Intelligent warehouse management method, device, equipment and storage medium based on the Internet of Things

Abstract

The present invention relates to an intelligent warehouse management method, device, equipment and storage medium based on the Internet of Things, comprising the following steps: using a preset RFID technology to scan goods entering a warehouse to obtain attribute data of the goods; calling warehouse layout data from a database, and extracting data from the warehouse layout data to obtain warehouse extraction data, and allocating positions of the goods based on the attribute data of the goods and the warehouse extraction data to obtain a goods allocation position; determining a planned path for the movement of the goods based on an initial position of the goods and a goods allocation position, and performing safety monitoring and identification on the planned path to obtain safety identification data; uploading the safety identification data, warehouse layout data and goods allocation position to a cloud platform to realize visual intelligent warehouse management; and solving the technical problem that an operation mode that relies on manual labor is prone to errors, has a slow processing speed, and is difficult to cope with large-scale or rapidly changing inventory demands.

Description

Technical Field

[0001] The present invention relates to the field of intelligent warehousing technology, and in particular to an intelligent warehousing management method, device, equipment and storage medium based on the Internet of Things. Background Art

[0002] In modern logistics and warehousing management, efficiency and safety are crucial considerations. Traditional warehouse management systems rely on manual operations, including the receipt, storage, retrieval and dispatch of goods. This manual operation mode is prone to errors and has a slow processing speed, making it difficult to cope with large-scale or rapidly changing inventory needs. In addition, safety issues are also a major challenge for traditional warehousing systems, including the safe storage of goods and safety issues during transportation. Summary of the invention

[0003] The main purpose of the present invention is to provide an intelligent warehouse management method, device, equipment and storage medium based on the Internet of Things, which solves the technical problems that the manual operation mode is prone to errors, has slow processing speed, and is difficult to cope with large-scale or rapidly changing inventory needs.

[0004] To achieve the above object, the present invention provides an intelligent warehouse management method based on the Internet of Things, comprising the following steps:

[0005] The goods entering the warehouse are scanned using the preset RFID technology to obtain the attribute data of the goods; wherein the attribute data of the goods include the production date of the goods, the shelf life of the goods, the urgency and safety level of the order, and the initial location of the goods;

[0006] Retrieving warehouse layout data from a database and performing data extraction on the warehouse layout data to obtain warehouse extraction data, wherein the warehouse layout data includes aisles and storage distribution areas, and shelf bearing weight;

[0007] Based on the attribute data of the goods and the warehouse extraction data, the goods are assigned positions to obtain goods assignment positions;

[0008] Determine a planned path for cargo movement based on the cargo initial position and the cargo allocation position, and perform safety monitoring and identification on the planned path to obtain safety identification data;

[0009] The security identification data, warehouse layout data and cargo distribution location are uploaded to the cloud platform to realize visual intelligent warehouse management.

[0010] Furthermore, the goods entering the warehouse are scanned using the preset RFID technology to obtain the attribute data of the goods, including:

[0011] Attach a preset RFID tag to each item or package of goods;

[0012] The RFID tags on the goods entering the warehouse are automatically scanned by the reader / writer deployed at the warehouse entrance to activate the RFID tags attached to the goods and obtain the EPC information of the goods;

[0013] The EPC information is decoded to obtain the attribute data of the goods.

[0014] Further, data extraction is performed on the warehouse layout data to obtain warehouse extraction data, including:

[0015] Obtaining labels preset in warehouse layout data, the labels including channel labels, shelf weight bearing labels, and storage distribution area labels;

[0016] Based on the label, the label data field of the warehouse layout data is identified to obtain label data; wherein the label data includes channel label data, shelf weight bearing label data and storage distribution area label data;

[0017] Parsing the channel label data by a preset graph search algorithm to obtain channel parsing data;

[0018] Using the channel analysis data to map the spatial distribution of the channel to obtain a visualized channel image;

[0019] Parsing the weight-bearing label data of the shelf to obtain shelf attribute information; wherein the shelf attribute information includes shelf size and load-bearing limit;

[0020] Calculating the storage capacity of the rack based on the rack dimensions and load-bearing limitations;

[0021] Parsing the storage distribution area label data to obtain identifier information and status information of the storage distribution area;

[0022] Tracking and navigating the storage distribution area based on the identifier information to obtain a location of the storage distribution area;

[0023] Taking the locations as nodes and the connection relationships between the storage distribution areas corresponding to the locations as edges, a storage distribution topology graph is constructed;

[0024] Warehouse extraction data is obtained based on the visualized channel image, the storage capacity of the shelf and the storage distribution topology map.

[0025] Furthermore, the goods are assigned positions based on the attribute data of the goods and the warehouse extraction data to obtain the goods assignment positions, including:

[0026] Conduct in-depth analysis of the cargo's attribute data to determine storage requirements;

[0027] Fusion the storage demand with the warehouse extraction data to obtain corresponding fused data;

[0028] Performing a space utilization evaluation on the warehouse based on the fused data to identify underutilized areas and obtain potential target areas for space optimization;

[0029] Based on the attribute data of the goods, a capacity constraint assessment is performed on the potential target area to obtain a safe storage allocation plan;

[0030] Based on the storage allocation plan, the goods are allocated positions to obtain preliminary allocation positions of the goods; wherein there are multiple goods;

[0031] Drawing a cargo label-position curve based on the preliminary allocation position of the cargo;

[0032] Identifying abnormal points of the cargo label-location curve; wherein the abnormal points include insufficient storage space and incorrect classification of cargo type;

[0033] Analyze the abnormal points and obtain corresponding solution strategies;

[0034] Optimizing the preliminary allocation position based on the solution strategy to obtain an optimized allocation position;

[0035] Constructing a cargo label-optimized position curve based on the optimized allocation position;

[0036] If the abnormal point on the cargo label-optimized position curve is within a preset variation range, the cargo label-optimized position curve is decomposed to obtain the cargo allocation position of the corresponding label.

[0037] Furthermore, based on the initial position of the cargo and the allocated position of the cargo, a planned path for cargo movement is determined, and security identification and monitoring of the planned path are performed to obtain security identification data, including:

[0038] Applying a preset graph search algorithm to calculate an initial planning path based on the initial location of the cargo and the cargo allocation location;

[0039] Identify obstacles and obstacles on the initial planned path, and update the information of the obstacles in real time to obtain the obstacle information on the updated path; wherein the obstacles include static obstacles and dynamic obstacles;

[0040] Monitoring safety parameters on the initial path based on the obstacle information on the updated path; wherein the safety parameters include the operating status of the handling equipment and the traffic flow;

[0041] Performing risk assessment on the safety parameters to obtain risk assessment results;

[0042] If the risk assessment result is within the preset risk range, the channel width and storage distribution are extracted from the visualized channel image and the storage distribution topology map respectively, and the channel width and storage distribution position are obtained accordingly;

[0043] The channel width and storage distribution position are used as constraint conditions, and a preset collision avoidance algorithm is used to perform safety monitoring on the initial planned path based on the constraint conditions to obtain safety identification data.

[0044] Furthermore, the security identification data, warehouse layout data and cargo distribution location are uploaded to the cloud platform to realize visual intelligent warehouse management, including:

[0045] integrating the security identification data, warehouse layout data, and cargo distribution locations to form a comprehensive data set;

[0046] Performing data encryption processing on the comprehensive data set to obtain encrypted data;

[0047] Upload the encrypted data to the cloud platform, and automatically decrypt the encrypted data using a decryption algorithm in the cloud platform to obtain decrypted data;

[0048] Based on the decrypted data, decrypted warehouse layout data, decrypted security identification data and cargo distribution location data are obtained, and a structured data set display of the warehouse is performed based on the decrypted warehouse layout data through a preset visualization tool to obtain an intuitive warehouse map; wherein virtual cargo is set in the warehouse map;

[0049] Marking the virtual goods based on the goods distribution location data to obtain the specific location of the goods on the map;

[0050] Using a preset three-dimensional modeling technology, the warehouse is virtually reconstructed based on the warehouse map, the specific location of the goods on the map, and the security identification data to obtain an interactive three-dimensional warehouse model;

[0051] Visual intelligent warehouse management is achieved for the warehouse based on the interactive three-dimensional warehouse model.

[0052] Furthermore, the warehouse is virtually reconstructed based on the warehouse map, the specific location of the goods on the map, and the security identification data using a preset 3D modeling technology to obtain an interactive 3D warehouse model, including:

[0053] Using a preset three-dimensional modeling technology, the warehouse is virtually reconstructed based on the warehouse map and the specific location of the goods on the map to obtain a virtual reconstruction model;

[0054] Applying a preset texture mapping technology to perform texturing on the virtual reconstructed model to obtain a model with a real texture;

[0055] Performing lighting processing on the model with real texture to obtain a model with natural lighting effect;

[0056] Adding interactive functions to the model with natural lighting effects to obtain an interactive three-dimensional model; wherein the interactive functions include navigation and exploration;

[0057] Mapping the security identification data to an interactive three-dimensional model to obtain a model with potential security problem areas highlighted;

[0058] The model with the potential safety problem areas highlighted is rendered to obtain an interactive three-dimensional warehouse model.

[0059] The present invention also provides an intelligent warehouse management device based on the Internet of Things, comprising:

[0060] A scanning module is used to scan the goods entering the warehouse using the preset RFID technology to obtain the attribute data of the goods; wherein the attribute data of the goods includes the production date of the goods, the shelf life of the goods, the urgency and safety level of the order, and the initial location of the goods;

[0061] A calling module, used to call warehouse layout data from a database and extract data from the warehouse layout data to obtain warehouse extraction data, wherein the warehouse layout data includes aisles and storage distribution areas, and shelf bearing weight;

[0062] An allocation module, used for allocating the location of the goods based on the attribute data of the goods and the warehouse extraction data to obtain the goods allocation location;

[0063] an identification module, used to determine a planned path for cargo movement based on the cargo initial position and the cargo allocation position, and to perform safety monitoring and identification on the planned path to obtain safety identification data;

[0064] The visualization module is used to upload the security identification data, warehouse layout data and cargo distribution location to the cloud platform to realize visual intelligent warehouse management.

[0065] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0066] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0067] The intelligent warehouse management method based on the Internet of Things provided by the present invention comprises the following steps: using a preset RFID technology to scan the goods entering the warehouse to obtain the attribute data of the goods; calling the warehouse layout data from the database, and extracting the warehouse layout data to obtain the warehouse extraction data, and allocating the position of the goods based on the attribute data of the goods and the warehouse extraction data to obtain the goods allocation position; determining the planned path of the goods movement based on the initial position of the goods and the goods allocation position, and performing safety monitoring and identification on the planned path to obtain safety identification data; uploading the safety identification data, warehouse layout data and goods allocation position to the cloud platform to realize visual intelligent warehouse management; through the above technical solution, the technical problem that the manual operation mode is prone to errors, the processing speed is slow, and it is difficult to cope with large-scale or rapidly changing inventory needs is solved, and the automated and intelligent goods processing is realized, which greatly reduces the manpower demand and the related time cost, makes the warehouse operation more rapid and efficient, and the integration of the cloud platform makes the warehouse management no longer limited to on-site operation, and the management personnel can remotely monitor and adjust the warehouse operation and respond to various emergencies in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a schematic diagram of the steps of an intelligent warehouse management method based on the Internet of Things in one embodiment of the present invention;

[0069] Figure 2 It is a structural block diagram of an intelligent warehouse management device based on the Internet of Things in one embodiment of the present invention;

[0070] Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0071] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0073] like Figure 1 As shown, Figure 1 It is a schematic diagram of the steps of an intelligent warehouse management method based on the Internet of Things in one embodiment of the present invention;

[0074] In one embodiment of the present invention, an intelligent warehouse management method based on the Internet of Things is provided, comprising the following steps:

[0075] Step S1, using the preset RFID technology to scan the goods entering the warehouse to obtain the attribute data of the goods; wherein the attribute data of the goods includes the production date of the goods, the shelf life of the goods, the urgency and safety level of the order and the initial location of the goods.

[0076] Specifically, this step is a key link in the intelligent warehouse management system, which realizes the automation and precision management of goods by using RFID technology. RFID technology application: RFID (Radio Frequency Identification) is a technology that can identify and track objects with RFID tags without direct contact. Cargo scanning process: When the goods enter the warehouse, the goods are scanned by the RFID reader preset at the warehouse entrance or on the key path. The reader sends out a radio frequency signal, activates the RFID tag on the goods, and reads the information stored in the tag. Attribute data acquisition: Through scanning, the system obtains the attribute data of the goods, which is stored in the RFID tag, including but not limited to the following aspects: Production date: The production date of the goods is key information for identifying the freshness and aging of the goods. Shelf life of goods: Shelf life information is crucial to ensure that the goods maintain their quality during storage and distribution. Order urgency: The urgency of the order affects the storage and sorting priority of the goods. Urgent orders may require faster processing and distribution. Security level: The security level indicates the safety measures required for the storage and handling of goods, such as special storage requirements for fragile, flammable or toxic items. Initial location of goods: records the initial location of goods when they enter the warehouse, providing a starting point for subsequent cargo positioning and tracking. Data storage and processing: The read attribute data is then transmitted to the warehouse management system (WMS) or enterprise resource planning (ERP) system for further analysis, processing and decision support. Basis for subsequent operations: The acquired cargo attribute data provides a basis for warehouse managers to perform subsequent operations, such as classified storage, route planning, and security management of goods. System automation and intelligence: The entire process reduces the need for manual input, reduces the error rate, and improves the level of automation and intelligence in warehouse management. Through this step, the intelligent warehouse management system can update the status of goods in real time, optimize inventory management, improve operational efficiency, and ensure the traceability and safety of goods throughout the supply chain.

[0077] Step S2, calling warehouse layout data from the database, and extracting the warehouse layout data to obtain warehouse extraction data, wherein the warehouse layout data includes channels and storage distribution areas, and shelf bearing weight.

[0078] Specifically, this step is a key link in the intelligent warehouse management system for optimizing warehouse space utilization and improving cargo storage efficiency. Database call: The system calls the stored warehouse layout data from the central database, which provides basic information for the spatial layout and use of the warehouse. Data extraction process: Analyze and process the warehouse layout data to extract key information. This process may involve data screening, cleaning and conversion. Warehouse extraction data content: The extracted data includes key layout information of the warehouse, including: Channel: The channel layout in the warehouse, including the size, location and available space of the channel, which is crucial for planning the cargo handling path. Storage distribution area: The various storage areas divided in the warehouse, each of which may be different according to the type, size or storage conditions of the goods. Shelf weight: The maximum load capacity of each shelf, which is very important for ensuring the safe storage of goods and avoiding damage to the shelf. Application of layout data: The extracted warehouse layout data will be used to support multiple functions in the intelligent warehouse management system, including but not limited to cargo storage location allocation, path planning and safety monitoring. Space optimization: Using warehouse layout data, the system can optimize the storage location of goods and ensure that the goods are placed in the appropriate area according to their attributes and storage requirements. Path planning: Channel information is used to plan the transportation path of goods from incoming warehouse to storage location, and from storage location to outgoing warehouse to reduce transportation time and cost. Safety monitoring: Data on the weight of shelves helps the system monitor storage safety, avoid overloading, and ensure the safety of warehouse operations. Data update and synchronization: Warehouse layout data may change due to warehouse operations or renovations. The system needs to update this data regularly to maintain its accuracy and synchronize it with the warehouse management system. Decision support: The extracted warehouse layout data provides decision support for managers to help them with warehouse space planning, capacity management, and resource allocation. Through this step, the intelligent warehouse management system can make more efficient use of warehouse space, improve the efficiency of goods storage and retrieval, and ensure the safety and compliance of warehouse operations.

[0079] Step S3, allocating the location of the goods based on the attribute data of the goods and the warehouse extraction data to obtain the goods allocation location.

[0080] Specifically, this step scheme is an important part of the intelligent warehouse management system for ensuring safe and efficient handling of goods. Initial position and allocation position determination: The system has obtained the initial position of the goods and the allocation position of the goods calculated based on the goods attributes and warehouse layout data. Planning path determination: Using the warehouse's channel and storage distribution area information, the system determines the best transportation path for the goods from the initial position to the allocation position. Path planning algorithm application: Apply path planning algorithms, such as the shortest path algorithm or the least time path algorithm, to consider the efficiency of the handling equipment and the traffic conditions in the warehouse. Safety monitoring and identification: While planning the path, the system implements safety monitoring measures, including the identification of obstacles on the path, traffic flow and other potential risk factors. Safety identification data collection: Collect safety-related data, such as the status of the handling equipment, the location of obstacles on the path, traffic flow changes, etc., for real-time safety analysis. Risk assessment: Evaluate the collected safety identification data to determine whether there are safety risks, such as the possibility of collision, path blockage, etc. Dynamic path adjustment: If potential safety risks are identified, the system will dynamically adjust the cargo handling path to avoid risks and ensure the safety of the goods. Real-time monitoring and alarm system: Implement a real-time monitoring system to monitor key points in the handling process and trigger an alarm when an anomaly is detected. Handling equipment control: Control the handling equipment to move along the planned path and make adjustments based on safety monitoring results when necessary. Data recording and analysis: Record path data and safety monitoring data throughout the handling process for subsequent analysis and optimization. User interface feedback: Provide real-time feedback on the user interface, including the current location of the goods, the handling path, and any safety alarms. Optimization and learning: Use the collected data to optimize the path planning and safety monitoring algorithms, and learn from historical data to improve the safety and efficiency of future handling. Compliance check: Ensure that the entire handling process complies with safety regulations and operating standards to reduce the risk of illegal operations.

[0081] Through this step, the intelligent warehouse management system can optimize the cargo handling path while ensuring the safety of the cargo, thereby improving the efficiency and safety of warehouse operations.

[0082] Step S4, determining a planned path for cargo movement based on the cargo initial position and the cargo allocation position, and performing security monitoring and identification on the planned path to obtain security identification data.

[0083] Specifically, this step plan is a key link in ensuring the safety and efficiency of the cargo handling process in the intelligent warehouse management system. Path planning basis: Using the known initial position of the cargo and the cargo distribution position calculated by the intelligent algorithm, the system determines the planned path for the cargo to move in the warehouse. Path planning execution: The system uses the built-in path planning algorithm to calculate an effective handling path considering the warehouse layout, channel direction and storage area distribution. Safety monitoring implementation: During the cargo handling process, the system conducts real-time safety monitoring of the planned path to identify potential obstacles, traffic congestion or other safety hazards. Risk identification: Safety monitoring includes the identification of path risks, such as the detection of fixed or moving obstacles on the path, and other factors that may affect the safety of handling. Safety identification data collection: The system collects data during the safety monitoring process, including obstacle location, handling equipment status, handling speed and surrounding environmental conditions. Dynamic path adjustment: If safety hazards are found during the monitoring process, the system will dynamically adjust the cargo handling path based on the safety identification data to avoid risks. Real-time feedback and control: The system provides real-time feedback, and the operator can manually or automatically adjust the operation of the handling equipment based on the feedback information. Emergency response mechanism: When a major safety risk is identified, the system triggers an emergency response mechanism, such as automatically stopping the handling equipment or changing its running direction. Data recording and analysis: All safety identification data is recorded and stored for subsequent data analysis and system optimization. User interface display: The planned path and safety monitoring results are displayed through the user interface, so that operators can intuitively understand the current handling status and safety information. System integration: Integrate the path planning and safety monitoring functions into the intelligent warehouse management system to ensure the smoothness and coordination of the entire handling process. Compliance and standard compliance: Ensure that the entire handling process complies with relevant safety standards and operating procedures to reduce operational risks. Through this step, the intelligent warehouse management system can improve handling efficiency and reduce the possibility of accidents while ensuring the safety of goods, thereby achieving more intelligent and automated warehouse operations.

[0084] Step S5, uploading the security identification data, warehouse layout data and cargo distribution location to the cloud platform to realize visual intelligent warehouse management.

[0085] Specifically, this step is a key link in realizing data integration and advanced management functions in intelligent warehouse management systems. Data integration: Integrate security identification data, warehouse layout data, and data on goods distribution locations to form a comprehensive data set that together supports the decision-making of intelligent warehouse management. Upload data to the cloud platform: Upload the integrated data to the cloud platform, and use the large-scale storage and computing capabilities of the cloud platform to ensure the security and accessibility of the data. Data synchronization: Ensure that the uploaded data is synchronized with the existing data on the cloud platform to ensure that all users and systems have access to the latest and consistent information. Visual intelligent warehouse management: Use visualization tools on the cloud platform to convert complex data into a graphical interface, so that managers can intuitively understand the operating status of the warehouse. Real-time monitoring: Realize real-time monitoring functions, and display the current location, status and handling path of goods, as well as the layout and use of the warehouse through a visual interface. Data analysis: Apply data analysis tools to conduct in-depth analysis of uploaded data to identify trends, predict demand, optimize storage, and improve efficiency. User interface customization: Allow users to customize the visualization interface according to their needs and select the type of data and level of detail to be displayed. Access control: Implement access control mechanisms to ensure that only authorized users can access sensitive data and perform management operations. Mobile and remote access: Support mobile devices and remote access, allowing managers to monitor and manage warehouses at any time and place. Reporting and notifications: Generate regular or on-demand reports, and send notifications through the cloud platform to promptly update warehouse status and important events. Collaboration and sharing: Promote team collaboration, share data and information through the cloud platform, and improve cross-departmental communication and collaboration efficiency. System scalability: Utilize the scalability of the cloud platform to support the addition and upgrade of future system functions to adapt to changing business needs. Through this step, the intelligent warehouse management system is able to provide a centralized, visual management platform to improve the transparency, efficiency and responsiveness of warehouse operations.

[0086] In a specific embodiment, the goods entering the warehouse are scanned using a preset RFID technology to obtain attribute data of the goods, including:

[0087] Attach a preset RFID tag to each item or package of goods;

[0088] The RFID tags on the goods entering the warehouse are automatically scanned by the reader / writer deployed at the warehouse entrance to activate the RFID tags attached to the goods and obtain the EPC information of the goods;

[0089] The EPC information is decoded to obtain the attribute data of the goods.

[0090] Specifically, this step is the basic link for realizing automatic identification and data collection of goods in the intelligent warehouse management system. RFID tag attachment: Attach the preset RFID tag to each piece of goods or the packaging of the goods. These tags contain the electronic identification information of the goods and are used to uniquely identify each piece of goods. Automated scanning: The reader / writer deployed at the warehouse entrance automatically scans the RFID tags on the goods entering the warehouse. This process does not require human intervention, which improves the efficiency of goods entering the warehouse. RFID tag activation: The radio frequency signal emitted by the reader / writer activates the RFID tag on the goods, putting it into working state and preparing to send the information stored inside it. EPC information acquisition: The activated RFID tag sends the EPC (Electronic Product Code) information of the goods back to the reader / writer. EPC information is the unique identifier of goods in the global supply chain. EPC information decoding: Decode the EPC information received from the RFID tag and convert it from a machine-readable format to a readable format for further processing and analysis. Attribute data extraction:

[0091] Extract the attribute data of the goods from the decoded EPC information. These data may include the production date, shelf life, order urgency, safety level, etc. of the goods. Data storage: Store the extracted attribute data of the goods in the warehouse management system (WMS) or enterprise resource planning (ERP) system to provide data support for subsequent inventory management and goods tracking. Data synchronization: Ensure that the attribute data of the goods is synchronized with the real-time data on the cloud platform for unified management and analysis. Data security: Take appropriate security measures such as data encryption and access control during data transmission and storage to protect the security and privacy of goods information. System integration: Integrate RFID technology with other information systems of the warehouse (such as WMS, ERP) to achieve seamless flow of data and collaborative work of the system. Through this step, the intelligent warehouse management system can realize the rapid identification and accurate management of goods, and improve the operational efficiency and accuracy of the warehouse. At the same time, it provides accurate data support for subsequent goods storage, sorting, handling and outbound delivery.

[0092] In a specific embodiment, data extraction is performed on the warehouse layout data to obtain warehouse extraction data, including:

[0093] Obtaining labels preset in warehouse layout data, the labels including channel labels, shelf weight bearing labels, and storage distribution area labels;

[0094] Based on the label, the label data field of the warehouse layout data is identified to obtain label data; wherein the label data includes channel label data, shelf weight bearing label data and storage distribution area label data;

[0095] Parsing the channel label data by a preset graph search algorithm to obtain channel parsing data;

[0096] Using the channel analysis data to map the spatial distribution of the channel to obtain a visualized channel image;

[0097] Parsing the weight-bearing label data of the shelf to obtain shelf attribute information; wherein the shelf attribute information includes shelf size and load-bearing limit;

[0098] Calculating the storage capacity of the rack based on the rack dimensions and load-bearing limitations;

[0099] Parsing the storage distribution area label data to obtain identifier information and status information of the storage distribution area;

[0100] Tracking and navigating the storage distribution area based on the identifier information to obtain a location of the storage distribution area;

[0101] Taking the locations as nodes and the connection relationships between the storage distribution areas corresponding to the locations as edges, a storage distribution topology graph is constructed;

[0102] Warehouse extraction data is obtained based on the visualized channel image, the storage capacity of the shelf and the storage distribution topology map.

[0103] Specifically, this step is a key link in the intelligent warehouse management system for in-depth analysis and utilization of warehouse space resources. Label acquisition: Get the preset labels of the warehouse layout data. These labels are important information carriers for identifying and classifying different areas and facilities in the warehouse. Label data field identification: Based on the acquired labels, the system identifies the label data field, including specific information of the channel, shelf load-bearing weight and storage distribution area. Label data parsing: Parse the identified label data to obtain specific channel label data, shelf load-bearing label data and storage distribution area label data. Graph search algorithm application: Use the preset graph search algorithm to parse the channel label data to find the connection relationship and spatial layout between channels. Channel spatial distribution mapping: Use channel parsing data to display the spatial distribution of channels in the form of visual images, which is convenient for understanding the channel layout of the warehouse. Shelf attribute information acquisition: Parse the shelf weight label data to obtain attribute information such as the shelf size and load-bearing limit. Shelf storage capacity calculation: Calculate the storage capacity of each shelf based on the shelf size and load-bearing limit to provide a basis for cargo allocation. Acquisition of storage distribution area identifier and status information: Parse the storage distribution area tag data to obtain the identifier information and current status information of each area. Storage distribution area tracking and navigation: Use the identifier information to track and navigate the storage distribution area to determine the specific location of each area in the warehouse. Storage distribution topology map construction: Take the location of the storage distribution area as a node and the connection relationship between the areas as an edge to construct a topology map reflecting the warehouse storage distribution. Warehouse extraction data generation: Comprehensive visualization channel images, shelf storage capacity and storage distribution topology map are integrated to form comprehensive warehouse extraction data. Data integration and analysis: Integrate and analyze the extracted data to provide decision support for warehouse management, such as optimizing the storage location of goods and improving the utilization rate of warehouse space. Visualization and interaction: Provide an interactive visualization interface so that managers can intuitively view the warehouse layout and perform related management and operations. Through this step, the intelligent warehouse management system can make full use of warehouse space resources, improve the efficiency of goods storage and retrieval, and provide strong data support for warehouse planning and management.

[0104] In a specific embodiment, the goods are assigned positions based on the attribute data of the goods and the warehouse extraction data to obtain the goods assignment positions, including:

[0105] Conduct in-depth analysis of the cargo's attribute data to determine storage requirements;

[0106] Fusion the storage demand with the warehouse extraction data to obtain corresponding fused data;

[0107] Performing a space utilization evaluation on the warehouse based on the fused data to identify underutilized areas and obtain potential target areas for space optimization;

[0108] Based on the attribute data of the goods, a capacity constraint assessment is performed on the potential target area to obtain a safe storage allocation plan;

[0109] Based on the storage allocation plan, the goods are allocated positions to obtain preliminary allocation positions of the goods; wherein there are multiple goods;

[0110] Drawing a cargo label-position curve based on the preliminary allocation position of the cargo;

[0111] Identifying abnormal points of the cargo label-location curve; wherein the abnormal points include insufficient storage space and incorrect classification of cargo type;

[0112] Analyze the abnormal points and obtain corresponding solution strategies;

[0113] Optimizing the preliminary allocation position based on the solution strategy to obtain an optimized allocation position;

[0114] Constructing a cargo label-optimized position curve based on the optimized allocation position;

[0115] If the abnormal point on the cargo label-optimized position curve is within a preset variation range, the cargo label-optimized position curve is decomposed to obtain the cargo allocation position of the corresponding label.

[0116] Specifically, this step solution is a high-level decision-making process in the intelligent warehouse management system for efficiently and accurately allocating storage locations for goods. Attribute data analysis: In-depth analysis of the attribute data of goods, including goods size, weight, storage requirements, fragility, etc., to clarify the storage requirements of each type of goods.

[0117] Fusion of storage demand and warehouse data: Fusion of analyzed storage demand and warehouse extracted data (such as channel layout, shelf capacity, storage area distribution) to form fused data for location allocation. Space utilization evaluation: Based on fused data, evaluate the space utilization of the warehouse and identify areas that are currently underutilized or have optimization space. Capacity constraint evaluation: Capacity constraint evaluation of potential space optimization target areas to ensure that the allocation plan meets the actual storage requirements such as the size and weight of the goods and meets safety standards. Preliminary location allocation: According to the storage allocation plan, preliminary location allocation is performed for each piece of goods to obtain the preliminary allocation location of the goods. Plotting the goods label-location curve: Plotting the data of the preliminary allocation location into a goods label-location curve to visualize the relationship between the goods and the storage location. Anomaly identification: Identify anomalies in the goods label-location curve, such as insufficient storage space or misclassification of goods types. Anomaly analysis and solution strategy: Conduct in-depth analysis of the identified anomalies, find out the cause of the problem, and formulate corresponding solution strategies. Preliminary allocation location optimization: According to the solution strategy, adjust and optimize the preliminary allocation location to obtain a more reasonable storage allocation plan. Constructing the optimized location curve: Use the optimized allocation location data to redraw the cargo label-optimized location curve. Determine the range of abnormal point changes: Determine the abnormal points on the optimized location curve to confirm whether they are within the preset range of changes to verify the effectiveness of the optimization plan. Label decomposition and final allocation: If the optimized location curve meets the requirements, label decomposition is performed to correspond each cargo label to a specific cargo allocation location. Confirmation of allocation location after optimization: Finally confirm the optimized allocation location to complete the storage location allocation process of the cargo. Through this step, the intelligent warehouse management system can achieve accurate allocation of cargo storage locations, optimize the efficiency of warehouse space use, ensure safe and compliant storage of cargo, and provide accurate data support for subsequent cargo management and retrieval.

[0118] In a specific embodiment, the planned path for cargo movement is determined based on the initial location of the cargo and the allocated location of the cargo, and the planned path is security identified and monitored to obtain security identification data, including:

[0119] Applying a preset graph search algorithm to calculate an initial planning path based on the initial location of the cargo and the cargo allocation location;

[0120] Identify obstacles and obstacles on the initial planned path, and update the information of the obstacles in real time to obtain the obstacle information on the updated path; wherein the obstacles include static obstacles and dynamic obstacles;

[0121] Monitoring safety parameters on the initial path based on the obstacle information on the updated path; wherein the safety parameters include the operating status of the handling equipment and the traffic flow;

[0122] Performing risk assessment on the safety parameters to obtain risk assessment results;

[0123] If the risk assessment result is within the preset risk range, the channel width and storage distribution are extracted from the visualized channel image and the storage distribution topology map respectively, and the channel width and storage distribution position are obtained accordingly;

[0124] The channel width and storage distribution position are used as constraint conditions, and a preset collision avoidance algorithm is used to perform safety monitoring on the initial planned path based on the constraint conditions to obtain safety identification data.

[0125] Specifically, this step plan is a path planning and safety monitoring process used in intelligent warehouse management systems to ensure safe and efficient handling of goods. Graph search algorithm application: Apply the preset graph search algorithm to calculate the initial planned path for cargo handling based on the initial position and distribution position of the goods. Obstacle identification and information update: Identify obstacles on the initial planned path, including static obstacles (such as fixed shelves, walls) and dynamic obstacles (such as other handling equipment or personnel), and update the location and status information of these obstacles in real time. Safety parameter monitoring: Based on the updated obstacle information, monitor the safety parameters on the initial path, including the operating status of the handling equipment and the current traffic flow. Risk assessment:

[0126] Perform risk assessment on the monitored safety parameters to determine whether there are potential risks in the handling process and obtain the risk assessment results. Risk range judgment: If the risk assessment result is within the preset risk range, it means that the current planned path is safe and can continue to be executed. Channel width and storage distribution extraction:

[0127] The channel width and storage distribution location are extracted from the visualized channel image and storage distribution topology map. This information will be used as constraints for subsequent safety monitoring. Constraint application: The extracted channel width and storage distribution location are used as constraints to ensure that the handling path meets the actual physical space constraints.

[0128] Collision avoidance algorithm execution: Through the preset collision avoidance algorithm, the initial planned path is safely monitored in combination with the constraints to avoid potential collisions with obstacles. Safety identification data acquisition: During the safety monitoring process, safety-related data, including path deviation, collision risk, etc., are collected to obtain safety identification data. Exception handling: If an abnormal situation is found during the monitoring process, such as a blocked path or safety parameters exceeding the preset range, the system will promptly adjust the handling strategy or replan the path. Real-time feedback and adjustment: The system provides a real-time feedback mechanism to dynamically adjust the operation of the handling equipment according to the safety monitoring results to ensure that the goods arrive at the destination safely. Data recording and analysis: Record the safety monitoring data of the entire handling process for subsequent data analysis and system optimization. Through this step, the intelligent warehouse management system can optimize the handling path while ensuring the safety of goods handling, reduce handling time and cost, and improve the efficiency and safety of warehouse operations.

[0129] In a specific embodiment, the security identification data, warehouse layout data and cargo distribution location are uploaded to a cloud platform to implement visual intelligent warehouse management, including:

[0130] integrating the security identification data, warehouse layout data, and cargo distribution locations to form a comprehensive data set;

[0131] Performing data encryption processing on the comprehensive data set to obtain encrypted data;

[0132] Upload the encrypted data to the cloud platform, and automatically decrypt the encrypted data using a decryption algorithm in the cloud platform to obtain decrypted data;

[0133] Based on the decrypted data, decrypted warehouse layout data, decrypted security identification data and cargo distribution location data are obtained, and a structured data set display of the warehouse is performed based on the decrypted warehouse layout data through a preset visualization tool to obtain an intuitive warehouse map; wherein virtual cargo is set in the warehouse map;

[0134] Marking the virtual goods based on the goods distribution location data to obtain the specific location of the goods on the map;

[0135] Using a preset three-dimensional modeling technology, the warehouse is virtually reconstructed based on the warehouse map, the specific location of the goods on the map, and the security identification data to obtain an interactive three-dimensional warehouse model;

[0136] Visual intelligent warehouse management is achieved for the warehouse based on the interactive three-dimensional warehouse model.

[0137] Specifically, this step is the key process for realizing data integration, secure transmission and advanced visual management in the intelligent warehouse management system. Data set integration: Integrate security identification data, warehouse layout data and cargo distribution location to form a comprehensive data set, which provides a complete information basis for subsequent analysis and management. Data encryption processing: Encrypt the integrated data set to ensure the security and confidentiality of the data during transmission. Encrypted data upload: Upload the encrypted data to the cloud platform, and use the storage and computing resources of the cloud platform to provide support for further processing and analysis of the data. Automatic decryption: Use the decryption algorithm in the cloud platform to automatically decrypt the uploaded encrypted data to obtain the decrypted data for subsequent visualization and analysis. Structured data set display: Based on the decrypted data, use the preset visualization tools to display the warehouse layout data in a structured data set to generate an intuitive warehouse map. Virtual goods setting: Set virtual goods in the warehouse map. These virtual goods represent the goods in the actual warehouse and provide a reference for visual management.

[0138] Cargo location marking: According to the cargo distribution location data, virtual cargo is marked on the warehouse map to clearly show the specific location of the cargo in the warehouse. Application of 3D modeling technology: Using the preset 3D modeling technology, combined with the warehouse map, cargo location and security identification data, the warehouse is virtually reconstructed to create an interactive 3D warehouse model. Interactive 3D warehouse model construction: The constructed 3D warehouse model not only shows the physical structure of the warehouse, but also reflects the storage status and security information of the cargo. Visual intelligent warehouse management: Based on the interactive 3D warehouse model, the visual intelligent management of the warehouse is realized, including monitoring, analysis, prediction and decision support. Real-time update and interaction: The model supports real-time data update and user interactive operations, such as zooming, rotating, selecting cargo, etc., to adapt to different management needs and scenarios. Decision support and optimization: Using the data and analysis tools provided by the 3D model, managers are supported to make more effective decisions and optimize warehouse operations. Through this step, the intelligent warehouse management system can provide a highly integrated, secure, intuitive and interactive management platform, which greatly improves the efficiency, transparency and intelligence level of warehouse management.

[0139] In a specific embodiment, a preset three-dimensional modeling technology is used to virtually reconstruct the warehouse based on the warehouse map, the specific location of the goods on the map, and the security identification data to obtain an interactive three-dimensional warehouse model, including:

[0140] Using a preset three-dimensional modeling technology, the warehouse is virtually reconstructed based on the warehouse map and the specific location of the goods on the map to obtain a virtual reconstruction model;

[0141] Applying a preset texture mapping technology to perform texturing on the virtual reconstructed model to obtain a model with a real texture;

[0142] Performing lighting processing on the model with real texture to obtain a model with natural lighting effect;

[0143] Adding interactive functions to the model with natural lighting effects to obtain an interactive three-dimensional model; wherein the interactive functions include navigation and exploration;

[0144] Mapping the security identification data to an interactive three-dimensional model to obtain a model with potential security problem areas highlighted;

[0145] The model with the potential safety problem areas highlighted is rendered to obtain an interactive three-dimensional warehouse model.

[0146] Specifically, this step is a key technical link for creating a highly simulated and interactive three-dimensional warehouse model in an intelligent warehouse management system. Application of three-dimensional modeling technology: Using the preset three-dimensional modeling technology, the warehouse is accurately virtually reconstructed according to the warehouse map and the specific location of the goods on the map to create a virtual reconstruction model. Texturing: Applying texture mapping technology to add real textures and materials to the virtual reconstruction model, so that the model is visually closer to the texture of the actual warehouse. Lighting processing: Lighting the model with real texture to simulate the illumination effect of natural light or artificial light source to enhance the realism and three-dimensional sense of the model. Adding interactive functions: Adding interactive functions to the model with natural lighting effects, allowing users to interact with the model through navigation and exploration, providing a richer user experience. Security identification data mapping: Mapping security identification data to an interactive three-dimensional model, which includes highlighting potential security problem areas, such as insufficient storage space, improper stacking of fragile items, etc. Model rendering: Rendering the model with added interactive functions and security identification data to generate the final interactive three-dimensional warehouse model. Interactive 3D warehouse model construction: The interactive 3D warehouse model constructed not only displays the 3D structure and cargo storage of the warehouse, but also integrates safety monitoring and early warning functions. Visual display: Through the interactive 3D model, managers can intuitively view the layout of the warehouse, the distribution of goods, and potential safety issues. Real-time monitoring and analysis: Realize real-time monitoring and analysis of warehouse status, timely discover and deal with safety issues, and improve the response speed and accuracy of warehouse management. Assisted decision making: The data and visual information provided by the 3D model assist managers in making more effective storage, handling and safety management decisions. Training and demonstration: The interactive 3D warehouse model can also be used for employee training and demonstration to improve the team's understanding of warehouse layout and operation procedures. Technology integration: Integrate 3D modeling technology and safety identification system into the intelligent warehouse management platform to achieve comprehensive application of technology and seamless docking of data. Through this step, the intelligent warehouse management system can provide a highly visual, interactive, safe and intuitive 3D warehouse model, which greatly improves the efficiency, safety and intelligence level of warehouse management.

[0147] The above describes the intelligent warehouse management method based on the Internet of Things in the embodiment of the present invention. The following describes the intelligent warehouse management method device based on the Internet of Things in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, an intelligent warehouse management method and device based on the Internet of Things includes:

[0148] The scanning module 21 is used to scan the goods entering the warehouse using the preset RFID technology to obtain the attribute data of the goods; wherein the attribute data of the goods includes the production date of the goods, the shelf life of the goods, the urgency and safety level of the order, and the initial location of the goods;

[0149] The calling module 22 is used to call warehouse layout data from the database and extract data from the warehouse layout data to obtain warehouse extraction data, wherein the warehouse layout data includes aisles and storage distribution areas, and shelf bearing weight;

[0150] An allocation module 23, configured to allocate the location of the goods based on the attribute data of the goods and the warehouse extraction data, and obtain a goods allocation location;

[0151] An identification module 24 is used to determine a planned path for cargo movement based on the cargo initial position and the cargo allocation position, and to perform safety monitoring and identification on the planned path to obtain safety identification data;

[0152] The visualization module 25 is used to upload the security identification data, warehouse layout data and cargo distribution location to the cloud platform to realize visualized intelligent warehouse management.

[0153] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to the above method embodiment, which will not be repeated here.

[0154] Reference Figure 3 The present invention also provides a computer device in an embodiment, wherein the internal structure of the computer device can be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0155] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0156] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0157] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0158] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0159] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An intelligent warehouse management method based on the Internet of Things, characterized in that: The following steps are involved: The goods entering the warehouse are scanned using the preset RFID technology to obtain the attribute data of the goods; wherein the attribute data of the goods include the production date of the goods, the shelf life of the goods, the urgency and safety level of the order, and the initial location of the goods; Retrieving warehouse layout data from a database and performing data extraction on the warehouse layout data to obtain warehouse extraction data, wherein the warehouse layout data includes aisles and storage distribution areas, and shelf bearing weight; Based on the attribute data of the goods and the warehouse extraction data, the goods are assigned positions to obtain goods assignment positions; Determine a planned path for cargo movement based on the cargo initial position and the cargo allocation position, and perform safety monitoring and identification on the planned path to obtain safety identification data; Uploading the security identification data, warehouse layout data and cargo distribution location to the cloud platform to achieve visual intelligent warehouse management; Extracting the warehouse layout data to obtain warehouse extraction data includes: Obtaining labels preset in warehouse layout data, the labels including channel labels, shelf weight bearing labels, and storage distribution area labels; Based on the label, the label data field of the warehouse layout data is identified to obtain label data; wherein the label data includes channel label data, shelf weight bearing label data and storage distribution area label data; Parsing the channel label data by a preset graph search algorithm to obtain channel parsing data; wherein the channel parsing data includes the connection relationship and spatial layout between the channels; Using the channel analysis data to map the spatial distribution of the channel to obtain a visualized channel image; Parsing the weight-bearing label data of the shelf to obtain shelf attribute information; wherein the shelf attribute information includes shelf size and load-bearing limit; Calculating the storage capacity of the rack based on the rack dimensions and load-bearing limitations; Parsing the storage distribution area label data to obtain identifier information and status information of the storage distribution area; Tracking and navigating the storage distribution area based on the identifier information to obtain a location of the storage distribution area; Taking the locations as nodes and the connection relationships between the storage distribution areas corresponding to the locations as edges, a storage distribution topology graph is constructed; Warehouse extraction data is obtained based on the visualized channel image, the storage capacity of the shelf and the storage distribution topology map.

2. According to claim 1, the intelligent warehouse management method based on the Internet of Things is characterized in that: Use the preset RFID technology to scan the goods entering the warehouse and obtain the attribute data of the goods, including: Attach a preset RFID tag to each item or package of goods; The RFID tags on the goods entering the warehouse are automatically scanned by the reader / writer deployed at the warehouse entrance to activate the RFID tags attached to the goods and obtain the EPC information of the goods; The EPC information is decoded to obtain the attribute data of the goods.

3. The intelligent warehouse management method based on the Internet of Things according to claim 1 is characterized in that: The goods are assigned positions based on the attribute data of the goods and the warehouse extraction data to obtain the goods assignment positions, including: Conduct in-depth analysis of the cargo's attribute data to determine storage requirements; Fusion the storage demand with the warehouse extraction data to obtain corresponding fused data; Performing a space utilization evaluation on the warehouse based on the fused data to identify underutilized areas and obtain potential target areas for space optimization; Based on the attribute data of the goods, a capacity constraint assessment is performed on the potential target area to obtain a safe storage allocation plan; Based on the storage allocation plan, the goods are allocated positions to obtain preliminary allocation positions of the goods; wherein there are multiple goods; Drawing a cargo label-position curve based on the preliminary allocation position of the cargo; Identifying abnormal points of the cargo label-location curve; wherein the abnormal points include insufficient storage space and incorrect classification of cargo type; Analyze the abnormal points and obtain corresponding solution strategies; Optimizing the preliminary allocation position based on the solution strategy to obtain an optimized allocation position; Constructing a cargo label-optimized position curve based on the optimized allocation position; If the abnormal point on the cargo label-optimized position curve is within a preset variation range, the cargo label-optimized position curve is subjected to label decomposition to obtain the cargo allocation position corresponding to the label.

4. The intelligent warehouse management method based on the Internet of Things according to claim 3 is characterized in that: Determine a planned path for cargo movement based on the cargo initial location and the cargo allocation location, and perform safety identification and monitoring on the planned path to obtain safety identification data, including: Applying a preset graph search algorithm to calculate an initial planning path based on the initial location of the cargo and the cargo allocation location; Identify obstacles on the initial planned path, and update the information of the obstacles in real time to obtain the obstacle information on the updated path; wherein the obstacles include static obstacles and dynamic obstacles; Monitoring safety parameters on the initial path based on the obstacle information on the updated path; wherein the safety parameters include the operating status of the handling equipment and the traffic flow; Performing risk assessment on the safety parameters to obtain risk assessment results; If the risk assessment result is within the preset risk range, the channel width and storage distribution are extracted from the visualized channel image and the storage distribution topology map respectively, and the channel width and storage distribution position are obtained accordingly; The channel width and storage distribution position are used as constraint conditions, and a preset collision avoidance algorithm is used to perform safety monitoring on the initial planned path based on the constraint conditions to obtain safety identification data.

5. The intelligent warehouse management method based on the Internet of Things according to claim 4 is characterized in that: Uploading the security identification data, warehouse layout data and cargo distribution location to the cloud platform to achieve visual intelligent warehouse management, including: integrating the security identification data, warehouse layout data, and cargo distribution locations to form a comprehensive data set; Performing data encryption processing on the comprehensive data set to obtain encrypted data; Upload the encrypted data to the cloud platform, and automatically decrypt the encrypted data using a decryption algorithm in the cloud platform to obtain decrypted data; Based on the decrypted data, decrypted warehouse layout data, decrypted security identification data and cargo distribution location data are obtained, and a structured data set display of the warehouse is performed based on the decrypted warehouse layout data through a preset visualization tool to obtain an intuitive warehouse map; wherein virtual cargo is set in the warehouse map; Marking the virtual goods based on the goods distribution location data to obtain the specific location of the goods on the map; Using a preset three-dimensional modeling technology, the warehouse is virtually reconstructed based on the warehouse map, the specific location of the goods on the map, and the security identification data to obtain an interactive three-dimensional warehouse model; Visual intelligent warehouse management is achieved for the warehouse based on the interactive three-dimensional warehouse model.

6. The intelligent warehouse management method based on the Internet of Things according to claim 5 is characterized in that: Using the preset 3D modeling technology, the warehouse is virtually reconstructed based on the warehouse map, the specific location of the goods on the map and the security identification data to obtain an interactive 3D warehouse model, including: Using a preset three-dimensional modeling technology, the warehouse is virtually reconstructed based on the warehouse map and the specific location of the goods on the map to obtain a virtual reconstruction model; Applying a preset texture mapping technology to perform texturing on the virtual reconstructed model to obtain a model with a real texture; Performing lighting processing on the model with real texture to obtain a model with natural lighting effect; Adding interactive functions to the model with natural lighting effects to obtain an interactive three-dimensional model; wherein the interactive functions include navigation and exploration; Mapping the security identification data to an interactive three-dimensional model to obtain a model with potential security problem areas highlighted; The model with the potential safety problem areas highlighted is rendered to obtain an interactive three-dimensional warehouse model.

7. An intelligent warehouse management device based on the Internet of Things, characterized in that: include: A scanning module is used to scan the goods entering the warehouse using the preset RFID technology to obtain the attribute data of the goods; wherein the attribute data of the goods includes the production date of the goods, the shelf life of the goods, the urgency and safety level of the order, and the initial location of the goods; A calling module, used to call warehouse layout data from a database and extract data from the warehouse layout data to obtain warehouse extraction data, wherein the warehouse layout data includes aisles and storage distribution areas, and shelf bearing weight; An allocation module, used for allocating the location of the goods based on the attribute data of the goods and the warehouse extraction data to obtain the goods allocation location; an identification module, used to determine a planned path for cargo movement based on the cargo initial position and the cargo allocation position, and to perform safety monitoring and identification on the planned path to obtain safety identification data; A visualization module is used to upload the security identification data, warehouse layout data and cargo distribution location to the cloud platform to realize visual intelligent warehouse management; Extracting the warehouse layout data to obtain warehouse extraction data includes: Obtaining labels preset in warehouse layout data, the labels including channel labels, shelf weight bearing labels, and storage distribution area labels; Based on the label, the label data field of the warehouse layout data is identified to obtain label data; wherein the label data includes channel label data, shelf weight bearing label data and storage distribution area label data; Parsing the channel label data by a preset graph search algorithm to obtain channel parsing data; wherein the channel parsing data includes the connection relationship and spatial layout between the channels; Using the channel analysis data to map the spatial distribution of the channel to obtain a visualized channel image; Parsing the weight-bearing label data of the shelf to obtain shelf attribute information; wherein the shelf attribute information includes shelf size and load-bearing limit; Calculating the storage capacity of the rack based on the rack dimensions and load-bearing limitations; Parsing the storage distribution area label data to obtain identifier information and status information of the storage distribution area; Tracking and navigating the storage distribution area based on the identifier information to obtain a location of the storage distribution area; Taking the locations as nodes and the connection relationships between the storage distribution areas corresponding to the locations as edges, a storage distribution topology graph is constructed; Warehouse extraction data is obtained based on the visualized channel image, the storage capacity of the shelf and the storage distribution topology map.

8. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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