Warehouse item management method, system, equipment and medium based on industrial Internet of Things

Abstract

The present invention relates to a warehouse item management method, system, equipment and medium based on the Industrial Internet of Things. By establishing a three-dimensional warehouse model and updating the cargo location status in real time, combined with the basic information of the goods to be stored and the predicted storage time, candidate storage areas and their priorities are determined; the optimal storage area is determined based on the area priority and the real-time storage status, and the optimal storage path is planned, and finally the cargo moving equipment is controlled to complete the storage operation of the warehouse items. This application solves the technical problem in the prior art that high-density stereoscopic warehouses lack the dynamic optimization capability based on the basic information of the goods and the predicted storage time, resulting in unreasonable allocation of storage space. It achieves the technical effect of real-time monitoring of the storage status based on the Industrial Internet of Things, and intelligent optimization and allocation of storage locations based on the basic information of the goods and the predicted storage time, thereby realizing reasonable allocation of storage space.

Description

Technical Field

[0001] The present invention relates to the field of industrial Internet of Things, and in particular to a warehouse item management method, system, equipment and medium based on the industrial Internet of Things. Background Art

[0002] With the rapid development of modern logistics, high-density three-dimensional warehouses are widely used across various industries. In the process of warehouse item management, reasonable space allocation is crucial for improving storage efficiency. Currently, storage location allocation in high-density three-dimensional warehouses is typically performed using fixed partitioning or empirical management, lacking a dynamic optimization mechanism. Specifically, existing warehouse item management systems often only consider the current storage space status when allocating storage locations, failing to fully utilize basic information about the goods (such as volume, weight, category, etc.) and storage duration. This results in inefficient use of storage space and leads to irrational space allocation. Summary of the Invention

[0003] The main purpose of this application is to provide a warehouse item management method, system, equipment and medium based on the Industrial Internet of Things, aiming to solve the technical problem in the existing technology that high-density three-dimensional warehouses lack dynamic optimization capabilities based on basic information of goods and predicted storage time, resulting in unreasonable allocation of storage space.

[0004] To achieve the above-mentioned purpose, the present application provides a warehouse item management method based on the industrial Internet of Things, which is applied to a warehouse item management system, wherein the warehouse item management system includes a management platform, a sensor network platform and an object platform, wherein the object platform includes a cargo space installation sensor; the method includes: establishing a three-dimensional model of a target warehouse; collecting cargo space occupancy status information in the target warehouse in real time through the cargo space installation sensor, and updating the three-dimensional model of the target warehouse based on the cargo space occupancy status information to obtain a real-time storage status model; obtaining basic information of the goods to be stored, wherein the basic information includes volume information, weight information, and category information of the goods to be stored; obtaining the goods to be stored according to the basic information ... the predicted storage duration of the goods to be stored; determining a plurality of candidate storage areas based on the basic information, and determining the storage area priorities of the plurality of candidate storage areas based on the basic information and the predicted storage duration; determining the optimal storage area for the goods to be stored according to the storage area priorities of the plurality of candidate storage areas and the real-time storage status model; planning a storage path for the goods to be stored from the entrance of the target warehouse to the optimal storage area using the real-time storage status model; controlling the cargo moving device to store the goods to be stored in the optimal storage area according to the storage path, and updating the real-time storage status model after the storage of the goods to be stored is completed.

[0005] Optionally, obtaining the predicted storage duration of the goods to be stored based on the basic information includes: constructing a goods storage prediction network, the goods storage prediction network including a goods analysis sub-model and a duration prediction sub-model; obtaining storage characteristics of the goods to be stored based on the basic information and the goods analysis sub-model; and obtaining the predicted storage duration of the goods to be stored based on the basic information, the storage characteristics and the duration prediction sub-model.

[0006] Optionally, the cargo storage prediction network is constructed, and the cargo storage prediction network includes a cargo analysis sub-model and a duration prediction sub-model, including: obtaining historical cargo storage records, and generating a historical cargo basic information set, a historical cargo storage feature set, and a historical cargo storage duration set based on the historical cargo storage records, wherein the historical cargo storage records have multiple historical storage categories; classifying the historical cargo basic information set and the historical cargo storage feature set according to the multiple historical storage categories to obtain multiple historical cargo basic information subsets, multiple historical cargo storage feature subsets, and multiple historical cargo storage duration subsets; based on A category information extraction layer is established based on the multiple historical storage categories, and multiple storage feature mapping layers are established based on multiple historical cargo basic information subsets and multiple historical cargo storage feature subsets; the multiple storage feature mapping layers are connected in parallel to the category information extraction layer to obtain the cargo analysis sub-model; multiple storage duration mapping layers are established based on the multiple historical cargo basic information subsets, multiple historical cargo storage feature subsets, and the multiple historical cargo storage duration subsets to form the duration prediction sub-model; the cargo analysis sub-model and the duration prediction sub-model are connected to obtain the cargo storage prediction network.

[0007] Optionally, the determining of multiple candidate storage areas based on the basic information and the determining of storage area priorities of the multiple candidate storage areas based on the basic information and the predicted storage duration include: determining applicable cargo location specifications based on the volume information and weight information in the basic information, and determining multiple candidate storage areas based on the applicable cargo location specifications and the three-dimensional model of the target warehouse; obtaining storage environment requirements for the goods to be stored based on the category information in the basic information; calculating the applicability scores of the multiple candidate storage areas based on the predicted storage duration and the storage environment requirements; and sorting the multiple candidate storage areas based on the applicability scores of the multiple candidate storage areas to obtain the storage area priority of each candidate storage area.

[0008] Optionally, calculating the suitability scores of the plurality of candidate storage areas according to the predicted storage duration and the storage environment requirement includes: constructing a suitability score calculation formula as shown in the following formula:

[0009]

[0010] in, represents the suitability score of the candidate storage area, Indicates the predicted storage duration, Indicates the average storage time of the candidate storage area, Indicates the required value of the i-th environmental indicator in the storage environment requirements, represents the actual value of the i-th environmental indicator of the candidate storage area, represents the weight of the i-th environmental indicator, represents the total number of environmental indicators, 、 is the weight coefficient, and ; Obtain the average storage time and actual values ​​of environmental indicators of each candidate storage area; Based on the predicted storage time of the goods to be stored, the storage environment requirements, and the average storage time and actual values ​​of the environmental indicators of each candidate storage area, combined with the suitability score calculation formula, obtain the suitability score of each candidate storage area.

[0011] Optionally, determining the optimal storage area for the goods to be stored based on the storage area priorities of multiple candidate storage areas and the real-time storage status model includes: traversing multiple candidate storage areas in sequence based on the storage area priorities; querying the occupancy status of the currently traversed candidate storage area in the real-time storage status model; if the currently traversed candidate storage area is in an idle state, determining the currently traversed candidate storage area as the optimal storage area.

[0012] Optionally, the use of the real-time storage status model to plan the storage path of the goods to be stored from the entrance of the target warehouse to the optimal storage area includes: establishing a traversable path network based on the real-time storage status model; in the traversable path network, the position coordinates of the entrance are used as the starting node, and the position coordinates of the optimal storage area are used as the target node; according to the starting node and the target node, the shortest path is searched in the traversable path network to obtain the storage path.

[0013] Furthermore, to achieve the above-mentioned objectives, the present application provides a warehouse item management system based on the Industrial Internet of Things, which is used to implement a warehouse item management method based on the Industrial Internet of Things. The warehouse item management system includes a management platform, a sensor network platform, and an object platform that are sequentially communicatively connected. The object platform includes sensors installed at cargo locations. The object platform is used to collect cargo location occupancy status information in a target warehouse in real time. The sensor network platform is used to obtain the cargo location occupancy status information and transmit the cargo location occupancy status information to the management platform. The management platform includes a model building module, a warehouse modeling module, a cargo information module, a duration prediction module, a region screening module, a region optimization module, a path planning module, and a storage execution module. Among them, the model building module is used to establish a three-dimensional model of the target warehouse; the warehouse modeling module is used to collect the occupancy status information of the cargo spaces in the target warehouse in real time through the sensors installed in the cargo spaces, and update the three-dimensional model of the target warehouse based on the occupancy status information to obtain a real-time storage status model; the cargo information module is used to obtain basic information of the cargo to be stored, wherein the basic information includes volume information, weight information, and category information of the cargo to be stored; the duration prediction module is used to obtain the predicted storage duration of the cargo to be stored based on the basic information; the area screening module is used to determine multiple candidate storage areas based on the basic information, and determine the storage area priority of the multiple candidate storage areas based on the basic information and the predicted storage duration; the area optimization module is used to determine the optimal storage area for the cargo to be stored based on the storage area priorities of the multiple candidate storage areas and the real-time storage status model; the path planning module is used to plan a storage path for the cargo to be stored from the entrance of the target warehouse to the optimal storage area using the real-time storage status model; the storage execution module is used to control the cargo moving device to store the cargo to be stored in the optimal storage area according to the storage path, and update the real-time storage status model after the storage of the cargo to be stored is completed.

[0014] In addition, to achieve the above-mentioned purpose, the present application also provides an electronic device, including a memory and a processor. The memory is used to store computer software programs; the processor is used to read and execute the computer software programs, thereby implementing a warehouse item management method based on the Industrial Internet of Things.

[0015] In addition, the present application also provides a non-transitory computer-readable storage medium, in which a computer software program is stored. When the computer software program is executed by a processor, a warehouse item management method based on the industrial Internet of Things is implemented.

[0016] The beneficial effects that can be achieved by this application are as follows:

[0017] A three-dimensional model of the target warehouse is established, providing a foundation for subsequent storage item management. Sensors installed at each shelf collect real-time occupancy information within the target warehouse. This information is then used to update the target warehouse model, resulting in a real-time storage status model. Using the Industrial Internet of Things (IIoT) system, the warehouse status is monitored in real time, allowing for the construction of a dynamic digital warehouse model. This provides an accurate data foundation for subsequent optimization decisions. Basic information about the goods to be stored, including volume, weight, and category, is obtained, providing essential cargo characteristics for allocating storage locations. Based on this basic information, the predicted storage duration of the goods to be stored is obtained, providing crucial decision-making support for optimal storage location allocation. Based on this basic information, multiple candidate storage areas are identified. Based on this information and the predicted storage duration, storage priority is determined for these candidate areas, and a storage area optimization mechanism is established. Based on the storage priority of these candidate areas and the real-time storage status model, the optimal storage area for the goods to be stored is determined, enabling intelligent storage location decisions and ensuring rational space allocation. The real-time storage status model is used to plan the storage path for the goods from the target warehouse's entry to the optimal storage area, improving storage efficiency. According to the storage path, the cargo moving equipment is controlled to store the goods to be stored in the optimal storage area, and the real-time storage status model is updated after the storage of the goods to be stored is completed, realizing the automated execution of the storage process and ensuring the real-time update of the warehouse status data.

[0018] Through the above steps, the technical problem in the existing technology that the storage item management lacks the dynamic optimization capability based on the basic information of the goods and the storage time, resulting in unreasonable allocation of storage space, is solved, and the reasonable allocation of storage space is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of the process of the warehouse goods management method based on the Industrial Internet of Things provided by the present invention;

[0020] Figure 2 This is a schematic diagram of the structure of the warehouse goods management system based on the Industrial Internet of Things provided by the present invention;

[0021] Figure 3 A schematic structural diagram of the electronic device provided by the present invention;

[0022] Figure 4 A schematic structural diagram of a computer-readable storage medium provided by the present invention.

[0023] In the accompanying drawings, the components represented by the reference numerals are as follows:

[0024] A warehouse item management system 100 based on the industrial Internet of Things, a management platform 101, a sensor network platform 102, an object platform 103, a model building module 11, a warehouse modeling module 12, a cargo information module 13, a duration prediction module 14, an area screening module 15, an area optimization module 16, a path planning module 17, a storage execution module 18, an electronic device 200, a memory 210, a processor 220, a computer program 211, and a computer-readable storage medium 300.

[0025] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0027] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the various components under a certain preset posture (as shown in the accompanying drawings). If the preset posture changes, the directional indication will also change accordingly.

[0028] In the present invention, unless otherwise specified or limited, the terms "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can mean fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will be able to understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0029] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the meaning of "and / or" appearing throughout the text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or a scheme in which A and B are satisfied at the same time. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0030] Example 1:

[0031] like Figure 1 As shown, an embodiment of the present invention provides a warehouse item management method based on the Industrial Internet of Things, which is applied to a warehouse item management system. The warehouse item management system can be, for example, a warehouse item management system based on the Industrial Internet of Things. The warehouse item management system based on the Industrial Internet of Things can include a management platform, a sensor network platform, and an object platform. The object platform includes sensors installed at cargo locations. The method includes:

[0032] S1: Establish a 3D model of the target warehouse;

[0033] S2: collecting cargo space occupancy status information in the target warehouse in real time through the cargo space installed sensors, and updating the target warehouse three-dimensional model based on the cargo space occupancy status information to obtain a real-time storage status model;

[0034] S3: Obtain basic information of the goods to be stored, wherein the basic information includes volume information, weight information, and category information of the goods to be stored;

[0035] S4: Obtaining a predicted storage time of the goods to be stored based on the basic information;

[0036] S5: determining a plurality of candidate storage areas based on the basic information, and determining storage area priorities of the plurality of candidate storage areas based on the basic information and the predicted storage duration;

[0037] S6: determining the optimal storage area for the goods to be stored according to the storage area priorities of the plurality of candidate storage areas and the real-time storage status model;

[0038] S7: Planning a storage path for the goods to be stored from the entrance of the target warehouse to the optimal storage area using the real-time storage status model;

[0039] S8: According to the storage path, control the cargo moving device to store the cargo to be stored in the optimal storage area, and update the real-time storage status model after the storage of the cargo to be stored is completed.

[0040] Specifically, the warehouse goods management method based on the Industrial Internet of Things proposed in the embodiment of the present application is specifically applied to a warehouse goods management system based on the Industrial Internet of Things. The warehouse goods management system based on the Industrial Internet of Things consists of three main platforms, namely the management platform, the sensor network platform and the object platform, wherein the object platform is equipped with sensors installed at cargo locations. The management platform serves as the central control unit of the system, responsible for data processing, business logic execution and user interaction interface presentation; the sensor network platform acts as an intermediate layer for data transmission, responsible for coordinating and managing the communication network between various sensor devices to ensure the reliability and stability of information transmission; the object platform is the physical entity layer, which includes sensors installed at cargo locations deployed at various cargo locations in the warehouse, which are used to collect information data such as the status and location of items in real time.

[0041] First, a high-density three-dimensional warehouse is used as the target warehouse. A laser rangefinder is used to obtain the target warehouse's spatial dimension data, including basic parameters such as length, width, and height. Then, using 3D modeling software, a basic framework model of the target warehouse is constructed based on the acquired spatial dimension data, resulting in a 3D model of the target warehouse. Next, based on the shelf layout information within the target warehouse, the shelf structure is added to the 3D model, and each shelf is assigned a unique 3D coordinate identifier. Subsequently, sensors installed in the shelf locations collect data from the target warehouse in real time. These sensors can be configured as needed, including, but not limited to, infrared sensors for detecting whether a shelf space is occupied, and gravity sensors for confirming the actual placement of goods. Next, the occupancy status of the shelf spaces in the target warehouse is determined based on the data collected by the sensors. The 3D model of the target warehouse is then updated based on this occupancy status information. Specifically, the occupancy status data collected by the sensor components is mapped to the attribute fields of the corresponding shelf spaces in the 3D model of the target warehouse using the shelf space's unique identifier, forming a real-time storage status model. Whenever the sensors detect a change in occupancy status, an update is triggered to ensure that the status information in the real-time storage status model remains consistent with the actual situation. Next, data collection equipment at the warehouse's incoming warehouse collects the volume, weight, and category information of the goods to be stored as basic information. Volume information, including length, width, and height, is obtained by scanning the goods using a 3D scanner. Weight information is collected using an electronic scale at the incoming warehouse, recording the actual weight of the goods. Category information, including attributes such as the type and specifications, is obtained by scanning the goods' electronic tags or barcodes. The collection of basic information is automated, ensuring data accuracy and timeliness. When goods arrive at the incoming warehouse, the data collection equipment automatically initiates the collection process, completing the collection and recording of all basic information. The collected data is also preprocessed and formatted to ensure standardization and usability. This basic information serves as an important basis for subsequent storage duration predictions and storage area determination.

[0042] Subsequently, the predicted storage duration of the goods to be stored is obtained based on the basic information. For example, historical basic information and actual storage duration data for goods to be stored are first extracted from the warehouse management system database to create a statistical sample set. Then, the correlation between the basic information and storage duration in the sample set is analyzed to establish a regression prediction model. Specifically, a multivariate linear regression method is used, with the volume and weight of the goods as independent variables and the storage duration as the dependent variable. The regression coefficients are calculated using the least squares method. Category information is converted into numerical features using one-hot encoding and then added to the regression model. Finally, the basic information of the goods to be stored is input into the trained regression model to obtain the predicted storage duration. Next, the storage areas in the target warehouse are classified and feature extracted. Based on the basic information of the goods to be stored, such as volume and weight, candidate storage areas that meet the storage requirements are selected from the classified storage areas. Then, a comprehensive evaluation is conducted, taking into account the storage requirements of the goods and the characteristic parameters of each candidate storage area, using the predicted storage duration as a key indicator, and combining it with other storage conditions to calculate a suitability score for each candidate storage area. Afterwards, the candidate storage areas are sorted according to the calculated suitability scores, thereby determining the storage area priority of each candidate storage area to ensure that the goods to be stored are allocated to the most suitable storage area.

[0043] Next, a priority traversal method is used to check the current status of each candidate storage area in the real-time storage state model, in descending order of priority. If a candidate storage area is found to be idle, it is directly identified as the optimal storage area, thus completing the storage area determination. The starting and ending locations of the cargo transportation are then determined. The starting point is the target warehouse's entrance, and the ending point is the location of the optimal storage area identified. The real-time storage state model is then used to analyze the current aisle conditions within the warehouse and avoid occupied or blocked paths. A path planning algorithm is then used to calculate a reasonable transportation path from the entrance to the optimal storage area, which serves as the storage path for the cargo to be stored, ensuring safe and efficient cargo transportation. The planned storage path is then converted into a sequence of motion instructions executable by the cargo moving device. These instructions are sent to the cargo moving device, which controls it to transport the cargo to the optimal storage area along the predetermined path. When the cargo moving device completes its storage task and receives completion status feedback, it updates the occupancy status of the corresponding storage location in the real-time storage state model to ensure consistency between the model and the actual warehouse status. This automated storage control method not only improves storage efficiency, but also ensures the real-time accuracy of warehouse status information.

[0044] As an optional implementation manner, obtaining the predicted storage time of the goods to be stored based on the basic information includes:

[0045] S41: Constructing a cargo storage prediction network, wherein the cargo storage prediction network includes a cargo analysis sub-model and a duration prediction sub-model;

[0046] S42: Obtaining storage characteristics of the goods to be stored based on the basic information and the goods analysis sub-model;

[0047] S43: Obtaining a predicted storage duration of the goods to be stored according to the basic information, the storage characteristics, and the duration prediction sub-model.

[0048] Specifically, to obtain the predicted storage duration of goods to be stored based on basic information, a cargo storage prediction network is first constructed, consisting of a cargo analysis sub-model and a duration prediction sub-model. This network adopts a dual-model structure, enabling in-depth analysis and prediction of cargo information in stages. The basic information of the goods to be stored is input into the cargo analysis sub-model for processing. The cargo analysis sub-model extracts and analyzes features of the basic information to obtain storage characteristics that characterize the storage characteristics of the goods to be stored. These storage characteristics contain key attribute information of the goods during the warehousing process. Subsequently, the basic information of the goods to be stored and the storage characteristics obtained by the cargo analysis sub-model are input into the duration prediction sub-model. Based on the input information and the established prediction mapping relationship, the duration prediction sub-model calculates the predicted storage duration of the goods to be stored. This dual-model-based prediction method improves prediction accuracy by extracting and utilizing cargo information in a step-by-step manner.

[0049] As an optional implementation, the cargo storage prediction network is constructed, and the cargo storage prediction network includes a cargo analysis sub-model and a duration prediction sub-model, including:

[0050] S411: Acquire historical cargo storage records, and generate a historical cargo basic information set, a historical cargo storage feature set, and a historical cargo storage duration set based on the historical cargo storage records, wherein the historical cargo storage records have multiple historical storage categories;

[0051] S412: Classifying the historical cargo basic information set and the historical cargo storage feature set according to the multiple historical storage categories to obtain multiple historical cargo basic information subsets, multiple historical cargo storage feature subsets, and multiple historical cargo storage duration subsets;

[0052] S413: Establishing a category information extraction layer based on the multiple historical storage categories, and establishing multiple storage feature mapping layers based on multiple historical cargo basic information subsets and multiple historical cargo storage feature subsets;

[0053] S414: Connecting the multiple storage feature mapping layers in parallel to the category information extraction layer to obtain the goods analysis sub-model;

[0054] S415: Establishing multiple storage duration mapping layers based on the multiple historical cargo basic information subsets, the multiple historical cargo storage feature subsets, and the multiple historical cargo storage duration subsets to form the duration prediction sub-model;

[0055] S416: Connect the cargo analysis sub-model and the duration prediction sub-model to obtain the cargo storage prediction network.

[0056] Specifically, when building a cargo storage prediction network, we first obtain historical cargo storage records. These records contain data on the complete storage cycle of cargo in the warehouse. Based on these records, we extract a historical cargo basic information set (including basic information such as volume, weight, and category), a historical cargo storage feature set (including characteristic data exhibited by historical cargo during storage), and a historical cargo storage duration set (including actual storage duration data). These historical cargo storage records cover multiple historical storage categories, ensuring data diversity and representativeness. Next, we classify and organize the extracted datasets based on these historical storage categories. Specifically, we divide the historical cargo basic information set and the historical cargo storage feature set by category, resulting in multiple historical cargo basic information subsets, multiple historical cargo storage feature subsets, and multiple historical cargo storage duration subsets. This classification process enables subsequent model training to better capture the characteristics of different cargo categories.

[0057] To establish the model structure, a category information extraction layer is first established based on multiple historical storage categories. This layer encodes and characterizes category information, mapping different categories of goods into corresponding feature vectors. Simultaneously, multiple storage feature mapping layers are established based on multiple historical basic information subsets of goods and multiple historical storage feature subsets of goods. Each mapping layer transforms basic information into storage features for different categories of goods. By connecting these multiple storage feature mapping layers in parallel to the category information extraction layer, a complete goods analysis sub-model is formed. This sub-model can derive storage features based on goods' categories and basic information. Next, multiple storage duration mapping layers are established using multiple historical basic information subsets, multiple historical storage feature subsets, and multiple historical storage duration subsets of goods. For each category, a storage duration mapping layer is trained based on the corresponding historical data, enabling it to predict storage duration based on the goods' basic information and storage features. These trained storage duration mapping layers form the duration prediction sub-model. The goods analysis sub-model and the duration prediction sub-model are then connected to form a complete goods storage prediction network. The network extracts cargo features through the cargo analysis sub-model, and then uses the storage duration prediction sub-model to predict the storage duration, thereby realizing storage duration prediction.

[0058] As an optional implementation manner, determining multiple candidate storage areas based on the basic information, and determining storage area priorities of the multiple candidate storage areas based on the basic information and the predicted storage duration, includes:

[0059] S51: Determine applicable cargo location specifications based on the volume information and weight information in the basic information, and determine multiple candidate storage areas based on the applicable cargo location specifications and the three-dimensional model of the target warehouse;

[0060] S52: Based on the category information in the basic information, obtaining the storage environment requirements of the goods to be stored;

[0061] S53: Calculating suitability scores of the plurality of candidate storage areas according to the predicted storage duration and the storage environment requirements;

[0062] S54: Sort the multiple candidate storage areas based on their applicability scores to obtain the storage area priority of each candidate storage area.

[0063] Specifically, when determining candidate storage areas and their priorities, the system first determines the applicable storage location specifications based on the volume and weight information of the goods to be stored. The volume information of the goods to be stored is matched with the dimensions of the various storage locations in the target warehouse, while the weight information is compared with the load-bearing capacity of the storage locations to select storage locations suitable for the goods. Based on the selected applicable storage location specifications and the distribution of storage locations in the three-dimensional model of the target warehouse, multiple candidate storage areas that meet the storage requirements are determined. Next, based on the category information of the goods to be stored, the storage environment requirements for that type of goods are retrieved from a preset storage environment parameter library. These requirements include the various environmental parameters that must be met during the storage of that type of goods.

[0064] Next, a suitability score is calculated for each candidate storage area, using the predicted storage duration and storage environment requirements as evaluation indicators. This scoring process comprehensively considers the match between the predicted storage duration and the characteristics of the candidate area, as well as the degree of compliance with environmental parameters. The candidate areas are then ranked based on the calculated suitability scores to determine their storage priority. The priority reflects the suitability of each storage area for the goods and provides a basis for subsequent selection of the optimal storage area.

[0065] As an optional implementation manner, calculating the suitability scores of the multiple candidate storage areas according to the predicted storage duration and the storage environment requirements includes:

[0066] S531: Construct the following formula to calculate the suitability score:

[0067]

[0068] in, represents the suitability score of the candidate storage area, Indicates the predicted storage duration, Indicates the average storage time of the candidate storage area, Indicates the required value of the i-th environmental indicator in the storage environment requirements, represents the actual value of the i-th environmental indicator of the candidate storage area, represents the weight of the i-th environmental indicator, represents the total number of environmental indicators, 、 is the weight coefficient, and ;

[0069] S532: Obtaining the average storage duration and actual value of the environmental indicator of each candidate storage area;

[0070] S533: Based on the predicted storage time of the goods to be stored, the storage environment requirements, the average storage time of each candidate storage area and the actual value of the environmental indicator, combined with the suitability score calculation formula, obtain the suitability score of each candidate storage area.

[0071] Specifically, when calculating the suitability score of a candidate storage area, first construct the suitability score calculation formula shown in the following formula:

[0072]

[0073] The formula consists of two parts: Reflects the matching degree of storage time by predicting storage time Average storage time with candidate storage areas The second part is to measure the difference between Reflects the degree of compliance with environmental indicators by calculating the required values ​​of each environmental indicator in the storage environment requirements The actual value of the candidate storage area The deviation is measured. Represents the weight of the i-th environmental indicator, which is used to reflect the importance of different environmental indicators. represents the total number of environmental indicators, 、 The weight coefficients are all between 0 and 1 and satisfy , used to balance the proportion of duration matching and environmental compliance in the score, and is set by the expert group based on the requirements for duration matching and environmental compliance, such as =0.65, =0.35. Among them, the duration difference term in the above applicability score calculation formula is Deviation from environmental indicators The smaller the value of , the higher the matching degree of storage duration and the better the compliance degree of environmental indicators. Therefore, the smaller the value of the suitability score, the more suitable the candidate storage area is for storing the goods to be stored.

[0074] Next, the average storage duration and actual environmental indicator values ​​for each candidate storage area are obtained. The average storage duration is obtained by statistically analyzing historical storage data, while the actual environmental indicator values ​​are collected by environmental monitoring equipment in the storage area. The predicted storage duration of the goods to be stored, the storage environment requirements, and the obtained average storage duration and actual environmental indicator values ​​for each candidate storage area are then substituted into the suitability scoring formula to calculate the suitability score for each candidate storage area. This scoring method comprehensively considers duration matching and environmental adaptability, objectively reflecting the suitability of each candidate storage area for the goods to be stored. The candidate storage areas are then sorted in ascending order based on their suitability scores, with candidate storage areas with lower suitability scores receiving higher storage area priority. This scoring and sorting method, which comprehensively considers duration matching and environmental adaptability, effectively identifies the most suitable storage area for the goods to be stored.

[0075] As an optional implementation manner, determining the optimal storage area for the goods to be stored based on the storage area priorities of the plurality of candidate storage areas and the real-time storage status model includes:

[0076] S61: traversing multiple candidate storage areas in sequence based on the storage area priority;

[0077] S62: querying the occupancy status of the currently traversed candidate storage area in the real-time storage status model;

[0078] S63: If the candidate storage area currently traversed is in an idle state, the candidate storage area currently traversed is determined as the optimal storage area.

[0079] Specifically, when determining the optimal storage area for goods to be stored based on the storage area priority and the real-time storage status model, multiple candidate storage areas are first traversed based on the storage area priority, and the candidate storage areas are visited one by one in descending order of priority. For the candidate storage area currently being traversed, its occupancy status information is queried in the real-time storage status model. The occupancy status information reflects whether the storage area is currently occupied by other goods. When the currently traversed candidate storage area is detected to be idle, it indicates that the area can be used to store goods, and the traversal is immediately stopped, and the candidate storage area is determined as the optimal storage area. This priority-based traversal method ensures that the most suitable storage area is preferentially selected under the premise that the storage conditions are met.

[0080] As an optional implementation, planning a storage path for the goods to be stored from the entrance of the target warehouse to the optimal storage area using the real-time storage status model includes:

[0081] S71: Establishing a traversable path network based on the real-time storage state model;

[0082] S72: In the navigable path network, the location coordinates of the storage entrance are used as a starting node, and the location coordinates of the optimal storage area are used as a target node;

[0083] S73: Searching for the shortest path in the traversable path network according to the starting node and the target node to obtain the storage path.

[0084] Specifically, when planning the storage paths for goods to be stored, a traversable path network is first established based on the real-time storage status model. Specifically, by analyzing the occupancy of storage spaces in the real-time storage status model, unoccupied aisles within the warehouse that are accessible to cargo transport equipment are identified. Occupied storage spaces or aisles are marked as impassable. By connecting all traversable aisles, a complete traversable path network is formed, which accurately reflects the actual traffic conditions within the warehouse. Next, the starting and ending points of the path planning are determined within this constructed traversable path network. The 3D coordinates of the target warehouse entrance are set as the starting node, representing the initial location where the goods enter the warehouse. The 3D coordinates of the determined optimal storage area are set as the target node, representing the final destination for the goods. The determination of these two nodes provides clear spatial constraints for subsequent path search. Next, a path search algorithm (such as the Dijkstra algorithm or the A* algorithm) is used within the traversable path network to search for a traversable path with the shortest distance, using the starting and target nodes as constraints. During the search process, constraints such as aisle width and turning radius are considered to ensure that the planned path meets the movement requirements of the cargo transport equipment. The shortest path found is the storage path from the warehouse entrance to the optimal storage area for the goods to be stored. This path ensures both the shortest transportation distance and the feasibility of the transportation process.

[0085] The second embodiment of the present application provides a warehouse goods management system based on the industrial Internet of Things, such as Figure 2As shown, the warehouse item management system 100 based on the industrial Internet of Things includes a management platform 101, a sensor network platform 102 and an object platform 103 that are communicatively connected in sequence. The object platform 103 includes a cargo space installation sensor. The object platform 103 is used to collect cargo space occupancy status information in the target warehouse in real time. The sensor network platform 102 is used to obtain cargo space occupancy status information and transmit the cargo space occupancy status information to the management platform 101. The management platform 101 includes a model building module 11, a warehouse modeling module 12, a cargo information module 13, a duration prediction module 14, an area screening module 15, an area optimization module 16, a path planning module 17 and a storage execution module 18. Among them, the model building module 11 is used to build a three-dimensional model of the target warehouse; the warehouse modeling module 12 is used to collect the cargo space occupancy status information in the target warehouse in real time through the cargo space installation sensor, and update the three-dimensional model of the target warehouse based on the cargo space occupancy status information to obtain a real-time storage status model; the cargo information module 13 is used to obtain the basic information of the cargo to be stored, wherein the basic information includes the volume information, weight information, and category information of the cargo to be stored; the duration prediction module 14 is used to obtain the predicted storage duration of the cargo to be stored based on the basic information; the area screening module 15 is used to determine multiple candidate storage areas based on the basic information, and based on The basic information and the predicted storage duration are used to determine the storage area priorities of multiple candidate storage areas; the area optimization module 16 is used to determine the optimal storage area for the goods to be stored based on the storage area priorities of multiple candidate storage areas and the real-time storage status model; the path planning module 17 is used to plan the storage path of the goods to be stored from the entrance of the target warehouse to the optimal storage area using the real-time storage status model; the storage execution module 18 is used to control the cargo moving device to store the goods to be stored in the optimal storage area according to the storage path, and update the real-time storage status model after the storage of the goods to be stored is completed.

[0086] As an optional embodiment, the duration prediction module 14 includes a prediction network construction unit, a storage feature acquisition unit, and a storage duration acquisition unit. The prediction network construction unit is configured to construct a cargo storage prediction network, which includes a cargo analysis sub-model and a duration prediction sub-model. The storage feature acquisition unit is configured to obtain the storage features of the cargo to be stored based on the basic information and the cargo analysis sub-model. The storage duration acquisition unit is configured to obtain the predicted storage duration of the cargo to be stored based on the basic information, the storage features, and the duration prediction sub-model.

[0087] As an optional implementation, the prediction network construction unit includes a data set acquisition subunit, a data set classification subunit, a hierarchy establishment subunit, a cargo analysis submodel establishment subunit, a duration prediction submodel establishment subunit, and a model connection subunit. The data set acquisition subunit is used to acquire historical cargo storage records, and generate a historical cargo basic information set, a historical cargo storage feature set, and a historical cargo storage duration set based on the historical cargo storage records, wherein the historical cargo storage records have multiple historical storage categories; the data set classification subunit is used to classify the historical cargo basic information set and the historical cargo storage feature set according to the multiple historical storage categories, and acquire multiple historical cargo basic information subsets, multiple historical cargo storage feature subsets, and multiple historical cargo storage duration subsets; the hierarchy establishment subunit is used to establish a category information extraction layer according to the multiple historical storage categories, and generate a historical cargo basic information set and a historical cargo storage feature set according to the multiple historical storage categories. A historical cargo basic information subset and a plurality of historical cargo storage feature subsets are used to establish a plurality of storage feature mapping layers; a cargo analysis submodel establishment subunit is used to connect the plurality of storage feature mapping layers in parallel to the category information extraction layer to obtain the cargo analysis submodel; a duration prediction submodel establishment subunit is used to establish a plurality of storage duration mapping layers based on the plurality of historical cargo basic information subsets, the plurality of historical cargo storage feature subsets and the plurality of historical cargo storage duration subsets to constitute the duration prediction submodel; a model connection subunit is used to connect the cargo analysis submodel and the duration prediction submodel to obtain the cargo storage prediction network.

[0088] As an optional embodiment, the area screening module 15 includes a candidate area determination unit, a storage environment requirement acquisition unit, a suitability scoring unit, and a priority determination unit. The candidate area determination unit is configured to determine applicable cargo space specifications based on the volume information and weight information in the basic information, and to determine multiple candidate storage areas based on the applicable cargo space specifications and the three-dimensional model of the target warehouse; the storage environment requirement acquisition unit is configured to acquire the storage environment requirements for the goods to be stored based on the category information in the basic information; the suitability scoring unit is configured to calculate the suitability scores of the multiple candidate storage areas based on the predicted storage duration and the storage environment requirements; and the priority determination unit is configured to sort the multiple candidate storage areas based on their suitability scores to obtain the storage area priority of each candidate storage area.

[0089] As an optional implementation, the suitability scoring unit includes a formula construction subunit, a region information acquisition subunit, and a score acquisition subunit. The formula construction subunit is used to construct the suitability score calculation formula shown below:

[0090]

[0091] in, represents the suitability score of the candidate storage area, Indicates the predicted storage duration, Indicates the average storage time of the candidate storage area, Indicates the required value of the i-th environmental indicator in the storage environment requirements, represents the actual value of the i-th environmental indicator of the candidate storage area, represents the weight of the i-th environmental indicator, represents the total number of environmental indicators, 、 is the weight coefficient, and .

[0092] The area information acquisition subunit is used to obtain the average storage time and actual values ​​of environmental indicators of each candidate storage area; the score acquisition subunit is used to obtain the suitability score of each candidate storage area based on the predicted storage time of the goods to be stored, the storage environment requirements, and the average storage time and actual values ​​of the environmental indicators of each candidate storage area, combined with the suitability score calculation formula.

[0093] As an optional embodiment, the region optimization module 16 includes a candidate storage region traversal unit, an occupancy status query unit, and an optimal storage region determination unit. The candidate storage region traversal unit is configured to sequentially traverse multiple candidate storage regions based on the storage region priority; the occupancy status query unit is configured to query the occupancy status of the currently traversed candidate storage region in the real-time storage status model; and the optimal storage region determination unit is configured to determine the currently traversed candidate storage region as the optimal storage region if the currently traversed candidate storage region is idle.

[0094] As an optional embodiment, the path planning module 17 includes a path network establishment unit, a starting target determination unit, and a storage path acquisition unit. The path network establishment unit is configured to establish a traversable path network based on the real-time storage state model; the starting target determination unit is configured to use the location coordinates of the storage entrance as the starting node and the location coordinates of the optimal storage area as the target node in the traversable path network; and the storage path acquisition unit is configured to search for the shortest path in the traversable path network based on the starting node and the target node to obtain the storage path.

[0095] A third embodiment of the present application provides an electronic device, such as Figure 3 As shown, the electronic device 200 includes a memory 210, a processor 220, and a computer program 211 stored in the memory 210 and executable on the processor 220. When the processor 220 executes the computer program 211, a warehouse item management method based on the industrial Internet of Things is implemented.

[0096] The fourth embodiment of the present application provides a computer-readable storage medium, such as Figure 4 As shown, a computer program 211 is stored on the computer-readable storage medium 300, and when the computer program 211 is executed by the processor, a warehouse item management method based on the industrial Internet of Things is implemented.

[0097] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A warehouse item management method based on the industrial Internet of Things, characterized in that: Applied to a warehouse goods management system, the warehouse goods management system includes a management platform, a sensor network platform, and an object platform, the object platform includes a cargo location-mounted sensor, and the method includes: Establish a 3D model of the target warehouse; The cargo space occupancy status information in the target warehouse is collected in real time by the cargo space installation sensor, and the three-dimensional model of the target warehouse is updated based on the cargo space occupancy status information to obtain a real-time storage status model; Obtaining basic information of the goods to be stored, wherein the basic information includes volume information, weight information, and category information of the goods to be stored; Obtaining a predicted storage time of the goods to be stored based on the basic information; determining a plurality of candidate storage areas based on the basic information, and determining storage area priorities of the plurality of candidate storage areas based on the basic information and the predicted storage duration; determining an optimal storage area for the goods to be stored according to the storage area priorities of the plurality of candidate storage areas and the real-time storage status model; Planning a storage path for the goods to be stored from the entrance of the target warehouse to the optimal storage area using the real-time storage status model; According to the storage path, control the cargo moving device to store the cargo to be stored in the optimal storage area, and update the real-time storage status model after the cargo to be stored is completed; Wherein, obtaining the predicted storage duration of the goods to be stored based on the basic information includes: constructing a goods storage prediction network, the goods storage prediction network including a goods analysis sub-model and a duration prediction sub-model; Obtaining storage characteristics of the goods to be stored based on the basic information and the goods analysis sub-model; Obtaining a predicted storage duration of the goods to be stored based on the basic information, the storage characteristics, and the duration prediction sub-model; The determining of a plurality of candidate storage areas based on the basic information, and determining storage area priorities of the plurality of candidate storage areas based on the basic information and the predicted storage duration, includes: Determining applicable cargo space specifications based on the volume information and weight information in the basic information, and determining a plurality of candidate storage areas based on the applicable cargo space specifications and the three-dimensional model of the target warehouse; Based on the category information in the basic information, obtaining the storage environment requirements of the goods to be stored; Calculating suitability scores of the plurality of candidate storage areas according to the predicted storage duration and the storage environment requirements; The plurality of candidate storage areas are sorted based on their applicability scores to obtain the storage area priority of each candidate storage area.

2. The method according to claim 1, characterized in that The said constructing of the cargo storage prediction network comprises: Acquire historical cargo storage records, and generate a historical cargo basic information set, a historical cargo storage feature set, and a historical cargo storage duration set based on the historical cargo storage records, wherein the historical cargo storage records have multiple historical storage categories; Classifying the historical cargo basic information set and the historical cargo storage feature set according to the multiple historical storage categories to obtain multiple historical cargo basic information subsets, multiple historical cargo storage feature subsets, and multiple historical cargo storage duration subsets; Establishing a category information extraction layer based on the multiple historical storage categories, and establishing multiple storage feature mapping layers based on multiple historical cargo basic information subsets and multiple historical cargo storage feature subsets; Connecting the multiple storage feature mapping layers in parallel to the category information extraction layer to obtain the goods analysis sub-model; Establishing multiple storage duration mapping layers based on the multiple historical cargo basic information subsets, the multiple historical cargo storage feature subsets, and the multiple historical cargo storage duration subsets to form the duration prediction sub-model; The cargo analysis sub-model and the duration prediction sub-model are connected to obtain the cargo storage prediction network.

3. The method according to claim 1, characterized in that The calculating, based on the predicted storage duration and the storage environment requirement, the suitability scores of the plurality of candidate storage areas includes: Construct the suitability score calculation formula as shown below: in, represents the suitability score of the candidate storage area, Indicates the predicted storage duration, Indicates the average storage time of the candidate storage area, Indicates the required value of the i-th environmental indicator in the storage environment requirements, represents the actual value of the i-th environmental indicator of the candidate storage area, represents the weight of the i-th environmental indicator, represents the total number of environmental indicators, 、 is the weight coefficient, and ; Obtaining an average storage duration and actual values ​​of environmental indicators of each candidate storage area; Based on the predicted storage time of the goods to be stored, the storage environment requirements, the average storage time of each candidate storage area and the actual value of the environmental indicator, combined with the suitability score calculation formula, the suitability score of each candidate storage area is obtained.

4. The method according to claim 1, wherein The determining the optimal storage area for the goods to be stored according to the storage area priorities of the plurality of candidate storage areas and the real-time storage status model includes: sequentially traverse a plurality of candidate storage areas based on the storage area priorities; querying the occupancy status of the currently traversed candidate storage area in the real-time storage status model; If the currently traversed candidate storage area is in an idle state, the currently traversed candidate storage area is determined as the optimal storage area.

5. The method according to claim 1, wherein The planning of a storage path for the goods to be stored from the entrance of the target warehouse to the optimal storage area using the real-time storage status model includes: Establishing a traversable path network based on the real-time storage state model; In the navigable path network, the location coordinates of the storage entrance are used as the starting node, and the location coordinates of the optimal storage area are used as the target node; According to the starting node and the target node, the shortest path is searched in the traversable path network to obtain the storage path.

6. The warehouse goods management system based on industrial Internet of Things is characterized by: The storage goods management system includes a management platform, a sensor network platform, and an object platform that are sequentially communicatively connected. The object platform includes sensors installed at cargo spaces. The object platform is used to collect cargo space occupancy status information in a target warehouse in real time. The sensor network platform is used to obtain the cargo space occupancy status information and transmit the cargo space occupancy status information to the management platform. The management platform includes: Model building module, used to build a three-dimensional model of the target warehouse; a warehouse modeling module, configured to collect cargo space occupancy status information in a target warehouse in real time through sensors installed in the cargo spaces, and update the three-dimensional model of the target warehouse based on the cargo space occupancy status information to obtain a real-time storage status model; A cargo information module is used to obtain basic information of the cargo to be stored, wherein the basic information includes volume information, weight information, and category information of the cargo to be stored; A duration prediction module, configured to obtain a predicted storage duration of the goods to be stored based on the basic information; an area screening module, configured to determine a plurality of candidate storage areas based on the basic information, and determine storage area priorities of the plurality of candidate storage areas based on the basic information and the predicted storage duration; an area optimization module, configured to determine an optimal storage area for the goods to be stored based on the storage area priorities of a plurality of candidate storage areas and the real-time storage status model; a path planning module, configured to plan a storage path for the goods to be stored from the entrance of the target warehouse to the optimal storage area using the real-time storage status model; a storage execution module, configured to control the cargo moving device to store the cargo to be stored in the optimal storage area according to the storage path, and to update the real-time storage status model after the cargo to be stored is completed; The duration prediction module is further configured to construct a cargo storage prediction network, the cargo storage prediction network comprising a cargo analysis sub-model and a duration prediction sub-model; obtain storage characteristics of the cargo to be stored based on the basic information and the cargo analysis sub-model; and obtain a predicted storage duration of the cargo to be stored based on the basic information, the storage characteristics, and the duration prediction sub-model; The area screening module is also used to determine the applicable cargo location specifications based on the volume information and weight information in the basic information, and determine multiple candidate storage areas based on the applicable cargo location specifications and the three-dimensional model of the target warehouse; obtain the storage environment requirements of the goods to be stored based on the category information in the basic information; calculate the suitability scores of the multiple candidate storage areas based on the predicted storage time and the storage environment requirements; sort the multiple candidate storage areas based on the suitability scores of the multiple candidate storage areas to obtain the storage area priority of each candidate storage area.

7. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor is used to read and execute the computer software program, thereby implementing the method described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium, characterized in that The storage medium stores a computer software program, which, when executed by a processor, implements the method according to any one of claims 1 to 5.

Images