Time-limited processing method, device, equipment and storage medium for logistics orders

Abstract

The present invention relates to the field of logistics technology, and in particular to a time-limited processing method, device, equipment and storage medium for logistics orders. The time-limited processing method for logistics orders includes the following steps: obtaining order information, the order information includes the delivery address, commodity category, order time and required delivery time, and classifying the urgency of the order according to the order information; formulating sorting rules, obtaining the classification results of the order, generating a sorting strategy according to the classification results and sorting rules of the order, and outputting the sorting strategy to the sorter; obtaining traffic data, planning the transportation route according to the traffic data, order information and classification results, and dispatching the order according to the pre-established dispatching rules to form dispatch information. The present invention can effectively ensure the timeliness of orders by reclassifying orders and performing targeted priority sorting and distribution, so that urgent orders can be completed on time.

Description

Technical Field

[0001] The present invention relates to the field of logistics technology, and in particular to a time-limit processing method, device, equipment and storage medium for logistics orders. Background Art

[0002] In the logistics service system, order timeliness is the most critical business indicator in logistics services. If the order timeliness cannot be accurately controlled, the order will be delivered overdue, which will lead to customer loss. In order to accurately control the order timeliness, the existing order distribution systems have adopted intelligent algorithms to distribute the delivery of orders. However, with the explosive growth of logistics orders, the original distribution method can no longer meet the needs. Especially for expedited orders, due to the large number of ordinary orders and heavy workload, the processing efficiency of expedited orders has also decreased. In addition, with the increase in the number of expedited orders and the occurrence of unforeseen factors on the delivery route, the processing of expedited orders has begun to seem inadequate. If the order distribution and processing of the system can be further optimized, it is expected to improve the processing efficiency of expedited orders. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to provide a method, device, equipment and storage medium for time-limited processing of logistics orders, aiming to improve the processing efficiency of orders and ensure that the processing time of orders is guaranteed when the number of orders increases dramatically.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] A first aspect of the present invention provides a time efficiency processing method for logistics orders, comprising the following steps: obtaining order information, the order information including the delivery address, commodity category, order time and required delivery time, and classifying the urgency of the order according to the order information; formulating sorting rules, obtaining the classification results of the order, generating a sorting strategy according to the classification results of the order and the sorting rules, and outputting the sorting strategy to the sorter; obtaining traffic data, planning the transportation route according to the traffic data, order information and classification results, and dispatching the order according to pre-established scheduling rules to form dispatch information.

[0006] Optionally, in a first implementation method of the first aspect of the present invention, the order information is obtained, and the order information includes a delivery address, a product category, an order time, and a required delivery time, and the urgency of the order is classified according to the order information, specifically including: obtaining order information, and the order information includes a delivery address, a product category, an order time, and a required delivery time; obtaining historical order data and regional distribution time efficiency data, and preprocessing the historical order data and regional distribution time efficiency data to obtain preprocessed data; using the preprocessed data as a training set, training a machine learning model to obtain a classification model; using the classification model to classify the urgency of the order according to the order information to obtain a classification result, and the classification result includes expedited, ordinary, and scheduled delivery.

[0007] Optionally, in a second implementation method of the first aspect of the present invention, the obtaining of historical order data and regional delivery time efficiency data, preprocessing the historical order data and regional delivery time efficiency data, and obtaining preprocessed data, specifically includes: obtaining historical order data and regional delivery time efficiency data, processing missing values ​​and abnormal values ​​of the data, and obtaining first data; identifying duplicate data in the first data and deleting it, unifying the data format, and obtaining second data; integrating the second data, labeling the integrated data, and obtaining preprocessed data.

[0008] Optionally, in a third implementation of the first aspect of the present invention, the formulating of sorting rules, obtaining classification results of orders, generating a sorting strategy according to the classification results of orders and the sorting rules, and outputting the sorting strategy to the sorter specifically includes: formulating sorting rules according to the urgency of the order, giving priority to sorting orders with urgent classification results, and sorting normally for orders with ordinary and scheduled classification results; obtaining the classification results of the order, and determining whether the order needs priority sorting according to the classification results of the order and the sorting rules; if the order needs priority sorting, obtaining warehouse layout data, adopting the shortest path algorithm, generating a sorting strategy according to the warehouse layout data, and outputting the sorting strategy to the sorter.

[0009] Optionally, in a fourth implementation method of the first aspect of the present invention, if the order needs priority sorting, the warehouse layout data is obtained, the shortest path algorithm is used, a sorting strategy is generated according to the warehouse layout data, and the sorting strategy is output to the sorter, specifically including: obtaining a list of warehouse sorters and their historical sorting records, sorting the list of warehouse sorters according to the historical sorting records to obtain an allocation sorting result; if the order needs priority sorting, the to-be-completed sorting data of all warehouse sorters is obtained, and the sorter of the order is matched according to the to-be-completed sorting data and the allocation sorting result; the warehouse layout data is obtained, the shortest path algorithm is used, a sorting strategy is generated according to the warehouse layout data, and the sorting strategy is output to the sorter.

[0010] Optionally, in a fifth implementation of the first aspect of the present invention, the acquiring of traffic data, planning of transport routes according to the traffic data, order information and classification results, and dispatching of orders according to pre-established dispatching rules to form dispatching information specifically include: acquiring traffic data, the traffic data including real-time road condition data, weather condition data and traffic restriction data, and planning of transport routes according to the traffic data, order information and classification results using a dynamic programming algorithm; acquiring vehicle information and deliveryman information of the site, and generating a vehicle priority list and a deliveryman priority list according to the vehicle information and the deliveryman information, respectively; establishing dispatching rules, and dispatching orders according to the dispatching rules, classification results, vehicle priority list and deliveryman priority list to form dispatching information.

[0011] Optionally, in a sixth implementation method of the first aspect of the present invention, the establishment of dispatch rules, dispatching orders according to the dispatch rules, classification results, vehicle priority lists and delivery personnel priority lists, and forming dispatch information, specifically includes: establishing dispatch rules, dispatching orders according to the dispatch rules, classification results, vehicle priority lists and delivery personnel priority lists, forming dispatch information and outputting it to the delivery personnel for execution, so as to obtain delivery process data; for orders with the classification result of expedited, obtaining delivery process data, using a delay judgment model, and judging whether there is a risk of delay for the expedited order based on the delivery process data; if there is a risk of delay for the expedited order, obtaining the location information of the vehicle corresponding to the order, re-planning the delivery plan based on the location information, and outputting the delivery plan to the relevant responsible person.

[0012] The second aspect of the present invention provides a time-efficiency processing device for logistics orders, including: a classification module, used to obtain order information, the order information includes the delivery address, commodity category, order time and required delivery time, and classify the urgency of the order according to the order information; a sorting module, used to formulate sorting rules, obtain the classification results of the order, generate a sorting strategy according to the classification results and sorting rules of the order, and output the sorting strategy to the sorter; a dispatching module, used to obtain traffic data, plan the transportation route according to the traffic data, order information and classification results, and dispatch the order according to pre-established scheduling rules to form dispatching information.

[0013] Optionally, in a first implementation method of the second aspect of the present invention, the classification module includes: an acquisition submodule, used to acquire order information, the order information including the delivery address, commodity category, order time and required delivery time; a preprocessing submodule, used to acquire historical order data and regional distribution time efficiency data, and preprocess the historical order data and regional distribution time efficiency data to obtain preprocessed data; a training submodule, used to train a machine learning model using the preprocessed data as a training set to obtain a classification model; a classification submodule, used to use the classification model to classify the urgency of the order according to the order information to obtain a classification result, and the classification result includes expedited, ordinary and scheduled delivery.

[0014] Optionally, in a second implementation method of the second aspect of the present invention, the preprocessing submodule includes: a first processing unit, used to obtain historical order data and regional delivery timeliness data, process missing values ​​and abnormal values ​​of the data, and obtain first data; a second processing unit, used to identify and delete duplicate data in the first data, unify the data format, and obtain second data; a labeling unit, used to integrate the second data, label the integrated data, and obtain preprocessed data.

[0015] Optionally, in a third implementation of the first aspect of the present invention, the sorting module includes: a formulation submodule, used to formulate sorting rules according to the urgency of the order, priority sorting for orders with urgent classification results, and normal sorting for orders with ordinary and scheduled classification results; a judgment submodule, used to obtain the classification results of the order, and judge whether the order needs priority sorting according to the classification results and sorting rules of the order; a calculation submodule, used to obtain warehouse layout data if the order needs priority sorting, adopt the shortest path algorithm, generate a sorting strategy according to the warehouse layout data, and output the sorting strategy to the sorter.

[0016] Optionally, in a fourth implementation of the second aspect of the present invention, the computing submodule includes: a sorting unit, used to obtain a list of warehouse sorters and their historical sorting records, and sort the list of warehouse sorters according to the historical sorting records to obtain an allocation sorting result; a matching unit, used to obtain the to-be-completed sorting data of all warehouse sorters if the order requires priority sorting, and match the sorters of the order according to the to-be-completed sorting data and the allocation sorting result; a computing unit, used to obtain warehouse layout data, adopt the shortest path algorithm, generate a sorting strategy according to the warehouse layout data, and output the sorting strategy to the sorter.

[0017] Optionally, in a fifth implementation of the second aspect of the present invention, the dispatch module includes: a planning submodule, used to obtain traffic data, the traffic data including real-time road condition data, weather condition data and traffic restriction data, and using a dynamic programming algorithm to plan a transportation route based on the traffic data, order information and classification results; a generation submodule, used to obtain the vehicle information and deliveryman information of the site, and generate a vehicle priority list and a deliveryman priority list based on the vehicle information and the deliveryman information, respectively; an order dispatch submodule, used to establish dispatch rules, and dispatch orders according to the dispatch rules, classification results, vehicle priority lists and deliveryman priority lists to form dispatch information.

[0018] Optionally, in a sixth implementation of the second aspect of the present invention, the dispatch submodule includes: a dispatch unit, used to establish dispatch rules, dispatch orders according to the dispatch rules, classification results, vehicle priority lists and delivery personnel priority lists, form dispatch information and output it to the delivery personnel for execution, so as to obtain delivery process data; a judgment unit, used to obtain delivery process data for orders with expedited classification results, adopt a delay judgment model, and judge whether there is a delay risk for the expedited order based on the delivery process data; a planning unit, used to obtain the location information of the vehicle corresponding to the order if there is a delay risk for the expedited order, re-plan the delivery plan based on the location information, and output the delivery plan to the relevant responsible person.

[0019] The third aspect of the present invention provides a time-limit processing device for logistics orders, comprising a memory and at least one processor, wherein the memory stores computer-readable instructions; the at least one processor calls the computer-readable instructions in the memory to execute each step of the time-limit processing method for logistics orders as described above.

[0020] A fourth aspect of the present invention provides a computer-readable storage medium having computer-readable instructions stored thereon, which, when executed by a processor, implement the various steps of the timeliness processing method for logistics orders as described above.

[0021] Beneficial effects: The present invention provides a time-efficiency processing method for logistics orders, which first obtains order information and classifies the urgency of the order according to the order information, so as to better process different types of orders; then formulates sorting rules, and generates sorting strategies according to the classification results and sorting rules of the orders, and outputs the sorting strategies to the sorters, thereby improving the sorting speed of the sorters, thereby reducing the stay time of urgent items in the warehouse; finally, by obtaining traffic data, planning transportation routes according to traffic data, order information and classification results, and dispatching orders according to pre-established scheduling rules, forming dispatch information, so that more professional personnel are responsible for the delivery of urgent items, and forming better distribution routes in advance. The time-efficiency processing method for logistics orders of the present invention can effectively improve the timeliness of logistics orders by optimizing the sorting, dispatching and transportation processes, so that packages can arrive on time. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A first flow chart of a time efficiency processing method for logistics orders provided by an embodiment of the present invention;

[0023] Figure 2 A second flow chart of the timeliness processing method for logistics orders provided by an embodiment of the present invention;

[0024] Figure 3 A third flow chart of the timeliness processing method for logistics orders provided by an embodiment of the present invention;

[0025] Figure 4 A fourth flow chart of the timeliness processing method for logistics orders provided by an embodiment of the present invention;

[0026] Figure 5 A fifth flow chart of the timeliness processing method for logistics orders provided by an embodiment of the present invention;

[0027] Figure 6 A sixth flow chart of the timeliness processing method for logistics orders provided by an embodiment of the present invention;

[0028] Figure 7 A seventh flow chart of the timeliness processing method for logistics orders provided by an embodiment of the present invention;

[0029] Figure 8 A schematic diagram of the structure of a time-limit processing device for logistics orders provided in an embodiment of the present invention;

[0030] Fig. 9 Another structural schematic diagram of the time-limit processing device for logistics orders provided by an embodiment of the present invention;

[0031] Fig.10A schematic diagram of the structure of a time-limited processing device for logistics orders provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The present invention provides a time-efficiency processing method, device, equipment and storage medium for logistics orders. The present invention first obtains order information, the order information includes the delivery address, commodity category, order time and required delivery time, and classifies the urgency of the order according to the order information, so that different types of orders are subsequently processed differently, so that the priority of expedited orders is further improved, and other businesses are prevented from squeezing out human resources; then, by formulating sorting rules, the classification results of the order are obtained, and a sorting strategy is generated according to the classification results and sorting rules of the order, and the sorting strategy is output to the sorter. By providing the sorter with a sorting strategy, the sorting efficiency of the sorter for expedited orders can be improved, thereby reducing the time occupied by sorting; finally, by obtaining traffic data, the transportation route is planned according to the traffic data, order information and classification results, and the order is dispatched according to the pre-established scheduling rules to form dispatching information, and the transportation resources and transportation routes are allocated according to the urgency of the order, so as to further ensure that the order can be delivered on time.

[0033] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 The first embodiment of the timeliness processing method of the logistics order in the embodiment of the present invention includes:

[0035] S101. Obtain order information, including the delivery address, product category, order time, and required delivery time, and classify the urgency of the order according to the order information;

[0036] For example, orders with remote delivery addresses and early order times can be considered expedited orders. For another example, orders requiring same-day delivery or fresh food products can also be considered expedited orders. Orders requiring a later delivery time can be considered regular orders. Other orders without expedited requirements and placed regularly are also considered regular orders.

[0037] By comprehensively considering multiple factors such as delivery address, product category, order time, and required delivery time to classify the urgency of orders, the order classification can be reasonably improved. In particular, for a small number of regular orders that are very close to the delivery date, the system will treat them as expedited orders, thereby avoiding overdue delivery of orders.

[0038] S102. Formulate sorting rules, obtain the classification results of the order, generate a sorting strategy based on the classification results of the order and the sorting rules, and output the sorting strategy to the sorter;

[0039] The logistics warehouse is large and contains all kinds of goods. If multiple urgent orders need to be loaded, the sorters need to take out the corresponding packages one by one in the warehouse. Since different packages enter the warehouse at different times, the storage locations may also be far away, which will cause the sorters to run back and forth and take a long time. By sorting orders according to the classification results, orders with high urgency can be sorted first, and the system can generate special sorting strategies as needed to optimize the sorting order of the sorters, thereby improving the sorting efficiency of the orders. By reducing the time spent on sorting, the packages corresponding to the orders can be loaded and sent out faster.

[0040] S103. Obtain traffic data, plan transportation routes according to traffic data, order information and classification results, and dispatch orders according to pre-established dispatch rules to form dispatch information.

[0041] Different dispatching strategies can be adopted for different types of orders, so that high-quality transportation resources can be used for urgent orders. For example, for urgent orders, vehicles with better hardware and faster speed can be arranged for delivery, while for ordinary orders, trailers with relatively slower speed can be arranged for transportation. By planning transportation routes in combination with traffic data, traffic jams can be avoided or slow-moving roads can be avoided.

[0042] See also Figure 2 The second embodiment of the timeliness processing method of the logistics order in the embodiment of the present invention includes:

[0043] S201. Obtain order information, including delivery address, product category, order time, and required delivery time;

[0044] S202. Obtain historical order data and regional delivery time efficiency data, pre-process the historical order data and regional delivery time efficiency data to obtain pre-processed data;

[0045] Specifically, regional delivery time efficiency data reflects the delivery time efficiency of different regions. For example, for areas with large land and sparse population, the delivery time will be longer, while for areas with concentrated population and convenient transportation, the delivery time will be shorter. There are corresponding deadline requirements for destinations in different regions. By obtaining the above data, the classification model trained later can be adapted to the conditions of different regions, and the classification of order urgency can be more accurate.

[0046] In addition, by preprocessing the data, the reliability of the data can be improved, and the data can be converted into a format suitable for training for easy use.

[0047] S203. Using the preprocessed data as a training set, training the machine learning model to obtain a classification model;

[0048] By training the machine learning model with historical order data and regional delivery time data, the resulting classification model can have more accurate classification results.

[0049] S204. Using a classification model, classify the urgency of the order according to the order information to obtain a classification result, wherein the classification result includes expedited, normal and scheduled delivery.

[0050] Specifically, for orders classified as urgent, the system will prioritize sorting and delivery. For ordinary orders, they will be processed according to the normal process. For orders that are scheduled to arrive, they will be processed according to the scheduled delivery method. For example, for orders with specific signing time requirements, the package cannot be sent out too early, otherwise it will not be signed for, and the package will occupy the storage or distribution resources of the lower-level outlets for a long time.

[0051] See also Figure 3 The third embodiment of the timeliness processing method of the logistics order in the embodiment of the present invention includes:

[0052] S301. Obtain historical order data and regional delivery time data, process missing values ​​and abnormal values ​​of the data, and obtain first data;

[0053] The existence of missing values ​​and outliers in the data will reduce the reliability of the data, and then the accuracy of the trained classification model will decrease. Among them, data that cannot be filled and has a lot of missing data can be deleted directly. Some missing content can be supplemented according to business logic, such as refining the address.

[0054] S302. Identify and delete duplicate data in the first data, unify the data format, and obtain second data;

[0055] Duplicate data increases the amount of computation required for data processing and analysis, and unnecessarily prolongs the model training time. Deleting duplicate data can optimize the data processing process and improve processing efficiency.

[0056] S303. Integrate the second data, annotate the integrated data, and obtain preprocessed data.

[0057] By properly labeling some historical order data and regional delivery time data, such as marking which orders are expedited orders and which are ordinary orders, the quality of the data can be improved. When it is further used to train the model, the obtained model can better meet the expected requirements.

[0058] See also Figure 4 The fourth embodiment of the timeliness processing method of the logistics order in the embodiment of the present invention includes:

[0059] S401. Formulate sorting rules according to the urgency of the order. For orders classified as expedited, prioritize sorting. For orders classified as ordinary and scheduled, sort normally.

[0060] By formulating sorting rules in advance, the system can automatically arrange according to different classification results and reduce labor costs.

[0061] S402. Obtain the classification result of the order, and determine whether the order needs to be sorted first according to the classification result and sorting rules of the order;

[0062] S403. If the order needs to be sorted first, obtain the warehouse layout data, use the shortest path algorithm, generate a sorting strategy based on the warehouse layout data, and output the sorting strategy to the sorter.

[0063] When parcels are put into storage, the placement area of ​​each parcel is recorded. When some urgent orders need to be sorted first, the system can obtain the warehouse layout data to know the location of the parcels currently shipped by priority loading. Through the shortest path algorithm, the most time-saving and efficient pickup and sorting strategy can be calculated, thereby reducing the sorters' repeated walking back and forth for sorting and improving sorting efficiency. By reducing the time spent on sorting, the vehicles responsible for urgent orders can set off earlier, better ensuring that the orders are completed on time.

[0064] See also Figure 5 The fifth embodiment of the timeliness processing method of the logistics order in the embodiment of the present invention includes:

[0065] S501. Obtain a list of warehouse sorters and their historical sorting records, and sort the list of warehouse sorters according to the historical sorting records to obtain a distribution sorting result;

[0066] Specifically, the historical sorting records may include the sorting volume, working time, etc. of each warehouse sorter. By analyzing the sorting volume in the recent period, the sorting efficiency and proficiency of each warehouse sorter can be known, so as to sort them.

[0067] S502. If the order needs to be sorted first, obtain the pending sorting data of all warehouse sorters, and match the sorter of the order according to the pending sorting data and the allocation sorting result;

[0068] For orders that need to be sorted first, they are generally assigned to skilled and efficient sorters. However, in the actual task allocation process, in addition to considering the picker's proficiency, the current picker's workload must also be considered. For example, if a picker already has multiple orders that need to be sorted first and have not been completed, the new order will be assigned to the sorter with less workload.

[0069] S503. Obtain warehouse layout data, use the shortest path algorithm, generate a sorting strategy based on the warehouse layout data, and output the sorting strategy to the sorter.

[0070] Furthermore, in this embodiment, before matching sorters to orders that require priority sorting, it is also necessary to obtain the employee's on-the-job data and update the list of sorters based on the on-the-job data to avoid assigning tasks to employees who are on leave or not on duty.

[0071] See also Figure 6 The sixth embodiment of the timeliness processing method of the logistics order in the embodiment of the present invention includes:

[0072] S601. Obtain traffic data, including real-time traffic data, weather data, and traffic restriction data, and use a dynamic programming algorithm to plan a transportation route based on traffic data, order information, and classification results;

[0073] Traffic data such as real-time traffic data, weather data, and traffic restrictions will affect the delivery time of the package. For example, in foggy or snowy weather, the highway may be temporarily closed. If the vehicle has already entered the highway at this time, it will cause a long delay. By obtaining current and predicted weather data, vehicles can avoid areas with bad weather or allow transport vehicles to leave the highway in advance to reduce the risk of long-term traffic jams on the road.

[0074] S602. Obtain the vehicle information and the delivery person information of the site, and generate a vehicle priority list and a delivery person priority list according to the vehicle information and the delivery person information;

[0075] In this embodiment, if it is an expedited order, vehicles in better condition and delivery personnel with more driving experience will be given priority to ensure that the expedited order can be delivered on time.

[0076] Specifically, vehicle information may include the rated load, whether it has refrigeration function, vehicle type, age, etc. Delivery person information may include driving experience, years of work experience, familiar driving routes, etc.

[0077] S603. Establish dispatching rules, dispatch orders according to the dispatching rules, classification results, vehicle priority list and delivery personnel priority list, and form dispatching information.

[0078] By combining the above factors and dispatching orders, the most suitable delivery personnel can be responsible for transportation, and the optimal delivery route can be planned to ensure that the orders are delivered on time.

[0079] See also Figure 7 The seventh embodiment of the timeliness processing method of the logistics order in the embodiment of the present invention includes:

[0080] S701. Establish dispatching rules, dispatch orders according to the dispatching rules, classification results, vehicle priority list and delivery personnel priority list, form dispatching information and output it to the delivery personnel for execution to obtain delivery process data;

[0081] Specifically, the delivery process data includes the real-time location of the transport vehicle, the stop time of the vehicle, the driving time of the vehicle, the driving speed of the vehicle, etc. The above data content can reflect the road conditions and vehicle conditions to a certain extent, such as predicting whether there is a traffic jam or a vehicle failure.

[0082] S702. For orders classified as expedited, obtain delivery process data, use a delay judgment model, and judge whether there is a delay risk for the expedited order based on the delivery process data;

[0083] Through the delay judgment model, a comprehensive judgment is made based on the real-time location of the transport vehicle, the vehicle's stop time, the vehicle's driving time, and the vehicle's driving speed data, which can effectively determine whether there is a risk of delay for expedited orders.

[0084] S703. If there is a risk of delay for an expedited order, obtain the location information of the vehicle corresponding to the order, re-plan the delivery plan based on the location information, and output the delivery plan to the relevant responsible person.

[0085] For orders with a risk of delay, the system can flexibly adjust the delivery plan based on the vehicle's location information. For example, if the road ahead is blocked, the route can be replanned. For another example, if a vehicle breaks down, the system can re-arrange the nearest vehicle to transfer the package.

[0086] The above describes the timeliness processing method of the logistics order in the embodiment of the present invention. The following describes the timeliness processing device of the logistics order in the embodiment of the present invention. Figure 8 In one embodiment of the present invention, a time-limit processing device for logistics orders includes:

[0087] The classification module 10 is used to obtain order information, including the delivery address, product category, order time and required delivery time, and classify the urgency of the order according to the order information;

[0088] A sorting module 20 is used to formulate sorting rules, obtain the classification results of the orders, generate a sorting strategy according to the classification results of the orders and the sorting rules, and output the sorting strategy to the sorters;

[0089] The dispatch module 30 is used to obtain traffic data, plan transportation routes according to the traffic data, order information and classification results, and dispatch orders according to pre-established dispatch rules to form dispatch information.

[0090] See also Fig. 9 In one embodiment of the present invention, a time-limit processing device for logistics orders includes:

[0091] The classification module 10 is used to obtain order information, including the delivery address, product category, order time and required delivery time, and classify the urgency of the order according to the order information;

[0092] A sorting module 20 is used to formulate sorting rules, obtain the classification results of the orders, generate a sorting strategy according to the classification results of the orders and the sorting rules, and output the sorting strategy to the sorters;

[0093] The dispatch module 30 is used to obtain traffic data, plan the transportation route according to the traffic data, order information and classification results, and dispatch the orders according to the pre-established dispatch rules to form dispatch information;

[0094] In this embodiment, the classification module 10 includes:

[0095] The acquisition submodule 11 is used to acquire order information, which includes the delivery address, product category, order time, and required delivery time;

[0096] A preprocessing submodule 12 is used to obtain historical order data and regional delivery time efficiency data, and preprocess the historical order data and regional delivery time efficiency data to obtain preprocessed data;

[0097] The training submodule 13 is used to train the machine learning model using the preprocessed data as a training set to obtain a classification model;

[0098] The classification submodule 14 is used to classify the urgency of the order according to the order information using a classification model to obtain a classification result, wherein the classification result includes expedited, normal and scheduled delivery;

[0099] In this embodiment, the preprocessing submodule 12 includes:

[0100] The first processing unit 121 is used to obtain historical order data and regional delivery time efficiency data, process missing values ​​and abnormal values ​​of the data, and obtain first data;

[0101] The second processing unit 122 is used to identify and delete duplicate data in the first data, unify the data format, and obtain second data;

[0102] A labeling unit 123, used to integrate the second data, label the integrated data, and obtain preprocessed data;

[0103] In this embodiment, the sorting module 20 includes:

[0104] The formulation submodule 21 is used to formulate sorting rules according to the urgency of the order. For orders classified as expedited, they are sorted first, and for orders classified as ordinary and scheduled orders, they are sorted normally.

[0105] The judgment submodule 22 is used to obtain the classification result of the order and judge whether the order needs to be sorted first according to the classification result and sorting rules of the order;

[0106] The calculation submodule 23 is used to obtain the warehouse layout data if the order needs to be sorted first, adopt the shortest path algorithm, generate the sorting strategy according to the warehouse layout data, and output the sorting strategy to the sorter;

[0107] In this embodiment, the calculation submodule 23 includes:

[0108] The sorting unit 231 is used to obtain a list of warehouse sorters and their historical sorting records, and sort the list of warehouse sorters according to the historical sorting records to obtain a distribution sorting result;

[0109] The matching unit 232 is used to obtain the to-be-completed sorting data of all warehouse sorters if the order needs to be sorted first, and match the sorter of the order according to the to-be-completed sorting data and the allocation sorting result;

[0110] The calculation unit 233 is used to obtain the warehouse layout data, use the shortest path algorithm, generate a sorting strategy according to the warehouse layout data, and output the sorting strategy to the sorter;

[0111] In this embodiment, the dispatch module 30 includes:

[0112] The planning submodule 31 is used to obtain traffic data, including real-time traffic data, weather data and traffic restriction data, and to plan the transportation route according to the traffic data, order information and classification results using a dynamic programming algorithm;

[0113] A generating submodule 32 is used to obtain the vehicle information and the delivery person information of the site, and to generate a vehicle priority list and a delivery person priority list according to the vehicle information and the delivery person information respectively;

[0114] The dispatching submodule 33 is used to establish dispatching rules, dispatch orders according to the dispatching rules, classification results, vehicle priority list and delivery personnel priority list, and form dispatching information;

[0115] In this embodiment, the dispatching submodule 33 includes:

[0116] The dispatching unit 331 is used to establish dispatching rules, dispatch orders according to the dispatching rules, classification results, vehicle priority list and delivery personnel priority list, form dispatching information and output it to the delivery personnel for execution, so as to obtain delivery process data;

[0117] The judgment unit 332 is used to obtain the delivery process data for the order classified as expedited, and use the delay judgment model to judge whether the expedited order has a delay risk according to the delivery process data;

[0118] The planning unit 333 is used to obtain the location information of the vehicle corresponding to the order if there is a risk of delay for the expedited order, re-plan the delivery plan based on the location information, and output the delivery plan to the relevant responsible person.

[0119] The time efficiency processing device for logistics orders of the present invention first obtains order information, wherein the order information includes the delivery address, commodity category, order time and required delivery time, and classifies the urgency of the order according to the order information, and can adjust the priority of the order according to the actual progress and actual situation of the order; formulates sorting rules, obtains the classification results of the order, generates a sorting strategy according to the classification results and the sorting rules of the order, and outputs the sorting strategy to the sorter, so that the appropriate sorter can be assigned to be responsible according to the urgency, thereby improving the sorting efficiency; obtains traffic data, plans the transportation route according to the traffic data, order information and classification results, and dispatches the order according to the pre-established dispatching rules to form dispatch information, so that more skilled delivery personnel and suitable vehicles are responsible for different types of orders, which can effectively avoid uneven distribution of transportation capacity and prevent urgent orders from being given priority.

[0120] The above is a detailed description of the timeliness processing device for logistics orders in the embodiment of the present invention from the perspective of modular functional entities. The following is a detailed description of the timeliness processing device for logistics orders in the embodiment of the present invention from the perspective of hardware processing.

[0121] Fig.10 A schematic diagram of the structure of a time-limited processing device for a logistics order provided in an embodiment of the present invention. The time-limited processing device 900 for a logistics order may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 910 (for example, one or more processors) and a memory 920, and one or more storage media 930 (for example, one or more mass storage devices) storing application programs 933 or data 932. Among them, the memory 920 and the storage medium 930 may be short-term storage or permanent storage. The program stored in the storage medium 930 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the time-limited processing device 900 for a logistics order. Furthermore, the processor 910 may be configured to communicate with the storage medium 930, and execute a series of instruction operations in the storage medium 930 on the time-limited processing device 900 for a logistics order to implement the steps of the time-limited processing method for a logistics order provided in the above-mentioned method embodiments.

[0122] The time-limit processing device 900 for logistics orders may also include one or more power supplies 940, one or more wired or wireless network interfaces 950, one or more input and output interfaces 960, and / or one or more operating systems 931, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Fig.10 The structure of the time-limit processing equipment for logistics orders shown does not constitute a limitation on the time-limit processing equipment for logistics orders, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0123] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the timeliness processing method for logistics orders.

[0124] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the equipment or device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0125] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0126] It is understandable that those skilled in the art can make equivalent substitutions or changes based on the technical solution and inventive concept of the present invention, and all these changes or substitutions should fall within the protection scope of the claims attached to the present invention.

Claims

1. A time efficiency processing method for logistics orders, characterized in that: The steps include: Obtaining order information, including the delivery address, product category, order time, and required delivery time, and classifying the urgency of the order according to the order information; Formulate sorting rules, obtain the classification results of the orders, generate sorting strategies according to the classification results of the orders and the sorting rules, and output the sorting strategies to the sorters; Obtain traffic data, plan transportation routes based on traffic data, order information and classification results, and dispatch orders according to pre-established dispatch rules to form dispatch information.

2. The time efficiency processing method for logistics orders according to claim 1 is characterized in that: The obtaining of order information, wherein the order information includes the delivery address, product category, order time, and required delivery time, and classifying the urgency of the order according to the order information, specifically includes: Obtain order information, including delivery address, product category, order time, and required delivery time; Acquire historical order data and regional delivery time efficiency data, and pre-process the historical order data and regional delivery time efficiency data to obtain pre-processed data; Using the preprocessed data as a training set, the machine learning model is trained to obtain a classification model; A classification model is used to classify the urgency of the order according to the order information to obtain a classification result, which includes expedited, ordinary and scheduled delivery.

3. The time efficiency processing method for logistics orders according to claim 2 is characterized in that: The obtaining of historical order data and regional delivery time efficiency data, and preprocessing of the historical order data and regional delivery time efficiency data to obtain preprocessed data specifically includes: Obtain historical order data and regional delivery time data, process missing values ​​and outliers in the data, and obtain the first data; Identify and delete duplicate data in the first data, unify the data format, and obtain second data; The second data is integrated, and the integrated data is labeled to obtain preprocessed data.

4. The time efficiency processing method for logistics orders according to claim 1 is characterized in that: The formulating of sorting rules, obtaining the classification results of the orders, generating sorting strategies according to the classification results of the orders and the sorting rules, and outputting the sorting strategies to the sorters specifically include: Formulate sorting rules based on the urgency of the order. Orders classified as expedited will be sorted first, and orders classified as ordinary and scheduled will be sorted normally. Obtain the classification results of the order, and determine whether the order needs to be sorted first based on the classification results and sorting rules; If the order needs to be sorted first, the warehouse layout data is obtained, the shortest path algorithm is used, a sorting strategy is generated according to the warehouse layout data, and the sorting strategy is output to the sorter.

5. The time efficiency processing method for logistics orders according to claim 4 is characterized in that: If the order needs to be sorted first, the warehouse layout data is obtained, the shortest path algorithm is used, a sorting strategy is generated according to the warehouse layout data, and the sorting strategy is output to the sorter, which specifically includes: Obtain the list of warehouse pickers and their historical sorting records, and sort the list of warehouse pickers according to the historical sorting records to obtain the allocation sorting results; If the order needs to be sorted first, obtain the pending sorting data of all warehouse pickers, and match the sorter of the order according to the pending sorting data and the allocation sorting result; The warehouse layout data is obtained, the shortest path algorithm is used, a sorting strategy is generated according to the warehouse layout data, and the sorting strategy is output to the sorter.

6. The time efficiency processing method for logistics orders according to claim 1 is characterized in that: The acquisition of traffic data, planning of transportation routes according to the traffic data, order information and classification results, and dispatching of orders according to pre-established dispatching rules to form dispatching information specifically include: Acquire traffic data, including real-time traffic data, weather data, and traffic restriction data, and use a dynamic programming algorithm to plan a transportation route based on the traffic data, order information, and classification results; Obtain the vehicle information and delivery personnel information of the site, and generate a vehicle priority list and a delivery personnel priority list according to the vehicle information and the delivery personnel information respectively; Establish dispatch rules, dispatch orders according to the dispatch rules, classification results, vehicle priority list and delivery personnel priority list, and form dispatch information.

7. The time efficiency processing method for logistics orders according to claim 6 is characterized in that: The establishing of dispatching rules, dispatching orders according to the dispatching rules, classification results, vehicle priority list and delivery personnel priority list, and forming dispatching information specifically includes: Establish dispatching rules, dispatch orders according to the dispatching rules, classification results, vehicle priority list and delivery personnel priority list, form dispatching information and output it to the delivery personnel for execution, so as to obtain delivery process data; For orders classified as expedited, the delivery process data is obtained, and a delay judgment model is used to determine whether the expedited order has a delay risk based on the delivery process data; If there is a risk of delay for an expedited order, obtain the location information of the vehicle corresponding to the order, re-plan the delivery plan based on the location information, and output the delivery plan to the relevant responsible person.

8. A time-limit processing device for logistics orders, characterized in that: include: A classification module is used to obtain order information, including the delivery address, product category, order time, and required delivery time, and classify the urgency of the order according to the order information; A sorting module is used to formulate sorting rules, obtain the classification results of orders, generate sorting strategies according to the classification results of orders and sorting rules, and output the sorting strategies to sorters; The dispatch module is used to obtain traffic data, plan transportation routes based on traffic data, order information and classification results, and dispatch orders according to pre-established scheduling rules to form dispatch information.

9. A time-limited processing device for logistics orders, characterized in that: comprising a memory and at least one processor, wherein the memory has computer-readable instructions stored therein; The at least one processor calls the computer-readable instructions in the memory to execute the various steps of the time-limit processing method for logistics orders as described in any one of claims 1-7.

10. A computer-readable storage medium having computer-readable instructions stored thereon, characterized in that: When the computer-readable instructions are executed by the processor, the various steps of the timeliness processing method for logistics orders as described in any one of claims 1-7 are implemented.

Images