Wireless monitoring alarm system and method applied to amphibious ship

Abstract

The invention relates to the technical field of ship monitoring and alarming, in particular to a wireless monitoring and alarming system and method applied to an amphibious ship. The method comprises the following steps: building a ship state monitoring alarm device on an amphibious ship; performing hull state monitoring on the amphibious ship through a ship state monitoring alarm device to obtain a real-time hull inclination angle value; performing motion dip angle compensation on the real-time ship body dip angle value to generate a compensation dip angle estimation value; carrying out ship alarm threshold fuzzy reasoning on the compensation inclination angle estimation value, and carrying out scene dynamic adjustment to obtain a dynamic alarm threshold; and performing intelligent alarm judgment on the compensation inclination angle estimation value by using the dynamic alarm threshold value, and generating a final ship alarm signal. According to the invention, the inclination angle change of the ship under different environmental conditions can be accurately identified, and accurate monitoring, intelligent analysis and timely early warning of the inclination angle state of the ship are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of ship monitoring and alarm, and in particular to a wireless monitoring and alarm system and method applied to amphibious ships. Background Art

[0002] As a special ship that can operate in both water and land environments, amphibious ships have higher requirements for inclination stability due to the complexity and particularity of their operating environments. Amphibious ships need to navigate and operate in both water and land environments. When sailing on water, they are affected by factors such as wind waves and water currents, and when driving on land, they are affected by factors such as ground undulations and load changes, all of which can cause the ship to tilt. Excessive tilt angles will not only affect the ship's navigation performance and operation efficiency, but in severe cases, may even cause the ship to capsize, resulting in casualties and property losses. Environmental factors such as waves and water currents will interfere with the inclination sensors. When the ship is sailing in waves, the hull will constantly rock with the rise and fall of the waves, and this rocking will affect the measurement results of the inclination sensors, resulting in deviations in the measurement data. In addition, the water current will also exert a force on the hull, affecting the hull's attitude and thus the measurement accuracy of the inclination sensors. The ship's own movement will also affect the inclination measurement. For example, when the ship turns, a centrifugal force will be generated, causing the hull to tilt outward. This tilt is part of the ship's normal movement, but traditional inclination sensors often cannot distinguish this movement tilt from the tilt caused by external factors, resulting in errors in the measurement results. However, traditional inclination monitoring of amphibious ships usually simply uses sensor monitoring data for monitoring, often unable to distinguish movement tilt from the tilt caused by external factors, resulting in errors in the measurement results and the problem of frequent false alarms, ultimately affecting the safe navigation of the ship. Summary of the Invention

[0003] Based on this, the present invention provides a wireless monitoring and alarm system and method applied to amphibious ships to solve at least one of the above technical problems.

[0004] To achieve the above object, a wireless monitoring and alarm method applied to amphibious ships includes the following steps: Step S1: Build a ship status monitoring and alarm device on the amphibious ship; monitor the hull status of the amphibious ship through the ship status monitoring and alarm device to generate calibrated ship status monitoring data; Step S2: Estimate the inclination observation value based on the calibrated ship status monitoring data to obtain the real-time hull inclination value; process the ship motion parameters according to the calibrated ship status monitoring data to obtain the ship motion turning centrifugal force data; compensate the real-time hull inclination value with the ship motion turning centrifugal force data to generate a compensated inclination estimation value; Step S3: Obtain real-time water condition and meteorological data; perform fuzzy inference on the ship alarm threshold according to the real-time water condition and meteorological data and the estimated value of the compensation inclination angle to obtain the roll angle alarm threshold and the pitch angle alarm threshold respectively; perform scenario-based dynamic adjustment on the roll angle alarm threshold and the pitch angle alarm threshold to obtain the dynamic alarm threshold. Step S4: Use the dynamic alarm threshold to perform intelligent alarm judgment on the estimated value of the compensation inclination angle to generate the final ship alarm signal. Step S5: Send the final ship alarm signal to the crew's mobile devices or the shore-based monitoring center through the LoRaWAN wireless communication module to obtain the wireless alarm monitoring information.

[0005] By building a ship status monitoring and alarm device, the present invention can comprehensively monitor the hull status of the amphibious ship. At the same time, through the processing of the ship's motion parameters, the system can accurately calculate the influence of the centrifugal force when the ship turns, and perform motion inclination compensation in combination with the real-time hull inclination value. This compensation mechanism can effectively distinguish the normal motion inclination of the ship from the inclination caused by the external environment, reduce the monitoring error caused by motion factors, and significantly improve the reliability of the data. Combining real-time water conditions and meteorological data, perform fuzzy inference processing on the hull inclination alarm threshold, and dynamically adjust the alarm threshold according to the ship's operation scenario. This dynamic adjustment mechanism avoids the limitations of traditional fixed threshold settings, enables the system to flexibly set reasonable alarm thresholds according to the actual environmental conditions, reduces false alarm phenomena under complex water conditions or extreme meteorological conditions, and can timely capture real dangerous inclination changes, providing more accurate early warning support for the safe operation of the ship. It realizes independent adjustment of the roll angle and pitch angle alarm thresholds, and can perform differential processing according to the risk characteristics of different inclination directions, further improving the accuracy of the alarm. Through the intelligent alarm judgment mechanism, comprehensively analyze the estimated value of the compensated inclination angle to generate the final ship alarm signal, ensuring the credibility and practicality of the alarm signal. This intelligent judgment ability effectively avoids false alarm phenomena caused by single data anomalies, making the alarm system more stable and reliable. Finally, through the LoRaWAN wireless communication module, the alarm signal is transmitted in real time to the crew's mobile devices or the shore-based monitoring center. The system can not only achieve the real-time nature of remote alarms, but also ensure that important information is quickly transmitted to relevant personnel in case of emergencies, facilitating the crew and shore-based management personnel to take corresponding measures in a timely manner, thus greatly improving the efficiency of ship safety management and the emergency handling ability. Therefore, an application of the present invention to an amphibious ship's wireless monitoring and alarm method addresses the problems of false alarms, missed alarms, and poor adaptability in traditional amphibious ship inclination monitoring, realizes precise monitoring, intelligent analysis, and timely early warning of the ship's inclination state, and significantly improves the navigation safety of amphibious ships in complex environments.

[0006] Preferably, step S1 includes the following steps: Step S11: Install multiple triaxial accelerometers, triaxial gyroscopes and GPS modules at the bow, stern and middle part of the hull of the amphibious ship, and perform initialization calibration to build a ship status monitoring and alarm device; Step S12: Set the sampling frequency according to the ship status monitoring and alarm device, and monitor the hull status of the amphibious ship to generate raw ship status monitoring data; Step S13: Transmit the raw ship status monitoring data to the on-board data processing center through the LoRaWAN wireless communication module, and perform multi-source data parsing to generate ship status monitoring metadata; Step S14: Perform noise filtering processing on the ship status monitoring metadata to obtain noise-reduced ship status monitoring data; Step S15: Perform timestamp synchronization processing on the noise-reduced ship status monitoring data, and perform time correction based on the time signal in the GPS module to generate calibrated ship status monitoring data.

[0007] In the present invention, by installing multiple triaxial accelerometers, triaxial gyroscopes and GPS modules at the bow, stern and middle part of the hull of the amphibious ship and performing initialization calibration, it is possible to realize the all-round and multi-angle status monitoring of the ship, effectively improving the comprehensiveness and accuracy of data collection. Through the reasonable setting of the sampling frequency, the system can adapt to the requirements of different navigation and operation scenarios, ensuring the real-time and effectiveness of the monitoring data. Using the LoRaWAN wireless communication module to transmit the collected raw data to the on-board data processing center can reduce the risk of data transmission delay and loss, ensuring the efficiency and reliability of data transmission. Through multi-source data parsing technology, the data collected by different sensors are fused and processed to generate structured ship status monitoring metadata, effectively improving the usability and consistency of the data. At the same time, through noise filtering processing technology, the system can significantly reduce the environmental noise interference in the collected data, improving the accuracy and stability of the monitoring data. Further adopting timestamp synchronization processing technology and performing time correction based on the time signal of the GPS module can ensure the time consistency of the monitoring data.

[0008] Preferably, step S2 includes the following steps: Step S21: Extract real-time measurement data according to the calibrated ship status monitoring data to obtain real-time GPS positioning data, real-time accelerometer measurement data and real-time gyroscope measurement data respectively; Step S22: Estimate the Kalman tilt observation value according to the real-time accelerometer measurement data and the real-time gyroscope measurement data to obtain the real-time hull tilt value; Step S23: Extract the ship's longitude and latitude coordinates through the real-time GPS positioning data to obtain the ship's longitude and latitude coordinate data; Step S24: Process the ship's motion parameter based on the ship's latitude and longitude coordinate data to obtain the ship's motion turning centrifugal force data; Step S25: Use the ship's motion turning centrifugal force data as the tilt compensation amount to perform motion tilt compensation on the real-time hull tilt value, and generate a compensated tilt estimated value.

[0009] Through the extraction and processing of real-time measurement data from the calibrated ship condition monitoring data, the present invention can comprehensively obtain the real-time GPS positioning data, accelerometer measurement data, and gyroscope measurement data of the ship, providing high-precision and multi-dimensional basic data support for subsequent tilt calculation and compensation. By using the Kalman filtering algorithm to fuse the data of the accelerometer and gyroscope, the noise and deviation in the data of a single sensor can be effectively reduced, and the calculation accuracy and stability of the real-time hull tilt value can be improved. At the same time, by extracting the ship's latitude and longitude coordinates from the GPS positioning data, the spatial position and motion trajectory of the ship can be accurately determined, providing an important basis for the calculation of the ship's dynamic motion parameters. Based on the latitude and longitude coordinates, the ship's motion parameters are processed to calculate the centrifugal force data generated during the ship's turning process. Further combining these data to perform motion tilt compensation on the real-time hull tilt value can effectively distinguish the normal motion tilt from the tilt deviation caused by external environmental interference, thereby generating a more accurate compensated tilt estimated value.

[0010] Preferably, step S22 includes the following steps: Step S221: Decompose the three-axis measurement values according to the real-time accelerometer measurement data and the real-time gyroscope measurement data, and define the measurement variables to obtain the hull monitoring variable data; Step S222: Construct a Kalman measurement tilt calculation model based on the hull monitoring variable data; Step S223: Calculate the components of the gravitational acceleration in the three axes using the real-time accelerometer measurement data to obtain the gravitational acceleration components; Step S224: Estimate the current tilt observation value according to the gravitational acceleration components to obtain the real-time hull tilt observation estimated value; Step S225: Perform real-time measurement noise processing on the real-time accelerometer measurement data and the real-time gyroscope measurement data, and calculate the tilt predicted value using the Kalman measurement tilt calculation model to obtain the hull tilt predicted value; Step S226: Calculate the difference between the real-time hull tilt observation estimated value and the hull tilt predicted value, and perform Kalman gain weighted update to generate the real-time hull tilt value.

[0011] Through the three-axis decomposition and variable definition of the real-time accelerometer measurement data and the real-time gyroscope measurement data, the present invention can accurately extract the monitoring variable data of the hull attitude, providing a clear physical basis for the inclination calculation. Based on these monitoring variable data, a Kalman measurement inclination calculation model is constructed, making the inclination estimation and prediction have higher mathematical accuracy and dynamic adaptability. By calculating the components of the gravitational acceleration in each axis and estimating the inclination observation value accordingly, the system can quickly obtain the real-time inclination observation and estimation value of the hull. At the same time, the noise in the measurement data is processed in real time, effectively reducing the influence of external interference on the inclination calculation and ensuring the stability and reliability of the data. Using the Kalman filtering algorithm to calculate the hull inclination prediction value, and combining the real-time inclination observation and estimation value for difference calculation and Kalman gain weighted update, the inclination prediction error can be dynamically corrected to generate a more accurate real-time hull inclination value. This joint observation and prediction processing mechanism not only improves the accuracy of the inclination calculation but also enhances the system's response ability to the rapid motion state of the hull, especially effectively reducing the cumulative error and transient error in the inclination calculation in a complex environment.

[0012] Preferably, step S222 includes the following steps: Define the hull state variables of the amphibious ship to obtain the hull state variable data; wherein, the hull state variable data includes the roll angle, pitch angle, roll angle rate, and pitch angle rate. Construct a Kalman filter calculation equation based on the hull monitoring variable data and the hull state variable data to obtain an initial Kalman measurement inclination calculation model. Obtain the historical navigation data of the amphibious ship. Use the historical navigation data of the amphibious ship to conduct an initial state statistics on the hull state variable data to generate the initial value data of the ship state vector. Conduct a measurement error analysis based on the accelerometer and gyroscope, and set the initial covariance matrix of the state vector to obtain the initial covariance matrix; wherein, the diagonal elements in the initial covariance matrix represent the initial uncertainty of each state variable, and the non-diagonal elements represent the correlation between state variables. Perform Kalman parameter combination on the initial covariance matrix and the initial value data of the ship state vector, and adjust the parameters of the initial Kalman measurement inclination calculation model to obtain the Kalman measurement inclination calculation model.

[0013] The present invention defines the hull state variables of an amphibious ship, clarifies the key monitoring variables including roll angle, pitch angle, roll angle rate, and pitch angle rate, and provides a comprehensive state description basis for inclination calculation. Combining the hull monitoring variable data and state variable data, an initial Kalman filter calculation equation is constructed, enabling the model to accurately reflect the change characteristics of the ship's dynamic state. By using the historical navigation data of the amphibious ship to perform initial state statistics on the state variables and generating the initial value data of the ship state vector, the historical characteristics and dynamic laws of the ship's operation can be fully considered in the model initialization stage, thereby improving the applicability of the model and the accuracy of the initial calculation. In addition, by analyzing the measurement errors of the accelerometer and gyroscope and combining the setting of the initial covariance matrix of the state vector, the system can effectively quantify the uncertainty of each state variable and its mutual correlation. The setting of the covariance matrix ensures that the Kalman filter can balance the weights between the observed value and the predicted value during the dynamic calculation process and reduce the cumulative effect of model errors. Finally, the initial covariance matrix and the initial value of the ship state vector are combined for parameter combination, and the parameters of the initial Kalman measurement inclination calculation model are adjusted to make the model more conform to the dynamic operation characteristics of the ship and the external environment interference characteristics.

[0014] Preferably, step S24 includes the following steps: Step S241: Calculate the real-time heading angle based on the ship's latitude and longitude coordinate data to generate real-time heading angle data; Step S242: Calculate the real-time speed based on the ship's latitude and longitude coordinate data to generate real-time navigation speed data; Step S243: Calculate the rate of change of the heading angle for the real-time heading angle data, and determine whether the change in the real-time heading angle exceeds the heading angle change threshold through a preset heading angle change threshold to obtain heading angle change judgment data; Step S244: When the heading angle change judgment data is true, it is determined that the ship is turning to obtain ship turning judgment data; Step S245: Calculate the centrifugal force during ship turning based on the real-time navigation speed data based on the ship turning judgment data to obtain ship motion turning centrifugal force data.

[0015] Through the processing of the ship's latitude and longitude coordinate data, the present invention can calculate the heading angle and navigation speed of the ship in real time, provide accurate motion state data, and lay a foundation for the determination of the ship's dynamic behavior. The calculation of the real-time heading angle change rate and the judgment of the heading angle change threshold help to quickly identify whether the ship is in a turning state, avoiding the lag and inaccuracy in the dynamic recognition of ship turning in traditional methods. When it is determined that the ship is turning, the system calculates the centrifugal force data during the ship's turning in combination with the real-time navigation speed data, providing key motion parameter support for subsequent inclination compensation. Through this centrifugal force calculation process, the system can accurately quantify the influence of the dynamic external force generated during the ship's turning and separate it from the actual inclination data, avoiding misjudging the inclination caused by the centrifugal force as external environmental interference, thereby significantly improving the accuracy of inclination monitoring.

[0016] Preferably, step S3 includes the following steps: Step S31: Set the fuzzy logic input variables according to the type of amphibious ship to obtain the fuzzy logic input variables; Step S32: Set the fuzzy logic output variables according to the fuzzy logic input variables to generate the fuzzy logic output variables; Step S33: Define the fuzzy sets according to the fuzzy logic input variables and the fuzzy logic output variables, thereby establishing a fuzzy logic inference model; Step S34: Calculate the change rates of the roll angle and the pitch angle according to the estimated compensation inclination value to obtain the inclination change rate data; Step S35: Obtain the real-time water condition and meteorological data; rate the water wave height according to the real-time water condition and meteorological data to obtain the real-time water wave height data; Step S36: Input the inclination change rate data and the real-time water wave height data into the fuzzy logic inference model and perform fuzzy inference to obtain the roll angle alarm threshold and the pitch angle alarm threshold respectively; Step S37: Perform scenario-based dynamic adjustment on the roll angle alarm threshold and the pitch angle alarm threshold to obtain the dynamic alarm threshold.

[0017] By setting fuzzy logic input variables according to the types of amphibious ships and generating fuzzy logic output variables, the present invention can perform personalized configuration for the operating characteristics and safety requirements of different types of ships, ensuring the applicability and accuracy of the system. By defining fuzzy sets and establishing a fuzzy logic inference model, the system can effectively handle the non-linear relationships of multiple factors in a complex environment. Especially under dynamically changing water conditions and meteorological conditions, it can flexibly adjust the alarm threshold, avoiding the limitations of traditional fixed threshold settings. By combining the compensated tilt angle estimation value to calculate the roll angle and pitch angle change rates of the ship, the dynamic change characteristics of the ship's tilt state are further quantified, providing real-time and accurate tilt angle change data support for the setting of the alarm threshold. At the same time, by obtaining real-time water condition and meteorological data and rating the water wave height, the system can fully consider the impact of the external environment on the safe operation of the ship, combine the water wave height data with the tilt angle change rate data and input them into the fuzzy logic inference model to comprehensively judge the tilt risk of the ship. Finally, through fuzzy inference, the alarm thresholds of the roll angle and pitch angle are generated and dynamically adjusted according to the real-time operating scenario to generate dynamic alarm thresholds that are more in line with the current environment and ship state.

[0018] Preferably, step S37 includes the following steps: Step S371: Obtain the real-time operation application scenario of the amphibious ship; Step S372: Match the safety margin coefficient according to the real-time operation application scenario of the amphibious ship to obtain the operation scenario safety margin coefficient; Step S373: Calculate the scenario adjustment coefficients for the roll angle alarm threshold and the pitch angle alarm threshold respectively through the operation scenario safety margin coefficient, and obtain the roll angle alarm threshold adjustment coefficient and the pitch angle alarm threshold adjustment coefficient respectively; Step S374: Dynamically adjust the corresponding thresholds of the roll angle alarm threshold and the pitch angle alarm threshold based on the roll angle alarm threshold adjustment coefficient and the pitch angle alarm threshold adjustment coefficient to generate a dynamic alarm threshold.

[0019] By obtaining the real-time operation application scenario of the amphibious ship, the present invention can accurately identify the operating environment and working conditions where the ship is currently located, providing a scenario-based basis for the dynamic adjustment of the alarm threshold. By matching the safety margin coefficient according to different operation scenarios, the system can reasonably consider the differences in the safety requirements of the ship under various scenarios. For example, in scenarios with a large operation load or relatively complex environmental conditions, the system can match a higher safety margin coefficient, thereby making a more conservative adjustment to the alarm threshold to ensure the operational safety of the ship. By calculating the scenario adjustment coefficients of the roll angle and pitch angle alarm thresholds, the system can make differential adjustments to the roll angle and pitch angle alarm thresholds respectively according to the specific requirements of the application scenario, and then generate dynamic alarm thresholds. This scenario-based adjustment method can significantly enhance the system's adaptability to different operating environments, realizing the refined management and dynamic optimization of the alarm threshold. The finally generated dynamic alarm threshold can better fit the actual operating state of the ship in the current operation scenario, not only effectively reducing the false alarm and missed alarm phenomena caused by too high or too low thresholds, but also improving the accuracy and reliability of the alarm.

[0020] Preferably, step S4 includes the following steps: Step S41: Compare the compensated tilt angle estimated value with the dynamic alarm threshold to determine whether it exceeds the threshold, and obtain an alarm trigger flag; Step S42: Calculate the degree of tilt angle exceeding the threshold according to the alarm trigger flag, and generate tilt angle overrun degree data; Step S43: Divide the tilt angle overrun degree data into the current alarm level according to the preset alarm level division standard, and obtain preliminary alarm level data; Step S44: Evaluate the alarm credibility according to the preliminary alarm level data, and generate alarm credibility evaluation data; Step S45: Calculate the historical tilt angle change rate of the compensated tilt angle estimated value, compare it with the preset tilt angle change rate threshold, and determine whether the tilt angle change is abnormal, obtaining a tilt angle change abnormality flag; Step S46: When both the alarm trigger flag and the tilt angle change abnormality flag are true, and the alarm credibility evaluation data is higher than the preset threshold, trigger an alarm to generate a final ship alarm signal, otherwise it is determined as a false alarm and the alarm is filtered.

[0021] By comparing the compensated tilt estimation value with a dynamic alarm threshold, the present invention can quickly determine whether the current tilt exceeds the safe range, thereby generating an alarm trigger flag to provide the first layer of screening for potential tilt risks. Further, by calculating the degree of tilt exceeding the threshold and generating tilt overrun degree data, the system can quantify the specific amplitude of tilt overrun, providing a scientific basis for alarm grading. According to the preset alarm level classification standard, the system can classify different degrees of tilt overrun, making the response to alarm signals more accurate and targeted. At the same time, by evaluating the credibility of the preliminary alarm level data, false alarm phenomena caused by data noise or short-term anomalies can be effectively identified, generating alarm signals with high credibility, thereby improving the reliability of the system. In addition, by calculating the historical tilt change rate, the system can dynamically monitor the tilt change trend and judge whether the tilt change is abnormal by comparing it with the preset change rate threshold. This trend monitoring mechanism can further enhance the accuracy of alarm signals and capture potential dangerous tilt dynamics. Finally, when the alarm trigger flag and the tilt change anomaly flag are both true, and the alarm credibility evaluation data is higher than the preset threshold, the system will trigger the final ship alarm signal; otherwise, it will be determined as a false alarm and filtered. This multi-level alarm determination and filtering mechanism can significantly reduce the occurrence of false alarms, avoid interfering with the crew's judgment due to false alarms, and ensure that real tilt anomalies can be captured in a timely manner and reliable alarm signals can be sent.

[0022] Preferably, the present invention also provides a wireless monitoring and alarm system for an amphibious ship, which executes the wireless monitoring and alarm method for an amphibious ship as described above. The wireless monitoring and alarm system for an amphibious ship includes: A hull state monitoring module, which is used to set up a ship state monitoring and alarm device on the amphibious ship; monitor the hull state of the amphibious ship through the ship state monitoring and alarm device, and generate calibrated ship state monitoring data; A tilt compensation calculation module, which is used to estimate the tilt observation value according to the calibrated ship state monitoring data to obtain the real-time hull tilt value; process the ship motion parameters according to the calibrated ship state monitoring data to obtain the ship motion turning centrifugal force data; compensate the motion tilt of the real-time hull tilt value through the ship motion turning centrifugal force data to generate a compensated tilt estimation value; A threshold dynamic adjustment module, which is used to obtain real-time water condition and meteorological data; perform fuzzy inference on the ship alarm threshold according to the real-time water condition and meteorological data and the compensated tilt estimation value to obtain the roll angle alarm threshold and the pitch angle alarm threshold respectively; perform scene-based dynamic adjustment on the roll angle alarm threshold and the pitch angle alarm threshold to obtain a dynamic alarm threshold; An intelligent alarm judgment module, which is used to perform intelligent alarm judgment on the compensated tilt estimation value by using the dynamic alarm threshold to generate the final ship alarm signal; A wireless signal transmission module is used to send the final ship alarm signal to the crew's mobile devices or the shore-based monitoring center through the LoRaWAN wireless communication module, so as to obtain wireless alarm monitoring information. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 FIG. is a schematic flow chart of the steps of the wireless monitoring and alarm method of the present invention applied to an amphibious ship; Figure 2 is Figure 1 a detailed implementation step flow chart of step S1 in; Figure 3 is Figure 1 a detailed implementation step flow chart of step S4 in; The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative work fall within the scope of protection of the present invention.

[0025] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0026] It should be understood that although the terms "first", "second", etc. may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0027] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides a wireless monitoring and alarm method applied to an amphibious ship, including the following steps: Step S1: Install a ship status monitoring and alarm device on the amphibious ship; monitor the hull status of the amphibious ship through the ship status monitoring and alarm device to generate calibrated ship status monitoring data; Step S2: Estimate the inclination observation value based on the calibrated ship status monitoring data to obtain the real-time hull inclination value; process the ship motion parameters according to the calibrated ship status monitoring data to obtain the ship motion turning centrifugal force data; compensate the motion inclination for the real-time hull inclination value through the ship motion turning centrifugal force data to generate a compensated inclination estimation value; Step S3: Obtain the real-time water condition and meteorological data; perform fuzzy inference on the ship alarm threshold according to the real-time water condition and meteorological data and the compensated inclination estimation value to obtain the roll angle alarm threshold and the pitch angle alarm threshold respectively; perform scenario-based dynamic adjustment on the roll angle alarm threshold and the pitch angle alarm threshold to obtain the dynamic alarm threshold; Step S4: Use the dynamic alarm threshold to perform intelligent alarm judgment on the compensated inclination estimation value to generate the final ship alarm signal; Step S5: Send the final ship alarm signal to the crew's mobile devices or the shore-based monitoring center through the LoRaWAN wireless communication module to obtain the wireless alarm monitoring information.

[0028] In the embodiment of the present invention, the wireless monitoring and alarm method applied to the amphibious ship includes the following steps: Step S1: Install a ship status monitoring and alarm device on the amphibious ship; monitor the hull status of the amphibious ship through the ship status monitoring and alarm device to generate calibrated ship status monitoring data; In the embodiments of the present invention, integrated sensing units are installed at key positions of the bow, stern and middle part of the hull of the amphibious ship. Each unit includes an MPU6050 sensor (configured with a sampling rate of 100 Hz) for measuring triaxial acceleration and triaxial angular velocity, and a NEO-6M GPS module (configured with a sampling rate of 1 Hz) for obtaining geographical location information. The raw data collected by each sensing unit is stably transmitted to the on-board data processing center based on the STM32F407 microcontroller through the RS485 bus. The on-board center is responsible for performing preliminary data processing, specifically including: zero-bias calibration and scale factor calibration of the raw output of the MPU6050 sensor to correct the inherent errors of the sensor; at the same time, satellite signal acquisition and positioning solution are performed on the raw signal of the NEO-6M GPS module, and preliminary longitude, latitude, speed and heading information are extracted. The data after preliminary processing is wirelessly transmitted to the (shore-based or remote) data processing center by using the LoRaWAN module (adopting the SX1278 chip, operating in the 433 MHz frequency band, and the transmit power is 10 dBm). After receiving the data packet, the data processing center parses it and assigns accurate timestamp metadata to it. To further improve the data quality, the center uses a Butterworth low-pass filter with a cut-off frequency of 10 Hz to filter the received triaxial acceleration and triaxial angular velocity data (which has been calibrated on board) to remove high-frequency noise. Finally, in order to unify the time reference of the sensor data with different sampling rates, with the accurate timestamp provided by the GPS module as a reference, all processed sensor data is time-synchronized through a linear interpolation algorithm to generate calibrated ship status monitoring data including timestamp, triaxial acceleration, triaxial angular velocity, longitude, latitude, speed (ground speed) and heading (ground heading).

[0029] Step S2: Estimate the inclination observation value according to the calibrated ship status monitoring data to obtain the real-time hull inclination value; process the ship motion parameters according to the calibrated ship status monitoring data to obtain the ship motion turning centrifugal force data; perform motion inclination compensation on the real-time hull inclination value through the ship motion turning centrifugal force data to generate a compensated inclination estimation value; In the embodiment of the present invention, GPS data (latitude and longitude, speed, course) at 1 Hz and IMU data (acceleration, angular velocity) at 10 Hz are extracted from calibration data. The Kalman filter algorithm is used to fuse the IMU data, the state vector is [roll angle, pitch angle, roll angle rate, pitch angle rate], and the observation vector is [estimated roll angle by accelerometer, estimated pitch angle by accelerometer, gyro roll angle rate, gyro pitch angle rate]. The state transition matrix is based on a uniform motion model, and the observation matrix is an identity matrix. The initial state vector and covariance matrix are obtained through statistics of historical navigation data, and the process noise and measurement noise covariance matrices are adjusted according to the sensor noise characteristics. The Kalman filter outputs the real-time hull inclination values (roll angle, pitch angle). The course change rate and haversine distance are calculated based on adjacent GPS data, and the turning radius and centripetal acceleration are calculated in combination with the speed. Assuming the ship mass is 1000 kg, the centrifugal force is calculated and decomposed into the hull coordinate system, and the inclination offset is calculated and subtracted from the real-time inclination value to obtain the compensated inclination estimate value.

[0030] Step S3: Obtain real-time water condition and meteorological data; perform fuzzy inference on the ship alarm threshold according to the real-time water condition and meteorological data and the compensated inclination estimate value to obtain the roll angle alarm threshold and the pitch angle alarm threshold respectively; perform scenario-based dynamic adjustment on the roll angle alarm threshold and the pitch angle alarm threshold to obtain the dynamic alarm threshold; In the embodiment of the present invention, data such as wind speed, wind direction, wave height, and wave period are obtained through on-board meteorological sensors. The water wave height level (e.g., low, medium, high) is divided according to the wave height. The change rate of the compensated inclination estimate value is calculated. The wind speed, wave height level, and inclination change rate are used as fuzzy logic input variables, and the roll angle alarm threshold and the pitch angle alarm threshold are used as output variables. Fuzzy sets and membership functions (e.g., triangular or trapezoidal) are defined, and a fuzzy rule base is established (e.g., IF wind speed is high AND wave height is high THEN roll angle alarm threshold is large). Fuzzy inference and defuzzification are performed to obtain the roll angle alarm threshold and the pitch angle alarm threshold. According to the real-time operation scenario of the ship (e.g., normal navigation, landing / offshore), the preset safety margin coefficient (e.g., 1.0, 1.2) is matched, and the adjustment coefficient is calculated and used to adjust the alarm threshold to obtain the dynamic alarm threshold.

[0031] Step S4: Use the dynamic alarm threshold to perform intelligent alarm judgment on the compensated inclination estimate value to generate the final ship alarm signal; In the embodiments of the present invention, the compensated inclination estimation value is compared with the dynamic alarm threshold, and an alarm trigger flag is set. The degree of inclination exceeding the limit is calculated. According to the preset alarm level division standard (for example: exceeding the limit by 1-5 degrees is a first-level alarm), the preliminary alarm level is obtained. The alarm credibility is evaluated by combining the preliminary alarm level, historical alarm data, and environmental factors to generate a credibility value between 0 and 1. The historical change rate of the compensated inclination estimation value is calculated and compared with the preset threshold to determine whether the inclination change is abnormal. When both the alarm trigger flag and the inclination change abnormality flag are true, and the alarm credibility is higher than the preset threshold (for example, 0.8), an alarm is triggered to generate a final ship alarm signal including the alarm type, level, and time. Otherwise, the alarm is filtered.

[0032] Step S5: Send the final ship alarm signal to the crew's mobile devices or the shore-based monitoring center through the LoRaWAN wireless communication module to obtain wireless alarm monitoring information.

[0033] In the embodiments of the present invention, it is sent to the crew's mobile devices or the shore-based monitoring center through the LoRaWAN wireless communication module (model: SX1278, operating frequency: 433 MHz). Each IMU and GPS module is equipped with a LoRaWAN module, which is connected to the sensor through the UART interface. The LoRaWAN module accesses the LoRaWAN network in the OTAA (Over-The-Air Activation) mode. The alarm signal is encapsulated into a LoRaWAN data packet, and the payload of the data packet includes the alarm type, alarm level, alarm time, compensated inclination estimation value, dynamic alarm threshold, and ship position information. The header of the data packet contains the target device address (the server of the crew's mobile device or the shore-based monitoring center). The LoRaWAN module sends the data packet to the nearby LoRaWAN gateway through the ISM frequency band. The gateway forwards the data packet to the LoRaWAN network server. The network server decrypts and authenticates the data packet and sends the payload to the server of the crew's mobile device or the shore-based monitoring center. After receiving the alarm information, the server of the crew's mobile device or the shore-based monitoring center gives a prompt in the form of a graphical interface or voice. For example, the alarm information is displayed on the crew's mobile device and an alarm sound is emitted to prompt the crew to pay attention to the tilting state of the ship.

[0034] As an example of the present invention, refer to Figure 2 shown, for Figure 1 the detailed implementation step flow diagram of step S1 in Step S11: Install multiple three-axis accelerometers, three-axis gyroscopes, and GPS modules at the bow, stern, and middle of the hull of the amphibious ship, and perform initialization calibration to build a ship status monitoring and alarm device; In the embodiment of the present invention, an integrated unit is installed at each of the bow, stern, and middle part of the hull of the amphibious ship. Each integrated unit includes a three-axis accelerometer and a three-axis gyroscope of model MPU6050, and a GPS module of model NEO-6M. Each integrated unit is connected to the on-board data processing center through the RS485 bus. After installation, each MPU6050 is initialized and calibrated, including zero-offset calibration and scale factor calibration. Zero-offset calibration is performed by collecting data for a certain period of time under static conditions and calculating the average value as the zero-offset value. Scale factor calibration is performed by placing the sensor in an environment with known acceleration and angular velocity and calculating the ratio between the actual value and the measured value. The initialization calibration of the GPS module includes acquiring satellite signals and performing positioning and resolution calculations.

[0035] Step S12: Set the sampling frequency according to the ship status monitoring and alarm device, and monitor the hull status of the amphibious ship to generate the original ship status monitoring data; In the embodiment of the present invention, the sampling frequencies of the three-axis accelerometer and the three-axis gyroscope are set to 100 Hz, and the sampling frequency of the GPS module is set to 1 Hz. The data acquisition module in each integrated unit acquires sensor data at the set sampling frequency. The data of the three-axis accelerometer and the three-axis gyroscope include three-axis acceleration values and three-axis angular velocity values, with units of g and ° / s respectively. The data of the GPS module includes information such as longitude, latitude, altitude, speed, heading, and timestamp. The acquired original data is stored in the buffer inside the integrated unit.

[0036] Step S13: Transmit the original ship status monitoring data to the on-board data processing center through the LoRaWAN wireless communication module, and perform multi-source data parsing to generate the meta-data of the ship status monitoring; In the embodiment of the present invention, each integrated unit sends the acquired original data to the on-board data processing center through the LoRaWAN wireless communication module. The LoRaWAN wireless communication module uses the SX1278 chip, with a working frequency of 433 MHz and a transmission power of 10 dBm. The on-board data processing center is based on the STM32F407 microcontroller and receives data from each integrated unit. The data processing center parses the received data packets, extracts the data of each sensor, and classifies them according to the data source and type to generate the meta-data of the ship status monitoring. The meta-data contains data from different sensors and the corresponding timestamp information.

[0037] Step S14: Perform noise filtering processing on the meta-data of the ship status monitoring to obtain the denoised ship status monitoring data; In the embodiments of the present invention, noise filtering is performed on the acceleration and angular velocity data in the ship status monitoring metadata. A Butterworth low-pass filter with a cut-off frequency of 10 Hz is used to filter out high-frequency noise. Since the GPS data has a relatively low sampling frequency, no filtering is performed on it.

[0038] Step S15: Perform timestamp synchronization on the noise-reduced ship status monitoring data, and use the time signal in the GPS module as a reference for time correction to generate calibrated ship status monitoring data.

[0039] In the embodiments of the present invention, due to the time delay in data acquisition and transmission of each sensor, it is necessary to perform timestamp synchronization on the noise-reduced ship status monitoring data. Using the time signal provided by the GPS module as the reference time, time correction is performed on the data of the accelerometer and gyroscope. The linear interpolation method is used to align the acceleration and angular velocity data with the GPS time. The data after time synchronization and correction is the calibrated ship status monitoring data, which includes information such as timestamps, three-axis acceleration, three-axis angular velocity, longitude and latitude, speed, and heading.

[0040] Preferably, step S2 includes the following steps: Step S21: Extract real-time measurement data according to the calibrated ship status monitoring data to obtain real-time GPS positioning data, real-time accelerometer measurement data, and real-time gyroscope measurement data respectively; Step S22: Estimate the Kalman tilt observation value based on the real-time accelerometer measurement data and the real-time gyroscope measurement data to obtain the real-time hull tilt value; Step S23: Extract the ship's longitude and latitude coordinates through the real-time GPS positioning data to obtain the ship's longitude and latitude coordinate data; Step S24: Process the ship's motion parameters according to the ship's longitude and latitude coordinate data to obtain the ship's motion turning centrifugal force data; Step S25: Use the ship's motion turning centrifugal force data as the tilt compensation amount to perform motion tilt compensation on the real-time hull tilt value to generate a compensated tilt estimation value.

[0041] In the embodiments of the present invention, real-time GPS positioning data including longitude, latitude, speed, and heading are extracted from the calibrated ship status monitoring data, and the data update frequency is 1 Hz. At the same time, real-time accelerometer measurement data including three-axis acceleration values (ax, ay, az) in g unit are extracted, and the data update frequency is 10 Hz. In addition, real-time gyroscope measurement data including three-axis angular velocity values (gx, gy, gz) in ° / s unit are extracted, and the data update frequency is 10 Hz. The Kalman filter algorithm is used to fuse the real-time accelerometer measurement data and the real-time gyroscope measurement data to obtain a more accurate hull inclination value. The state variables of the Kalman filter include hull inclination and angular velocity, and the observed variables are the measurement values of the accelerometer and the gyroscope. Through the prediction and update steps, the Kalman filter continuously corrects the estimated values of the state variables, and finally obtains the real-time hull inclination value including roll angle and pitch angle. The units of the roll angle and the pitch angle are degrees. The longitude and latitude coordinate data of the ship are extracted from the real-time GPS positioning data in degrees, and these data are used to calculate the motion parameters of the ship, such as speed and heading change rate. According to the longitude and latitude coordinate data at two adjacent times, the heading change rate and displacement of the ship are calculated. Combining the speed information in the real-time GPS positioning data, the turning radius of the ship is calculated. The turning radius is calculated using the formula R = d / (2 sin(Δθ / 2)), where d is the distance between two points and Δθ is the heading change. Then, the centripetal acceleration of the ship is calculated using the formula a = v^2 / R, where v is the ship speed. Finally, according to Newton's second law F = ma, the turning centrifugal force of the ship's motion is calculated, where m is the ship mass and is set to a fixed value. The turning centrifugal force of the ship's motion calculated in step S24 is decomposed into the x-axis and y-axis directions of the hull coordinate system to obtain Fx and Fy. According to Fx and Fy, the inclination offset caused by the centrifugal force is calculated: Δθx = arctan(Fx / (mg)), Δθy = arctan(Fy / (mg)), where g is the acceleration due to gravity. The real-time hull inclination value obtained in step S22 is subtracted by the corresponding inclination offset to obtain the compensated inclination estimated value: compensated roll angle = roll angle - Δθy, compensated pitch angle = pitch angle - Δθx.

[0042] Preferably, step S22 includes the following steps: Step S221: Decompose the three-axis measurement values according to the real-time accelerometer measurement data and the real-time gyroscope measurement data, and define the measurement variables to obtain the hull monitoring variable data; Step S222: Construct a Kalman measurement inclination calculation model according to the hull monitoring variable data; Step S223: Calculate the components of the acceleration due to gravity in the three axes using the real-time accelerometer measurement data to obtain the gravity acceleration components; Step S224: Estimate the current tilt observation value based on the gravity acceleration component to obtain the real-time hull tilt observation and estimation value; Step S225: Perform real-time measurement noise processing on the real-time accelerometer measurement data and the real-time gyroscope measurement data, and calculate the tilt prediction value using the Kalman measurement tilt calculation model to obtain the hull tilt prediction value; Step S226: Calculate the difference between the real-time hull tilt observation and estimation value and the hull tilt prediction value, and perform Kalman gain weighted update to generate the real-time hull tilt value.

[0043] In the embodiments of the present invention, the real-time accelerometer measurement data is decomposed into acceleration components in three axial directions, denoted as ax, ay, and az respectively, with the unit of g. The real-time gyroscope measurement data is decomposed into angular velocity components in three axial directions, denoted as gx, gy, and gz respectively, with the unit of ° / s. Define the hull monitoring variable data, including the roll angle φ, the pitch angle θ, and the angular velocities gx, gy, and gz. Construct a Kalman filter model for estimating the hull inclination. The state vector X is defined as [φ, θ, gx, gy, gz]T, representing the roll angle, the pitch angle, and the three-axis angular velocities respectively. The state transition matrix F is determined according to the hull kinematic model. The observation vector Z is defined as [φ_meas, θ_meas, gx_meas, gy_meas, gz_meas]T, where gx_meas is the measured value of the angular velocity on the x-axis, gy_meas is the measured value of the angular velocity on the y-axis, gz_meas is the measured value of the angular velocity on the z-axis, and T is the matrix transpose symbol. Respectively represent the roll angle, the pitch angle, and the three-axis angular velocities measured by the accelerometer and the gyroscope. The observation matrix H maps the state vector to the observation vector. The process noise covariance matrix Q and the measurement noise covariance matrix R are determined according to the sensor noise characteristics. According to the real-time accelerometer measurement data ax, ay, and az, calculate the components of the gravitational acceleration in the three axial directions. Assuming that when the hull coordinate system is aligned with the direction of gravity, the gravitational acceleration components are [0, 0, g]T, where g is the gravitational acceleration constant with a value of 9.8 m / s². Through coordinate transformation, the gravitational acceleration components are transformed into the components in the current attitude of the hull, denoted as gx_acc, gy_acc, and gz_acc. Use the gravitational acceleration components gx_acc, gy_acc, and gz_acc to estimate the current hull inclination observation value. The calculation formula is: φ_meas = arctan(gy_acc / gz_acc); θ_meas = arctan(gx_acc / sqrt(gy_acc² + gz_acc²)). The obtained φ_meas and θ_meas are the estimated values of the real-time hull inclination, with the unit of degrees. Perform noise processing on the real-time accelerometer measurement data and the real-time gyroscope measurement data, such as using a low-pass filter to remove high-frequency noise. Use the Kalman filter model constructed in step S222 to calculate the predicted value of the inclination. According to the state estimate value at the previous moment and the state transition matrix F, predict the state vector X_pred at the current moment. Calculate the difference between the estimated value of the real-time hull inclination observation and the predicted value of the hull inclination, that is, the measurement residual. Calculate the Kalman gain K for fusing the predicted value and the observed value. Use the Kalman gain to update the state predicted value to obtain the state estimate value X_est at the current moment, which includes the real-time hull inclination values φ and θ, with the unit of degrees. X_est = X_pred + K (Z - H X_pred). The updated state estimate is used as the prediction input for the next moment, and the Kalman filter calculation is performed iteratively.

[0044] Preferably, step S222 includes the following steps: Define the hull state variables of the amphibious ship to obtain the hull state variable data; wherein, the hull state variable data includes the roll angle, pitch angle, roll rate, and pitch rate. Construct a Kalman filter calculation equation based on the hull monitoring variable data and the hull state variable data to obtain an initial Kalman measurement inclination calculation model. Obtain the historical navigation data of the amphibious ship. Use the historical navigation data of the amphibious ship to perform an initial state statistics on the hull state variable data to generate the initial value data of the ship state vector. Perform measurement error analysis based on the accelerometer and gyroscope, and set the initial covariance matrix of the state vector to obtain the initial covariance matrix; wherein, the diagonal elements in the initial covariance matrix represent the initial uncertainties of each state variable, and the non-diagonal elements represent the correlations between state variables. Combine the initial covariance matrix and the initial value data of the ship state vector for Kalman parameter combination, and adjust the parameters of the initial Kalman measurement inclination calculation model to obtain the Kalman measurement inclination calculation model.

[0045] In the embodiment of the present invention, the hull state variables are defined to describe the attitude and motion state of the hull. The hull state variable data includes the roll angle (roll, φ), pitch angle (pitch, θ), roll rate (rollrate, φ̇), and pitch rate (pitchrate, θ̇). The units of the roll angle and pitch angle are radians, and the units of the roll rate and pitch rate are radians / second. Based on the hull state variables and the hull monitoring variables (accelerometer and gyroscope data), a Kalman filter calculation equation is constructed. The state vector X is defined as . The state transition matrix F describes the variation relationship of the hull state variables over time. The initial Kalman measurement inclination calculation model includes the state transition matrix F, the observation matrix H, and the process noise covariance matrix Q and measurement noise covariance matrix R that have not been initialized. Collect the historical navigation data of the amphibious ship under different sea conditions and working conditions, including accelerometer, gyroscope, and GPS data. Analyze the historical navigation data, and statistically calculate the average values of the roll angle, pitch angle, roll rate, and pitch rate of the ship in the stationary state as the initial value data of the ship state vector. For example, the data for 10 seconds in the stationary state can be taken for averaging. Analyze the measurement errors of the accelerometer and gyroscope, for example, quantify the measurement noise by calculating the standard deviation of the sensor output. Based on the results of the measurement error analysis, set the initial covariance matrix P0 of the state vector. P0 is a 4x4 matrix, the diagonal elements of which represent the initial uncertainties of the respective state variables, and the off-diagonal elements represent the correlations between the state variables. For example, if it is assumed that there is no correlation between the state variables, P0 can be set as a diagonal matrix, and the diagonal elements are the squares of the initial uncertainties of the respective state variables. Substitute the initial covariance matrix P0 and the initial value data X0 of the ship state vector into the initial Kalman measurement tilt calculation model. Adjust the process noise covariance matrix Q and the measurement noise covariance matrix R according to the historical navigation data and the actual application scenario to obtain the best filtering effect. Finally, obtain the Kalman measurement tilt calculation model, including the state transition matrix F, the observation matrix H, the process noise covariance matrix Q, the measurement noise covariance matrix R, the initial state vector X0, and the initial covariance matrix P0.

[0046] Preferably, step S24 includes the following steps: Step S241: Calculate the real-time heading angle based on the ship's latitude and longitude coordinate data to generate real-time heading angle data; Step S242: Calculate the real-time speed based on the ship's latitude and longitude coordinate data to generate real-time navigation speed data; Step S243: Calculate the rate of change of the heading angle for the real-time heading angle data, and determine whether the change in the real-time heading angle exceeds the heading angle change threshold through a preset heading angle change threshold to obtain heading angle change judgment data; Step S244: When the heading angle change judgment data is true, it is determined that the ship is turning to obtain ship turning judgment data; Step S245: Calculate the centrifugal force when the ship is turning based on the real-time navigation speed data based on the ship turning judgment data to obtain ship motion turning centrifugal force data.

[0047] In the embodiments of the present invention, the real-time heading angle is calculated using the ship's latitude and longitude coordinate data (latitude1, longitude1) and (latitude2, longitude2) at two adjacent times. First, the latitude and longitude coordinates are converted into coordinates in the Cartesian coordinate system, and the WGS84 ellipsoid model is used for the conversion. Then, the arctangent function is used to calculate the angle of the line connecting the two coordinate points, that is, the heading angle. The calculation formula is: heading = atan2(longitude2 - longitude1, latitude2 - latitude1). The calculated heading angle is converted into degrees and limited between 0 and 360 degrees. Using the ship's latitude and longitude coordinate data and timestamps at two adjacent times, the real-time sailing speed is calculated. First, the distance between the two coordinate points is calculated, and the Haversine formula is used to calculate the spherical distance. Then, the distance is divided by the time difference to obtain the real-time sailing speed, with the unit of meters per second. The difference between the real-time heading angle data at two adjacent times is calculated to obtain the heading angle change amount. The heading angle change amount is divided by the time difference to obtain the heading angle change rate, with the unit of degrees per second. A preset heading angle change threshold is set, for example, 5 degrees per second. The absolute value of the heading angle change rate is compared with the preset threshold. If the absolute value of the heading angle change rate is greater than the threshold, the heading angle change judgment data is true; otherwise, it is false. According to the heading angle change judgment data obtained in step S243, it is judged whether the ship is turning. If the heading angle change judgment data is true, the ship turning judgment data is true, indicating that the ship is turning; otherwise, the ship turning judgment data is false, indicating that the ship is not turning or is in a straight sailing state. When the ship turning judgment data is true, the centrifugal force when the ship turns is calculated according to the real-time sailing speed data and the heading angle change rate. First, the turning radius is calculated according to the heading angle change rate and the real-time sailing speed. The formula is: radius = velocity / (angular_velocity * pi / 180), where velocity is the real-time sailing speed and angular_velocity is the heading angle change rate. Then, the centrifugal force is calculated according to the turning radius and the real-time sailing speed. The formula is: centrifugal_force = mass * velocity^2 / radius, where mass is the ship's mass, which is set to a fixed value, for example, 1000 kg. The ship motion turning centrifugal force data is obtained, with the unit of Newton. If the ship turning judgment data is false, the ship motion turning centrifugal force data is set to 0.

[0048] Preferably, step S3 includes the following steps: Step S31: Set the fuzzy logic input variables according to the amphibious ship type to obtain the fuzzy logic input variables; Step S32: Set the fuzzy logic output variables according to the fuzzy logic input variables to generate fuzzy logic output variables; Step S33: Define the fuzzy sets according to the fuzzy logic input variables and the fuzzy logic output variables, thereby establishing a fuzzy logic inference model; Step S34: Calculate the roll angle and pitch angle change rates based on the compensated tilt angle estimation value to obtain tilt angle change rate data; Step S35: Obtain real-time water condition and meteorological data; rate the water wave height according to the real-time water condition and meteorological data to obtain real-time water wave height data; Step S36: Input the tilt angle change rate data and the real-time water wave height data into the fuzzy logic inference model, and perform fuzzy inference to obtain the roll angle alarm threshold and the pitch angle alarm threshold respectively; Step S37: Perform scenario-based dynamic adjustment on the roll angle alarm threshold and the pitch angle alarm threshold to obtain dynamic alarm thresholds.

[0049] In the embodiments of the present invention, according to the type and operating characteristics of the amphibious ship, fuzzy logic input variables are set. The input variables include real-time water condition and meteorological data and inclination rate of change data. The real-time water condition and meteorological data include parameters such as wind speed, wind direction, wave height, and wave period. The inclination rate of change data includes the roll angle rate of change and the pitch angle rate of change. For example, for a certain type of amphibious ship, the wind speed, wave height, roll angle rate of change, and pitch angle rate of change are set as fuzzy logic input variables. According to a predetermined alarm strategy, fuzzy logic output variables are set. The output variables are the roll angle alarm threshold and the pitch angle alarm threshold. These two thresholds are used to determine whether the ship's inclination degree reaches the alarm level. Fuzzy sets are defined for each fuzzy logic input variable and output variable. For example, the wind speed is divided into three fuzzy sets: "low", "medium", and "high", and each set corresponds to a membership function, such as a triangular membership function or a trapezoidal membership function. Similarly, the wave height, roll angle rate of change, pitch angle rate of change, roll angle alarm threshold, and pitch angle alarm threshold are also divided into corresponding fuzzy sets, and the corresponding membership functions are defined. According to the pre-set rules, a fuzzy logic inference model is established. The fuzzy rule base contains a series of IF-THEN rules, such as: IF the wind speed is "high" AND the wave height is "high" THEN the roll angle alarm threshold is "large"; IF the wind speed is "low" AND the wave height is "low" THEN the roll angle alarm threshold is "small". According to the estimated values of the compensated inclination angles at two adjacent moments, the roll angle rate of change and the pitch angle rate of change are calculated. The calculation formula is: rate of change = (current value - previous moment value) / time interval. Real-time water condition and meteorological data, including wind speed, wind direction, wave height, and wave period, are obtained through on-board meteorological sensors. According to the numerical range of the wave height, it is divided into different levels, such as three levels: "low", "medium", and "high", corresponding to different wave height ranges. For example, when the wave height is less than 1 meter, it is "low", when it is between 1 and 2 meters, it is "medium", and when it is greater than 2 meters, it is "high". The wave height level is used as the real-time water wave height data. The inclination rate of change data obtained in step S34 and the real-time water wave height data obtained in step S35 are input into the fuzzy logic inference model established in step S33. According to the fuzzy rule base and the membership function, fuzzy inference is performed to calculate the fuzzy values of the output variables. Then, the fuzzy values of the output variables are defuzzified to obtain the clear roll angle alarm threshold and pitch angle alarm threshold. According to the current operating scenario of the amphibious ship, the roll angle alarm threshold and pitch angle alarm threshold obtained in step S36 are dynamically adjusted. For example, during landing / offshore operations, since the hull attitude changes greatly, the alarm threshold can be appropriately relaxed. During normal navigation, the standard alarm threshold is adopted. The adjusted threshold is the dynamic alarm threshold.

[0050] Preferably, step S37 includes the following steps: Step S371: Obtain the real-time operation application scenario of the amphibious ship; Step S372: Match the safety margin coefficient according to the real-time operation application scenario of the amphibious ship to obtain the safety margin coefficient of the operation scenario; Step S373: Calculate the scenario adjustment coefficients for the roll angle alarm threshold and the pitch angle alarm threshold respectively through the safety margin coefficient of the operation scenario, and obtain the roll angle alarm threshold adjustment coefficient and the pitch angle alarm threshold adjustment coefficient respectively; Step S374: Dynamically adjust the corresponding thresholds of the roll angle alarm threshold and the pitch angle alarm threshold based on the roll angle alarm threshold adjustment coefficient and the pitch angle alarm threshold adjustment coefficient to generate a dynamic alarm threshold.

[0051] In the embodiment of the present invention, the real-time operation application scenario information of the amphibious ship is obtained through the sensors and control systems on the ship. The real-time operation application scenario information includes the current state of the ship, such as: normal navigation, landing / offshore, loading / unloading goods, berthing, etc. The real-time operation application scenario of the ship can be judged through information such as GPS data, speed, course, and the ship's own state sensors. For example, by analyzing the GPS data and speed, it can be judged whether the ship is approaching the shore, so as to judge whether the ship is in the landing / offshore state. The safety margin coefficients corresponding to different operation application scenarios are preset in advance. The safety margin coefficient is a value used to adjust the alarm threshold to meet the safety requirements in different scenarios. For example, in the normal navigation state, the safety margin coefficient is set to 1.0; in the landing / offshore state, since the ship's attitude changes greatly, the safety margin coefficient is set to 1.2; in the loading / unloading goods state, since the ship's stability requirements are higher, the safety margin coefficient is set to 0.8; in the berthing state, the safety margin coefficient is set to 1.5. According to the real-time operation application scenario obtained in step S371, the corresponding safety margin coefficient is matched. According to the safety margin coefficient of the operation scenario obtained in step S372, the roll angle alarm threshold adjustment coefficient and the pitch angle alarm threshold adjustment coefficient are calculated. The calculation formula is: adjustment coefficient = safety margin coefficient. For example, if the safety margin coefficient of the operation scenario is 1.2, then both the roll angle alarm threshold adjustment coefficient and the pitch angle alarm threshold adjustment coefficient are 1.2. Multiply the roll angle alarm threshold and the pitch angle alarm threshold obtained in step S36 by the corresponding adjustment coefficients respectively to obtain the dynamic alarm threshold. The calculation formula is: dynamic alarm threshold = original alarm threshold × adjustment coefficient. For example, if the original roll angle alarm threshold is 15 degrees and the roll angle alarm threshold adjustment coefficient is 1.2, then the dynamic roll angle alarm threshold is 15 × 1.2 = 18 degrees. Similarly, calculate the dynamic pitch angle alarm threshold. The finally generated dynamic alarm threshold will be used for the alarm judgment in step S4.

[0052] As an example of the present invention, refer to Figure 3 as shown, for Figure 1Schematic diagram of the detailed implementation steps of step S4. In this example, step S4 includes: Step S41: Compare the compensated tilt angle estimate with the dynamic alarm threshold to determine whether it exceeds the threshold, and obtain an alarm trigger flag. In the embodiment of the present invention, the compensated roll angle estimate is compared with the dynamic roll angle alarm threshold, and the compensated pitch angle estimate is compared with the dynamic pitch angle alarm threshold respectively. If the absolute value of the compensated roll angle estimate is greater than the dynamic roll angle alarm threshold, the roll angle alarm trigger flag is set to true; otherwise, the roll angle alarm trigger flag is set to false. Similarly, if the absolute value of the compensated pitch angle estimate is greater than the dynamic pitch angle alarm threshold, the pitch angle alarm trigger flag is set to true; otherwise, the pitch angle alarm trigger flag is set to false. If the roll angle alarm trigger flag or the pitch angle alarm trigger flag is true, the total alarm trigger flag is true, indicating that at least one tilt angle exceeds the corresponding dynamic alarm threshold; otherwise, the total alarm trigger flag is false.

[0053] Step S42: Calculate the degree of tilt angle exceeding the threshold according to the alarm trigger flag, and generate tilt angle exceeding threshold degree data. In the embodiment of the present invention, if the alarm trigger flag is true, the degree of tilt angle exceeding the threshold is calculated. For the roll angle, the calculation formula is: roll angle exceeding threshold degree = abs(compensated roll angle estimate) - dynamic roll angle alarm threshold. For the pitch angle, the calculation formula is: pitch angle exceeding threshold degree = abs(compensated pitch angle estimate) - dynamic pitch angle alarm threshold. The calculated roll angle exceeding threshold degree and pitch angle exceeding threshold degree are respectively stored as tilt angle exceeding threshold degree data. If the alarm trigger flag is false, the tilt angle exceeding threshold degree data is set to 0.

[0054] Step S43: Divide the tilt angle exceeding threshold degree data according to the preset alarm level division standard to obtain preliminary alarm level data. In the embodiment of the present invention, the alarm level division standard is preset, for example: Level 1 alarm: The tilt angle exceeds the threshold by 1 - 5 degrees; Level 2 alarm: The tilt angle exceeds the threshold by 5 - 10 degrees; Level 3 alarm: The tilt angle exceeds the threshold by more than 10 degrees. According to the tilt angle exceeding threshold degree data and the preset alarm level division standard, the current alarm level is divided. For example, if the roll angle exceeding threshold degree is 3 degrees, the preliminary roll angle alarm level data is a Level 1 alarm; if the pitch angle exceeding threshold degree is 7 degrees, the preliminary pitch angle alarm level data is a Level 2 alarm.

[0055] Step S44: Evaluate the alarm credibility according to the preliminary alarm level data, and generate alarm credibility evaluation data. In the embodiments of the present invention, based on information such as preliminary alarm level data, historical alarm data, and current environmental factors (such as wind speed, wave height), an alarm credibility evaluation is performed. For example, the Bayesian method or the fuzzy logic method can be used for alarm credibility evaluation. The evaluation result is quantified as a value between 0 and 1, indicating the credibility of the alarm, and alarm credibility evaluation data is generated. The larger the value, the higher the credibility.

[0056] Step S45: Calculate the historical inclination change rate of the compensated inclination estimation value, compare it with a preset inclination change rate threshold, determine whether the inclination change is abnormal, and obtain an inclination change abnormality flag. In the embodiments of the present invention, calculate the historical change rate of the compensated inclination estimation value, such as the average change rate over a past period of time (such as 5 seconds). Preset an inclination change rate threshold, such as 10 degrees / second. Compare the calculated inclination change rate with the preset threshold. If the absolute value of the inclination change rate is greater than the preset threshold, the inclination change abnormality flag is true, indicating that the inclination change is abnormal; otherwise, the inclination change abnormality flag is false.

[0057] Step S46: When both the alarm trigger flag and the inclination change abnormality flag are true, and the alarm credibility evaluation data is higher than the preset threshold, trigger an alarm, generate a final ship alarm signal; otherwise, determine it as a false alarm and perform alarm filtering.

[0058] In the embodiments of the present invention, if both the alarm trigger flag and the inclination change abnormality flag are true, and the alarm credibility evaluation data is higher than the preset threshold (such as 0.8), then trigger an alarm and generate a final ship alarm signal. The final ship alarm signal includes information such as the alarm type (roll angle or pitch angle), alarm level, and alarm time. If the above conditions are not met, it is determined as a false alarm, and alarm filtering is performed without generating an alarm signal.

[0059] Preferably, the present invention also provides a wireless monitoring and alarm system applied to an amphibious ship, which executes the wireless monitoring and alarm method applied to an amphibious ship as described above. The wireless monitoring and alarm system applied to an amphibious ship includes: A hull state monitoring module, used to build a ship state monitoring and alarm device on the amphibious ship; monitor the hull state of the amphibious ship through the ship state monitoring and alarm device, and generate calibrated ship state monitoring data; An inclination compensation calculation module, used to estimate the inclination observation value according to the calibrated ship state monitoring data to obtain the real-time hull inclination value; process the ship motion parameters according to the calibrated ship state monitoring data to obtain the ship motion turning centrifugal force data; perform motion inclination compensation on the real-time hull inclination value through the ship motion turning centrifugal force data to generate a compensated inclination estimation value; The threshold dynamic adjustment module is used to obtain real-time water condition and meteorological data; perform fuzzy inference on the ship alarm threshold based on the real-time water condition and meteorological data and the estimated value of the compensation inclination angle to obtain the roll angle alarm threshold and the pitch angle alarm threshold respectively; perform scenario-based dynamic adjustment on the roll angle alarm threshold and the pitch angle alarm threshold to obtain the dynamic alarm threshold. The intelligent alarm judgment module is used to perform intelligent alarm judgment on the estimated value of the compensation inclination angle by using the dynamic alarm threshold to generate the final ship alarm signal. The wireless signal transmission module is used to send the final ship alarm signal to the crew's mobile devices or the shore-based monitoring center through the LoRaWAN wireless communication module to obtain the wireless alarm monitoring information.

[0060] The present application lies in that by building a ship status monitoring and alarm device on an amphibious ship, the hull status can be comprehensively and real-time monitored. The construction of this device not only improves the comprehensiveness and accuracy of data collection, but also ensures the time consistency and availability of data through multi-source data analysis and timestamp synchronization processing, effectively reducing the risk of data transmission delay and loss, and ensuring the efficiency and reliability of data transmission. Through further processing of the calibrated ship status monitoring data, the real-time hull inclination value can be accurately estimated, and the motion inclination compensation can be performed in combination with the ship motion parameters. This not only effectively distinguishes the normal motion inclination of the ship from the inclination caused by the external environment, reducing the monitoring error caused by motion factors, but also improves the accuracy and stability of inclination calculation through the fusion processing of the Kalman filter algorithm. Also, through the acquisition of real-time water condition and meteorological data, fuzzy inference and scenario-based dynamic adjustment of the ship alarm threshold are performed in combination with the estimated value of the compensation inclination angle to generate the dynamic alarm threshold. This mechanism avoids the limitations of traditional fixed threshold settings, enables the system to flexibly set reasonable alarm thresholds according to the actual environmental conditions, reduces false alarm phenomena under complex water conditions or extreme meteorological conditions, and at the same time can timely capture real dangerous inclination changes, providing more accurate early warning support for the safe operation of the ship.

[0061] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to cover all changes falling within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0062] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A wireless monitoring and alarm method applied to amphibious ships, characterized in that: The following steps are involved: Step S1: Building a ship status monitoring and alarm device on an amphibious ship; monitoring the hull status of the amphibious ship through the ship status monitoring and alarm device, and generating calibration ship status monitoring data; Step S2: Estimating the inclination observation value according to the calibrated ship status monitoring data to obtain the real-time hull inclination value; Process the ship motion parameters according to the calibrated ship status monitoring data to obtain the ship motion turning centrifugal force data; perform motion inclination compensation on the real-time hull inclination value through the ship motion turning centrifugal force data to generate a compensated inclination estimation value; Step S3: acquiring real-time water condition and meteorological data; performing fuzzy reasoning on the ship alarm threshold according to the real-time water condition and meteorological data and the estimated value of the compensated tilt angle, and obtaining the roll angle alarm threshold and the pitch angle alarm threshold respectively; performing scenario-based dynamic adjustment on the roll angle alarm threshold and the pitch angle alarm threshold, and obtaining the dynamic alarm threshold; Step S4: using the dynamic alarm threshold to perform intelligent alarm judgment on the estimated value of the compensation tilt angle to generate a final ship alarm signal; Step S5: The final ship alarm signal is sent to the crew's mobile device or shore-based monitoring center through the LoRaWAN wireless communication module to obtain wireless alarm monitoring information.

2. The wireless monitoring and alarm method for amphibious ships according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: multiple three-axis accelerometers, three-axis gyroscopes and GPS modules are installed at the bow, stern and middle of the hull of the amphibious ship, and initial calibration is performed to build a ship status monitoring and alarm device; Step S12: setting the sampling frequency according to the ship status monitoring alarm device, monitoring the hull status of the amphibious ship, and generating raw data for ship status monitoring; Step S13: transmitting the raw data of ship status monitoring to the ship-borne data processing center through the LoRaWAN wireless communication module, and performing multi-source data analysis to generate ship status monitoring metadata; Step S14: performing noise filtering on the ship status monitoring metadata to obtain noise-reduced ship status monitoring data; Step S15: Perform time stamp synchronization processing on the noise reduction ship status monitoring data, and use the time signal in the GPS module as a reference to perform time correction to generate calibrated ship status monitoring data.

3. The wireless monitoring and alarm method for amphibious ships according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: extracting real-time measurement data according to the calibrated ship status monitoring data, and obtaining real-time GPS positioning data, real-time accelerometer measurement data and real-time gyroscope measurement data respectively; Step S22: Estimating the Kalman inclination observation value according to the real-time accelerometer measurement data and the real-time gyroscope measurement data to obtain the real-time hull inclination value; Step S23: extracting the longitude and latitude coordinates of the ship through real-time GPS positioning data to obtain the longitude and latitude coordinate data of the ship; Step S24: Processing the ship motion parameters according to the ship's latitude and longitude coordinate data to obtain the ship motion turning centrifugal force data; Step S25: using the ship motion turning centrifugal force data as the inclination compensation amount, performing motion inclination compensation on the real-time hull inclination value, and generating a compensated inclination estimation value.

4. The wireless monitoring and alarm method for amphibious ships according to claim 3 is characterized in that: Step S22 includes the following steps: Step S221: performing three-axis measurement value decomposition according to the real-time accelerometer measurement data and the real-time gyroscope measurement data, and defining measurement variables to obtain hull monitoring variable data; Step S222: constructing a Kalman measurement inclination angle calculation model according to the hull monitoring variable data; Step S223: Calculate the components of gravity acceleration in three axes using the real-time accelerometer measurement data to obtain gravity acceleration components; Step S224: estimating the current inclination observation value according to the gravity acceleration component to obtain the real-time inclination observation estimation value of the hull; Step S225: performing real-time measurement noise processing on the real-time accelerometer measurement data and the real-time gyroscope measurement data, and calculating the inclination prediction value using the Kalman measurement inclination calculation model to obtain the hull inclination prediction value; Step S226: perform difference calculation on the predicted value of the hull inclination angle through the real-time hull inclination angle observation estimate, and perform Kalman gain weighted update to generate a real-time hull inclination angle value.

5. The wireless monitoring and alarm method for amphibious ships according to claim 4 is characterized in that: Step S222 includes the following steps: Defining hull state variables of an amphibious ship to obtain hull state variable data; wherein the hull state variable data includes a roll angle, a pitch angle, a roll angle rate, and a pitch angle rate; Construct a Kalman filter calculation equation based on the hull monitoring variable data and the hull state variable data to obtain an initial Kalman measurement inclination angle calculation model; Obtain historical navigation data of amphibious ships; The historical navigation data of amphibious ships are used to perform initial state statistics on the hull state variable data to generate the initial value data of the ship state vector; Perform measurement error analysis based on the accelerometer and the gyroscope, and set the initial covariance matrix of the state vector to obtain an initial covariance matrix; wherein the diagonal elements in the initial covariance matrix represent the initial uncertainty of each state variable, and the off-diagonal elements represent the correlation between the state variables; The initial covariance matrix and the initial value data of the ship state vector are combined with Kalman parameters, and the parameters of the initial Kalman measurement tilt angle calculation model are adjusted to obtain the Kalman measurement tilt angle calculation model.

6. The wireless monitoring and alarm method for amphibious ships according to claim 3 is characterized in that: Step S24 includes the following steps: Step S241: Calculate the real-time heading angle according to the longitude and latitude coordinate data of the ship to generate real-time heading angle data; Step S242: Calculate the real-time speed according to the longitude and latitude coordinate data of the ship to generate real-time sailing speed data; Step S243: calculating the heading angle change rate of the real-time heading angle data, and judging whether the change of the real-time heading angle exceeds the heading angle change threshold by using a preset heading angle change threshold, thereby obtaining heading angle change judgment data; Step S244: when the heading angle change judgment data is true, it is determined that the ship is turning, and the ship turning judgment data is obtained; Step S245: Calculate the centrifugal force when the ship turns based on the ship turning judgment data through the real-time navigation speed data to obtain the ship motion turning centrifugal force data.

7. The wireless monitoring and alarm method for amphibious ships according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: setting fuzzy logic input variables according to the type of amphibious ship to obtain fuzzy logic input variables; Step S32: setting fuzzy logic output variables according to fuzzy logic input variables to generate fuzzy logic output variables; Step S33: defining a fuzzy set according to the fuzzy logic input variables and the fuzzy logic output variables, thereby establishing a fuzzy logic reasoning model; Step S34: Calculating the roll angle and pitch angle change rates according to the estimated value of the compensated tilt angle to obtain tilt angle change rate data; Step S35: acquiring real-time water condition and meteorological data; performing wave height rating according to the real-time water condition and meteorological data to obtain real-time wave height data; Step S36: inputting the inclination angle change rate data and the real-time wave height data into the fuzzy logic reasoning model, and performing fuzzy reasoning to obtain the roll angle alarm threshold and the pitch angle alarm threshold respectively; Step S37: dynamically adjust the roll angle alarm threshold and the pitch angle alarm threshold according to the scenario to obtain a dynamic alarm threshold.

8. The wireless monitoring and alarm method for amphibious ships according to claim 7 is characterized in that: Step S37 includes the following steps: Step S371: Acquire the real-time operation application scenario of the amphibious ship; Step S372: matching the safety margin coefficient according to the real-time operation application scenario of the amphibious ship to obtain the operation scenario safety margin coefficient; Step S373: Calculating the scene adjustment coefficients for the roll angle alarm threshold and the pitch angle alarm threshold by using the operation scene safety margin coefficient, and obtaining the roll angle alarm threshold adjustment coefficient and the pitch angle alarm threshold adjustment coefficient respectively; Step S374: dynamically adjusting the roll angle alarm threshold and the pitch angle alarm threshold based on the roll angle alarm threshold adjustment coefficient and the pitch angle alarm threshold adjustment coefficient to generate a dynamic alarm threshold.

9. The wireless monitoring and alarm method for amphibious ships according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: using the dynamic alarm threshold to compare the estimated value of the compensation tilt angle, determine whether it exceeds the threshold, and obtain an alarm triggering flag; Step S42: calculating the degree to which the inclination exceeds the threshold value according to the alarm triggering flag, and generating inclination exceeding limit degree data; Step S43: classifying the inclination over-limit degree data into current alarm levels according to a preset alarm level classification standard to obtain preliminary alarm level data; Step S44: performing alarm credibility assessment according to the preliminary alarm level data to generate alarm credibility assessment data; Step S45: Calculate the historical tilt change rate of the compensated tilt estimate value, and compare it with a preset tilt change rate threshold to determine whether the tilt change is abnormal, and obtain an abnormal tilt change flag; Step S46: When the alarm trigger flag and the inclination change abnormal flag are both true, and the alarm credibility assessment data is higher than the preset threshold, the alarm is triggered and the final ship alarm signal is generated; otherwise, it is determined to be a false alarm and the alarm is filtered.

10. A wireless monitoring and alarm system for amphibious ships, characterized in that: Used to execute the wireless monitoring and alarm method for amphibious ships as claimed in claim 1, the wireless monitoring and alarm system for amphibious ships comprises: The hull status monitoring module is used to build a ship status monitoring alarm device on an amphibious ship; the hull status of the amphibious ship is monitored through the ship status monitoring alarm device to generate calibrated ship status monitoring data; The inclination compensation calculation module is used to estimate the inclination observation value according to the calibration ship state monitoring data to obtain the real-time hull inclination value; process the ship motion parameters according to the calibration ship state monitoring data to obtain the ship motion turning centrifugal force data; perform motion inclination compensation on the real-time hull inclination value through the ship motion turning centrifugal force data to generate a compensated inclination estimation value; The threshold dynamic adjustment module is used to obtain real-time water condition and meteorological data; perform fuzzy reasoning on the ship alarm threshold according to the real-time water condition and meteorological data and the estimated value of the compensation tilt angle to obtain the roll angle alarm threshold and the pitch angle alarm threshold respectively; perform scenario-based dynamic adjustment on the roll angle alarm threshold and the pitch angle alarm threshold to obtain the dynamic alarm threshold; An intelligent alarm judgment module is used to make intelligent alarm judgment on the estimated value of the compensation tilt angle by using a dynamic alarm threshold to generate a final ship alarm signal; The wireless signal transmission module is used to send the final ship alarm signal to the crew's mobile device or shore-based monitoring center through the LoRaWAN wireless communication module to obtain wireless alarm monitoring information.

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