Intelligent storage industry Internet of Things system and control method

Abstract

The invention discloses an intelligent warehousing industry Internet of Things system and a control method, and particularly relates to the technical field of intelligent warehousing systems.The intelligent warehousing industry Internet of Things system comprises a sensing module, an information processing module, an automatic operation module, a control decision module, a data storage module and a user interface module; a sensor in the sensing module is at least one of a temperature and humidity sensor, a gas sensor and a vibration sensor, the warehouse environment can be monitored in real time, abnormal conditions can be detected, and a plurality of sensors are selected to be installed at different positions according to the size of a storage space. Data analysis and decision optimization are carried out in combination with an artificial intelligence algorithm, the automation and intelligence level and the actual application effect of the warehousing system are improved, the warehouse environment can be monitored by using various sensors and real-time data, and in combination with an intelligent early warning mechanism, potential risks can be recognized in time, an alarm can be given out, and the safety and stability of the warehousing environment are ensured.

Description

Technical Field

[0001] The present invention relates to the field of intelligent warehousing technology, and in particular to an intelligent warehousing industrial Internet of Things system and a control method. Background Art

[0002] With the development of modern industry and the continuous advancement of internet technology, intelligent warehousing systems are becoming increasingly widely used across various industries. Traditional warehousing management suffers from issues such as low levels of informatization, low operational efficiency, and inaccurate inventory management, resulting in high costs and low efficiency for manufacturers. To address these issues, intelligent warehousing systems leverage emerging technologies such as the Internet of Things, automated equipment, cloud computing, and big data to achieve intelligent and informatized warehousing management, significantly improving warehousing efficiency and accuracy.

[0003] Existing intelligent warehousing systems primarily focus on the digital transformation of infrastructure, such as tracking and managing items through technologies like barcodes and RFID. However, these systems primarily focus on item location and tracking, lacking systematic integration across multiple dimensions such as inventory management, dynamic scheduling, and anomaly warnings. Furthermore, they fail to adequately consider warehouse scheduling and the collaborative management of personnel and equipment in complex environments. This results in a low level of intelligence and poor practical application effectiveness. Summary of the Invention

[0004] The present invention proposes an intelligent warehousing industrial Internet of Things system and control method to solve the problems of lack of systematic integration of multi-dimensional management such as inventory management, dynamic scheduling, and abnormal warning in intelligent warehousing management, and lack of sufficient consideration for warehouse scheduling, collaborative management of personnel and equipment in complex environments, resulting in low intelligence level and poor actual application effect.

[0005] To achieve the above objectives, the present invention adopts the following technical solutions: an intelligent warehousing industrial Internet of Things system and control method, comprising: Perception module: It includes at least one RFID reader, sensor, and camera, which are used together to locate, identify, and monitor goods in the warehouse in real time, and collect warehouse environmental data; Information processing module: includes a central processing unit, which is used to receive data transmitted from the perception module, pre-process the received data, filter out noise data, and analyze cargo information and warehouse environment; Automated operation module: includes at least one automated storage and retrieval device (which can be a stacker or other storage equipment) and a transport robot, which is used to perform cargo storage and retrieval operations according to the instructions issued by the central processing unit of the information processing module; Control and decision-making module: This includes an artificial intelligence algorithm module that automatically generates optimal cargo access strategies, scheduling strategies, and inventory management strategies based on inventory information, environmental data, and business needs; Data storage module: used to store warehouse data, equipment status data and operation records; User interface module: provides an interactive interface between users and the system, used to display warehouse status, inventory warnings and abnormal situations, and allows users to operate and manage.

[0006] The effect achieved by the above components is: by integrating multiple functional modules, the system can realize all-round intelligent management from warehouse environment monitoring to inventory management and cargo scheduling, reducing the complexity of relying on multiple decentralized systems. Each module can be independently optimized and upgraded according to needs, enhancing the flexibility and scalability of the system. The collaborative work between modules enables various operations in the warehousing process to be carried out efficiently and accurately, significantly improving the overall warehouse management efficiency.

[0007] Preferably, the sensor in the perception module is at least one of a temperature and humidity sensor, a gas sensor, and a vibration sensor, which can monitor the warehouse environment in real time and detect abnormal conditions. Multiple sensors can be installed in different locations according to the size of the storage space.

[0008] The effect achieved by the above components is: by monitoring the warehouse environment (such as temperature, humidity, gas concentration, etc.), the system can prevent environmental problems from affecting the quality of goods. This is especially important for commodities with high environmental requirements (such as medicines, food, etc.). When the sensor detects an abnormal situation, it can send an alarm signal in time to prevent the problem from worsening and ensure warehouse safety. The multi-dimensional environmental perception improves the system's intelligent decision-making ability, and can independently judge the warehouse status and respond.

[0009] Preferably, the automated operation module includes a stacker, an automatic guided vehicle (AGV) and a robotic arm, which are used to perform automated storage, retrieval and transportation operations of goods.

[0010] The effects achieved by the above components are: the application of automated equipment can reduce manual intervention, lower labor costs, and improve operational efficiency. Mechanized operations can greatly reduce manual errors and ensure the accuracy and efficiency of goods storage and retrieval. Through automated equipment and robots, goods in the warehouse can be stored and retrieved quickly and efficiently, reducing waiting time and bottlenecks in the transportation process, and improving overall operational speed.

[0011] Preferably, the control decision module includes a prediction model based on deep learning, which is used to predict future inventory demand based on historical data and intelligently optimize the warehousing process.

[0012] The effects achieved by the above components are: artificial intelligence algorithms can make intelligent decisions based on large amounts of historical data and real-time data, so that warehouse operations no longer rely on manual experience, but instead generate optimal decision-making plans through data analysis. AI algorithms can adjust operating strategies based on real-time changes in demand, inventory conditions, and environmental data, achieve accurate inventory scheduling and cargo access, optimize resource allocation, and generate strategies through algorithms, which can greatly reduce possible errors in the manual decision-making process and ensure the accuracy of warehouse management.

[0013] Preferably, the information processing module further includes a data fusion algorithm for fusing data from different sensors and improving the accuracy and reliability of the data.

[0014] The effects achieved by the above components are: by fusing data from different sensors, the possible errors of a single sensor can be eliminated, and the reliability and accuracy of the overall data can be improved. Data fusion enhances the system's adaptability to environmental changes or sensor failures, enabling the system to operate stably under various conditions. Data fusion provides more comprehensive and accurate data support for the control decision-making module, thereby improving the quality and effectiveness of decision-making.

[0015] Preferably, the method further includes a control method for an intelligent warehousing industrial Internet of Things system, comprising the following steps: S1, obtain real-time information about goods in the warehouse and real-time data about the warehouse environment through the perception module; S2. The raw data collected by the perception module is transmitted to the information processing module, and the data is preprocessed to obtain usable data; S3. Based on the data processed by the information processing module, the artificial intelligence algorithm in the control decision module is used to generate warehouse operation strategies, including cargo storage and retrieval, inventory adjustment and equipment scheduling strategies; S4. Cargo storage, retrieval and transportation operations are performed through the automated operation module; S5. Feedback the operation results to the information processing module, and display relevant status information through the user interface module to perform abnormal early warning and alarm processing.

[0016] The effect achieved by the above components is: this method realizes the full-chain control from data collection to execution feedback, ensuring that each link can respond in real time and efficiently, reducing missed links or delays. The combination of automated execution and real-time data feedback can significantly improve the efficiency of warehouse operations and ensure fast and accurate operations. Through the control and feedback mechanism based on real-time data, the system can flexibly respond to changes in different warehouse environments and different cargo demands, and has strong adaptability.

[0017] Preferably, the artificial intelligence algorithm in step S3 further predicts inventory demand based on a deep learning model, and dynamically adjusts the warehousing strategy according to the prediction results.

[0018] The effects achieved by the above components are: deep learning algorithms can deeply explore the potential patterns in historical data, provide accurate predictions for inventory demand, and improve the accuracy of inventory management. By predicting future demand, the system can optimize inventory levels, avoid excessive inventory backlogs, and reduce warehousing costs. Based on the prediction results, warehouse management strategies can be dynamically adjusted according to future demand, improving the system's responsiveness and adaptability.

[0019] Preferably, the automated operation module in step S4 further includes a machine learning module for optimizing warehousing operation strategies based on historical operation data and improving operation efficiency.

[0020] The effects achieved by the above components are: continuous optimization of operation strategies through machine learning, so that the system can continuously improve and improve efficiency during actual use. The system can customize optimization strategies based on the historical data of specific warehouses, realize personalized adjustments, and further improve the effect of warehouse management. The machine learning module can automatically extract patterns from historical data, reduce manual intervention, reduce manual management costs, and improve operational accuracy.

[0021] In summary, the beneficial effects of the present invention are: (1) Collect real-time data through Internet of Things technology, combine it with artificial intelligence algorithms for data analysis and decision optimization, and improve the automation and intelligence level of the warehousing system and its actual application effect.

[0022] (2) Using deep learning and prediction models, we can predict future inventory demand based on historical data and dynamically adjust inventory management and operation strategies to achieve more efficient and accurate warehouse management.

[0023] (3) The present invention not only includes inventory management, but also covers warehouse environment monitoring, cargo storage and retrieval, equipment scheduling and other aspects, building a comprehensive and systematic intelligent warehousing solution.

[0024] (4) Using a variety of sensors and real-time data to monitor the warehouse environment, combined with an intelligent early warning mechanism, it can promptly identify potential risks and issue alarms to ensure the safety and stability of the storage environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a flowchart of the process module of this application; DETAILED DESCRIPTION In the first embodiment, the intelligent warehousing system of this embodiment includes multiple hardware modules, mainly including: a perception module, an information processing module, an automated operation module, a control and decision module, a data storage module, and a user interface module. All modules are connected through the Internet of Things technology to form a highly integrated intelligent warehousing system; The sensing module uses multiple sensors, including temperature and humidity sensors, gas sensors, and vibration sensors. The sensors are installed in different locations in the warehouse and transmit the monitored data to the information processing module via a wireless network. The temperature and humidity sensors mainly monitor the temperature and humidity of the warehouse environment and are suitable for storing goods with strict environmental requirements (such as medicines and food); the gas sensor is used to detect the gas concentration in the warehouse to prevent toxic gas leaks; the vibration sensor monitors the operating status of the equipment in the warehouse and the vibration of the storage environment in real time; The information processing module is responsible for receiving real-time data collected by the perception module and integrating data from multiple sensors using data fusion algorithms to ensure data accuracy and reliability. The information processing module interacts with other modules through IoT technology, processes data, and transmits the results to the control decision module. The automated operation module consists of stackers, automated guided vehicles (AGVs), and robotic arms, and is used to perform warehousing operations, including the storage, retrieval, and handling of goods. Stackers are used to efficiently stack and retrieve goods in the warehouse; automated guided vehicles (AGVs) are responsible for transporting goods to designated locations, reducing manual operations; and robotic arms are used for refined handling operations, such as picking up and placing small items. The control and decision-making module uses deep learning models or other artificial intelligence algorithms to analyze and determine warehouse operation strategies. Based on real-time collected environmental data, cargo storage status, and inventory requirements, the control and decision-making module dynamically generates the optimal operation strategy and transmits the strategy instructions to the automated operation module. The deep learning-based prediction model can use historical operation data, environmental data, and inventory information to predict demand and optimize warehouse operations. The data storage module is used to store all operational and sensor-collected data over the long term, including warehouse status, inventory levels, and environmental data. This data will be used for subsequent data analysis and optimization. The user interface module provides an interactive interface between warehouse managers and the intelligent warehousing system. Through this module, managers can view warehouse status, operation records, and inventory information in real time and adjust warehousing operations as needed.

[0026] In the second embodiment, the system in this embodiment predicts future demand through the control decision module and automatically adjusts the inventory level. For example, during peak demand periods, the system will increase the storage of warehouse materials in advance to avoid shortages; during low demand periods, the system will reduce storage levels to reduce storage costs. The system in this embodiment can also dynamically adjust the warehouse's internal environment based on environmental data provided by the perception module. For example, if the temperature and humidity are too high, the system can adjust ventilation and cooling equipment to ensure a suitable warehouse environment. If a storage device malfunctions, the perception module can monitor it in real time and transmit the information to the processing module. The system will automatically determine the type of malfunction and generate repair recommendations or directly activate backup equipment to replace it, reducing equipment downtime.

[0027] Workflow: In actual operation, sensors in the perception module continuously monitor warehouse environmental parameters (such as temperature, humidity, gas concentration, and vibration), and transmit this data in real time to the information processing module for processing. The information processing module uses a data fusion algorithm to integrate data from different sensors, eliminating potential sensor errors and improving data accuracy. This processed data is then used by the control and decision-making module to help the system make correct operational decisions. Based on the collected environmental data and inventory requirements, the control and decision-making module uses deep learning or other intelligent algorithms to predict future storage needs and generate corresponding operational strategies (such as the order of goods storage and retrieval, and the scheduling strategy for automated equipment). These strategies are transmitted to the automated operation module to guide specific operations, and the automated operation module executes tasks according to the instructions of the control and decision-making module. Stackers, automated guided vehicles, and robotic arms work together to perform operations such as storage, retrieval, and handling of goods, ensuring efficient and accurate warehouse operations. After the operations are completed, the system provides feedback to the control and decision-making module. If any operational issues are detected, the system can adjust the operational strategy based on this feedback to optimize the overall warehouse process. Based on historical data and real-time feedback, the control and decision-making module's deep learning algorithm can accurately predict inventory needs and automatically adjust warehouse operations. By predicting future demand, the system can make inventory adjustments in advance, reduce inventory backlogs, and avoid shortages or surpluses of materials.

Claims

1. Intelligent warehousing industrial Internet of Things system, characterized by: include: Perception module: It includes at least one RFID reader, sensor, and camera, which are used together to locate, identify, and monitor goods in the warehouse in real time, and collect warehouse environmental data; Information processing module: includes a central processing unit, which is used to receive data transmitted from the perception module, pre-process the received data, filter out noise data, and analyze cargo information and warehouse environment; Automated operation module: includes at least one automated storage and retrieval device (which can be a stacker or other storage equipment) and a transport robot, which is used to perform cargo storage and retrieval operations according to the instructions issued by the central processing unit of the information processing module; Control and decision-making module: This includes an artificial intelligence algorithm module that automatically generates optimal cargo access strategies, scheduling strategies, and inventory management strategies based on inventory information, environmental data, and business needs; Data storage module: used to store warehouse data, equipment status data and operation records; User interface module: provides an interactive interface between users and the system, used to display warehouse status, inventory warnings and abnormal situations, and allows users to operate and manage.

2. The intelligent warehousing industrial Internet of Things system according to claim 1, characterized in that: The sensor in the perception module is at least one of a temperature and humidity sensor, a gas sensor, and a vibration sensor, which can monitor the warehouse environment in real time and detect abnormal situations. Multiple sensors can be installed in different locations according to the size of the storage space.

3. The intelligent warehousing industrial Internet of Things system according to claim 1, characterized in that: The automated operation module includes a stacker, an automated guided vehicle (AGV) and a robotic arm, which are used to perform automated storage, retrieval and transportation operations of goods.

4. The intelligent warehousing industrial Internet of Things system according to claim 1, characterized in that: The control decision module includes a deep learning-based prediction model for predicting future inventory demand based on historical data and intelligently optimizing warehousing processes.

5. The intelligent warehousing industrial Internet of Things system according to claim 1, characterized in that: The information processing module also includes a data fusion algorithm for fusing data from different sensors and improving the accuracy and reliability of the data.

6. A control method for an intelligent warehousing industrial Internet of Things system, characterized in that: The following steps are involved: S1, obtain real-time information about goods in the warehouse and real-time data about the warehouse environment through the perception module; S2. The raw data collected by the perception module is transmitted to the information processing module, and the data is preprocessed to obtain usable data; S3. Based on the data processed by the information processing module, the artificial intelligence algorithm in the control decision module is used to generate warehouse operation strategies, including cargo storage and retrieval, inventory adjustment and equipment scheduling strategies; S4. Cargo storage, retrieval and transportation operations are performed through the automated operation module; S5. Feedback the operation results to the information processing module, and display relevant status information through the user interface module to perform abnormal early warning and alarm processing.

7. The control method of the intelligent warehousing industrial Internet of Things system according to claim 6, characterized in that: The artificial intelligence algorithm in step S3 further predicts inventory demand based on the deep learning model and dynamically adjusts the warehousing strategy according to the prediction results.

8. The control method of the intelligent warehousing industrial Internet of Things system according to claim 6, characterized in that: The automated operation module in step S4 further includes a machine learning module for optimizing warehousing operation strategies based on historical operation data and improving operation efficiency.

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