Multimodal transport cargo transport state monitoring system based on standardized intelligent tray group

Abstract

The invention discloses a multimodal transport cargo transportation state monitoring system based on a standardized intelligent tray group. The system comprises a modular tray group composed of a main control tray and at least one functional tray; the main control tray is integrated with an edge calculator, an RFID reader-writer, a temperature and humidity sensor and a power supply module; the functional tray comprises a strain sensor and an acceleration sensor; the tray groups are rigidly connected through electromagnetic lock catches, and a voltage sensor is arranged to monitor the connection state; the tray group transmits data through a mixed communication channel; LoRa wireless communication is adopted by default, and magnetic attraction wired connection is switched when the packet loss rate is greater than 1%; after being preprocessed by the edge calculator, the data are encrypted by the Beidou system and uploaded to the background server; the background server executes overload and unbalanced load analysis, vibration grade evaluation, environmental risk early warning and lock catch state monitoring; and triggering a user side to give an alarm when an exception is detected. The safety monitoring problem of goods transportation is effectively solved, the transportation safety is improved, and the accident risk is reduced.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of multimodal transport and monitoring of goods, and particularly relates to a multimodal transport state monitoring system based on a standardized intelligent pallet group. BACKGROUND

[0002] Under the background of global trade integration and sustained economic growth, multimodal transport has become the core driving force of the modern logistics system. In 2024, China's multimodal transport freight volume further expanded, with the amount of container rail-water intermodal transport reaching 1335 million standard containers, an increase of 15.4% year-on-year. The railway container shipment volume reached 3668 million standard containers, an increase of about 10.7% year-on-year. The number of China-Europe freight trains reached 100,000, with a shipment of over 110 million standard containers and a value of over 420 billion US dollars, showing strong development momentum. The efficient coordination of sea, land and air transportation modes has become the core competitiveness of the logistics industry.

[0003] However, there are still many obstacles in the development process of multimodal transport. First, the standards, communication protocols and monitoring systems of different transport links lack unified specifications, resulting in data gaps and monitoring blind spots in scenarios such as sea-to-land transport and land-to-air transport. Second, in existing freight transport monitoring solutions, traditional pallets only bear the function of carrying goods and lack intelligent sensing capabilities, making it difficult to capture the state of goods in real time. Although individual intelligent pallets have basic monitoring functions, they are difficult to adapt to diversified transportation demands in complex and variable multi-scenario and large-volume freight transport tasks. SUMMARY

[0004] To solve the above technical problems, the present application provides a multimodal transport state monitoring system based on a standardized intelligent pallet group, which upgrades traditional pallets to intelligent sensing nodes and data interaction hubs that monitor the state of goods transport. Through the modularization of master control pallets and functional pallets, a dynamic monitoring network covering the entire multimodal transport chain is constructed, providing a solid guarantee for the safe transport of high-risk materials such as ammunition and lithium batteries, and comprehensively improving strategic delivery capabilities and transport operation efficiency.

[0005] To achieve the above purpose, the present application provides a multimodal transport state monitoring system based on a standardized intelligent pallet group, which includes:

[0006] A modular pallet group composed of a master control pallet and at least one functional pallet;

[0007] The master control pallet integrates an edge computer, an RFID reader / writer, a temperature and humidity sensor, and a power supply module;

[0008] The functional pallet includes a strain sensor and an acceleration sensor;

[0009] The tray groups are rigidly connected through electromagnetic buckles, and a voltage sensor is arranged to monitor the connection state;

[0010] The tray groups transmit data through a mixed communication channel: LoRa wireless communication is used by default, and magnetic attraction wired connection is switched when the packet loss rate is >1%;

[0011] After the data is preprocessed by the edge calculator, it is encrypted and uploaded to the background server through the Beidou system;

[0012] The background server performs super-biased load analysis, vibration level evaluation, environmental risk warning, and buckle state monitoring;

[0013] When an anomaly is detected, the user end alarm is triggered.

[0014] Optionally, the main control tray comprises:

[0015] The edge calculator is connected with an RFID reader / writer for scanning the electronic tags of goods;

[0016] The power supply module uses a detachable lithium battery to supply power to the entire group of trays through wires;

[0017] The temperature and humidity sensor is located in the groove on the surface of the tray.

[0018] Optionally, the strain sensors of the functional tray are symmetrically arranged at the four corners, and a redundant design and data fusion algorithm are used to eliminate installation errors;

[0019] The acceleration sensor is a three-axis MEMS device with a range of ±16g, located in the center area of the tray.

[0020] Optionally, the data acquisition process comprises:

[0021] The strain sensor monitors the weight distribution of the goods in real time;

[0022] The acceleration sensor captures the transport jolt data;

[0023] The voltage sensor detects the adsorption state of the electromagnetic buckle;

[0024] The RFID reader / writer scans the attribute information of the electronic tags of goods.

[0025] Optionally, the mixed communication channel switching process comprises:

[0026] Real-time monitoring of the packet loss rate of the LoRa wireless channel;

[0027] Maintain LoRa transmission when the packet loss rate is ≤1%;

[0028] When the packet loss rate is >1%, activate the magnetic attraction interface to establish a wired connection.

[0029] Optionally, the lock state monitoring process comprises:

[0030] The voltage sensor continuously collects the electromagnetic lock input voltage;

[0031] When the voltage is lower than the threshold, trigger the local audible and visual alarm;

[0032] The alarm signal is uploaded to the background server synchronously.

[0033] Optionally, the super bias load analysis process comprises:

[0034] The empty load calibration obtains the average value of the sensor baseline;

[0035] The full load calibration calculates the sensitivity coefficient;

[0036] The temperature drift compensation corrects the sensitivity;

[0037] The total load and the center of gravity offset are calculated in real time.

[0038] Optionally, the background server processing process comprises:

[0039] The median filter and sliding window peak detection are performed on the acceleration data;

[0040] The ADC quantization error elimination and first-order calibration are performed on the temperature and humidity data;

[0041] The state profile is generated by integrating the cargo attributes, environmental parameters and attitude data.

[0042] Technical effects of the present application:

[0043] (1) The system avoids the data fault problem caused by the protocol difference of the transportation tools in multimodal transport through the independent communication capability of the standardized intelligent pallet, so that it can interact with the cloud platform in real time, realize the seamless connection of the cargo transportation data in the multimodal transport scene, eliminate the monitoring blind area, and effectively improve the logistics transportation management efficiency.

[0044] (2) The strain sensor is used to monitor the weight distribution of the goods in the container in real time, and the overload, bias load or local overload risk can be identified from the completion of loading to the transportation, so as to avoid the damage of the transportation tools and the damage of the goods caused by unreasonable loading.

[0045] (3) Realize the multi-dimensional state perception and monitoring of the whole process of cargo transportation. BRIEF DESCRIPTION OF DRAWINGS

[0046] The drawings constituting a part of this application are used to provide further understanding of the application, the illustrative embodiments of the application and the description thereof are used to explain the application, and do not constitute improper limitation on the application. In the drawings:

[0047] Figure 1 A flowchart of a goods transportation state monitoring method according to an embodiment of the present application is shown in FIG. 1.

[0048] Figure 2 A main control tray arrangement according to an embodiment of the present application is shown in FIG. 2.

[0049] Figure 3 A function tray arrangement according to an embodiment of the present application is shown in FIG. 3.

[0050] Figure 4 A smart tray group information transmission flowchart according to an embodiment of the present application is shown in FIG. 4.

[0051] Figure 5 A smart tray group and external data transmission flowchart according to an embodiment of the present application is shown in FIG. 5.

[0052] Figure 6 A smart tray group and external data transmission flowchart according to an embodiment of the present application is shown in FIG. 5. DETAILED DESCRIPTION

[0053] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0054] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a group of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0055] In the present embodiment, a multi-modal goods transportation state monitoring system based on a standardized smart tray group is provided, and a smart tray group is designed, which innovatively integrates a high-precision sensor, an edge computing module and an RFID technology, and constructs a three-dimensional monitoring system of "environment + posture + attribute". In the environmental monitoring layer, the main control tray and the function tray are equipped with temperature and humidity sensors, which can obtain the transportation environment temperature and humidity in real time, and provide data guarantee for temperature and humidity sensitive goods; the electromagnetic lock voltage sensor is responsible for monitoring the tray connection state, and ensures the stability and reliability of the transportation process. In the posture monitoring, the strain sensor can capture the overloading condition of the goods in time, and the acceleration sensor can accurately record the vibration frequency and intensity of the goods, which provides protection for the transportation of fragile goods and precision instruments. With the help of the electronic tag, the attribute monitoring obtains the data of the goods batch, specification, hazard level and the like, which is convenient for logistics tracing, transportation space planning and hazardous goods protection. Through the edge computer and the communication module built in the main control tray, these rich information can be efficiently processed and interacted. Compared with the traditional tray, the system breaks the defect of single function of the traditional tray, and brings a new solution of more intelligent and efficient goods management for multi-modal transportation, and realizes the stereoscopic monitoring of the goods transportation state.

[0056] S1 first carries out data acquisition, real-time monitoring of container weight distribution through strain sensors during cargo loading and transportation process, accurately identifying overload, unbalanced load or local overload risk. The adsorption state of the electromagnetic lock buckle between the tray groups is monitored through the voltage sensor, the cargo information is collected through the reader, the jolt condition suffered by the cargo is sensed through the acceleration sensor, and the cargo transportation environment is sensed through the temperature sensor and the humidity sensor; S2: through the edge calculator built-in the main control tray, the collected data is preprocessed, and the preliminary identification and filtering of abnormal data are completed; S3: stable data communication is carried out through wireless transmission, Beidou and other communication modes, that is, wireless transmission is mainly used between trays, LoRa wireless communication technology is used for daily data transmission, wired connection is used as backup, the data collected by the multi-source sensor is transmitted to the edge calculator, and after being processed by the edge calculator, it is uploaded to the background server through the Beidou system, ensuring smooth and unobstructed data communication; S4: the background server intelligently analyzes the received data, and respectively carries out overload and unbalanced load analysis, vibration level evaluation, environmental risk early warning and lock buckle state monitoring for different sensor data sets; S5: based on the analysis result, the cargo state database is automatically updated in the normal state, and real-time alarm is pushed to the user end in the abnormal condition. The specific working process is shown in Figure 1 .

[0057] S1-S2: The system adopts modular combination design, which is composed of one main control tray and at least one functional tray, and the number of functional trays can be flexibly adjusted according to the cargo carrying capacity. The main control tray integrates core processing and control functions, and is equipped with an edge calculator for real-time processing of data collected by each sensor. Among them, the voltage sensor is responsible for monitoring the working state of the electromagnetic lock buckle to ensure stable connection between trays; the strain sensor continuously monitors the load distribution of the cargo during transportation to accurately identify overload and unbalanced load conditions and ensure transportation safety. The reader on the main control tray can quickly read the cargo information, and cooperates with the directional antenna array to receive the cargo electronic tag data, realizing intelligent cargo management; the electromagnetic lock buckle serves as the connection hub between trays and can reliably connect each functional tray; the lithium battery pack provides stable power support for the entire intelligent tray group; the temperature sensor and the humidity sensor work together to monitor the cargo transportation environment in real time, providing comprehensive data protection for cargo storage and transportation. The specific layout of the main control sensor is shown in Figure 2 .

[0058] The functional tray, as a direct cargo bearing unit, symmetrically arranges high-precision strain sensors at four corners, adopts redundancy design and data fusion algorithm to eliminate installation error influence; a three-axis MEMS acceleration sensor with a range of ±16g and a resolution of 1mg is arranged in the center to capture vibration data at a sampling frequency of 1kHz, and the wavelet transform algorithm is used to realize feature identification and hierarchical early warning of abnormal vibration such as collision and jolt. The specific layout of the functional tray is shown in Figure 3As shown.

[0059] S3 Regarding the information transmission process, as follows: Figure 4 As shown, information collection begins first. Goods are affixed with electronic tags to store their information. The smart pallet group consists of a main control pallet and functional pallets. The main control pallet is equipped with sensors for temperature, humidity, voltage, and strain gauges, while the functional pallets are equipped with strain gauge and acceleration sensors. These sensors are responsible for collecting information on the temperature and humidity of the transport environment, the overloading and vibration of the goods, and the electromagnetic latch voltage of the pallet connections. They also collect background information on the characteristics of the external environment under different transport modes. During information transmission, data is primarily transmitted between pallets and between pallets and electronic tags using LoRa wireless communication technology. After the electronic tag information is read by the main control pallet reader, it is transmitted within the smart pallet group via LoRa technology or a wired connection. Information collected by various sensors between the main control pallet and functional pallets also interacts via LoRa wireless communication or a wired connection, enabling data sharing.

[0060] Regarding the data transmission process, as follows: Figure 5 As shown, the main control pallet and functional pallets are equipped with sensors to collect data on cargo status and the environment. The pallets are networked and transmitted via LoRa wireless communication technology. The data collected by the pallet group is routinely aggregated via LoRa wireless communication to the edge computer of the main control pallet. The main control pallet then transmits the data to the backend server via BeiDou short message communication, passing through BeiDou satellites and satellite receiving stations, and using TCP / IP+SM4 encryption. The backend server processes the data, and based on this information, users can monitor the cargo transportation status, implement overload alarms and abnormal environmental temperature and humidity warnings, and also complete cargo identification and traceability management through electronic tag information. When the system detects abnormalities such as overload or exceeding environmental standards, it will trigger an alarm response according to a preset procedure. It can also adapt to different transportation scenarios such as sea, rail, road, and air transport according to the transportation environment and cargo status, ensuring efficient, safe, and controllable transportation processes.

[0061] To address the potential interruption of data transmission during high-speed travel or in areas with unstable signal coverage, an intelligent dual-channel transmission mechanism is employed, prioritizing LoRa wireless communication technology by default. During transmission, channel quality is monitored in real time. If the packet loss rate is ≤1%, normal data transmission is performed using LoRa wireless communication technology. If the loss rate exceeds 1%, an emergency switch is triggered, activating the magnetic interface for wired transmission. Both data streams are aggregated at the edge data processing stage. After processing, the data is uploaded using BeiDou encryption and ultimately received by the backend server, ensuring the stability and reliability of data transmission and adapting to the information exchange needs of various transportation scenarios. The intelligent pallet group data transmission dual-channel switching control process is as follows: Figure 6 As shown.

[0062] S4~S5: The following are the functional solutions for monitoring cargo overloading / uneven loading, the bumps encountered, the transportation environment, and the status of the locks.

[0063] The first functional implementation plan: monitoring overloading and off-center loading during cargo transportation.

[0064] Strain gauge sensors are used to determine whether goods are overloaded or off-center during transportation. Assume the intelligent pallet group consists of one main control pallet and N functional pallets. First, an unloaded calibration is performed. When the intelligent pallet group is unloaded, for each corner sensor of the main control pallet and all functional pallets, the k-th (1,2,...,M) sensor continuously collects 100 voltage readings to obtain the baseline average value V. k Where M = 4 × (N + 1). The baseline average value is the average value of the sensor's output signal when unloaded, used to eliminate the sensor's own zero-point drift, initial strain caused by installation preload, and environmental noise.

[0065] Next, perform a full-load calibration by placing a standard weight at the center of the intelligent tray assembly. The weight should be 80% of the rated total load, i.e., F. cal =0.8×F max This is to ensure safety and cover the main measurement range. The output voltage value V of each strain sensor is recorded. loaded,k And calculate the sensitivity S k .

[0066]

[0067] Check the consistency of sensitivity coefficients of all sensors: calculate the ratio of the maximum value to the minimum value. If it exceeds 1.15 (i.e., the difference is greater than 15%), it is considered a calibration abnormality and the sensor or installation structure needs to be checked.

[0068] To mitigate the impact of environmental interference on measurement accuracy, temperature drift compensation is performed. The formula for calculating the compensated sensitivity coefficient is as follows:

[0069] S k ′=S k ·[1+α·(TT c )],(α=-2×10 -4 / ℃);

[0070] Where T c The temperature recorded during full-load calibration.

[0071] After calibration and compensation are completed, real-time load calculations are performed to determine over-offset load. The formulas for calculating the load of a single sensor and the total load are as follows:

[0072]

[0073]

[0074] This embodiment uses standard container trucks for cargo transportation. According to the "Railway Container Transport Rules" and the "Regulations on the Management of Overweight and Oversized Vehicles on Highways," the maximum gross weight is 24 tons, i.e., F... 额 =24t.

[0075] Based on the degree of overweight, overweight goods are divided into three levels: Level 1 overweight, Level 2 overweight, and Super Overweight. The following inequality is used to determine if goods are overweight.

[0076] Level 1 Overweight: F 额 <F 总 ≤1.05×F 额 ;

[0077] Level 2 overweight: 1.05 × F 额 <F 总 ≤1.09×F 额 ;

[0078] Super Overweight: F 总 >1.09×F 额 .

[0079] The total length L and total width W of the pallet assembly are centered at (x). g ,y g Based on the splicing method, the position coordinates of each sensor are calculated as (x...). k ,y k If the centroid coordinates are (x, y), then the centroid coordinates are (x, y). c ,y c )for:

[0080]

[0081] Perform off-center load determination:

[0082] When |x c -x g | > 0.1L, judged as lateral offset;

[0083] When |y c -y g |>0.05W, judged as vertical offset;

[0084] If all of the above conditions are met, it is determined to be a composite offset.

[0085] The second functional implementation plan: monitoring the bumps and jolting experienced by goods during transportation.

[0086] The X, Y, and Z axis acceleration values ​​of the MEMS accelerometer are transmitted to the edge calculator via LoRa wireless communication technology.

[0087] The acquired acceleration data undergoes necessary preprocessing, including median filtering to reduce noise. N consecutive samples are selected, sorted, and the median value is used as the filtered result for the current moment. Peak values ​​are calculated using a sliding window method, defining a time window and searching for the maximum value within this window. This is combined with a difference method, calculating the difference between adjacent samples; a change from negative to positive indicates a potential trough, and a change from positive to negative indicates a potential peak. Feature values ​​such as vibration frequency, amplitude, and duration are extracted, which helps determine the severity and type of vibration.

[0088] Set vibration thresholds and rules to determine whether goods have experienced vibrations exceeding the permissible range. Within a set sliding time window, if the cumulative vibration energy of the Z-axis acceleration data after median filtering exceeds the preset threshold, it is considered abnormal.

[0089] The third function implementation plan: monitoring the temperature and humidity environment during cargo transportation.

[0090] The temperature sensor generates a temperature-related voltage signal ΔV through the temperature sensing module. AC and V AC , where ΔV AC As the input voltage of the ADC, V AC The input voltage serves as the reference voltage for the ADC. The ADC quantizes the input voltage signal, converting it into a series of discrete digital codes proportional to the input voltage magnitude. These codes are then processed by a digital calibration system. The digital signal is then decimated and filtered by a Sinc3 filter. The Sinc3 filter is a special filter structure used to smooth the input signal and remove high-frequency noise and clutter. In this process, the filter converts the ADC output bitstream into a 16-bit quantized output.

[0091] The temperature data calculation module performs calculations on the filtered digital signal to eliminate the systematic error V introduced during the ADC quantization process. b Two sets of quantized data were obtained during the two quantization stages of the ADC. The output of the first set of quantized data is represented as (4 Δ V AC +V b ) / V AC The output of the second set of quantized data is (-4) Δ V AC +V b ) / V AC By calculating the difference between these two sets of quantization results and averaging them, most of the systematic errors can be effectively removed, thus obtaining the accurate value of the quantized output X. The quantization results are then corrected using a formula to obtain a more accurate temperature data signal. The formula for calculating the quantized output X is as follows:

[0092]

[0093] The temperature data calculation module eliminates systematic errors in the quantization results, obtaining accurate temperature data signals. The calibration module calculates the temperature value using a first-order calibration formula based on the calibration coefficients written in the register and the temperature data signal. Finally, the pulse output module outputs a corresponding number of pulse signals based on the calculated temperature value, completing the temperature measurement and output. The entire process ensures that the measurement error is controlled within ±1℃ within the temperature range of -40 to 125℃.

[0094] The calibration module substitutes the obtained quantized value X into the formula to calculate β. After obtaining β, it inputs the calibration coefficient written to the register into the calibration formula, converting the digital signal into the actual temperature value C. The calibration coefficient is used to adjust the parameters of the calibration formula to improve the accuracy of the measurement.

[0095]

[0096] C = A·β + B;

[0097] A register module is used to store these calibration coefficients and the calibrated temperature data. The pulse output module calculates the corresponding number of pulses based on the calibrated temperature data and generates corresponding pulse signals for output through specific circuitry. These pulse signals are received and processed by the edge calculator to achieve the temperature monitoring function.

[0098] Monitoring humidity during cargo transportation.

[0099] According to the humidity sensor's specifications, the raw data is converted into actual humidity values. To improve measurement accuracy, the humidity sensor needs to be calibrated. This is done by comparing the sensor readings with standard humidity measurements. Since data readings can be affected by temperature, temperature compensation algorithms can improve accuracy. Furthermore, data filtering can further enhance data stability and reliability.

[0100] After the data is transmitted to the edge calculator, when the humidity sensor detects that the humidity change during the cargo transportation process exceeds the preset range, the system will automatically trigger an alarm mechanism to promptly remind relevant personnel to take necessary countermeasures to ensure the safety and stability of cargo transportation.

[0101] The fourth function implementation scheme: the electromagnetic lock adsorption state during cargo transportation.

[0102] The magnetic attraction force of the electromagnetic latch is determined by the magnetic field generated by the energized coil, and its strength is approximately linearly related to the operating voltage. Insufficient voltage can cause a decrease in magnetic force, potentially leading to latch failure. The latch's attraction status can be indirectly determined by real-time monitoring of the voltage at its power input terminal.

[0103] Normal adsorption: When the voltage is stable within the rated voltage range, the electromagnetic latch can provide sufficient adsorption force to ensure normal operation of the latch;

[0104] Insufficient adsorption force: The voltage remains below the threshold. In this case, the electromagnetic latch's adsorption force is insufficient, which may cause the latch to fail to close completely or become loose, posing a safety hazard.

[0105] Complete failure: The voltage is close to 0V or fluctuates abnormally. In this state, the electromagnetic latch completely loses its attraction force, and the latch fails.

[0106] In case of any abnormality, a local alarm will be triggered first, using an audible and visual alarm, namely a buzzer (85dB) + red LED. This will be triggered immediately upon voltage abnormality. On-site personnel will quickly locate the equipment based on the alarm sound and red light indication. The power connection of the electromagnetic latch will be checked for proper functioning, including the power module and wiring connectors.

[0107] Fifth function implementation scheme: Obtaining cargo attribute information during cargo transportation.

[0108] Writing information such as weight, category, and name of goods onto RFID passive tags aims to achieve more efficient, transparent, and low-cost supply chain management and provide fundamental support for the Internet of Things (IoT) and digital transformation, with significant benefits. Using passive tags enhances data autonomy and independence; the tags store key information without database dependence, enabling rapid data retrieval. This makes them suitable for environments with unstable networks or offline operations, and simplifies operational processes. During logistics transfers or temporary inspections, basic information can be obtained directly by reading the tag without needing to access system data, thus improving response speed.

[0109] RFID systems achieve data collection and interaction through the collaborative work of tags, readers, and backend systems. The complete process encompasses initialization, communication establishment, data transmission, and interference optimization. Upon system startup, the reader first powers on and initializes its internal circuitry, loads the communication protocol, and simultaneously transmits radio frequency carrier signals via its antenna to provide power to the passive tags.

[0110] During the data transmission phase, the passive tag's antenna receives electromagnetic energy and converts it into electrical energy, activating the chip. Then, the communication establishment phase begins, where the reader sends a query command, the tag responds and returns a unique identifier, and the two parties establish a communication link through a protocol handshake.

[0111] The data transmission phase is divided into two modes: read and write. In read mode, the reader sends a command to retrieve the cargo information stored on the tag. In write mode, the reader sends an update command to modify the tag content, and the tag returns an operation status. After the interaction is complete, the reader sends a silence command to put the tag to sleep and uploads the communication log to the backend system for subsequent data analysis and business processing.

[0112] This embodiment employs a modular design, consisting of a main control pallet and at least one functional pallet, with the number of functional pallets adjustable according to cargo load. The pallet structure utilizes a composite molding technology combining high-strength composite materials and a lightweight metal frame, ensuring load-bearing capacity while reducing weight. The bottom features positioning protrusions that precisely match the container's bottom slots, while the top surface is textured with anti-slip material and equipped with cargo anchoring points, forming a dual anti-displacement protection mechanism. The pallet's interior employs a modular space design, supporting rapid integration and functional upgrades of sensors, communication modules, and edge computing units. Pressure sensors are installed at the pallet connection points for monitoring the electromagnetic lock status.

[0113] The main control pallet integrates an edge computing unit, power supply system, main reader / writer, and temperature and humidity sensors to form the core of data processing and transmission. An RFID reader / writer is integrated on the upper side of the main control pallet, working in conjunction with electronic tags affixed to the outer packaging of the goods to quickly read attribute information such as batch number, specifications, and hazard level. This information is then integrated with environmental and attitude data to form a comprehensive cargo status file. Functional pallets are divided into two categories: the basic type has strain gauge sensors and reader / writer antennas deployed at the four corners to continuously collect weight data during loading and transportation. Algorithms analyze weight distribution to accurately identify overloading, uneven loading, or localized overload issues. The other type of functional pallet is mostly equipped with acceleration sensors to monitor bumps and collisions during transportation in real time.

[0114] This invention utilizes a multi-sensor system to monitor the transport status of goods. During loading, an RFID reader integrated on the top of the main control pallet quickly scans the electronic tags affixed to the outer packaging of the goods to obtain information such as batch number, specifications, and hazard level. Simultaneously, strain gauge sensors and reader antennas at the four corners of the basic functional pallet begin collecting weight data to build an initial weight distribution model. Algorithms are then used to analyze and determine if overloading, uneven loading, or localized overload issues exist, and timely warnings are issued if any anomalies are detected. After loading, pressure sensors at the pallet connection points monitor the electromagnetic lock status in real time, ensuring a safe and reliable connection for the pallet assembly. During transport, the functional pallets work collaboratively: the basic pallet continuously collects weight data for dynamic weight monitoring during transport; functional pallets equipped with accelerometers capture real-time impact data such as bumps and collisions during transport; and the temperature and humidity sensor integrated on the main control pallet accurately senses changes in the temperature and humidity of the surrounding environment. The main control pallet, as the core of data processing and transmission, integrates and analyzes the collected cargo attribute information, environmental parameters, posture data, and weight distribution data to form a comprehensive cargo status profile, which is then transmitted to the monitoring center in real time via a communication module. Based on this data, the monitoring center can remotely monitor the status of cargo transportation, promptly identify potential risks, and take corresponding measures to ensure the safe and efficient transportation of goods. The solutions to cargo transportation safety measures are as follows:

[0115] To address the data gaps caused by differences in communication protocols among transport vehicles in multimodal transport, this system adopts an intelligent pallet group autonomous communication solution. The pallet group integrates a unified wireless transmission system, establishing a direct connection to the cloud backend, completely independent of the communication networks of ships, trucks, and other transport vehicles. Through this design, all cargo status data is directly collected by the pallet and uploaded to the cloud platform in real time, ensuring continuous monitoring throughout the entire process from loading and transshipment to unloading, fundamentally avoiding protocol compatibility issues between different modes of transport. This architecture, independent of the communication systems of transport vehicles, ensures reliable data transmission while significantly reducing system complexity.

[0116] To address the communication interruption issues inherent in traditional monitoring systems during multimodal transport, the intelligent pallet group employs a hybrid connection mode: wireless transmission is the primary method, utilizing LoRa wireless communication technology for low-power, short-range wireless communication, used for daily data transmission. A wired connection is provided as a backup, established via a magnetic interface in emergencies such as high data loss rates or when high security requirements are present. Data is then uploaded to the backend server via the BeiDou system, ensuring uninterrupted data communication.

[0117] To address the issue of traditional containers and pallets being connected only by simple mechanical latches, lacking deep integration of electrical and data interfaces, a matrix electromagnetic latch system is adopted, embedded in the bottom of the pallet. This system automatically aligns and locks with the guide groove at the bottom of the container's inner layer, forming a rigid connection. Simultaneously, voltage sensors are installed close to the electromagnetic latches to monitor the engagement status between the pallet and container. If the latches become loose or detach, a connection anomaly is reported in real time, ensuring a rigid connection of the transport unit. This avoids the risk of container and pallet separation due to electromagnetic latch failure.

[0118] To address the issue of overloading and off-center loading during cargo loading and transportation, strain gauge sensors are used, with sensing nodes deployed at the four corners of the pallet to monitor the container's weight distribution in real time after loading and during transportation. Differences in strain values ​​are used to determine whether the cargo has shifted or whether the center of gravity is unbalanced, accurately identifying risks of overloading, off-center loading, or localized overload.

[0119] For information transmission of multimodal transport goods, an RFID system is used, with passive RFID tags affixed to each shipment. Readers are installed on pallets to automatically scan goods during loading, unloading, or transportation.

[0120] To address potential issues such as cargo movement and changes in environmental conditions during transportation, a multi-sensor combined monitoring method is employed to obtain comprehensive data. Accelerometers are used to collect cargo vibration data, while digital temperature and humidity sensors are used to monitor temperature and humidity changes in real time during transport.

[0121] To address the challenges of efficient fusion and real-time processing of multi-sensor data, data processing is performed on the backend server. Specifically, this includes: comprehensively analyzing information from different sensors through a multimodal fusion network; monitoring the vibration experienced by goods in real time to help assess whether the goods have undergone potentially damaging severe vibrations during transportation or storage; and monitoring temperature and humidity changes, automatically triggering alarms when they exceed preset ranges to ensure safe and stable cargo transportation.

[0122] To address the power supply issue of the intelligent tray assembly, a removable lithium battery pack is placed inside the main control tray, with a chip controlling its charging and discharging management. Power is then supplied to all other trays' electromagnetic latches and accelerometer sensors via wiring, avoiding the complexity of distributed power supply wiring. The wiring connects to the main control tray's power supply through pre-drilled holes in the bottom of the trays.

[0123] To address the need for timely early warnings when cargo conditions are abnormal, a comprehensive analysis and decision support system is adopted. Based on the cargo condition identification results and combined with information such as acceleration status, overload status, temperature and humidity, a comprehensive analysis is performed to determine whether to trigger an alarm. The alarm information is then transmitted to the user terminal through a pre-set early warning system to ensure timely response measures are taken.

[0124] The above methods can effectively solve the problem of safety monitoring of goods during transportation, improve transportation safety, and reduce the risk of accidents.

[0125] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A multimodal cargo transportation status monitoring system based on standardized intelligent pallet groups, characterized in that, include: A modular tray group consisting of a main control tray and at least one functional tray; The main control tray integrates an edge calculator, an RFID reader / writer, a temperature and humidity sensor, and a power supply module; The functional tray includes a strain gauge sensor and an acceleration sensor; The trays are rigidly connected by electromagnetic latches, and voltage sensors are installed to monitor the connection status. The tray assembly transmits data through a hybrid communication channel: it uses LoRa wireless communication by default, and switches to magnetic wired connection when the packet loss rate is >1%. After being preprocessed by the edge calculator, the data is encrypted and uploaded to the backend server via the BeiDou system. The backend server performs overload analysis, vibration level assessment, environmental risk warning, and latch status monitoring. When an anomaly is detected, a client-side alarm is triggered.

2. The multimodal cargo transportation status monitoring system based on standardized intelligent pallet groups as described in claim 1, characterized in that, The main control tray includes: The edge calculator connects to an RFID reader / writer for scanning electronic tags on goods; The power supply module uses a removable lithium battery to supply power to the entire tray via wires; The temperature and humidity sensor is located in a groove on the surface of the tray.

3. The multimodal cargo transportation status monitoring system based on standardized intelligent pallet groups as described in claim 1, characterized in that, The strain gauges of the functional tray are symmetrically arranged at the four corners, and redundant design and data fusion algorithm are used to eliminate installation errors. The accelerometer is a triaxial MEMS device with a range of ±16g, located in the center of the tray.

4. The multimodal cargo transportation status monitoring system based on standardized intelligent pallet groups as described in claim 1, characterized in that, The data acquisition process includes: Strain gauge sensors monitor cargo weight distribution in real time; Accelerometers capture data on transport bumps; A voltage sensor detects the adsorption state of the electromagnetic latch; RFID readers scan the attribute information of electronic tags on goods.

5. The multimodal cargo transportation status monitoring system based on standardized intelligent pallet groups as described in claim 1, characterized in that, The hybrid communication channel switching process includes: Real-time monitoring of LoRa wireless channel packet loss rate; LoRa transmission is maintained when the packet loss rate is ≤1%; When the packet loss rate is greater than 1%, the magnetic interface is activated to establish a wired connection.

6. The multimodal cargo transportation status monitoring system based on standardized intelligent pallet groups as described in claim 1, characterized in that, The latch status monitoring process includes: The voltage sensor continuously collects the voltage at the input terminal of the electromagnetic latch; A local audible and visual alarm is triggered when the voltage is determined to be below a threshold. The alarm signal is simultaneously uploaded to the backend server.

7. The multimodal cargo transportation status monitoring system based on standardized intelligent pallet groups as described in claim 1, characterized in that, The off-center load analysis process includes: Obtain the average baseline value of the sensor during no-load calibration; Full-load calibration calculates the sensitivity coefficient; Temperature drift compensation correction sensitivity; Calculate the total load and center of gravity offset in real time.

8. The multimodal cargo transportation status monitoring system based on standardized intelligent pallet groups as described in claim 1, characterized in that, The backend server processing procedure includes: Median filtering and sliding window peak detection are performed on the acceleration data; Perform ADC quantization error elimination and first-order calibration on the temperature and humidity data; Integrate cargo attributes, environmental parameters, and attitude data to generate a status profile.

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