Goods tracking system based on Internet of Things

Abstract

The invention discloses a cargo tracking system based on the Internet of Things, and relates to the technical field of Internet of Things transportation, and the key points of the technical scheme are that the system comprises a sensor module which can flexibly configure a sensor according to the characteristics and demands of transported cargoes, and the sensor is installed on the cargoes and is used for tracking and detecting the state of the cargoes in the transportation process; the data transmission module is used for immediately sending related data to the vehicle-mounted module when the sensor module detects the abnormity; when the vehicle-mounted module receives the data sent by the data transmission module, the vehicle-mounted module intercepts road condition images in a short time before an abnormality occurs through front and rear cameras, and the road condition images are stored in the data processing module in combination with the current driving speed of the vehicle and the data of the sensor module; and the data processing module processes the data stored in the vehicle-mounted module, makes a judgment and gives an alarm according to the processed result, and realizes real-time monitoring of the state of the goods in the transportation process so as to improve the safety and transparency of logistics transportation.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things transportation technology, and more particularly, to a cargo tracking system based on the Internet of Things. Background Art

[0002] With the rapid development of globalization and e-commerce, logistics is facing tremendous pressure to improve efficiency, control costs and upgrade customer experience. Traditional logistics methods can no longer meet the needs of rapid response and real-time tracking. IoT technology provides a bridge between the physical world and the digital world, allowing logistics nodes such as equipment, vehicles, and warehouses to be seamlessly integrated into an intelligent network to achieve real-time tracking and monitoring of goods.

[0003] In modern logistics and transportation, ensuring the safety and integrity of goods is crucial, especially for the transportation of valuable goods. However, traditional cargo monitoring methods mainly rely on manual inspection, which is inefficient and cannot provide real-time feedback, and cannot monitor the status of goods in real time during transportation.

[0004] Especially when the goods are finally inspected and found to be damaged, it is difficult to explain the cause of the damage and determine the responsibility, which often leads to disputes. Not only does it cause financial losses, but it also takes up time and brings mental pressure to both the carrier and the shipper.

[0005] Therefore, in order to solve the above technical problems, the present invention proposes a cargo tracking system based on the Internet of Things. Summary of the invention

[0006] In view of the deficiencies in the prior art, an object of the present invention is to provide a cargo tracking system based on the Internet of Things.

[0007] To achieve the above object, the present invention provides the following technical solution: a cargo tracking system based on the Internet of Things, comprising:

[0008] A sensor module, including but not limited to a three-axis acceleration sensor, a gyroscope, and a gravity sensor, is installed on the cargo and is used to monitor the status of the cargo in real time during transportation;

[0009] A data transmission module, the data transmission module is used to send the data detected by the sensor module to the vehicle-mounted module when the data detected by the sensor module is abnormal;

[0010] The vehicle-mounted module, when receiving the data sent by the data transmission module, saves the images captured by the front and rear cameras of the vehicle within the time t before and after the abnormality occurs. The vehicle-mounted module has a neural network for analyzing the impact of road conditions, which is used to classify the impact of road conditions into "safe road surface" and "bumpy road surface" and save the vehicle driving data;

[0011] A data processing module processes the data stored in the vehicle-mounted module. If the road surface is a "bumpy road surface", the data is marked as a "risk path"; if the road surface is a "safe road surface", it is marked as "dangerous driving". The "risk path" data will be used by the dispatch center to optimize the transportation route planning in the future, and "dangerous driving" will be recorded as a high-risk behavior, and both "dangerous driving" and "risk path" will issue cargo damage risk warning reminders.

[0012] Furthermore, the data transmission module transmits the data of the sensor module via Bluetooth, and the low power consumption characteristic of Bluetooth makes it suitable for long-term operation without frequent battery replacement.

[0013] Furthermore, when the vehicle-mounted module receives the data sent by the data transmission module, it captures road condition images within 2-10 seconds before and after the abnormality occurs through the front and rear cameras of the vehicle.

[0014] Furthermore, the vehicle driving data stored in the vehicle-mounted module includes continuous driving mileage and time, vehicle speed, and acceleration, which are used to determine whether the driver is driving fatigued.

[0015] Furthermore, the vehicle-mounted module includes a neural network for analyzing the impact of road conditions, which adopts a CNN structure and is used to classify the impact of road conditions into "safe road surface" and "bumpy road surface".

[0016] Furthermore, after receiving the data from the sensor module, the data processing module will use the mass of the object measured by the gravity sensor and the acceleration value measured by the three-axis acceleration sensor to calculate the magnitude of the impact force currently exerted on the object.

[0017] Furthermore, after receiving the data from the sensor module, the data processing module uses the gyroscope data to perform angular acceleration analysis and measure the rotation of the goods. By combining the acceleration and angular acceleration, it can be determined whether the goods are excessively rotated or tilted.

[0018] Furthermore, when the data processing module receives the number of "risky paths" and "dangerous driving" exceeding the threshold, it will add a risk value count. When the risk count reaches a certain number of times, a risk prompt will appear.

[0019] Furthermore, the threshold is set between 1 and 20 according to the nature of the transported goods, and when the result is greater than or equal to the threshold, the transport data recorded this time is marked as high-risk data that may cause damage.

[0020] Furthermore, the sensor module data and the vehicle-mounted module data will be grouped together with the information of the truck and driver responsible for the transportation and automatically archived by the system to form a complete transportation record.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] 1. In the present invention, the status of the goods in the transportation process is tracked and detected by the sensor module, and then the abnormal data detected by the sensor module is immediately sent to the vehicle-mounted module through the data transmission module. Next, the vehicle-mounted module captures the road condition image for a short time before the abnormality occurs through the front and rear cameras, combines the current driving speed of the vehicle with the sensor module data, and stores it in the data processing module. Finally, the data processing module processes the data stored in the vehicle-mounted module, and makes a judgment based on the processed results, gives a warning, and realizes real-time monitoring of the status of the goods in the transportation process, so as to improve the safety and transparency of logistics transportation, and finally provide logistics companies and customers with safer and more transparent transportation services. DETAILED DESCRIPTION

[0023] The present invention provides a cargo tracking system based on the Internet of Things. The solution consists of the following modules: a sensor module, a data transmission module, a vehicle-mounted module, and a data processing module.

[0024] The sensor module can flexibly configure its sensors according to the characteristics and requirements of the transported goods, and the sensors are installed on the goods to track and detect the status of the goods during transportation. This embodiment mainly monitors the status of the goods during transportation in real time through the following sensors:

[0025] The three-axis acceleration sensor is used to monitor the acceleration changes of goods during transportation in real time, including vibration and impact in the lateral, longitudinal and vertical directions. By detecting the acceleration changes of different frequencies and intensities, it can be determined whether the goods have experienced sudden braking, severe bumps or other abnormal conditions, so as to facilitate subsequent data analysis and problem tracing;

[0026] The gyroscope is used to detect the direction change and tilt angle of the goods during transportation to ensure that the goods are not excessively tilted or rotated during transportation. This is especially important for fragile goods to prevent damage caused by improper operation. The real-time data of the gyroscope is combined with the monitoring results of the three-axis acceleration sensor to provide a more comprehensive assessment of the transportation status.

[0027] Temperature and humidity sensors: used to monitor the sensitivity of goods to environmental changes, especially for goods such as precision instruments that have strict temperature and humidity requirements. These sensors can issue alarms in time when environmental parameters exceed the set range and transmit data to the on-board module;

[0028] Pressure sensor: Suitable for fragile goods that may be squeezed or impacted during transportation. It can monitor in real time whether the goods are affected by external pressure, thereby ensuring their safety.

[0029] The data transmission module transmits the data of the sensor module via Bluetooth. Bluetooth has a significant low power consumption feature, which makes it extremely suitable for scenarios that require long-term operation and strict energy consumption requirements. This means that the use of the Bluetooth data transmission module can be used for a long time without frequent battery replacement, thereby reducing maintenance costs and inconvenience;

[0030] The data transmission module has a unique number. This unique numbering mechanism ensures that the on-board module can accurately identify and distinguish the data on each cargo. In a complex logistics environment, this unique identification is crucial for achieving accurate tracking and management of cargo.

[0031] The vehicle-mounted module, during the transportation process, when the sensor module detects that the state of the goods is abnormal (such as sudden braking, severe bumps, etc.), the system will immediately send the relevant data (including acceleration value, timestamp and location, etc.) to the vehicle-mounted module through the data transmission module. At the same time, the vehicle-mounted module captures the road condition image of a short time (such as 2 seconds) before the abnormality occurs through the front and rear cameras, and stores it in the data processing module in combination with the current driving speed of the vehicle and other sensor data. If the goods are indeed damaged after inspection, the stored data can be used as evidence for accountability and explain the cause of the abnormal driving situation that caused the damage, so as to be realistic. There is a neural network in the vehicle-mounted module for analyzing the impact of road conditions. The CNN structure is selected and trained with a large number of road condition pictures. It is used to classify the impact of road conditions (binary) into "safe road surface" and "bumpy road surface". If the road surface is "bumpy road surface", the group of data is marked as "path risk path", and if the road surface is safe, it is marked as "dangerous driving". The risk path data will be used by the dispatch center to optimize the transportation path planning in the future, and "dangerous driving" will be recorded as a high-risk behavior.

[0032] After receiving the data, the data processing module will use the mass of the item measured by the gravity sensor and the acceleration value measured by the acceleration sensor to calculate the impact force currently exerted on the item. The three-axis acceleration sensor can simultaneously monitor the acceleration changes of the goods in the three directions of x, y, and z to identify the impact and vibration generated in different directions. For example, sudden braking usually causes an increase in the acceleration in front of the vehicle, while sharp turns will produce lateral acceleration. At the same time, the gyroscope data is used for angular acceleration analysis to measure the rotation of the goods. By combining acceleration and angular acceleration, it can be determined whether the goods have excessive rotation or tilt, thereby warning of possible damage risks. The system is set with an impact force threshold, which can be determined according to the specific type of transported goods. For example, 30N. When the calculated result of the impact force is greater than or equal to this threshold, the recorded data is marked as high-risk data that may cause damage;

[0033] When the "bumpy road" and impact force exceed the threshold, a risk value count will be added to the cargo each time it occurs. When the risk count reaches a certain threshold (for example, 1, 3, 6), it will be classified as low, medium, or high damage risk, respectively. When a medium damage risk prompt appears, the driver is warned to pay attention. When a high damage risk prompt appears, the warning is further upgraded, requiring the driver to stop and check as soon as possible.

[0034] All sensor data and camera images will be grouped together with the truck information responsible for transportation and automatically archived by the system to form a complete transportation record. These records not only provide a basis for later problems such as damage to objects, but also provide important data support for logistics companies to analyze potential risks in the transportation process and optimize transportation routes and operating procedures.

[0035] Through the implementation of the present invention, comprehensive monitoring and real-time feedback of the status of each cargo can be achieved, significantly improving the safety and transparency of cargo transportation, and ultimately providing logistics companies and customers with more efficient and reliable transportation services.

[0036] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any ordinary technician in the industry can smoothly implement the present invention as described above. However, any equivalent changes, modifications and evolutions made by technicians familiar with the profession without departing from the scope of the technical solution of the present invention using the technical content disclosed above are all equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the technical solution of the present invention.

Claims

1. A cargo tracking system based on the Internet of Things, characterized in that: include: A sensor module, which includes a three-axis acceleration sensor, a gyroscope, and a gravity sensor, is installed on the cargo and is used to monitor the status of the cargo in real time during transportation; A data transmission module, the data transmission module is used to send the data detected by the sensor module to the vehicle-mounted module when the data detected by the sensor module is abnormal; An on-board module, which saves the data sent by the data transmission module upon receipt, and also saves the images captured by the front and rear cameras of the vehicle within a time t before and after the abnormality occurs and the vehicle driving data, and classifies the road conditions into "safe road" and "bumpy road" through a neural network, and saves the vehicle driving data; A data processing module processes the data stored in the vehicle-mounted module to determine the cause of the sensor data anomaly. If the road surface is a "bumpy road surface", the data is marked as a "risky path". If the road surface is a "safe road surface", the data is marked as "dangerous driving". At the same time, a cargo damage risk warning is issued to the driver and recorded.

2. The cargo tracking system based on the Internet of Things according to claim 1, characterized in that: The data transmission module transmits data via Bluetooth.

3. The cargo tracking system based on the Internet of Things according to claim 1, characterized in that: When the vehicle-mounted module receives the data sent by the data transmission module, it captures the road condition images within 2-10 seconds before and after the abnormality occurs through the front and rear cameras of the vehicle.

4. The cargo tracking system based on the Internet of Things according to claim 3 is characterized in that: The vehicle driving data stored in the vehicle-mounted module includes continuous driving mileage and time, vehicle speed, and acceleration.

5. The cargo tracking system based on the Internet of Things according to claim 4 is characterized in that: The neural network in the vehicle-mounted module uses a CNN structure to classify the impact of road conditions.

6. The cargo tracking system based on the Internet of Things according to claim 5, characterized in that: After receiving the data from the sensor module, the data processing module calculates the magnitude of the impact force on the object through the mass and acceleration value of the object obtained by the sensor.

7. The cargo tracking system based on the Internet of Things according to claim 6, characterized in that: After receiving the data from the sensor module, the data processing module uses the gyroscope data to perform angular acceleration analysis on the rotation of the goods, and combines the acceleration and angular acceleration to determine the degree of rotation or tilt of the goods.

8. The cargo tracking system based on the Internet of Things according to claim 1, characterized in that: When the data processing module receives the number of "risky paths" and "dangerous driving" exceeding the threshold, it will add a risk value count. When the risk count reaches a certain number of times, a risk prompt will appear.

9. The cargo tracking system based on the Internet of Things according to claim 8, characterized in that: The threshold is set between 1 and 20 according to the nature of the transported goods. When the result is greater than or equal to the threshold, the transport data recorded this time is marked as high-risk data that may cause damage.

10. The cargo tracking system based on the Internet of Things according to claim 9, characterized in that: The sensor module data and the vehicle-mounted module data will be grouped together with the truck and driver information responsible for the transportation and automatically archived by the system to form a complete transportation record.