Logistics order statistical management method and device, equipment and storage medium

Abstract

The invention provides a logistics order statistical management method, apparatus and device, and a storage medium. The method comprises the steps of obtaining order data and logistics data based on an e-commerce platform server and a logistics system server; the obtained order data and logistics data are preprocessed; classifying the preprocessed order data according to the geographic area; according to logistics data corresponding to the order data in the target area after classification, counting logistics timeliness of all the order data in the target area; the logistics time efficiency which does not meet the preset requirement is obtained; and when the logistics aging ratio which does not meet the preset requirement in the target area is greater than a preset value, optimizing logistics management in the target area. According to the invention, the ratio of express items in a province / region can be increased, the demand of consumers for express item distribution is met, and the shopping experience of the consumers is improved.

Description

Technical Field

[0001] The present invention relates to, specifically, a logistics order statistics management method, device, equipment and storage medium, and more specifically, to a logistics order statistics management method, device, equipment and storage medium within a province / region. Background Art

[0002] With the rapid development of e-commerce, e-commerce warehousing networks are constantly expanding, and major customers are building warehouses in various places to meet the needs of fast delivery. At the same time, e-commerce platforms have increasingly higher requirements for express delivery timeliness, especially for express delivery within one day. The statistics of timeliness achieved have a higher weight in the platform assessment, and such express delivery is more likely to receive good reviews from consumers. However, there is currently a lack of effective means to monitor and improve the proportion of express delivery within a province / region, resulting in irrational allocation of logistics resources and low logistics efficiency, which affects consumers' shopping experience. Summary of the invention

[0003] The main purpose of the present invention is to solve the problem that the existing technology lacks effective means for monitoring and improving the proportion of express parcels within a province / region, which leads to unreasonable allocation of logistics resources and low logistics efficiency.

[0004] A first aspect of the present invention provides a logistics order statistics management method, including: obtaining order data and logistics data based on an e-commerce platform server and a logistics system server respectively; preprocessing the obtained order data and logistics data respectively; classifying the preprocessed order data according to geographical areas; according to the logistics data corresponding to the order data in the target area after classification, counting the logistics timeliness of all order data in the target area; and obtaining the logistics timeliness that does not meet the preset requirements; when the proportion of logistics timeliness that does not meet the preset requirements in the target area is greater than a preset value, optimizing the logistics management in the target area.

[0005] Optionally, in a first implementation method of the first aspect of the present invention, the order data and logistics data are respectively obtained based on the e-commerce platform server and the logistics system server, including: sending a data acquisition request to the target server in response to an operation instruction triggered by a data acquisition control; so that the target server obtains corresponding target data based on the data acquisition request; receiving the corresponding target data sent by the target server; wherein the target server includes: an e-commerce platform server and a logistics system server; and the target data includes: order data and logistics data.

[0006] Optionally, in a second implementation of the first aspect of the present invention, the acquired order data and logistics data are pre-processed separately, including: cleaning the acquired order data and logistics data; wherein the cleaning includes: at least one of deduplication processing, outlier processing, and data format unification processing.

[0007] Optionally, in a third implementation method of the first aspect of the present invention, the logistics data corresponding to the order data in the target area after classification is used to count the logistics timeliness of all order data in the target area; based on the logistics timeliness of all order data in the target area obtained by statistics, the logistics timeliness that does not meet the preset requirements is obtained; when the proportion of logistics timeliness that does not meet the preset requirements in the target area is greater than a preset value, the current logistics management is optimized, including: according to the logistics data corresponding to the order data in the target area, obtaining the corresponding shipping time information and arrival time information of the order data; calculating the transportation time according to the shipping time information and the arrival time information; counting the transportation time of all order data in the target area, and calculating the proportion of the transportation time of all order data in the target area that is less than the preset value, and when the proportion is less than a threshold, optimizing the logistics management in the target area.

[0008] Optionally, in a fourth implementation of the first aspect of the present invention, the optimization of logistics management in the target area includes: optimizing at least one of warehouse layout, logistics routes, transportation resource allocation, and information system support in the target area.

[0009] Optionally, in a fifth implementation of the first aspect of the present invention, the optimizing at least one of warehouse layout, logistics path, transportation resource allocation, and information system support in the target area includes:

[0010] Re-plan the warehouse layout using the WMS system based on order volume, cargo characteristics, and transportation requirements to optimize the warehouse layout;

[0011] Use the TMS system to analyze the efficiency and cost of existing logistics routes, optimize the current logistics routes based on the analysis results, and achieve logistics route optimization;

[0012] Use the TMS system to monitor the use of transportation resources in real time, intelligently allocate transportation resources based on real-time data and demand conditions, and optimize transportation resource allocation;

[0013] The introduction of automated equipment and unmanned delivery vehicles will improve transportation speed and accuracy and optimize information system support.

[0014] Optionally, in a sixth implementation of the first aspect of the present invention, the logistics time efficiency statistical report, the proportion analysis results and the optimization strategy are displayed in a visual manner;

[0015] The logistics time efficiency statistical report includes: logistics time efficiency of all order data in the target area;

[0016] The proportion analysis results include: the proportion of logistics timeliness that does not meet the preset requirements in the target area and the proportion of logistics timeliness that meets the preset requirements in the target area;

[0017] The optimization strategies include: warehouse layout optimization strategy, logistics path optimization strategy, transportation resource allocation optimization strategy, and information system support optimization strategy.

[0018] The second aspect of the present invention provides a logistics order statistics management device, including: a data acquisition module, used to obtain order data and logistics data based on an e-commerce platform server and a logistics system server respectively; a preprocessing module, used to preprocess the acquired order data and logistics data respectively; a classification module, used to classify the preprocessed order data according to geographical areas; an optimization module, used to count the logistics timeliness of all order data in the target area according to the logistics data corresponding to the order data in the target area after classification; and obtain the logistics timeliness that does not meet the preset requirements; when the proportion of logistics timeliness that does not meet the preset requirements in the target area is greater than the preset value, the logistics management in the target area is optimized.

[0019] Optionally, in a first implementation method of the second aspect of the present invention, the data acquisition module includes: sending a data acquisition request to a target server in response to an operation instruction triggered by a data acquisition control; so that the target server acquires corresponding target data based on the data acquisition request; receiving corresponding target data sent by the target server; wherein the target server includes: an e-commerce platform server and a logistics system server; and the target data includes: order data and logistics data.

[0020] Optionally, in a second implementation of the second aspect of the present invention, the preprocessing module includes: cleaning the acquired order data and logistics data; wherein the cleaning process includes: at least one of deduplication processing, outlier processing, and data format unification processing.

[0021] Optionally, in a third implementation method of the second aspect of the present invention, the optimization module includes: obtaining the delivery time information and arrival time information corresponding to the order data in the target area according to the logistics data corresponding to the order data in the target area; calculating the transportation time according to the delivery time information and the arrival time information; counting the transportation time of all order data in the target area, and calculating the proportion of transportation time of all order data in the target area that is less than a preset value, and when the proportion is less than a threshold, optimizing the logistics management in the target area.

[0022] Optionally, in a fourth implementation method of the second aspect of the present invention, the optimization of logistics management in the target area includes: optimizing at least one of warehouse layout, logistics routes, transportation resource allocation, and information system support in the target area.

[0023] Optionally, in a fifth implementation of the second aspect of the present invention, the optimizing at least one of warehouse layout, logistics path, transportation resource allocation, and information system support in the target area includes:

[0024] Re-plan the warehouse layout using the WMS system based on order volume, cargo characteristics, and transportation requirements to optimize the warehouse layout;

[0025] Use the TMS system to analyze the efficiency and cost of existing logistics routes, optimize the current logistics routes based on the analysis results, and achieve logistics route optimization;

[0026] Use the TMS system to monitor the use of transportation resources in real time, intelligently allocate transportation resources based on real-time data and demand conditions, and optimize transportation resource allocation;

[0027] The introduction of automated equipment and unmanned delivery vehicles will improve transportation speed and accuracy and optimize information system support.

[0028] Optionally, in a sixth implementation of the second aspect of the present invention, the logistics time efficiency statistical report, the proportion analysis results and the optimization strategy are displayed in a visual manner;

[0029] The logistics time efficiency statistical report includes: logistics time efficiency of all order data in the target area;

[0030] The proportion analysis results include: the proportion of logistics timeliness that does not meet the preset requirements in the target area and the proportion of logistics timeliness that meets the preset requirements in the target area;

[0031] The optimization strategies include: warehouse layout optimization strategy, logistics path optimization strategy, transportation resource allocation optimization strategy, and information system support optimization strategy.

[0032] The third aspect of the present invention provides an electronic device, comprising: a memory and at least one processor, wherein instructions are stored in the memory, and the memory and the at least one processor are interconnected via lines; the at least one processor calls the instructions in the memory so that the electronic device executes the above-mentioned logistics order statistics management method.

[0033] A fourth aspect of the present invention provides a computer-readable storage medium, in which instructions are stored, and when the computer-readable storage medium is run on a computer, the computer executes the above-mentioned logistics order statistics management method.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. Real-time monitoring of the proportion and time efficiency of express parcels within the province / region, providing data support for the rational allocation of logistics resources;

[0036] 2. The present invention analyzes the proportion, obtains the factors affecting the proportion, and proposes targeted optimization strategies to improve logistics efficiency;

[0037] 3. After optimizing logistics management, the present invention can increase the proportion of express parcels within the province / region, meet consumers' demand for fast delivery, and improve consumers' shopping experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0039] Figure 1 A first flow chart of the logistics order statistics management method provided in an embodiment of the present invention.

[0040] Figure 2 A second flow chart of the logistics order statistics management method provided in an embodiment of the present invention.

[0041] Figure 3 This is a third flow chart of the logistics order statistics management method provided in an embodiment of the present invention.

[0042] Figure 4 A schematic diagram of the structure of a logistics order statistics management device provided in an embodiment of the present invention.

[0043] Figure 5 Another structural schematic diagram of the logistics order statistics management device provided in an embodiment of the present invention.

[0044] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The embodiment of the present invention provides a logistics order statistics management method, device, equipment and storage medium. First, order data and logistics data are obtained based on an e-commerce platform server and a logistics system server respectively; the obtained order data and logistics data are preprocessed respectively; the preprocessed order data are classified according to geographical areas; according to the logistics data corresponding to the order data in the target area after classification, the logistics timeliness of all order data in the target area is counted; and the logistics timeliness that does not meet the preset requirements is obtained; when the proportion of logistics timeliness that does not meet the preset requirements in the target area is greater than a preset value, the logistics management in the target area is optimized, including: optimizing at least one of the warehouse layout, logistics route, transportation resource allocation, and information system support in the target area.

[0046] 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.

[0047] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 The first embodiment of the logistics order statistics management method in the embodiment of the present invention includes:

[0048] 101. Obtain order data and logistics data based on the e-commerce platform server and the logistics system server respectively;

[0049] In this embodiment, in response to an operation instruction triggered by a data acquisition control, a data acquisition request is sent to an e-commerce platform server; so that the e-commerce platform server acquires order data based on the data acquisition request; and the order data sent by the e-commerce platform server is received;

[0050] In response to an operation instruction triggered by a data acquisition control, a data acquisition request is sent to a logistics system server; so that the logistics system server acquires logistics data based on the data acquisition request; and the logistics data sent by the logistics system server is received.

[0051] 102. Pre-process the acquired order data and logistics data respectively;

[0052] In this embodiment, the collected data is cleaned, including: removing duplicate, erroneous or invalid data; and then integrating the cleaned data to form a unified data format and structure to facilitate subsequent analysis and processing.

[0053] 103. Classify the pre-processed order data according to geographic regions;

[0054] In this embodiment, the order data belonging to intra-province items are filtered out according to the shipping place and receiving place information in the order data; the filtered intra-province item data are classified according to the province to ensure that independent timeliness statistics can be performed for each province later.

[0055] 104. According to the logistics data corresponding to the order data in the target area after classification, the logistics timeliness of all order data in the target area is counted; and the logistics timeliness that does not meet the preset requirements is obtained; when the proportion of logistics timeliness that does not meet the preset requirements in the target area is greater than the preset value, the logistics management in the target area is optimized.

[0056] In this embodiment, according to the logistics data corresponding to the order data in the target area, the delivery time information and arrival time information corresponding to the order data are obtained;

[0057] Calculate the transportation time based on the shipping time information and arrival time information;

[0058] Statistics are collected on the transportation time of all order data in the target area, and the proportion of the transportation time of all order data in the target area that is less than the preset value is calculated. When the proportion is less than the threshold, the logistics management in the target area is optimized.

[0059] In this embodiment, the logistics management in the target area is optimized, including: optimizing at least one of warehouse layout, logistics path, transportation resource allocation, and information system support in the target area.

[0060] Among them, the warehouse layout is re-planned and optimized using the WMS system based on order volume, cargo characteristics and transportation requirements;

[0061] Use the TMS system to analyze the efficiency and cost of existing logistics routes, optimize the current logistics routes based on the analysis results, and achieve logistics route optimization;

[0062] Use the TMS system to monitor the use of transportation resources in real time, intelligently allocate transportation resources based on real-time data and demand conditions, and optimize transportation resource allocation;

[0063] The introduction of automated equipment and unmanned delivery vehicles will improve transportation speed and accuracy and optimize information system support.

[0064] This embodiment monitors the proportion and timeliness of express parcels within the province / region in real time through order data and corresponding logistics data, thereby providing data support for logistics management optimization; based on the monitored proportion of express parcels within the province / region, the specific factors affecting the proportion are analyzed, including: warehouse layout, logistics routes, transportation resource allocation, information system support, etc. According to the specific factors obtained from the analysis, corresponding optimization strategies are proposed to improve logistics efficiency.

[0065] See also Figure 2 Another embodiment of the logistics order statistics management method in the embodiment of the present invention includes:

[0066] 201. Obtain order data, logistics data, and warehouse layout data based on the e-commerce platform server and the logistics system server respectively;

[0067] In this embodiment, the purpose and requirements of data collection are first clarified, and the types of data and specific fields of the data to be collected are determined. According to the requirements of the present invention, the data to be collected include order data, logistics data, and warehouse layout data; wherein, based on the currently acquired warehouse layout data, the warehouse layout is further optimized;

[0068] Collecting order data includes: obtaining order data from the e-commerce platform by writing corresponding data collection programs or scripts based on the data interface and data transmission protocol of the e-commerce platform; at the same time, setting the frequency and time period of data collection to ensure the real-time and integrity of the data.

[0069] Collecting logistics data includes: first obtaining access rights to logistics data, extracting logistics data from the logistics system by writing corresponding data collection programs or scripts based on the data structure and field definitions of the logistics system, and setting appropriate data collection strategies considering the real-time and dynamic nature of logistics data to ensure the accuracy and timeliness of the data.

[0070] Collecting warehouse layout data includes: obtaining access rights to warehouse layout data, analyzing the data structure of the warehouse management system, and determining the warehouse layout data fields that need to be collected; extracting warehouse layout data from the warehouse management system by writing corresponding data collection programs or scripts, and associating it with order data and logistics data.

[0071] 202. Preprocessing the acquired order data, logistics data, and warehouse layout data;

[0072] In this embodiment, the collected order data, logistics data and warehouse layout data are cleaned to remove duplicate, erroneous or invalid data; the cleaned data are integrated to form a unified data format and structure to facilitate subsequent analysis and processing.

[0073] 203. Process the pre-processed order data and logistics data, calculate the time efficiency of each province's internal items, including delivery time, arrival time, transportation time, etc., and generate a time efficiency statistical report;

[0074] In this embodiment, data screening and classification: screening of intra-province data, based on the shipping location information and receiving location information in the order data, screening out order data belonging to intra-province items.

[0075] Classification by province: Classify the provincial data by province to ensure that independent timeliness statistics can be performed for each province later;

[0076] Time calculation and statistics: Delivery time extraction: Extract the delivery time information of each intra-province item from the order data. Arrival time extraction: Extract the arrival time information of each intra-province item from the logistics data. Transportation time calculation: Calculate the transportation time of each intra-province item based on the delivery time and arrival time.

[0077] Time efficiency statistics: According to the province classification, the time efficiency of each province is counted, including average transportation time, longest transportation time, shortest transportation time and other indicators.

[0078] 204. Analyze the proportion of express parcels within each province / region according to the timeliness statistics report, and find out the reasons for the low proportion, including: unreasonable warehouse layout, unoptimized logistics routes, unreasonable allocation of transportation resources, unreasonable staffing, lack of information system support, etc.;

[0079] Interpretation of the time-efficiency statistics report: Read the time-efficiency statistics report in depth to understand the meaning and calculation method of each data indicator in the report. Pay attention to the proportion of express shipments within the province / region, compare the data of different provinces, and find out the provinces with relatively low or abnormal proportions.

[0080] Proportion analysis: Conduct in-depth analysis of provinces with low proportions to understand the basic situation of the province, such as order volume, shipment volume, and transportation capacity. Compare the proportion data of other provinces to analyze the impact of factors such as the province's position in the logistics network, warehouse distribution, and transportation resources on the proportion.

[0081] Cause mining: Warehouse layout analysis: Investigate whether the warehouse layout of the province is reasonable, and whether there are problems such as uneven warehouse distribution and insufficient warehouse capacity. Analyze the impact of warehouse layout on order processing, cargo sorting, transportation and distribution, and determine whether there are efficiency problems caused by unreasonable layout. Logistics path analysis: Analyze whether the logistics path of the province is optimized, and whether there are problems such as too long paths and too many transfers. Combined with the transportation time data in the timeliness statistics report, determine whether the logistics path has a negative impact on the timeliness of express delivery. Consider other factors: In addition to warehouse layout and logistics paths, other factors that may affect the proportion of express delivery within the province / region should also be considered, such as transportation resource allocation, staffing, information system support, etc. Analyze whether there are problems or deficiencies in these factors, and whether they have a negative impact on the proportion of express delivery.

[0082] 205. Based on the analysis results, propose targeted optimization strategies, including warehouse layout adjustment, logistics route optimization, etc., so as to improve transportation efficiency and increase the proportion of express delivery within the province / region;

[0083] In this example, we deeply understand the results of the proportion analysis: carefully read the proportion analysis report, and fully understand the current situation, problems and reasons of the proportion of express parcels within each province / region. We focus on provinces with low proportions and deeply understand the specific conditions and existing problems in their warehouse layout, logistics routes, transportation efficiency, etc.

[0084] Propose targeted optimization strategies: Adjust warehouse layout: Re-plan warehouse layout according to order volume, cargo characteristics, transportation demand and other factors to ensure reasonable warehouse distribution and sufficient capacity. Consider adding warehouses at key nodes or areas with greater demand to shorten transportation distances and improve distribution efficiency. Optimize logistics routes: Analyze the efficiency and cost of existing logistics routes and find out the links and nodes that can be optimized. Formulate new logistics route plans to reduce the number of transfers, shorten transportation time, and improve logistics efficiency. Consider introducing advanced logistics technologies, such as route optimization algorithms and intelligent scheduling systems, to improve the intelligence level of logistics routes. Improve transportation efficiency: Strengthen the allocation and management of transportation resources to ensure sufficient and efficient use of transportation tools. Introduce advanced transportation technologies and equipment, such as automated equipment and unmanned delivery vehicles, to improve transportation speed and accuracy. Strengthen the training and management of transportation personnel to improve their professionalism and service quality.

[0085] See also Figure 3 Another embodiment of the logistics order statistics management method in the embodiment of the present invention includes:

[0086] 301. Obtain order data and logistics data based on the e-commerce platform server and the logistics system server respectively;

[0087] 302. Preprocess the acquired order data and logistics data respectively;

[0088] In this embodiment, the collected data is cleaned, including: removing duplicate, erroneous or invalid data; and then integrating the cleaned data to form a unified data format and structure to facilitate subsequent analysis and processing.

[0089] 303. Process and analyze the pre-processed order data and logistics data, and calculate the time efficiency of each province's internal items, including delivery time, arrival time, transportation time, etc., and generate a time efficiency statistical report;

[0090] In this embodiment, the order data belonging to the intra-province items are screened out according to the shipping place and receiving place information in the order data;

[0091] Extract the delivery time information of each intra-province item from the order data; extract the arrival time information of each intra-province item from the logistics data; calculate the transportation time of each intra-province item based on the delivery time and arrival time.

[0092] Time efficiency statistics: According to the province classification, the time efficiency of each province is counted, including average transportation time, longest transportation time, shortest transportation time and other indicators.

[0093] Generate time-efficiency statistical report: Design report template: Design the template of time-efficiency statistical report according to statistical requirements, including table header, table structure, data display method, etc. Fill in report data: Fill in the statistical time-efficiency data into the report according to the requirements of the template to ensure the accuracy and readability of the data. Report visualization: Use visualization tools such as charts, bar charts, and line charts to visualize the time-efficiency statistical data, so that relevant personnel can intuitively understand the time-efficiency situation of provincial items in each province.

[0094] Report review and release: Report review: Review the generated time efficiency statistics report to ensure the accuracy and completeness of the data and avoid errors or omissions. Report release: Release the approved time efficiency statistics report to relevant personnel, including e-commerce platform operators, logistics managers, warehouse managers, etc., so that they can understand the time efficiency of provincial items in each province and take corresponding optimization measures.

[0095] 304. Generate a time efficiency statistical report within the province to obtain the logistics time efficiency that does not meet the preset requirements; when the proportion of logistics time efficiency that does not meet the preset requirements within the province is greater than the preset value, optimize the logistics management within the province.

[0096] In this embodiment, according to the logistics data corresponding to the order data in the target area, the delivery time information and arrival time information corresponding to the order data are obtained;

[0097] Calculate the transportation time based on the shipping time information and arrival time information;

[0098] Statistics are collected on the transportation time of all order data in the target area, and the proportion of the transportation time of all order data in the target area that is less than the preset value is calculated. When the proportion is less than the threshold, the logistics management in the target area is optimized.

[0099] In this embodiment, the logistics management in the target area is optimized, including: optimizing at least one of warehouse layout, logistics path, transportation resource allocation, and information system support in the target area.

[0100] Among them, the warehouse layout is re-planned and optimized using the WMS system based on order volume, cargo characteristics and transportation requirements;

[0101] Use the TMS system to analyze the efficiency and cost of existing logistics routes, optimize the current logistics routes based on the analysis results, and achieve logistics route optimization;

[0102] Use the TMS system to monitor the use of transportation resources in real time, intelligently allocate transportation resources based on real-time data and demand conditions, and optimize transportation resource allocation;

[0103] The introduction of automated equipment and unmanned delivery vehicles will improve transportation speed and accuracy and optimize information system support.

[0104] 305. Display the timeliness statistics, percentage analysis results and optimization strategies to relevant personnel in a visual manner, so that they can quickly understand the situation of express delivery within the province / region and take corresponding measures.

[0105] In this example, prepare the materials required for visualization: organize the timeliness statistical report, ensure the data is accurate and complete, and classify and organize it according to the needs of visualization. Review the analysis results, extract key information and problems found, and provide strong data support for visualization. Sort out the proposed optimization strategies, clarify the specific content and goals of each measure, so that they can be clearly presented in the visualization.

[0106] Choose appropriate visualization tools and methods: According to the presentation content and target audience, choose appropriate visualization tools, such as data visualization software, PPT, chart library, etc. Determine the method of visualization, such as using charts, tables, maps, etc., so as to display data and information intuitively and vividly.

[0107] Create visual presentation content: Design a clear and concise presentation interface to ensure that information is presented in an orderly and easy-to-understand manner. Use charts to display key data in the timeliness statistics report, such as transportation time, proportion, etc., so that relevant personnel can quickly understand the overall situation. Use maps or tables to display the proportion analysis results of each province, highlight provinces with low proportions and existing problems, and provide intuitive basis for optimization strategies. Describe the optimization strategy in detail, and use flowcharts, schematic diagrams, etc. to show the specific implementation steps and goals of the strategy to help relevant personnel understand and adopt it.

[0108] The above describes the logistics order statistics management method in the embodiment of the present invention. The following describes the logistics order statistics management device in the embodiment of the present invention. Figure 4 In one embodiment of the present invention, a logistics order statistics management device includes:

[0109] The data acquisition module 401 is used to acquire order data and logistics data based on the e-commerce platform server and the logistics system server respectively;

[0110] In this embodiment, in response to an operation instruction triggered by a data acquisition control, a data acquisition request is sent to an e-commerce platform server; so that the e-commerce platform server acquires order data based on the data acquisition request; and the order data sent by the e-commerce platform server is received;

[0111] In response to an operation instruction triggered by a data acquisition control, a data acquisition request is sent to a logistics system server; so that the logistics system server acquires logistics data based on the data acquisition request; and the logistics data sent by the logistics system server is received.

[0112] A preprocessing module 402 is used to preprocess the acquired order data and logistics data respectively;

[0113] In this embodiment, the collected data is cleaned, including: removing duplicate, erroneous or invalid data; and then integrating the cleaned data to form a unified data format and structure to facilitate subsequent analysis and processing.

[0114] A classification module 403, for classifying the pre-processed order data according to geographical regions;

[0115] In this embodiment, the order data belonging to intra-province items are filtered out according to the shipping place and receiving place information in the order data; the filtered intra-province item data are classified according to the province to ensure that independent timeliness statistics can be performed for each province later.

[0116] The optimization module 404 counts the logistics timeliness of all order data in the target area according to the logistics data corresponding to the order data in the classified target area, and obtains the logistics timeliness that does not meet the preset requirements; when the proportion of logistics timeliness that does not meet the preset requirements in the target area is greater than the preset value, the logistics management in the target area is optimized.

[0117] In this embodiment, according to the logistics data corresponding to the order data in the target area, the delivery time information and arrival time information corresponding to the order data are obtained;

[0118] Calculate the transportation time based on the shipping time information and arrival time information;

[0119] Statistics are collected on the transportation time of all order data in the target area, and the proportion of the transportation time of all order data in the target area that is less than the preset value is calculated. When the proportion is less than the threshold, the logistics management in the target area is optimized.

[0120] In this embodiment, the logistics management in the target area is optimized, including: optimizing at least one of warehouse layout, logistics path, transportation resource allocation, and information system support in the target area.

[0121] Among them, the warehouse layout is re-planned and optimized using the WMS system based on order volume, cargo characteristics and transportation requirements;

[0122] Use the TMS system to analyze the efficiency and cost of existing logistics routes, optimize the current logistics routes based on the analysis results, and achieve logistics route optimization;

[0123] Use the TMS system to monitor the use of transportation resources in real time, intelligently allocate transportation resources based on real-time data and demand conditions, and optimize transportation resource allocation;

[0124] The introduction of automated equipment and unmanned delivery vehicles will improve transportation speed and accuracy and optimize information system support.

[0125] See also Figure 5 Another embodiment of the logistics order statistics management device in the embodiment of the present invention includes:

[0126] The data acquisition module 501 is used to acquire order data, logistics data, and warehouse layout data based on the e-commerce platform server and the logistics system server respectively;

[0127] In this embodiment, the purpose and requirements of data collection are first clarified, and the types of data and specific fields of the data to be collected are determined. According to the requirements of the present invention, the data to be collected include order data, logistics data, and warehouse layout data; wherein, based on the currently acquired warehouse layout data, the warehouse layout is further optimized;

[0128] Collecting order data includes: obtaining order data from the e-commerce platform by writing corresponding data collection programs or scripts based on the data interface and data transmission protocol of the e-commerce platform; at the same time, setting the frequency and time period of data collection to ensure the real-time and integrity of the data.

[0129] Collecting logistics data includes: first obtaining access rights to logistics data, extracting logistics data from the logistics system by writing corresponding data collection programs or scripts based on the data structure and field definitions of the logistics system, and setting appropriate data collection strategies considering the real-time and dynamic nature of logistics data to ensure the accuracy and timeliness of the data.

[0130] Collecting warehouse layout data includes: obtaining access rights to warehouse layout data, analyzing the data structure of the warehouse management system, and determining the warehouse layout data fields that need to be collected; extracting warehouse layout data from the warehouse management system by writing corresponding data collection programs or scripts, and associating it with order data and logistics data.

[0131] A preprocessing module 502 is used to preprocess the acquired order data, logistics data, and warehouse layout data;

[0132] In this embodiment, the collected order data, logistics data and warehouse layout data are cleaned to remove duplicate, erroneous or invalid data; the cleaned data are integrated to form a unified data format and structure to facilitate subsequent analysis and processing.

[0133] The time efficiency statistical report generation module 503 is used to process the pre-processed order data and logistics data, count the time efficiency of each province's internal items, including delivery time, arrival time, transportation time, etc., and generate a time efficiency statistical report;

[0134] In this embodiment, the time-effectiveness statistical report generation module 503 includes:

[0135] The data screening and classification module 5031 is used to screen the intra-province data and screen out the order data belonging to intra-province items according to the shipping place and receiving place information in the order data.

[0136] The province classification module 5032 is used to classify the provincial data of the screening process according to the province, so as to ensure that independent timeliness statistics can be performed for each province later;

[0137] The time efficiency calculation and statistics module 5033 is used for delivery time extraction: extracting the delivery time information of each intra-province item from the order data. Arrival time extraction: extracting the arrival time information of each intra-province item from the logistics data. Transportation time calculation: calculating the transportation time of each intra-province item based on the delivery time and arrival time.

[0138] The time efficiency statistics module 5034 is used to classify by province and to collect statistics on the time efficiency of intra-provincial items in each province, including indicators such as average transportation time, longest transportation time, and shortest transportation time.

[0139] The analysis module 504 is used to analyze the proportion of express parcels within the province / region of each province according to the timeliness statistical report, and find out the reasons for the low proportion, including: unreasonable warehouse layout, unoptimized logistics path, unreasonable allocation of transportation resources, unreasonable staffing, lack of information system support, etc.;

[0140] Interpretation of the time-efficiency statistics report: Read the time-efficiency statistics report in depth to understand the meaning and calculation method of each data indicator in the report. Pay attention to the proportion of express shipments within the province / region, compare the data of different provinces, and find out the provinces with relatively low or abnormal proportions.

[0141] Proportion analysis: Conduct in-depth analysis of provinces with low proportions to understand the basic situation of the province, such as order volume, shipment volume, and transportation capacity. Compare the proportion data of other provinces to analyze the impact of factors such as the province's position in the logistics network, warehouse distribution, and transportation resources on the proportion.

[0142] Cause mining: Warehouse layout analysis: Investigate whether the warehouse layout of the province is reasonable, and whether there are problems such as uneven warehouse distribution and insufficient warehouse capacity. Analyze the impact of warehouse layout on order processing, cargo sorting, transportation and distribution, and determine whether there are efficiency problems caused by unreasonable layout. Logistics path analysis: Analyze whether the logistics path of the province is optimized, and whether there are problems such as too long paths and too many transfers. Combined with the transportation time data in the timeliness statistics report, determine whether the logistics path has a negative impact on the timeliness of express delivery. Consider other factors: In addition to warehouse layout and logistics paths, other factors that may affect the proportion of express delivery within the province / region should also be considered, such as transportation resource allocation, staffing, information system support, etc. Analyze whether there are problems or deficiencies in these factors, and whether they have a negative impact on the proportion of express delivery.

[0143] The optimization module 505 is used to propose targeted optimization strategies based on the proportion analysis results, including: warehouse layout adjustment, logistics path optimization, etc., so as to improve transportation efficiency and increase the proportion of express shipments within the province / region;

[0144] In this example, we deeply understand the results of the proportion analysis: carefully read the proportion analysis report, and fully understand the current situation, problems and reasons of the proportion of express parcels within each province / region. We focus on provinces with low proportions and deeply understand the specific conditions and existing problems in their warehouse layout, logistics routes, transportation efficiency, etc.

[0145] Propose targeted optimization strategies: Adjust warehouse layout: Re-plan warehouse layout according to order volume, cargo characteristics, transportation demand and other factors to ensure reasonable warehouse distribution and sufficient capacity. Consider adding warehouses at key nodes or areas with greater demand to shorten transportation distances and improve distribution efficiency. Optimize logistics routes: Analyze the efficiency and cost of existing logistics routes and find out the links and nodes that can be optimized. Formulate new logistics route plans to reduce the number of transfers, shorten transportation time, and improve logistics efficiency. Consider introducing advanced logistics technologies, such as route optimization algorithms and intelligent scheduling systems, to improve the intelligence level of logistics routes. Improve transportation efficiency: Strengthen the allocation and management of transportation resources to ensure sufficient and efficient use of transportation tools. Introduce advanced transportation technologies and equipment, such as automated equipment and unmanned delivery vehicles, to improve transportation speed and accuracy. Strengthen the training and management of transportation personnel to improve their professionalism and service quality.

[0146] above Figure 4 and Figure 5 The logistics order statistics management device in the embodiment of the present invention is described in detail from the perspective of modular functional entities, and the electronic device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0147] Figure 66 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device 600 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 610 (for example, one or more processors) and a memory 620, and one or more storage media 630 (for example, one or more mass storage devices) storing application programs 633 or data 632. Among them, the memory 620 and the storage medium 630 can be short-term storage or permanent storage. The program stored in the storage medium 630 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the electronic device 600. Furthermore, the processor 610 can be configured to communicate with the storage medium 630 to execute a series of instruction operations in the storage medium 630 on the electronic device 600.

[0148] The electronic device 600 may also include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input and output interfaces 650, and / or one or more operating systems 631, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 6 The structure of the electronic device shown does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine some components, or arrange the components differently.

[0149] 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 logistics order statistics management method.

[0150] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0151] 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.

[0152] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A logistics order statistics management method, characterized in that: include: Obtain order data and logistics data based on the e-commerce platform server and logistics system server respectively; Pre-process the acquired order data and logistics data respectively; Classify the pre-processed order data according to geographic regions; According to the logistics data corresponding to the order data in the target area after classification, the logistics timeliness of all order data in the target area is counted; And obtain the logistics timeliness that does not meet the preset requirements; When the proportion of logistics timeliness that does not meet the preset requirements in the target area is greater than the preset value, the logistics management in the target area will be optimized.

2. The logistics order statistics management method according to claim 1 is characterized in that: The method of obtaining order data and logistics data based on the e-commerce platform server and the logistics system server respectively includes: In response to an operation instruction triggered by a data acquisition control, a data acquisition request is sent to a target server, so that the target server acquires corresponding target data based on the data acquisition request; Receiving corresponding target data sent by the target server; Wherein, the target servers include: e-commerce platform servers and logistics system servers; The target data includes: order data and logistics data.

3. The logistics order statistics management method according to claim 1 is characterized in that: The obtained order data and logistics data are preprocessed separately, including: Clean the acquired order data and logistics data; The cleaning process includes at least one of deduplication processing, abnormal value processing, and data format unification processing.

4. The logistics order statistics management method according to claim 1 is characterized in that: The logistics data corresponding to the order data in the target area after classification is used to count the logistics timeliness of all order data in the target area; Based on the logistics time efficiency of all order data in the target area obtained by statistics, obtain the logistics time efficiency that does not meet the preset requirements; When the proportion of logistics timeliness that does not meet the preset requirements in the target area is greater than the preset value, the current logistics management is optimized, including: According to the order data and corresponding logistics data in the target area, obtain the corresponding delivery time information and arrival time information of the order data; Calculate the transportation time according to the shipping time information and the arrival time information; Statistics are collected on the transportation time of all order data in the target area, and the proportion of the transportation time of all order data in the target area that is less than the preset value is calculated. When the proportion is less than the threshold, the logistics management in the target area is optimized.

5. The logistics order statistics management method according to claim 1 is characterized in that: The optimizing of the logistics management in the target area includes optimizing at least one of warehouse layout, logistics route, transportation resource allocation and information system support in the target area.

6. The logistics order statistics management method according to claim 5 is characterized in that: The optimization of at least one of warehouse layout, logistics route, transportation resource allocation, and information system support in the target area includes: Re-plan the warehouse layout using the WMS system based on order volume, cargo characteristics, and transportation requirements to optimize the warehouse layout; Use the TMS system to analyze the efficiency and cost of existing logistics routes, optimize the current logistics routes based on the analysis results, and achieve logistics route optimization; Use the TMS system to monitor the use of transportation resources in real time, intelligently allocate transportation resources based on real-time data and demand conditions, and optimize transportation resource allocation; The introduction of automated equipment and unmanned delivery vehicles will improve transportation speed and accuracy and optimize information system support.

7. The logistics order statistics management method according to claim 6 is characterized in that: Display logistics time statistics, proportion analysis results and optimization strategies in a visual way; The logistics time efficiency statistical report includes: logistics time efficiency of all order data in the target area; The proportion analysis results include: the proportion of logistics timeliness that does not meet the preset requirements in the target area and the proportion of logistics timeliness that meets the preset requirements in the target area; The optimization strategies include: warehouse layout optimization strategy, logistics path optimization strategy, transportation resource allocation optimization strategy, and information system support optimization strategy.

8. A logistics order statistics management device, characterized in that: include: A data acquisition module is used to acquire order data and logistics data based on the e-commerce platform server and the logistics system server respectively; A preprocessing module is used to preprocess the acquired order data and logistics data respectively; A classification module, used to classify the pre-processed order data according to geographical areas; The optimization module is used to count the logistics timeliness of all order data in the target area according to the logistics data corresponding to the order data in the classified target area; And obtain the logistics timeliness that does not meet the preset requirements; When the proportion of logistics timeliness that does not meet the preset requirements in the target area is greater than the preset value, the logistics management in the target area will be optimized.

9. An electronic device, comprising a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory so that the electronic device executes each step of the logistics order statistics management method as described in any one of claims 1-7.

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

Images