Trailer logistics monitoring system based on Beidou positioning and AI

Abstract

The invention belongs to the technical field of logistics monitoring, and particularly discloses a trailer logistics monitoring system based on Beidou positioning and AI, the system comprises an algorithm optimization sub-module, a positioning optimization module, an energy management module and a data transmission module, the Beidou positioning terminal is used for acquiring positioning data of the trailer; the positioning optimization module is used for optimizing the positioning data of the trailer based on a positioning optimization algorithm and / or a neural network to obtain optimized positioning data; the energy management module is used for performing energy management on equipment in the trailer so as to increase the endurance of the equipment; and the data transmission module is used for realizing data transmission in the trailer logistics monitoring system based on Beidou positioning and AI and data transmission between the trailer logistics monitoring system based on Beidou positioning and AI and other systems.

Description

Technical Field

[0001] The present application belongs to the field of logistics monitoring technology, and more specifically, to a trailer logistics monitoring system based on Beidou positioning and artificial intelligence (AI). Background Art

[0002] In the existing technology, when monitoring trailer logistics, generally only a positioning device is installed in the trailer. However, the positioning accuracy is poor in sea areas with poor signals. Although existing high-precision positioning technologies, such as lidar, can provide centimeter-level positioning accuracy, their hardware costs and maintenance costs are high, making them difficult to use in trailers. The high-precision positioning algorithm consumes high power and is not suitable for trailer scenarios that require long-term operation. Summary of the invention

[0003] In view of the defects of the prior art, the purpose of this application is to provide a trailer logistics monitoring system based on Beidou positioning and AI, aiming to solve the problem that the prior art cannot take into account positioning accuracy, power consumption and cost when monitoring trailer logistics.

[0004] To achieve the above objectives, in the first aspect, the present application provides a trailer logistics monitoring system based on Beidou positioning and AI, including a positioning acquisition module, a positioning optimization module, an energy management module and a data transmission module, wherein: The positioning acquisition module includes a Beidou positioning terminal with multiple metamaterial antennas, and the Beidou positioning terminal is used to obtain the positioning data of the trailer; The positioning optimization module is used to optimize the positioning data of the trailer based on a positioning optimization algorithm and / or a neural network to obtain optimized positioning data; The energy management module is used to manage the energy of the equipment in the trailer to increase the endurance of the equipment; The data transmission module is used to realize data transmission within the trailer logistics monitoring system based on Beidou positioning and AI, as well as data transmission between the trailer logistics monitoring system based on Beidou positioning and AI and other systems.

[0005] This application improves the positioning accuracy of the trailer by using a Beidou positioning terminal equipped with a metamaterial antenna in the trailer, and combines positioning optimization algorithms, neural networks and other positioning data optimization technologies. Energy management is performed through the equipment in the trailer to increase the equipment's endurance and reduce energy consumption costs. Through the data transmission module, various types of data in the monitoring system can be connected to external systems to increase system benefits and reduce costs, so that the monitoring system can improve the positioning accuracy of the trailer while reducing power consumption and costs.

[0006] According to a trailer logistics monitoring system based on Beidou positioning and AI provided by the present application, the positioning optimization module includes an algorithm optimization submodule and / or a neural network optimization submodule, wherein: The algorithm optimization submodule is used for one or more of the following: Based on different scenarios, adjusting the working states of the multiple metamaterial antennas; fusing the automatic identification system data of the ship where the trailer is located with the positioning data of the trailer; The neural network optimization submodule is used for one or more of the following: Based on the positioning data and the historical trajectory of the trailer, a spatiotemporal graph model is constructed, and based on the spatiotemporal graph model, a future position of the trailer is predicted and a multipath error is corrected; Training a convolutional neural network to learn Beidou positioning error patterns and correct coordinate deviations of the trailer in real time; The extended Kalman filter combined with the long short-term memory network (LSTM) is used to predict the dynamic trajectory of the trailer.

[0007] The present application improves the positioning accuracy of the trailer through a variety of high-precision positioning algorithms and / or a variety of neural networks.

[0008] According to a trailer logistics monitoring system based on Beidou positioning and AI provided by this application, the system also includes: The federated learning module is used to share local model parameters with other trailer equipment using federated learning collaborative optimization.

[0009] This application uses federated learning collaborative optimization to improve model performance, communication efficiency, privacy protection, and overall system efficiency through the collaboration and optimization technology of each trailer.

[0010] According to a trailer logistics monitoring system based on Beidou positioning and AI provided by the present application, the system also includes an anomaly detection module, and the anomaly detection module includes: An abnormality identification submodule, used to identify abnormal conditions of the trailer; An alarm submodule is used to generate an alarm after the abnormality identification submodule identifies an abnormality; The abnormal signal recovery submodule is used to switch the backup signal source when the abnormal identification submodule identifies that the abnormal situation is signal loss or distortion.

[0011] This application uses an anomaly detection module to identify, report and recover trailer anomalies in real time.

[0012] According to a trailer logistics monitoring system based on Beidou positioning and AI provided by this application, the energy management module includes one or more of the following: The data reporting management submodule is used to dynamically adjust the data reporting frequency based on reinforcement learning; An energy consumption performance balancing submodule, used for dynamically adjusting the operation mode of the equipment in the trailer based on reinforcement learning; The path planning submodule is used to dynamically adjust the ship's route based on real-time weather and ocean current data.

[0013] This application uses various energy management algorithms to reduce the energy consumption of equipment in the trailer and increase battery life.

[0014] According to a trailer logistics monitoring system based on Beidou positioning and AI provided by this application, the energy management module also includes one or more of the following: a kinetic energy generator, mounted on the trailer, for converting vibration energy of the trailer into electrical energy; A flexible solar film, covering the surface of the trailer, for collecting solar energy; Devices containing edge computing chips; The sampling rate adjustment submodule is used to dynamically adjust the sampling rate of the sensor in the trailer.

[0015] This application can reduce energy consumption by installing a kinetic generator, flexible solar film, etc. on the trailer, use edge computing chips in the equipment, and dynamically adjust the sampling rate of the sensor to reduce energy consumption and increase battery life.

[0016] According to a trailer logistics monitoring system based on Beidou positioning and AI provided by the present application, a Tiny Machine Learning (TinyML) model compressed from a Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM) is run in the Beidou positioning terminal.

[0017] This application compresses the CNN-LSTM hybrid network into a TinyML model and runs it directly on the Beidou positioning terminal, which can reduce cloud dependence and achieve millisecond-level localized response to abnormal events.

[0018] According to a trailer logistics monitoring system based on Beidou positioning and AI provided by this application, the system also includes one or more of the following: Edge computing module, used to process sensor data in real time and generate warning signals, and upload key data to the cloud; The digital twin modeling module is used to build a digital twin of the trailer and cargo status, and combine external data to simulate transportation risks and optimize routes in advance.

[0019] According to a trailer logistics monitoring system based on Beidou positioning and AI provided by this application, the data transmission module is specifically used for: Utilize the Beidou short message function and the 4th generation mobile communication technology (4G) or the 5th generation mobile communication technology (5G) network for dual-channel data transmission.

[0020] This application utilizes the Beidou short message function and 4G / 5G network dual-channel transmission to improve communication reliability in areas without network coverage at sea.

[0021] According to a trailer logistics monitoring system based on Beidou positioning and AI provided by this application, the system also includes: The data display module is used to visualize the preset indicator data.

[0022] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the prior art: By using Beidou positioning terminals equipped with metamaterial antennas in trailers and combining positioning data optimization technologies such as AI, positioning optimization algorithms and deep learning models, the positioning accuracy of trailers can be improved. Energy management is performed through the equipment in the trailer to increase the battery life of the equipment and reduce energy consumption costs. Through the data transmission module, various types of data in the monitoring system can be connected to external systems to increase system benefits and reduce costs. This allows the monitoring system to improve the positioning accuracy of the trailer while reducing power consumption and costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the present application or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 It is a structural diagram of a trailer logistics monitoring system based on Beidou positioning and AI provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0026] The term "and / or" in this article is a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The symbol " / " in this article indicates that the associated objects are in an or relationship, for example, A / B means A or B.

[0027] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0028] In the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more than two. For example, multiple processing units refer to two or more processing units, etc.; multiple elements refer to two or more elements, etc.

[0029] Next, combine Figure 1 The trailer logistics monitoring system based on Beidou positioning and AI provided in the embodiment of the present application is introduced.

[0030] Figure 1 is a structural diagram of a trailer logistics monitoring system based on Beidou positioning and AI provided in an embodiment of the present application, such as Figure 1 As shown, the system includes a positioning acquisition module 110, a positioning optimization module 120, an energy management module 130 and a data transmission module 140: The positioning acquisition module 110 includes a Beidou positioning terminal with multiple metamaterial antennas, and the Beidou positioning terminal is used to obtain the positioning data of the trailer; By installing a Beidou positioning terminal in the trailer, the trailer's positioning data can be obtained in real time.

[0031] In order to improve the signal reception sensitivity of the Beidou positioning terminal, the use of metamaterial antennas in the Beidou positioning terminal can take advantage of its miniaturization and high gain characteristics to further improve the Beidou signal reception sensitivity and reduce the metal shielding effect.

[0032] Optionally, the number of metamaterial antennas can be freely set according to actual needs, and this application does not limit this.

[0033] The positioning optimization module 120 is used to optimize the positioning data of the trailer based on the positioning optimization algorithm and the neural network to obtain the optimized positioning data; In order to improve the positioning accuracy of the trailer, the positioning data obtained by the Beidou positioning terminal can be combined with a variety of high-precision positioning algorithms and technologies such as positioning optimization algorithms and neural networks to optimize the positioning data of the trailer and improve the positioning accuracy of the trailer.

[0034] Optionally, the positioning optimization algorithm and neural network used may be any positioning optimization algorithm and neural network that can optimize positioning data and improve positioning accuracy.

[0035] The energy management module 130 is used to manage the energy of the equipment in the trailer to increase the endurance of the equipment; The energy management module can increase the endurance of the trailer equipment by adjusting the working time and / or working intensity of the equipment, or by converting other energy into electrical energy, or by other means.

[0036] The data transmission module 140 is used to realize data transmission within the trailer logistics monitoring system based on Beidou positioning and AI, as well as data transmission between the trailer logistics monitoring system based on Beidou positioning and AI and other systems.

[0037] The data transmission module can not only realize data transmission between modules within the system, but also transmit data with other systems through the standard Application Programming Interface (API).

[0038] For example, it can be integrated with logistics management systems, such as transportation management systems (TMS, WMS), to automatically synchronize cargo status and transportation tasks, and realize "monitoring-scheduling-execution" closed-loop management.

[0039] For example, it can be linked with port automation equipment, such as intelligent handling robots (Automated Guided Vehicle, AGV) or smart gates, to achieve automatic queuing and loading and unloading scheduling of trailers based on Beidou high-precision positioning, thereby reducing waiting time.

[0040] For example, it can transmit data with user terminals, allowing users to view data such as the current logistics location and cargo status. It can also automatically generate multi-dimensional transportation analysis reports based on natural language processing (NLP), such as causes of cargo damage, route efficiency, etc., support voice query and interaction, and collect user operation pain points to quickly iterate interfaces and functions.

[0041] By transmitting data with other systems, the value of data can be increased, efficiency can be improved and system costs can be reduced.

[0042] The present application provides a trailer logistics monitoring system based on Beidou positioning and AI. By using a Beidou positioning terminal equipped with a metamaterial antenna in the trailer and combining positioning data optimization technologies such as AI, positioning optimization algorithms and deep learning models, the positioning accuracy of the trailer is improved. Energy management is performed through the equipment in the trailer to increase the battery life of the equipment and reduce energy consumption costs. Through the data transmission module, various types of data in the monitoring system can be connected with external systems to increase system benefits and reduce costs, so that the monitoring system can reduce power consumption and costs while improving the positioning accuracy of the trailer.

[0043] In some embodiments, the positioning optimization module 120 includes an algorithm optimization submodule and / or a neural network optimization submodule, wherein: The algorithm optimization submodule is used for one or more of the following: Adjust the working status of multiple metamaterial antennas based on different scenarios; Fusion of the trailer’s ship’s automatic identification system data and the trailer’s positioning data; The Neural Network Optimization submodule is used for one or more of the following: Based on the positioning data and the trailer's historical trajectory, a spatiotemporal graph model is constructed, and the future position of the trailer is predicted based on the spatiotemporal graph model and multipath errors are corrected; Train the convolutional neural network to learn the Beidou positioning error pattern and correct the coordinate deviation of the trailer in real time; The extended Kalman filter combined with the long short-term memory network (LSTM) is used to predict the dynamic trajectory of the trailer.

[0044] Optionally, the algorithm optimization submodule can design an automatic switching mechanism for multiple antenna arrays for multi-path interference scenarios, such as near port yards or bridges, to dynamically select the optimal signal source and improve the positioning accuracy of the Beidou positioning terminal.

[0045] Optionally, the hardware reliability can be verified in extreme environments, such as high humidity, strong electromagnetic interference, etc., and data can be collected to optimize the antenna design.

[0046] Optionally, the algorithm optimization submodule can combine the ship automatic identification system (Automatic Identification System, AIS) data with Beidou positioning data, and fuse multi-source information through the Kalman filter algorithm to improve the positioning accuracy.

[0047] Optionally, the neural network optimization submodule can build and train a graph neural network model based on Beidou positioning data and the trailer's historical trajectory, and use the trained graph neural network model to predict the future position of the trailer and correct multipath errors.

[0048] Optionally, this application does not limit the specific graph neural network model used.

[0049] Optionally, the neural network optimization submodule can train a convolutional neural network to learn Beidou positioning error patterns, such as multipath effects, and then use the trained convolutional neural network to correct the coordinate deviation of the trailer in real time to improve the positioning accuracy of the trailer.

[0050] Optionally, the neural network optimization submodule can use an extended Kalman filter combined with an LSTM network to predict the dynamic trajectory of the trailer and reduce signal jitter.

[0051] In one embodiment of the present application, an extended Kalman filter is combined with an LSTM network to predict the dynamic trajectory of a trailer. The steps are as follows: first, the sensor data of the trailer is collected, and then the data is preprocessed such as cleaning and normalization. Then, the extended Kalman filter algorithm is used to estimate the state of the trailer, and the estimated state is used as input to train the LSTM network to predict the future state. The prediction performance of the model is evaluated using test data, the model training is completed, and finally the trained model is used to predict the dynamic trajectory of the trailer.

[0052] In some embodiments, the system further comprises: The federated learning module 150 is used to share local model parameters with other trailer equipment using federated learning collaborative optimization.

[0053] Optionally, collaborative optimization can be performed through federated learning, where each trailer terminal shares local model parameters through federated learning and globally optimizes the positioning correction algorithm to avoid data privacy leakage.

[0054] Federated learning collaborative optimization refers to the process of improving model performance, communication efficiency, privacy protection, and overall system efficiency through multi-party collaboration and optimization technology under the Federated Learning (FL) framework. Federated learning is a distributed machine learning paradigm that allows multiple participants to collaboratively train a global model without sharing original data.

[0055] In some embodiments, the system further includes an anomaly detection module, the anomaly detection module including: An abnormality identification submodule, used to identify abnormal conditions of the trailer; The alarm submodule is used to generate an alarm after the abnormality identification submodule identifies an abnormal situation; The abnormal signal recovery submodule is used to switch the backup signal source when the abnormal identification submodule identifies that the abnormal situation is a detected signal loss or distortion.

[0056] Optionally, based on the isolation forest or autoencoder algorithm, abnormal stopping, detour or deviation behavior in the trailer trajectory can be identified to trigger an early warning.

[0057] Optionally, the anomaly identification submodule can also identify various common trailer anomalies such as signal loss or distortion.

[0058] After the abnormality identification submodule identifies the abnormality, it can trigger an alarm through the alarm submodule, and then report the alarm information through the data transmission module.

[0059] Optionally, the autoencoder can be used to detect signal loss or distortion and automatically switch to an alternative signal source, such as Beidou short message, to ensure data transmission continuity.

[0060] In some embodiments, the energy management module 130 includes one or more of the following: The data reporting management submodule is used to dynamically adjust the data reporting frequency based on reinforcement learning; The energy consumption and performance balance submodule is used to dynamically adjust the operation mode of the equipment in the trailer based on reinforcement learning; The path planning submodule is used to dynamically adjust the ship's route based on real-time weather and ocean current data.

[0061] Optionally, the data reporting management submodule can dynamically adjust the data reporting frequency through reinforcement learning, such as increasing the sampling rate in complex near-shore waterways (reporting every 1 minute) and reducing the frequency in low-risk ocean areas (reporting every 30 minutes), balancing power consumption and accuracy.

[0062] Optionally, the data reporting management submodule can dynamically adjust the data reporting frequency of the Beidou terminal according to the ship speed and route complexity, for example, reporting every 5 minutes near the shore and every 30 minutes at sea.

[0063] Optionally, the data reporting management submodule can identify abnormal events through AI algorithms, such as deviation from the route, sensor alarms, etc., and immediately trigger the transmission of high-priority data.

[0064] Optionally, the energy-performance balance submodule can dynamically adjust the device operation mode through reinforcement learning, such as turning off some sensors at night, to extend the hardware life and save energy.

[0065] Optionally, the path planning submodule can use reinforcement learning to dynamically adjust the ship's route, combined with real-time weather and ocean current data, to reduce transportation time and energy consumption.

[0066] In some embodiments, the energy management module 130 further includes one or more of the following: A kinetic energy generator, mounted on the trailer, is used to convert the vibration energy of the trailer into electrical energy; A flexible solar film, covering the surface of the trailer, for collecting solar energy; Devices containing edge computing chips; The sampling rate adjustment submodule is used to dynamically adjust the sampling rate of the sensors in the trailer.

[0067] Optionally, a micro kinetic generator can be installed on the trailer wheels or suspension system to convert the trailer's vibration energy into electrical energy, supplementing the insufficient solar power supply and increasing the equipment's endurance.

[0068] Optionally, the surface of the trailer can be covered with a flexible solar film, such as perovskite photovoltaic materials, which are lighter and more impact-resistant than traditional solar panels, improve energy collection efficiency, and increase equipment endurance.

[0069] Optionally, the equipment in the trailer can use edge computing chips, such as RISC-V architecture, to optimize terminal power consumption.

[0070] Optionally, the sensor sampling rate can be dynamically adjusted through artificial intelligence (AI) to extend battery life.

[0071] In some embodiments, a micro machine learning TinyML model that compresses a convolutional neural network-long short-term memory network CNN-LSTM is run in the Beidou positioning terminal.

[0072] The CNN-LSTM hybrid network is compressed into a TinyML model and run directly on the Beidou terminal, reducing cloud dependence and achieving millisecond-level localized response to abnormal events. It can also reduce power consumption and increase the battery life of the Beidou terminal.

[0073] In one embodiment of the present application, the steps for compressing a CNN-LSTM hybrid network into a TinyML model are as follows: first, train the CNN-LSTM hybrid network to ensure that the model performs well on the target task, then compress the model using methods such as quantization and pruning, verify whether the performance of the compressed model meets the requirements, then convert it into TinyML format, use the TinyML framework to deploy the model to the Beidou terminal, and finally debug and optimize it.

[0074] In some embodiments, the system further comprises one or more of the following: Edge computing module: used to process sensor data in real time, generate warning signals, and upload key data to the cloud; The digital twin modeling module is used to build a digital twin of the trailer and cargo status, and combine external data to simulate transportation risks and optimize routes in advance.

[0075] Optionally, an edge computing module can be deployed on the trailer to process sensor data in real time and generate warning signals, such as temperature exceeding the limit, so that only critical data is uploaded to the cloud to reduce bandwidth usage.

[0076] Optionally, a digital twin of the trailer and cargo status can be constructed, combined with external data such as weather and road conditions, to simulate transportation risks and optimize routes in advance.

[0077] In some embodiments, the data transmission module 140 is specifically used to: Utilize Beidou short message function and 4G or 5G network for dual-channel data transmission.

[0078] By utilizing the Beidou short message function and 4G / 5G network dual-channel transmission, the communication reliability in areas without network coverage at sea can be ensured.

[0079] In some embodiments, the system further comprises: The data display module is used to visualize the preset indicator data.

[0080] Optionally, a visualization platform can be developed using a Web-based Geographic Information System (WebGIS) to integrate the real-time location of ships and trailers, trajectory history, and sensor data such as temperature, humidity, and vibration, support multi-layer switching, and display route planning, weather warnings, and heat maps of congested areas.

[0081] Optionally, a mobile app or mini-program can be provided, so that customers can view the location of goods in real time and receive abnormal alarm push notifications. Customers can set attention indicators, and the system automatically generates analysis reports.

[0082] Optionally, core indicators can be defined, such as positioning error rate <1%, alarm response time <5 seconds, etc., to display system performance in real time through the dashboard and drive continuous optimization.

[0083] Optionally, transport carbon emissions can be calculated in conjunction with energy consumption data to provide green logistics certification support to customers.

[0084] It is understandable that the processor in the embodiment of the present application may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0085] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0086] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.

[0087] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A trailer logistics monitoring system based on Beidou positioning and AI, characterized in that: It includes positioning acquisition module, positioning optimization module, energy management module and data transmission module, among which: The positioning acquisition module includes a Beidou positioning terminal with multiple metamaterial antennas, and the Beidou positioning terminal is used to obtain the positioning data of the trailer; The positioning optimization module is used to optimize the positioning data of the trailer based on a positioning optimization algorithm and / or a neural network to obtain optimized positioning data; The energy management module is used to manage the energy of the equipment in the trailer to increase the endurance of the equipment; The data transmission module is used to realize data transmission within the trailer logistics monitoring system based on Beidou positioning and AI, as well as data transmission between the trailer logistics monitoring system based on Beidou positioning and AI and other systems.

2. The trailer logistics monitoring system based on Beidou positioning and AI according to claim 1 is characterized in that: The positioning optimization module includes an algorithm optimization submodule and / or a neural network optimization submodule, wherein: The algorithm optimization submodule is used for one or more of the following: Based on different scenarios, adjusting the working states of the multiple metamaterial antennas; fusing the automatic identification system data of the ship where the trailer is located with the positioning data of the trailer; The neural network optimization submodule is used for one or more of the following: Based on the positioning data and the historical trajectory of the trailer, a spatiotemporal graph model is constructed, and based on the spatiotemporal graph model, a future position of the trailer is predicted and a multipath error is corrected; Training a convolutional neural network to learn Beidou positioning error patterns and correct coordinate deviations of the trailer in real time; The extended Kalman filter combined with the long short-term memory network (LSTM) is used to predict the dynamic trajectory of the trailer.

3. The trailer logistics monitoring system based on Beidou positioning and AI according to claim 1 or 2 is characterized in that: The system further comprises: The federated learning module is used to share local model parameters with other trailer equipment using federated learning collaborative optimization.

4. The trailer logistics monitoring system based on Beidou positioning and AI according to claim 1 is characterized in that: The system further includes an anomaly detection module, wherein the anomaly detection module includes: An abnormality identification submodule, used to identify abnormal conditions of the trailer; An alarm submodule is used to generate an alarm after the abnormality identification submodule identifies an abnormality; The abnormal signal recovery submodule is used to switch the backup signal source when the abnormal identification submodule identifies that the abnormal situation is signal loss or distortion.

5. The trailer logistics monitoring system based on Beidou positioning and AI according to claim 1 is characterized in that: The energy management module includes one or more of the following: The data reporting management submodule is used to dynamically adjust the data reporting frequency based on reinforcement learning; An energy consumption performance balancing submodule, used for dynamically adjusting the operation mode of the equipment in the trailer based on reinforcement learning; The path planning submodule is used to dynamically adjust the ship's route based on real-time weather and ocean current data.

6. The trailer logistics monitoring system based on Beidou positioning and AI according to claim 1 or 5, characterized in that: The energy management module also includes one or more of the following: a kinetic energy generator, mounted on the trailer, for converting vibration energy of the trailer into electrical energy; A flexible solar film, covering the surface of the trailer, for collecting solar energy; Devices containing edge computing chips; The sampling rate adjustment submodule is used to dynamically adjust the sampling rate of the sensor in the trailer.

7. The trailer logistics monitoring system based on Beidou positioning and AI according to claim 1 is characterized in that: The Beidou positioning terminal runs a micro machine learning TinyML model that compresses the convolutional neural network-long short-term memory network CNN-LSTM.

8. The trailer logistics monitoring system based on Beidou positioning and AI according to claim 1 is characterized in that: The system may further include one or more of the following: Edge computing module, used to process sensor data in real time and generate warning signals, and upload key data to the cloud; The digital twin modeling module is used to build a digital twin of the trailer and cargo status, and combine external data to simulate transportation risks and optimize routes in advance.

9. The trailer logistics monitoring system based on Beidou positioning and AI according to claim 1 is characterized in that: The data transmission module is specifically used for: Utilize the Beidou short message function and the fourth-generation mobile communication technology 4G or the fifth-generation mobile communication technology 5G network for dual-channel data transmission.

10. The trailer logistics monitoring system based on Beidou positioning and AI according to claim 1 is characterized in that: The system further comprises: The data display module is used to visualize the preset indicator data.

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