CN112633793B - Method for optimizing warehouse entry allocation of goods space through big data analysis by automatic stereo warehouse - Google Patents
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Abstract
The invention provides a method for optimizing warehouse entry allocation of a goods space by big data analysis in an automatic stereo warehouse, which comprises the following steps: defining a multi-dimensional feature for big data analysis; establishing a SKU ex-warehouse heat table of a product in a database, and inserting the SKU name into the SKU ex-warehouse heat table in a row form; analyzing historical warehouse-in and warehouse-out data, and counting according to the SKU names to obtain the characteristic value of each row of SKU products in the SKU warehouse-out heat meter; sequencing the SKU ex-warehouse heat table by using the characteristic value, and taking the sequenced sequence number as the ex-warehouse heat level; establishing a heat query function, and querying the ex-warehouse heat level of the product through the SKU name and the warehouse-in date; and optimizing the distribution and warehousing of the cargo space according to the ex-warehouse heat level of the product when the product is warehoused or the equipment is idle for cargo management. The invention solves the problem of low delivery efficiency caused by that hot delivery products are stored in more goods positions when delivering the goods when the three-dimensional warehouse is put in storage or the goods are put in a storage.
Description
Technical Field
The invention relates to the field of logistics, in particular to a method for optimizing warehouse entry allocation of a goods space by big data analysis of an automatic three-dimensional warehouse.
Background
The existing warehouse entry allocation of the goods is carried out according to the regional or time sequence, and the allocation modes have the condition of low efficiency in practice, and the allocation speed is influenced.
The patent document CN107862403A discloses a method and a system for scheduling a delivery sequence of goods to be delivered by an unmanned aerial vehicle. The method comprises the following steps: s1: acquiring weight, volume and destination information of each goods to be distributed; s2: establishing an objective function of an unmanned aerial vehicle cargo delivery and distribution sequence by taking the maximum loading weight, the maximum loading volume and the current electric quantity of the unmanned aerial vehicle as constraint conditions; s3: on the premise of meeting constraint conditions, generating an initial solution of an unmanned aerial vehicle cargo delivery sequence, and optimizing the unmanned aerial vehicle cargo delivery sequence based on the objective function to obtain an optimal solution set of the unmanned aerial vehicle cargo delivery sequence; s4: and distributing goods according to the optimal solution set of the unmanned aerial vehicle goods delivery sequence. According to the invention, the cargo delivery sequence at the delivery point is optimized according to the weight, the volume and the destination information of the cargoes to be delivered, so that the purposes of saving the flight energy consumption of the unmanned aerial vehicle and increasing the delivery weight and the volume of the unmanned aerial vehicle are achieved. However, the scheduling problem of hot products cannot be realized by the scheme.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method for optimizing warehouse entry allocation of a goods space by big data analysis in an automatic three-dimensional warehouse.
The invention provides a method for optimizing warehouse entry allocation of a goods space by big data analysis in an automatic stereo warehouse, which comprises the following steps:
The feature definition step: defining a multi-dimensional feature for big data analysis;
The SKU ex-warehouse heat meter establishment step: establishing a SKU ex-warehouse heat table of a product in a database, wherein a table field comprises the SKU name, the ex-warehouse heat level and the multidimensional feature;
SKU name input step: counting the SKU type names of the products through historical warehouse-in and warehouse-out data, and inserting the SKU names into a SKU warehouse-out heat meter in a row mode;
and (3) characteristic value statistics: analyzing historical warehouse-in and warehouse-out data, and counting according to the SKU names to obtain the characteristic value of each row of SKU products in the SKU warehouse-out heat meter;
and a characteristic value ordering step: sequencing the SKU ex-warehouse heat table by using the characteristic value, and taking the sequenced sequence number as the ex-warehouse heat level;
The establishment step of the heat query function: establishing a heat query function, and querying the ex-warehouse heat grade of the product through the sku name and the warehouse-in date;
And (3) distribution and storage: and optimizing the distribution and warehousing of the cargo space according to the ex-warehouse heat level of the product when the product is warehoused or the equipment is idle for cargo management.
Preferably, the heat query function is: f (sku name, date of warehouse entry) =ex-warehouse heat level.
Preferably, a record matching the SKU name is queried through the SKU ex-warehouse heat meter, whether the remaining inventory period of the product to be queried is smaller than the set number of days is judged, if so, the queried ex-warehouse heat level is returned as a result, and if not less than the set number of days, 0 is returned, wherein 0 is the lowest heat.
Preferably, the higher the product delivery heat level, the faster the delivery of the product to the cargo space.
Preferably, the multi-dimensional features include average inventory cycles of products and last week of product stocking.
Preferably, the feature value statistics step includes:
inquiring the warehouse-in date: inquiring the warehouse-in date in the history warehouse-in data according to the bar code value of the product;
Calculating the number of days: inquiring the SKU name and the date of delivery in the historical delivery list according to the bar code value, obtaining the number of days of the bar code delivered to the delivery by subtracting the delivery date from the delivery date, and storing the SKU name and the number of days in the temporary data list 1;
Clustering calculation: clustering calculation is carried out on different days of each SKU product by using the data of the temporary data table 1 to obtain the SKU name, the days and the duty ratio of the result, and the result is stored in the temporary data table 2;
Setting an average inventory period: inquiring the highest 1 day of the duty ratio in each SKU product by using a temporary data table 2, wherein the duty ratio exceeds a preset value as an average inventory period;
an average inventory period assignment step: assigning the average inventory period of the query to an average inventory period field of the SKU ex-warehouse heat meter;
And (3) assigning the number of the warehouse-out: and classifying, summarizing and inquiring the quantity of the last week of delivery in the historical delivery table according to the SKU name, and assigning the quantity of delivery to a field of the last week of delivery of the SKU delivery heat table.
Preferably, the step of distributing and warehousing includes:
dividing: dividing the goods space into a plurality of areas according to the ex-warehouse time of each goods space, and storing the areas into a temporary data table 3;
the distribution step: inquiring ex-warehouse heat level by using a heat inquiry function through SKU names when the products are put in warehouse and distributed with goods places, and distributing the products to a proper goods place area for warehouse entry according to the ex-warehouse heat level;
And a transferring step: and when the equipment is idle, inquiring all ex-warehouse heat levels stored in the warehouse through a heat inquiry function, and moving the products with high ex-warehouse heat stored in the warehouse-out warehouse with low ex-warehouse speed to the warehouse-out warehouse with high ex-warehouse speed.
Preferably, the method further comprises the step of assigning the average-free inventory period: when the average inventory period is not found, a maximum value is given to the field of the average inventory period, which indicates that the change of the inventory period of the SKU is large and irregular.
Preferably, the fields in temporary data table 3 contain the cargo space number, the time of shipment, the area.
Preferably, the areas are divided according to the speed of shipment.
Compared with the prior art, the invention has the following beneficial effects:
1. According to the invention, by adopting a mode of analyzing the product ex-warehouse heat to optimize the cargo space distribution, the problem of low ex-warehouse efficiency caused by storing hot ex-warehouse products in more cargo spaces during ex-warehouse use when a three-dimensional warehouse enters or a tally moves the warehouse is solved;
2. According to the invention, through reasonably configuring the corresponding relation between the products and the goods space, the hot products are planned to the goods space with high shipment speed, so that the efficiency is greatly improved, and the resources are saved.
3. According to the method, the historical warehouse-in and warehouse-out data of the three-dimensional warehouse are analyzed by utilizing a big data analysis technology, the multidimensional characteristic data of the warehouse-in and warehouse-out of the product are extracted, the warehouse-out heat level of the product is divided, and the distribution of goods positions is optimized according to the warehouse-out heat level when the three-dimensional warehouse is in warehouse-in or idle for tally, so that the warehouse-out time cost is reduced, and the warehouse-out efficiency of the three-dimensional warehouse is improved.
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Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
FIG. 1 is a flowchart of method steps for optimizing warehouse entry allocation of a warehouse through big data analysis in an automated stereoscopic warehouse.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present invention.
As shown in fig. 1, the method for optimizing warehouse entry allocation of a warehouse through big data analysis according to the automatic stereoscopic warehouse provided by the invention comprises the following steps:
step 1: defining multidimensional features for big data analysis, and supposing that 2 features are defined 1. Average inventory period of products 2. Last week of delivery of products.
Step 2: and designing a SKU ex-warehouse heat meter of a product in the database, wherein the field of the table comprises the SKU name, the ex-warehouse heat level, the characteristics 1 and the characteristics 2.
Step 3: and counting the SKU type names of the products through the historical database access data, and inserting the SKU names into the SKU database access heat table in a row mode.
Step 4: and analyzing the historical warehouse-in and warehouse-out data, and counting according to the SKU names to obtain the characteristic value of each row of SKU of the SKU warehouse-out heat meter. Specifically, the feature value statistics include:
Step 4.1: and inquiring the warehousing date of the historical warehousing data according to the bar code value of the product.
Step 4.2: inquiring the SKU name and the date of delivery in the historical delivery list according to the bar code value, and obtaining the number of days of delivering the bar code from delivery according to the delivery date minus the delivery date. SKU name, days are stored in temporary data table 1.
Step 4.3: and (3) carrying out clustering calculation on different days of each SKU by using the data of the temporary data table 1 to obtain the name, the number of days and the duty ratio of the SKU, and storing the result into the temporary data table 2.
Step 4.4: the temporary data table 2 is used to look up the average inventory period for the highest 1 day in each sku and for the ratio exceeding a preset value (e.g., 70%).
Step 4.5: assigning the average inventory period queried in the step 4.4 to an average inventory period field of a SKU ex-warehouse heat table; some SKUs assign a maximum value to the average inventory period field, such as 9999, without checking for average inventory periods at step 4.4, which indicates that the SKU's inventory period varies widely and irregularly.
Step 4.6: and classifying, summarizing and inquiring the quantity of the last week of delivery in the historical delivery table according to the SKU name, and assigning the quantity of delivery to a field of the last week of delivery of the SKU delivery heat table.
Step 5: and (3) sequencing the SKU ex-warehouse heat table by using the characteristic value, for example, firstly descending the average inventory period and then ascending the ex-warehouse quantity in the last week, wherein the sequencing sequence number is larger when the average inventory period is shorter, and the sequencing sequence number is used as the ex-warehouse heat level.
Step 6: a heat query function, f (sku name, date of entry) =ex-warehouse heat level, was designed. The product delivery heat level can be inquired when the goods are distributed. Internal logic of the function: and inquiring records matched with the SKU names through the SKU ex-warehouse heat meter, judging whether the residual inventory period of the product to be checked is less than 2 days, returning the inquired ex-warehouse heat level as a result if the residual inventory period is less than 2 days, and returning to 0 if the residual inventory period is more than 1 day, wherein 0 indicates that the heat is the lowest.
Step 7: and when the products are put in storage or the equipment is idle for tallying, inquiring the heat degree grade of the products to be put out of the warehouse according to the SKU names and the warehouse-in dates of the products, and optimizing the distribution and warehouse-in of goods positions. The goods with high ex-warehouse heat level is distributed to the goods with high ex-warehouse speed, and the goods with low ex-warehouse heat level is distributed to the goods with low ex-warehouse speed.
Step 7 comprises the following steps:
Step 7.1: the goods space is divided into a plurality of areas according to the ex-warehouse time of each goods space, the areas are stored in a temporary data table 3, the fields of the table contain the goods space numbers, and the areas are used when the goods are ex-warehouse. The area indicates the speed of delivery of the cargo space, which may be a, B, c.
Step 7.2: when the product is put into storage and allocated with goods space, the heat inquiry function is used for inquiring the heat grade of the product to be put out of the warehouse, and the product is allocated to a proper goods space area for storage according to the heat grade of the product to be put out of the warehouse. For example, the heat level of the warehouse out of 10-50 can be allocated to the cargo space in zone B and 51-79 to the cargo space in zone C.
Step 7.3: and when the equipment is idle, inquiring all ex-warehouse heat levels stored in the warehouse through a heat inquiry function, and moving the products with high ex-warehouse heat stored in the warehouse-out warehouse with low ex-warehouse speed to the warehouse-out warehouse with high ex-warehouse speed.
According to the method, the historical warehouse-in and warehouse-out data of the three-dimensional warehouse are analyzed by utilizing a big data analysis technology, the multidimensional characteristic data of the warehouse-in and warehouse-out of the product are extracted, the warehouse-out heat level of the product is divided, and the distribution of goods positions is optimized according to the warehouse-out heat level when the three-dimensional warehouse is in warehouse-in or idle for tally, so that the warehouse-out time cost is reduced, and the warehouse-out efficiency of the three-dimensional warehouse is improved.
In the description of the present application, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application.
The foregoing describes specific embodiments of the present application. It is to be understood that the application is not limited to the particular embodiments described above, and that various changes or modifications may be made by those skilled in the art within the scope of the appended claims without affecting the spirit of the application. The embodiments of the application and the features of the embodiments may be combined with each other arbitrarily without conflict.
Claims (9)
1. The method for optimizing warehouse entry allocation of the goods places by big data analysis of the automatic stereo warehouse is characterized by comprising the following steps:
The feature definition step: defining a multi-dimensional feature for big data analysis;
The SKU ex-warehouse heat meter establishment step: establishing a SKU ex-warehouse heat table of a product in a database, wherein a table field comprises the SKU name, the ex-warehouse heat level and the multidimensional feature;
SKU name input step: counting the SKU type names of the products through historical warehouse-in and warehouse-out data, and inserting the SKU names into a SKU warehouse-out heat meter in a row mode;
and (3) characteristic value statistics: analyzing historical warehouse-in and warehouse-out data, and counting according to the SKU names to obtain the characteristic value of each row of SKU products in the SKU warehouse-out heat meter;
and a characteristic value ordering step: sequencing the SKU ex-warehouse heat table by using the characteristic value, and taking the sequenced sequence number as the ex-warehouse heat level;
The establishment step of the heat query function: establishing a heat query function, and querying the ex-warehouse heat grade of the product through the sku name and the warehouse-in date;
And (3) distribution and storage: when products are put in storage or equipment is idle for goods management, goods space allocation and put in storage are optimized according to the heat grade of the products when the products are put in storage;
the characteristic value statistics step comprises the following steps:
inquiring the warehouse-in date: inquiring the warehouse-in date in the history warehouse-in data according to the bar code value of the product;
Calculating the number of days: inquiring the SKU name and the date of delivery in the historical delivery list according to the bar code value, obtaining the number of days of the bar code delivered to the delivery by subtracting the delivery date from the delivery date, and storing the SKU name and the number of days in the temporary data list 1;
Clustering calculation: clustering calculation is carried out on different days of each SKU product by using the data of the temporary data table 1 to obtain the SKU name, the days and the duty ratio of the result, and the result is stored in the temporary data table 2;
Setting an average inventory period: inquiring the highest 1 day of the duty ratio in each SKU product by using a temporary data table 2, wherein the duty ratio exceeds a preset value as an average inventory period;
an average inventory period assignment step: assigning the average inventory period of the query to an average inventory period field of the SKU ex-warehouse heat meter;
And (3) assigning the number of the warehouse-out: and classifying, summarizing and inquiring the quantity of the last week of delivery in the historical delivery table according to the SKU name, and assigning the quantity of delivery to a field of the last week of delivery of the SKU delivery heat table.
2. The method for optimizing warehouse entry allocation of a cargo space by big data analysis of an automated stereoscopic warehouse according to claim 1, wherein the heat query function is: f (sku name, date of warehouse entry) =ex-warehouse heat level.
3. The method for optimizing warehouse entry allocation of a cargo space by big data analysis according to claim 2, wherein a record matching SKU name is searched by a SKU ex-warehouse heat table, whether the remaining inventory period of the product to be checked is smaller than a set number of days is judged, if the remaining inventory period is smaller than the set number of days, the searched ex-warehouse heat level is returned as a result, and if the searched ex-warehouse heat level is larger than or equal to the set number of days, 0 is returned, wherein the heat is the lowest.
4. The method for optimizing warehouse entry allocation of a cargo space by big data analysis according to claim 1, wherein the higher the product ex-warehouse heat level is, the faster the ex-warehouse speed of the cargo space to which the product is allocated is.
5. The method for optimizing warehouse entry allocation of a cargo space through big data analysis according to claim 1, wherein the multi-dimensional features include average inventory period of products and last week of shipment of products.
6. The method for optimizing the warehousing distribution of a cargo space by big data analysis of an automated stereoscopic warehouse according to claim 1, wherein the distribution warehousing step comprises:
dividing: dividing the goods space into a plurality of areas according to the ex-warehouse time of each goods space, and storing the areas into a temporary data table 3;
the distribution step: inquiring ex-warehouse heat level by using a heat inquiry function through SKU names when the products are put in warehouse and distributed with goods places, and distributing the products to a proper goods place area for warehouse entry according to the ex-warehouse heat level;
And a transferring step: and when the equipment is idle, inquiring all ex-warehouse heat levels stored in the warehouse through a heat inquiry function, and moving the products with high ex-warehouse heat stored in the warehouse-out warehouse with low ex-warehouse speed to the warehouse-out warehouse with high ex-warehouse speed.
7. The method for optimizing warehouse entry allocation of a warehouse through big data analysis in an automated stereoscopic warehouse of claim 1, further comprising the step of assigning a mean-free inventory period: when the average inventory period is not found, a maximum value is given to the field of the average inventory period, which indicates that the change of the inventory period of the SKU is large and irregular.
8. The method for optimizing warehouse entry allocation of a cargo space by big data analysis according to claim 6, wherein the fields in temporary data table 3 contain cargo space number, time of shipment, area.
9. The method for optimizing warehouse entry allocation of a cargo space through big data analysis according to claim 8, wherein the areas are divided according to the speed of shipment.
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| CN114261668A (en) * | 2021-11-29 | 2022-04-01 | 广东嘉腾机器人自动化有限公司 | Allocation strategy based on dispatching system and multi-storage-position system joint dispatching AGV |
| CN117436795B (en) * | 2023-12-21 | 2024-03-08 | 武汉佰思杰科技有限公司 | Warehouse material monitoring method and system for hierarchical management |
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