CN111080028A - Real-time human notch estimation system and using method - Google Patents
Real-time human notch estimation system and using method Download PDFInfo
- Publication number
- CN111080028A CN111080028A CN201911368131.4A CN201911368131A CN111080028A CN 111080028 A CN111080028 A CN 111080028A CN 201911368131 A CN201911368131 A CN 201911368131A CN 111080028 A CN111080028 A CN 111080028A
- Authority
- CN
- China
- Prior art keywords
- picking
- module
- time
- task
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
Abstract
The invention relates to the technical field of real-time manpower estimation, and discloses a real-time manpower gap estimation system, wherein the output end of the real-time manpower estimation system is electrically connected with a goods picking quantity relation module and a goods picking category module, the output end of the goods picking quantity relation module is electrically connected with a shop goods picking module and a goods picking module in a bin, the output end of the shop goods picking module, the goods picking module in the bin and the goods picking category module are electrically connected with an analysis module, the output end of the analysis module is electrically connected with an effective range module, the output end of the effective range module is electrically connected with a calling module, the output end of the calling module is electrically connected with a calculation module, the output end of the calculation module is electrically connected with a display module, the output end of the goods picking quantity relation module is electrically connected with the shop goods picking module and the goods picking module in the bin, the relation of the time for picking goods at different places and, the problem of rational distribution manpower is solved.
Description
Technical Field
The invention relates to the technical field of real-time human forecast, in particular to a real-time human notch forecast system and a use method.
Background
At present, in an online store, too many orders are overstocked, so that people are needed to digest the overstocked orders, the requirement is that the extra manpower needed is predicted according to the overstocked orders aiming at the actual goods picking condition of the current store, and the real-time manpower prediction system is not detailed enough when the excessive orders generated under various different conditions are processed and analyzed, so that the manpower is not conveniently and reasonably distributed.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a real-time human notch estimation system and a use method, has the advantages of carrying out detailed analysis on excessive single quantity generated under various different conditions, and the like, and solves the problem of reasonable human power distribution.
(II) technical scheme
In order to achieve the purpose of reasonably distributing the manpower, the invention provides the following technical scheme: the real-time human notch pre-estimation system comprises a pre-estimation distribution system, wherein electric signals at the output end of the pre-estimation distribution system are all connected with a computer, and the output end of the computer is electrically connected with a picking quantity relation module and a picking category module;
the output end of the goods picking quantity relation module is in electric signal connection with a goods picking module in a store and a goods picking module in a bin;
the electric signals of the output ends of the goods picking module in the store, the goods picking module in the warehouse and the goods picking category module are all connected with an analysis module, the electric signal of the output end of the analysis module is connected with an effective range module, the electric signal of the output end of the effective range module is connected with a calling module, the electric signal of the output end of the calling module is connected with a calculation module, and the electric signal of the output end of the calculation module is connected with a display module.
Preferably, the picking number relation module is used for calculating the relation between the picking task time length and the task number, the output end of the picking number relation module is electrically connected with the order picking module and the in-bin picking module, the order picking module is used for indicating the relation between the task time length of order picking and the number of categories in tasks, the relation between the task time length of order picking and the number of categories in tasks is y 0.615x +5.7346, R2 is 0.9707, the relation between the task time length of order picking in bins and the number of categories in tasks is y 0.3029x +3.9381, and R2 is 0.7832.
Preferably, the order picking category module is the relationship between order picking duration and the specific category contained, and when the order picking duration is refined to the specific category combination, the data fluctuation is large due to the sharp reduction of the number of samples. The method comprises the steps of screening a TOP30 category combination in about 1 month (2019/4/1-2019/5/9) of green colorful shops, wherein the TOP2 categories exceed 50 categories, the number of other categories is less than 50 categories, the number of 30-th categories is less than 10, the data reliability is reduced, the actual picking time is basically distributed between 5min and 8min, two TOP10 categories are taken for analysis, the actual picking time is displayed on a histogram, the histogram distribution of the actual picking time and the domestic fruit and vegetable (avg is 6.5/std is 2.5) and the actual picking time and the domestic fruit and low-temperature milk (avg is 6.5min/std is 4.1), the histogram distribution of the actual picking time and the histogram distribution of the actual picking time are found to be close, but the fluctuation of the histogram itself is large, but a small peak appears obviously at the picking time and is mainly caused by picking, due to the fact that false picking exists in stores, when the types are specifically selected, the accuracy of the time duration is greatly influenced, when the types are specifically selected, the data volume accumulation is too small, when the samples are too small, the data accuracy is greatly influenced, the fact that the types are refined is not significant, and finally the time duration of picking is predicted according to the types.
Preferably, the analysis module estimates the credibility of the data transmitted by the order picking module, the in-bin picking module and the order picking category module, and if the average value is taken as the picking time length according to the distribution of the picking time length, the picking time length of 60% tasks is shorter than the average value, namely the estimated probability that the estimated personnel can meet the production is more than or equal to 60%, namely: the reliability of the estimated result is more than or equal to 60 percent;
the estimation formula is a single quantity Nmax derivation formula which can be processed currently: nmax × (TJHC _ C1 × pJHC _ C1+ TJHC _ C2 × pJHC _ C2+ · TJHC _ C2 × pJHC _ C2) ═ SCRNT × 15min (equation 1), the required number of people Sneed derives the equation: KJHC _ C1 × TJHC _ C1+ TJHC _ C2 × KJHC _ C2+ ·+ TJHC _ C2 × KJHC _ C2 Sneed × 15min (formula 2), picker gap Sgap, Sgap ed-Sneed-SCRNT;
the estimated process is 1.Step 1: the current backlogged task situation, for example, backlogged: task 1, task 2, and task 3, find the corresponding picking duration according to the "number of secondary categories" and "picking partition" in task 1, assuming that task 1 is MC01 and contains 2 categories, then take TMC01_ C2, and find T of all tasks by such pushing, all picking partitions are MC01 and contain 2 categories, KJHC _ C1, the percentage of which is pJHC _ C1, then find Sneed according to TMC01_ C2 and KJHC _ C1 (formula 1), and find Nmax according to TMC01_ C2 and pJHC _ C1 (formula 2).
Preferably, the retrieval module retrieves the data of the half-warehouse stores in the retrieval effective range module and transmits the data to the calculation module.
Preferably, the calculation module is provided with an ID field and a picking member field, the picking member field is used for recording who operates the task, the last person and the picking time field are recorded, the picking time field is the time when the picking member clicks the order, the order is recorded only if the order is successfully picked, the initial value is blank, the picking time field is the time when the picking member clicks the order, the initial value is blank, the picking time field is the time when the picking member scans the upper wall of the code, the initial value is blank, the picking subarea field is MC01, the second-level category number field is the background category of the middle platform, the number of 2-level categories is taken, the picking task ID field is the ID of the picking task in the confluence wall, the updating time field is the last updating time, only the order is picked, the picking is completed, the picking is carried out, the order ID and the order-picking task number are updated, 1. the list is updated each time the picker takes an order, completes a pick, or scans the wall 2. there may be fields empty, such as combineTime, where the combineTime for the pick task is empty if the user clicks on "pick complete" and the order has been cancelled or pick completed on another platform.
Preferably, the display module displays each data calculated by the calculation module, and the data is automatically refreshed after a period of time, and the refresh time is: and once in 15min, the user can scroll left and right and click to display data.
The use method of the real-time human notch estimation system comprises the following steps:
the first step is as follows: the goods picking number module calculates the relation between the goods picking time and the goods picking number, and transmits the data to the goods picking module in the store and the goods picking module in the warehouse for classification calculation.
The second step is that: and the goods picking category module calculates the relation between the goods picking duration and the specific categories contained in the goods picking duration, and then transmits the data to the analysis module.
The third step: the analysis module analyzes the reliability of the transmitted data and then transmits the reliability to the validation module, and the validation range module stores the transmitted data.
The fourth step: the calling module calls effective data from the effective range module and transmits the effective data to the calculation module, and the calculation module calculates the data by using a specified formula and then transmits a calculation result to the display module.
The fifth step: and the display module displays the calculation result.
(III) advantageous effects
Compared with the prior art, the invention provides a real-time human notch estimation system and a use method, and the system has the following beneficial effects:
1. the real-time human notch estimation system and the use method thereof are characterized in that a shop order picking module and an in-bin order picking module are connected through an output end electric signal of an order picking number relation module, the shop order picking module is used for realizing the relation between the task time length of the shop order picking and the number of categories in the task, the relation between the task time length of the shop order picking and the number of the categories in the task is y 0.615x +5.7346, R2 is 0.9707, the in-bin order picking module is used for realizing the relation between the task time length of the in-bin order picking and the number of the categories in the task is y 0.3029x +3.9381, R2 is 0.7832, the relation between the time length of the different places and the number of the categories is distinguished for analyzing and calculating, and the problem of reasonable labor distribution is solved.
2. The real-time human notch estimation system and the use method thereof, the analysis module estimates the credibility of the data transmitted by the goods picking module in the market, the goods picking module in the warehouse and the goods picking category module, and the estimation process is 1.Step 1: the current backlogged task situation, for example, backlogged: task 1, task 2, and task 3, find the corresponding picking duration according to the "number of secondary categories" and "picking partition" in task 1, assuming that task 1 is MC01 and includes 2 categories, then take TMC01_ C2, and so on to find T of all tasks, all picking partitions are MC01 and include 2 categories, including KJHC _ C1, whose percentage is pJHC _ C1, then find Sneed according to TMC01_ C2 and KJHC _ C1 (formula 1), and find Nmax according to TMC01_ C2 and pJHC _ C1 (formula 2), so as to analyze the credibility of the data.
3. According to the real-time human notch estimation system and the use method, the data of the half-warehouse stores in the transfer effective range module are transferred and conveyed to the calculation module through the transfer module, the data which does not need to be calculated are screened out, and the calculation time is saved.
Drawings
FIG. 1 is a block diagram of the system of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings, wherein like elements are designated by like reference numerals, wherein the terms "front", "rear", "left", "right", "upper" and "lower", "bottom" and "top" used in the following description refer to directions in the drawings, and the terms "inner" and "outer" refer to directions toward and away from, respectively, the geometric center of a particular element.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides a technical solution: the system comprises a pre-estimation distribution system, wherein electric signals at the output end of the pre-estimation distribution system are connected with a computer, the output end of the computer is electrically connected with a goods picking quantity relation module and a goods picking category module, the output end of the goods picking quantity relation module is electrically connected with a shop goods picking module and a goods picking module in a warehouse, the output end electric signals of the shop goods picking module, the goods picking module in the warehouse and the goods picking category module are respectively connected with an analysis module, the output end of the analysis module is electrically connected with an effective range module, the output end of the effective range module is electrically connected with a calling module, the output end of the calling module is electrically connected with a calculation module, and the output end of the calculation module is electrically connected with a display module;
the picking quantity relation module is used for calculating the relation between the picking task time length and the task quantity, the output end of the picking quantity relation module is in electric signal connection with a shop picking module and an in-bin picking module, and the shop picking module is used for calculating the relation between the task time length of the shop picking and the category quantity in the task;
the relation between the task time of picking the goods in the store and the number of the categories in the task is y which is 0.615x +5.7346, R2 which is 0.9707, the relation between the task time of picking the goods in the store and the number of the categories in the task is y which is 0.3029x +3.9381, and R2 which is 0.7832;
the picking category module is a relation between picking duration and specific categories contained, when the picking category module is refined to the specific category combination, the number of samples is sharply reduced, the data fluctuation is large, the TOP30 category combination is screened out by about 1 month (2019/4/1-2019/5/9) in a colorful green area store, except that the TOP2 categories exceed 50, the number of other categories is less than 50, the number of 30-th products is less than 10, the data reliability is reduced, the actual picking time is basically distributed between 5min and 8min, two TOP10 categories are taken for analysis, the home fruit and vegetable (avg is 6.5/std is 2.5) and the home fruit and low temperature milk product (avg is 6.5min/std is 4.1), the actual picking time is respectively presented on a histogram, the histogram distribution of the two histograms is found to be relatively close, however, the fluctuation of the histogram is relatively large, but a small peak appears obviously at the picking time of 0, which is mainly caused by false picking, and because the store has false picking, when the type is specifically reached, the calculation of the time length accuracy which is greatly influenced is carried out, when the type is specifically reached, the data amount accumulation is too little, when the sample is too little, the data accuracy is greatly influenced, at the moment, the thinning of the type has no obvious meaning, and finally, the picking time length is predicted through the number of the types when the time is actually estimated;
the analysis module estimates the credibility of the data transmitted by the shop order picking module, the in-warehouse order picking module and the order picking category module, and if the average value is taken as the order picking time length according to the distribution of the order picking time length, the order picking time length of 60 percent of tasks is shorter than the average value, namely the estimated probability that the personnel can meet the production is more than or equal to 60 percent, namely: the credibility of the estimation result is more than or equal to 60 percent, and the estimation formula is a single quantity Nmax derivation formula which can be processed currently: nmax × (TJHC _ C1 × pJHC _ C1+ TJHC _ C2 × pJHC _ C2+ · TJHC _ C2 × pJHC _ C2) ═ SCRNT × 15min (equation 1), the desired person number Sneed is derived as: KJHC _ C1 × TJHC _ C1+ TJHC _ C2 × KJHC _ C2+ __. + TJHC _ C2 × KJHC _ C2 Sneed × 15min (formula 2), picker gap Sgap, Sgap ed-Sneed-SCRNT, predicted course 1.Step 1: current backlogged task conditions, such as backlogged: task 1, task 2 and task 3, finding corresponding picking duration according to the number of secondary categories and picking partitions in task 1, assuming that task 1 is MC01 and comprises 2 categories, taking TMC01_ C2, and finding T of all tasks by analogy, wherein all picking partitions are at MC01 and comprise 2 categories of KJHC _ C1 with the ratio of pJHC _ C1, Sneed can be obtained according to the above TMC01_ C2 and KJHC _ C1 (formula 1), and Nmax can be obtained according to the above TMC01_ C2 and pJHC _ C1 (formula 2);
the calling module is used for calling the half-warehouse store data in the calling effective range module and conveying the data to the calculation module;
the calculation module is provided with an ID field and a picking member field, the picking member field is used for recording the task of who operates, the last-time person and the picking time field are recorded, the picking time field is the time when the picking member clicks the order and is recorded only when the order is successfully picked, the initial value is blank, the picking time field is the time when the picking member clicks the order to finish, the initial value is blank, the wall-up time field is the time when the picking member scans the wall, the initial value is blank, the picking partition field is MC01, the secondary category number field is the background category of the middle platform, the number of 2-level categories is taken, the picking task ID field is the ID of the picking task in the confluence wall, the updating time field is the last updating time, only the order receiving, the picking is finished, the picking is carried out, the wall is updated, whether the order picking, the order picking ID and the order picking task number are performed, 1. the picker updates the table each time the picker takes an order, completes the pick, or scans the wall, 2. there may be fields empty, such as combineTime, if the user clicks on "pick complete", the combineTime for the pick task is empty if the order has been cancelled or pick complete on another platform;
the display module displays each data calculated by the calculation module, and the data can be automatically refreshed after a period of time, wherein the refreshing time is as follows: the data can be displayed by left-right scrolling and clicking once in 15 min;
the use method of the real-time human notch estimation system comprises the following steps:
the first step is as follows: the goods picking number module calculates the relation between the goods picking time and the goods picking number, and transmits the data to the goods picking module in the store and the goods picking module in the warehouse for classification calculation.
The second step is that: and the goods picking category module calculates the relation between the goods picking duration and the specific categories contained in the goods picking duration, and then transmits the data to the analysis module.
The third step: the analysis module analyzes the reliability of the transmitted data and then transmits the reliability to the validation module, and the validation range module stores the transmitted data.
The fourth step: the calling module calls effective data from the effective range module and transmits the effective data to the calculation module, and the calculation module calculates the data by using a specified formula and then transmits a calculation result to the display module.
The fifth step: and the display module displays the calculation result.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. Real-time manpower breach estimation system, including estimating distribution system, its characterized in that: the output end electric signal of the pre-estimation distribution system is connected with a computer, the output end electric signal of the computer is connected with a goods picking quantity relation module and a goods picking category module, the output end electric signal of the goods picking quantity relation module is connected with a goods picking module in a shop and a goods picking module in a warehouse, the output end electric signal of the goods picking module in the shop, the goods picking module in the warehouse and the goods picking category module is connected with an analysis module, the output end electric signal of the analysis module is connected with an effective range module, the output end electric signal of the effective range module is connected with a calling module, the output end electric signal of the calling module is connected with a calculation module, and the output end electric signal of the calculation module.
2. The real-time human notch prediction system of claim 1, further comprising: the picking quantity relation module is used for calculating the relation between the picking task duration and the task quantity;
the output end of the picking number relation module is electrically connected with a shop picking module and an in-bin picking module, the shop picking module is used for realizing the relation between the task time length of the shop picking and the number of categories in the task, the relation between the task time length of the shop picking and the number of the categories in the task is y equal to 0.615x +5.7346, R2 equal to 0.9707, the in-bin picking module is used for realizing the relation between the task time length of the in-bin picking and the number of the categories in the task is y equal to 0.3029x +3.9381, and R2 equal to 0.7832.
3. The real-time human notch prediction system of claim 1, wherein: the picking category module is a relation between picking duration and specific categories contained, when the picking category module is refined to the specific category combination, due to the fact that the number of samples is sharply reduced, data fluctuation is large, a TOP30 category combination is screened out in about 1 month (2019/4/1-2019/5/9) of a colorful store in a green area, except that the TOP2 categories exceed 50, the number of other categories is less than 50, the data credibility is reduced when the number of the 30 th name is less than 10, the actual picking time is basically distributed between 5min and 8min, two of the TOP10 categories are taken for analysis, the actual picking time is respectively presented on a histogram, and the histogram distribution of the two histograms is found to be close, however, the fluctuation of the histogram is relatively large, but a small peak appears obviously at the picking time of 0, which is mainly caused by false picking, when the store has false picking, and specifically reaches the category, the calculation of the time duration accuracy which is greatly influenced is carried out, when the category is specifically reached, the data amount accumulation is too little, when the sample is too little, the data accuracy is greatly influenced, at the moment, the refinement of the category has no obvious meaning, and finally, the picking time duration is predicted through the number of the categories when the time is actually estimated.
4. The real-time human notch prediction system of claim 1, wherein: the analysis module estimates the credibility of the data transmitted by the shop order picking module, the in-warehouse order picking module and the order picking category module, and if the average value is taken as the order picking time length according to the distribution of the order picking time length, the order picking time length of 60% tasks is shorter than the average value, namely the time is used for estimation:
the estimated possibility that the personnel can meet the production is more than or equal to 60 percent, namely: the reliability of the estimated result is more than or equal to 60 percent;
the estimation formula is a single quantity Nmax derivation formula which can be processed currently: nmax × (TJHC _ C1 × pJHC _ C1+ TJHC _ C2 × pJHC _ C2+ · TJHC _ C2 × pJHC _ C2) ═ SCRNT × 15min (equation 1), the required number of people Sneed derives the equation: KJHC _ C1 × TJHC _ C1+ TJHC _ C2 × KJHC _ C2+ ·+ TJHC _ C2 × KJHC _ C2 Sneed × 15min (formula 2), picker gap Sgap, Sgap ed-Sneed-SCRNT;
the estimated process is 1.Step 1: the current backlogged task situation, for example, backlogged: task 1, task 2, and task 3, finding corresponding picking duration according to "number of secondary categories" and "picking partition" in task 1, assuming that task 1 is MC01 and includes 2 categories, taking TMC01_ C2, and so on to find T of all tasks, all picking partitions are MC01 and include 2 categories including KJHC _ C1, the percentage of which is pJHC _ C1, Sneed can be found according to (formula 1) of TMC01_ C2 and KJHC _ C1, and Nmax can be found according to (formula 2) of TMC01_ C2 and pJHC _ C1.
5. The real-time human notch prediction system of claim 1, wherein: the retrieval module retrieves the data of the half-warehouse stores in the retrieval effective range module and transmits the data to the calculation module.
6. The real-time human notch prediction system of claim 1, wherein: the calculation module is provided with an ID field and a picking member field, wherein the picking member field is used for recording the task of who is operated, the last-time-of-operation person and the picking-starting time field are recorded, the picking-starting time field is the time when the picking member clicks the order and is recorded only when the order is successfully picked, the initial value is blank, the picking-ending time field is the time when the picking member clicks the order and is finished, the initial value is blank, the wall-mounting time field is the time when the picking member scans the code and mounts the wall, the initial value is blank, the picking partition field is MC01, the secondary category number field is the background category of the middle platform, the number of 2-level categories is taken, the picking task ID field is the ID of the picking task in the confluence wall, the updating time field is the last updating time, only the order receiving, the picking is finished and the wall is mounted, the updating, whether the order picking, the order-combining and picking ID and the order combining task number are carried out, and the order combining task number is updated, 1, the picking member operates the order and the order receiving, The "pick complete" and "scan the wall" are updated 2. there may be fields empty, such as combineTime, where the combineTime for the pick task is empty if the user clicks on "pick complete" and the order has been cancelled or the pick is complete on another platform.
7. The real-time human notch prediction system of claim 1, wherein: the display module displays each data calculated by the calculation module, and the data can be automatically refreshed after a period of time, wherein the refreshing time is as follows: and the data can be displayed by left and right scrolling and clicking once in 15 min.
8. The use method of the real-time human notch estimation system is characterized in that: the method comprises the following steps:
the first step is as follows: the goods picking number module calculates the relation between the goods picking time and the goods picking number, and transmits the data to the goods picking module in the store and the goods picking module in the bin for classification calculation;
the second step is that: the calculation result is transmitted to an analysis module, and a picking category module calculates the relationship between picking duration and specific categories contained in the picking duration and transmits data to the analysis module;
the third step: the analysis module analyzes the reliability of the transmitted data and then transmits the reliability to the validation module, and the validation range module stores the transmitted data;
the fourth step: the calling module calls effective data from the effective range module and transmits the effective data to the calculation module, and the calculation module calculates the data by using a specified formula and then transmits a calculation result to the display module;
the fifth step: and the display module displays the calculation result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911368131.4A CN111080028A (en) | 2019-12-26 | 2019-12-26 | Real-time human notch estimation system and using method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911368131.4A CN111080028A (en) | 2019-12-26 | 2019-12-26 | Real-time human notch estimation system and using method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111080028A true CN111080028A (en) | 2020-04-28 |
Family
ID=70318380
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911368131.4A Pending CN111080028A (en) | 2019-12-26 | 2019-12-26 | Real-time human notch estimation system and using method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111080028A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113283826A (en) * | 2021-03-23 | 2021-08-20 | 北京京东振世信息技术有限公司 | Method and system for delivering articles out of warehouse |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN205221675U (en) * | 2015-12-22 | 2016-05-11 | 智达慧远（天津）科技咨询有限公司 | Past heritage intelligent classification device |
| CN105608549A (en) * | 2016-01-27 | 2016-05-25 | 郭兵 | Dispensing storage method |
| CN108304980A (en) * | 2017-01-11 | 2018-07-20 | 顺丰速运有限公司 | Sorter and piece supplying table stand-by period configuration method and system |
| CN108629436A (en) * | 2017-03-15 | 2018-10-09 | 阿里巴巴集团控股有限公司 | A kind of method and electronic equipment of estimation warehouse picking ability |
-
2019
- 2019-12-26 CN CN201911368131.4A patent/CN111080028A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN205221675U (en) * | 2015-12-22 | 2016-05-11 | 智达慧远（天津）科技咨询有限公司 | Past heritage intelligent classification device |
| CN105608549A (en) * | 2016-01-27 | 2016-05-25 | 郭兵 | Dispensing storage method |
| CN108304980A (en) * | 2017-01-11 | 2018-07-20 | 顺丰速运有限公司 | Sorter and piece supplying table stand-by period configuration method and system |
| CN108629436A (en) * | 2017-03-15 | 2018-10-09 | 阿里巴巴集团控股有限公司 | A kind of method and electronic equipment of estimation warehouse picking ability |
Non-Patent Citations (1)
| Title |
|---|
| 张瑾: "《模块化分拣下的需求预测与补货问题研究》", 《中国优秀硕士学位论文全文数据库经济与管理科学辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113283826A (en) * | 2021-03-23 | 2021-08-20 | 北京京东振世信息技术有限公司 | Method and system for delivering articles out of warehouse |
| CN113283826B (en) * | 2021-03-23 | 2023-11-07 | 北京京东振世信息技术有限公司 | Method and system for delivering articles out of warehouse |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104050187B (en) | Search result methods of exhibiting and system | |
| CN108549851A (en) | Method and device for identifying goods in intelligent container and intelligent container | |
| CN104636933A (en) | Method and device for positioning oversell reasons of e-commerce website | |
| CN103884152A (en) | Refrigerator capable of providing health indexes and system | |
| CN112633788A (en) | Method and device for determining warehousing storage position, electronic equipment and storage medium | |
| CN114219558B (en) | Intelligent agricultural product recommendation system based on data mining | |
| CN112053168A (en) | Material monitoring method and device, electronic equipment and storage medium | |
| CN111340425A (en) | Warehouse recommendation method and device, storage medium and electronic device | |
| CN113689258A (en) | Commodity recommendation heat generation method and system | |
| CN113689259A (en) | Commodity personalized recommendation method and system based on user behaviors | |
| CN111080028A (en) | Real-time human notch estimation system and using method | |
| US20210279784A1 (en) | Analysis method and system for the item on the supermarket shelf | |
| CN113627990A (en) | Big data analysis system and analysis method for accurate sale of agricultural products | |
| CN108629467B (en) | Sample information processing method and system | |
| CN104933601B (en) | A kind of method and apparatus exporting merchandise news | |
| CN111209368A (en) | Information prompting method and device, computer readable storage medium and electronic device | |
| CN109656540B (en) | Replenishment stock space recommendation method based on Apriori algorithm | |
| CN112396496B (en) | Intelligent commodity recommendation management system of electronic commerce platform based on cloud computing | |
| CN111707034A (en) | Food taking management method, device and equipment and storage medium | |
| CN110826948A (en) | Warehouse product selection method and device | |
| CN110487016B (en) | Method, device and system for precisely pushing information to intelligent refrigerator | |
| CN114418474A (en) | E-commerce logistics cold chain management method and system based on Internet | |
| CN113781183A (en) | Commodity display method and device, computer equipment and computer-readable storage medium | |
| CN110675552A (en) | Self-service selling system | |
| CN112396363A (en) | Method, device, equipment and computer readable medium for shelving articles |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |