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CN-122022691-A - Big data-based supply chain logistics storage service analysis and management system and method

CN122022691ACN 122022691 ACN122022691 ACN 122022691ACN-122022691-A

Abstract

The invention discloses a supply chain logistics storage service analysis management system and method based on big data, which relate to the technical field of storage service management and comprise the following steps: and after the warehousing operation is finished, carrying out visual management on information of a cargo storage area and a storage area, entering a picking stage after the warehousing and storage tasks are finished, carrying out wave division optimization in a mode of intercepting orders according to time and carrying out aggregate value analysis, setting a wave size threshold interval, arranging cargo picking work and carrying out visual management on picking data according to the optimized wave size within the wave size threshold interval, and collecting and carrying out visual management on the picking data when the cargoes are picked out of the warehouse, so that the whole process of picking from the warehouse in, storage and warehouse out is carried out in real time and transparent management through data visualization, and the picking efficiency is improved.

Inventors

  • YAN WENLIANG
  • YANG DAN
  • LI WANFENG

Assignees

  • 青岛远航致高供应链有限公司

Dates

Publication Date
20260512
Application Date
20260210

Claims (10)

  1. 1. The large data-based supply chain logistics storage service analysis and management method is characterized by comprising the following steps of: Carrying out goods warehousing operation and visual management of warehousing data; After the warehousing operation is completed, carrying out goods storage and visual management of storage area information; entering a picking stage after the completion of warehousing and storage tasks, performing wave division optimization in a mode of intercepting orders according to time and performing aggregate value analysis, and setting a wave size threshold interval so that the divided wave size is within the wave size threshold interval; goods picking work is arranged according to the optimized wave number, and visual management of picking data is carried out; And when the goods are delivered, the delivery data are collected and visual management is carried out on the delivery data.
  2. 2. The method for analyzing and managing big data based supply chain logistics warehouse services of claim 1, wherein the performing the optimization of the division of the wave number comprises the steps of: retrieving order information to be picked, wherein the order information to be picked comprises order creation time, the quantity of cargoes contained in each order and regional data of the cargoes; Acquiring the creation time T 1 of the order with the earliest creation time in the order to be picked, intercepting the order with the creation time within the [ T 1 ,T 1 +t ] time period at the time interval T as a candidate aggregation order, intercepting n candidate aggregation orders altogether, extracting a cargo quantity set A= { A 1 ,A 2 ,...,A n } contained in the n candidate aggregation orders, preprocessing the region data of the cargo storage, acquiring the region number of the cargo storage contained in the n candidate aggregation orders, acquiring the number k with the largest occurrence number, taking the region with the numbers k, k-1 and k+1 as a matching region, counting the number m of the matching regions in the region of the cargo storage contained in the n candidate aggregation orders according to a formula Calculating the aggregate value W of n candidate aggregate orders, wherein i represents the ith order in the n candidate aggregate orders, setting the aggregate value threshold as W, comparing W with W, if W is less than W, not dividing the n candidate aggregate orders into the same wave number, and if W is more than or equal to W, dividing the n candidate aggregate orders into the same wave number.
  3. 3. The method for analyzing and managing supply chain logistics warehouse service based on big data as set forth in claim 2, wherein if W is greater than or equal to W, counting the total number of cargoes contained in n candidate aggregated orders as B, setting a threshold interval of wave size as [ a, B ], wherein a and B are respectively a lower limit and an upper limit of the threshold interval of wave size, the wave size refers to the number of cargoes contained in orders divided into the same wave, comparing B with the threshold interval of wave size, if B < a, adding candidate aggregated orders and then analyzing the aggregate value again, if B > B, randomly dividing the orders divided into f wave times, wherein the size of each wave time in f wave times is within the interval of [ a, B ], if a is less than or equal to B, confirming that the corresponding order is the order divided into the same wave, and performing wave division in the next time period, wherein the next time period refers to (T 1 +t,T 1 +2t ].
  4. 4. The method for analyzing and managing large data based supply chain logistics warehouse service of claim 3, wherein the method for adding candidate aggregate orders is characterized in that the last order created after T 1 +t time is obtained as order C 1 , the quantity of goods contained in order C 1 is r 1 , if B+r 1 > a, order C 1 is added to candidate aggregate orders, aggregate value analysis is carried out on n candidate aggregate orders and order C 1 , if B+r 1 is less than or equal to a, the last order created after order C 1 is obtained as order C 2 , the quantity of goods contained in order C 2 is r 2 , if B+r 1 +r 2 > a, order C 1 and order C 2 are added to candidate aggregate orders, aggregate value analysis is carried out on n candidate aggregate orders and order C 1 , And B+r 1 +r 2 is less than or equal to a, adding the order to the candidate aggregate order according to the order from the beginning to the end of the creation time until the sum of the quantity of goods contained in the added order and B is greater than a, and after the candidate aggregate order is added, carrying out aggregate value analysis and wave number division again on all the candidate aggregate orders until the wave number after division is within the [ a, B ] interval.
  5. 5. The method for analyzing and managing large data based supply chain logistics warehouse services according to claim 3, wherein the step of setting a threshold interval of the wave number comprises setting a lower limit of the threshold interval of the wave number and setting an upper limit of the threshold interval of the wave number, the step of setting the lower limit of the threshold interval of the wave number comprises calling historical data of wave number dividing and picking in a fixed mode, wherein the fixed mode is a mode of cutting orders at fixed time intervals and dividing all cut orders into the same wave number and arranging pickers to pick according to the wave number, and the historical data comprises an order arrival rate, a time interval of each time interval, The divided wave number and the picking path length are obtained, and the wave number of each division when the order arrival rate is u 1 , And after dividing, arranging the picking path length of the pickers when picking, comparing the picking path lengths, screening out the wave sizes corresponding to the two shortest picking path lengths to be H 1 and H 1 ,H 1 >h 1 respectively, generating a lower threshold setting training sample to be { (u 1 ,h 1 ),(u 2 ,h 2 ),...,(u v ,h v ) }, and an upper threshold setting training sample to be { (u 1 ,H 1 ),(u 2 ,H 2 ),...,(u v ,H v ) }, wherein v represents a total of v different order arrival rates, H v represents the minimum value of the wave sizes corresponding to the two shortest picking path lengths screened out when the order arrival rate is u v , H v represents the maximum value of the wave sizes corresponding to the two shortest picking path lengths screened out when the order arrival rate is u v , performing straight line fitting on the lower threshold setting training sample, and establishing a lower threshold setting model: y=βx+θ, where β and θ represent fitting coefficients, x represents a variable in the lower threshold setting model that refers to an order arrival rate, y represents a variable in the lower threshold setting model that refers to a wave order size, the order arrival rate in the [ T 1 ,T 1 +t ] time period is obtained as p, p is input into the lower threshold setting model, let x=p, and obtain an output result of β×p+θ, and then set a=β×p+θ, where the upper limit of the wave order size threshold interval is set in the same manner as the lower limit.
  6. 6. The method for analyzing and managing the supply chain logistics warehouse service based on big data according to claim 3, wherein the method for arranging the cargo picking work according to the optimized wave number and performing the visual management of the picking data comprises the steps of arranging a picker to pick the cargo according to the divided wave number after the wave number division is completed, guiding the picker to reach the cargo area of each wave number, and uploading the picking progress data to a visual billboard when the picker picks the cargo.
  7. 7. The method for analyzing and managing big data-based supply chain logistics warehouse services of claim 1, wherein the performing cargo warehouse entry operation and visual management of warehouse entry data comprises: The RFID scanning equipment is utilized to automatically collect the warehousing data, the scanning equipment is in butt joint with the WMS so as to automatically synchronize the data, operators, operation time and equipment information are recorded during scanning, the upper limit and the lower limit of the inventory are set, early warning is automatically triggered when the inventory does not meet the upper limit and the lower limit conditions, a visual billboard is built, the collected warehousing data is uploaded to the visual billboard, and the warehousing task and the warehousing progress information are displayed in real time by the contents of the billboard.
  8. 8. The method for analysis and management of big data based supply chain logistics warehouse services of claim 1, wherein the performing of visual management of information of goods storage and storage areas comprises: After the warehouse entry is completed, the goods are stored in the corresponding storage areas, and the area data stored by the goods are uploaded to the visual billboard for display.
  9. 9. The method for analyzing and managing big data-based supply chain logistics warehouse services of claim 1, wherein the steps of collecting and visually managing the warehouse-out data comprise: When goods are delivered, the RFID scanning equipment is utilized to automatically collect delivery data, the delivery data is uploaded to the visual billboard, and delivery progress, inventory state after delivery and abnormal order data are displayed in real time through the visual billboard.
  10. 10. The large data-based supply chain logistics storage service analysis and management system is applied to the large data-based supply chain logistics storage service analysis and management method as set forth in any one of claims 1-9, and is characterized by comprising a storage management module, a goods storage management module, a picking optimization module, a goods picking module and a delivery management module; Carrying out cargo warehousing operation and visual management of warehousing data through the warehousing management module; After the warehouse-in operation is completed, the goods storage management module performs visual management on the goods storage and the storage area information; performing wave division optimization in a picking stage through the picking optimization module; arranging cargo picking work according to the optimized wave number through the cargo picking module and carrying out visual management on picking data; And when the goods are delivered, the delivery management module collects delivery data and performs visual management on the delivery data.

Description

Big data-based supply chain logistics storage service analysis and management system and method Technical Field The invention relates to the technical field of warehouse service management, in particular to a supply chain logistics warehouse service analysis and management system and method based on big data. Background The construction of the digital intelligent logistics platform can realize the seamless connection of cooperative units such as a goods right party, a trade party, a warehouse party and the like, can realize real-time cooperative business full process, realize information cooperation, real-time sharing and real-time processing, in a warehouse management link, the operation and the efficiency are put in important first place, the operation personnel of the most basic layer are integrated into the top layer design of the whole business process so as to reduce the whole operation cost of the warehouse and improve the operation efficiency, the digital intelligent logistics platform is a core target of modern supply chain operation, the whole process real-time transparent management from warehouse entry, storage and picking to warehouse exit can be automatically replaced by manual recording and paper documents, the operation time of each link is obviously shortened, for example, warehouse entry data acquisition is realized through code scanning during warehouse entry, accurate inventory control can be realized on the basis of accelerating warehouse entry data acquisition speed, the picking efficiency can be improved in the optimization picking stage, and the warehouse exit link can realize rapid and accurate order form, and the efficiency of the warehouse operation can be greatly improved; however, in the picking stage, the scattered orders are generally divided into groups according to a specific rule, so that a picker can process all the orders in one group at a time, in the prior art, a part of the orders are usually intercepted according to the order creation time sequence at a fixed time interval, the intercepted all the orders are divided into one wave without any analysis, the size of the wave is a fixed value, goods in the orders divided into one wave can be easily scattered in each area, and the picker needs to frequently and repeatedly run to each area to pick when picking, so that the picking efficiency cannot be effectively improved. Disclosure of Invention The invention aims to provide a large data-based supply chain logistics storage service analysis and management system and method, which are used for solving the problems in the prior art. In order to achieve the purpose, the invention provides the following technical scheme that the supply chain logistics storage service analysis and management method based on big data comprises the following steps: The warehouse-in management stage is to manage warehouse-in information; The RFID scanning equipment is utilized to automatically collect warehouse-in data, the scanning equipment is in butt joint with a WMS (i.e. warehouse management system) to automatically synchronize the data, operators, operation time and equipment information are recorded during scanning, upper and lower limits of the inventory are set, early warning is automatically triggered when the inventory does not meet the upper and lower limit conditions, a visual billboard is built, the collected warehouse-in data is uploaded to the visual billboard, and warehouse-in tasks and warehouse-in progress information are displayed in real time by the billboard content. Preferably, the goods are stored in the corresponding storage areas after the warehouse entry is completed, and the data of the areas where the goods are stored are uploaded to the visual bulletin board for display. Preferably, the picking optimization stage comprises entering a picking stage after finishing the task of warehousing and storage, and retrieving order information to be picked, wherein the order information to be picked comprises order creation time, the quantity of cargoes contained in each order and the regional data of the cargoes storage; Acquiring the creation time of an order with the earliest creation time in the orders to be picked as T 1, intercepting the orders with the creation time within the [ T 1,T1 +t ] time period as candidate aggregation orders at a time interval T, intercepting n candidate aggregation orders altogether, extracting a cargo quantity set A= { A 1,A2,...,An } contained in the n candidate aggregation orders, acquiring the region numbers of cargo stores contained in the n candidate aggregation orders, acquiring the number k with the largest occurrence number, taking the regions with the numbers of k, k-1 and k+1 as matching regions, counting the number m of the matching regions in the cargo stores contained in the n candidate aggregation orders according to a formula Calculating the aggregate value W of n candidate aggregate orders, wherein i represents the ith order in t