Dynamic goods allocation intelligent storage method and system based on Internet of Things

Abstract

The invention discloses a dynamic goods allocation intelligent storage method and system based on the Internet of Things, and relates to the technical field of warehouse storage, and the method comprises the following specific steps: judging whether a goods is a high-frequency type goods or a conventional type goods based on a goods type judgment model, and building a goods historical information database; reading and identifying the basic attributes of the goods newly arriving at the storage warehouse, comparing the basic attributes of the goods with the goods historical information database, and judging that the conventional goods shelves of the conventional goods in the storage warehouse of the corresponding type are placed according to the goods storage adaptive model; and the high-frequency type goods are put into the temporary goods shelves in the storage warehouses of the corresponding types. According to the method, the goods type judgment model and the goods types are conveniently utilized for division, then the goods are adaptively stored through the goods storage adaptive model, and therefore the storage position can be adjusted in real time according to the change of the goods demand quantity, and the storage box can be conveniently taken out.

Description

Technical Field

[0001] This invention relates to the field of warehouse storage technology, and in particular to a dynamic intelligent storage method and system for cargo locations based on the Internet of Things. Background Technology

[0002] With the development of logistics and storage technologies, warehousing has emerged. Warehousing refers to the storage and safekeeping of goods and items in warehouses. "Warehouse" is a general term for buildings and sites used for storing, safekeeping, and preserving goods. These can be buildings, caves, large containers, or specific sites, and have the function of storing and protecting goods. "Storage" means to store or reserve, indicating that goods are collected and stored for future use, and has the meaning of receiving, safekeeping, and delivering goods for use.

[0003] Currently, due to the development of Internet technology, more and more storage warehouses are being integrated with the Internet, resulting in smart warehouses that utilize the powerful computing capabilities of the Internet for efficient storage.

[0004] However, most of the aforementioned smart warehouses utilize the internet to plan reasonable storage locations for goods. But there are some goods that suddenly become popular. These popular goods have the characteristic of a sudden surge in demand, followed by a gradual decrease in demand over time. Existing smart warehouses cannot replan storage locations in real time, which may result in popular goods being located deep inside the smart warehouse, making each storage and retrieval inconvenient. To address this, we propose a dynamic location-based smart storage method and system based on the Internet of Things. Summary of the Invention

[0005] The purpose of this invention is to provide a dynamic intelligent storage method and system based on the Internet of Things. This method facilitates the classification of goods by using a goods type determination model, and then uses a goods storage adaptation model to adapt the goods for storage. This allows for real-time adjustment of storage locations in response to changes in goods demand, thereby facilitating the retrieval of storage boxes.

[0006] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides a dynamic intelligent storage method for cargo locations based on the Internet of Things, comprising the following specific steps: Step 1: Based on the internal layout of the storage warehouse, divide it into different types of storage warehouses, and plan the corresponding temporary shelves and regular shelves in each type of storage warehouse. Step 2: Capture all historical purchase volume data, sales volume data, and sales frequency data of goods, as well as their basic attribute data. Then, based on the goods type determination model, determine whether the goods are high-frequency or regular types of goods, and establish a historical information database of goods. Step 3: Read and identify the basic attributes of the newly arrived goods in the storage warehouse, transmit the basic attributes of the goods to the back-end terminal, and compare the basic attributes of the goods with the historical information database of the goods to determine whether the goods are regular or high-frequency goods. Step 4: Based on the results of determining whether the goods are regular or high-frequency goods in Step 3, and according to the goods storage adaptation model, determine whether regular goods are placed on regular shelves in the corresponding type of storage warehouse, and whether high-frequency goods are placed on temporary shelves in the corresponding type of storage warehouse.

[0007] Preferably, the specific content of the cargo type determination model is as follows: Set the switching threshold between high-frequency goods and regular goods as Z. Capture the inbound volume, outbound volume, and outbound frequency data of goods within time T, and record the inbound volume as A, the outbound volume as B, and the outbound frequency as C. Then, substitute them into the judgment formula to obtain the judgment factor S. Then compare the value of S with Z. When S is less than or equal to Z, the goods are regular goods. When S is greater than Z, the goods are high-frequency goods.

[0008] Preferably, the determination formula is: S = α*A + β*B + γ*C, where α ranges from 0.3 to 0.4, β ranges from 0.5 to 0.6, and γ ranges from 0.8 to 1.

[0009] Preferably, the cargo storage adaptation model contains the following specific details: In different types of storage warehouses, temporary shelves No. 1, No. 2, and No. 3 are set up in order of distance from the warehouse entrance, from closest to furthest. Regular shelves No. 1, No. 2, and No. 3 are also set up. Then, based on the decision factor S for selecting all goods, the goods are prioritized from largest to smallest. For high-frequency goods, storage locations in temporary shelves No. 1, No. 2, or No. 3 are selected first. For regular goods, storage locations in regular shelves No. 1, No. 2, or No. 3 are selected first.

[0010] Preferably, in step two, the goods type determination model determines whether the goods are high-frequency or regular goods, and updates the data automatically every t time interval. The unit of t can be a day, a week, or something else. After each t time interval, the backend terminal re-captures all historical purchase volume data, sales volume data, sales frequency data, and basic attribute data of the goods, and then determines whether the goods are high-frequency or regular goods based on the goods type determination model, and updates the goods history information database. When the goods are determined to be high-frequency goods, if the goods were already high-frequency goods before this determination, they are still stored on the temporary shelf. If the goods were regular goods before this determination, the goods storage adaptation model determines that the high-frequency goods are placed on the temporary shelf in the corresponding type of storage warehouse. When the goods are determined to be regular goods, if the goods were already high-frequency goods before this determination, the goods storage adaptation model determines that the regular goods are placed on the regular shelf in the corresponding type of storage warehouse. If the goods were already regular goods before this determination, they are still stored on the regular shelf.

[0011] Preferably, once the storage location of the goods located on the temporary shelf has been planned, the location of the goods will not change as long as the determination factor S is still greater than the switching threshold Z between high-frequency type goods and regular type goods.

[0012] Preferably, the different types of storage warehouses in step one specifically include constant temperature storage warehouses, low temperature storage warehouses, high temperature storage warehouses, and refrigerated and frozen storage warehouses. Temporary shelves and regular shelves are then set up in each of these different types of storage warehouses, with the temporary shelves located near the warehouse entrance.

[0013] Preferably, the basic information of the goods includes the model, quantity, specifications, storage conditions and shelf life of the goods.

[0014] Preferably, once the storage location of the goods located on the regular shelf has been planned, the location of the goods will not change as long as the determination factor S is still less than or equal to the switching threshold Z between high-frequency type goods and regular type goods.

[0015] Secondly, the present invention provides an IoT-based dynamic storage system for intelligent cargo locations, which implements the IoT-based dynamic storage method as described above. The system includes: The data acquisition module is used to collect environmental parameters within the storage warehouse, as well as the location and status of the goods. The transmission module transmits the information data collected by the data acquisition module using wired or wireless communication. The analysis and processing module is used to analyze and process the information data transmitted from the transmission module, store the information data, establish a warehouse information database and a cargo information database, and issue delivery instructions. The conveying module sends conveying instructions through the processing module to transport goods to the designated location.

[0016] The technical effects and advantages of this invention are as follows: By capturing historical purchase volume, sales volume, and sales frequency data for all goods, and then inputting them into the goods type determination model, it is possible to determine which goods are high-frequency goods and which are regular goods. A goods history information database is also established. When new goods arrive at the storage warehouse, after reading and identifying them, the back-end terminal determines whether the goods are regular or high-frequency goods. According to the goods storage adaptation model, high-frequency goods are stored in temporary shelves, and regular goods are stored in regular shelves. The goods type determination model is dynamically updated using an updatable goods history information database, so as to dynamically store goods based on their purchase volume, sales volume, and sales frequency, thereby facilitating storage or retrieval. By updating the cargo history information database in real time, the cargo in the storage warehouse is dynamically determined based on the cargo type determination model, thereby dynamically storing the cargo and facilitating its later storage or retrieval. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the dynamic intelligent storage method for cargo locations based on the Internet of Things according to the present invention.

[0018] Figure 2 This is a schematic diagram of the IoT-based dynamic cargo location intelligent storage system of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] This invention provides, for example Figure 1-2 The IoT-based dynamic storage method for warehouse locations, as shown, includes the following specific steps: Step 1: Based on the internal layout of the storage warehouse, divide it into different types of storage warehouses, and plan the corresponding temporary shelves and regular shelves in each type of storage warehouse. Step 2: Capture all historical purchase volume data, sales volume data, and sales frequency data of goods, as well as their basic attribute data. Then, based on the goods type determination model, determine whether the goods are high-frequency or regular types of goods, and establish a historical information database of goods. Step 3: Read and identify the basic attributes of the newly arrived goods in the storage warehouse, transmit the basic attributes of the goods to the back-end terminal, and compare the basic attributes of the goods with the historical information database of the goods to determine whether the goods are regular or high-frequency goods. Step 4: Combining the results of Step 3 in determining whether goods are regular or high-frequency, and based on the goods storage adaptation model, regular goods are placed on regular shelves in the corresponding type of storage warehouse, while high-frequency goods are placed on temporary shelves in the corresponding type of storage warehouse. Different types of storage warehouses are pre-established according to the warehouse layout. Corresponding historical shelves and regular shelves are then created in each type of storage warehouse. Historical inbound, outbound, and outbound frequency data for all goods are captured and input into the goods type determination model to determine which goods are high-frequency and which are regular. A goods history information database is established. When new goods arrive at the storage warehouse, after reading and identification, the backend terminal determines whether the goods are regular or high-frequency. Based on the goods storage adaptation model, high-frequency goods are stored on temporary shelves, and regular goods are stored on regular shelves. The updatable goods history information database allows the goods type determination model to be dynamically updated, thus dynamically storing goods based on their inbound, outbound, and outbound frequencies for easy storage or retrieval.

[0021] Furthermore, the specific details of the cargo type determination model are as follows: A switching threshold Z is set between high-frequency cargo and regular cargo. The model captures the cargo's inbound volume, outbound volume, and outbound frequency data within a time period T. Inbound volume is recorded as A, outbound volume as B, and outbound frequency as C. These data are then substituted into the determination formula to obtain the determination factor S. S is then compared with Z. When S is less than or equal to Z, the cargo is considered a regular cargo; when S is greater than Z, the cargo is considered a high-frequency cargo. The determination formula is: S = α*A + β*B + γ*C, where α ranges from 0.3 to 0.4, β ranges from 0.5 to 0.6, and γ ranges from 0.8~1; The specific content of the cargo storage adaptation model is as follows: In different types of storage warehouses, temporary shelves No. 1, No. 2, and No. 3 are set up in order of distance from the warehouse entrance, from closest to furthest. Then, based on the decision factor S for all selected goods, the goods are prioritized from largest to smallest. For high-frequency goods, storage locations are selected first from temporary shelves No. 1, No. 2, or No. 3. For regular goods, storage locations are selected first from regular shelves No. 1. The storage location is on the shelf, or on the second or third regular shelf. Here, goods J, K, and L are identified. Then, the historical purchase volume, sales volume, and sales frequency data for each of goods J, K, and L are retrieved. Specifically, the purchase volume for goods J is set to A1, the sales volume to B1, and the sales frequency to C1; the purchase volume for goods K is set to A2, the sales volume to B2, and the sales frequency to C2; and the purchase volume for goods L is set to A3, the sales volume to B3, and the sales frequency to C3. Then, A1, B1, C1 and A2, B2, C2... Substituting A3, B3, and C3 into the judgment formula, we obtain the judgment factor S1 = α*A1 + β*B1 + γ*C1 for goods J, S2 = α*A2 + β*B2 + γ*C2 for goods K, and S3 = α*A3 + β*B3 + γ*C3 for goods L. When S1, S2, and S3 are all greater than Z, they are all judged as high-frequency goods. Then, they are prioritized according to their values ​​from largest to smallest. The priority order is set as S1, S2, and S3. Goods J are then prioritized for temporary shelf number one or temporary shelf number two. The system prioritizes finding a storage location on temporary shelf number 1 or 3, followed by goods K and L. If S1, S2, and S3 are all less than or equal to Z, they are considered regular goods. In this case, they are prioritized according to their values ​​from largest to smallest, set as S1, S2, and S3. Goods J are then prioritized for storage on regular shelf number 1, 2, or 3, followed by goods K and L. In practice, the number of temporary and regular shelves can be adjusted according to actual needs.

[0022] Furthermore, in step two, based on the cargo type determination model, the system determines whether the cargo is a high-frequency or regular type. This is then automatically updated every t time interval, where t can be a day, week, or other unit. Each time interval t, the backend terminal re-captures all historical cargo purchase volume data, shipment volume data, shipment frequency data, and basic cargo attribute data. Based on the cargo type determination model again, it determines whether the cargo is a high-frequency or regular type and updates the cargo history information database. If the cargo is determined to be a high-frequency type, and if the cargo was already a high-frequency type before this determination, it remains stored on the temporary shelf. If the cargo is a high-frequency type in this determination... Previously, goods were classified as regular items. Based on the goods storage adaptation model, these high-frequency items were placed on temporary shelves in the corresponding type of storage warehouse. If the goods were already classified as high-frequency items before this determination, they were placed on regular shelves in the corresponding type of storage warehouse. If they were already classified as regular items before this determination, they remained on regular shelves. By updating the historical goods information database in real time, the goods in the storage warehouse are dynamically determined based on the goods type determination model, thus enabling dynamic storage and facilitating later storage or retrieval.

[0023] Furthermore, once the storage location of goods located on temporary shelves has been planned, the location of the goods will not change as long as the decision factor S is still greater than the switching threshold Z between high-frequency goods and regular goods; this avoids frequent changes in the storage location of high-frequency goods, which could lead to storage chaos and increased workload.

[0024] Furthermore, in step one, the different types of storage warehouses specifically include temperature-controlled storage warehouses, low-temperature storage warehouses, high-temperature storage warehouses, and refrigerated / frozen storage warehouses. Temporary shelves and regular shelves are installed in each of these different types of storage warehouses, with the temporary shelves located near the warehouse entrance. The basic information of the goods includes the model, quantity, specifications, storage conditions, and shelf life. This allows for the selection of the appropriate type of storage warehouse based on the basic attributes of the goods, thus facilitating storage.

[0025] Furthermore, once the storage location of goods located on regular shelves has been planned, the location of the goods will not change as long as the decision factor S is still less than or equal to the switching threshold Z between high-frequency goods and regular goods; this avoids frequent changes in the storage location of regular goods, which could lead to storage chaos and increased workload.

[0026] The IoT-based dynamic storage system implements the aforementioned IoT-based dynamic storage method. The system includes: The data acquisition module is used to collect environmental parameters within the storage warehouse, as well as the location and status of the goods. The transmission module transmits the information data collected by the data acquisition module using wired or wireless communication. The analysis and processing module is used to analyze and process the information data transmitted from the transmission module, store the information data, establish a warehouse information database and a cargo information database, and issue delivery instructions. The conveying module sends conveying instructions through the processing module to transport goods to the designated location.

[0027] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dynamic goods location intelligent storage method based on the Internet of Things, characterized in that, Comprise the following specific steps: Step one, according to the internal situation of storage warehouse layout, divide different types of storage warehouse, and plan the corresponding temporary shelves and corresponding regular shelves in different types of storage warehouse; Step two, grab all the goods history purchase quantity data, sales quantity data and sales frequency data and its goods basic attribute data, and then determine the goods type judgment model to determine whether the goods is high frequency type goods or regular type goods, and establish goods history information database; Step three, read and identify the basic attributes of new goods in the storage warehouse, and pass the basic attributes of the goods to the background terminal, and compare the basic attributes of the goods with the goods history information database to determine whether the goods is a regular type goods or a high frequency type goods; Step four, combine the results of step three to determine whether the goods is a regular type goods or a high frequency type goods, and determine the regular type goods to be placed in the corresponding type storage warehouse according to the goods storage adaptation model, and the high frequency type goods is placed in the temporary shelf in the corresponding type storage warehouse.

2. The IoT-based dynamic storage method for intelligent cargo location as described in claim 1, characterized in that, The specific content of the goods type judgment model is as follows: The switching threshold between high frequency type goods and regular type goods is Z, the purchase quantity data, sales quantity data and sales frequency data of goods T are grabbed, and the purchase quantity is recorded as A, the sales quantity is recorded as B, and the sales frequency is recorded as C, then the determination factor S is obtained by substituting into the determination formula, then S and Z are compared, when S is less than or equal to Z, the goods is a regular type goods, when S is greater than Z, the goods is a high frequency type goods. 3.The dynamic storage method of the Internet of Things based intelligent storage according to claim 2, characterized in that, The determination formula is: S=α*A+β*B+γ*C, wherein the value of α is 0.3~0.4, the value of β is 0.5~0.6, and the value of γ is 0.8~1.

4. The IoT-based dynamic storage method for intelligent cargo location as described in claim 3, characterized in that, The specific content of the goods storage adaptation model is as follows: Set up a temporary shelf, a temporary shelf and a temporary shelf, a regular shelf, a regular shelf and a regular shelf in different types of storage warehouse according to the distance from the door of the storage warehouse from near to far, then select all the goods determination factor S, then arrange the priority according to the size of the determination factor S, then select the storage position in the first temporary shelf or the second temporary shelf or the third temporary shelf according to the priority of high frequency type goods, and select the storage position in the first regular shelf or the second regular shelf or the third regular shelf according to the priority of regular type goods. 5.The dynamic storage method of the Internet of Things based intelligent storage according to claim 4, characterized in that, The step two determines whether the goods are high-frequency type goods or regular type goods based on a goods type determination model. The t unit can be days or weeks. After every t time period, the background terminal re-grabs all the historical goods purchase quantity data, goods shipment quantity data, goods shipment frequency data, and goods basic attribute data, and determines whether the goods are high-frequency type goods or regular type goods based on the goods type determination model, and updates the goods historical information database. When the goods are determined to be high-frequency type goods, if the goods were high-frequency type goods before the current determination, they are still stored on the temporary shelves. If the goods were regular type goods before the current determination, the high-frequency type goods are determined to be placed on the temporary shelves in the corresponding type storage warehouse according to the goods storage adaptation model. When the goods are determined to be regular type goods, if the goods were high-frequency type goods before the current determination, the regular type goods are determined to be placed on the regular shelves in the corresponding type storage warehouse according to the goods storage adaptation model. If the goods were regular type goods before the current determination, they are still stored on the regular shelves. 6.The dynamic storage method of the Internet of Things based intelligent storage according to claim 5, characterized in that, After the goods in the temporary shelves have been planned for storage positions, if the determination factor S is still greater than the switching threshold Z between high-frequency type goods and regular type goods, the positions of the goods will not change. 7.The dynamic storage method of intelligent storage of goods based on Internet of Things according to claim 1, characterized in that, The different type storage warehouses in the step one specifically include constant temperature type storage warehouses, low temperature type storage warehouses, high temperature type storage warehouses, and refrigeration and freezing storage warehouses, and temporary shelves and regular shelves are arranged in the different type storage warehouses, and the temporary shelves are located near the warehouse doors of the different type storage warehouses. 8.The dynamic storage method of the Internet of Things based smart storage of goods according to claim 1, wherein, The goods basic information includes the model, quantity, specification, storage condition, and shelf life of the goods. 9.The dynamic storage method of intelligent storage of goods based on Internet of Things according to claim 6, characterized in that, After the goods in the regular shelves have been planned for storage positions, if the determination factor S is still less than or equal to the switching threshold Z between high-frequency type goods and regular type goods, the positions of the goods will not change.

10. A dynamic storage system for goods based on Internet of Things, which implements the dynamic storage method for goods based on Internet of Things as claimed in any one of claims 1 to 9, characterized in that, The system comprises: A data acquisition module is configured to acquire environmental parameters in the storage warehouse, and positions and states of goods. A transmission module is configured to transmit information data acquired by the data acquisition module by using wired communication or wireless communication. An analysis and processing module is configured to analyze and process the information data transmitted by the transmission module, store the information data, establish a storage warehouse information database and a goods information database, and issue a conveying instruction. A conveying module is configured to convey the goods to a designated position according to the conveying instruction issued by the processing module.

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