Ship in-transit cargo monitoring system

Abstract

The invention discloses a ship in-transit cargo monitoring system, and relates to the technical field of ship in-transit monitoring, the ship in-transit cargo monitoring system comprises a camera module and a control module, the camera module comprises a plurality of cameras arranged on a ship and facing a container, the control module comprises a shipborne terminal and a cloud server, and the shipborne terminal is wirelessly connected with the cloud server; the shipborne terminal is configured to obtain video data of the camera and upload the video data to the cloud server; the cloud server is configured to judge whether the shipborne terminal is offline or not based on a preset API interface used for being connected with the shipborne terminal, and if yes, the frame supplementing process, the frame intercepting process and the container state verification process are executed in sequence; and if not, executing a frame interception process and a container state verification process. According to the invention, the monitoring and tracking effects during cargo transportation of the ship are improved.

Description

Technical Field

[0001] This application relates to the field of ship in-transit monitoring technology, and in particular to a ship in-transit cargo monitoring system. Background Technology

[0002] Logistics trajectory monitoring refers to the use of technical means to locate and track ships and their transported goods, and to use the location and fixed-point data at different time periods to connect the data of each fixed point to complete the logistics trajectory and obtain the full logistics data.

[0003] However, the aforementioned location data can only reveal the physical trajectory of import and export goods, but cannot reveal whether the status of the transport vehicle changes during the transportation process; moreover, during the transportation of goods by ship, due to the unstable signal coverage of the operator, the signal may be interrupted, resulting in incomplete video data transmission, which affects the monitoring and tracking effect of the logistics chain. Therefore, this application proposes a new technical solution. Summary of the Invention

[0004] To improve the monitoring and tracking of cargo transported by ships, this application provides a cargo monitoring system for ships en route.

[0005] This application provides a ship cargo monitoring system in transit, which adopts the following technical solution: A ship cargo monitoring system includes a camera module and a control module. The camera module includes multiple cameras deployed on the ship and facing the containers. The control module includes: Shipborne terminal, which is installed on the ship and connects to the camera for data; A cloud server, wirelessly connected to the shipboard terminal; The shipborne terminal is configured to acquire video data from the camera and upload it to a cloud server. The cloud server is configured as follows: Based on the preset API interface used to connect to the shipborne terminal, it is determined whether the shipborne terminal is offline. If it is, the frame interpolation process, frame capture process, and container status verification process are executed in sequence; if not, the frame capture process and container status verification process are executed. The frame interpolation process includes: Obtain the offline and online times of the shipborne terminal, and analyze to obtain the offline time periods; Record the offline time period as a reference value and generate a video retrieval task; the video retrieval task includes retrieving video data from the offline time period after the shipborne terminal is brought back online; The frame capture process includes: Image data is obtained by extracting key frames from video data uploaded by the shipborne terminal using a preset image extraction logic. Add a timestamp and pre-acquired AIS tracking information to any image; Images are sorted based on their timestamps to generate continuous tracking data.

[0006] Optionally, the container verification process includes: if the identification result meets the preset container abnormality alarm conditions, then an alarm message is output.

[0007] Optionally, the cloud server is further configured to perform a video authenticity verification process before executing the frame interpolation process, the video authenticity verification process including: The current position information of the vessel is obtained based on AIS tracking information; Weather information can be obtained by searching for weather forecasts online based on location information; If the offline time meets the preset weather verification conditions, the weather information for the offline time period is retrieved and recorded as weather data one. The video data for the offline time period is retrieved, and weather information is identified based on the video data to obtain the second weather data for the same period. If the weather data for weather data 1 and weather data 2 are different, a video error message will be output, and the frame interpolation process will be stopped.

[0008] Optionally, the video authenticity verification process may further include: If the weather data for weather data one and weather data two correspond to the same weather, proceed to the next step; If the weather data is sunny or cloudy, then light and shadow features are identified based on the video data before or during the offline period and the video data during the offline period, respectively, to obtain light and shadow feature one and light and shadow feature two. The ship's navigation trajectory during the offline period is obtained based on AIS tracking information; If the second light and shadow feature does not conform to the changing trend of the first light and shadow feature over time and the navigation trajectory, then output a video error message and stop the frame interpolation process.

[0009] Optionally, the shipborne terminal is configured to: acquire the ship's real-time speed, add a timestamp, and upload it to a cloud server; The video authenticity verification process also includes: If the weather data for weather data one and weather data two correspond to the same weather, proceed to the next step; If weather data one meets the preset wind auxiliary judgment conditions, then wind direction feature recognition is performed based on video data before or during offline time and video data during the offline time period to obtain wind direction feature one and wind direction feature two. The ship's navigation trajectory during the offline period is obtained based on AIS tracking information; If wind direction feature 2 does not conform to the changing trend of wind direction feature 1 with wind direction and navigation trajectory, then output a video abnormality prompt message and stop the frame interpolation process.

[0010] Optionally, the shipborne terminal is configured to: acquire the ship's real-time speed, add a timestamp, and upload it to a cloud server; The cloud server is configured as follows: Water surface and ship hull recognition are performed on video data from offline time periods; If a set of video data meets the preset ship wave observation conditions, then ship wave feature recognition is performed to obtain ship wave feature one. The ship's customs and port registration data are obtained, and the ship's wave characteristics are calculated and analyzed by combining multiple speed thresholds. If the ship's wave characteristic one does not conform to the changing trend of the ship's wave characteristic two with the speed, then an abnormal video prompt message will be output and the frame interpolation process will be terminated.

[0011] Optionally, the system also includes multiple electronic seal locks, which are attached to the container and used to lock the container doors. Each electronic seal lock is embedded with an RFID tag. Multiple readers are installed on the ship's hull, and each reader is paired with an RFID tag. The reader receives signals from the RFID tag. When the electronic seal lock is closed, the RFID circuit is activated, and the reader receives the signal from the RFID tag. The reader is connected to a shipboard terminal, which is configured as follows: If the reader is detected to have lost pairing with the RFID tag, an alarm message will be output.

[0012] Optionally, the electronic seal lock is equipped with a battery, and the outer shell of the electronic seal lock is embedded with a solar panel, which is connected in series with the battery.

[0013] In summary, this application has the following beneficial technical effects: Through the collaborative operation of the shipborne terminal and the cloud server, real-time monitoring of containers and timely alarms for abnormal conditions are achieved; retrieving offline video data and uploading it to the cloud server, along with frame interpolation, effectively prevents interruptions in the cloud server's video data, ensuring the integrity of the video data. Furthermore, the integrity of the video data further enhances the monitoring of cargo on board the ship, improving the monitoring and tracking effectiveness of cargo transported by the ship. Attached Figure Description

[0014] Figure 1 This is a schematic diagram illustrating the connection between the shipborne terminal and the cloud server in this application; Figure 2 This is a schematic diagram of the shipborne terminal connection in this application; Figure 3This is a cross-sectional view of the electronic seal lock of this application; Figure 4 This is a schematic diagram of the cloud server operation platform page of this application; Figure 5 This is a schematic diagram of the cloud server operation platform monitoring in this application.

[0015] Explanation of reference numerals in the attached diagram: 1. Control module; 11. Shipborne terminal; 12. Cloud server; 2. Camera; 3. Reader; 4. RFID electronic tag. Detailed Implementation

[0016] The following is in conjunction with the appendix Figure 1-5 This application will be described in further detail.

[0017] This application discloses a ship cargo monitoring system in transit.

[0018] Reference Figure 1 and Figure 2 The ship's cargo monitoring system includes a camera module and a control module 1. The camera module includes multiple cameras 2 deployed on the ship and facing the containers. The cameras 2 can be installed on the ship's deck, the edge of the ship, or on a bracket for mounting the cameras 2. The cameras 2 are mounted on the bracket and take pictures of the containers from an upper perspective. At least one camera 2 is required within 3 meters of each container, so that the cameras 2 can monitor the status of the containers more comprehensively.

[0019] The control module 1 includes: a shipborne terminal 11, which is installed on the ship and connected to the camera 2. The shipborne terminal 11 can be a dedicated computer device in the ship's cockpit or equipment room, serving as the local control center of this application.

[0020] The cloud server 12 is wirelessly connected to the shipborne terminal 11. The shipborne terminal 11 may have a built-in 3G / 4G module for encrypted interaction with the cloud server 12. The cloud server 12 can be a remote management platform deployed on shore. It may include a video acquisition platform for storing all monitoring records uploaded from the ship, and an intelligent control platform for pushing alarm information to relevant personnel and performing real-time monitoring and analysis of the video.

[0021] Cloud server 12 is configured as follows: Based on the preset API interface used to connect to the shipborne terminal 11, it is determined whether the shipborne terminal 11 is offline (e.g., the cloud server 12 provides an API interface to query whether the front-end device is online, which is used to confirm whether the device is offline. If no response is received three times in a row, it is determined that the shipborne terminal 11 is offline). If it is, the frame interpolation process, frame capture process and container status verification process are executed in sequence; if not, the frame capture process and container status verification process are executed.

[0022] If the device is offline, it means the video cannot be uploaded. A frame interpolation process needs to be performed after the shipboard terminal 11 is restored to complete the video from the offline period. This ensures the integrity of video data transmission, improves monitoring and tracking of cargo during transport, and enhances cargo safety. After frame interpolation, a frame truncation process and a container verification process are required to monitor the safety of the shipboard containers and identify any anomalies during the offline period. If no offline status was found, the process directly proceeds to the frame truncation and container status verification processes.

[0023] The frame interpolation process includes: Obtain the offline and online times of the shipborne terminal 11, and analyze them to obtain the offline time period. For example, if the shipborne terminal 11 is detected to be offline at 5:30 and successfully reconnects to the shipborne terminal 11 at 6:00, then the time period from 5:30 to 6:00 is defined as the offline time period.

[0024] Record the offline time period as a reference value and generate video retrieval tasks. There can be multiple video retrieval tasks. If there are multiple video retrieval tasks, a video retrieval task sequence is generated and the video is retrieved in sequence according to the task sequence number. The video retrieval task includes calling the video data of the offline time period after the shipborne terminal 11 is restored to online. The frame interpolation process can fill in the gaps in video that was not uploaded during offline periods, thereby improving the completeness of logistics supervision.

[0025] The frame capture process includes: Based on the video data uploaded by the shipborne terminal 11, key frames are extracted using a preset image extraction logic to obtain image data; wherein, the preset image extraction logic can be the frame extraction speed, such as 1 frame / second.

[0026] Add a timestamp and pre-acquired AIS tracking information to any image; where AIS is a real-time positioning and navigation data broadcasting system required for ships, which can obtain the ship's real-time position, heading, speed, etc.; for example: if an image is captured at 25° North latitude at 5:30:30, then add a timestamp watermark of 5:30:30 and a latitude and longitude watermark of 25° North latitude.

[0027] Images are sorted based on their timestamps to generate continuous tracking data. For example, if multiple images have timestamps of 5:30:30, 5:31:30, and 5:32:30, they are sorted according to the order of their timestamps.

[0028] According to the above design, retrieving offline video data and uploading it to the cloud server 12 for frame interpolation effectively prevents video data interruption on the cloud server 12, ensuring the integrity of the video data. This improved video data integrity further enhances the monitoring of cargo on board, improving the monitoring and tracking effectiveness of transported goods. Furthermore, through the collaborative operation of the shipborne terminal 11 and the cloud server 12, real-time monitoring of containers, timely alarms for abnormal situations, and timely uploading and interpolation of offline video data significantly improve the safety of cargo on board.

[0029] Container status verification process: If the identification result meets the preset container abnormality alarm conditions, an alarm message will be output.

[0030] The abnormal container alarm conditions can include detecting changes to the container seal (e.g., being opened) or opening of the container door. The alarm information can be sent to the display screen of the ship's onboard terminal 11 to remind the staff, or it can be sent to the staff via SMS using the staff's pre-stored mobile phone number. The alarm information should include at least the ship's pre-stored number, time, and the location of the abnormal container on the ship or the pre-stored number of that container.

[0031] Verifying the condition of containers can improve the detection of container security and ensure the safety of the goods inside the containers as much as possible.

[0032] Cloud server 12 is also configured to perform a video authenticity verification process before executing the frame interpolation process. The video authenticity verification process includes: The current position information of the vessel is obtained based on AIS tracking information; By connecting to the internet and searching for weather forecasts based on location information, weather information can be obtained. Combining this with the previous step, based on the ship's location information, the weather forecast for the ship's location can be obtained, such as: sunny.

[0033] If the offline time meets the preset weather verification conditions, the weather information for the offline time period is retrieved and recorded as weather data one. The preset weather verification conditions are that the weather forecast obtained from the query is consistent with the weather in the video. For example, if the weather forecast query shows sunny weather, the verification is to check whether the weather in the video before the offline time is sunny. The weather before the offline time should be at least within 5 minutes before the offline time, and it is best to retrieve the weather information at the time when the offline time is detected. Otherwise, the possibility of misjudgment may be increased. The verification criteria may include whether there are obvious light shadows or reflections, and whether the picture is bright and clear. The system retrieves video data from the offline time period and performs weather information recognition based on the video data to obtain weather data for the same period. It then verifies the video data from the offline time period by recognizing the images in the video data and analyzing whether it is consistent with or conforms to the meteorological change pattern of the weather at the ship's location as queried before the offline time (e.g., the weather forecast is cloudy turning to light rain, and the time period of turning to light rain is within the offline time period; the weather was cloudy before the offline time period, and gradually changed from cloudy to light rain during the offline time period). If the weather data corresponding to Weather Data 1 and Weather Data 2 is different, a video anomaly message will be output, and the frame interpolation process will be stopped. If the weather data of Weather Data 1 and Weather Data 2 is the same, there are two possibilities: one is that the weather is completely consistent, and the other is that the weather is not completely consistent, but conforms to the meteorological change pattern of the weather forecast, such as: it is cloudy before the offline time, and gradually changes from cloudy to light rain during the offline time period. Based on the above possibilities, it can also be analyzed and judged that the weather data corresponding to Weather Data 1 and Weather Data 2 is the same. If the weather data corresponding to Weather Data 1 and Weather Data 2 is different, that is, Weather Data 1 shows sunny weather and there is no forecast of weather change during the offline time period, while Weather Data 2 shows cloudy weather, a video anomaly message will be output. The message should include at least the time, specific location, and the ship's pre-stored number.

[0034] Since the probability of identical weather conditions is not very small, even if weather data one and weather data two show the same weather, it cannot fully guarantee that the video data is free of any anomalies. Therefore, the following design was adopted: In another embodiment of this application: The video authenticity verification process also includes: If the weather data for weather data 1 and weather data 2 are the same, proceed to the next step; if, after analysis, it is determined that the weather data for weather data 1 and weather data 2 may both be sunny or cloudy. If the weather data is not sunny or cloudy, then light and shadow feature recognition is performed on the video data at or before the offline time (at least within 5 minutes of the offline time) and the video data during the offline time period to obtain light and shadow feature one and light and shadow feature two. Among them, the images of multiple cameras 2 can be selected for light and shadow feature recognition, and the light and shadow of the video data must be obvious. Based on AIS tracking information, the ship's navigation trajectory during the offline period can be obtained; thus, the position of the sun relative to the ship can be determined based on the navigation trajectory and the time, and the trend of light and shadow changes can be inferred. If the second light and shadow feature does not conform to the changing trend of the first light and shadow feature over time and along the navigation trajectory, a video anomaly warning should be output, and the frame interpolation process should be terminated. For example: Based on the navigation trajectory, the sun's position relative to the ship is determined (e.g., southeast). The shadow is located in the northwest. At this time, the navigation trajectory continues to move towards the northwest. If the light and shadow change with the time axis and the navigation trajectory, the shadow direction will slowly shift in a clockwise direction (from northwest → west → southwest). However, the shadow direction of the second light and shadow feature shifts in a counterclockwise direction, indicating that there is an anomaly in the video. A video anomaly warning should be output, and the anomaly warning information should include at least the information of camera 2 that captured the abnormal video footage, the time of the anomaly, and the specific location coordinates of the anomaly.

[0035] Based on the above design, the presence of anomalies in the video can be analyzed by comparing the changing trends of light and shadow, further enhancing the security of the monitoring.

[0036] Because some weather conditions (such as cloudy days) may not have obvious light and shadow surfaces, which can easily lead to misjudgment, the following design was made: In another embodiment of this application: The shipborne terminal 11 is configured to: acquire the ship's real-time speed, add a timestamp, and upload it to the cloud server 12; acquiring the real-time speed helps to determine the direction of the flag or the object on the ship.

[0037] The authenticity verification process within the video also includes: If the weather data for weather data 1 and weather data 2 are the same, proceed to the next step; if, after analysis, the weather data for weather data 1 and weather data 2 may both be cloudy and there is no obvious light and shadow surface to analyze and determine whether the video data matches, then the final data may not be completely accurate and the next step is required for further verification.

[0038] If weather data one meets the preset wind-assisted judgment conditions, then wind direction feature identification is performed based on video data at or before offline (at least within 5 minutes of offline time) and video data during the offline time period to obtain wind direction feature one and wind direction feature two. The wind-assisted judgment conditions can be that the wind force is greater than the preset threshold, such as level 6 wind force, and the ship needs to be at a medium speed (can be 14 knots) or below, so that the flag or other swaying objects on the ship can be changed by the influence of wind direction.

[0039] Based on AIS tracking information, the ship's navigation trajectory during the offline period can be obtained; thus, the ship's course during the offline period can be determined, and the wind direction at the ship's location can also be queried.

[0040] If wind direction feature two does not conform to the changing trend of wind direction feature one with wind and navigation trajectory, a video anomaly message is output, and the frame interpolation process is terminated. Combining the navigation trajectory information obtained above, the ship's orientation can be determined. At the same time, wind information (including at least wind force and wind direction) and navigation speed (reported by shipborne terminal 11 when reconnected to the shipborne terminal 11) are referenced. When the navigation speed is medium or below, and the wind force is level 6 or above, the wind direction will have a significant directional influence on the waving direction of the flag on the ship (such as forming an angle with the navigation direction, or waving directly with the wind direction). The wind direction in the wind data can be used to analyze and determine whether the waving direction of the flag conforms to the predicted value. If the ship speed is high (e.g., above 14 knots) or the wind force is low (e.g., below level 6), the flag or other waving objects may change with the ship's navigation direction. If the flag in the video of the offline time period does not change with the navigation direction or has a large angle, and the wind force is obviously high, it can be analyzed as an anomaly.

[0041] Shipborne terminal 11 is configured as follows: Obtain the ship's real-time speed, add a timestamp, and upload it to the cloud server 12; by knowing the ship's real-time speed, the size of the ship's waves can be analyzed.

[0042] Cloud server 12 is configured as follows: The video data from the offline time period is used to identify the water surface and the ship's side; in particular, identifying the water surface and the ship's side can help locate the camera 2 that can see the ship's waves, and extract the image information of the camera 2 to perform the following process.

[0043] If a set of video data meets the preset ship wave observation conditions, then ship wave feature recognition is performed to obtain ship wave feature one; the ship wave observation conditions are that ship waves can be seen in the picture captured by camera 2 and the ship waves are obvious. The ship waves in the video data are extracted, their changing patterns are analyzed and defined as ship wave feature one.

[0044] The process involves obtaining customs and port registration data for vessels, combining this data with various speed thresholds to calculate and analyze the second characteristic of ship wave patterns. The registration data includes at least the vessel's specifications (such as specific model, volume, and load capacity) and the weight of the containers it carries. Then, based on the state of ship wave patterns at various speeds (such as low speed, medium speed, and high speed, where low speed can be less than 8 knots, medium speed is 8-14 knots, and high speed is greater than 14 knots), the process is combined with the vessel's weight and draft for calculation and analysis. The calculation of ship wave patterns is an existing technology and will not be elaborated here.

[0045] If the first characteristic of the ship's traveling wave does not conform to the changing trend of the second characteristic of the ship's traveling wave with speed, a video anomaly message is output, and the frame interpolation process is terminated. Based on the calculated and analyzed second characteristic of the ship's traveling wave, the first characteristic of the ship's traveling wave is compared. If the first characteristic of the ship's traveling wave in the video does not conform to the calculated and predicted second characteristic of the ship's traveling wave, and the deviation is large (e.g., the calculated and predicted second characteristic of the ship's traveling wave has a longer wavelength and higher wave height for a long period of time, while the wavelength length and wave height of the ship's traveling wave in the first characteristic of the ship's traveling wave do not meet the requirements of the second characteristic of the ship's traveling wave most of the time), a video anomaly message is output, and the frame interpolation process is terminated. The video anomaly message includes at least the time, specific location, and analysis results.

[0046] refer to Figure 3 It also includes multiple electronic seal locks, which are attached to the container and used to physically lock the container doors. The electronic seal locks and containers can be coded separately, and the electronic seal locks and each container can be paired and bound with a code and stored in a preset database. This database can be formed by staff entering information. The electronic seal lock is embedded with an RFID electronic tag 4 (the code of the electronic seal lock can be the code of the RFID electronic tag 4). Multiple readers 3 are installed on both sides of the ship, and the readers 3 and RFID electronic tags 4 are paired one by one. When the electronic seal lock is closed, the connection line of the RFID electronic tag 4 is connected. The line can be installed inside the lock tongue and the lock slot. The side walls where the lock tongue and the lock slot abut against each other are provided with metal plates that can conduct the circuit. The lock tongue is provided with a groove, and a spring is fixed in the groove. One end of the spring is connected to the metal plate of the lock tongue, and the other end is connected to the groove wall. The metal plate is slidably connected to the end of the lock tongue. The metal plate can be pushed outward by the force of the spring, thereby effectively preventing the lock tongue and the lock slot from loosening when they are connected.

[0047] After the circuit is connected, the RFID electronic tag 4 can be identified by the reader 3 (the RFID electronic tag 4 can be scanned periodically, such as once every 5 seconds to check the lock status); when the electronic seal lock is illegally opened, the circuit is disconnected and the signal is interrupted, at which time the RFID tag cannot be identified by the reader 3.

[0048] The reader 3 is connected to the shipboard terminal 11, which can be via the ship's wireless local area network. The shipboard terminal 11 is configured to output an alarm message if it detects that the pairing between the reader 3 and the RFID tag 4 has been disconnected. The alarm message includes at least the RFID tag 4 of the electronic seal lock whose signal was disconnected and its paired container, the time of the signal disconnection, and the location of the lock or container on the ship.

[0049] The RFID electronic tag 4 in this application can be an active RFID electronic tag 4. The electronic seal lock is equipped with a battery. The battery and the RFID electronic tag 4 are connected in series and used for power supply. The outer shell of the electronic seal lock is embedded with a solar panel. The receiving surface of the solar panel faces the outside of the electronic seal lock. The solar panel and the battery are connected in series.

[0050] Equipped with solar panels, the electronic seal lock can be charged by sunlight to meet its power requirements. Because of its minimal power consumption, it remains usable even during prolonged periods of cloudy or rainy weather. When using the electronic seal lock, it's important to ensure the solar panels face outwards to avoid being blocked by the container, thus facilitating solar energy absorption.

[0051] According to the above design, the camera module deploys multiple cameras 2 to ensure comprehensive monitoring of the containers; the control module 1 uploads video via the shipborne terminal 11 for frame interpolation, and verifies the authenticity of the video through a cloud server 12. The cloud server 12 verifies the video using various methods, including weather comparison (such as light and shadow comparison), wind characteristic comparison (such as the direction of flag waving on the ship), and ship wave pattern comparison (such as the wavelength and wave height of ship waves), analyzing and verifying the authenticity of the monitoring video; the electronic seal lock uses RFID technology to lock and monitor the containers, combined with solar power to ensure long-term stable operation. This coordinated approach effectively ensures the safety of goods during ship transportation.

[0052] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A ship in-transit cargo monitoring system, characterized in that: The system includes a camera module and a control module (1). The camera module includes multiple cameras (2) deployed on the ship and facing the containers. The control module (1) includes: Shipborne terminal (11), which is installed on the ship and connected to the camera (2); A cloud server (12) is wirelessly connected to a shipboard terminal (11). The shipborne terminal (11) is configured to: acquire video data from the camera (2) and upload it to the cloud server (12); The cloud server (12) is configured as follows: Based on the preset API interface for connecting the shipborne terminal (11), it is determined whether the shipborne terminal (11) is offline. If it is offline, the frame interpolation process, frame capture process and container status verification process are executed in sequence; if not, the frame capture process and container status verification process are executed. The frame interpolation process includes: Obtain the offline and online times of the shipborne terminal (11) and analyze the offline time period; Record the offline time period as a reference value and generate a video retrieval task; the video retrieval task includes calling the video data of the offline time period after the shipborne terminal (11) is restored to online; The frame capture process includes: Based on the video data uploaded by the shipborne terminal (11), key frames are extracted using a preset image extraction logic to obtain image data; Add a timestamp and pre-acquired AIS tracking information to any image; Images are sorted based on their timestamps to generate continuous tracking data.

2. The ship in-transit cargo monitoring system according to claim 1, characterized in that: The container verification process includes: if the identification result meets the preset container abnormality alarm conditions, then an alarm message is output.

3. The ship in-transit cargo monitoring system according to claim 2, characterized in that: The cloud server (12) is further configured to perform a video authenticity verification process before executing the frame interpolation process, the video authenticity verification process including: The current position information of the vessel is obtained based on AIS tracking information; Weather information can be obtained by searching for weather forecasts online based on location information; If the offline time meets the preset weather verification conditions, the weather information for the offline time period is retrieved and recorded as weather data one. The video data for the offline time period is retrieved, and weather information is identified based on the video data to obtain the second weather data for the same period. If the weather data for weather data 1 and weather data 2 are different, a video error message will be output, and the frame interpolation process will be stopped.

4. The ship in-transit cargo monitoring system according to claim 3, characterized in that: The video authenticity verification process also includes: If the weather data for weather data one and weather data two correspond to the same weather, proceed to the next step; If the weather data is sunny or cloudy, then light and shadow features are identified based on the video data before or during the offline period and the video data during the offline period, respectively, to obtain light and shadow feature one and light and shadow feature two. The ship's navigation trajectory during the offline period is obtained based on AIS tracking information; If the second light and shadow feature does not conform to the changing trend of the first light and shadow feature over time and the navigation trajectory, then output a video error message and stop the frame interpolation process.

5. The ship in-transit cargo monitoring system according to claim 3, characterized in that: The shipborne terminal (11) is configured to: obtain the real-time speed of the ship, add a timestamp and upload it to the cloud server (12). The video authenticity verification process also includes: If the weather data for weather data one and weather data two correspond to the same weather, proceed to the next step; If weather data one meets the preset wind auxiliary judgment conditions, then wind direction feature recognition is performed based on video data before or during offline time and video data during the offline time period to obtain wind direction feature one and wind direction feature two. The ship's navigation trajectory during the offline period is obtained based on AIS tracking information; If wind direction feature 2 does not conform to the changing trend of wind direction feature 1 with wind direction and navigation trajectory, then output a video abnormality prompt message and stop the frame interpolation process.

6. The ship in-transit cargo monitoring system according to claim 2, characterized in that: The shipborne terminal (11) is configured to: obtain the real-time speed of the ship, add a timestamp and upload it to the cloud server (12). The cloud server (12) is configured as follows: Water surface and ship hull recognition are performed on video data from offline time periods; If a set of video data meets the preset ship wave observation conditions, then ship wave feature recognition is performed to obtain ship wave feature one. The ship's customs and port registration data are obtained, and the ship's wave characteristics are calculated and analyzed by combining multiple speed thresholds. If the ship's wave characteristic one does not conform to the changing trend of the ship's wave characteristic two with the speed, then an abnormal video prompt message will be output and the frame interpolation process will be terminated.

7. The ship in-transit cargo monitoring system according to claim 1, characterized in that: It also includes multiple electronic seal locks, which are mounted on the container and used to lock the container doors. Each electronic seal lock is embedded with an RFID electronic tag (4). The ship's hull is equipped with multiple readers (3), and each reader (3) is paired with an RFID electronic tag (4). The reader (3) is used to receive signals from the RFID electronic tag (4). When the electronic seal lock is closed, the RFID-connected circuit is turned on, and the reader (3) receives the signal from the RFID electronic tag (4). The reader (3) is connected to a shipboard terminal (11), which is configured as follows: If the reader (3) is detected to be disconnected from the RFID tag (4), an alarm message will be output.

8. The ship in-transit cargo monitoring system according to claim 7, characterized in that: The electronic seal lock contains a battery, and its outer casing is embedded with a solar panel, which is connected in series with the battery.

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