Warehouse management method based on industrial Internet of Things

Abstract

The invention discloses a warehouse management method based on the industrial Internet of Things, and the method comprises the following steps: automatically collecting image data of goods through Internet of Things equipment, taking the image data as goods information, and storing the goods information in a central database for recognition; image data and temperature and humidity data of a warehouse shelf are obtained through Internet of Things equipment to serve as shelf information, and the shelf information is stored in a central database to be recognized; matching is carried out according to the goods information and the goods shelf information, the optimal goods shelf is selected to store goods on the basis of goods types, sizes, storage requirements and storage condition factors, and a unique classification number is distributed to each piece of goods; the goods are stored to the corresponding goods shelf positions according to the distributed classification numbers and the matching results; through automatic and intelligent warehouse management, the operation efficiency of the warehouse and the accuracy of inventory management are improved, and complex storage conditions and dynamically changing inventory requirements can be dealt with, so that the overall efficiency and precision of warehouse management are effectively improved.

Description

Technical Field

[0001] The invention relates to a warehouse management method based on industrial Internet of Things. Background Art

[0002] With the rapid development of industries such as e-commerce and intelligent manufacturing, warehouse management plays an increasingly important role in the modern supply chain. In order to meet the growing market demand, the traditional warehouse management model faces many challenges and urgently needs to improve management efficiency, reduce operating costs, and ensure the accuracy and safety of the logistics process through technological innovation. At present, the traditional warehouse management method has obvious defects in the following aspects, which limits the efficiency and scalability of the warehouse system.

[0003] Traditional warehouse management mostly relies on manual operations to collect information about goods and shelves, which has problems such as inaccurate data and untimely updates. Especially in large-scale warehouses, manually collected data often does not match the actual situation due to operational errors, omissions or delays. Operations such as goods warehousing, outbound delivery, and inventory counting require manual recording, which not only increases the complexity of operations, but also easily leads to errors in inventory data, affecting the efficiency of the entire warehousing system.

[0004] In traditional warehouse management, the storage of goods is usually decided manually by warehouse managers based on experience and rules, which may ignore the actual storage requirements of goods (such as temperature and humidity, load-bearing restrictions, etc.) and the space utilization efficiency of shelves. The selection of storage locations for goods is not precise enough, resulting in the failure to optimize the use of warehouse space. Due to the lack of systematic and intelligent decision-making support, warehouse management often has mismatched storage conditions for goods, resulting in reduced storage safety and even waste of shelf space.

[0005] As inventory changes, the storage location, quantity, and specifications of goods will be dynamically adjusted. The traditional warehouse management model relies on manual inventory counting and adjustment, which has problems such as slow response and untimely adjustment. In the case of a large variety of goods in the warehouse and complex storage requirements, the manual intervention management method seems to be powerless and cannot guarantee the reasonable allocation of inventory and the optimal optimization of the location of goods. Especially when dealing with large-scale warehousing and frequent adjustment needs, the limitations of traditional methods are particularly prominent.

[0006] In traditional warehouse management, manual operations and manual data entry occupy a lot of manpower and material resources. Whether it is the registration of goods entering and leaving the warehouse, location marking, or inventory and inquiry, they all rely on manual labor and judgment, which is prone to errors or delays. The efficiency of warehouse management is often restricted by manual capacity and work intensity, and it is impossible to obtain and process a large amount of dynamic information in real time. This inefficient management method makes the warehousing system respond slowly and cannot quickly adapt to changes in market demand, affecting the operational efficiency of the entire supply chain.

[0007] The large amount of data (such as inventory quantity, cargo type, temperature and humidity, etc.) and dynamically changing conditions that need to be processed by modern warehouse management make traditional management methods incapable of coping. Traditional warehouse management systems often rely on manual judgment and simple rules to make decisions, lacking intelligent data analysis and automated management methods. This not only fails to fully utilize the advantages of modern information technology, but may also lead to a long response time for warehouse management and an inability to adjust warehouse layout and cargo storage strategies in a timely manner. Summary of the invention

[0008] In view of the deficiencies in the prior art, the present invention provides a warehouse management method based on the industrial Internet of Things. Through the industrial Internet of Things technology, the problems of inaccurate data collection, unoptimized matching of goods and shelves, and difficulty in dynamic adjustment in traditional warehouse management are solved. Through automated and intelligent warehouse management, the warehouse operation efficiency and the accuracy of inventory management are improved, and it can cope with complex storage conditions and dynamically changing inventory needs, thereby effectively improving the overall efficiency and accuracy of warehouse management.

[0009] The technical solution adopted by the present invention to solve its technical problem is:

[0010] A warehouse management method based on industrial Internet of Things includes the following steps:

[0011] Automatically collect image data of goods as cargo information through IoT devices and store it in a central database for identification;

[0012] The image data and temperature and humidity data of warehouse shelves are obtained through IoT devices as shelf information, which is stored in the central database for identification;

[0013] Match the cargo information with the shelf information, select the best shelf to store the cargo based on cargo type, size, storage requirements and storage conditions, and assign a unique classification number to each cargo;

[0014] Store the goods in the corresponding shelf positions according to the assigned classification numbers and matching results;

[0015] The information of goods and shelves in the warehouse is scanned, monitored and updated regularly through IoT devices, and the position of goods is adjusted regularly according to the scanning results.

[0016] Preferably, the Internet of Things device includes a camera, a pressure sensor, a load sensor and a temperature and humidity sensor.

[0017] As a preferred method, the method of automatically collecting image data of goods as goods information through the Internet of Things device is:

[0018] Use a camera to take images of the goods and input them into CNN, which uses image recognition technology to classify the goods and detect different types of goods;

[0019] CNN extracts high-level features of goods, including shape, volume, weight, and packaging method;

[0020] CNN extracts the specific shape of the goods in the image through the convolution layer and obtains its length, width and height;

[0021] The above information is regarded as cargo information.

[0022] As a preferred method, the method of obtaining the image data and temperature and humidity data of the warehouse shelves as the shelf information through the Internet of Things device is:

[0023] By obtaining the layout image of a local area of ​​the warehouse and inputting it into CNN, CNN is used to extract the location of the shelves in the warehouse, the distribution of the space, and the width of the aisles between the shelves;

[0024] Install pressure sensors on each shelf to detect whether there are goods on the shelf;

[0025] Install load sensors on the supporting points of the shelves to monitor the load-bearing capacity of the shelves in real time;

[0026] Install temperature and humidity sensors on each shelf in the warehouse to monitor the temperature and humidity of the surrounding environment in real time;

[0027] The above information is used as shelf information.

[0028] As a preferred method, the method of matching the cargo information with the shelf information, selecting the best shelf to store the cargo based on the cargo type, size, storage requirements and storage conditions, and assigning a unique classification number to each cargo is as follows:

[0029] According to the type of goods, the goods are divided into fragile goods, temperature-controlled goods, heavy goods and regular goods, and their storage requirements are set in combination with the shape, volume, weight, packaging method and length, width and height dimensions of the goods;

[0030] Set the storage conditions through shelf information and confirm the shelf availability;

[0031] According to the storage requirements of the goods and the storage conditions of the shelves, select the shelves that meet the storage requirements;

[0032] Verify whether the selected shelf is free, and select the best shelf to store the goods based on the shelf's space utilization efficiency, load-bearing conditions, and environmental conditions;

[0033] Each cargo is assigned a unique classification number, which consists of cargo type code, size code, storage condition code and storage location.

[0034] As a preferred method, the method of verifying whether the selected shelf is free and selecting the best shelf to store the goods according to the shelf's space utilization efficiency, load-bearing conditions, and environmental conditions is:

[0035] According to the size of the goods, choose the area that best matches the shelf space

[0036] Use the optimized layout algorithm to automatically calculate the storage space utilization of each shelf area;

[0037] Each shelf is assigned a comprehensive score based on the idle state, space utilization efficiency, load-bearing capacity and environmental conditions. The scoring formula is as follows:

[0038] Comprehensive score = w1×idle state score + w2×space utilization score + w3×load-bearing score + w4×environmental condition score

[0039] Among them, w1, w2, w3, and w4 are the weights of idle state, space utilization, load-bearing capacity, and environmental conditions, respectively.

[0040] Another technical problem to be solved by the present invention is to provide a warehouse management system based on industrial Internet of Things, including:

[0041] The cargo information collection module automatically collects cargo image data through IoT devices and stores the collected cargo image data in a central database;

[0042] The shelf information collection module obtains the image data and temperature and humidity data of the warehouse shelves through the Internet of Things devices, and stores the collected shelf image data and temperature and humidity data in the central database;

[0043] A matching module, which automatically selects the best shelf to store the goods based on the stored goods information and shelf information, based on the goods type, size, storage requirements and storage conditions, and assigns a unique classification number to each piece of goods;

[0044] The goods storage module stores the goods in the corresponding shelf positions according to the assigned classification number and matching results, ensuring the best match between the goods and the shelves;

[0045] The monitoring and updating module regularly scans and monitors the location and status information of goods and shelves in the warehouse through IoT devices, and regularly adjusts the storage location of goods based on the scanning results to ensure the real-time accuracy of inventory information and goods location.

[0046] Another technical problem to be solved by the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a warehouse management method based on the industrial Internet of Things as described above is implemented.

[0047] Another technical problem to be solved by the present invention is to provide a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, it implements any of the warehouse management methods based on the industrial Internet of Things as described above.

[0048] The beneficial effects of the present invention are:

[0049] The IoT devices automatically collect cargo image data and warehouse shelf image, temperature and humidity data, realize real-time collection and update of cargo and shelf information, reduce manual intervention, and improve data accuracy and timeliness; intelligent matching is performed based on cargo information (such as type, size, storage requirements) and shelf information (such as shelf load capacity, storage conditions, etc.), and the optimal shelf is selected to store cargo. This intelligent matching method based on multi-factor analysis optimizes the use of storage space and improves cargo storage efficiency and safety.

[0050] The location of goods and shelves is scanned and monitored regularly through IoT devices, and the storage location of goods is automatically adjusted according to the scanning results. The system can dynamically adjust according to changes in inventory, storage requirements of goods and availability of shelves to ensure that the goods are always in the best storage location; through industrial IoT technology, fully automated information collection, storage and management are achieved, greatly improving the efficiency and accuracy of warehouse management. The system can update inventory data in real time, automatically detect the storage status of goods, reduce manual intervention, and avoid problems such as inventory differences, loss or damage; by combining image recognition, temperature and humidity monitoring, automatic matching and real-time adjustment technologies, the entire warehouse management system is more intelligent, able to automatically make decisions and optimizations, improve management efficiency, and meet the complex needs of modern warehousing environments. Specific implementation methods

[0052] The principles and features of the present invention are described below. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. Example

[0054] A warehouse management method based on industrial Internet of Things includes the following steps:

[0055] Automatically collect image data of goods as cargo information through IoT devices and store it in a central database for identification;

[0056] The image data and temperature and humidity data of warehouse shelves are obtained through IoT devices as shelf information, which is stored in the central database for identification;

[0057] Match the cargo information with the shelf information, select the best shelf to store the cargo based on cargo type, size, storage requirements and storage conditions, and assign a unique classification number to each cargo;

[0058] Store the goods in the corresponding shelf positions according to the assigned classification numbers and matching results;

[0059] The information of goods and shelves in the warehouse is scanned, monitored and updated regularly through IoT devices, and the position of goods is adjusted regularly according to the scanning results.

[0060] By automatically collecting the image data of goods and the status data of warehouse shelves through IoT devices, detailed information of goods and shelves (such as size, shape, temperature and humidity, etc.) can be obtained in real time. This automated data collection not only reduces errors in manual operations and manual records, but also ensures the real-time and accuracy of data, thereby improving the overall warehouse management efficiency; based on the matching of goods information and shelf information, it can intelligently select the most suitable shelf to store goods according to the type, size, storage requirements (such as temperature and humidity, etc.) of the goods and the storage conditions of the warehouse. This intelligent matching mechanism helps maximize the use of warehouse space, reduce waste and confusion during the storage of goods, and ensure that goods are stored in the most appropriate conditions to ensure the safety and quality of goods.

[0061] Assigning a unique classification number to each piece of goods helps to manage the goods in a refined and standardized manner. With the classification number, warehouse managers can quickly locate the location of the goods, reduce search time, and improve the efficiency of goods storage and retrieval. This digital and systematic management method can greatly reduce the interference of human factors and reduce the error rate; IoT devices regularly scan and monitor the goods and shelf information in the warehouse to ensure that the data in the system is always consistent with the actual situation. This regularly updated mechanism can promptly detect changes in the storage location of goods or situations that do not meet storage requirements, and support dynamic adjustment of the location and storage plan of goods. This helps to cope with the daily flow and changes of goods in warehouse management and improve the flexibility of warehouse management.

[0062] Through automated information collection and real-time monitoring, accurate inventory management can be achieved, human errors can be avoided, and the error probability and management costs during inventory counting can be reduced. At the same time, dynamic adjustment of goods location and storage solutions can also optimize inventory layout, reduce inventory backlogs and excessive storage, thereby reducing inventory costs and improving inventory turnover; due to real-time information collection and intelligent storage solutions, warehouses can quickly respond to market demand and customer order changes. The storage location of goods can be flexibly adjusted according to demand, improving the warehouse's response speed and overall efficiency, and further improving customer satisfaction. Especially during order processing and outbound delivery, the system can quickly retrieve and determine the location of goods, shortening the order fulfillment cycle.

[0063] The IoT device includes a camera, a pressure sensor, a load sensor, and a temperature and humidity sensor. The method for automatically collecting image data of goods as cargo information through the IoT device is as follows:

[0064] Use a camera to take images of the goods and input them into CNN, which uses image recognition technology to classify the goods and detect different types of goods;

[0065] CNN extracts high-level features of goods, including shape, volume, weight, and packaging method;

[0066] CNN extracts the specific shape of the goods in the image through the convolution layer and obtains its length, width and height;

[0067] The above information is regarded as cargo information.

[0068] By using a camera to capture images of goods and inputting them into CNN for image recognition, different goods can be automatically classified. Compared with traditional manual recognition methods, this deep learning-based technology can classify a large number of goods more quickly and accurately, reduce manual errors, and improve classification efficiency. CNN can extract high-level features of goods (such as shape, volume, weight, etc.) from images, further enhance recognition accuracy, and adapt to the processing needs of different types of goods.

[0069] CNN extracts the specific shape and size information (such as length, width, and height) of the goods through the convolution layer, and can accurately obtain the spatial attributes of the goods. This is crucial for warehouse management and can ensure the accurate storage of goods and the optimization of space utilization. Compared with relying on manual measurement or traditional sensors, this automated image recognition method can not only improve the accuracy of dimensional measurement, but also reduce errors or omissions caused by manual operation.

[0070] By integrating a variety of IoT sensors (such as cameras, pressure sensors, load sensors, and temperature and humidity sensors), the system can simultaneously obtain multi-dimensional information such as image data, weight, pressure, and storage environment of the goods, and conduct a comprehensive analysis. This makes the management of goods more comprehensive and intelligent, and can automatically optimize the processing according to the physical characteristics of the goods, storage requirements, and environmental conditions. For example, some goods may need to be stored under specific temperature and humidity conditions. The system can adjust the storage location or storage environment of the goods based on this data to improve management efficiency and ensure the safety of the goods.

[0071] The method of obtaining image data and temperature and humidity data of warehouse shelves as shelf information through IoT devices is as follows:

[0072] By obtaining the layout image of a local area of ​​the warehouse and inputting it into CNN, CNN is used to extract the location of the shelves in the warehouse, the distribution of the space, and the width of the aisles between the shelves;

[0073] Install pressure sensors on each shelf to detect whether there are goods on the shelf;

[0074] Install load sensors on the supporting points of the shelves to monitor the load-bearing capacity of the shelves in real time;

[0075] Install temperature and humidity sensors on each shelf in the warehouse to monitor the temperature and humidity of the surrounding environment in real time;

[0076] The above information is used as shelf information.

[0077] By acquiring the layout image of the warehouse area and using CNN to extract the location, spatial distribution and aisle width information between shelves, we can fully understand the spatial structure of the warehouse. This provides important data support for warehouse optimization. With this information, we can accurately plan the layout of shelves and optimize the aisle width between shelves, thereby improving the access efficiency of the warehouse and the utilization of storage space. Automated recognition and analysis greatly reduces the complexity of manual measurement and planning, while improving the flexibility and response speed of space management.

[0078] By installing pressure sensors, load sensors, and temperature and humidity sensors, the load condition of each shelf, inventory status, and changes in the surrounding environment can be monitored in real time. For example, the pressure sensor can automatically detect whether there are goods on the shelf, and the load sensor can monitor the load capacity of the shelf in real time to avoid damage to the shelf or goods due to overloading. The temperature and humidity sensor can monitor the temperature and humidity changes in the warehouse environment to ensure that the goods are stored in ideal conditions, thereby reducing the risk of damage to the goods due to an unsuitable environment.

[0079] Combining image data with various sensor data can realize the automation and intelligence of warehouse management. For example, the system can automatically generate shelf load status reports, space utilization efficiency reports, and environmental monitoring data through data analysis, helping warehouse managers to understand warehouse operations in real time and make timely decisions. Data-driven management not only reduces the frequency of manual inspections, but also accurately predicts and optimizes processes such as cargo storage, handling, and inventory replenishment, thereby improving the overall warehouse operation efficiency and safety.

[0080] The method of matching the cargo information with the shelf information, selecting the best shelf to store the cargo based on the cargo type, size, storage requirements and storage conditions, and assigning a unique classification number to each cargo is as follows:

[0081] According to the type of goods, the goods are divided into fragile goods, temperature-controlled goods, heavy goods and regular goods, and their storage requirements are set in combination with the shape, volume, weight, packaging method and length, width and height dimensions of the goods;

[0082] Set the storage conditions through shelf information and confirm the shelf availability;

[0083] According to the storage requirements of the goods and the storage conditions of the shelves, select the shelves that meet the storage requirements;

[0084] Verify whether the selected shelf is free, and select the best shelf to store the goods based on the shelf's space utilization efficiency, load-bearing conditions, and environmental conditions;

[0085] Each cargo is assigned a unique classification number, which consists of cargo type code, size code, storage condition code and storage location.

[0086] By accurately screening and allocating shelf space according to the specific size, weight, shape, packaging method and other requirements of the goods, the utilization rate of warehouse space can be maximized. Shelf selection takes into account the storage needs of the goods and the availability of the shelves, ensuring that each item is stored on the most suitable shelf. This method avoids the waste of shelf space and optimizes the density of goods storage, thereby increasing the overall storage capacity of the warehouse.

[0087] Different types of goods (such as fragile goods, temperature-controlled goods, heavy goods, etc.) have different storage requirements. By matching the storage requirements of the goods with the storage conditions of the shelves, it is possible to effectively avoid damage or deterioration of the goods due to inappropriate storage conditions. For example, temperature-controlled goods can be stored on shelves with temperature control functions, and fragile goods will be placed in appropriate places to prevent vibration and pressure. This can ensure the safety of the goods during storage and meet their environmental adaptability requirements, avoiding losses caused by improper storage.

[0088] Assigning a unique classification number to each piece of cargo can systematically record and track the cargo's location, storage conditions, and other key information. This classification number not only helps warehouse managers find each cargo quickly and accurately, but also improves cargo traceability and management efficiency through the classification system. The setting of classification numbers also helps automate inventory management, reduces manual errors through the coding system, and facilitates subsequent inventory replenishment and inbound and outbound management. Systematic management of classification numbers improves the accuracy and efficiency of the entire warehouse management.

[0089] The method to verify whether the selected shelf is free and select the best shelf to store the goods according to the shelf's space utilization efficiency, load-bearing conditions, and environmental conditions is as follows:

[0090] According to the size of the goods, choose the area that best matches the shelf space

[0091] Use the optimized layout algorithm to automatically calculate the storage space utilization of each shelf area;

[0092] Each shelf is assigned a comprehensive score based on the idle state, space utilization efficiency, load-bearing capacity and environmental conditions. The scoring formula is as follows:

[0093] Comprehensive score = w1×idle state score + w2×space utilization score + w3×load-bearing score + w4×environmental condition score

[0094] Among them, w1, w2, w3, and w4 are the weights of idle state, space utilization, load-bearing capacity, and environmental conditions, respectively.

[0095] By using the optimization layout algorithm to automatically calculate the storage space utilization of each shelf area, it can ensure that the shelf space is fully utilized. The area that best matches the goods is automatically selected according to their size and storage requirements, avoiding space waste and unnecessary idle areas. The space utilization of each shelf is accurately calculated, which can effectively maximize the available space and improve the overall storage capacity of the warehouse, especially when space is limited.

[0096] When selecting the optimal shelf, in addition to considering space utilization, the load-bearing capacity of the shelf will also be evaluated. By adding the load-bearing score to the comprehensive score, it can be ensured that each item is stored on a shelf that can bear its weight, avoiding damage to the shelf or loss of goods due to overloading. In addition, environmental conditions (such as temperature, humidity, ventilation and other factors) will also be taken into consideration to ensure that the goods are stored in a suitable environment, especially for temperature-controlled goods, fragile goods and other goods with special storage requirements, which can greatly improve the safety and stability of goods storage.

[0097] By assigning a comprehensive score to each shelf, managers can quickly and accurately evaluate the pros and cons of the shelves through a numerical scoring system, and then make reasonable choices. This method avoids the bias caused by manual judgment and improves the scientificity and transparency of decision-making. At the same time, the weight coefficients (w1, w2, w3, w4) can be flexibly adjusted according to actual conditions, so that this plan can be optimized according to the actual needs of different warehouses. For example, if the storage of goods with higher load-bearing requirements is more important, the weight of the load-bearing score can be increased. This flexibility further enhances the adaptability and efficiency of the system.

[0098] A warehouse management system based on industrial Internet of Things, including:

[0099] The cargo information collection module automatically collects cargo image data through IoT devices and stores the collected cargo image data in a central database;

[0100] The shelf information collection module obtains the image data and temperature and humidity data of the warehouse shelves through the Internet of Things devices, and stores the collected shelf image data and temperature and humidity data in the central database;

[0101] A matching module, which automatically selects the best shelf to store the goods based on the stored goods information and shelf information, based on the goods type, size, storage requirements and storage conditions, and assigns a unique classification number to each piece of goods;

[0102] The goods storage module stores the goods in the corresponding shelf positions according to the assigned classification number and matching results, ensuring the best match between the goods and the shelves;

[0103] The monitoring and updating module regularly scans and monitors the location and status information of goods and shelves in the warehouse through IoT devices, and regularly adjusts the storage location of goods based on the scanning results to ensure the real-time accuracy of inventory information and goods location.

[0104] The system automatically collects image data, temperature and humidity information of goods and shelves through IoT devices, realizing the automation of information collection. This automation reduces manual operations and human errors, and improves the accuracy and real-time nature of information. The matching module automatically selects the most suitable shelf according to the type, size and storage requirements of the goods, eliminating errors and inconsistencies in human judgment, and ensuring that each piece of goods can be stored in the best way.

[0105] Through the intelligent matching module, the system can automatically allocate shelves according to the specific conditions of goods and shelves (such as space, load-bearing capacity, temperature and humidity, etc.), thereby ensuring that the storage space is optimized and utilized to the greatest extent. This not only reduces the space waste during the storage of goods, but also dynamically adjusts the storage location according to the demand for goods, maximizing the space utilization efficiency of the warehouse. At the same time, the monitoring and update module ensures the real-time and accurate update of the location and inventory information of goods, effectively avoiding problems such as inventory backlog or shortage.

[0106] The system can regularly scan and monitor the location and status information of goods and shelves through IoT devices, and make dynamic adjustments based on these data. In this way, warehouse managers can grasp the exact location and status of each piece of goods in the warehouse in real time, avoiding the problems of manual inventory and information lag. In addition, the system can also provide real-time inventory data and analysis reports to help decision makers adjust inventory strategies in a timely manner and improve the flexibility and responsiveness of supply chain management.

[0107] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a warehouse management method based on the industrial Internet of Things as described above is implemented.

[0108] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the warehouse management method based on the industrial Internet of Things as described above is implemented.

[0109] 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 used in the embodiments provided in this application can 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 many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), 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 (RDRAM).

[0110] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0111] The above embodiments of the present invention are not intended to limit the protection scope of the present invention, and the implementation modes of the present invention are not limited thereto. All other modifications, replacements or changes made to the above structures of the present invention based on the above contents of the present invention, in accordance with common technical knowledge and customary means in the art, without departing from the above basic technical ideas of the present invention, should fall within the protection scope of the present invention.

Claims

1. A warehouse management method based on industrial Internet of Things, characterized in that: The following steps are involved: Automatically collect image data of goods as cargo information through IoT devices and store it in a central database for identification; The image data and temperature and humidity data of warehouse shelves are obtained through IoT devices as shelf information, which is stored in the central database for identification; Match the cargo information with the shelf information, select the best shelf to store the cargo based on cargo type, size, storage requirements and storage conditions, and assign a unique classification number to each cargo; Store the goods in the corresponding shelf positions according to the assigned classification numbers and matching results; The information of goods and shelves in the warehouse is scanned, monitored and updated regularly through IoT devices, and the position of goods is adjusted regularly according to the scanning results.

2. The warehouse management method based on industrial Internet of Things according to claim 1 is characterized in that: The Internet of Things device includes a camera, a pressure sensor, a load sensor, and a temperature and humidity sensor.

3. The warehouse management method based on industrial Internet of Things according to claim 2 is characterized in that: The method of automatically collecting image data of goods as goods information through IoT devices is: Use a camera to take images of the goods and input them into CNN, which uses image recognition technology to classify the goods and detect different types of goods; CNN extracts high-level features of goods, including shape, volume, weight, and packaging method; CNN extracts the specific shape of the goods in the image through the convolution layer and obtains its length, width and height; The above information is regarded as cargo information.

4. The warehouse management method based on industrial Internet of Things according to claim 3 is characterized in that: The method of obtaining image data and temperature and humidity data of warehouse shelves as shelf information through IoT devices is as follows: By obtaining the layout image of a local area of ​​the warehouse and inputting it into CNN, CNN is used to extract the location of the shelves in the warehouse, the distribution of the space, and the width of the aisles between the shelves; Install pressure sensors on each shelf to detect whether there are goods on the shelf; Install load sensors on the supporting points of the shelves to monitor the load-bearing capacity of the shelves in real time; Install temperature and humidity sensors on each shelf in the warehouse to monitor the temperature and humidity of the surrounding environment in real time; The above information is used as shelf information.

5. The warehouse management method based on industrial Internet of Things according to claim 4 is characterized in that: The method of matching the cargo information with the shelf information, selecting the best shelf to store the cargo based on the cargo type, size, storage requirements and storage conditions, and assigning a unique classification number to each cargo is as follows: According to the type of goods, the goods are divided into fragile goods, temperature-controlled goods, heavy goods and regular goods, and their storage requirements are set in combination with the shape, volume, weight, packaging method and length, width and height dimensions of the goods; Set the storage conditions through shelf information and confirm the shelf availability; According to the storage requirements of the goods and the storage conditions of the shelves, select the shelves that meet the storage requirements; Verify whether the selected shelf is free, and select the best shelf to store the goods based on the shelf's space utilization efficiency, load-bearing conditions, and environmental conditions; Each cargo is assigned a unique classification number, which consists of cargo type code, size code, storage condition code and storage location.

6. The warehouse management method based on industrial Internet of Things according to claim 5 is characterized in that: The method to verify whether the selected shelf is free and select the best shelf to store the goods according to the shelf's space utilization efficiency, load-bearing conditions, and environmental conditions is as follows: According to the size of the goods, choose the area that best matches the shelf space Use the optimized layout algorithm to automatically calculate the storage space utilization of each shelf area; Each shelf is assigned a comprehensive score based on the idle state, space utilization efficiency, load-bearing capacity and environmental conditions. The scoring formula is as follows: Comprehensive score = w1×idle state score + w2×space utilization score + w3×load-bearing score + w4×environmental condition score Among them, w1, w2, w3, and w4 are the weights of idle state, space utilization, load-bearing capacity, and environmental conditions, respectively.

7. A warehouse management system based on industrial Internet of Things, characterized in that: Included are: The cargo information collection module automatically collects cargo image data through IoT devices and stores the collected cargo image data in a central database; The shelf information collection module obtains the image data and temperature and humidity data of the warehouse shelves through the Internet of Things devices, and stores the collected shelf image data and temperature and humidity data in the central database; A matching module, which automatically selects the best shelf to store the goods based on the stored goods information and shelf information, based on the goods type, size, storage requirements and storage conditions, and assigns a unique classification number to each piece of goods; The goods storage module stores the goods in the corresponding shelf positions according to the assigned classification number and matching results, ensuring the best match between the goods and the shelves; The monitoring and updating module regularly scans and monitors the location and status information of goods and shelves in the warehouse through IoT devices, and regularly adjusts the storage location of goods based on the scanning results to ensure the real-time accuracy of inventory information and goods location.

8. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the warehouse management method based on the industrial Internet of Things as claimed in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the warehouse management method based on industrial Internet of Things as described in any one of claims 1 to 6 is implemented.