Warehouse informatization management method and system based on Internet of Things

Abstract

The invention provides a warehouse informatization management method and system based on the Internet of Things, and belongs to the technical field of warehouse management, and the method specifically comprises the steps: obtaining the storage data of a region at different dates, determining a high-load region in the region through the storage data, and storing the high-load region in the region; the method comprises the following steps: determining change frequency coefficients of different types of cargos and frequently-changed cargos based on change conditions of cargo storage data of different types of cargos at different dates in a high-load area, obtaining storage data of different frequently-changed cargos in the area, and obtaining storage data of different frequently-changed cargos in combination with the change frequency coefficients of different frequently-changed cargos; the warehouse informatization management strategy based on the Internet of Things equipment in the area is determined, and the accuracy of the storage data of the warehouse is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of warehouse management, and in particular relates to a warehouse information management method and system based on the Internet of Things. Background Art

[0002] Unlike other goods, pharmaceutical goods often have larger deviations in storage environment data requirements depending on the type of goods, which means that the reliability requirements for warehouse management of medical goods are relatively high.

[0003] Therefore, in order to realize the management of pharmaceutical warehouses, the existing technical solutions often combine a variety of IoT devices to carry out information management of pharmaceutical warehouses. Specifically, similar solutions are given in the existing technical solutions such as the invention patent application CN202410700314.6 "An intelligent pharmaceutical storage system and management method based on digital twins", but the above technical solutions all have the following defects: In the existing technical solutions, Chinese medicine goods are often counted based on the in-and-out data. This will inevitably lead to deviations in the inventory data in the information system over a long period of time. Therefore, how to combine the Internet of Things devices for automatic inventory processing has become a technical problem that needs to be solved urgently.

[0004] In response to the above technical problems, the present application specifically provides a warehouse information management method and system based on the Internet of Things. Summary of the invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: In a first aspect, the present application provides a warehouse information management method based on the Internet of Things, specifically comprising: S1 divides the warehouse into multiple areas, determines the storage time data of different types of goods in the area, and determines that the failure risk of the electronic tags of the goods in the area meets the requirements based on the storage data of different types of goods in the area, and then proceeds to the next step; S2 obtains storage data of the area on different dates, and uses the storage data to determine a high-load area in the area according to a preset AI model; S3: determining the frequency of change coefficients of different types of goods and frequently changing goods based on the change of goods storage data of different types of goods in the high-load area on different dates; S4 obtains storage data of different frequently changing goods in the high-load area, and determines a warehouse information management strategy based on Internet of Things devices in the high-load area in combination with the frequency of change coefficients of different frequently changing goods.

[0006] The beneficial effects of the present invention are: Based on the storage time data and storage data of different types of goods in the area, it is determined whether the failure risk of the electronic tags of the goods in the area meets the requirements. Full consideration is given to the differences in consumption cycles of different types of goods in the area due to differences in type, which in turn leads to differences in the abnormal probability of electronic tag failure. This ensures the reliability of inventory processing in areas with a large storage volume and a high probability of electronic tag failure, and promptly identifies and processes failed electronic tags, thereby improving the reliability of information management.

[0007] Based on the storage data and frequency coefficient of different frequently changing goods in the area, the warehouse information management strategy based on Internet of Things devices in the area is determined. Not only the frequent changes in the entry and exit of goods in and out of the warehouse are taken into account, but also the storage data of the above-mentioned frequently changing goods in the current area is taken into account, thereby ensuring the reliability of inventory processing in areas where inventory levels frequently change, and thus ensuring the accuracy of the data in the information system.

[0008] A further technical solution is to divide the warehouse into multiple areas, including: The warehouse is divided into a plurality of areas according to a preset unit area.

[0009] A further technical solution is that the storage data of the goods includes the storage quantity of the different types of goods in the area.

[0010] A further technical solution is to determine whether the failure risk of the electronic tags of the goods in the area meets the requirements, specifically including: Determine the average storage time of different types of goods based on the storage time data of goods in the area; Determining long-life goods among the goods based on the average storage time; Based on the storage data of the goods, the total storage amount of long-cycle goods in the area is determined, and the total storage amount of goods is used to determine whether the failure risk of the electronic tags of the goods in the area meets the requirements.

[0011] A further technical solution is that the average storage time is determined according to the average storage time of goods corresponding to the type of goods in the warehouse.

[0012] A further technical solution is that the method for determining the warehouse information management strategy based on the Internet of Things devices in the high-load area is: Determine the cargo storage amount of different frequently changing cargoes in the high load area based on the storage data of different frequently changing cargoes in the high load area; Determining cargo weight coefficients of different frequently changing cargoes based on preset weight coefficients corresponding to the cargo storage volume; The monitoring demand coefficient of the high-load area is determined according to the sum of the products of the cargo weight coefficients and the frequent change coefficients of different frequently changing cargoes, and the warehouse information management strategy based on the Internet of Things devices in the high-load area is determined using the monitoring demand coefficient.

[0013] A further technical solution is to use the monitoring demand coefficient to determine the warehouse information management strategy based on the Internet of Things devices in the high-load area, specifically including: When the monitoring demand coefficient is greater than the preset demand coefficient threshold, the setting of the reading IoT device of the electronic tag is performed according to the matching setting quantity corresponding to the monitoring demand coefficient, and the reading IoT device is used to perform the information inventory processing of the goods in the high-load area; When the monitoring demand coefficient is not greater than the preset demand coefficient threshold, there is no need to set up the Internet of Things device for reading the electronic tag.

[0014] In a second aspect, the present invention provides a warehouse information management system based on the Internet of Things, which adopts the above-mentioned warehouse information management method based on the Internet of Things, specifically comprising: Failure risk assessment module, area identification module, cargo classification module, management strategy determination module; The failure risk assessment module is responsible for determining whether the failure risk of the electronic tags of the goods in the area meets the requirements; The area identification module is responsible for using the stored data to determine the high-load area in the area according to a preset AI model; The cargo classification module is responsible for determining the frequently changing cargo among different types of cargo; The management strategy determination module is responsible for determining the warehouse information management strategy based on Internet of Things devices in the high-load area.

[0015] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0016] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings; Figure 1 It is a flow chart of a warehouse information management method based on the Internet of Things; Figure 2 It is a flow chart for determining whether the failure risk of the electronic tags of goods in the area meets the requirements; Figure 3 is a flow chart of a method for determining a high load area in a region; Figure 4 It is a flow chart of the method for determining the frequent coefficient of change of goods; Figure 5 It is a flow chart of a method for determining a warehouse information management strategy based on IoT devices in a region; Figure 6 It is a framework diagram of a warehouse information management system based on the Internet of Things. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0019] In the present application, the storage volume of goods that enter and leave the warehouse frequently in the corresponding area of ​​the warehouse is used to determine the warehouse information association strategy for the Internet of Things devices in the corresponding area. When the storage volume of goods that enter and leave the warehouse frequently is large, the information inventory of the goods in the area is performed by reading the Internet of Things devices; when the storage volume of goods that enter and leave the warehouse frequently is not large, there is no need to set up the Internet of Things devices for reading electronic tags.

[0020] When the storage volume of goods whose storage time is longer than the preset storage time is within the preset storage volume range, it is determined that the failure risk of the electronic tags of the goods in the area meets the requirement.

[0021] According to the storage volume of goods, the proportion of dates on which the storage volume is greater than the preset storage volume is calculated. When the proportion of dates on which the storage volume in the area is greater than the preset storage volume is greater than 0.7, the area is determined to be a high-load area.

[0022] In another embodiment, the number of dates on which the storage volume is greater than the preset storage volume and the proportion of the number are used as input of a preset AI model, and the output of the preset AI model is used as the load coefficient of the area. When the load coefficient of the area is greater than 0.6, the area is determined to be a high-load area.

[0023] It should be noted that the AI ​​model is built using one or more of the CNN convolutional neural network, BP neural network, LSTM neural network and RNN convolutional neural network.

[0024] Optionally, the specific steps of building the AI ​​model are: 1. Data Preparation and Preprocessing ‌Data Split‌: Divide the dataset into training set, validation set, and test set for training, parameter adjustment, and final evaluation‌.

[0025] 2. Network structure design ‌Define input layer‌: Set the number of input layer nodes according to the feature dimension, here it is 2.

[0026] ‌Build the hidden layer‌: Select the number of hidden layers and the number of neurons in each layer (e.g. a single hidden layer contains 59 neurons); Use linear transformations (such as torch.nn.Linear) to connect layers.

[0027] Activation function selection: Add nonlinear activation functions (such as ReLU, Sigmoid) between the hidden layer and the output layer to enhance the model's expressiveness.

[0028] ‌Define output layer‌: Set the output structure according to the task type, here it is 1.

[0029] 3. Training Configuration ‌Loss function definition‌: Select the loss function (such as mean squared error MSE, cross entropy loss) according to the task.

[0030] Optimizer selection: Use gradient descent algorithms (such as SGD, Adam) to update weight parameters.

[0031] Regularization and Hyperparameters: Set the L2 regularization coefficient to prevent overfitting, and adjust the learning rate, batch size, and maximum number of iterations.

[0032] 4. Model training and verification ‌Forward propagation‌: The input data is calculated through the network to obtain the prediction result, forming the calculation result, and the calculation result is back-propagated with the parameter update to calculate the gradient of the loss function with respect to each parameter; The weights are updated through the optimizer, and forward propagation and back propagation are performed in a loop until the preset number of iterations is reached or convergence occurs, resulting in a trained AI model.

[0033] Using the trained AI model, the number of dates with storage volume greater than the preset storage volume and the percentage of the number are used as the input of the AI ​​model, and the load factor is determined based on the output of the AI ​​model. The date when the number of changes in the cargo storage quantity is greater than the preset number of changes is taken as the cargo change date, and the proportion of the number of cargo change dates of the type of cargo is taken as the frequency of change coefficient. When the frequency of change coefficient is greater than 0.3, the cargo is determined to be frequently changing cargo.

[0034] The storage data of different frequently changing goods in the area are used to determine the storage volume of different frequently changing goods in the area. The cargo weight coefficients of different frequently changing goods are determined based on the preset weight coefficients corresponding to the cargo storage volume. The monitoring demand coefficient of the area is determined according to the sum of the products of the cargo weight coefficients of different frequently changing goods and the frequent change coefficients. When the monitoring demand coefficient is greater than the preset demand coefficient threshold, the setting of the Internet of Things device for reading the electronic tag is performed with the matching setting quantity corresponding to the monitoring demand coefficient, and the information inventory processing of the goods in the area is performed using the reading Internet of Things device. When the monitoring demand coefficient is not greater than the preset demand coefficient threshold, there is no need to set the Internet of Things device for reading the electronic tag.

[0035] Example 1 Figure 1 As shown, in the first aspect, the present application provides a warehouse information management method based on the Internet of Things, which specifically includes: S1 divides the warehouse into multiple areas, determines the storage time data of different types of goods in the area, and determines that the failure risk of the electronic tags of the goods in the area meets the requirements based on the storage data of different types of goods in the area, and then proceeds to the next step; Furthermore, the warehouse is divided into multiple areas, including: The warehouse is divided into a plurality of areas according to a preset unit area.

[0036] Specifically, the cargo storage data includes the cargo storage quantity of the different types of cargo in the area.

[0037] It should be noted that if Figure 2 As shown, it is determined that the failure risk of the electronic tags of the goods in the area meets the requirements, specifically including: Determine the average storage time of different types of goods based on the storage time data of goods in the area; Determining long-life goods among the goods based on the average storage time; Based on the storage data of the goods, the total storage amount of long-cycle goods in the area is determined, and the total storage amount of goods is used to determine whether the failure risk of the electronic tags of the goods in the area meets the requirements.

[0038] Optionally, the average storage time is determined based on an average storage time of goods corresponding to the type of goods in the warehouse.

[0039] It should also be noted that the long-cycle goods are goods whose average storage time is longer than the preset storage time.

[0040] Specifically, when the total amount of the long-life cargo stored in the area is greater than a preset amount of long-life cargo, it is determined that the failure risk of the electronic tags of the cargo in the area does not meet the requirement.

[0041] It is understandable that when the failure risk of the electronic tags of the goods in the area does not meet the requirements, the setting of the electronic tag reading IoT devices is performed in the area according to the preset number, and the reading IoT devices are used to perform information inventory processing of the goods in the area.

[0042] Optionally, determining whether the failure risk of the electronic tags of the goods in the area meets the requirement specifically includes: Determine the average storage time of different types of goods based on the storage time data of goods in the area; Determining failure probabilities of different goods based on the failure ratio of the electronic tags under the average storage time; The total amount of goods stored in the area whose failure probability is greater than a preset failure probability is used to determine whether the failure risk of the electronic tags of the goods in the area meets the requirements.

[0043] Optionally, determining whether the failure risk of the electronic tags of the goods in the area meets the requirement specifically includes: The storage time data of the goods in the area is used to obtain the storage time of different goods in the area. When there is a goods whose storage time is longer than a preset time threshold: When the number of goods whose storage time is longer than the preset time threshold does not meet the requirement, it is determined that the failure risk of the electronic tags of the goods in the area does not meet the requirement: When there is no cargo whose storage time exceeds the preset time threshold or the number of cargo whose storage time exceeds the preset time threshold meets the requirement: Determine the average storage time of different types of goods. When the average storage time of different types of goods is less than the preset storage time threshold: When it is determined based on the historical storage data of different types of goods that no goods of different types have a historical storage time greater than a preset time threshold, it is determined that the failure risk of the electronic tags of the goods in the area meets the requirements: When there is a type of goods whose historical storage time is longer than the preset time threshold: Obtaining the proportion of goods of different types whose historical storage time is longer than a preset time threshold, and when the proportion of goods of different types whose historical storage time is longer than the preset time threshold all meet the requirements, determining that the failure risk of the electronic tags of the goods in the area meets the requirements; When there are goods whose average storage time is not less than the preset storage time threshold or goods whose historical storage time is longer than the preset storage time threshold and the proportion of goods does not meet the requirements of the type of goods: The failure probability of the electronic tags of different types of goods is determined based on the average storage time of different types of goods and the proportion of goods with a historical storage time greater than a preset time threshold. When there is no type of goods with a failure probability greater than the preset failure probability threshold, it is determined that the failure risk of the electronic tags of the goods in the area meets the requirement; When there is a type of goods whose failure probability is greater than the preset failure probability threshold: Based on the failure probability, long-period goods among the goods are determined, based on the storage data of the goods, the total storage amount of long-period goods in the area is determined, and the total storage amount of goods is used to determine whether the failure risk of the electronic tags of the goods in the area meets the requirements.

[0044] S2 obtains storage data of the area on different dates, and uses the storage data to determine a high-load area in the area according to a preset AI model; Specifically, Figure 3 As shown, the method for determining the high load area in the area is: Determine the total amount of goods stored in the area on different dates using the storage data; determining a high-load date among the dates based on the total cargo storage amount; According to the number of the high-load dates, a preset AI model is used to determine the load factor of the area, and the load factor is used to determine whether it is a high-load area.

[0045] Optionally, the high-load date is a date when the total storage amount of goods is greater than a preset storage amount threshold.

[0046] Furthermore, the input of the AI ​​model is the number of high-load dates, and the output is the load factor of the area.

[0047] It should also be noted that when the load factor of the area is greater than a preset load factor threshold, the area is determined to be a high-load area.

[0048] S3: determining the frequency of change coefficients of different types of goods and frequently changing goods based on the change of goods storage data of different types of goods in the high-load area on different dates; Specifically, the change of the goods storage data includes the number of times the goods of the type enter and leave the warehouse on different dates and the quantity of goods with different numbers of times of entry and exit.

[0049] Specifically, Figure 4 As shown, the method for determining the frequent change coefficient of the goods is: Determine the number of times the goods of the type are in and out of the warehouse on different dates based on the changes in the goods storage data of the type of goods on different dates; The frequent change date of the goods of the type is determined by using the times of entry and exit of the goods; The frequency of change coefficient of the type of goods is determined based on the quantity ratio of the frequently changing dates.

[0050] Furthermore, the frequently changing dates are dates when the number of in-and-out times is within a preset range.

[0051] It should also be noted that the value range of the frequency change coefficient of the type of goods is between 0 and 1. When the frequency change coefficient of the type of goods is greater than the preset frequency coefficient threshold, the type of goods is determined to be frequently changing goods.

[0052] Optionally, the method for determining the frequent change coefficient of the goods is: Determine the number of times the goods of the type are in and out of the warehouse on different dates based on the changes in the goods storage data of the type of goods on different dates; The average number of times the goods of the type are in and out of the warehouse on different dates is taken as the mean number of times of in and out of the warehouse; The frequency coefficient of change of the goods of the type is determined according to the average number of times of entering and leaving the warehouse.

[0053] Further, determining the change frequency coefficient of the type of goods according to the average number of times of in-and-out storage, specifically includes: The product of the average number of times of in-and-out storage and the preset proportional factor is used as the frequency coefficient of change of the type of goods.

[0054] Optionally, the method for determining the frequent change coefficient of the goods is: S31 determines the number of times the goods of the type are in and out of the warehouse on different dates based on the change of the goods storage data of the type of goods on different dates; S32 determines the cargo variation coefficient of the cargo of the type on different dates based on the number of times the cargo of the type enters and leaves the warehouse on different dates and in combination with the quantity of cargo at different times of entry and exit; S33 determines the frequent change coefficient of the goods of the type according to the goods change coefficients on different dates.

[0055] Optionally, the above step S31 includes the following contents: S311 determines the number of times the goods of the type are in and out of the warehouse on different dates based on the change of the goods storage data of the type of goods on different dates, obtains the date with the in and out data, and uses it as the storage change date. When the number of the storage change date is less than the preset number of dates, it is determined that the goods of the type are not frequently changing goods. When the number of the storage change date is not less than the preset number of dates, it proceeds to step S312. S312 obtains the number of in-and-out times on different storage change dates. When the number of in-and-out times on different storage change dates is less than the preset number threshold, the process proceeds to step S313. When there is a storage change date with the number of in-and-out times not less than the preset number threshold, the process proceeds to step S32. S313 obtains the quantity ratio of the storage change date. When the quantity ratio of the storage change date is within the preset date quantity ratio interval, it is determined that the type of goods does not belong to frequently changing goods. When the quantity ratio of the storage change date is not within the preset date quantity ratio interval, go to step S32.

[0056] Optionally, the above step S32 includes the following contents: S321 determines the changed quantity of the goods of the type at different storage change dates based on the quantity of the goods of the type at different times of in-and-out storage. When the changed quantity of the goods at different storage change dates are all within the preset change quantity interval, the process proceeds to step S322. When there is a storage change date when the changed quantity of the goods is not within the preset change quantity interval, the process proceeds to step S323. S322: When the sum of the quantity changes of the goods of the type on different storage change dates is less than the preset quantity threshold, it is determined that the goods of the type do not belong to frequently changing goods; when the sum of the quantity changes of the goods of the type on different storage change dates is not less than the preset quantity threshold, the process proceeds to step S323; S323 determines the cargo variation coefficient of the cargo of the type on different dates based on the number of times the cargo of the type enters and leaves the warehouse on different dates and in combination with the quantity of cargo of different numbers of times of entry and exit. When the cargo variation coefficient of the cargo of the type on different dates is within the preset variation coefficient range, the process proceeds to step S324. When there is a date when the cargo variation coefficient of the cargo of the type is not within the preset variation coefficient range, the process proceeds to step S33. S324 determines the basic change coefficient of the goods of the type concerned based on the number of storage change dates and the proportion of the number of goods of the type concerned. When the basic change coefficient of the goods of the type concerned is less than the preset change coefficient threshold, it is determined that the goods of the type concerned do not belong to frequently changing goods. When the basic change coefficient of the goods of the type concerned is not less than the preset change coefficient threshold, proceed to step S33.

[0057] S4 obtains storage data of different frequently changing goods in the high-load area, and determines a warehouse information management strategy based on Internet of Things devices in the high-load area in combination with the frequency of change coefficients of different frequently changing goods.

[0058] Specifically, Figure 5 As shown, the method for determining the warehouse information management strategy based on the Internet of Things devices in the high-load area is: Determine the cargo storage amount of different frequently changing cargoes in the high load area based on the storage data of different frequently changing cargoes in the high load area; Determining cargo weight coefficients of different frequently changing cargoes based on preset weight coefficients corresponding to the cargo storage volume; The monitoring demand coefficient of the area is determined according to the sum of the products of the cargo weight coefficients and the frequent change coefficients of different frequently changing cargoes, and the warehouse information management strategy based on the Internet of Things devices in the high-load area is determined using the monitoring demand coefficient.

[0059] Further, the monitoring demand coefficient is used to determine the warehouse information management strategy based on the Internet of Things device in the high-load area, specifically including: When the monitoring demand coefficient is greater than the preset demand coefficient threshold, the setting of the reading IoT device of the electronic tag is performed according to the matching setting quantity corresponding to the monitoring demand coefficient, and the reading IoT device is used to perform the information inventory processing of the goods in the area; When the monitoring demand coefficient is not greater than the preset demand coefficient threshold, there is no need to set up the Internet of Things device for reading the electronic tag.

[0060] It should be noted that when the area does not belong to a high-load area, there is no need to set up the Internet of Things device for reading the electronic tag.

[0061] In one possible embodiment, the method for determining the warehouse information management strategy based on the Internet of Things devices in the high-load area is: Determine the storage volume of different frequently changing goods in the high-load area based on the storage data of different frequently changing goods in the high-load area; when the total storage volume of different frequently changing goods in the high-load area does not meet the requirement, set the reading IoT device of the electronic tag with a preset number, and use the reading IoT device to perform information inventory processing of the goods in the high-load area; When the total storage volume of different frequently changing goods in the high load area meets the requirements: When it is determined that there is no frequently changing cargo whose storage amount in the high-load area is greater than a preset storage amount based on the cargo storage amounts of different frequently changing cargoes in the high-load area: Acquire the number of frequently changing goods in the high-load area. When the number of frequently changing goods in the high-load area is less than the preset number of frequently changing goods, there is no need to set the Internet of Things device for reading the electronic tag. When there are frequently changing goods whose storage amount in the high-load area is greater than the preset storage amount or the number of frequently changing goods in the high-load area is not less than the preset number of frequently changing goods: Frequently changing goods whose storage amount in the high-load area is greater than a preset storage amount are used as screening goods. When the sum of the frequent change coefficients of the screening goods is greater than a preset coefficient threshold, the setting of the reading IoT device of the electronic tag is performed with a preset setting number, and the information inventory processing of the goods in the high-load area is performed by using the reading IoT device; When the sum of the change frequency coefficients of the screened goods is not greater than the preset coefficient threshold: Based on the preset weight coefficient corresponding to the cargo storage volume, the cargo weight coefficients of different frequently changing cargoes are determined, and the monitoring demand coefficient of the high-load area is determined according to the sum of the products of the cargo weight coefficients of different frequently changing cargoes and the frequent change coefficient. The monitoring demand coefficient is used to determine the warehouse information management strategy based on Internet of Things devices in the high-load area.

[0062] Embodiment 2 The second aspect, as Figure 6 As shown, the present invention provides a warehouse information management system based on the Internet of Things, which adopts the above-mentioned warehouse information management method based on the Internet of Things, specifically including: Failure risk assessment module, area identification module, cargo classification module, management strategy determination module; The failure risk assessment module is responsible for determining whether the failure risk of the electronic tags of the goods in the area meets the requirements; The area identification module is responsible for using the stored data to determine the high-load area in the area according to a preset AI model; The cargo classification module is responsible for determining the frequently changing cargo among different types of cargo; The management strategy determination module is responsible for determining the warehouse information management strategy based on Internet of Things devices in the high-load area.

[0063] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0064] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0065] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A warehouse information management method based on the Internet of Things, characterized in that: Specifically include: Divide the warehouse into multiple areas, determine the storage time data of different types of goods in the area, and determine that the failure risk of the electronic tags of the goods in the area meets the requirements based on the storage data of the different types of goods in the area, and then proceed to the next step; Acquire storage data of the area on different dates, and use the storage data to determine a high-load area in the area according to a preset AI model; Determine the frequency of change coefficients of different types of goods and frequently changing goods based on the change of goods storage data of different types of goods in the high-load area on different dates; The storage data of different frequently changing goods in the high-load area are obtained, and the warehouse information management strategy based on the Internet of Things devices in the high-load area is determined in combination with the frequency of change coefficients of different frequently changing goods.

2. The warehouse information management method based on the Internet of Things according to claim 1, characterized in that: Divide the warehouse into multiple areas, including: The warehouse is divided into a plurality of areas according to a preset unit area.

3. The warehouse information management method based on the Internet of Things according to claim 1, characterized in that: The cargo storage data includes the cargo storage quantity of the different types of cargo in the area.

4. The warehouse information management method based on the Internet of Things according to claim 1, characterized in that: Determine that the failure risk of the electronic tags of the goods in the area meets the requirements, including: Determine the average storage time of different types of goods based on the storage time data of goods in the area; Determining long-life goods among the goods based on the average storage time; Based on the storage data of the goods, the total storage amount of long-cycle goods in the area is determined, and the total storage amount of goods is used to determine whether the failure risk of the electronic tags of the goods in the area meets the requirements.

5. The warehouse information management method based on the Internet of Things as claimed in claim 4 is characterized in that: The average storage time is determined according to the average storage time of goods corresponding to the type of goods in the warehouse.

6. The warehouse information management method based on the Internet of Things according to claim 1, characterized in that: When the failure risk of the electronic tags of the goods in the area does not meet the requirements, the setting of the electronic tag reading Internet of Things devices is performed in the area according to the preset number, and the reading Internet of Things devices are used to perform information inventory processing of the goods in the area.

7. The warehouse information management method based on the Internet of Things according to claim 1, characterized in that: The change of the goods storage data includes the number of times the goods of the type enter and leave the warehouse on different dates and the quantity of goods at different times of entry and exit.

8. The warehouse information management method based on the Internet of Things as claimed in claim 1, characterized in that: The method for determining the warehouse information management strategy based on IoT devices in the high-load area is: Determine the storage volume of different frequently changing goods in the area based on the storage data of different frequently changing goods in the area; Determining cargo weight coefficients of different frequently changing cargoes based on preset weight coefficients corresponding to the cargo storage volume; The monitoring demand coefficient of the area is determined according to the sum of the products of the cargo weight coefficients and the frequent change coefficients of different frequently changing cargoes, and the warehouse information management strategy based on the Internet of Things devices in the high-load area is determined using the monitoring demand coefficient.

9. The warehouse information management method based on the Internet of Things according to claim 8, characterized in that: Determining a warehouse information management strategy based on IoT devices in the high-load area by using the monitoring demand coefficient specifically includes: When the monitoring demand coefficient is greater than the preset demand coefficient threshold, the setting of the reading IoT device of the electronic tag is performed according to the matching setting quantity corresponding to the monitoring demand coefficient, and the reading IoT device is used to perform the information inventory processing of the goods in the area; When the monitoring demand coefficient is not greater than the preset demand coefficient threshold, there is no need to set up the Internet of Things device for reading the electronic tag.

10. A warehouse information management system based on the Internet of Things, characterized in that: The method for warehouse information management based on the Internet of Things according to any one of claims 1 to 9 specifically comprises: Failure risk assessment module, area identification module, cargo classification module, management strategy determination module; The failure risk assessment module is responsible for determining whether the failure risk of the electronic tags of the goods in the area meets the requirements; The area identification module is responsible for using the stored data to determine the high-load area in the area according to a preset AI model; The cargo classification module is responsible for determining the frequently changing cargo among different types of cargo; The management strategy determination module is responsible for determining the warehouse information management strategy based on Internet of Things devices in the high-load area.

Images