Intelligent warehousing service management device based on Internet of Things and use method

Abstract

The invention discloses an intelligent warehousing service management device based on the Internet of Things and a use method, and relates to the technical field of intelligent warehousing, and the device comprises a cargo attribute monitoring unit, a warehouse layout updating unit and a cargo dynamic adjustment module. The cargo dynamic adjustment module, the cargo attribute monitoring unit and the warehouse layout updating unit are connected through a data bus of the Internet of Things, and the cargo dynamic adjustment module is used for monitoring the storage state and order change of the cargo in real time according to cargo attribute data and warehouse layout data and dynamically adjusting the storage position and priority of the cargo. According to the invention, by installing the cargo dynamic adjustment module, the storage state of the cargo and the order change condition can be detected in real time according to the real-time attribute data of the cargo and the warehouse layout data, so that the storage position and the priority of the cargo are dynamically adjusted; therefore, the problem that an immobilized storage strategy in a traditional warehouse management system is difficult to adapt to frequent order change and cargo turnover requirements is effectively solved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent warehousing technology, and specifically to an intelligent warehousing service management device based on the Internet of Things and a method for using the same. Background Art

[0002] In modern warehousing management, with the rapid development of the logistics industry, the complexity of the warehousing environment and the diversity of goods have put forward higher requirements for warehousing management, especially in terms of dynamic adjustment of goods. Traditional warehousing management systems often adopt fixed storage strategies, which are difficult to adapt to frequent order changes and goods turnover needs.

[0003] Patent CN118761712B discloses an intelligent warehouse management method, device, equipment and storage medium based on the Internet of Things. The above patent 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] The above patent obtains the attribute data of the goods and the warehouse extraction data, allocates the location of the goods, obtains the cargo allocation location, determines the planned path of the cargo movement based on the initial location of the goods and the cargo allocation location, and performs security detection and identification on the planned path to obtain security identification data. The security identification data, warehouse layout data and cargo allocation location are uploaded to the cloud platform to realize visual intelligent warehousing management. However, the above patent still has room for optimization in terms of dynamic adjustment of goods.

[0005] To this end, the present application proposes an IoT-based intelligent warehousing service management device and usage method that can dynamically adjust goods in real time. Summary of the Invention

[0006] The purpose of the present invention is to provide an intelligent warehousing service management device and a method of use based on the Internet of Things to solve the technical problem of the inability to dynamically adjust goods in real time proposed in the above background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solutions: an intelligent warehousing service management device based on the Internet of Things, comprising a cargo attribute monitoring unit, a warehouse layout updating unit, and a cargo dynamic adjustment module. The cargo dynamic adjustment module is configured to detect cargo storage status and order changes in real time based on cargo attribute data and warehouse layout data, and dynamically adjust cargo storage location and priority.

[0008] The cargo dynamic adjustment module includes: a priority evaluation unit, a storage location optimization unit, an AI intelligent scheduling algorithm and a real-time feedback mechanism;

[0009] The priority evaluation unit dynamically calculates the priority of goods based on order urgency, remaining shelf life, and environmental parameters;

[0010] The storage location optimization unit allocates the best storage location through a space optimization algorithm based on the priority evaluation results, environmental parameters and shelf load limits;

[0011] The AI ​​intelligent scheduling algorithm predicts the inbound and outbound demand of goods based on historical and real-time data, adjusts the storage location of goods in advance, and reduces handling time and costs;

[0012] The real-time feedback mechanism dynamically adjusts the cargo storage strategy based on the latest cargo attributes and warehouse layout information.

[0013] Preferably, the cargo attribute monitoring unit is used to collect cargo attribute data in real time and detect cargo storage environment parameters at the same time, so as to evaluate and adjust the storage status of the cargo in real time to ensure the rationality and safety of cargo storage;

[0014] The cargo attribute monitoring unit includes: RFID read / write array and environmental sensor network;

[0015] RFID reader / writer arrays are deployed on shelves and warehouse entrances to read RFID tag data of goods in batches through radio frequency signals;

[0016] The environmental sensing network integrates temperature and humidity sensors and vibration sensors to collect cargo storage environment parameters in real time and trigger abnormal warnings.

[0017] Preferably, the warehouse layout updating unit is used to update warehouse layout data in real time, including aisle locations, storage area divisions, and shelf load-bearing information;

[0018] The warehouse layout update unit includes: a laser scanning module and an adaptive data synchronization engine;

[0019] The laser scanning module dynamically constructs a three-dimensional point cloud map of the warehouse through TOF laser radar;

[0020] The adaptive data synchronization engine automatically triggers layout data updates and pushes them to the path planning module when the shelf moves or the load-bearing capacity changes.

[0021] Preferably, the management device is further designed with a path planning unit, which is in communication with the cargo dynamic adjustment module and the warehouse layout update unit, and is used to generate a cargo handling path according to the cargo dynamic adjustment instruction and the latest warehouse layout data;

[0022] The path planning unit includes: conflict avoidance algorithm unit and energy consumption optimization unit;

[0023] The conflict avoidance algorithm unit uses AGV scheduling algorithm and dynamic time window technology to calculate the collision-free transport path in real time;

[0024] The energy consumption optimization unit generates an AGV scheduling plan with optimal energy consumption based on the equipment power status and path length.

[0025] Preferably, the management device is further designed with an equipment control unit, which is connected to the path planning unit by signal and is used to control the storage equipment to execute the handling task generated by the path planning unit;

[0026] The device control unit includes: a multi-device collaborative scheduler and a device health monitoring unit;

[0027] The multi-device collaborative scheduler coordinates the operation sequence of AGVs, stackers, and robotic arms based on task queue priorities;

[0028] The equipment health monitoring unit predicts equipment failures through vibration spectrum analysis and triggers preventive maintenance instructions.

[0029] Preferably, the management device is further designed with a visual management platform, which interacts with data from all modules and displays cargo status, warehouse layout, route planning and equipment operation status in real time;

[0030] The visual management platform includes: 3D visualization engine and interactive alarm panel;

[0031] The 3D visualization engine dynamically renders warehouse panoramas and cargo location heat maps based on WebGL technology;

[0032] The interactive alarm panel uses color coding to mark expired goods, route congestion areas and abnormal equipment status.

[0033] Preferably, the management device is further designed with a distributed data storage cluster, which is connected to the cargo attribute monitoring unit, the cargo dynamic adjustment module and the equipment control unit via an Internet of Things data bus, and is used to store cargo attribute data, storage location allocation data and equipment operation logs in real time;

[0034] Distributed data storage cluster includes: data sharding system and hierarchical storage architecture;

[0035] The data sharding system stores cargo attribute data and storage location allocation data in shards by timestamp, and equipment operation logs in shards by device ID, using a consistent hashing algorithm to ensure balanced data distribution.

[0036] The hierarchical storage architecture is divided into hot data layer and cold data layer. The hot data layer stores cargo attribute data and high-frequency environmental parameters collected by sensors in real time through a time series database, while the cold data layer stores location allocation data and equipment operation logs through a graph database.

[0037] Preferably, the management device is further designed with a data traceability system, which interacts with the distributed data storage cluster through a blockchain network to provide tamper-proof evidence of cargo flow records;

[0038] The data traceability system includes: blockchain evidence storage engine and visual audit interface;

[0039] The blockchain evidence storage engine uses distributed ledger technology. Once data is written, it will be hashed using an encryption algorithm and permanently recorded. No one can modify or delete it, ensuring the authenticity and immutability of the data.

[0040] The visual audit interface provides a timeline view of the flow of goods, can trace back the operation records of any node, and mark abnormal events in the form of heat maps, supporting one-click generation of compliance reports.

[0041] Preferably, the method of use comprises the following steps:

[0042] S1. Data Collection and Status Assessment: RFID reader / writer arrays are used to batch scan cargo tags to obtain cargo attribute data. The environmental sensor network is used to collect storage environment parameters in real time. The three-dimensional point cloud map constructed by the laser scanning module is then used to dynamically update warehouse layout data.

[0043] S2. Dynamic Storage Strategy Adjustment: Based on order urgency, remaining shelf life, and environmental parameters, the priority evaluation unit calculates the priority of goods, uses a space optimization algorithm to allocate the best storage location, and uses an AI-powered intelligent scheduling algorithm to predict inbound and outbound demand and adjust storage locations in advance.

[0044] S3, Path Planning and Equipment Collaboration: Based on dynamic adjustment instructions and the latest warehouse layout data, a collision avoidance algorithm is used to generate collision-free transport paths. Energy consumption is optimized by combining equipment power status and path length. A multi-device collaborative scheduler is used to control AGVs, stackers, and robotic arms to perform transport tasks.

[0045] S4. Visual Monitoring and Abnormal Response: A 3D visualization engine is used in the visual management platform to render a panoramic view of the warehouse and a heat map of the cargo. An interactive alarm panel marks abnormal conditions and triggers preventive maintenance instructions in the equipment health monitoring unit.

[0046] S5. Data storage and traceability audit: Goods attributes and equipment log data are stored in shards in a distributed data storage cluster. The blockchain evidence storage engine provides tamper-proof evidence storage for goods flow records, and a visual audit interface generates a goods flow timeline and compliance report.

[0047] Preferably, the method of use further comprises the following steps:

[0048] S11. By linking the RFID reader array with the laser scanning module, the cargo tag data and the three-dimensional coordinates of the shelf are synchronously collected at a frequency of 10Hz. The temperature and humidity data of the environmental sensor network are integrated using the Kalman filter algorithm to generate a warehouse holographic state matrix with a time stamp.

[0049] S21. When the AI ​​intelligent scheduling algorithm predicts that high-frequency inbound and outbound operations will occur within the next two hours, the priority evaluation unit is triggered to recalculate the cargo weight coefficient and generate an emergency filling instruction set based on the shelf load safety threshold;

[0050] S31. Based on the initial path output by the conflict avoidance algorithm, the AGV running trajectory is detected every 30 seconds using the dynamic time window technology. If the path overlap exceeds the threshold, the energy consumption optimization unit is called to generate an alternative path and update it to the multi-device collaborative scheduler.

[0051] S41. When the interactive alarm panel detects expired goods or equipment anomalies, the 3D visualization engine automatically focuses on the abnormal area and overlays a heat map layer, where the color gradient maps the risk level (red for high risk and yellow for warning);

[0052] S51. When the goods flow record is processed by the blockchain evidence engine, the SHA-256 algorithm is used to hash and encrypt the operation time, equipment ID and warehouse location coordinates to generate an irreversible evidence block and synchronize it to the timeline node of the visual audit interface.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] 1. The present invention is equipped with a cargo dynamic adjustment module, which can detect the storage status of goods and order changes in real time according to the real-time attribute data of the goods and warehouse layout data, and then dynamically adjust the storage location and priority of the goods. The module combines intelligent algorithms, such as machine learning and deep learning algorithms, to predict the demand for goods in and out of the warehouse, optimize the storage location of the goods in advance, ensure that high-turnover goods are close to the exit, reduce handling costs, and improve warehousing efficiency. At the same time, the module can also automatically adjust the storage location of environmentally sensitive goods such as cold chain goods according to the shelf life and environmental parameters of the goods, ensure that the goods are stored in a suitable environment, extend the shelf life of the goods, and reduce cargo losses, thereby effectively solving the problem that fixed storage strategies in traditional warehouse management systems are difficult to adapt to frequent order changes and cargo turnover needs.

[0055] 2. The present invention realizes the function of recording and analyzing cargo data by installing a distributed data storage cluster and a data traceability system, solving the problems of incomplete data records, difficulty in tracing and analyzing in existing warehouse management, improving the level of informatization and scientific decision-making in warehouse management, and providing strong support for warehouse optimization and management.

[0056] 3. The present invention realizes the conflict avoidance function of avoiding path conflicts by installing a path planning unit, solves the problems of unreasonable cargo handling path planning, easy collision and congestion in existing warehouse management, improves the operating efficiency and handling safety of warehouse equipment, and ensures that goods can move efficiently and smoothly in the warehouse environment.

[0057] 4. The present invention realizes the equipment safety automatic detection function and equipment failure prediction function by installing an equipment control unit, solves the problems of untimely equipment maintenance and frequent failures in existing warehouse management, improves the reliability and service life of warehouse equipment, reduces equipment maintenance costs and downtime, and ensures the stable operation of the warehouse system. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 Schematic diagram of the control workflow of the present invention; DETAILED DESCRIPTION

[0059] 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0060] In the description of the present invention, it should be noted that the terms "upper," "lower," "inner," "outer," "front end," "rear end," "both ends," "one end," "the other end," and the like, indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0061] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "installed," "provided with," "connected," etc., should be understood in a broad sense. For example, "connected" may refer to a fixed connection, a detachable connection, or an integral connection; it may refer to a mechanical connection or an electrical connection; it may refer to a direct connection or an indirect connection through an intermediate medium; it may refer to internal communication between two components. 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.

[0062] See also Figure 1The present invention provides an embodiment of an intelligent warehousing service management device based on the Internet of Things, comprising a cargo attribute monitoring unit, a warehouse layout updating unit, and a cargo dynamic adjustment module. The cargo dynamic adjustment module is configured to detect cargo storage status and order changes in real time based on cargo attribute data and warehouse layout data, and dynamically adjust cargo storage location and priority.

[0063] The cargo dynamic adjustment module includes: a priority evaluation unit, a storage location optimization unit, an AI intelligent scheduling algorithm and a real-time feedback mechanism;

[0064] The priority evaluation unit dynamically calculates the priority of goods based on order urgency, remaining shelf life, and environmental parameters;

[0065] The storage location optimization unit allocates the best storage location through a space optimization algorithm based on the priority evaluation results, environmental parameters and shelf load limits;

[0066] The AI ​​intelligent scheduling algorithm predicts the inbound and outbound demand of goods based on historical and real-time data, adjusts the storage location of goods in advance, and reduces handling time and costs;

[0067] The real-time feedback mechanism dynamically adjusts the cargo storage strategy based on the latest cargo attributes and warehouse layout information;

[0068] Furthermore, when goods enter the warehouse, the goods attribute monitoring unit first reads the RFID tag data of the goods in batches through the RFID read-write array to obtain attribute information such as the production date, shelf life, order urgency and safety level of the goods. At the same time, the environmental sensor network collects parameters such as temperature and humidity of the goods storage environment in real time to ensure that the goods are stored in a suitable environment. These data are transmitted to the goods dynamic adjustment module through the Internet of Things data bus. The priority evaluation unit in the goods dynamic adjustment module dynamically calculates the priority of each piece of goods based on the acquired goods attribute data, combined with factors such as the urgency of the order, the remaining shelf life of the goods, and the safety level of the goods. For example, goods with a shorter shelf life and a higher order urgency will be given a higher priority to ensure that these goods can be shipped out first or placed in a location that is easy to access quickly.

[0069] Next, the storage location optimization unit uses a space optimization algorithm to assign the best storage location for each item based on the priority assessment results and the load-bearing capacity of the shelves. This algorithm comprehensively considers the size and weight of the items and the spatial layout of the shelves within the warehouse to ensure that the items are reasonably placed within the load-bearing range, while maximizing warehouse space utilization and reducing space waste. For example, heavier items are prioritized for allocation to the bottom shelf to avoid exceeding the shelf's load-bearing capacity, while lighter and smaller items can be placed higher on the shelves to fully utilize vertical space.

[0070] Throughout the entire process, the warehouse layout update unit monitors layout changes within the warehouse in real time, such as shelf adjustments and aisle changes, and dynamically constructs a three-dimensional point cloud map of the warehouse through the laser scanning module. Once a layout change is detected, the adaptive data synchronization engine automatically triggers an update of the layout data and pushes the updated data to the cargo dynamic adjustment module to ensure that the cargo dynamic adjustment module can make reasonable storage location allocation and priority adjustments based on the latest warehouse layout.

[0071] See also Figure 1 The present invention provides an embodiment of an intelligent warehousing service management device based on the Internet of Things, wherein the management device is further designed with a path planning unit, the path planning unit being communicatively connected to the cargo dynamic adjustment module and the warehouse layout update unit, and being used to generate a cargo handling path according to the cargo dynamic adjustment instruction and the latest warehouse layout data;

[0072] The path planning unit includes: conflict avoidance algorithm unit and energy consumption optimization unit;

[0073] The conflict avoidance algorithm unit uses AGV scheduling algorithm and dynamic time window technology to calculate the collision-free transport path in real time;

[0074] The energy consumption optimization unit generates an AGV scheduling plan with optimal energy consumption based on the equipment power status and path length;

[0075] Furthermore, when the cargo dynamic adjustment module issues cargo handling instructions based on order requirements or inventory changes, the path planning unit is immediately activated. The conflict avoidance algorithm unit first collects the current position, speed, and handling task information of all AGVs (automated guided vehicles), and combines it with the real-time shelf position, aisle width and other layout data provided by the warehouse layout update unit, and uses the AGV scheduling algorithm to calculate the preliminary handling path of each AGV. On this basis, dynamic time window technology is introduced. It will allocate a dynamic time window to each AGV based on the urgency of the cargo and the current task progress of the AGV. Within this time window, the AGV needs to complete a specific handling task, thereby ensuring that in the case of multiple tasks concurrently, the paths between the AGVs will not conflict, effectively avoiding the occurrence of collision accidents;

[0076] At the same time, the energy optimization unit begins operating, monitoring the battery status of each AGV in real time and, combined with the preliminary transport routes generated by the path planning unit, calculating the energy consumption estimate for each route. The energy optimization unit comprehensively considers factors such as the AGV's remaining battery life, route length, cargo weight, and transport speed. Using a complex algorithm model, it optimizes and adjusts the preliminary routes, ultimately generating an energy-optimized AGV scheduling plan. This plan not only ensures that the AGVs can complete their tasks within the permitted power limits but also minimizes the energy consumption of the entire warehouse system, improving energy efficiency.

[0077] See also Figure 1 The present invention provides an embodiment of an intelligent warehousing service management device based on the Internet of Things, wherein the management device is further designed with a device control unit, which is connected to a path planning unit by signal and is used to control the warehousing equipment to execute the handling task generated by the path planning unit;

[0078] The device control unit includes: a multi-device collaborative scheduler and a device health monitoring unit;

[0079] The multi-device collaborative scheduler coordinates the operation sequence of AGVs, stackers, and robotic arms based on task queue priorities;

[0080] The equipment health monitoring unit predicts equipment failures through vibration spectrum analysis and triggers preventive maintenance instructions;

[0081] Furthermore, after the path planning unit generates the cargo handling path, the device control unit receives the handling task instructions sent by the path planning unit. The multi-device collaborative scheduler coordinates the operation sequence of AGVs (automated guided vehicles), stackers, and robotic arms based on the priority of the task queue. For example, in a typical warehousing operation, the multi-device collaborative scheduler first dispatches the AGV to transport the cargo to the designated stacker location. After the stacker receives the cargo, it moves the cargo to the corresponding shelf area according to the instructions of the path planning unit. Finally, the robotic arm completes the precise placement of the cargo. Throughout the entire process, the multi-device collaborative scheduler ensures seamless operation sequence between devices, avoids conflicts and congestion between devices, and improves the operating efficiency of storage equipment.

[0082] At the same time, the equipment health monitoring unit monitors the operating status of the storage equipment in real time. Through the vibration sensors installed on the key components of the equipment, the equipment health monitoring unit can collect vibration data during the operation of the equipment. Using advanced vibration spectrum analysis technology, the equipment health monitoring unit analyzes the vibration data in real time to predict possible equipment failures. For example, when the vibration spectrum analysis results show that the drive motor of a certain AGV has an abnormal vibration frequency, the equipment health monitoring unit will immediately trigger a preventive maintenance instruction. The maintenance personnel will inspect and repair the AGV according to the instruction, replace worn parts in time, avoid downtime caused by equipment failure, ensure the stable operation of the storage equipment, reduce equipment maintenance costs, and improve the reliability of warehouse management.

[0083] See Figure 1 The present invention provides an embodiment of an intelligent warehousing service management device based on the Internet of Things. The management device is further designed with a visual management platform that interacts with data from all modules and displays cargo status, warehouse layout, route planning, and equipment operating status in real time.

[0084] The visual management platform includes: 3D visualization engine and interactive alarm panel;

[0085] The 3D visualization engine dynamically renders warehouse panoramas and cargo location heat maps based on WebGL technology;

[0086] The interactive alarm panel uses color coding to mark expired goods, route congestion areas and abnormal equipment status;

[0087] Furthermore, during the warehousing operation process, the visual management platform receives and processes data from various modules in real time. The three-dimensional visualization engine dynamically renders the warehouse panorama based on WebGL technology, generating an intuitive warehouse layout map and cargo location heat map. Managers can clearly see the real-time storage status and distribution of goods through the platform. The interactive alarm panel uses color coding to mark expired goods, route congestion areas and equipment abnormalities in real time. Once an abnormal situation is detected, such as goods exceeding the shelf life, transportation route congestion or equipment failure, the alarm panel will immediately notify the manager with eye-catching colors and prompt information so that timely measures can be taken to solve the problem.

[0088] See also Figure 1The present invention provides an embodiment of an intelligent warehousing service management device based on the Internet of Things. The management device is further designed with a distributed data storage cluster, which is connected to a cargo attribute monitoring unit, a cargo dynamic adjustment module, and an equipment control unit via an Internet of Things data bus, and is used to store cargo attribute data, storage location allocation data, and equipment operation logs in real time. The management device is also designed with a data traceability system, which interacts with the distributed data storage cluster via a blockchain network to provide tamper-proof evidence of cargo flow records.

[0089] Distributed data storage cluster includes: data sharding system and hierarchical storage architecture;

[0090] The data sharding system stores cargo attribute data and storage location allocation data in shards by timestamp, and equipment operation logs in shards by device ID, using a consistent hashing algorithm to ensure balanced data distribution.

[0091] The hierarchical storage architecture is divided into hot data layers and cold data layers. The hot data layer uses a time series database to store cargo attribute data and high-frequency environmental parameters collected by sensors in real time, while the cold data layer uses a graph database to store location allocation data and equipment operation logs.

[0092] The data traceability system includes: blockchain evidence storage engine and visual audit interface;

[0093] The blockchain evidence storage engine uses distributed ledger technology. Once data is written, it will be hashed using an encryption algorithm and permanently recorded. No one can modify or delete it, ensuring the authenticity and immutability of the data.

[0094] The visual audit interface provides a timeline view of the cargo flow, allows tracing back the operation records of any node, and marks abnormal events in the form of heat maps, supporting one-click generation of compliance reports;

[0095] Furthermore, when goods enter the storage area, the goods attribute monitoring unit first collects the goods attribute data (such as production date, shelf life, order urgency and safety level) and storage environment parameters (such as temperature, humidity and vibration) in real time through the RFID read-write array and environmental sensor network. This data is transmitted to the distributed data storage cluster through the IoT data bus. The data sharding system shards and stores the goods attribute data and storage location allocation data according to the timestamp, and shards and stores the equipment operation logs according to the equipment ID. The consistent hashing algorithm is used to ensure the balanced distribution of data in the storage cluster. The hot data layer in the hierarchical storage architecture uses a time series database to store the high-frequency data collected by sensors in real time, facilitating rapid query and analysis of the current storage status. The cold data layer uses a graph database to store the location allocation data and equipment operation logs for long-term storage and complex queries.

[0096] At the same time, the data traceability system uses a blockchain evidence storage engine to provide tamper-proof evidence of cargo flow records. The blockchain evidence storage engine utilizes distributed ledger technology and encryption algorithms to ensure that once written data cannot be modified or deleted, thereby guaranteeing the authenticity and immutability of the data. Managers can view a timeline view of cargo flow through a visual audit interface, trace back operation records at any node, and intuitively view abnormal events through heat maps. In addition, the visual audit interface also supports the one-click generation of compliance reports, providing strong data support and decision-making basis for warehouse management, ensuring the efficiency, accuracy, and compliance of warehouse operations.

[0097] Working Principle: First, the device collects cargo attribute data and storage environment parameters in real time through the cargo attribute monitoring unit. This data is transmitted to the distributed data storage cluster via the IoT data bus for storage. The distributed data storage cluster utilizes a data sharding system and hierarchical storage architecture to ensure efficient data storage and fast access, while also ensuring balanced data distribution and long-term preservation.

[0098] Next, the device's cargo dynamic adjustment module dynamically adjusts the cargo's storage location and priority based on the collected cargo attribute data and the latest layout information provided by the warehouse layout update unit. The path planning unit uses this information to generate the optimal cargo handling route. The equipment control unit, following the instructions of the path planning unit, directs the storage equipment to carry out cargo handling tasks, ensuring that the cargo reaches the designated location efficiently and accurately. During this process, the equipment health monitoring unit monitors the equipment's operating status in real time, predicts equipment failures, and triggers preventive maintenance instructions to ensure stable equipment operation.

[0099] Finally, the data traceability system uses blockchain technology to store tamper-proof records of cargo flow, ensuring the authenticity and immutability of the data. The visual management platform displays the warehouse status in real time, including cargo location, warehouse layout, route planning, and equipment operating status, and marks abnormal situations through an interactive alarm panel to support managers in making quick decisions. Through the collaborative work of these functional modules, the device realizes intelligent, automated, and efficient warehouse management, significantly improving warehouse operation efficiency and management level.

[0100] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. An intelligent warehousing service management device based on the Internet of Things, characterized by: It includes a cargo attribute monitoring unit, a warehouse layout updating unit, and a cargo dynamic adjustment module. The cargo dynamic adjustment module is used to detect the storage status and order changes of cargo in real time based on cargo attribute data and warehouse layout data, and dynamically adjust the storage location and priority of cargo; The cargo dynamic adjustment module includes: a priority evaluation unit, a storage location optimization unit, an AI intelligent scheduling algorithm and a real-time feedback mechanism; The priority evaluation unit dynamically calculates the priority of goods based on order urgency, remaining shelf life, and environmental parameters; The storage location optimization unit allocates the best storage location through a space optimization algorithm based on the priority evaluation results, environmental parameters and shelf load limits; The AI ​​intelligent scheduling algorithm predicts the inbound and outbound demand of goods based on historical and real-time data, adjusts the storage location of goods in advance, and reduces handling time and costs; The real-time feedback mechanism dynamically adjusts the cargo storage strategy based on the latest cargo attributes and warehouse layout information.

2. The intelligent warehousing service management device based on the Internet of Things according to claim 1, characterized in that: The cargo attribute monitoring unit is used to collect cargo attribute data in real time and detect cargo storage environment parameters at the same time, so as to evaluate and adjust the storage status of the cargo in real time to ensure the rationality and safety of cargo storage; The cargo attribute monitoring unit includes: RFID read / write array and environmental sensor network; RFID reader / writer arrays are deployed on shelves and warehouse entrances to read RFID tag data of goods in batches through radio frequency signals; The environmental sensing network integrates temperature and humidity sensors and vibration sensors to collect cargo storage environment parameters in real time and trigger abnormal warnings.

3. The intelligent warehousing service management device based on the Internet of Things according to claim 1, characterized in that: The warehouse layout update unit is used to update warehouse layout data in real time, including aisle locations, storage area divisions, and shelf load-bearing information; The warehouse layout update unit includes: a laser scanning module and an adaptive data synchronization engine; The laser scanning module dynamically constructs a three-dimensional point cloud map of the warehouse through TOF laser radar; The adaptive data synchronization engine automatically triggers layout data updates and pushes them to the path planning module when the shelf moves or the load-bearing capacity changes.

4. The intelligent warehousing service management device based on the Internet of Things according to claim 1, characterized in that: The management device is further designed with a path planning unit, which is in communication with the cargo dynamic adjustment module and the warehouse layout update unit and is used to generate a cargo handling path based on the cargo dynamic adjustment instructions and the latest warehouse layout data; The path planning unit includes: conflict avoidance algorithm unit and energy consumption optimization unit; The conflict avoidance algorithm unit uses AGV scheduling algorithm and dynamic time window technology to calculate the collision-free transport path in real time; The energy consumption optimization unit generates an AGV scheduling plan with optimal energy consumption based on the equipment power status and path length.

5. The intelligent warehousing service management device based on the Internet of Things according to claim 1, characterized in that: The management device is further designed with an equipment control unit, which is connected to the path planning unit by signal and is used to control the storage equipment to execute the handling task generated by the path planning unit; The device control unit includes: a multi-device collaborative scheduler and a device health monitoring unit; The multi-device collaborative scheduler coordinates the operation sequence of AGVs, stackers, and robotic arms based on task queue priorities; The equipment health monitoring unit predicts equipment failures through vibration spectrum analysis and triggers preventive maintenance instructions.

6. The intelligent warehousing service management device based on the Internet of Things according to claim 1, characterized in that: The management device is also designed with a visual management platform, which interacts with all module data and displays the cargo status, warehouse layout, route planning and equipment operation status in real time; The visual management platform includes: 3D visualization engine and interactive alarm panel; The 3D visualization engine dynamically renders warehouse panoramas and cargo location heat maps based on WebGL technology; The interactive alarm panel uses color coding to mark expired goods, route congestion areas and abnormal equipment status.

7. The intelligent warehousing service management device based on the Internet of Things according to claim 1, characterized in that: The management device is also designed with a distributed data storage cluster, which is connected to the cargo attribute monitoring unit, the cargo dynamic adjustment module and the equipment control unit via the Internet of Things data bus, and is used to store cargo attribute data, storage location allocation data and equipment operation logs in real time; Distributed data storage cluster includes: data sharding system and hierarchical storage architecture; The data sharding system stores cargo attribute data and storage location allocation data in shards by timestamp, and equipment operation logs in shards by device ID, using a consistent hashing algorithm to ensure balanced data distribution. The hierarchical storage architecture is divided into hot data layer and cold data layer. The hot data layer stores cargo attribute data and high-frequency environmental parameters collected by sensors in real time through a time series database, while the cold data layer stores location allocation data and equipment operation logs through a graph database.

8. The intelligent warehousing service management device based on the Internet of Things according to claim 1, characterized in that: The management device is also designed with a data traceability system, which interacts with the distributed data storage cluster through the blockchain network to provide tamper-proof evidence of cargo flow records; The data traceability system includes: blockchain evidence storage engine and visual audit interface; The blockchain evidence storage engine uses distributed ledger technology. Once data is written, it will be hashed using an encryption algorithm and permanently recorded. No one can modify or delete it, ensuring the authenticity and immutability of the data. The visual audit interface provides a timeline view of the flow of goods, can trace back the operation records of any node, and mark abnormal events in the form of heat maps, supporting one-click generation of compliance reports.

9. A method for using an intelligent warehousing service management device based on the Internet of Things, applicable to the intelligent warehousing service management device based on the Internet of Things according to any one of claims 1 to 8, characterized in that: The method of use comprises the following steps: S1. Data Collection and Status Assessment: RFID reader / writer arrays are used to batch scan cargo tags to obtain cargo attribute data. The environmental sensor network is used to collect storage environment parameters in real time. The three-dimensional point cloud map constructed by the laser scanning module is then used to dynamically update warehouse layout data. S2. Dynamic Storage Strategy Adjustment: Based on order urgency, remaining shelf life, and environmental parameters, the priority evaluation unit calculates the priority of goods, uses a space optimization algorithm to allocate the best storage location, and uses an AI-powered intelligent scheduling algorithm to predict inbound and outbound demand and adjust storage locations in advance. S3, Path Planning and Equipment Collaboration: Based on dynamic adjustment instructions and the latest warehouse layout data, a collision avoidance algorithm is used to generate collision-free transport paths. Energy consumption is optimized by combining equipment power status and path length. A multi-device collaborative scheduler is used to control AGVs, stackers, and robotic arms to perform transport tasks. S4. Visual Monitoring and Abnormal Response: A 3D visualization engine is used in the visual management platform to render a panoramic view of the warehouse and a heat map of the cargo. An interactive alarm panel marks abnormal conditions and triggers preventive maintenance instructions in the equipment health monitoring unit. S5. Data storage and traceability audit: Goods attributes and equipment log data are stored in shards in a distributed data storage cluster. The blockchain evidence storage engine provides tamper-proof evidence storage for goods flow records, and a visual audit interface generates a goods flow timeline and compliance report.

10. The method for using the intelligent warehousing service management device based on the Internet of Things according to claim 9, characterized in that: The method of use further comprises the following steps: S11. By linking the RFID reader array with the laser scanning module, the cargo tag data and the three-dimensional coordinates of the shelf are synchronously collected at a frequency of 10Hz. The temperature and humidity data of the environmental sensor network are integrated using the Kalman filter algorithm to generate a warehouse holographic state matrix with a time stamp. S21. When the AI ​​intelligent scheduling algorithm predicts that high-frequency inbound and outbound operations will occur within the next two hours, the priority evaluation unit is triggered to recalculate the cargo weight coefficient and generate an emergency filling instruction set based on the shelf load safety threshold; S31. Based on the initial path output by the conflict avoidance algorithm, the AGV running trajectory is detected every 30 seconds using the dynamic time window technology. If the path overlap exceeds the threshold, the energy consumption optimization unit is called to generate an alternative path and update it to the multi-device collaborative scheduler. S41. When the interactive alarm panel detects expired goods or equipment anomalies, the 3D visualization engine automatically focuses on the abnormal area and overlays a heat map layer, where the color gradient maps the risk level (red for high risk and yellow for warning); S51. When the goods flow record is processed by the blockchain evidence engine, the SHA-256 algorithm is used to hash and encrypt the operation time, equipment ID and warehouse location coordinates to generate an irreversible evidence block and synchronize it to the timeline node of the visual audit interface.

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