Logistics life cycle shortening method and system in Internet of Things environment

Abstract

The invention discloses a logistics life cycle shortening method and system in an Internet of Things environment, and relates to the technical field of Internet of Things, and the method comprises the steps: carrying out the identification of a cargo through an RFID tag, collecting the comprehensive data of the cargo, and carrying out the preprocessing; state information of goods is monitored in real time, a storage environment is adjusted, a storage position is allocated based on comprehensive data of the goods, an emergency degree value is allocated for each piece of goods, shelf layout and the storage position are adjusted, and a cloud data center receives data of each warehouse, carries out global inventory management and path planning and generates inventory layout data and path planning data. The multi-source sensor is used for monitoring the cargo state in real time, dynamically adjusting the storage environment and optimizing the warehouse space utilization rate, the shelf layout is adjusted in combination with emergency degree value distribution and an intelligent algorithm, it is ensured that high-priority cargos are quickly delivered out of a warehouse, the warehousing efficiency and the overall operation intelligence level are remarkably improved, and the labor intensity of workers is lowered. And a scientific basis and a dynamic optimization capability are provided for efficient shortening of the logistics life cycle.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a method and system for shortening the logistics life cycle in an Internet of Things environment. Background Art

[0002] The development of IoT technology is transforming the logistics industry. Traditional logistics relies on manual and centralized processing, which is inefficient and prone to errors. RFID, sensors, cloud computing, and edge computing are driving automation and intelligence. RFID tags enable rapid identification and tracking, while sensor networks monitor cargo status and ensure safety. Cloud computing analyzes data, while edge computing reduces latency and improves response speed.

[0003] Despite advances in logistics technology, shortcomings remain. Current logistics systems lack comprehensive and dynamic monitoring of cargo status, focusing solely on single sensor data. Inventory management and route planning rely on static data, lacking real-time and global optimization, leading to low warehouse utilization and inefficiency. Route planning relies on historical data and cannot be dynamically adjusted, impacting overall efficiency. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for shortening the logistics life cycle in an Internet of Things environment to solve the problems of incomplete cargo status monitoring and lack of real-time and global optimization capabilities in inventory management and route planning.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for shortening the logistics life cycle in an Internet of Things environment, which comprises:

[0008] Use RFID tags to identify goods, collect comprehensive data of goods, and perform pre-processing;

[0009] Monitor the status of goods in real time and adjust the storage environment. Allocate storage locations based on comprehensive goods data, assign urgency values to each item, and adjust shelf layouts and storage locations. The cloud data center receives data from each warehouse, performs global inventory management and route planning, and generates inventory layout data and route planning data.

[0010] Combine real-time traffic information and historical traffic data to generate transportation routes and delivery routes for transport vehicles, monitor vehicle status, handle abnormal situations, and adjust transportation routes and delivery routes in real time based on path planning data;

[0011] After the goods arrive at the destination, the goods information is confirmed through the RFID reader, and the delivery data is recorded after the signing operation is completed;

[0012] Integrate global data for global optimization and global management.

[0013] As a preferred solution of the method for shortening the logistics life cycle under the Internet of Things environment of the present invention, wherein: the RFID tags are used to identify the goods, collect the comprehensive data of the goods, and perform preprocessing, the specific steps are as follows:

[0014] Attach RFID tags to the goods and write basic information of the goods, storage requirements, order information and transportation requirements;

[0015] Read basic information, storage requirements, order information, and transportation requirements from RFID tags, and collect cargo status information through temperature sensors, humidity sensors, and vibration sensors;

[0016] The cargo status information refers to the temperature, humidity and vibration intensity of the cargo; the basic information, storage requirements, order information, transportation requirements and status information of the cargo are integrated into comprehensive cargo data, and the comprehensive cargo data is cleaned and standardized.

[0017] As a preferred solution of the method for shortening the logistics life cycle under the Internet of Things environment described in the present invention, the real-time monitoring of the status information of the goods and the adjustment of the storage environment are carried out, the storage location is allocated based on the comprehensive data of the goods, the urgency value is assigned to each goods, the shelf layout and storage location are adjusted, and the cloud data center receives the data of each warehouse, performs global inventory management and route planning, and generates inventory layout data and route planning data. The specific steps are as follows:

[0018] Set storage environment range based on the storage requirements of goods and adjust the storage environment;

[0019] Use a greedy algorithm to assign a storage location to each item;

[0020] Assign an urgency value to each item based on storage requirements and order information, use intelligent algorithms to optimize shelf layout, and adjust storage locations based on urgency values;

[0021] Upload inventory data, order data, and status data to the cloud data center, perform global inventory management based on inventory data and order data, plan routes based on the traffic volume, traffic capacity, node processing capacity, and node load rate of each route, record the shelf location, storage location, storage requirements, inventory quantity, inventory turnover rate, and safety stock level of each warehouse as inventory layout data, and record the starting point, end point, waypoints, distance, and estimated travel time of the route as route planning data.

[0022] As a preferred solution of the method for shortening the logistics life cycle under the Internet of Things environment of the present invention, wherein: the real-time traffic information and historical traffic data are combined to generate transportation routes and delivery routes for transportation vehicles, the specific steps are as follows:

[0023] Obtain real-time traffic information from the traffic data platform, extract historical traffic data from the historical database, clean the real-time traffic information and historical traffic data, and integrate them into traffic data;

[0024] Use the A* algorithm to generate transportation routes and delivery routes based on traffic data.

[0025] As a preferred solution of the method for shortening the logistics life cycle under the Internet of Things environment of the present invention, wherein: the monitoring of vehicle status, handling of abnormal situations, and real-time adjustment of transportation routes and delivery routes based on path planning data are carried out in the following specific steps:

[0026] The vehicle status refers to the vehicle's location information, remaining fuel, fuel consumption rate, engine temperature, engine speed and fault code;

[0027] Collect vehicle status data in real time through on-board sensors and upload it to the cloud data center;

[0028] The abnormal conditions refer to traffic abnormalities, vehicle abnormalities and cargo abnormalities;

[0029] Send abnormal situations to drivers and dispatchers for processing;

[0030] Obtain path planning data from the cloud data center, combine vehicle status, abnormal conditions, and cargo status information, use the A* algorithm to estimate the distance from the current node to the destination, and calculate the actual cost and total cost from the starting point to the current node;

[0031] The total cost refers to the sum of the distance from the current node to the end point and the actual cost from the starting point to the current node;

[0032] Define the starting point as the vehicle's current location and the end point as the customer's address, initialize the open list and closed list, add the starting point to the open list, select the target point from the open list for path planning, and trace back the path to generate the optimal transportation route and the optimal delivery route.

[0033] As a preferred solution of the method for shortening the logistics life cycle under the Internet of Things environment of the present invention, wherein: after the goods arrive at the destination, the goods information is confirmed by an RFID reader, and the delivery data is recorded after the signing operation is completed. The specific steps are as follows:

[0034] Use an RFID reader to scan the goods label, read the order information of the goods, verify the read order information of the goods, and confirm the receipt through the mobile device;

[0035] Record cargo information, order information, receipt information, and exceptions as delivery data.

[0036] As a preferred solution of the method for shortening the logistics life cycle under the Internet of Things environment of the present invention, wherein: the integration of global data for global optimization and global management, the specific steps are as follows:

[0037] Integrate inventory layout data, transportation scheduling data, and delivery data into global data;

[0038] The transport scheduling data refers to transport routes, transport times, delivery routes, delivery times, vehicle status, cargo status information and real-time traffic information;

[0039] Use Python to analyze global data, optimize inventory layout based on the analysis results, and use machine learning algorithms to optimize transportation routes, vehicle scheduling, delivery routes, and order processing processes.

[0040] In a second aspect, the present invention provides a logistics life cycle shortening system in an Internet of Things environment, comprising:

[0041] The pre-processing module uses RFID tags to identify goods, collect comprehensive data of goods, and perform pre-processing;

[0042] The warehouse management module monitors the status of goods in real time and adjusts the storage environment. It allocates storage locations based on the comprehensive data of goods, assigns urgency values to each item, and adjusts shelf layout and storage locations. The cloud data center receives data from each warehouse, performs global inventory management and route planning, and generates inventory layout data and route planning data.

[0043] The transportation and distribution module combines real-time traffic information and historical traffic data to generate transportation routes and distribution routes for transport vehicles, monitor vehicle status, handle abnormal situations, and adjust transportation routes and distribution routes in real time based on path planning data;

[0044] The receipt module verifies the cargo information through an RFID reader after the cargo arrives at the destination, and records the delivery data after the receipt operation is completed;

[0045] The optimization and management module integrates global data for global optimization and global management.

[0046] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for shortening the logistics life cycle in an Internet of Things environment as described in the first aspect of the present invention is implemented.

[0047] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for shortening the logistics life cycle in an Internet of Things environment as described in the first aspect of the present invention is implemented.

[0048] The beneficial effects of the present invention are: real-time monitoring of cargo status through multi-source sensors, dynamic adjustment of storage environment and optimization of warehouse space utilization, adjustment of shelf layout in combination with urgency value allocation and intelligent algorithm, ensuring rapid delivery of high-priority cargo, and integration of global data through cloud data center to achieve precise inventory management and route planning, significantly improving warehousing efficiency and overall operational intelligence, providing a scientific basis and dynamic optimization capabilities for the efficient shortening of the logistics life cycle, generating optimal transportation and delivery routes using the A* algorithm through real-time traffic information and historical traffic data, combining real-time monitoring of vehicle status with dynamic adjustment of route planning data, effectively reducing transportation time, cost and abnormal delays, and significantly improving delivery efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 This is a flow chart of the method for shortening the logistics life cycle in the Internet of Things environment in Example 1.

[0051] Figure 2 This is a schematic diagram of the logistics life cycle shortening system in the Internet of Things environment in Example 1. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0054] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0055] Example 1, with reference to Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a method for shortening the logistics life cycle in an Internet of Things environment, comprising the following steps:

[0056] S1: Use RFID tags to identify goods, collect comprehensive data of goods, and perform pre-processing.

[0057] The specific steps are as follows:

[0058] When goods are put into storage, RFID tags are attached and basic information, storage requirements, order information and transportation requirements of the goods are written;

[0059] The basic information of goods refers to the goods ID, name, specifications, weight, volume, production batch, production date and shelf life.

[0060] Storage requirements refer to the optimal storage temperature, humidity, storage period and special storage conditions (such as avoiding light and shock).

[0061] Transportation requirements refer to transportation temperature range, shockproof level and transportation priority.

[0062] Order information refers to the order number, customer information and delivery time requirements.

[0063] Through the RFID reader, tag information can be read at a long distance and in batches, improving data collection efficiency.

[0064] It should also be noted that RFID tags not only contain basic information, but can also record the historical operation records and maintenance information of the goods, ensuring traceability and transparency throughout the process.

[0065] Read basic information, storage requirements, order information, and transportation requirements from RFID tags, and collect cargo status information through temperature sensors, humidity sensors, and vibration sensors;

[0066] The cargo status information refers to the current temperature, humidity and vibration intensity.

[0067] It should also be noted that multi-sensor fusion technology can monitor the status of goods during transportation and storage in real time, ensuring their safety and quality. Sensor data can be uploaded to the cloud in real time via wireless networks for subsequent analysis and decision-making.

[0068] Integrate the basic information, storage requirements, order information, transportation requirements and status information of the goods into comprehensive data of the goods, and perform data cleaning and standardization on the comprehensive data of the goods to ensure that the data formats from different sources are consistent, so as to facilitate unified management and analysis.

[0069] Data cleaning involves removing duplicate data, filling in missing values, and correcting erroneous data. For example, if a temperature sensor's reading is abnormal (e.g., outside a reasonable range), it is marked as abnormal data and removed.

[0070] It should also be noted that Python (Pandas library) can be used for data cleaning and integration.

[0071] S2: Monitor the status of goods in real time and adjust the storage environment. Allocate storage locations based on the comprehensive data of goods, assign urgency values to each item, adjust shelf layout and storage locations. The cloud data center receives data from each warehouse, performs global inventory management and route planning, and generates inventory layout data and route planning data.

[0072] The specific steps are as follows:

[0073] Set storage environment range based on the storage requirements of goods and adjust the storage environment;

[0074] If the temperature is outside the preset range, start the cooling device or heating device to adjust the temperature to the optimal range.

[0075] If the humidity is too high or too low, start a humidifier or dehumidifier to adjust the humidity to the optimal range.

[0076] If ventilation is insufficient, start ventilation equipment to improve air circulation.

[0077] It should also be noted that through intelligent control, the storage environment (such as temperature and humidity control) can be dynamically adjusted according to real-time monitoring data to ensure that the goods are always in the best storage conditions.

[0078] A greedy algorithm is used to assign a storage location to each item based on storage requirements, current inventory, inventory turnover, safety stock level order quantity, order priority, and delivery time.

[0079] Assign an urgency value to each item based on storage requirements and order information, use intelligent algorithms to optimize shelf layout, and adjust storage locations based on urgency values;

[0080] Place frequently accessed goods close to the sorting area.

[0081] Place perishable and fragile goods in areas with optimal environmental conditions.

[0082] Adjust storage locations according to inventory turnover rates to reduce transportation distances.

[0083] It should also be noted that the greedy algorithm can quickly find the optimal storage location based on the current inventory situation, reducing warehousing time and improving space utilization.

[0084] Upload inventory data, order data, and status data to the cloud data center, perform global inventory management based on inventory data and order data, plan routes based on the traffic volume, traffic capacity, node processing capacity, and node load rate of each route, record the shelf location, storage location, storage requirements, inventory quantity, inventory turnover rate, and safety stock level of each warehouse as inventory layout data, and record the starting point, end point, waypoints, distance, and estimated travel time of the route as route planning data.

[0085] Inventory data refers to the inventory volume, inventory turnover rate and storage conditions of each warehouse.

[0086] Order data refers to the order volume, order priority, and delivery time of each warehouse.

[0087] Status data refers to the environmental status of each warehouse, the status information of goods, and the status of equipment.

[0088] It's also important to note that global inventory management and routing data not only helps optimize internal warehouse operations but also enables cross-warehouse collaborative scheduling, ensuring optimal resource allocation. This data can be used to predict future demand, enabling proactive adjustments and further shortening logistics cycles.

[0089] S3: Combine real-time traffic information and historical traffic data to generate transportation routes and delivery routes for transportation vehicles.

[0090] The specific steps are as follows:

[0091] Obtain real-time traffic information from the traffic data platform, extract historical traffic data from the historical database, clean the real-time traffic information and historical traffic data, and integrate them into traffic data;

[0092] Real-time traffic information refers to congestion index, road speed limits, road closure information and accident reports.

[0093] Historical traffic data refers to historical average travel time, historical congestion conditions and weather impacts.

[0094] It should also be noted that outliers and duplicate data will be removed during the data cleaning process to ensure data accuracy and consistency. The integrated traffic data can more comprehensively reflect the current road conditions and help generate more reasonable transportation routes.

[0095] Use the A* algorithm to generate transportation routes and delivery routes based on traffic data.

[0096] When planning a route, we must comprehensively consider shorter distances, shorter travel times, lower fuel consumption, and avoiding high-risk sections. We must also evaluate the rationality of the generated route (e.g., whether it meets delivery time requirements and transportation requirements).

[0097] It's also important to note that the A* algorithm not only considers the shortest path but also takes into account factors like traffic conditions and road speed limits to generate the optimal route. This dynamic route planning method can effectively respond to unexpected traffic conditions and reduce delays.

[0098] S4: Monitor vehicle status, handle abnormal situations, and adjust transportation and delivery routes in real time based on path planning data.

[0099] The specific steps are as follows:

[0100] Vehicle status refers to the vehicle's location information, remaining fuel, fuel consumption rate, engine temperature, engine speed and fault codes;

[0101] It should also be noted that real-time monitoring of vehicle status helps to promptly identify potential problems and avoid delays caused by vehicle failures. In addition, refueling plans can be optimized based on vehicle status, reducing operating costs.

[0102] Vehicle status data is collected in real time through on-board sensors and uploaded to the cloud data center through wireless communication modules;

[0103] Abnormal situations refer to traffic abnormalities, vehicle abnormalities and cargo abnormalities;

[0104] It should also be noted that: multiple anomaly detection rules can be set up. Once an abnormal situation is found, an alarm will be immediately issued and relevant personnel will be notified to handle it, ensuring that the problem is resolved in a timely manner.

[0105] Send abnormal situations to drivers and dispatchers for processing;

[0106] If the current route is congested or road closed, immediately re-plan the route to avoid the congested or closed sections.

[0107] If the vehicle is in an abnormal state (such as insufficient fuel or engine failure), go to the nearest gas station or dispatch a spare vehicle to take over the transportation task.

[0108] If the status information of the goods is abnormal (such as temperature exceeding the standard or vibration intensity being too high), start the backup refrigeration equipment, adjust the ventilation, or adjust the driving speed and route.

[0109] Obtain path planning data from the cloud data center, combine vehicle status, abnormal conditions, and cargo status information, use the A* algorithm to estimate the distance from the current node to the destination, and calculate the actual cost and total cost from the starting point to the current node;

[0110] The total cost is the sum of the distance from the current node to the end point and the actual cost from the starting point to the current node;

[0111] Define the starting point as the vehicle's current location and the end point as the customer's address. Initialize the open list and closed list. The open list stores the nodes to be explored, and the closed list stores the nodes that have been explored. Add the starting point to the open list and set the actual cost from the starting point to the current node to 0.

[0112] Select the node with the smallest distance from the current node to the destination from the open list as the current node. If the current node is the destination, the path planning is completed, and the backtracking path generates the optimal transportation route and the optimal delivery route. Otherwise, move the current node from the open list to the closed list.

[0113] It should also be noted that dynamically adjusting transportation routes not only improves transportation efficiency, but also reduces the impact of traffic congestion or weather changes, ensuring that goods arrive at their destination on time.

[0114] S5: After the goods arrive at the destination, the goods information is confirmed through the RFID reader, and the delivery data is recorded after the signing operation is completed.

[0115] The specific steps are as follows:

[0116] Use an RFID reader to scan the goods label, read the order information of the goods, verify the read order information of the goods, and confirm the receipt through the mobile device;

[0117] If the cargo information does not match or the cargo is damaged, mark the exception and initiate the subsequent processing process.

[0118] During the signing process, the signatory, signing time and signing status (such as normal, abnormal) are recorded, and photos of the goods are taken and uploaded to the cloud as evidence of completed delivery.

[0119] It should also be noted that the combination of RFID readers and mobile devices simplifies the sign-off process and reduces manual errors. At the same time, sign-off data can be uploaded to the cloud in real time, ensuring information synchronization and traceability.

[0120] Record cargo information, order information, receipt information, and exceptions as delivery data.

[0121] Receipt information refers to the signatory, receipt time and receipt status.

[0122] Abnormal conditions refer to damaged goods, discrepancies in quantity and discrepancies in goods.

[0123] It should also be noted that detailed delivery data records facilitate subsequent tracking and auditing, ensuring the transparency and traceability of the entire logistics process.

[0124] S6: Integrate global data for global optimization and global management.

[0125] The specific steps are as follows:

[0126] Integrate inventory layout data, transportation scheduling data, and delivery data into global data;

[0127] Remove duplicate data, fill missing values and correct erroneous data from global data, and classify and store global data by type (such as warehouse data, transportation data, order data) to facilitate subsequent analysis.

[0128] It should also be noted that this detailed data not only helps to monitor and adjust transportation plans in real time, but also provides a basis for future optimization and continuously improves the level of intelligent logistics.

[0129] Transport scheduling data refers to transport routes, transport times, delivery routes, delivery times, vehicle status, cargo status information, and real-time traffic information;

[0130] It should also be noted that this detailed data not only helps to monitor and adjust transportation plans in real time, but also provides a basis for future optimization and continuously improves the level of intelligent logistics.

[0131] Use Python to analyze global data, optimize inventory layout based on the analysis results, and use machine learning algorithms to optimize transportation routes, vehicle scheduling, delivery routes, and order processing processes.

[0132] It's also worth noting that Python, as a powerful programming language, can efficiently process large amounts of data and, combined with machine learning algorithms, achieve automated optimization. This approach not only improves the scientific nature and accuracy of decision-making, but also significantly shortens logistics cycles and reduces costs.

[0133] This embodiment also provides a logistics life cycle shortening system in an Internet of Things environment, including:

[0134] The pre-processing module uses RFID tags to identify goods, collect comprehensive data of goods, and perform pre-processing;

[0135] The warehouse management module monitors the status of goods in real time and adjusts the storage environment. It allocates storage locations based on the comprehensive data of goods, assigns urgency values to each item, and adjusts shelf layout and storage locations. The cloud data center receives data from each warehouse, performs global inventory management and route planning, and generates inventory layout data and route planning data.

[0136] The transportation and distribution module combines real-time traffic information and historical traffic data to generate transportation routes and distribution routes for transport vehicles, monitor vehicle status, handle abnormal situations, and adjust transportation routes and distribution routes in real time based on path planning data;

[0137] The receipt module verifies the cargo information through an RFID reader after the cargo arrives at the destination, and records the delivery data after the receipt operation is completed;

[0138] The optimization and management module integrates global data for global optimization and global management.

[0139] This embodiment also provides a computer device, which is suitable for the method of shortening the logistics life cycle in the Internet of Things environment, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the method of shortening the logistics life cycle in the Internet of Things environment proposed in the above embodiment.

[0140] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0141] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for shortening the logistics life cycle in an Internet of Things environment as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk.

[0142] In summary, the present invention uses: multi-source sensors to monitor the status of goods in real time, dynamically adjusts the storage environment and optimizes warehouse space utilization, combines urgency value allocation with intelligent algorithms to adjust shelf layout, ensures that high-priority goods are quickly shipped out, and integrates global data through cloud data centers to achieve accurate inventory management and route planning, significantly improving warehousing efficiency and the overall level of operational intelligence, providing a scientific basis and dynamic optimization capabilities for the efficient shortening of the logistics life cycle, and using real-time traffic information and historical traffic data to generate optimal transportation and delivery routes using the A* algorithm. Combined with real-time monitoring of vehicle status and dynamic adjustment of route planning data, it effectively reduces transportation time, cost and abnormal delays, and significantly improves delivery efficiency.

[0143] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for shortening the logistics life cycle in an Internet of Things environment, characterized by: include, Use RFID tags to identify goods, collect comprehensive data of goods, and perform pre-processing; Monitor the status of goods in real time and adjust the storage environment. Allocate storage locations based on comprehensive goods data, assign urgency values to each item, and adjust shelf layouts and storage locations. The cloud data center receives data from each warehouse, performs global inventory management and route planning, and generates inventory layout data and route planning data. Combine real-time traffic information and historical traffic data to generate transportation routes and delivery routes for transport vehicles, monitor vehicle status, handle abnormal situations, and adjust transportation routes and delivery routes in real time based on path planning data; After the goods arrive at the destination, the goods information is confirmed through the RFID reader, and the delivery data is recorded after the signing operation is completed; Integrate global data for global optimization and global management.

2. The method for shortening the logistics life cycle in an Internet of Things environment according to claim 1, characterized in that: The specific steps of using RFID tags to identify goods, collect comprehensive data of goods, and perform preprocessing are as follows: Attach RFID tags to the goods and write basic information of the goods, storage requirements, order information and transportation requirements; Read basic information, storage requirements, order information, and transportation requirements from RFID tags, and collect cargo status information through temperature sensors, humidity sensors, and vibration sensors; The cargo status information refers to the temperature, humidity and vibration intensity of the cargo; the basic information, storage requirements, order information, transportation requirements and status information of the cargo are integrated into comprehensive cargo data, and the comprehensive cargo data is cleaned and standardized.

3. The method for shortening the logistics life cycle in an Internet of Things environment according to claim 2, characterized in that: The real-time monitoring of the status information of the goods and the adjustment of the storage environment are carried out. The storage location is allocated based on the comprehensive data of the goods, the urgency value is assigned to each goods, and the shelf layout and storage location are adjusted. The cloud data center receives the data of each warehouse, performs global inventory management and route planning, and generates inventory layout data and route planning data. The specific steps are as follows: Set storage environment range based on the storage requirements of goods and adjust the storage environment; Use a greedy algorithm to assign a storage location to each item; Assign an urgency value to each item based on storage requirements and order information, use intelligent algorithms to optimize shelf layout, and adjust storage locations based on urgency values; Upload inventory data, order data, and status data to the cloud data center, perform global inventory management based on inventory data and order data, plan routes based on the traffic volume, traffic capacity, node processing capacity, and node load rate of each route, record the shelf location, storage location, storage requirements, inventory quantity, inventory turnover rate, and safety stock level of each warehouse as inventory layout data, and record the starting point, end point, waypoints, distance, and estimated travel time of the route as route planning data.

4. The method for shortening the logistics life cycle in an Internet of Things environment according to claim 3, characterized in that: The specific steps of combining real-time traffic information and historical traffic data to generate transportation routes and delivery routes for transportation vehicles are as follows: Obtain real-time traffic information from the traffic data platform, extract historical traffic data from the historical database, clean the real-time traffic information and historical traffic data, and integrate them into traffic data; Use the A* algorithm to generate transportation routes and delivery routes based on traffic data.

5. The method for shortening the logistics life cycle in an Internet of Things environment according to claim 4, characterized in that: The specific steps of monitoring vehicle status, handling abnormal situations, and adjusting transportation routes and delivery routes in real time based on path planning data are as follows: The vehicle status refers to the vehicle's location information, remaining fuel, fuel consumption rate, engine temperature, engine speed and fault code; Vehicle status data is collected in real time through on-board sensors and uploaded to the cloud data center; abnormal conditions refer to traffic abnormalities, vehicle abnormalities, and cargo abnormalities; Send abnormal situations to drivers and dispatchers for processing; Obtain path planning data from the cloud data center, combine vehicle status, abnormal conditions, and cargo status information, use the A* algorithm to estimate the distance from the current node to the destination, and calculate the actual cost and total cost from the starting point to the current node; The total cost refers to the sum of the distance from the current node to the end point and the actual cost from the starting point to the current node; Define the starting point as the vehicle's current location and the end point as the customer's address, initialize the open list and closed list, add the starting point to the open list, select the target point from the open list for path planning, and trace back the path to generate the optimal transportation route and the optimal delivery route.

6. The method for shortening the logistics life cycle in an Internet of Things environment according to claim 5, characterized in that: After the goods arrive at the destination, the goods information is confirmed through the RFID reader, and the delivery data is recorded after the signing operation is completed. The specific steps are as follows: Use an RFID reader to scan the goods label, read the order information of the goods, verify the read order information of the goods, and confirm the receipt through the mobile device; Record cargo information, order information, receipt information, and exceptions as delivery data.

7. The method for shortening the logistics life cycle in an Internet of Things environment according to claim 6, characterized in that: The specific steps of integrating global data for global optimization and global management are as follows: Integrate inventory layout data, transportation scheduling data, and delivery data into global data; The transport scheduling data refers to transport routes, transport times, delivery routes, delivery times, vehicle status, cargo status information and real-time traffic information; Use Python to analyze global data, optimize inventory layout based on the analysis results, and use machine learning algorithms to optimize transportation routes, vehicle scheduling, delivery routes, and order processing processes.

8. A system for shortening the logistics life cycle in an Internet of Things environment, based on the method for shortening the logistics life cycle in an Internet of Things environment according to any one of claims 1 to 7, characterized in that: include, The pre-processing module uses RFID tags to identify goods, collect comprehensive data of goods, and perform pre-processing; The warehouse management module monitors the status of goods in real time and adjusts the storage environment. It allocates storage locations based on the comprehensive data of goods, assigns urgency values to each item, and adjusts shelf layout and storage locations. The cloud data center receives data from each warehouse, performs global inventory management and route planning, and generates inventory layout data and route planning data. The transportation and distribution module combines real-time traffic information and historical traffic data to generate transportation routes and distribution routes for transport vehicles, monitor vehicle status, handle abnormal situations, and adjust transportation routes and distribution routes in real time based on path planning data; The receipt module verifies the cargo information through an RFID reader after the cargo arrives at the destination, and records the delivery data after the receipt operation is completed; The optimization and management module integrates global data for global optimization and global management.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for shortening the logistics life cycle in an Internet of Things environment according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for shortening the logistics life cycle in an Internet of Things environment according to any one of claims 1 to 7 are implemented.

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