Commodity cloud storage method and management system

Abstract

The invention relates to the technical field of cloud storage, in particular to a commodity cloud storage method and management system, and the method comprises the steps: collecting various parameter classification codes of a commodity to generate a unique identifier, binding the unique identifier to a cloud end to obtain cloud file data, obtaining storage node parameters, and comparing the storage node parameters with commodity attributes to generate an adaptive list; and in combination with order information, inventory is called to calculate sorting time to generate a scheduling instruction, warehouse-out transfer parameters are collected and compared to obtain circulation tracking data, and warehouse operation parameters are collected to calculate resource coefficient marking nodes to generate allocation scheme data. According to the invention, commodity parameters are classified and coded to generate unique identifiers and bound and stored to realize accurate filing, warehousing node parameters and commodity attributes are compared to improve the matching accuracy, order data and warehousing inventory are compared to calculate a sorting time optimization processing flow, commodity circulation data is collected and compared with a scheduling instruction to realize whole-course tracking, and the matching accuracy is improved. Resources are reasonably allocated according to the storage resource demand coefficient marking node state, the storage circulation configuration problem is solved, and it is guaranteed that the whole commodity cloud storage process is efficiently and orderly propelled.

Description

Technical Field

[0001] This invention relates to the field of cloud warehousing technology, and in particular to a cloud warehousing method and management system for goods. Background Technology

[0002] The field of cloud warehousing technology encompasses technologies related to online commodity trading, commodity storage, commodity distribution, order processing, and electronic voucher management. The core of this field revolves around the collaborative online and offline management of the entire process of commodity circulation and use, encompassing technologies such as intelligent cloud order processing, digital commodity warehousing management, cloud-based design and storage of electronic vouchers, and cross-user traceability of commodities. It involves real-time statistical analysis and encrypted storage of data such as commodity information, quantity, weight, volume, and energy consumption on cloud platforms, as well as seamless integration between offline counter transactions and cloud management systems, providing digital technical support for commodity storage, trading, and inheritance.

[0003] One of the product cloud warehousing methods and management systems refers to a method and management system that relies on cloud software and warehousing hardware to collaboratively realize product storage, transaction, and voucher management. The technical aspects it addresses include cloud-based product archiving, intelligent warehouse location allocation, electronic voucher issuance, product circulation records, dynamic inventory monitoring, and offline delivery integration. Specific methods include: after a user purchases a product through the software, the system assigns a unique digital identifier and binds the product information to a cloud database; the software company completes centralized storage of products or creates and sends products on demand based on cloud order information; the system issues electronic vouchers to users, which are counted according to product attributes and synchronized to cloud storage, supporting real-time querying and status updates; the cloud platform records product storage location, quantity changes, and circulation trajectory, allowing users to query, transfer, or retrieve products offline using electronic vouchers; under specific circumstances, product production can be adjusted based on cloud inventory data, optimizing production rhythm through order pre-deposit or cloud warehouse reserves; the products cover daily necessities such as food, water, electricity, oil, pots and pans, and various other necessities required for social operation, and their storage information can be inherited and transferred through the cloud system.

[0004] Existing technologies only build technical systems around the collaborative management of the entire product process, without accurately encoding and binding product parameters. This results in a lack of uniformity in product information management. Warehouse management only mentions digital management without addressing the precise comparison of node parameters and product attributes, which can easily lead to improper storage in product storage adaptation scenarios. Order processing focuses on intelligent processing but does not involve sorting time calculation and priority judgment, making it difficult to guarantee order execution efficiency. Product flow traceability is only covered at the system level without implementing data collection and comparison at all stages, making it impossible to accurately track the status of products in actual flow. Warehouse resource allocation lacks quantitative coefficient support and relies solely on inventory data to regulate production, leading to frequent resource redundancy or shortages and affecting the overall operational efficiency of cloud-based product warehousing. Summary of the Invention

[0005] To address the technical problems existing in the prior art, embodiments of the present invention provide a cloud-based commodity warehousing method and management system. The technical solution is as follows:

[0006] A cloud warehousing method for goods includes the following steps:

[0007] S1: Obtain the product name, specifications, weight, volume, shelf life, storage temperature range, and storage humidity range; classify and encode each parameter to generate a unique product identifier; bind the identifier and each parameter to the cloud database to obtain the product cloud profile data;

[0008] S2: Based on the product cloud archive data, obtain the available volume of the storage node, the weight capacity of the storage device, the real-time temperature, and the real-time humidity. Compare these with the product volume, weight, storage temperature range, and storage humidity range respectively to determine the compatibility and generate a storage node compatibility list.

[0009] S3: Based on the product cloud archive data and the warehouse node adaptation list, obtain the order product identifier, required quantity, and delivery time, call the corresponding inventory quantity of the adapted warehouse node, compare the required quantity with the inventory quantity, calculate the total time required for sorting and the latest start time, compare the time to determine priority sorting, and generate order warehouse scheduling instruction data.

[0010] S4: Based on the order warehousing scheduling instruction data, collect parameters such as product outbound time, outbound node identifier, and real-time transfer location, bind the parameters with the product identifier, and compare them with the scheduling instruction delivery time and target node to obtain product flow status tracking data;

[0011] S5: Call the commodity circulation status tracking data and the warehouse node adaptation list, collect the warehouse node inventory occupancy rate and equipment utilization rate, calculate the resource demand coefficient and compare it with the average value, mark the scarce and redundant nodes, and generate warehouse resource allocation plan data.

[0012] As a further aspect of the present invention, the product cloud archive data includes a unique product identifier, product name, product specifications, product weight, product storage temperature range, and product storage humidity range; the warehouse node adaptation list includes an adapted warehouse node identifier, available warehouse node volume, real-time warehouse node temperature, and warehouse node adaptation status identifier; the order warehouse scheduling instruction data includes an order identifier, an adapted warehouse node identifier, total sorting time, latest sorting start time, and priority sorting trigger identifier; the product flow status tracking data includes a product identifier, product outbound time, real-time product transfer location, product inbound time, and flow and scheduling instruction comparison results; and the warehouse resource allocation scheme data includes a warehouse node identifier, warehouse node inventory occupancy rate, node resource demand coefficient, node resource status marker, and product allocation quantity.

[0013] As a further aspect of the present invention, the step of acquiring the commodity cloud archive data is as follows:

[0014] S101: Obtain product name, specifications, weight, volume, shelf life, storage temperature range, and storage humidity range; classify and encode each parameter; combine them according to the coding rules to generate a unique product identifier; establish the corresponding association between each parameter and the code; and generate product parameter coding data.

[0015] S102: Based on the product parameter coding data, retrieve the product's unique identifier and corresponding associated parameters, bind the product's unique identifier to each parameter one by one, upload it to the cloud database to complete the classification and storage, and obtain the product cloud archive data.

[0016] As a further aspect of the present invention, the step of obtaining the warehouse node adaptation list is as follows:

[0017] S201: Based on the commodity cloud archive data, obtain the available volume of the storage node, the load-bearing capacity of the storage device, the real-time temperature, and the real-time humidity, record the specific values ​​of each parameter, organize the parameters according to the storage node number, and establish a basic parameter set for the storage node.

[0018] S202: Call the product cloud archive data and the basic parameter set of the warehouse node, compare the available volume of the warehouse node with the volume of the product, compare the load-bearing capacity of the storage device with the weight of the product, compare the real-time temperature and humidity with the storage temperature and humidity range, determine the compatibility, and generate a warehouse node compatibility list.

[0019] As a further aspect of the present invention, the step of obtaining the order warehousing scheduling instruction data is as follows:

[0020] S301: Based on the product cloud archive data and the warehouse node adaptation list, obtain the product identifier, required quantity, and delivery time in the order, call the inventory quantity of the corresponding product identifier in the adapted warehouse node, record the various values, and form order inventory matching data;

[0021] S302: Based on order inventory matching data, obtain the required quantity and the processing capacity of the sorting equipment at the warehouse node per unit time. Divide the required quantity by the processing capacity per unit time to calculate the total time required for sorting. Subtract the current time from the delivery time to calculate the latest start time for sorting and obtain the sorting time calculation result.

[0022] S303: Call the sorting time calculation result, compare the total time required for sorting with the latest start time for sorting, determine whether to trigger priority sorting, integrate the order identifier, adapt the warehouse node information and the judgment result, and generate order warehouse scheduling instruction data.

[0023] As a further aspect of the present invention, the step of acquiring the commodity circulation status tracking data is as follows:

[0024] S401: Based on order warehousing scheduling instructions, collect the product outbound time, outbound node identifier, real-time transfer location, transfer node identifier, inbound time, and receiving node identifier, organize the parameters according to the product flow sequence, and form the original product flow data;

[0025] S402: Call the original data of commodity circulation and order warehousing scheduling instructions, bind each collected parameter with the commodity identifier, compare it with the delivery time and target node in the scheduling instructions, record the comparison results, and obtain commodity circulation status tracking data.

[0026] As a further aspect of the present invention, the step of obtaining the warehousing resource allocation plan data is as follows:

[0027] S501: Call the commodity flow status tracking data and the warehouse node adaptation list, collect the inventory occupancy rate and equipment utilization rate of each warehouse node, calculate the total commodity demand in the orders of the past 7 days, divide the total demand by the corresponding warehouse node inventory occupancy rate to calculate the resource demand coefficient, and establish warehouse resource demand coefficient data.

[0028] S502: Based on the warehouse resource demand coefficient data, calculate the average value of the resource demand coefficient of all nodes, compare the resource demand coefficient of each node with the average value, mark the resource-scarce and redundant nodes, integrate the node information and marking results, and generate warehouse resource allocation plan data.

[0029] A cloud-based commodity warehousing management system, the system comprising:

[0030] The product parameter filing module obtains the product name, specifications, weight, volume, shelf life, storage temperature range, and storage humidity range, classifies and codes each parameter to generate a unique product identifier, binds each parameter to the cloud database, and obtains the product cloud file data;

[0031] The warehouse node adaptation module obtains the available volume, storage device load capacity, real-time temperature, and real-time humidity of the warehouse node based on the product cloud archive data. It compares these data with the product volume, weight, and storage temperature and humidity range to determine the adaptation status and generate a warehouse node adaptation list.

[0032] The order inventory scheduling module obtains the order product identifier, required quantity, and delivery time based on the product cloud archive data and the warehouse node adaptation list. It then calls the inventory quantity of the adapted warehouse node, compares the required quantity with the inventory quantity, calculates the total time required for sorting and the latest start time, compares the two to determine the priority of sorting, and generates order warehouse scheduling instruction data.

[0033] The product circulation tracking module collects product outbound time, real-time transfer location, and inbound time based on order warehousing scheduling instruction data, binds product identifiers and compares them with scheduling instructions to obtain product circulation status tracking data;

[0034] The warehouse resource allocation module calls commodity circulation status tracking data and warehouse node adaptation list, collects warehouse node inventory occupancy rate and equipment utilization rate, calculates resource demand coefficient and compares it with the average value, marks scarce and redundant nodes, and generates warehouse resource allocation plan data.

[0035] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0036] This invention classifies and encodes various parameters of goods to generate unique identifiers and binds them for storage, enabling accurate filing of goods information. By comparing multiple parameters of warehousing nodes with goods attributes one by one to determine the compatibility, the accuracy of warehousing node matching is improved. By comparing order data with warehousing inventory, sorting time is calculated and time comparison is performed to determine sorting priority, optimizing the order processing flow. Data from all stages of goods circulation is comprehensively collected and compared with scheduling instructions to achieve accurate tracking of goods circulation throughout the entire process. The operating parameters of warehousing nodes are collected to calculate resource demand coefficients and compare them with average values ​​to mark node resource status, enabling reasonable allocation of warehousing resources. This solves various problems in the allocation of resources in goods storage and circulation, ensuring the efficient and orderly progress of the entire cloud warehousing process. Attached Figure Description

[0037] Figure 1 This is a flowchart of the commodity cloud warehousing method of the present invention;

[0038] Figure 2 This is a flowchart of the product cloud archive data acquisition process of the present invention;

[0039] Figure 3 This is a flowchart of the process for obtaining the warehouse node adaptation list in this invention;

[0040] Figure 4 This is a flowchart of the order warehousing scheduling instruction data acquisition process of the present invention;

[0041] Figure 5 This is a flowchart of the process for acquiring commodity circulation status tracking data according to the present invention;

[0042] Figure 6 This is a flowchart illustrating the data acquisition process for the warehousing resource allocation scheme of the present invention.

[0043] Figure 7 This is a flowchart of the commodity cloud warehousing management system of the present invention. Detailed Implementation

[0044] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0045] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0046] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0047] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0048] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0049] Please see Figure 1 This invention provides a technical solution: a cloud warehousing method for goods, comprising the following steps:

[0050] S1: Obtain the product name, specifications, weight, volume, shelf life, storage temperature range, and storage humidity range; classify and encode each parameter to generate a unique product identifier; bind the unique product identifier with each parameter and store it in the cloud database to obtain the product cloud profile data.

[0051] S2: Based on the product cloud archive data, obtain the available volume of the storage node, the load-bearing capacity of the storage device, the real-time temperature, and the real-time humidity. Compare the available volume of the storage node with the volume of the product, compare the load-bearing capacity of the storage device with the weight of the product, compare the real-time temperature with the storage temperature range, compare the real-time humidity with the storage humidity range, determine whether the storage node is suitable for product storage, and generate a list of compatible storage nodes.

[0052] S3: Based on the product cloud archive data and the warehouse node adaptation list, obtain the product identifier, required quantity, and delivery time in the order; call the inventory quantity of the corresponding product identifier in the adapted warehouse node; compare the required quantity with the inventory quantity; call the unit time processing capacity of the warehouse node sorting equipment; divide the required quantity by the unit time processing capacity to calculate the total time required for sorting; subtract the current time from the delivery time to calculate the latest start time for sorting; compare the total time required for sorting with the latest start time for sorting to determine whether priority sorting is triggered; and generate order warehouse scheduling instruction data.

[0053] S4: Based on the order warehousing scheduling instruction data, collect the product outbound time, outbound node identifier, real-time transfer location, transfer node identifier, inbound time, and receiving node identifier. Bind each collected parameter to the product identifier and compare it with the delivery time and target node in the scheduling instruction to obtain product flow status tracking data.

[0054] S5: Call the commodity circulation status tracking data and the warehouse node adaptation list, collect the inventory occupancy rate and equipment utilization rate of each warehouse node, calculate the resource demand coefficient by dividing the total commodity demand in the past 7 days' orders by the corresponding warehouse node's inventory occupancy rate, compare the resource demand coefficient of each node with the average of all node resource demand coefficients, mark resource-scarce and redundant nodes, and generate warehouse resource allocation plan data.

[0055] The product cloud archive data includes a unique product identifier, product name, product specifications, product weight, product storage temperature range, and product storage humidity range. The warehouse node adaptation list includes an adapted warehouse node identifier, available warehouse node volume, real-time warehouse node temperature, and warehouse node adaptation status identifier. The order warehouse scheduling instruction data includes an order identifier, an adapted warehouse node identifier, total sorting time, latest sorting start time, and priority sorting trigger identifier. The product flow status tracking data includes a product identifier, product outbound time, real-time product transfer location, product inbound time, and flow and scheduling instruction comparison results. The warehouse resource allocation scheme data includes a warehouse node identifier, warehouse node inventory occupancy rate, node resource demand coefficient, node resource status marker, and product allocation quantity.

[0056] Please see Figure 2 The steps for obtaining product cloud archive data are as follows:

[0057] S101: Obtain product name, specifications, weight, volume, shelf life, storage temperature range, and storage humidity range; classify and encode each parameter; combine them according to the coding rules to generate a unique product identifier; establish the corresponding association between each parameter and the code; and generate product parameter coding data.

[0058] To obtain the basic parameters of a product, taking mineral water as an example, the product name is coded as A001, 500ml as B002, weight as C003, volume as D004, shelf life as E005 (12 months), temperature range as F006 (2-25℃), and humidity range as G007 (40-60%). These parameters are then combined and coded in order of category to establish a one-to-one correspondence between parameters and codes, generating product parameter coding data.

[0059] S102: Based on the product parameter coding data, retrieve the product's unique identifier and corresponding associated parameters, bind the product's unique identifier to each parameter one by one, upload it to the cloud database to complete the classification and storage, and obtain the product cloud archive data.

[0060] Based on the product parameter coding data, codes A001B002C003D004E005F006G007 and their corresponding mineral water parameters are retrieved. The codes and parameters are bound one by one, and the data is uploaded to the cloud database for categorized storage according to food category, thus obtaining the product cloud archive data.

[0061] Please see Figure 3 The steps to obtain the warehouse node adaptation list are as follows:

[0062] S201: Based on the commodity cloud archive data, obtain the available volume of the storage node, the load-bearing capacity of the storage device, the real-time temperature, and the real-time humidity, record the specific values ​​of each parameter, organize the parameters according to the storage node number, and establish a basic parameter set for the storage node.

[0063] Based on the commodity cloud archive data, the available volume of warehouse node C01 is 50m³, the load capacity is 10t, the real-time temperature is 22℃, and the real-time humidity is 55%. All values ​​are recorded, and the parameters are organized according to the node number C01 to establish a basic parameter set for the warehouse node.

[0064] S202: Call the product cloud archive data and the basic parameter set of the warehouse node, compare the available volume of the warehouse node with the volume of the product, compare the load-bearing capacity of the storage device with the weight of the product, compare the real-time temperature and humidity with the storage temperature and humidity range, determine the compatibility, and generate a warehouse node compatibility list.

[0065] The system calls upon the product cloud archive data and the basic parameter set of the warehouse node, compares the volume of node C01 (50m³) with the product volume (0.0005m³), the load capacity (10t) with the product weight (0.5kg), and compares the real-time temperature and humidity with the storage range to determine node compatibility and generate a warehouse node compatibility list.

[0066] Please see Figure 4 The steps for obtaining order warehousing scheduling instruction data are as follows:

[0067] S301: Based on the product cloud archive data and the warehouse node adaptation list, obtain the product identifier, required quantity, and delivery time in the order, call the inventory quantity of the corresponding product identifier in the adapted warehouse node, record the various values, and form order inventory matching data;

[0068] Based on the product cloud archive data and the warehouse node adaptation list, obtain the order product identifier A001B002..., the demand of 500 units, and the delivery time. Then, call the corresponding product inventory of 800 units at node C01, record the various values, and form the order inventory matching data.

[0069] S302: Based on order inventory matching data, obtain the required quantity and the processing capacity of the sorting equipment at the warehouse node per unit time. Divide the required quantity by the processing capacity per unit time to calculate the total time required for sorting. Subtract the current time from the delivery time to calculate the latest start time for sorting and obtain the sorting time calculation result.

[0070] Based on order inventory matching data, the demand is 500 pieces and the sorting equipment processing capacity is 100 pieces / hour. The total sorting time is calculated to be 5 hours. The current time is taken as 8 hours away from the delivery time. The latest start time is calculated as the delivery time minus 8 hours, and the sorting time calculation result is obtained.

[0071] S303: Call the sorting time calculation result, compare the total time required for sorting with the latest start time for sorting, determine whether to trigger priority sorting, integrate the order identifier, adapt the warehouse node information and the judgment result, and generate order warehouse scheduling instruction data.

[0072] The sorting time calculation result is called, the total sorting time of 5 hours is compared with the latest start time of 8 hours, and it is determined that priority sorting is not triggered. The order number D001, node C01 information and judgment result are integrated to generate order warehouse scheduling instruction data.

[0073] Please see Figure 5 The steps for obtaining product circulation status tracking data are as follows:

[0074] S401: Based on order warehousing scheduling instructions, collect the product outbound time, outbound node identifier, real-time transfer location, transfer node identifier, inbound time, and receiving node identifier, organize the parameters according to the product flow sequence, and form the original product flow data;

[0075] Based on the order warehousing scheduling instruction data, the following parameters are collected: mineral water outbound time 10:00, outbound node identifier C01, real-time transfer location 116°E, 39°N, transfer node identifier Z02, inbound time 14:00, and receiving node identifier C03. The parameters are then organized according to the outbound to inbound flow sequence to form the original commodity flow data.

[0076] S402: Call the original data of commodity circulation and order warehousing scheduling instructions, bind each collected parameter with the commodity identifier, compare it with the delivery time and target node in the scheduling instructions, record the comparison results, and obtain commodity circulation status tracking data.

[0077] The system retrieves raw data on product circulation and order warehousing scheduling instructions, binds the collected six parameters to product identifiers A001, B002, etc., compares the inbound time 14:00 with the delivery time 15:00, and compares the receiving node C03 with the target node, records the consistency of the comparisons, and obtains product circulation status tracking data.

[0078] Please see Figure 6 The steps for obtaining data for the warehousing resource allocation plan are as follows:

[0079] S501: Call the commodity flow status tracking data and the warehouse node adaptation list, collect the inventory occupancy rate and equipment utilization rate of each warehouse node, calculate the total commodity demand in the orders of the past 7 days, divide the total demand by the corresponding warehouse node inventory occupancy rate to calculate the resource demand coefficient, and establish warehouse resource demand coefficient data.

[0080] Call the commodity circulation status tracking data and the warehouse node adaptation list, collect the inventory occupancy rate of node C01 (80%) and equipment utilization rate (75%), and calculate the total commodity demand of 1000 units in the past 7 days. Divide 1000 by 0.8 to obtain the resource demand coefficient of 1250, and establish the warehouse resource demand coefficient data.

[0081] S502: Based on the warehouse resource demand coefficient data, calculate the average value of the resource demand coefficient of all nodes, compare the resource demand coefficient of each node with the average value, mark the resource-scarce and redundant nodes, integrate the node information and marking results, and generate warehouse resource allocation plan data.

[0082] Based on the warehouse resource demand coefficient data, the average value of nodes C01 (1250), C02 (800), and C03 (950) is calculated to be 1000. The coefficients of each node are compared with 1000, and C01 is marked as a scarce node and C02 as a redundant node. The node information and marking results are integrated to generate warehouse resource allocation plan data.

[0083] Please see Figure 7 A cloud-based commodity warehousing management system, the system comprising:

[0084] The product parameter filing module obtains the product name, specifications, weight, volume, shelf life, storage temperature range, and storage humidity range, classifies and codes each parameter to generate a unique product identifier, binds each parameter to the cloud database, and obtains the product cloud file data;

[0085] The warehouse node adaptation module obtains the available volume, storage device load capacity, real-time temperature, and real-time humidity of the warehouse node based on the product cloud archive data. It compares these data with the product volume, weight, and storage temperature and humidity range to determine the adaptation status and generate a warehouse node adaptation list.

[0086] The order inventory scheduling module obtains the order product identifier, required quantity, and delivery time based on the product cloud archive data and the warehouse node adaptation list. It then calls the inventory quantity of the adapted warehouse node, compares the required quantity with the inventory quantity, calculates the total time required for sorting and the latest start time, compares the two to determine the priority of sorting, and generates order warehouse scheduling instruction data.

[0087] The product circulation tracking module collects product outbound time, real-time transfer location, and inbound time based on order warehousing scheduling instruction data, binds product identifiers and compares them with scheduling instructions to obtain product circulation status tracking data;

[0088] The warehouse resource allocation module calls commodity circulation status tracking data and warehouse node adaptation list, collects warehouse node inventory occupancy rate and equipment utilization rate, calculates resource demand coefficient and compares it with the average value, marks scarce and redundant nodes, and generates warehouse resource allocation plan data.

[0089] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A commodity cloud storage method, characterized in that, Includes the following steps: S1: Obtain the product name, specifications, weight, volume, shelf life, storage temperature range, and storage humidity range; classify and encode each parameter to generate a unique product identifier; bind the identifier and each parameter to the cloud database to obtain the product cloud profile data; S2: Based on the product cloud archive data, obtain the available volume of the storage node, the weight capacity of the storage device, the real-time temperature, and the real-time humidity. Compare these with the product volume, weight, storage temperature range, and storage humidity range respectively to determine the compatibility and generate a storage node compatibility list. S3: Based on the product cloud archive data and the warehouse node adaptation list, obtain the order product identifier, required quantity, and delivery time, call the corresponding inventory quantity of the adapted warehouse node, compare the required quantity with the inventory quantity, calculate the total time required for sorting and the latest start time, compare the time to determine priority sorting, and generate order warehouse scheduling instruction data. S4: Based on the order warehousing scheduling instruction data, collect parameters such as product outbound time, outbound node identifier, and real-time transfer location, bind the parameters with the product identifier, and compare them with the scheduling instruction delivery time and target node to obtain product flow status tracking data; S5: Call the commodity circulation status tracking data and the warehouse node adaptation list, collect the warehouse node inventory occupancy rate and equipment utilization rate, calculate the resource demand coefficient and compare it with the average value, mark the scarce and redundant nodes, and generate warehouse resource allocation plan data.

2. The commodity cloud warehousing method according to claim 1, characterized in that: The product cloud archive data includes a unique product identifier, product name, product specifications, product weight, product storage temperature range, and product storage humidity range. The warehouse node adaptation list includes an adapted warehouse node identifier, available warehouse node volume, real-time warehouse node temperature, and warehouse node adaptation status identifier. The order warehouse scheduling instruction data includes an order identifier, an adapted warehouse node identifier, total sorting time, latest sorting start time, and priority sorting trigger identifier. The product flow status tracking data includes a product identifier, product outbound time, real-time product transfer location, product inbound time, and flow and scheduling instruction comparison results. The warehouse resource allocation scheme data includes a warehouse node identifier, warehouse node inventory occupancy rate, node resource demand coefficient, node resource status marker, and product allocation quantity.

3. The commodity cloud warehousing method according to claim 1, characterized in that: The steps for obtaining the product cloud archive data are as follows: S101: Obtain product name, specifications, weight, volume, shelf life, storage temperature range, and storage humidity range; classify and encode each parameter; combine them according to the coding rules to generate a unique product identifier; establish the corresponding association between each parameter and the code; and generate product parameter coding data. S102: Based on the product parameter coding data, retrieve the product's unique identifier and corresponding associated parameters, bind the product's unique identifier to each parameter one by one, upload it to the cloud database to complete the classification and storage, and obtain the product cloud archive data.

4. The commodity cloud warehousing method according to claim 1, characterized in that: The steps for obtaining the storage node adaptation list are as follows: S201: Based on the commodity cloud archive data, obtain the available volume of the storage node, the load-bearing capacity of the storage device, the real-time temperature, and the real-time humidity, record the specific values ​​of each parameter, organize the parameters according to the storage node number, and establish a basic parameter set for the storage node. S202: Call the product cloud archive data and the basic parameter set of the warehouse node, compare the available volume of the warehouse node with the volume of the product, compare the load-bearing capacity of the storage device with the weight of the product, compare the real-time temperature and humidity with the storage temperature and humidity range, determine the compatibility, and generate a warehouse node compatibility list.

5. The commodity cloud warehousing method according to claim 1, characterized in that: The steps for obtaining the order warehousing scheduling instruction data are as follows: S301: Based on the product cloud archive data and the warehouse node adaptation list, obtain the product identifier, required quantity, and delivery time in the order, call the inventory quantity of the corresponding product identifier in the adapted warehouse node, record the various values, and form order inventory matching data; S302: Based on order inventory matching data, obtain the required quantity and the processing capacity of the sorting equipment at the warehouse node per unit time. Divide the required quantity by the processing capacity per unit time to calculate the total time required for sorting. Subtract the current time from the delivery time to calculate the latest start time for sorting and obtain the sorting time calculation result. S303: Call the sorting time calculation result, compare the total time required for sorting with the latest start time for sorting, determine whether to trigger priority sorting, integrate the order identifier, adapt the warehouse node information and the judgment result, and generate order warehouse scheduling instruction data.

6. The commodity cloud warehousing method according to claim 1, characterized in that: The steps for obtaining the product circulation status tracking data are as follows: S401: Based on order warehousing scheduling instructions, collect the product outbound time, outbound node identifier, real-time transfer location, transfer node identifier, inbound time, and receiving node identifier, organize the parameters according to the product flow sequence, and form the original product flow data; S402: Call the original data of commodity circulation and order warehousing scheduling instructions, bind each collected parameter with the commodity identifier, compare it with the delivery time and target node in the scheduling instructions, record the comparison results, and obtain commodity circulation status tracking data.

7. The commodity cloud warehousing method according to claim 1, characterized in that: The steps for obtaining the data are as follows: The steps for obtaining the data of the warehousing resource allocation plan are as follows: S501: Call the commodity flow status tracking data and the warehouse node adaptation list, collect the inventory occupancy rate and equipment utilization rate of each warehouse node, calculate the total commodity demand in the orders of the past 7 days, divide the total demand by the corresponding warehouse node inventory occupancy rate to calculate the resource demand coefficient, and establish warehouse resource demand coefficient data. S502: Based on the warehouse resource demand coefficient data, calculate the average value of the resource demand coefficient of all nodes, compare the resource demand coefficient of each node with the average value, mark the resource-scarce and redundant nodes, integrate the node information and marking results, and generate warehouse resource allocation plan data.

8. A commodity cloud warehouse management system, characterized in that, The system is used in the commodity cloud warehousing method according to any one of claims 1-7, the system comprising: The product parameter filing module obtains the product name, specifications, weight, volume, shelf life, storage temperature range, and storage humidity range, classifies and codes each parameter to generate a unique product identifier, binds each parameter to the cloud database, and obtains the product cloud file data; The warehouse node adaptation module obtains the available volume, storage device load capacity, real-time temperature, and real-time humidity of the warehouse node based on the product cloud archive data. It compares these data with the product volume, weight, and storage temperature and humidity range to determine the adaptation status and generate a warehouse node adaptation list. The order inventory scheduling module obtains the order product identifier, required quantity, and delivery time based on the product cloud archive data and the warehouse node adaptation list. It then calls the inventory quantity of the adapted warehouse node, compares the required quantity with the inventory quantity, calculates the total time required for sorting and the latest start time, compares the two to determine the priority of sorting, and generates order warehouse scheduling instruction data. The product circulation tracking module collects product outbound time, real-time transfer location, and inbound time based on order warehousing scheduling instruction data, binds product identifiers and compares them with scheduling instructions to obtain product circulation status tracking data; The warehouse resource allocation module calls commodity circulation status tracking data and warehouse node adaptation list, collects warehouse node inventory occupancy rate and equipment utilization rate, calculates resource demand coefficient and compares it with the average value, marks scarce and redundant nodes, and generates warehouse resource allocation plan data.

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