Trailer logistics monitoring system based on beidou positioning and ai

Abstract

The application belongs to the technical field of logistics monitoring, and specifically discloses a trailer logistics monitoring system based on Beidou positioning and AI, which comprises an algorithm optimization submodule, a positioning optimization module, an energy management module and a data transmission module, wherein the positioning acquisition module comprises a Beidou positioning terminal with multiple metamaterial antennas, and the Beidou positioning terminal is used for acquiring positioning data of a 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 the equipment in the trailer to increase the endurance of the equipment; and the data transmission module is used for realizing data transmission within 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] This application belongs to the field of logistics monitoring technology, and more specifically, relates to a trailer logistics monitoring system based on Beidou positioning and artificial intelligence (AI). Background Technology

[0002] In existing technologies, when monitoring trailer logistics, positioning devices are generally installed only inside the trailer. However, this results in poor positioning accuracy in sea areas with poor signal. While existing high-precision positioning technologies, such as lidar, can provide centimeter-level positioning accuracy, their hardware costs and maintenance expenses are high, making them difficult to apply in trailers. Furthermore, the high power consumption of high-precision positioning algorithms makes them unsuitable for trailer scenarios that require long-term operation. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the purpose of this application is to provide a trailer logistics monitoring system based on BeiDou positioning and AI, aiming to solve the problems of existing technologies being unable to simultaneously consider positioning accuracy, power consumption, and cost when monitoring trailer logistics.

[0004] To achieve the above objectives, firstly, this 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:

[0005] The positioning acquisition module includes a Beidou positioning terminal with multiple metamaterial antennas, which is used to acquire the positioning data of the trailer.

[0006] 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.

[0007] The energy management module is used to manage the energy of the equipment in the trailer to increase the equipment's range;

[0008] 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.

[0009] This application improves the positioning accuracy of trailers by using a Beidou positioning terminal equipped with a metamaterial antenna and combining positioning optimization algorithms and positioning data optimization technologies such as neural networks. It also increases the equipment's battery life and reduces energy consumption costs through energy management through equipment in the trailer. Furthermore, it connects various data in the monitoring system to external systems through a data transmission module, increasing system revenue and reducing costs. This allows the monitoring system to improve the positioning accuracy of trailers while reducing power consumption and costs.

[0010] According to the trailer logistics monitoring system based on BeiDou positioning and AI provided in this application, the positioning optimization module includes an algorithm optimization submodule and / or a neural network optimization submodule, wherein:

[0011] The algorithm optimization submodule is used for one or more of the following:

[0012] Adjust the operating status of the multiple metamaterial antennas according to different scenarios;

[0013] The Automatic Identification System (AIS) data of the vessel to which the trailer is located and the positioning data of the trailer are fused together;

[0014] The neural network optimization submodule is used for one or more of the following:

[0015] Based on the location 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.

[0016] The convolutional neural network is trained to learn the BeiDou positioning error pattern and the coordinate deviation of the trailer is corrected in real time.

[0017] The dynamic trajectory of the trailer is predicted by using an extended Kalman filter combined with a Long Short-Term Memory (LSTM) network.

[0018] This application improves the positioning accuracy of trailers by using a variety of high-precision positioning algorithms and / or a variety of neural networks.

[0019] According to the trailer logistics monitoring system based on BeiDou positioning and AI provided in this application, the system further includes:

[0020] The federated learning module is used to collaboratively optimize the sharing of local model parameters with other trailer equipment using federated learning.

[0021] This application utilizes federated learning and collaborative optimization techniques to improve model performance, communication efficiency, privacy protection, and overall system efficiency through collaboration and optimization among various trailers.

[0022] According to the trailer logistics monitoring system based on BeiDou positioning and AI provided in this application, the system further includes an anomaly detection module, which includes:

[0023] An anomaly identification submodule is used to identify abnormal conditions of the trailer;

[0024] The alarm submodule is used to trigger an alarm after the anomaly identification submodule identifies an abnormal situation.

[0025] The abnormal signal recovery submodule is used to switch to a backup signal source when the abnormality identification submodule identifies an abnormality as signal loss or distortion.

[0026] This application uses an anomaly detection module to identify, report, and recover from trailer anomalies in real time.

[0027] According to the trailer logistics monitoring system based on BeiDou positioning and AI provided in this application, the energy management module includes one or more of the following:

[0028] The data reporting management submodule is used to dynamically adjust the data reporting frequency based on reinforcement learning;

[0029] The energy consumption performance balancing submodule is used to dynamically adjust the operating mode of the equipment in the trailer based on reinforcement learning.

[0030] The route planning submodule is used to dynamically adjust ship routes based on real-time weather and ocean current data.

[0031] This application reduces the energy consumption of equipment in trailers and increases battery life through various energy management algorithms.

[0032] According to the trailer logistics monitoring system based on BeiDou positioning and AI provided in this application, the energy management module further includes one or more of the following:

[0033] A kinetic energy generator, installed on the trailer, is used to convert the vibration energy of the trailer into electrical energy;

[0034] A flexible solar film is applied to the surface of the trailer to collect solar energy.

[0035] Devices that include edge computing chips;

[0036] The sampling rate adjustment submodule is used to dynamically adjust the sampling rate of the sensors in the trailer.

[0037] This application reduces energy consumption by installing kinetic generators and flexible solar films on trailers. It also reduces energy consumption and increases battery life by using edge computing chips in the equipment and dynamically adjusting the sampling rate of sensors.

[0038] According to the trailer logistics monitoring system based on Beidou positioning and AI provided in this application, the Beidou positioning terminal runs a Tiny Machine Learning (TinyML) model that is compressed from a Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM).

[0039] This application reduces cloud dependency and enables millisecond-level localized response to abnormal events by compressing the CNN-LSTM hybrid network into a TinyML model and running it directly on the BeiDou positioning terminal.

[0040] According to the trailer logistics monitoring system based on BeiDou positioning and AI provided in this application, the system further includes one or more of the following:

[0041] The edge computing module is used to process sensor data in real time, generate early warning signals, and upload key data to the cloud.

[0042] The digital twin modeling module is used to build a digital twin of the trailer and cargo status, and combine it with external data to simulate transportation risks and optimize routes in advance.

[0043] According to the trailer logistics monitoring system based on BeiDou positioning and AI provided in this application, the data transmission module is specifically used for:

[0044] Dual-channel data transmission is achieved by utilizing the BeiDou short message function and either 4G or 5G networks.

[0045] This application utilizes the BeiDou short message function and dual-channel transmission of 4G / 5G networks to improve communication reliability in areas without network coverage at sea.

[0046] According to the trailer logistics monitoring system based on BeiDou positioning and AI provided in this application, the system further includes:

[0047] The data display module is used to visualize preset indicator data.

[0048] Overall, the technical solutions conceived in this application have the following beneficial effects compared with the prior art:

[0049] By using a BeiDou positioning terminal equipped with a metamaterial antenna in the trailer, and combining it with positioning data optimization technologies such as AI, positioning optimization algorithms, and deep learning models, the positioning accuracy of the trailer can be improved. Energy management through equipment in the trailer can increase the equipment's endurance and reduce energy consumption costs. Through the data transmission module, various data in the monitoring system can be connected to external systems, increasing system revenue and reducing costs. This allows the monitoring system to improve the positioning accuracy of the trailer while reducing power consumption and costs. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a schematic diagram of the structure of the trailer logistics monitoring system based on Beidou positioning and AI provided in the embodiments of this application. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0053] In this article, the term "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The symbol " / " in this article indicates that the related objects are in an "or" relationship; for example, A / B means A or B.

[0054] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0055] In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more, for example, multiple processing units means two or more processing units, multiple elements means two or more elements, etc.

[0056] Next, combined Figure 1 This application provides an introduction to the trailer logistics monitoring system based on BeiDou positioning and AI provided in the embodiments of this application.

[0057] Figure 1 This is a schematic diagram of the structure of a trailer logistics monitoring system based on BeiDou positioning and AI provided in an embodiment of this application, as shown below. 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.

[0058] The positioning acquisition module 110 includes a Beidou positioning terminal with multiple metamaterial antennas, which is used to acquire the positioning data of the trailer.

[0059] Installing a Beidou positioning terminal in the trailer allows for real-time acquisition of the trailer's positioning data.

[0060] To improve the signal reception sensitivity of BeiDou positioning terminals, metamaterial antennas can be used in BeiDou positioning terminals. Their miniaturization and high gain characteristics can be utilized to further improve the signal reception sensitivity of BeiDou and reduce the metal shielding effect.

[0061] Optionally, the number of metamaterial antennas can be freely set according to actual needs, and this application does not limit this.

[0062] The positioning optimization module 120 is used to optimize the positioning data of the trailer based on the positioning optimization algorithm and neural network to obtain optimized positioning data;

[0063] To improve trailer positioning accuracy, positioning data obtained from BeiDou positioning terminals can be combined with various high-precision positioning algorithms and technologies such as positioning optimization algorithms and neural networks to optimize trailer positioning data and improve trailer positioning accuracy.

[0064] Optionally, the positioning optimization algorithm and neural network used can be any positioning optimization algorithm and neural network that can optimize positioning data and improve positioning accuracy.

[0065] Energy management module 130 is used to manage the energy of the equipment in the trailer to increase the equipment's range;

[0066] The energy management module can increase the trailer equipment's range by adjusting the equipment's operating time and / or operating intensity, or by converting other forms of energy into electrical energy, or by other means.

[0067] 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.

[0068] 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).

[0069] For example, it can be integrated with logistics management systems, such as Transportation Management Systems (TMS) and Warehouse Management Systems (WMS), to automatically synchronize cargo status and transportation tasks, and achieve closed-loop management of "monitoring-scheduling-execution".

[0070] For example, it can be linked with port automation equipment, such as Automated Guided Vehicles (AGVs) or smart gates, to achieve automatic queuing and loading / unloading scheduling of trailers based on BeiDou high-precision positioning, reducing waiting time.

[0071] For example, it can transmit data with user terminals, allowing users to view data such as the current location of logistics and the status of goods. It can also automatically generate multi-dimensional transportation analysis reports based on natural language processing (NLP), such as the cause of cargo damage and route efficiency. It supports voice query and interaction, and can collect user pain points to quickly iterate the interface and functions.

[0072] By transmitting data with other systems, the value of data can be increased, efficiency can be improved, and system costs can be reduced.

[0073] This 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 system improves the positioning accuracy of the trailer. Energy management is performed through equipment in the trailer to increase equipment endurance and reduce energy consumption costs. Through a data transmission module, various data in the monitoring system can be connected to external systems to increase system revenue and reduce costs. This allows the monitoring system to improve trailer positioning accuracy while reducing power consumption and costs.

[0074] In some embodiments, the positioning optimization module 120 includes an algorithm optimization submodule and / or a neural network optimization submodule, wherein:

[0075] The algorithm optimization submodule is used for one or more of the following:

[0076] Adjust the operating status of multiple metamaterial antennas based on different scenarios;

[0077] The Automatic Identification System (AIS) data of the vessel to which the trailer is located is fused with the trailer's positioning data;

[0078] The neural network optimization submodule is used for one or more of the following:

[0079] Based on location data and trailer historical trajectories, a spatiotemporal graph model is constructed, and the future location of the trailer is predicted and multipath errors are corrected based on the spatiotemporal graph model.

[0080] Train a convolutional neural network to learn the BeiDou positioning error pattern and correct the coordinate deviation of the trailer in real time;

[0081] An extended Kalman filter combined with a long short-term memory (LSTM) network is used to predict the dynamic trajectory of the trailer.

[0082] Optionally, the algorithm optimization submodule can design an automatic switching mechanism for multiple antenna arrays to dynamically select the optimal signal source in multi-path interference scenarios, such as near port yards or bridges, thereby improving the positioning accuracy of the BeiDou positioning terminal.

[0083] Optionally, hardware reliability can be verified and data collected to optimize antenna design in extreme environments, such as high humidity and strong electromagnetic interference.

[0084] Optionally, the algorithm optimization submodule can combine Automatic Identification System (AIS) data and BeiDou positioning data, and use the Kalman filter algorithm to fuse multi-source information to improve positioning accuracy.

[0085] Optionally, the neural network optimization submodule can construct 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 trailer's future position and correct multipath errors.

[0086] Optionally, this application does not limit the specific graph neural network model used.

[0087] 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 trailer's coordinate deviation in real time, thereby improving the trailer's positioning accuracy.

[0088] 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.

[0089] In one embodiment of this application, the following steps are taken to predict the dynamic trajectory of a trailer using an extended Kalman filter combined with an LSTM network: First, sensor data of the trailer is collected. Then, the data is preprocessed, such as cleaning and normalization. Next, the extended Kalman filter algorithm is used to estimate the trailer state. 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 to complete the model training. Finally, the trained model is used to predict the dynamic trajectory of the trailer.

[0090] In some embodiments, the system further includes:

[0091] Federated learning module 150 is used to collaboratively optimize the sharing of local model parameters with other trailer equipment using federated learning.

[0092] Alternatively, federated learning can be used for collaborative optimization, where each trailer terminal shares local model parameters through federated learning to globally optimize the positioning correction algorithm and avoid data privacy leaks.

[0093] 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 techniques within 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 the original data.

[0094] In some embodiments, the system further includes an anomaly detection module, which includes:

[0095] The anomaly detection submodule is used to identify abnormal situations of the trailer;

[0096] The alarm submodule is used to trigger an alarm after the anomaly identification submodule identifies an abnormal situation.

[0097] The abnormal signal recovery submodule is used to switch to a backup signal source when the abnormality identification submodule detects an abnormal situation, such as signal loss or distortion.

[0098] Optionally, an isolated forest or autoencoder algorithm can be used to identify abnormal stopping, detours, or deviations in the trailer's trajectory and trigger an alert.

[0099] Optionally, the anomaly identification submodule can also identify various common trailer anomalies such as signal loss or distortion.

[0100] After the anomaly identification submodule detects an anomaly, it can trigger an alarm through the alarm submodule, and then report the alarm information through the data transmission module.

[0101] Optionally, a self-encoder can be used to detect signal loss or distortion and automatically switch to a backup signal source, such as BeiDou short message service, to ensure continuous data transmission.

[0102] In some embodiments, the energy management module 130 includes one or more of the following:

[0103] The data reporting management submodule is used to dynamically adjust the data reporting frequency based on reinforcement learning;

[0104] The energy consumption performance balancing submodule is used to dynamically adjust the operating mode of equipment in trailers based on reinforcement learning.

[0105] The route planning submodule is used to dynamically adjust ship routes based on real-time weather and ocean current data.

[0106] 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) to balance power consumption and accuracy.

[0107] Optionally, the data reporting management submodule can dynamically adjust the data reporting frequency of the Beidou terminal according to the ship's speed and the complexity of the route, for example, reporting every 5 minutes when near the shore and every 30 minutes when at sea.

[0108] Optionally, the data reporting management submodule can use AI algorithms to identify abnormal events, such as deviations from the flight path or sensor alarms, and immediately trigger high-priority data feedback.

[0109] Optionally, the energy consumption performance balancing submodule can dynamically adjust the device's operating mode through reinforcement learning, such as turning off some sensors at night, to extend hardware life and save energy.

[0110] Optionally, the route 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.

[0111] In some embodiments, the energy management module 130 further includes one or more of the following:

[0112] A kinetic energy generator, installed on a trailer, is used to convert the vibration energy of the trailer into electrical energy;

[0113] A flexible solar film is applied to the surface of the trailer to collect solar energy.

[0114] Devices that include edge computing chips;

[0115] The sampling rate adjustment submodule is used to dynamically adjust the sampling rate of the sensors in the trailer.

[0116] Optionally, a micro kinetic energy generator can be installed on the trailer wheels or suspension system to convert the trailer's vibration energy into electrical energy, supplementing insufficient solar power supply and increasing the equipment's range.

[0117] Optionally, a flexible solar film, such as perovskite photovoltaic material, can be covered on the trailer surface. Compared with traditional solar panels, it is lighter, more impact-resistant, improves energy collection efficiency, and increases the equipment's range.

[0118] Optionally, the equipment in the trailer can use edge computing chips, such as those based on the RISC-V architecture, to optimize terminal power consumption.

[0119] Optionally, the sensor sampling rate can be dynamically adjusted using artificial intelligence (AI) to extend battery life.

[0120] In some embodiments, the BeiDou positioning terminal runs a miniature machine learning model TinyML, which is a compressed convolutional neural network-long short-term memory network (CNN-LSTM).

[0121] By compressing the CNN-LSTM hybrid network into a TinyML model and running it directly on the BeiDou terminal, the dependence on the cloud is reduced, enabling millisecond-level localized response to abnormal events. It can also reduce power consumption and increase the battery life of the BeiDou terminal.

[0122] In one embodiment of this 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, use methods such as quantization and pruning to compress the model and verify whether the performance of the compressed model meets the requirements. Then, convert it to TinyML format, use the TinyML framework to deploy the model to the Beidou terminal, and finally perform debugging and optimization.

[0123] In some embodiments, the system further includes one or more of the following:

[0124] Edge computing module: Used to process sensor data in real time, generate early warning signals, and upload key data to the cloud;

[0125] The digital twin modeling module is used to build a digital twin of the trailer and cargo status, and combine it with external data to simulate transportation risks and optimize routes in advance.

[0126] Optionally, an edge computing module can be deployed on the trailer to process sensor data in real time and generate early warning signals, such as temperature exceeding limits, while only critical data is uploaded to the cloud, reducing bandwidth usage.

[0127] Optionally, a digital twin of the trailer and cargo status can be constructed, and combined with external data such as weather and road conditions, to simulate transportation risks and optimize routes in advance.

[0128] In some embodiments, the data transmission module 140 is specifically used for:

[0129] Utilize the BeiDou short message function and 4G or 5G networks for dual-channel data transmission.

[0130] By utilizing the BeiDou short message function and dual-channel transmission via 4G / 5G networks, communication reliability can be ensured in areas at sea without network coverage.

[0131] In some embodiments, the system further includes:

[0132] The data display module is used to visualize preset indicator data.

[0133] Optionally, a visualization platform can be developed using a Web Geographic Information System (WebGIS) to integrate the real-time location, trajectory history, and sensor data of ships and trailers, such as temperature, humidity, and vibration, and to support multi-layer switching to display route planning, weather warnings, heat maps of congested areas, etc.

[0134] Optionally, a mobile app or mini-program can be provided, allowing customers to view the real-time location of goods, receive abnormal alarm push notifications, set monitoring indicators, and automatically generate analysis reports.

[0135] Optionally, core metrics can be defined, such as positioning error rate <1% and alarm response time <5 seconds, and the system performance can be displayed in real time through the dashboard to drive continuous optimization.

[0136] Optionally, energy consumption data can be combined to calculate transportation carbon emissions, providing customers with green logistics certification support.

[0137] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0138] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as 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, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0139] It is understood that the various numerical designations used in the embodiments of this application are merely for the convenience of description and are not intended to limit the scope of the embodiments of this application.

[0140] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A Beidou positioning and AI-based trailer logistics monitoring system, characterized in that, The system comprises a positioning acquisition module, a positioning optimization module, an energy management module, and a data transmission module, wherein: The positioning acquisition module comprises a Beidou positioning terminal with multiple metamaterial antennas, which is used to acquire 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 a neural network, to obtain optimized positioning data; The energy management module is used to manage the energy of the devices in the trailer to increase the endurance of the devices; The data transmission module is used to realize data transmission within the Beidou positioning and AI-based trailer logistics monitoring system, and between the system and other systems; The energy management module comprises: A data reporting management submodule for dynamically adjusting data reporting frequency based on reinforcement learning; An energy consumption performance balancing submodule for dynamically adjusting the operating mode of the devices in the trailer based on reinforcement learning; A path planning submodule for dynamically adjusting the ship route based on real-time weather and ocean current data; The positioning optimization module comprises an algorithm optimization submodule and a neural network optimization submodule, wherein: The algorithm optimization submodule is used to: Adjust the working state of the multiple metamaterial antennas based on different scenarios; Fuse the automatic identification system data of the ship where the trailer is located and the positioning data of the trailer; The neural network optimization submodule is used to: Based on the positioning data and the historical trajectory of the trailer, construct a spatio-temporal graph model, and predict the future position of the trailer and correct the multi-path error based on the spatio-temporal graph model; Train a convolutional neural network to learn the Beidou positioning error pattern and correct the coordinate deviation of the trailer in real time; Use extended Kalman filtering combined with long short-term memory network (LSTM) to predict the dynamic trajectory of the trailer.

2. The Beidou positioning and AI-based trailer logistics monitoring system according to claim 1, characterized in that, The system further comprises: A federated learning module for sharing local model parameters with the devices of other trailers through federated learning collaborative optimization. 3.The Beidou positioning and AI-based trailer logistics monitoring system according to claim 1, characterized in that, The system further comprises an anomaly detection module, which comprises: An anomaly identification submodule for identifying abnormal conditions of the trailer; An alarm submodule for alarming after the anomaly identification submodule identifies abnormal conditions; An anomaly signal recovery submodule for switching to a backup signal source when the anomaly identification submodule identifies that the abnormal condition is signal loss or distortion.

4. The Beidou positioning and AI-based trailer logistics monitoring system according to claim 1, characterized in that, The energy management module further comprises one or more of the following: A kinetic energy generator installed on the trailer for converting vibration energy of the trailer into electrical energy; A flexible solar thin film covering the surface of the trailer for collecting solar energy; A device containing an edge computing chip; A sampling rate adjustment submodule for dynamically adjusting the sampling rate of sensors in the trailer.

5. The Beidou positioning and AI-based trailer logistics monitoring system according to claim 1, characterized in that, A convolutional neural network-long short-term memory network (CNN-LSTM) TinyML model is run in the Beidou positioning terminal. 6.The Beidou positioning and AI-based trailer logistics monitoring system according to claim 1, characterized in that, The system further comprises one or more of the following: An edge computing module for real-time processing of sensor data and generation of early warning signals, and uploading of key data to the cloud; A digital twin modeling module is configured to construct a digital twin of the trailer and the cargo state, and simulate transportation risks and optimize a path in advance in combination with external data.

7. The Beidou positioning and AI-based trailer logistics monitoring system according to claim 1, characterized in that, The data transmission module is specifically configured to: The data transmission module is specifically configured to: 8.The Beidou positioning and AI-based trailer logistics monitoring system according to claim 1, characterized in that, The data transmission module is specifically configured to: The system further includes: A data display module is configured to visually display the preset index data.

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