CN116051005B - Product management method and system in intelligent warehouse system - Google Patents
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Abstract
The invention provides a product management method and system in an intelligent warehousing system, comprising the following steps: acquiring identification information of a product to be stored, inputting the identification information into a classification model for classification, determining a corresponding warehouse type, and acquiring a warehouse of the corresponding warehouse type; dividing a storage space of a warehouse into a plurality of storage space areas, and configuring corresponding first parameter values for the storage space areas; respectively acquiring the attribute of each product; sorting according to the order of the due dates of the products from near to far to obtain a product sorting table, and obtaining a first historical shipment table and a second historical shipment table; respectively configuring second parameter values for the products according to the product sorting table, the first historical shipment table and the second historical shipment table; selecting a first storage space region with the maximum first parameter value, and sequentially storing a product with the maximum second parameter value into the first storage space region; the invention automatically determines the storage space area of the product, thereby storing the product in the storage position in the storage space area.
Description
Technical Field
The invention belongs to the technical field of warehouse management systems, and particularly relates to a product management method and system in an intelligent warehouse system.
Background
Currently, after finishing product processing, in order to ensure orderly shipment of products, manufacturers generally need to store the products in a warehouse and then perform shipment operation. Different products should be stored in different warehouses or in different areas to avoid cluttering the products together.
In the prior art, the storage position of a product in a warehouse is generally determined by relying on experience of a user, and the storage position of the product in the warehouse cannot be automatically generated, so that the efficiency of storing the product is low.
Disclosure of Invention
The invention mainly aims to provide a product management method and system in an intelligent warehouse system, which aim to overcome the defect that the storage position of a product in a warehouse cannot be automatically generated.
In order to achieve the above-mentioned object, the present invention provides a method for managing products in an intelligent warehouse system, comprising the following steps:
acquiring identification information of a product to be stored; wherein the identification information comprises a product type and a product size; inputting the identification information of the product into a classification model for classification, determining the warehouse type corresponding to the product according to the classification result, and acquiring a warehouse corresponding to the warehouse type; wherein the classification model is a deep learning model which is trained in advance;
dividing a storage space capable of storing products in a warehouse into a plurality of storage space regions, and configuring corresponding first parameter values for each storage space region; wherein the warehouse stores only one type of product, each storage space region comprising a plurality of storage locations of the same size, each storage location for storing a first quantity of products;
respectively acquiring the attribute of each product to be stored in the warehouse; wherein the attribute comprises a product expiration date;
sorting the products according to the sequence from near to far of the expiration date of the products to obtain a product sorting table, and obtaining a first historical shipment table and a second historical shipment table of the warehouse; respectively configuring second parameter values for the products according to the product sorting table, the first historical shipment table and the second historical shipment table;
selecting a first storage space region with the largest first parameter value from the storage space regions capable of storing products at present, and sequentially storing the products with the largest second parameter value into the first storage space region; and when the first storage space region cannot continuously store the products, selecting a second storage space region with the maximum first parameter value from the rest storage space regions capable of storing the products, and storing the non-stored products in the second storage space region until all the products are stored.
Further, the warehouse also comprises a transit space and a shipment space;
under the condition that the number of products to be shipped is an integer multiple of the first number, directly moving the products in the storage positions of the integer number to the shipment space for shipment;
and under the condition that the number of products to be shipped is not an integral multiple of the first number, moving the corresponding number of products to the transit space for quantity statistics, and then moving the products to the shipment space for shipment.
Further, configuring a corresponding first parameter value for each storage space region, including:
the first parameter value=1/(distance of storage space area from transfer space+distance of storage space area from shipment space).
Further, the first historical shipment table of the warehouse includes a first shipment record in the case that the number of products to be shipped is an integer multiple of the first number, the first shipment record including shipment time and shipment number of products;
the second historical shipment table of the warehouse comprises a second shipment record in the case that the number of products to be shipped is not an integer multiple of the first number, and the second shipment record comprises shipment time and shipment number.
Further, the configuring, according to the product sorting table, the first historical shipment table, and the second historical shipment table, second parameter values for the products respectively includes:
acquiring a third quantity of products currently stored in the warehouse; obtaining a second number of products in the product ordering table; obtaining the total product quantity according to the sum of the third quantity and the second quantity;
calculating the average daily product quantity of the warehouse based on the first historical shipment table and the second historical shipment table, and calculating estimated shipment days for all products in the product sorting table to be completely shipped: wherein, estimated number of days of shipment = total number of products/average number of products shipped per day in warehouse;
respectively counting the number of the first shipment records and the number of the second shipment records in the first historical shipment table and the second historical shipment table, and adding to obtain the total shipment record number;
calculating the total quantity of products delivered from the warehouse based on the first historical delivery table and the second historical delivery table, and calculating to obtain the average quantity of each delivery according to the total quantity of products delivered from the warehouse and the total delivery record quantity; wherein, the average number of products per shipment = total number of products shipped from the warehouse/total number of records of shipment;
calculating estimated shipment times based on the total product quantity and the average shipment quantity per time; wherein, the estimated shipment times = total product quantity/average shipment quantity per time;
calculating the second parameter value according to the estimated shipment times and the estimated shipment days:
second parameter value = estimated number of shipments/estimated number of shipments days.
Further, the user stores the product to be stored in a warehouse, including storing the product in a storage space area of the warehouse, and storing the product in the storage space area in a usable storage position.
Further, in the process that the user stores the product to be stored in the warehouse, the personal ID of the user, the time point of receiving the storage task and the time point of completing the storage task are recorded to generate a storage record table.
Further, at different time points in the process of storing the products to be stored into a warehouse by a user, recording the personal ID, the personal speed and the speed acquisition time point of the user to generate a mobile record table.
Further, the stored analysis table is generated by the steps of:
extracting a personal ID (identity) in a first data record, a time point for receiving a storage task and a time point for completing the storage task aiming at the first data record in the storage record table;
selecting a second data record from the mobile record table; the second data record comprises a personal ID, a personal speed and a speed acquisition time point; the personal ID in the second data record is the same as the personal ID in the first data record, the speed acquisition time point is between the time point of receiving the storage task and the time point of completing the storage task in the first data record, and the earliest speed acquisition time point in the second data record is taken as a first time point;
acquiring a first personal speed in a second data record corresponding to the first time point;
judging whether the first personal speed meets the condition of less than or equal to a preset speed threshold value, if so, updating a speed acquisition time point which is later than a first time point in the second data record and is closest to the first time point into the first time point, and jumping to the previous step; if the speed is greater than the preset speed threshold, the first time point is taken as a starting time point;
taking the latest speed acquisition time point from the second data record as a second time point;
acquiring a second person speed in a second data record corresponding to a second point in time;
judging whether the second personal speed meets the condition of being smaller than or equal to a preset speed threshold value, if yes, updating a speed acquisition time point which is in the second data record and is closest to the second time point into the second time point, and jumping to the last step, wherein if the speed acquisition time point is larger than the preset speed threshold value, the second time point is taken as an ending time point;
and adding a start time point and an end time point to the first data record to generate a data record in a storage analysis table.
The invention also provides a product management system in the intelligent warehouse system, which comprises:
the classification module is used for acquiring the identification information of the products to be stored; wherein the identification information comprises a product type and a product size; inputting the identification information of the product into a classification model for classification, determining the warehouse type corresponding to the product according to the classification result, and acquiring a warehouse corresponding to the warehouse type; wherein the classification model is a deep learning model which is trained in advance;
the dividing module is used for dividing a storage space capable of storing products in the warehouse into a plurality of storage space areas, and configuring corresponding first parameter values for each storage space area; wherein the warehouse stores only one type of product, each storage space region comprising a plurality of storage locations of the same size, each storage location for storing a first quantity of products;
the acquisition module is used for respectively acquiring the attributes of the products aiming at each product required to be stored in the warehouse; wherein the attribute comprises a product expiration date;
the configuration module is used for sequencing the products according to the sequence from near to far of the expiration date of the products to obtain a product sequencing table, and acquiring a first historical shipment table and a second historical shipment table of the warehouse; respectively configuring second parameter values for the products according to the product sorting table, the first historical shipment table and the second historical shipment table;
the storage module is used for selecting a first storage space region with the largest first parameter value from the storage space regions capable of storing products at present, and storing the products with the largest second parameter value into the first storage space region in sequence; and when the first storage space region cannot continuously store the products, selecting a second storage space region with the maximum first parameter value from the rest storage space regions capable of storing the products, and storing the non-stored products in the second storage space region until all the products are stored.
Compared with the prior art, the invention has the following beneficial effects:
in the method, firstly, identification information of products to be stored is acquired, the products are classified and stored into corresponding warehouses according to the identification information, and then all storage spaces capable of storing the products in the warehouses are divided into different storage space areas, and first parameter values are respectively configured for the different storage space areas; secondly, for products to be stored in a warehouse, obtaining the expiration date of the products, generating a product sorting table, and respectively configuring second parameter values for the products by combining the first historical shipment table and the second historical shipment table; and finally, storing the products according to the second parameter value and the first parameter values of different storage space areas in the warehouse. The invention can automatically determine the storage space area of the product, thereby storing the product in the storage position in the storage space area.
Drawings
FIG. 1 is a flow chart of steps of a method for product management in an intelligent warehousing system according to the present invention;
fig. 2 is a block diagram of a product management system in an intelligent warehousing system according to the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
It will be understood that the terms "first," "second," and the like, as used herein, may be used to describe various elements, but these elements are not limited by these terms unless otherwise specified. These terms are only used to distinguish one element from another element. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script, without departing from the scope of the present application.
The invention provides a product management method in an intelligent warehousing system as shown in fig. 1, which is realized mainly by executing the following steps:
step S1, obtaining identification information of a product to be stored; wherein the identification information comprises a product type and a product size; inputting the identification information of the product into a classification model for classification, determining the warehouse type corresponding to the product according to the classification result, and acquiring a warehouse corresponding to the warehouse type; wherein the classification model is a deep learning model which is trained in advance;
step S2, dividing a storage space capable of storing products in a warehouse into a plurality of storage space areas, and configuring corresponding first parameter values for each storage space area; wherein the warehouse stores only one type of product, each storage space region comprising a plurality of storage locations of the same size, each storage location for storing a first quantity of products;
step S3, respectively acquiring the attribute of each product required to be stored in the warehouse; wherein the attribute comprises a product expiration date;
s4, sorting the products according to the sequence from near to far of the expiration date of the products to obtain a product sorting table, and acquiring a first historical shipment table and a second historical shipment table of the warehouse; respectively configuring second parameter values for the products according to the product sorting table, the first historical shipment table and the second historical shipment table;
step S5, selecting a first storage space region with the largest first parameter value from the storage space regions capable of storing products at present, and storing the products with the largest second parameter value into the first storage space region in sequence; and when the first storage space region cannot continuously store the products, selecting a second storage space region with the maximum first parameter value from the rest storage space regions capable of storing the products, and storing the non-stored products in the second storage space region until all the products are stored.
In particular, the inventor finds that in the prior art, the storage position of a product in a warehouse is generally determined by relying on experience of a user, and the storage position of the product in the warehouse cannot be automatically generated, so that the efficiency of storing the product is low.
Aiming at the technical problems, the proposal is provided, firstly, the identification information of the product to be stored is obtained; wherein the identification information comprises a product type and a product size; inputting the identification information of the product into a classification model for classification, determining the warehouse type corresponding to the product according to the classification result, and acquiring a warehouse corresponding to the warehouse type; the classification model is a deep learning model which is trained in advance and has a model effect of efficient classification after a large amount of data training. It will be appreciated that an enterprise may include multiple warehouses, with different warehouses being used to store different types of products.
The first parameter values are respectively configured for storage space regions in the warehouse, wherein one storage space region comprises a plurality of storage positions, and one storage position can store a first quantity (such as four, twenty and the like) of products. For example, a storage space region may correspond to a shelf, and storage locations may correspond to different layers of the shelf. And secondly, obtaining a product sorting table of different products required to be stored in the warehouse, simultaneously combining the first historical shipment table and the second historical shipment table to respectively configure second parameter values for the products, and finally storing the products with the largest second parameter values in the warehouse. Through the steps, the storage space regions of different products can be automatically determined, and the storage efficiency of the products is improved.
In an embodiment, the warehouse further comprises a transit space and a shipment space.
Under the condition that the number of products to be shipped is an integer multiple of the first number, directly moving the products in the storage positions of the integer number to the shipment space for shipment;
and under the condition that the number of products to be shipped is not an integral multiple of the first number, moving the corresponding number of products to the transit space for quantity statistics, and then moving the products to the shipment space for shipment.
In an embodiment, configuring a corresponding first parameter value for each storage space region includes:
the first parameter value=1/(distance of storage space area from transfer space+distance of storage space area from shipment space).
Specifically, the inventor considers that when necessary, a certain amount of products need to be taken out from a warehouse to be sent through logistics, namely, shipment operation, and in order to improve shipment efficiency, when the amount of the products to be shipped is an integer multiple of the first amount, namely, the amount of the products to be shipped is the amount of the products corresponding to the integer storage positions, the products can be directly moved to a shipment space to be shipped, and the amount of the products does not need to be counted one by one.
And when the number of the products which are delivered is not integral multiple of the first number, moving the products to a transit space for counting the number of the products, and after counting the number of the products is completed, moving the products to the delivery space. Furthermore, the inventors consider in turn the storage space area further from the transfer space and the shipment space, the longer the shipment time from which the product is selected for shipment, because the product needs to be moved from the storage space area to the transfer space and the shipment space at shipment. In this way, the above formula is proposed to calculate the first parameter values of different storage space regions, according to which the first parameter values are greater for the storage space regions closer to the transfer space and the shipment space.
In an embodiment, the first historical shipment table of the warehouse includes a first shipment record for a case where the number of products to be shipped is an integer multiple of the first number, the first shipment record including shipment time, and shipment number of products;
the second historical shipment table of the warehouse comprises a second shipment record in the case that the number of products to be shipped is not an integer multiple of the first number, and the second shipment record comprises shipment time and shipment number.
In an embodiment, the configuring the second parameter values for the products according to the product sort table, the first historical shipment table, and the second historical shipment table respectively includes:
acquiring a third quantity of products currently stored in the warehouse; obtaining a second number of products in the product ordering table; obtaining the total product quantity according to the sum of the third quantity and the second quantity;
calculating the average daily product quantity of the warehouse based on the first historical shipment table and the second historical shipment table, and calculating estimated shipment days for all products in the product sorting table to be completely shipped: wherein, estimated number of days of shipment = total number of products/average number of products shipped per day in warehouse;
respectively counting the number of the first shipment records and the number of the second shipment records in the first historical shipment table and the second historical shipment table, and adding to obtain the total shipment record number;
calculating the total quantity of products delivered from the warehouse based on the first historical delivery table and the second historical delivery table, and calculating to obtain the average quantity of each delivery according to the total quantity of products delivered from the warehouse and the total delivery record quantity; wherein, the average number of products per shipment = total number of products shipped from the warehouse/total number of records of shipment;
calculating estimated shipment times based on the total product quantity and the average shipment quantity per time; wherein, the estimated shipment times = total product quantity/average shipment quantity per time;
calculating the second parameter value according to the estimated shipment times and the estimated shipment days:
second parameter value = estimated number of shipments/estimated number of shipments days.
Specifically, the purpose of configuring the second parameter value for the product is achieved through the steps, and the third quantity of the products stored in the warehouse at present is obtained; obtaining a second number of products in the product ordering table; obtaining total product quantity according to the sum of the third quantity and the second quantity, calculating the average daily product quantity of the warehouse based on the first historical shipment table and the second historical shipment table, and calculating estimated shipment days for all products in the product sorting table to be completely shipped: wherein, estimated number of days of shipment = total number of products/average number of products shipped per day in warehouse; respectively counting the number of the first shipment records and the number of the second shipment records in the first historical shipment table and the second historical shipment table, and adding to obtain the total shipment record number; calculating the total quantity of products delivered from the warehouse based on the first historical delivery table and the second historical delivery table, and calculating the average quantity of each delivery according to the total quantity of products delivered from the warehouse and the total delivery record quantity; wherein, the average number of products per shipment = total number of products shipped from the warehouse/total number of records of shipment; calculating estimated shipment times based on the total product quantity and the average shipment quantity per time; wherein, the estimated shipment times = total product quantity/average shipment quantity per time; and calculating the second parameter value according to the estimated shipment times and the estimated shipment days.
Wherein, the calculation formula of the second parameter value is: second parameter value = estimated number of shipments/estimated number of shipments days.
And finally calculating a second parameter value of the product by using the formula, wherein the smaller the estimated shipment number is, the larger the estimated shipment number is, and the larger the second parameter value of the product is.
In one embodiment, the user deposits the product to be deposited to a warehouse, including depositing the product to a storage space area of the warehouse, and depositing the product to the usable storage location in the storage space area.
In one embodiment, during the process of storing the product to be stored in the warehouse, the personal ID of the user, the time point of receiving the storage task, and the time point of completing the storage task are recorded to generate a storage record table.
In one embodiment, the personal ID, personal speed, and speed collection time point of the user are recorded at different time points in the process of storing the product to be stored in the warehouse by the user, so as to generate a mobile record table.
Specifically, after automatically determining a storage space region of a product, when a user receives a storage task, the user usually moves the product to the corresponding storage space region first by a human, then stores the product to the corresponding storage position, so that the user completes the storage task, but the prior art lacks fine management to the process, that is, the prior art cannot know how much time the user spends moving the product to the corresponding storage space region and how much time the user spends storing the product to the corresponding storage position in the process, so that in order to further solve the technical problem, the storage record table and the movement record table are generated, and the storage analysis table is generated accordingly.
In one embodiment, a solution for generating a stored analysis table is provided. Specifically, the deposit analysis table is generated by the steps of:
extracting a personal ID (identity) in a first data record, a time point for receiving a storage task and a time point for completing the storage task aiming at the first data record in the storage record table;
selecting a second data record from the mobile record table; the second data record comprises a personal ID, a personal speed and a speed acquisition time point; the personal ID in the second data record is the same as the personal ID in the first data record, the speed acquisition time point is between the time point of receiving the storage task and the time point of completing the storage task in the first data record, and the earliest speed acquisition time point in the second data record is taken as a first time point;
acquiring a first personal speed in a second data record corresponding to the first time point;
judging whether the first personal speed meets the condition of less than or equal to a preset speed threshold value, if so, updating a speed acquisition time point which is later than a first time point in the second data record and is closest to the first time point into the first time point, and jumping to the previous step; if the speed is greater than the preset speed threshold, the first time point is taken as a starting time point;
taking the latest speed acquisition time point from the second data record as a second time point;
acquiring a second person speed in a second data record corresponding to a second point in time;
judging whether the second personal speed meets the condition of being smaller than or equal to a preset speed threshold value, if yes, updating a speed acquisition time point which is in the second data record and is closest to the second time point into the second time point, and jumping to the last step, wherein if the speed acquisition time point is larger than the preset speed threshold value, the second time point is taken as an ending time point;
and adding a start time point and an end time point to the first data record to generate a data record in a storage analysis table.
Referring to fig. 2, in an embodiment of the present invention, there is further provided a product management system in an intelligent warehousing system, including:
the classification module 10 is used for acquiring identification information of products to be stored; wherein the identification information comprises a product type and a product size; inputting the identification information of the product into a classification model for classification, determining the warehouse type corresponding to the product according to the classification result, and acquiring a warehouse corresponding to the warehouse type; wherein the classification model is a deep learning model which is trained in advance;
a dividing module 20, configured to divide a storage space capable of storing products in a warehouse into a plurality of storage space areas, and configure a corresponding first parameter value for each storage space area; wherein the warehouse stores only one type of product, each storage space region comprising a plurality of storage locations of the same size, each storage location for storing a first quantity of products;
an obtaining module 30, configured to obtain, for each product that needs to be stored in the warehouse, an attribute of the product respectively; wherein the attribute comprises a product expiration date;
a configuration module 40, configured to sort the products according to the order of the due dates of the products from near to far to obtain a product sorting table, and obtain a first historical shipment table and a second historical shipment table of the warehouse; respectively configuring second parameter values for the products according to the product sorting table, the first historical shipment table and the second historical shipment table;
a storage module 50, configured to select a first storage space region with a maximum first parameter value from storage space regions capable of storing products currently, and store products with a maximum second parameter value into the first storage space region sequentially; and when the first storage space region cannot continuously store the products, selecting a second storage space region with the maximum first parameter value from the rest storage space regions capable of storing the products, and storing the non-stored products in the second storage space region until all the products are stored.
In this embodiment, the specific implementation of each module in the above system embodiment is described in the above method embodiment, and will not be described herein.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of computer programs, which may be stored on a non-transitory computer readable storage medium, and which, when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the foregoing embodiments may be arbitrarily combined, and for brevity, all of the possible combinations of the technical features of the foregoing embodiments are not described, however, they should be considered as the scope of the disclosure as long as there is no contradiction between the combinations of the technical features.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (5)
1. The product management method in the intelligent warehousing system is characterized by comprising the following steps of:
acquiring identification information of a product to be stored; wherein the identification information comprises a product type and a product size; inputting the identification information of the product into a classification model for classification, determining the warehouse type corresponding to the product according to the classification result, and acquiring a warehouse corresponding to the warehouse type; wherein the classification model is a deep learning model which is trained in advance;
dividing a storage space capable of storing products in a warehouse into a plurality of storage space regions, and configuring corresponding first parameter values for each storage space region; wherein the warehouse stores only one type of product, each storage space region comprising a plurality of storage locations of the same size, each storage location for storing a first quantity of products; the first parameter value=1/(distance between storage space area and transfer space+distance between storage space area and shipment space);
respectively acquiring the attribute of each product to be stored in the warehouse; wherein the attribute comprises a product expiration date;
sorting the products according to the sequence from near to far of the expiration date of the products to obtain a product sorting table, and obtaining a first historical shipment table and a second historical shipment table of the warehouse; respectively configuring second parameter values for the products according to the product sorting table, the first historical shipment table and the second historical shipment table; the second parameter value expresses the ratio of the estimated shipment times to the estimated shipment days of each product;
selecting a first storage space region with the largest first parameter value from the storage space regions capable of storing products at present, and sequentially storing the products with the largest second parameter value into the first storage space region; when the first storage space region cannot continuously store the products, selecting a second storage space region with the maximum first parameter value from the rest storage space regions capable of storing the products, and storing the non-stored products in the second storage space region until all the products are stored;
storing the product to be stored in a warehouse by a user, wherein the storing of the product in a storage space area of the warehouse is performed, and the product is stored in a usable storage position in the storage space area;
recording the personal ID of the user, the time point of receiving the storage task and the time point of completing the storage task to generate a storage record list in the process that the user stores the product to be stored in a warehouse;
recording the personal ID, personal speed and speed acquisition time points of the user at different time points in the process that the user stores the product to be stored in a warehouse so as to generate a mobile record list;
the stored analysis table is generated by the steps of:
extracting a personal ID (identity) in a first data record, a time point for receiving a storage task and a time point for completing the storage task aiming at the first data record in the storage record table;
selecting a second data record from the mobile record table; the second data record comprises a personal ID, a personal speed and a speed acquisition time point; the personal ID in the second data record is the same as the personal ID in the first data record, the speed acquisition time point is between the time point of receiving the storage task and the time point of completing the storage task in the first data record, and the earliest speed acquisition time point in the second data record is taken as a first time point;
acquiring a first personal speed in a second data record corresponding to the first time point;
judging whether the first personal speed meets the condition of less than or equal to a preset speed threshold value, if so, updating a speed acquisition time point which is later than a first time point in the second data record and is closest to the first time point into the first time point, and jumping to the previous step; if the speed is greater than the preset speed threshold, the first time point is taken as a starting time point;
taking the latest speed acquisition time point from the second data record as a second time point;
acquiring a second person speed in a second data record corresponding to a second point in time;
judging whether the second personal speed meets the condition of being smaller than or equal to a preset speed threshold value, if yes, updating a speed acquisition time point which is in the second data record and is closest to the second time point into the second time point, and jumping to the last step, wherein if the speed acquisition time point is larger than the preset speed threshold value, the second time point is taken as an ending time point;
and adding a start time point and an end time point to the first data record to generate a data record in a storage analysis table.
2. The method of claim 1, wherein the warehouse further comprises a transfer space and a shipment space;
under the condition that the number of products to be shipped is an integer multiple of the first number, directly moving the products in the storage positions of the integer number to the shipment space for shipment;
and under the condition that the number of products to be shipped is not an integral multiple of the first number, moving the corresponding number of products to the transit space for quantity statistics, and then moving the products to the shipment space for shipment.
3. The method of claim 1, wherein the first historical shipment table of the warehouse includes a first shipment record for a case where the number of products to be shipped is an integer multiple of the first number, the first shipment record including shipment time and shipment number;
the second historical shipment table of the warehouse comprises a second shipment record in the case that the number of products to be shipped is not an integer multiple of the first number, and the second shipment record comprises shipment time and shipment number.
4. The method for product management in an intelligent warehousing system according to claim 3, wherein the configuring second parameter values for the products according to the product sort table, the first historical shipment table, and the second historical shipment table, respectively, includes:
acquiring a third quantity of products currently stored in the warehouse; obtaining a second number of products in the product ordering table; obtaining the total product quantity according to the sum of the third quantity and the second quantity;
calculating the average daily product quantity of the warehouse based on the first historical shipment table and the second historical shipment table, and calculating estimated shipment days for all products in the product sorting table to be completely shipped: wherein, estimated number of days of shipment = total number of products/average number of products shipped per day in warehouse;
respectively counting the number of the first shipment records and the number of the second shipment records in the first historical shipment table and the second historical shipment table, and adding to obtain the total shipment record number;
calculating the total quantity of products delivered from the warehouse based on the first historical delivery table and the second historical delivery table, and calculating to obtain the average quantity of each delivery according to the total quantity of products delivered from the warehouse and the total delivery record quantity; wherein, the average number of products per shipment = total number of products shipped from the warehouse/total number of records of shipment;
calculating estimated shipment times based on the total product quantity and the average shipment quantity per time; wherein, the estimated shipment times = total product quantity/average shipment quantity per time;
calculating the second parameter value according to the estimated shipment times and the estimated shipment days:
second parameter value = estimated number of shipments/estimated number of shipments days.
5. A product management system in an intelligent warehousing system comprising:
the classification module is used for acquiring the identification information of the products to be stored; wherein the identification information comprises a product type and a product size; inputting the identification information of the product into a classification model for classification, determining the warehouse type corresponding to the product according to the classification result, and acquiring a warehouse corresponding to the warehouse type; wherein the classification model is a deep learning model which is trained in advance;
the dividing module is used for dividing a storage space capable of storing products in the warehouse into a plurality of storage space areas, and configuring corresponding first parameter values for each storage space area; wherein the warehouse stores only one type of product, each storage space region comprising a plurality of storage locations of the same size, each storage location for storing a first quantity of products; the first parameter value=1/(distance between storage space area and transfer space+distance between storage space area and shipment space);
the acquisition module is used for respectively acquiring the attributes of the products aiming at each product required to be stored in the warehouse; wherein the attribute comprises a product expiration date;
the configuration module is used for sequencing the products according to the sequence from near to far of the expiration date of the products to obtain a product sequencing table, and acquiring a first historical shipment table and a second historical shipment table of the warehouse; respectively configuring second parameter values for the products according to the product sorting table, the first historical shipment table and the second historical shipment table; the second parameter value expresses the ratio of the estimated shipment times to the estimated shipment days of each product;
the storage module is used for selecting a first storage space region with the largest first parameter value from the storage space regions capable of storing products at present, and storing the products with the largest second parameter value into the first storage space region in sequence; when the first storage space region cannot continuously store the products, selecting a second storage space region with the maximum first parameter value from the rest storage space regions capable of storing the products, and storing the non-stored products in the second storage space region until all the products are stored;
storing the product to be stored in a warehouse by a user, wherein the storing of the product in a storage space area of the warehouse is performed, and the product is stored in a usable storage position in the storage space area;
recording the personal ID of the user, the time point of receiving the storage task and the time point of completing the storage task to generate a storage record list in the process that the user stores the product to be stored in a warehouse;
recording the personal ID, personal speed and speed acquisition time points of the user at different time points in the process that the user stores the product to be stored in a warehouse so as to generate a mobile record list;
the stored analysis table is generated by the steps of:
extracting a personal ID (identity) in a first data record, a time point for receiving a storage task and a time point for completing the storage task aiming at the first data record in the storage record table;
selecting a second data record from the mobile record table; the second data record comprises a personal ID, a personal speed and a speed acquisition time point; the personal ID in the second data record is the same as the personal ID in the first data record, the speed acquisition time point is between the time point of receiving the storage task and the time point of completing the storage task in the first data record, and the earliest speed acquisition time point in the second data record is taken as a first time point;
acquiring a first personal speed in a second data record corresponding to the first time point;
judging whether the first personal speed meets the condition of less than or equal to a preset speed threshold value, if so, updating a speed acquisition time point which is later than a first time point in the second data record and is closest to the first time point into the first time point, and jumping to the previous step; if the speed is greater than the preset speed threshold, the first time point is taken as a starting time point;
taking the latest speed acquisition time point from the second data record as a second time point;
acquiring a second person speed in a second data record corresponding to a second point in time;
judging whether the second personal speed meets the condition of being smaller than or equal to a preset speed threshold value, if yes, updating a speed acquisition time point which is in the second data record and is closest to the second time point into the second time point, and jumping to the last step, wherein if the speed acquisition time point is larger than the preset speed threshold value, the second time point is taken as an ending time point;
and adding a start time point and an end time point to the first data record to generate a data record in a storage analysis table.
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