Multi-source interference compensation tsunami monitoring and early warning method and system and storage medium

Abstract

The invention relates to the technical field of tsunami monitoring, and discloses a tsunami monitoring and early warning method and system with multi-source interference compensation and a storage medium, the tsunami monitoring and early warning method with multi-source interference compensation comprises the following steps: S1, collecting seabed pressure, atmospheric pressure, temperature and humidity, wind speed, wind direction and wave data; the wave data at least comprise wave height, wave period and wave direction; s2, transmitting the acquired data to a shore-based tsunami data monitoring and early warning platform through a satellite communication system; s3, supplementing the data at different sampling intervals, and filtering abnormal data; and S4, dynamically distributing the weights of the seabed pressure, the atmospheric pressure and the wave data by adopting a weighted fusion algorithm, and generating comprehensive monitoring data and the like. According to the method, the tsunami monitoring accuracy is effectively improved, the tsunami can effectively work in different sea areas and under different environment conditions, and the adaptability of the system is improved.

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] Traditionally, tsunami monitoring mainly relies on an array network of seafloor pressure gauges and a seismic monitoring network. A seafloor pressure sensor is an important tool for tsunami monitoring, which detects tsunami waves by monitoring changes in seafloor pressure. When a tsunami wave passes through, it changes the sea surface height, which in turn affects the seafloor pressure, and the seafloor pressure sensor can capture this change to achieve tsunami monitoring. However, this method faces various interference factors in practical applications, affecting the monitoring accuracy.

[0003] In actual tsunami monitoring, the signals measured by seafloor pressure sensors are affected and interfered by various factors, mainly including: 1. Changes in atmospheric pressure directly affect the sea surface height, which in turn affects the seafloor pressure, causing measurement errors; 2. The undulation of sea surface waves also changes the sea surface height, interfering with the measurement of seafloor pressure; 3. Others such as seafloor geological activities and marine biological activities may also interfere with the measurement.

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

[0005] Aiming at the deficiencies of the prior art, 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] To achieve the above purpose, the present invention provides the following technical solutions: A tsunami monitoring and early warning method with multi-source interference compensation, comprising: 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 the onshore tsunami data monitoring and early warning platform through a satellite communication system; S3, filling in the data with different sampling intervals and filtering abnormal data; S4, using a weighted fusion algorithm to dynamically allocate the weights of seabed pressure, atmospheric pressure and wave data to generate comprehensive monitoring data; S5, constructing a basic interference model of seabed pressure, and decoupling the atmospheric pressure interference and wave interference from the basic interference model by using the comprehensive monitoring data to generate a multi-source interference corrected seabed pressure reference signal; S6, establishing a tidal level change rule model and generating a tsunami characteristic parameter set according to the seabed pressure reference signal; S7, when the seabed pressure reference signal shows the characteristics of a tsunami wave, the onshore tsunami data monitoring and early warning platform triggers an early warning.

[0007] In the present invention, preferably, the S3 includes: S31, realizing the time axis alignment of data with different sampling intervals through a sliding window interpolation method to eliminate the phase deviation caused by different sampling intervals; S32, using a dynamic prediction model to fill in the missing data of the sensor and establishing a compensation mechanism based on historical characteristics; S33, filtering noise by constructing an adaptive threshold model and combining residual analysis.

[0008] In the present invention, preferably, the S5 includes: S51, constructing a basic interference model based on the non-linear response relationship between atmospheric pressure and sea level height; S52, separating the hydrostatic pressure interference component caused by atmospheric pressure by using the high-order observer principle in the electronic compass interference compensation algorithm through atmospheric pressure and wave data; S53, using the comprehensive monitoring data, combining the wave lag characteristics, and adopting a model predictive control method to suppress the high-frequency wave signal by adjusting the wave interference weight coefficient and decouple the atmospheric pressure interference and wave interference; S54, extracting characteristic parameters such as pressure change rate and fluctuation period stability to generate a multi-source interference corrected seabed pressure reference signal.

[0009] 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 period distribution of pressure fluctuations through a 24-hour sliding window, identifying the typical characteristics of normal tides and storm surges, and establishing a tidal level change rule 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 for more than 5 minutes, and at the same time combining the atmospheric pressure gradient change and sudden increase in wave spectrum energy for multi-source data cross-verification; S63, calculating the time interval between adjacent abnormal wave peaks through a continuous wave peak detection algorithm, and determining the wave height by using the absolute difference between the maximum wave peak and the baseline to generate a tsunami characteristic parameter set.

[0010] In the present invention, preferably, the S7 includes: S71, when the tsunami wave characteristics appear in the underwater pressure reference signal, according to the extracted tsunami wave characteristics, upload the encrypted measurement data after triggering the tsunami characteristic warning to the onshore tsunami data monitoring and warning platform; S72, check the warning data through the onshore tsunami data monitoring and warning platform, and send out a warning message after the check is confirmed.

[0011] A multi-source interference compensation tsunami monitoring and warning system includes: a data acquisition module for acquiring underwater pressure, atmospheric pressure, temperature and humidity, wind speed, wind direction and wave data, where the wave data at least includes wave height, wave period and wave direction; a data transmission module for transmitting the acquired data to the onshore tsunami data monitoring and warning platform through a satellite communication system; a data preprocessing module for filling in the data with different sampling intervals and filtering abnormal data; a data fusion module for dynamically allocating the weights of underwater pressure, atmospheric pressure and wave data by using a weighted fusion algorithm to generate comprehensive monitoring data; an interference compensation module for constructing a basic interference model of underwater pressure, and decoupling the atmospheric pressure interference and wave interference from the basic interference model by using the comprehensive monitoring data to generate an underwater pressure reference signal corrected by multi-source interference; a feature extraction module for establishing a tidal level change rule model and generating a set of tsunami characteristic parameters according to the underwater pressure reference signal; a warning module for triggering a warning on the onshore tsunami data monitoring and warning platform when the tsunami wave characteristics appear in the underwater pressure reference signal.

[0012] In the present invention, preferably, the data acquisition module at least includes: an underwater pressure sensor placed on the seabed for measuring the underwater pressure; a meteorological sensor placed on the water surface for acquiring atmospheric pressure, temperature and humidity, wind speed and wind direction; a radar wave gauge placed on the water surface for measuring wave height, wave period and wave direction.

[0013] In the present invention, preferably, the data preprocessing module includes: an alignment unit for achieving the time-axis alignment of data with different sampling intervals through a sliding window interpolation method to eliminate the phase deviation caused by different sampling intervals; a filling unit for filling the missing data of the sensor by using a dynamic prediction model and establishing a compensation mechanism based on historical features; a filtering unit for filtering noise by constructing an adaptive threshold model in combination with residual analysis. The interference compensation module includes: a model construction unit for constructing a basic interference model based on the non-linear response relationship between atmospheric pressure and sea level height; a separation unit for separating the hydrostatic pressure interference component caused by atmospheric pressure by using the atmospheric pressure and wave data and the principle of the high-order observer in the electronic compass interference compensation algorithm; a decoupling unit for using the comprehensive monitoring data, combining the wave hysteresis characteristics, and adopting a model predictive control method to suppress the high-frequency wave signal by adjusting the wave interference weight coefficient and decouple the atmospheric pressure interference and wave interference; a reference signal generation unit for extracting the characteristic parameters of the pressure change rate and the fluctuation period stability and generating a seabed pressure reference signal corrected by multi-source interference.

[0014] In the present invention, preferably, the feature extraction module includes: a pattern recognition unit for establishing a dynamic reference baseline based on the seabed pressure reference signal, calculating the standard deviation and period distribution of the pressure fluctuation through a 24-hour sliding window, identifying the typical features of normal tides and storm surges, and establishing a tidal level change pattern model; a verification unit for extracting the time-frequency features of the pressure signal by using an adaptive wavelet threshold denoising algorithm, detecting low-frequency abnormal fluctuations lasting for more than 5 minutes, and simultaneously performing multi-source data cross-verification in combination with the change of the atmospheric pressure gradient and the sudden increase of the wave spectrum energy; a tsunami feature generation unit for calculating the time interval between adjacent abnormal wave crests through a continuous wave crest detection algorithm, determining the wave height by using the absolute difference between the maximum wave crest and the baseline, and generating a set of tsunami feature parameters. The early warning module includes: an early warning uploading unit for uploading the encrypted measurement data after triggering the tsunami feature warning to the onshore tsunami data monitoring and early warning platform according to the extracted tsunami wave features when the seabed pressure reference signal shows the characteristics of a tsunami wave; an early warning verification unit installed on the onshore tsunami data monitoring and early warning platform for verifying the early warning data and sending out an early warning message after verification and confirmation.

[0015] A computer-readable storage medium, the computer-readable storage medium includes instructions, when the instructions run on a computer, enabling the computer to execute the multi-source interference compensation-based tsunami monitoring and early warning method as described in any one of the above.

[0016] Compared with the prior art, the beneficial effects of the present invention are: The method and system of the present invention effectively improve the accuracy of tsunami monitoring by integrating multi-source data and adopting a new interference compensation algorithm, enabling it to work effectively in different sea areas and different environmental conditions, improving the adaptability of the system. The application sea area of the tsunami monitoring system based on high-precision pressure measurement is expanded, thereby improving the accuracy and timeliness of tsunami monitoring and reducing the casualties and property losses caused by tsunami disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of the tsunami monitoring and early warning method with multi-source interference compensation according to an embodiment of the present invention.

[0018] Figure 2 It 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.

[0019] Figure 3 It 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.

[0020] Figure 4 It 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.

[0021] Figure 5 It 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.

[0022] Figure 6 It is a schematic structural diagram of the tsunami monitoring and early warning system with multi-source interference compensation according to another embodiment of the present invention.

[0023] Figure 7 It is a schematic structural diagram of the data acquisition module in the tsunami monitoring and early warning system with multi-source interference compensation according to another embodiment of the present invention.

[0024] Figure 8 It is a schematic structural diagram of the data preprocessing module in the tsunami monitoring and early warning system with multi-source interference compensation according to another embodiment of the present invention.

[0025] Figure 9 It is a schematic structural diagram of the interference compensation module in the tsunami monitoring and early warning system with multi-source interference compensation according to another embodiment of the present invention.

[0026] Figure 10 It is a schematic structural diagram of the feature extraction module and the early warning module in the tsunami monitoring and early warning system with multi-source interference compensation according to another embodiment of the present invention.

[0027] In the accompanying drawings: 1. Data acquisition module; 101. Submarine pressure sensor; 102. Meteorological sensor; 103. Radar wave gauge; 2. Data transmission module; 3. Data preprocessing module; 301. Alignment unit; 302. Filling unit; 303. Filtering unit; 4. Data fusion module; 5. Interference compensation module; 501. Model construction 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 implementation manners

[0028] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] It should be noted that when a component is referred to as being "fixed to" another component, it can be directly on the other component or there may also be an intermediate component. When a component is considered to be "connected to" another component, it can be directly connected to the other component or there may be an intermediate component at the same time. When a component is considered to be "disposed on" another component, it can be directly disposed on the other component or there may be an intermediate component at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used herein in the description of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0031] Please refer to Figure 1 , a preferred embodiment of the present invention provides a tsunami monitoring and early warning method with multi-source interference compensation, including: S1. Collect submarine 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.

[0032] Data such as seabed pressure, atmospheric pressure, temperature and humidity, wind speed, wind direction, wave height, and wave period are collected through sensors such as seabed pressure sensors, meteorological sensors, and radar wave gauges. Specifically, seabed pressure sensors, meteorological sensors, and radar wave gauges can be used.

[0033] Seabed pressure sensor: Placed on the seabed to measure changes in seabed pressure. In the present invention, it is in millimeters.

[0034] Meteorological sensor: Placed on the water surface to collect data such as atmospheric pressure, temperature and humidity, wind speed, and wind direction.

[0035] Radar wave gauge: Placed on the water surface to measure data such as wave height and wave period of waves.

[0036] S2. Transmit the collected data to the onshore tsunami data monitoring and early warning platform through the satellite communication system.

[0037] The satellite communication system can be used to transmit the collected data to the onshore tsunami data monitoring and early warning platform in real time to ensure the timeliness and continuity of the data.

[0038] S3. Complement data with different sampling intervals and filter abnormal data.

[0039] Perform complement processing on data with different sampling intervals to eliminate phase deviation; at the same time, use a dynamic prediction model to fill in missing data, and construct an adaptive threshold model combined with residual analysis to filter noise and improve data quality.

[0040] Specifically, as Figure 2 shown, S3 includes: S31. Align the time axes of data with different sampling intervals through the sliding window interpolation method to eliminate the phase deviation caused by different sampling intervals.

[0041] Since there are differences in the sampling intervals of different sensors, first use the sliding window interpolation method to perform time axis alignment processing on the data. Eliminate the phase deviation caused by different sampling intervals so that all data can be fused and analyzed on a unified time axis.

[0042] S32. Use a dynamic prediction model to fill in the missing data of the sensor and establish a compensation mechanism based on historical characteristics.

[0043] Due to the different sampling time intervals of different sensors, there are data gaps on the time axis between different sensors thereafter. For example, the sampling time interval of the meteorological sensor is 60s, and the sampling time interval of the seabed pressure sensor is 15s. After the time axis is aligned, the sampling data of the meteorological sensor will have gaps relative to the seabed pressure sensor. For the missing data, a dynamic prediction model is used to fill in the missing data based on the characteristics of historical data. For example, for the missing atmospheric pressure data of the meteorological sensor, the model predicts and fills in the missing values based on the previous atmospheric pressure change trend and correlation data, and establishes a compensation mechanism based on historical characteristics to ensure the integrity of the data.

[0044] S33. Filter noise by constructing an adaptive threshold model and combining residual analysis.

[0045] Construct an adaptive threshold model and combine residual analysis to filter noise from the data. For the seabed pressure data, by analyzing its residual distribution, the threshold is automatically adjusted to remove abnormal data points caused by factors such as marine biological activities and sensor self-noise, improving the data quality and ensuring the reliability of subsequent analysis.

[0046] S4. Use a weighted fusion algorithm to dynamically allocate the weights of seabed pressure, atmospheric pressure, and wave data to generate comprehensive monitoring data.

[0047] Apply a weighted fusion algorithm to dynamically allocate the weights of seabed pressure, atmospheric pressure, and wave data. According to the importance and real-time reliability of each data in tsunami monitoring, comprehensive monitoring data is generated to reflect the influence of different interference sources on the seabed pressure signal. Among them, the seabed pressure data is the core monitoring source, the atmospheric pressure data is used to correct the hydrostatic pressure interference, and the wave data extracts low-frequency characteristic components through frequency domain analysis. For example, the seabed pressure data contains the influence of reference pressure, atmospheric pressure, and wave data. The hydrostatic pressure interference generated by the atmospheric pressure is an approximately linear influence, and the weight value is fixed. The composition of the wave data is more complex, including parts of various high-frequency and low-frequency waves. The high-frequency part has a very small influence on the seabed pressure, and a smaller weight value can be allocated. The low-frequency part has a greater influence, and a larger weight value can be allocated. The influence can also be determined according to the depth. The deeper the depth, the greater the influence on the seabed pressure. The frequency and depth of the wave can be comprehensively considered to determine its weight value, and the wave is divided into several segments to allocate weight values, so as to obtain the influence of the wave data.

[0048] S5. Construct a basic interference model for seabed pressure, and use the comprehensive monitoring data to decouple the atmospheric pressure interference and wave interference from the basic interference model to generate a seabed pressure reference signal corrected by multi-source interference.

[0049] Specifically, as Figure 3 shown, S5 includes: S51. Construct a basic interference model based on the non-linear response relationship between atmospheric pressure and sea level height.

[0050] Through a large amount of historical data and experimental analysis, determine the influence function of atmospheric pressure changes on sea level height. This function can describe the law of hydrostatic pressure changes caused by atmospheric pressure changes, providing a theoretical basis for subsequent interference separation.

[0051] S52. Using the atmospheric pressure and wave data, and applying the principle of high-order observer in the electronic compass interference compensation algorithm, separate the hydrostatic pressure interference component caused by atmospheric pressure.

[0052] Input the collected atmospheric pressure data and wave data into the basic interference model, and use the principle of high-order observer in the electronic compass interference compensation algorithm to separate the hydrostatic pressure interference component caused by atmospheric pressure. The high-order observer can accurately estimate the state variables of the system, thus effectively distinguishing the atmospheric pressure interference signal from the true seabed pressure change signal and achieving precise separation of the atmospheric pressure interference.

[0053] S53. Using the comprehensive monitoring data, combined with the wave lag characteristics, adopt the model predictive control method, and by adjusting the wave interference weight coefficient, achieve high-frequency wave signal suppression and decouple the atmospheric pressure interference and wave interference.

[0054] The wave lag characteristic refers to the fact that the influence of waves on the seabed pressure has certain delay and attenuation characteristics. The model predictive control method predicts the influence of wave interference on the future seabed pressure according to the real-time changes and historical change trends of wave data, and accordingly adjusts the wave interference weight coefficient to reduce the interference of high-frequency wave signals on seabed pressure monitoring. After the above wave interference suppression processing based on model predictive control, combined with specific algorithms (such as adaptive filters, Kalman filters, sliding window analysis, wavelet transforms, Hilbert-Huang transforms, etc.), separate the atmospheric pressure interference and wave interference from the comprehensive monitoring data and achieve decoupling of the two.

[0055] S54. Extract characteristic parameters such as the pressure change rate and the stability of the fluctuation period, and generate a seabed pressure reference signal corrected by multi-source interference.

[0056] This reference signal can more truly reflect the dynamic changes of the seabed pressure, eliminating the interference of environmental factors such as atmospheric pressure and waves, and providing key data support for subsequent tsunami characteristic extraction and early warning.

[0057] S6. According to the seabed pressure reference signal, establish a tidal level change rule model and generate a set of tsunami characteristic parameters.

[0058] Based on the seabed pressure reference signal, a tidal level change law model is established. The adaptive wavelet threshold denoising algorithm is used to extract the time-frequency characteristics of the pressure signal. Combining the atmospheric pressure gradient change and the sudden increase in wave spectrum energy for multi-source data cross-validation, a tsunami characteristic parameter set is generated to accurately capture the characteristic information of the tsunami wave.

[0059] Specifically, as Figure 4 shown, S6 includes: S61, establish a dynamic reference baseline based on the seabed pressure reference signal, calculate the standard deviation and period distribution of the pressure fluctuation through a 24-hour sliding window, identify the typical characteristics of normal tides and storm surges, and establish a tidal level change law model.

[0060] Calculate the standard deviation and period distribution of the pressure fluctuation through a 24-hour sliding window to deeply analyze the tidal change law. Normal tides have relatively stable period and amplitude characteristics. By statistically analyzing historical tidal data, identify the typical characteristics of normal tides and storm surges, and establish a tidal level change law model. This model can compare the current tidal level change situation in real time and provide a reference basis for tsunami characteristic identification.

[0061] S62, use the adaptive wavelet threshold denoising algorithm to extract the time-frequency characteristics of the pressure signal, detect low-frequency abnormal fluctuations lasting more than 5 minutes, and at the same time combine the atmospheric pressure gradient change and the sudden increase in wave spectrum energy for multi-source data cross-validation.

[0062] Wavelet transform can decompose the signal for analysis at different scales. The adaptive wavelet threshold denoising algorithm adaptively adjusts the threshold according to the characteristics of the signal, effectively removes noise interference, and extracts the low-frequency abnormal fluctuation characteristics in the pressure signal. At the same time, detect low-frequency abnormal fluctuations lasting more than 5 minutes, and combine the atmospheric pressure gradient change and the sudden increase in wave spectrum energy for multi-source data cross-validation. When the seabed pressure reference signal shows low-frequency abnormal fluctuations with a long duration, and is accompanied by a significant change in the atmospheric pressure gradient and a sudden increase in wave spectrum energy, it can be preliminarily judged as a tsunami wave characteristic signal.

[0063] S63, calculate the time interval between adjacent abnormal wave peaks through the continuous wave peak detection algorithm, determine the wave height using the absolute difference between the maximum wave peak and the baseline, and generate a tsunami characteristic parameter set.

[0064] Further calculate the time interval between adjacent abnormal wave peaks through the continuous wave peak detection algorithm, determine the wave height, and generate a tsunami characteristic parameter set. The tsunami characteristic parameter set includes key parameters such as the period, wave height, and propagation speed of the tsunami wave, providing detailed and accurate characteristic information for tsunami early warning.

[0065] S7, when the seabed pressure reference signal shows tsunami wave characteristics, the onshore tsunami data monitoring and early warning platform triggers an early warning.

[0066] Specifically, as Figure 5 shown, S7 includes: S71, when the tsunami wave characteristics appear in the submarine pressure reference signal, according to the extracted tsunami wave characteristics, upload the encrypted measurement data after triggering the tsunami characteristic warning to the onshore tsunami data monitoring and warning platform.

[0067] S72, check the warning data through the onshore tsunami data monitoring and warning platform, and send out a warning message after verification.

[0068] The warning verification unit of the platform strictly verifies the warning data, and verifies it through multiple aspects such as comparing historical tsunami data, analyzing the current marine environmental conditions and the consistency of multi-source data. After verification, the platform quickly sends out a warning message to notify the relevant departments and personnel in the coastal areas to take emergency evacuation, protection and other emergency measures to minimize the casualties and property losses that may be caused by the tsunami disaster.

[0069] The embodiment of the present invention also provides a tsunami monitoring and warning system with multi-source interference compensation, as Figure 6 shown, including: Data acquisition module 1, used to acquire submarine pressure, atmospheric pressure, temperature and humidity, wind speed, wind direction and wave data, and the wave data at least includes wave height, wave period and wave direction.

[0070] Data transmission module 2, used to transmit the acquired data to the onshore tsunami data monitoring and warning platform through the satellite communication system.

[0071] Data preprocessing module 3, used to supplement the data with different sampling intervals and filter out abnormal data.

[0072] Data fusion module 4, used to dynamically allocate the weights of submarine pressure, atmospheric pressure and wave data by using the weighted fusion algorithm to generate comprehensive monitoring data.

[0073] Interference compensation module 5, used to construct a basic interference model of submarine pressure, and decouple the atmospheric pressure interference and wave interference from the basic interference model by using the comprehensive monitoring data to generate a submarine pressure reference signal corrected by multi-source interference.

[0074] Feature extraction module 6, used to establish a tidal level change rule model and generate a set of tsunami characteristic parameters according to the submarine pressure reference signal.

[0075] Warning module 7, used to trigger a warning on the onshore tsunami data monitoring and warning platform when the tsunami wave characteristics appear in the submarine pressure reference signal.

[0076] In a preferred embodiment of the present invention, as Figure 7 shown, the data acquisition module 1 at least includes: The seabed pressure sensor 101 is deployed on the seabed and used to measure the seabed pressure.

[0077] The meteorological sensor 102 is deployed on the water surface and used to collect atmospheric pressure, temperature and humidity, wind speed and wind direction.

[0078] The radar wave gauge 103 is deployed on the water surface and used to measure wave height, wave period and wave direction.

[0079] In a preferred embodiment of the present invention, as Figure 8 shown, the data preprocessing module 3 includes: The alignment unit 301 is used to achieve the time-axis alignment of data with different sampling intervals by the sliding window interpolation method, and eliminate the phase deviation caused by different sampling intervals.

[0080] The filling unit 302 is used to fill the missing data of the sensor by using a dynamic prediction model and establish a compensation mechanism based on historical features.

[0081] The filtering unit 303 is used to filter noise by constructing an adaptive threshold model and combining residual analysis.

[0082] As Figure 9 shown, the interference compensation module 5 includes: The model construction unit 501 is used to construct a basic interference model based on the non-linear response relationship between atmospheric pressure and sea level height.

[0083] The separation unit 502 is used to separate the hydrostatic pressure interference component caused by atmospheric pressure by using atmospheric pressure and wave data and applying the high-order observer principle in the electronic compass interference compensation algorithm.

[0084] The decoupling unit 503 is used to use the integrated monitoring data, combine the wave hysteresis characteristics, adopt the model predictive control method, and realize the suppression of high-frequency wave signals by adjusting the wave interference weight coefficient, and decouple the atmospheric pressure interference and wave interference.

[0085] The reference signal generation unit 504 is used to extract characteristic parameters such as pressure change rate and fluctuation period stability, and generate a seabed pressure reference signal corrected by multi-source interference.

[0086] In a preferred embodiment of the present invention, as Figure 10 shown, the feature extraction module 6 includes: The law 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 tidal level change law model.

[0087] The verification unit 602 is used to extract the time-frequency characteristics of the pressure signal by using the adaptive wavelet threshold denoising algorithm, detect low-frequency abnormal fluctuations lasting for more than 5 minutes, and at the same time perform multi-source data cross-verification by combining the atmospheric pressure gradient change and the sudden increase in wave spectrum energy.

[0088] The tsunami feature generation unit 603 calculates the time interval between adjacent abnormal wave crests through the continuous wave crest detection algorithm, determines the wave height by using the absolute difference between the maximum wave crest and the baseline, and generates a set of tsunami feature parameters.

[0089] As Figure 10 shown, the early warning module 7 includes: The early warning upload unit 701 is used to upload the encrypted measurement data after triggering the tsunami feature warning to the onshore tsunami data monitoring and early warning platform according to the extracted tsunami wave features when the tsunami wave features appear in the submarine pressure reference signal. The early warning verification unit 702 is installed on the onshore tsunami data monitoring and early warning platform and is used to verify the early warning data and send out an early warning message after verification and confirmation.

[0090] The embodiment of the present invention also provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, it realizes each process of the above-mentioned embodiment of the tsunami monitoring and early warning method for multi-source interference compensation and can achieve the same technical effect. Among them, the computer-readable storage medium can be of various types, such as read-only memory (ROM for short), random access memory (RAM for short), magnetic disk or optical disc, etc.

[0091] The above description is a detailed description of the preferred and feasible embodiment of the present invention, but the embodiment is not used to limit the patent application scope of the present invention. Any equivalent changes or modifications completed under the technical spirit prompted by the present invention shall 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, Including: S1. Collect seabed pressure, atmospheric pressure, temperature and humidity, wind speed, wind direction and wave data. The wave data at least includes wave height, wave period and wave direction; S2. Transmit the collected data to the onshore tsunami data monitoring and early warning platform through the satellite communication system; S3. Complement the data with different sampling intervals and filter out abnormal data; S4. Use the weighted fusion algorithm to dynamically allocate the weights of seabed pressure, atmospheric pressure and wave data to generate comprehensive monitoring data; S5. Construct a basic interference model of seabed pressure, and decouple the atmospheric pressure interference and wave interference from the basic interference model by using the comprehensive monitoring data to generate a seabed pressure reference signal corrected by multi-source interference; S6. According to the seabed pressure reference signal, establish a tidal level change rule model and generate a set of tsunami characteristic parameters; S7. When the seabed pressure reference signal shows the characteristics of a tsunami wave, the onshore tsunami data monitoring and early warning platform triggers an early warning.

2. The method for tsunami monitoring and early warning with multi-source interference compensation according to claim 1, wherein The S3 includes: S31. Realize the time-axis alignment of data with different sampling intervals through the sliding window interpolation method to eliminate the phase deviation caused by different sampling intervals; S32. Use a dynamic prediction model to fill in the missing data of the sensor and establish a compensation mechanism based on historical characteristics; S33. Filter out noise by constructing an adaptive threshold model and combining residual analysis.

3. The multi-source interference compensation-based tsunami monitoring and early warning method according to claim 1, characterized in that, The S5 includes: S51. Construct a basic interference model based on the non-linear response relationship between atmospheric pressure and sea level height; S52. Through atmospheric pressure and wave data, use the principle of the high-order observer in the electronic compass interference compensation algorithm to separate the hydrostatic pressure interference component caused by atmospheric pressure; S53. Use the comprehensive monitoring data, combine the wave lag characteristics, adopt the model predictive control method, and decouple the atmospheric pressure interference and wave interference by adjusting the wave interference weight coefficient to suppress the high-frequency wave signal; S54. Extract characteristic parameters such as pressure change rate and fluctuation period stability to generate a seabed pressure reference signal corrected by multi-source interference.

4. The tsunami monitoring and early warning method for multi-source interference compensation according to claim 1, wherein The S6 includes: S61. 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 tidal level change rule model; S62. Use the adaptive wavelet threshold denoising algorithm to extract the time-frequency characteristics of the pressure signal, detect low-frequency abnormal fluctuations lasting more than 5 minutes, and at the same time combine the atmospheric pressure gradient change and the sudden increase in wave spectrum energy for multi-source data cross-verification; S63. Calculate the time interval between adjacent abnormal wave peaks through the continuous wave peak detection algorithm, and determine the wave height by using the absolute difference between the maximum wave peak and the baseline to generate a set of tsunami characteristic parameters.

5. The tsunami monitoring and early warning method for multi-source interference compensation according to claim 1, characterized in that The S7 includes: S71. When the seabed pressure reference signal shows the characteristics of a tsunami wave, upload the encrypted measurement data after triggering the tsunami characteristic early warning to the onshore tsunami data monitoring and early warning platform according to the extracted tsunami wave characteristics; S72. Check the early warning data through the onshore tsunami data monitoring and early warning platform, and issue an early warning message after the check is confirmed.

6. A tsunami monitoring and early warning system with multi-source interference compensation, characterized in that, Including: The data acquisition module is used to collect seabed pressure, atmospheric pressure, temperature and humidity, wind speed, wind direction and wave data, and the wave data at least includes wave height, wave period and wave direction; The data transmission module is used to transmit the collected data to the onshore tsunami data monitoring and early warning platform through the satellite communication system; The data preprocessing module is used to complement data with different sampling intervals and filter abnormal data; The data fusion module is used to dynamically allocate the weights of seabed pressure, atmospheric pressure and wave data by using a weighted fusion algorithm to generate comprehensive monitoring data; The interference compensation module is used to construct a basic interference model of seabed pressure, and decouple the atmospheric pressure interference and wave interference from the basic interference model by using the comprehensive monitoring data to generate a seabed pressure reference signal corrected by multi-source interference; The feature extraction module is used to establish a tidal level change rule model and generate a tsunami feature parameter set according to the seabed pressure reference signal; The early warning module is used to trigger an early warning on the onshore tsunami data monitoring and early warning platform when the seabed pressure reference signal shows the characteristics of a tsunami wave; 7. The tsunami monitoring and early warning system for multi-source interference compensation according to claim 6, characterized in that, The data acquisition module at least includes: A seabed pressure sensor placed on the seabed for measuring seabed pressure; A meteorological sensor placed on the water surface for collecting atmospheric pressure, temperature and humidity, wind speed and wind direction; A radar wave gauge placed on the water surface for measuring wave height, wave period and wave direction.

8. The tsunami monitoring and early warning system for multi-source interference compensation according to claim 6, wherein, The data preprocessing module includes: An alignment unit for realizing the time axis alignment of data with different sampling intervals by using the sliding window interpolation method to eliminate the phase deviation caused by different sampling intervals; A filling unit for filling the missing data of the sensor by using a dynamic prediction model and establishing a compensation mechanism based on historical features; 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 non-linear response relationship between atmospheric pressure and sea level height; A separation unit for separating the hydrostatic pressure interference component caused by atmospheric pressure by using atmospheric pressure and wave data and the principle of a high-order observer in the electronic compass interference compensation algorithm; A decoupling unit for using the comprehensive monitoring data, combining the wave hysteresis characteristics, adopting a model predictive control method, and realizing the suppression of high-frequency wave signals by adjusting the wave interference weight coefficient to decouple the atmospheric pressure interference and wave interference; A reference signal generation unit for extracting characteristic parameters such as pressure change rate and fluctuation period stability to generate a seabed pressure reference signal corrected by multi-source interference; 9. The tsunami monitoring and early warning system for multi-source interference compensation according to claim 6, characterized in that, The feature extraction module includes: A rule recognition unit for establishing a dynamic reference baseline based on the seabed pressure reference signal, calculating the standard deviation and period distribution of pressure fluctuations through a 24-hour sliding window, identifying the typical characteristics of normal tides and storm surges, and establishing a tidal level change rule model; A verification unit for extracting the time-frequency characteristics of the pressure signal by using the adaptive wavelet threshold denoising algorithm, detecting low-frequency abnormal fluctuations lasting for more than 5 minutes, and simultaneously performing multi-source data cross-verification by combining the change of atmospheric pressure gradient and the sudden increase of wave spectrum energy; A tsunami feature generation unit calculates the time interval between adjacent abnormal wave crests through a continuous wave crest detection algorithm, determines the wave height using the absolute difference between the maximum wave crest and the baseline, and generates a set of tsunami feature parameters; The early warning module includes: An early warning upload unit, when a tsunami wave feature appears in the submarine pressure reference signal, uploads the encrypted measurement data after triggering the tsunami feature early warning to the onshore tsunami data monitoring and early warning platform according to the extracted tsunami wave features; An early warning verification unit, installed on the onshore tsunami data monitoring and early warning platform, is used to verify the early warning data and issue an early warning message after verification and confirmation.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes instructions that, when the instructions are run on a computer, cause the computer to execute the multi-source interference compensation-based tsunami monitoring and early warning method according to any one of claims 1 to 5.

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