Tsunami monitoring and early warning method, system and storage medium with multi-source interference compensation

Abstract

The present invention relates to the technical field of tsunami monitoring, and discloses a tsunami monitoring and early warning method, system and storage medium with multi-source interference compensation. The tsunami monitoring and early warning method with multi-source interference compensation comprises: S1, collecting seabed pressure, atmospheric pressure, temperature and humidity, wind speed, wind direction and wave data, wherein the wave data at least includes wave height, wave period and wave direction; S2, transmitting the collected data to a shore-based tsunami data monitoring and early warning platform via a satellite communication system; S3, filling data with different sampling intervals and filtering abnormal data; S4, using a weighted fusion algorithm to dynamically allocate weights of seabed pressure, atmospheric pressure and wave data to generate comprehensive monitoring data, etc. The method effectively improves the accuracy of tsunami monitoring, enables it to work effectively in different sea areas and different environmental conditions, and improves the adaptability of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of tsunami monitoring, and in particular to a tsunami monitoring and early warning method, system and storage medium with multi-source interference compensation. Background Art

[0002] Tsunami monitoring has traditionally relied on seafloor pressure gauge arrays and seismic monitoring networks. Seafloor pressure sensors are a key tsunami monitoring tool, detecting tsunami waves by monitoring changes in seafloor pressure. As tsunami waves pass through, they alter sea level, which in turn affects seafloor pressure. Seafloor pressure sensors can capture these changes, enabling tsunami monitoring. However, this method faces numerous interference factors in practical applications, impacting monitoring accuracy.

[0003] In actual tsunami monitoring, the signals measured by seabed pressure sensors are affected and interfered with by many factors, mainly including: 1. Changes in atmospheric pressure will directly affect the sea level, and then affect the seabed pressure, causing measurement errors; 2. The ups and downs of sea surface waves will also change the sea level, interfering with the seabed pressure measurement; 3. Other factors such as seabed geological activities and marine biological activities may also interfere with the measurement.

[0004] These interference factors cause the signals measured by seafloor pressure sensors to be mixed with a variety of non-tsunami-related signals, increasing the difficulty of tsunami monitoring. Therefore, how to accurately extract tsunami wave characteristics from multi-source interference data has become an important research topic in the field of tsunami monitoring. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a tsunami monitoring and early warning method, system and storage medium with multi-source interference compensation, which can improve the accuracy of tsunami monitoring.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A tsunami monitoring and early warning method with multi-source interference compensation comprises: S1, collecting seabed pressure, atmospheric pressure, temperature and humidity, wind speed, wind direction and wave data, wherein the wave data at least includes wave height, wave period and wave direction; S2, transmitting the collected data to a shore-based tsunami data monitoring and early warning platform via a satellite communication system; S3, filling in data with different sampling intervals and filtering abnormal data; S4, dynamically allocating weights of seabed pressure, atmospheric pressure and wave data using a weighted fusion algorithm to generate comprehensive monitoring data; S5, constructing a basic interference model for seabed pressure, decoupling atmospheric pressure interference and wave interference from the basic interference model using the comprehensive monitoring data, and generating a seabed pressure reference signal corrected for multi-source interference; S6, establishing a tide level variation law model and generating a tsunami characteristic parameter set based on the seabed pressure reference signal; S7, when tsunami wave characteristics appear in the seabed pressure reference signal, the shore-based tsunami data monitoring and early warning platform triggers an early warning.

[0008] In the present invention, preferably, the S3 includes: S31, realizing time axis alignment of data with different sampling intervals through a sliding window interpolation method, eliminating phase deviation caused by different sampling intervals; S32, using a dynamic prediction model to fill in missing sensor data, and establishing a compensation mechanism based on historical characteristics; S33, filtering noise by constructing an adaptive threshold model and combining residual analysis.

[0009] In the present invention, preferably, the S5 includes: S51, constructing a basic interference model based on the nonlinear response relationship between atmospheric pressure and sea level; S52, separating the hydrostatic pressure interference component caused by atmospheric pressure through atmospheric pressure and wave data and utilizing the high-order observer principle in the electronic compass interference compensation algorithm; S53, utilizing comprehensive monitoring data, combining wave hysteresis characteristics, and adopting a model predictive control method to achieve high-frequency wave signal suppression by adjusting the wave interference weight coefficient, and decoupling the atmospheric pressure interference and wave interference; S54, extracting characteristic parameters of pressure change rate and fluctuation period stability, and generating a seabed pressure reference signal corrected for multi-source interference.

[0010] In the present invention, preferably, the S6 includes: S61, establishing a dynamic reference baseline based on the seabed pressure reference signal, calculating the standard deviation and periodic distribution of pressure fluctuations through a 24-hour sliding window, identifying the typical characteristics of normal tides and storm surges, and establishing a tide level change law model; S62, using an adaptive wavelet threshold denoising algorithm to extract the time-frequency characteristics of the pressure signal, detecting low-frequency abnormal fluctuations lasting more than 5 minutes, and performing multi-source data cross-validation in combination with atmospheric pressure gradient changes and wave spectrum energy surges; S63, calculating the time intervals between adjacent abnormal peaks through a continuous peak detection algorithm, determining the wave height using the absolute difference between the maximum peak and the baseline, and generating a tsunami characteristic parameter set.

[0011] In the present invention, preferably, the S7 includes: S71, when tsunami wave characteristics appear in the seabed pressure reference signal, uploading the encrypted measurement data after triggering the tsunami characteristic warning to the shore-based tsunami data monitoring and early warning platform according to the extracted tsunami wave characteristics; S72, verifying the warning data through the shore-based tsunami data monitoring and early warning platform, and issuing a warning information after verification and confirmation.

[0012] A tsunami monitoring and early warning system with multi-source interference compensation comprises: a data acquisition module for collecting seabed pressure, atmospheric pressure, temperature and humidity, wind speed, wind direction and wave data, wherein the wave data includes at least wave height, wave period and wave direction; a data transmission module for transmitting the collected data to a shore-based tsunami data monitoring and early warning platform via a satellite communication system; a data preprocessing module for completing data at different sampling intervals and filtering abnormal data; a data fusion module for dynamically allocating weights of seabed pressure, atmospheric pressure and wave data using a weighted fusion algorithm to generate comprehensive monitoring data; an interference compensation module for constructing a basic interference model for seabed pressure, decoupling atmospheric pressure interference and wave interference from the basic interference model using comprehensive monitoring data, and generating a seabed pressure reference signal corrected for multi-source interference; a feature extraction module for establishing a tide level variation law model and generating a tsunami feature parameter set based on the seabed pressure reference signal; and an early warning module for triggering an early warning on the shore-based tsunami data monitoring and early warning platform when tsunami wave characteristics appear in the seabed pressure reference signal.

[0013] In the present invention, preferably, the data acquisition module includes at least: a seabed pressure sensor, which is placed on the seabed and is used to measure the seabed pressure; a meteorological sensor, which is placed on the water surface and is used to collect atmospheric pressure, temperature and humidity, wind speed and wind direction; and a radar wave meter, which is placed on the water surface and is used to measure wave height, wave period and wave direction.

[0014] In the present invention, preferably, the data preprocessing module includes: an alignment unit for realizing time axis alignment of data with different sampling intervals through a sliding window interpolation method, and eliminating phase deviations caused by different sampling intervals; a filling unit for filling missing sensor data with a dynamic prediction model, and establishing a compensation mechanism based on historical characteristics; a filtering unit for filtering noise by constructing an adaptive threshold model and combining residual analysis; the interference compensation module includes: a model construction unit for constructing a basic interference model based on the nonlinear response relationship between atmospheric pressure and sea level; a separation unit for separating the hydrostatic pressure interference component caused by atmospheric pressure through atmospheric pressure and wave data by utilizing the high-order observer principle in the electronic compass interference compensation algorithm; a decoupling unit for utilizing comprehensive monitoring data, combined with wave hysteresis characteristics, and adopting a model predictive control method to achieve high-frequency wave signal suppression by adjusting the wave interference weight coefficient, and decoupling atmospheric pressure interference and wave interference; a reference signal generation unit for extracting characteristic parameters of pressure change rate and fluctuation period stability, and generating a seabed pressure reference signal corrected for multi-source interference.

[0015] In the present invention, preferably, the feature extraction module includes: a pattern recognition unit, which is used to establish a dynamic reference baseline based on the seabed pressure reference signal, calculate the standard deviation and periodic distribution of pressure fluctuations through a 24-hour sliding window, identify typical characteristics of normal tides and storm surges, and establish a tidal level change pattern model; a verification unit, which is used to extract the time-frequency characteristics of the pressure signal using an adaptive wavelet threshold denoising algorithm, detect low-frequency abnormal fluctuations lasting more than 5 minutes, and perform multi-source data cross-validation in combination with atmospheric pressure gradient changes and wave spectrum energy surges; a tsunami feature generation unit, which calculates the time interval between adjacent abnormal peaks through a continuous peak detection algorithm, determines the wave height using the absolute difference between the maximum peak and the baseline, and generates a tsunami feature parameter set; the early warning module includes: an early warning uploading unit, which is used to upload encrypted measurement data after triggering a tsunami feature warning to a shore-based tsunami data monitoring and early warning platform based on the extracted tsunami wave characteristics when tsunami wave characteristics appear in the seabed pressure reference signal; and an early warning verification unit, which is installed on the shore-based tsunami data monitoring and early warning platform, and is used to verify the warning data and issue an early warning message after verification and confirmation.

[0016] A computer-readable storage medium includes instructions. When the instructions are executed on a computer, the computer is caused to execute the tsunami monitoring and early warning method with multi-source interference compensation as described in any one of the above.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] By integrating multi-source data and employing a novel interference compensation algorithm, the method and system of the present invention effectively improve the accuracy of tsunami monitoring, enabling it to operate effectively in diverse sea areas and environmental conditions, thereby enhancing the system's adaptability. This also expands the scope of application for high-precision pressure measurement-based tsunami monitoring systems, thereby increasing the accuracy and timeliness of tsunami monitoring and reducing casualties and property losses caused by tsunami disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of a tsunami monitoring and early warning method with multi-source interference compensation according to an embodiment of the present invention.

[0020] Figure 2 This is a flowchart of S3 in the tsunami monitoring and early warning method with multi-source interference compensation according to an embodiment of the present invention.

[0021] Figure 3 This is a flowchart of S5 in the tsunami monitoring and early warning method with multi-source interference compensation according to an embodiment of the present invention.

[0022] Figure 4 This is a flowchart of S6 in the tsunami monitoring and early warning method with multi-source interference compensation according to an embodiment of the present invention.

[0023] Figure 5 This is a flowchart of S7 in the tsunami monitoring and early warning method with multi-source interference compensation according to an embodiment of the present invention.

[0024] Figure 6 This is a schematic structural diagram of a tsunami monitoring and early warning system with multi-source interference compensation according to another embodiment of the present invention.

[0025] Figure 7 This is a structural diagram of a data acquisition module in a tsunami monitoring and early warning system with multi-source interference compensation according to another embodiment of the present invention.

[0026] Figure 8 This is a structural diagram of a data preprocessing module in a tsunami monitoring and early warning system with multi-source interference compensation according to another embodiment of the present invention.

[0027] Figure 9 This is a structural diagram of an interference compensation module in a tsunami monitoring and early warning system with multi-source interference compensation according to another embodiment of the present invention.

[0028] Figure 10 This is a schematic structural diagram of a feature extraction module and an early warning module in a tsunami monitoring and early warning system with multi-source interference compensation according to another embodiment of the present invention.

[0029] In the accompanying drawings: 1. Data acquisition module; 101. Seabed pressure sensor; 102. Meteorological sensor; 103. Radar wave meter; 2. Data transmission module; 3. Data preprocessing module; 301. Alignment unit; 302. Padding unit; 303. Filtering unit; 4. Data fusion module; 5. Interference compensation module; 501. Model building unit; 502. Separation unit; 503. Decoupling unit; 504. Reference signal generation unit; 6. Feature extraction module; 601. Pattern recognition unit; 602. Verification unit; 603. Tsunami feature generation unit; 7. Early warning module; 701. Early warning upload unit; 702. Early warning verification unit. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0031] It should be noted that when a component is referred to as being "fixed to" another component, it may be directly on the other component or there may be a central component. When a component is considered to be "connected to" another component, it may be directly connected to the other component or there may be a central component. When a component is considered to be "disposed on" another component, it may be directly disposed on the other component or there may be a central component. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0033] See Figure 1 A preferred embodiment of the present invention provides a tsunami monitoring and early warning method with multi-source interference compensation, comprising:

[0034] S1, collects seabed pressure, atmospheric pressure, temperature and humidity, wind speed, wind direction and wave data, where the wave data includes at least wave height, wave period and wave direction.

[0035] The seabed pressure sensor, meteorological sensor and radar wave meter are used to collect data such as seabed pressure, atmospheric pressure, temperature and humidity, wind speed, wind direction, wave height and wave period. Specifically, seabed pressure sensor, meteorological sensor and radar wave meter can be used.

[0036] Submarine pressure sensor: placed on the seabed to measure seabed pressure changes. This invention uses millimeter-level sensors.

[0037] Meteorological sensors: deployed on the water surface to collect data such as atmospheric pressure, temperature and humidity, wind speed, and wind direction.

[0038] Radar wave meter: placed on the water surface to measure wave height, wave period and other data.

[0039] S2, transmits the collected data to the shore-based tsunami data monitoring and early warning platform via the satellite communication system.

[0040] Satellite communication systems can be used to transmit the collected data in real time to the shore-based tsunami data monitoring and early warning platform to ensure the timeliness and continuity of the data.

[0041] S3, fills in the data at different sampling intervals and filters out abnormal data.

[0042] The data with different sampling intervals are padded to eliminate phase deviation. At the same time, a dynamic prediction model is used to fill in the missing data, and an adaptive threshold model is constructed in combination with residual analysis to filter noise and improve data quality.

[0043] Specifically, such as Figure 2 As shown, S3 includes:

[0044] S31, using a sliding window interpolation method to achieve time axis alignment of data with different sampling intervals, thereby eliminating phase deviations caused by different sampling intervals.

[0045] Because different sensors have different sampling intervals, we first use a sliding window interpolation method to align the data on the time axis. This eliminates the phase deviation caused by different sampling intervals and enables all data to be fused and analyzed on a unified time axis.

[0046] S32, uses a dynamic prediction model to fill in the missing sensor data and establishes a compensation mechanism based on historical characteristics.

[0047] Because different sensors have different sampling intervals, data may be missing between different sensors after the timeline. For example, if the sampling interval for a meteorological sensor is 60 seconds, and the sampling interval for a submarine pressure sensor is 15 seconds, then after the timelines are aligned, the meteorological sensor's sampled data will be missing relative to the submarine pressure sensor's. For missing data, a dynamic prediction model is used to fill in the gaps based on historical data characteristics. For example, if atmospheric pressure data from a meteorological sensor is missing, the model predicts and fills in the missing values ​​based on previous atmospheric pressure trends and correlation data. This establishes a compensation mechanism based on historical characteristics to ensure data integrity.

[0048] S33, by building an adaptive threshold model and combining it with residual analysis to filter noise.

[0049] An adaptive threshold model is constructed and combined with residual analysis to filter noise from the data. For seafloor pressure data, the residual distribution is analyzed to automatically adjust the threshold and remove anomalous data points caused by factors such as marine biological activity and sensor noise, improving data quality and ensuring the reliability of subsequent analysis.

[0050] S4 uses a weighted fusion algorithm to dynamically assign weights to seabed pressure, atmospheric pressure, and wave data to generate comprehensive monitoring data.

[0051] A weighted fusion algorithm dynamically assigns weights to seafloor pressure, atmospheric pressure, and wave data. Based on the importance and real-time reliability of each data point for tsunami monitoring, comprehensive monitoring data is generated, reflecting the impact of different interference sources on the seafloor pressure signal. Seafloor pressure data serves as the core monitoring source, atmospheric pressure data is used to correct for hydrostatic pressure interference, and wave data is analyzed in the frequency domain to extract low-frequency characteristic components. For example, seafloor pressure data incorporates the influence of baseline pressure, atmospheric pressure, and wave data. The hydrostatic pressure interference caused by atmospheric pressure is a nearly linear effect with a fixed weight. Wave data, on the other hand, is more complex, including various high- and low-frequency wave components. High-frequency components have minimal impact on seafloor pressure and can be assigned a smaller weight, while low-frequency components have a greater impact and can be assigned a larger weight. The influence can also be determined based on depth; deeper depths have a greater impact on seafloor pressure. Weights can be determined by comprehensively considering wave frequency and depth, dividing waves into segments and assigning weights to understand the impact of wave data.

[0052] S5, construct a basic interference model of seabed pressure, use comprehensive monitoring data to decouple atmospheric pressure interference and wave interference from the basic interference model, and generate a seabed pressure reference signal corrected by multi-source interference.

[0053] Specifically, such as Figure 3 As shown, S5 includes:

[0054] S51, construct a basic interference model based on the nonlinear response relationship between atmospheric pressure and sea level.

[0055] Through a large amount of historical data and experimental analysis, the influence function of atmospheric pressure changes on sea level height was determined. This function can describe the law of hydrostatic pressure changes caused by atmospheric pressure changes, providing a theoretical basis for subsequent interference separation.

[0056] S52, through atmospheric pressure and wave data, uses the high-order observer principle in the electronic compass interference compensation algorithm to separate the hydrostatic pressure interference component caused by atmospheric pressure.

[0057] The collected atmospheric pressure and wave data are input into the basic interference model. The high-order observer principle, part of the electronic compass interference compensation algorithm, is used to separate the hydrostatic pressure interference component caused by atmospheric pressure. The high-order observer accurately estimates the system's state variables, effectively distinguishing the atmospheric pressure interference signal from the actual seabed pressure change signal, achieving precise separation of the atmospheric pressure interference.

[0058] S53 uses comprehensive monitoring data, combines the wave hysteresis characteristics, and adopts the model predictive control method to adjust the wave interference weight coefficient to achieve high-frequency wave signal suppression and decouple atmospheric pressure interference and wave interference.

[0059] Wave hysteresis refers to the delay and attenuation of waves' impact on seabed pressure. Model predictive control methods predict the impact of wave interference on future seabed pressure based on real-time and historical trends in wave data. They adjust the wave interference weighting coefficient accordingly to reduce the interference of high-frequency wave signals on seabed pressure monitoring. This model predictive control-based wave interference suppression process, combined with specific algorithms (such as adaptive filters, Kalman filters, sliding window analysis, wavelet transforms, and Hilbert-Huang transforms), separates atmospheric pressure interference and wave interference from the integrated monitoring data, achieving decoupling between the two.

[0060] S54, extracting characteristic parameters of pressure change rate and fluctuation period stability, and generating a seabed pressure reference signal corrected for multi-source interference.

[0061] This benchmark signal can more realistically reflect the dynamic changes in seabed pressure, eliminate the interference of environmental factors such as atmospheric pressure and waves, and provide key data support for subsequent tsunami feature extraction and early warning.

[0062] S6. Based on the seabed pressure reference signal, a tide level variation model is established and a tsunami characteristic parameter set is generated.

[0063] A tide level change model is established based on the seabed pressure reference signal, and an adaptive wavelet threshold denoising algorithm is used to extract the time-frequency characteristics of the pressure signal. Multi-source data cross-validation is performed based on the atmospheric pressure gradient changes and the sudden increase in wave spectrum energy to generate a tsunami characteristic parameter set and accurately capture the characteristic information of tsunami waves.

[0064] Specifically, such as Figure 4 As shown, S6 includes:

[0065] S61, establishes a dynamic reference baseline based on the seabed pressure reference signal, calculates the standard deviation and periodic distribution of pressure fluctuations through a 24-hour sliding window, identifies the typical characteristics of normal tides and storm surges, and establishes a model for the regularity of tidal changes.

[0066] The standard deviation and period distribution of pressure fluctuations are calculated over a 24-hour sliding window, enabling in-depth analysis of tidal patterns. Normal tides have relatively stable periods and amplitudes. Statistical analysis of historical tidal data identifies the typical characteristics of normal tides and storm surges, and establishes a tidal level variation model. This model enables real-time comparison of current tidal level changes, providing a reference for identifying tsunami characteristics.

[0067] S62 uses an adaptive wavelet threshold denoising algorithm to extract the time-frequency characteristics of the pressure signal and detect low-frequency abnormal fluctuations lasting more than 5 minutes. At the same time, it combines the changes in atmospheric pressure gradient and the sudden increase in wave spectrum energy to perform multi-source data cross-validation.

[0068] The wavelet transform decomposes the signal into different scales for analysis. The adaptive wavelet threshold denoising algorithm adaptively adjusts the threshold based on the signal's characteristics, effectively removing noise interference and extracting the low-frequency abnormal fluctuation characteristics in the pressure signal. Furthermore, the algorithm detects low-frequency abnormal fluctuations lasting for more than five minutes and performs multi-source data cross-validation based on changes in atmospheric pressure gradients and sudden increases in wave spectrum energy. When the seafloor pressure reference signal exhibits long-lasting low-frequency abnormal fluctuations accompanied by significant changes in atmospheric pressure gradients and sudden increases in wave spectrum energy, it can be preliminarily identified as a tsunami wave characteristic signal.

[0069] S63, calculating the time interval between adjacent abnormal wave peaks using a continuous wave peak detection algorithm, determining the wave height using the absolute difference between the maximum wave peak and the baseline, and generating a tsunami characteristic parameter set.

[0070] The continuous wave peak detection algorithm is then used to calculate the time intervals between adjacent abnormal wave peaks, determine the wave height, and generate a tsunami characteristic parameter set. This tsunami characteristic parameter set includes key parameters such as the tsunami wave period, wave height, and propagation speed, providing detailed and accurate characteristic information for tsunami warnings.

[0071] S7: When the seabed pressure reference signal shows tsunami wave characteristics, the shore-based tsunami data monitoring and early warning platform triggers an early warning.

[0072] Specifically, such as Figure 5 As shown, S7 includes:

[0073] S71, when tsunami wave characteristics appear in the seabed pressure reference signal, the encrypted measurement data after triggering the tsunami characteristic warning is uploaded to the shore-based tsunami data monitoring and early warning platform based on the extracted tsunami wave characteristics.

[0074] S72: Verify the warning data through the shore-based tsunami data monitoring and warning platform, and issue a warning message after verification and confirmation.

[0075] The platform's early warning verification unit rigorously verifies warning data by comparing it to historical tsunami data, analyzing current marine environmental conditions, and verifying the consistency of multi-source data. Once verified, the platform promptly issues a warning message, notifying relevant departments and personnel in coastal areas to take emergency measures, including evacuation and protection, to minimize potential casualties and property losses from the tsunami.

[0076] The embodiment of the present invention also provides a tsunami monitoring and early warning system with multi-source interference compensation, such as Figure 6 As shown, including:

[0077] The data acquisition module 1 is used to collect seabed pressure, atmospheric pressure, temperature and humidity, wind speed, wind direction and wave data, and the wave data includes at least wave height, wave period and wave direction.

[0078] The data transmission module 2 is used to transmit the collected data to the shore-based tsunami data monitoring and early warning platform via a satellite communication system.

[0079] The data preprocessing module 3 is used to fill in the data at different sampling intervals and filter out abnormal data.

[0080] The data fusion module 4 is used to dynamically allocate the weights of seabed pressure, atmospheric pressure and wave data using a weighted fusion algorithm to generate comprehensive monitoring data.

[0081] The interference compensation module 5 is used to construct a basic interference model of seabed pressure, decouple atmospheric pressure interference and wave interference from the basic interference model using comprehensive monitoring data, and generate a seabed pressure reference signal corrected for multi-source interference.

[0082] The feature extraction module 6 is used to establish a tide level variation model and generate a tsunami feature parameter set based on the seabed pressure reference signal.

[0083] The early warning module 7 is used to trigger an early warning on the shore-based tsunami data monitoring and early warning platform when the seabed pressure reference signal shows tsunami wave characteristics.

[0084] In a preferred embodiment of the present invention, Figure 7 As shown, the data acquisition module 1 at least includes:

[0085] The seabed pressure sensor 101 is placed on the seabed to measure the seabed pressure.

[0086] Meteorological sensors 102 are placed on the water surface to collect atmospheric pressure, temperature and humidity, wind speed and direction.

[0087] The radar wave meter 103 is placed on the water surface to measure the wave height, wave period and wave direction.

[0088] In a preferred embodiment of the present invention, Figure 8 As shown, the data preprocessing module 3 includes:

[0089] The alignment unit 301 is used to achieve time axis alignment of data with different sampling intervals by using a sliding window interpolation method, thereby eliminating phase deviations caused by different sampling intervals.

[0090] The filling unit 302 is used to fill in the missing sensor data using a dynamic prediction model and establish a compensation mechanism based on historical features.

[0091] The filtering unit 303 is used to filter noise by constructing an adaptive threshold model and combining residual analysis.

[0092] like Figure 9 As shown, the interference compensation module 5 includes:

[0093] The model building unit 501 is used to build a basic interference model based on the nonlinear response relationship between atmospheric pressure and sea level.

[0094] The separation unit 502 is used to separate the hydrostatic pressure interference component caused by the atmospheric pressure from the atmospheric pressure and wave data by using the high-order observer principle in the electronic compass interference compensation algorithm.

[0095] The decoupling unit 503 is used to utilize the comprehensive monitoring data, combine the wave hysteresis characteristics, adopt the model predictive control method, and adjust the wave interference weight coefficient to suppress the high-frequency wave signal and decouple the atmospheric pressure interference and wave interference.

[0096] The reference signal generating unit 504 is used to extract characteristic parameters of pressure change rate and fluctuation period stability, and generate a seabed pressure reference signal corrected for multi-source interference.

[0097] In a preferred embodiment of the present invention, Figure 10 As shown, the feature extraction module 6 includes:

[0098] The pattern recognition unit 601 is used to establish a dynamic reference baseline based on the seabed pressure reference signal, calculate the standard deviation and period distribution of pressure fluctuations through a 24-hour sliding window, identify the typical characteristics of normal tides and storm surges, and establish a tide level change pattern model.

[0099] The verification unit 602 is used to extract the time-frequency characteristics of the pressure signal using an adaptive wavelet threshold denoising algorithm, detect low-frequency abnormal fluctuations lasting more than 5 minutes, and perform multi-source data cross-validation in combination with atmospheric pressure gradient changes and sudden increases in wave spectrum energy.

[0100] The tsunami feature generation unit 603 calculates the time interval between adjacent abnormal peaks using a continuous peak detection algorithm, determines the wave height using the absolute difference between the maximum peak and the baseline, and generates a tsunami feature parameter set.

[0101] like Figure 10 As shown, the early warning module 7 includes:

[0102] The early warning uploading unit 701 is used to upload the encrypted measurement data after the tsunami characteristic warning is triggered to the shore-based tsunami data monitoring and early warning platform according to the extracted tsunami wave characteristics when the seabed pressure reference signal shows tsunami wave characteristics;

[0103] The early warning verification unit 702 is installed on the shore-based tsunami data monitoring and early warning platform, and is used to verify the early warning data and issue early warning information after verification and confirmation.

[0104] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program implements the various processes of the aforementioned embodiment of the tsunami monitoring and early warning method with multi-source interference compensation, achieving the same technical effect. The computer-readable storage medium can be of various types, such as read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk.

[0105] The above description is a detailed description of the preferred embodiments of the present invention, but the embodiments are not intended to limit the scope of the patent application of the present invention. Any equivalent changes or modifications completed under the technical spirit suggested by the present invention should fall within the patent scope covered by the present invention.

Claims

1. A tsunami monitoring and early warning method with multi-source interference compensation, characterized in that: include: S1, collecting seabed pressure, atmospheric pressure, temperature and humidity, wind speed, wind direction and wave data, wherein the wave data includes at least wave height, wave period and wave direction; S2, transmits the collected data to the shore-based tsunami data monitoring and early warning platform via a satellite communication system; S3, fills in the data at different sampling intervals and filters out abnormal data; S4, uses a weighted fusion algorithm to dynamically assign weights to seabed pressure, atmospheric pressure, and wave data to generate comprehensive monitoring data; S5, constructing a basic interference model of seabed pressure, decoupling atmospheric pressure interference and wave interference from the basic interference model using comprehensive monitoring data, and generating a seabed pressure reference signal corrected for multi-source interference; S6, based on the seabed pressure reference signal, establishes a tide level variation model and generates a tsunami characteristic parameter set; S7: When the seabed pressure reference signal shows tsunami wave characteristics, the shore-based tsunami data monitoring and early warning platform triggers an early warning; The S5 includes: S51, constructing a basic interference model based on the nonlinear response relationship between atmospheric pressure and sea level; S52, through atmospheric pressure and wave data, uses the high-order observer principle in the electronic compass interference compensation algorithm to separate the hydrostatic pressure interference component caused by atmospheric pressure; S53, using comprehensive monitoring data and combining it with the hysteresis characteristics of waves, adopts a model predictive control method to achieve high-frequency wave signal suppression by adjusting the wave interference weight coefficient, thereby decoupling atmospheric pressure interference and wave interference. The model predictive control method predicts the impact of wave interference on future seabed pressure based on real-time changes and historical trends in wave data, and adjusts the wave interference weight coefficient accordingly. After wave interference suppression processing based on model predictive control, combined with adaptive filters, Kalman filters, sliding window analysis, wavelet transforms, or Hilbert-Huang transform algorithms, atmospheric pressure interference and wave interference are separated from the comprehensive monitoring data, achieving decoupling between the two. S54, extracting characteristic parameters of pressure change rate and fluctuation period stability, and generating a seabed pressure reference signal corrected for multi-source interference.

2. The tsunami monitoring and early warning method with multi-source interference compensation according to claim 1, characterized in that: The S3 includes: S31, aligning the time axes of data with different sampling intervals by a sliding window interpolation method to eliminate phase deviation caused by different sampling intervals; S32, uses a dynamic prediction model to fill in the missing sensor data and establishes a compensation mechanism based on historical characteristics; S33, by building an adaptive threshold model and combining it with residual analysis to filter noise.

3. The tsunami monitoring and early warning method with multi-source interference compensation according to claim 1, characterized in that: The S6 includes: S61, establishes a dynamic reference baseline based on the seabed pressure reference signal, calculates the standard deviation and period distribution of pressure fluctuations through a 24-hour sliding window, identifies the typical characteristics of normal tides and storm surges, and establishes a tide level variation model; S62 uses an adaptive wavelet threshold denoising algorithm to extract the time-frequency characteristics of the pressure signal, detecting low-frequency abnormal fluctuations lasting more than 5 minutes, and combining atmospheric pressure gradient changes and sudden increases in wave spectrum energy to perform multi-source data cross-validation; S63, calculating the time interval between adjacent abnormal wave peaks using a continuous wave peak detection algorithm, determining the wave height using the absolute difference between the maximum wave peak and the baseline, and generating a tsunami characteristic parameter set.

4. The tsunami monitoring and early warning method with multi-source interference compensation according to claim 1, characterized in that: The S7 includes: S71, when the seabed pressure reference signal shows tsunami wave characteristics, the encrypted measurement data after triggering the tsunami characteristic warning is uploaded to the shore-based tsunami data monitoring and early warning platform according to the extracted tsunami wave characteristics; S72: Verify the warning data through the shore-based tsunami data monitoring and warning platform, and issue a warning message after verification and confirmation.

5. A tsunami monitoring and early warning system with multi-source interference compensation, characterized in that: include: a data acquisition module for collecting seabed pressure, atmospheric pressure, temperature and humidity, wind speed, wind direction, and wave data, wherein the wave data includes at least wave height, wave period, and wave direction; A data transmission module, used to transmit the collected data to a shore-based tsunami data monitoring and early warning platform via a satellite communication system; Data preprocessing module, used to fill in data with different sampling intervals and filter out abnormal data; Data fusion module, which uses a weighted fusion algorithm to dynamically assign weights to seabed pressure, atmospheric pressure, and wave data to generate comprehensive monitoring data; The interference compensation module is used to build a basic interference model of seabed pressure, decouple atmospheric pressure interference and wave interference from the basic interference model using comprehensive monitoring data, and generate a seabed pressure reference signal corrected for multi-source interference; The feature extraction module is used to establish a tide level variation model and generate a tsunami characteristic parameter set based on the seabed pressure reference signal; The early warning module is used to trigger an early warning on the shore-based tsunami data monitoring and early warning platform when the seabed pressure reference signal shows tsunami wave characteristics; The data preprocessing module includes: An alignment unit is used to achieve time axis alignment of data with different sampling intervals through a sliding window interpolation method, thereby eliminating phase deviations caused by different sampling intervals; The filling unit is used to fill in the missing sensor data using a dynamic prediction model and establish a compensation mechanism based on historical characteristics; A filtering unit is used to filter noise by building an adaptive threshold model combined with residual analysis; The interference compensation module includes: A model building unit, used for building a basic interference model based on the nonlinear response relationship between atmospheric pressure and sea level; A separation unit is used to separate the hydrostatic pressure interference component caused by atmospheric pressure by using atmospheric pressure and wave data and the high-order observer principle in the electronic compass interference compensation algorithm; A decoupling unit is used to utilize the comprehensive monitoring data, combined with the wave hysteresis characteristics, and adopt a model predictive control method to achieve high-frequency wave signal suppression by adjusting the wave interference weight coefficient, thereby decoupling the atmospheric pressure interference and wave interference. The model predictive control method predicts the impact of wave interference on future seabed pressure based on real-time changes and historical change trends of wave data, and adjusts the wave interference weight coefficient accordingly. After wave interference suppression processing based on model predictive control, combined with an adaptive filter, Kalman filter, sliding window analysis, wavelet transform, or Hilbert-Huang transform algorithm, the atmospheric pressure interference and wave interference are separated from the comprehensive monitoring data to achieve decoupling of the two. The reference signal generation unit is used to extract characteristic parameters of pressure change rate and fluctuation period stability, and generate a seabed pressure reference signal corrected for multi-source interference.

6. The tsunami monitoring and early warning system with multi-source interference compensation according to claim 5, characterized in that: The data acquisition module at least includes: Subsea pressure sensors are placed on the seabed to measure seabed pressure; Meteorological sensors are placed on the water surface to collect atmospheric pressure, temperature and humidity, wind speed and direction; Radar wavemeters are deployed on the water surface to measure wave height, wave period and wave direction.

7. The tsunami monitoring and early warning system with multi-source interference compensation according to claim 5, characterized in that: The feature extraction module includes: The pattern recognition unit is used to establish a dynamic reference baseline based on the seabed pressure reference signal, calculate the standard deviation and period distribution of pressure fluctuations through a 24-hour sliding window, identify the typical characteristics of normal tides and storm surges, and establish a tide level change pattern model; The verification unit is used to extract the time-frequency characteristics of the pressure signal using an adaptive wavelet threshold denoising algorithm, detect low-frequency abnormal fluctuations lasting more than 5 minutes, and perform multi-source data cross-validation based on atmospheric pressure gradient changes and sudden increases in wave spectrum energy; The tsunami feature generation unit calculates the time interval between adjacent abnormal peaks through a continuous peak detection algorithm, determines the wave height using the absolute difference between the maximum peak and the baseline, and generates a tsunami feature parameter set; The early warning module includes: The early warning upload unit is used to upload the encrypted measurement data after the tsunami characteristic warning is triggered to the shore-based tsunami data monitoring and early warning platform based on the extracted tsunami wave characteristics when the seabed pressure reference signal shows tsunami wave characteristics; The early warning verification unit is installed on the shore-based tsunami data monitoring and early warning platform. It is used to verify the early warning data and issue early warning information after verification and confirmation.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium includes instructions, and when the instructions are executed on a computer, the computer is caused to execute the tsunami monitoring and early warning method with multi-source interference compensation according to any one of claims 1 to 4.

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