single cargos in the current order information is not within the range of the average weight benchmark value interval according to the result fed back by the judging module;
the first scheduling module is used for scheduling a proper vehicle to go to a pickup according to the total weight of the goods;
a third obtaining module, configured to obtain historical order cargo weight data of the target customer from the customer order storage module, so as to obtain a cargo weight data set;
a calculation module for calculating the average weight benchmark value mu and the standard deviation sigma of the single piece of cargo according to the cargo weight data set, wherein,
miactual average weight of each single piece of goods in the historical orders, wherein n is the corresponding historical order number;
and the setting module is used for setting the range of the average weight benchmark value according to the average weight benchmark value mu and the standard deviation sigma.
6. The system of claim 5, further comprising:
and the removing module is used for removing the historical order goods weight data of which the actual average weight of the single piece of goods belongs to abnormal fluctuation from the goods weight data set after obtaining the goods weight data set.
7. The system of claim 5, further comprising:
the second scheduling module is used for scheduling the vehicle to go to the pickup according to the total weight of the goods when the target customer does not exist according to the result fed back by the matching module;
and the customer order storage module is also used for creating and storing a customer file according to the customer information when no target customer exists according to the result fed back by the matching module.
8. The system for vehicle dispatch taking off a single weight based on intelligent adjustment of claims 5 or 7, further comprising:
the actual weight obtaining module is used for obtaining the actual average weight of the single goods of the current order after the goods taking is finished;
and the customer order storage module is also used for storing the actual average weight of the single goods.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811271119.7A CN109583806B (en) | 2018-10-29 | 2018-10-29 | Vehicle dispatching and pickup method and system based on intelligent adjustment of order weight |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811271119.7A CN109583806B (en) | 2018-10-29 | 2018-10-29 | Vehicle dispatching and pickup method and system based on intelligent adjustment of order weight |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109583806A CN109583806A (en) | 2019-04-05 |
| CN109583806B true CN109583806B (en) | 2020-07-07 |
Family
ID=65921206
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811271119.7A Active CN109583806B (en) | 2018-10-29 | 2018-10-29 | Vehicle dispatching and pickup method and system based on intelligent adjustment of order weight |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109583806B (en) |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110210707B (en) * | 2019-04-30 | 2022-07-29 | 跨越速运集团有限公司 | Entropy weight method based point automatic scheduling efficiency evaluation method and system |
| CN110348084B (en) * | 2019-06-26 | 2022-08-19 | 西南交通大学 | Subway vehicle technical index dynamic weight acquisition system and method for multi-subject customers |
| CN110543963B (en) * | 2019-07-19 | 2022-08-05 | 深圳市跨越新科技有限公司 | XGboost model-based client order weight forecasting method and system |
| CN110543964A (en) * | 2019-07-19 | 2019-12-06 | 深圳市跨越新科技有限公司 | XGboost model-based abnormal singleweight early warning method and system |
| CN112258117B (en) * | 2020-10-27 | 2021-09-21 | 上海寻梦信息技术有限公司 | Sending method and device, electronic equipment and storage medium |
| CN112396372A (en) * | 2020-11-16 | 2021-02-23 | 上海燕汐软件信息科技有限公司 | Goods delivery information determining method, device and system |
| CN112686602A (en) * | 2020-12-29 | 2021-04-20 | 安徽运通达物流科技有限公司 | Transport platform waybill information pushing method |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7739202B2 (en) * | 2003-04-22 | 2010-06-15 | United Parcel Service Of America, Inc. | Computer system for routing package deliveries |
| CN106570573B (en) * | 2015-10-13 | 2022-05-27 | 菜鸟智能物流控股有限公司 | Method and device for predicting package attribute information |
| CN107094165B (en) * | 2016-08-31 | 2021-01-08 | 阿里巴巴集团控股有限公司 | Distribution capacity determining method, distribution task obtaining method, distribution resource scheduling method and equipment |
| CN106960302A (en) * | 2017-03-23 | 2017-07-18 | 江苏金易达供应链管理有限公司 | A kind of Intelligent logistics management method |
-
2018
- 2018-10-29 CN CN201811271119.7A patent/CN109583806B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN109583806A (en) | 2019-04-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109583806B (en) | Vehicle dispatching and pickup method and system based on intelligent adjustment of order weight | |
| US11093893B2 (en) | Apparatus and method of maintaining accurate perpetual inventory information | |
| US10546258B2 (en) | Apparatus and method of identifying an overstated perpetual inventory in a retail space | |
| EP1315107A1 (en) | Demand forecasting method in parts inventory control | |
| US20090089150A1 (en) | System and method for operation management of logistics center | |
| CN111754113A (en) | Automatic control method, system and medium for scheduling and scheduling in production process | |
| CN101118625A (en) | Stock managing system and method | |
| US20080243646A1 (en) | Methods for proactively reconciling bin denials in inventory management environments | |
| US20150142494A1 (en) | System and method for scheduling temporary resources | |
| JP6740860B2 (en) | Safety stock determination device, method and program | |
| CN111833594A (en) | Traffic flow prediction method, traffic flow prediction device, electronic device, and storage medium | |
| CN109902858A (en) | A kind of logistics picking operates time-consuming intelligent Forecasting and system | |
| US20090228312A1 (en) | Method and system for a calendaring tool for claim code and workload determination | |
| JP2009289056A (en) | Device for supporting personnel arrangement planning | |
| US6401056B1 (en) | Methods and apparatus for evaluating tool performance | |
| GB2547089A (en) | Systems and methods for forecasting on-shelf product availability | |
| JP2001092520A (en) | Equipment maintenance/management system | |
| JP2004334326A (en) | System for predicting demand of merchandise and system for adjusting number of merchandise sold | |
| CN116823349B (en) | Advertisement delivery monitoring and managing method and system based on data acquisition | |
| JP2003256702A (en) | Stock information referring method | |
| CN113283844B (en) | Inventory determination method and device for aging sensitive materials and electronic equipment | |
| CN115081961A (en) | Logistics transport capacity intelligent dispatching method based on big data | |
| CN110097211B (en) | Logistics service prediction method and system based on Monte Carlo combination | |
| CN112397147B (en) | Gene sequencing delivery method and system for realizing collinear production based on intelligent scheduling technology | |
| JP2022118011A (en) | Feed management device, feed management method and program |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 518000 floor 1-8, building a, Lufthansa aviation Park, Shenzhen Airport Terminal 4, hourui community, Hangcheng street, Bao'an District, Shenzhen City, Guangdong Province Applicant after: KUAYUE EXPRESS GROUP Co.,Ltd. Address before: 518000 Shenxiang Logistics Base, No. 5 Airport City Industrial Park, Fuyong Street, Heping Community, Baoan District, Shenzhen City, Guangdong Province Applicant before: KUAYUE EXPRESS GROUP Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |