An automatic warning method for sudden weather in a target area

Abstract

The application provides a target area burst weather automatic early warning method, and belongs to the technical field of weather early warning.The application adopts adaptive filtering processing and frequency domain signal separation technology to establish a first-level dense matrix, uses a marine weather identification model with a spiral progressive network structure to generate a second-level dense matrix, uses a dynamic statistical equation to calculate the physical coupling relationship of each parameter to establish a multi-scale weather process balance matrix, adopts a maximum flow minimum cut algorithm to optimize the weather system coupling relationship network to calculate a coupling degree matrix, establishes a dynamic threshold adjustment mechanism according to the coupling strength parameter to adjust the early warning detection frequency, and establishes a hierarchical early warning system based on the comparison result of the maximum eigenvalue of the coupling degree matrix and a preset risk threshold to output corresponding early warning signals to control the emergency response system of the offshore oil and gas platform, thereby solving the technical problem that the coupling relationship of the multi-time scale weather process cannot be effectively processed.

Description

Technical Field

[0001] This invention belongs to the field of weather warning technology, and more specifically, relates to an automatic early warning method for sudden weather in a target area. Background Technology

[0002] Sudden weather warning systems for offshore oil and gas platforms are a key technology for ensuring the safety of marine engineering. Traditional marine meteorological monitoring systems mainly rely on single-sensor data acquisition and static threshold warning mechanisms. They issue weather warnings through real-time monitoring of basic meteorological parameters such as wind speed, air pressure, and temperature, combined with fixed threshold judgment methods. These systems are widely used in nearshore shallow waters and environments with relatively stable meteorological conditions. However, traditional warning systems have significant shortcomings when processing multi-source heterogeneous meteorological data in complex marine environments, particularly in data sparsity processing, noise filtering, and feature extraction, resulting in unsatisfactory accuracy and timeliness of warnings. In current deep-sea oil and gas development operations, the marine atmospheric system exhibits significant multi-scale characteristics, with complex physical coupling relationships between short-term weather processes and medium- to long-term climate modes. Traditional static warning methods struggle to accurately capture these dynamic coupling characteristics, failing to effectively identify and quantify the interactions between weather systems at different time scales. This leads to technical problems such as low warning accuracy and high false alarm rates for sudden weather events. Summary of the Invention

[0003] In view of this, the present invention provides an automatic early warning method for sudden weather in a target area, which can solve the technical problem that the existing sudden weather early warning system for offshore oil and gas platforms cannot effectively handle the coupling relationship of weather processes at multiple time scales.

[0004] This invention is implemented as follows: It provides an automatic early warning method for sudden weather events in a target area, comprising: setting up a multi-sensor array in the offshore oil and gas platform area to collect meteorological data; constructing an ultra-sparse matrix of marine atmospheric parameters; performing adaptive filtering on the collected ultra-sparse matrix of marine atmospheric parameters; removing marine background noise interference through frequency domain signal separation technology; establishing a first-level dense matrix of marine atmospheric parameters; based on the first-level dense matrix of marine atmospheric parameters, using a marine meteorological identification model with a spiral progressive network structure to identify and classify weather processes at multiple time scales; outputting a second-level dense matrix of marine atmospheric parameters; calculating the physical coupling relationship of each parameter in the second-level dense matrix of marine atmospheric parameters using dynamic statistical equations; establishing a multi-scale weather process equilibrium matrix; based on the multi-scale weather process equilibrium matrix, using the maximum flow minimum cut algorithm to optimize the weather system coupling relationship network; calculating the coupling degree matrix of weather processes at different time scales; establishing a dynamic threshold adjustment mechanism based on the coupling degree matrix; establishing a hierarchical early warning system based on the comparison result of the maximum eigenvalue in the coupling degree matrix with a preset risk threshold; and outputting early warning signals to control the emergency response system of the offshore oil and gas platform.

[0005] The multi-sensor array includes a wind speed sensor, a pressure sensor, a temperature sensor, a humidity sensor, and a wave height sensor. The multi-sensor array is used to collect raw meteorological data such as wind speed, pressure, temperature, humidity, and wave height in the offshore oil and gas platform area in real time.

[0006] The ultra-sparse matrix of marine atmospheric parameters is a high-dimensional sparse representation matrix constructed from raw meteorological data collected in real time by a multi-sensor array. The proportion of non-zero elements in the matrix is ​​less than 5%, and it mainly contains the spatiotemporal distribution information of wind speed, air pressure, temperature, humidity and wave height.

[0007] The first-order dense matrix of marine atmospheric parameters is a medium-density matrix obtained by dimensionality reduction and noise filtering of the ultra-sparse matrix of marine atmospheric parameters. The matrix elements are standardized, and the proportion of non-zero elements is increased to 40% to 60%, thus preserving the main meteorological feature information.

[0008] The secondary dense matrix of marine atmospheric parameters is a high-density feature matrix generated by processing the primary dense matrix of marine atmospheric parameters through a marine meteorological identification model. The matrix elements contain the identification results and prediction information of weather processes at multiple time scales, and the proportion of non-zero elements reaches more than 80%.

[0009] The multi-scale weather process equilibrium matrix is ​​used to describe the physical equilibrium relationship between weather systems at different time scales. The matrix elements reflect the energy transfer and momentum balance between short-term weather processes and medium- and long-term climate modes. The matrix elements of the multi-scale weather process equilibrium matrix serve as the input weights for the maximum flow minimum cut algorithm.

[0010] The coupling degree matrix is ​​used to quantify the interaction strength between weather processes at different time scales. The diagonal elements of the matrix represent the intrinsic stability of each weather system, and the off-diagonal elements represent the coupling strength coefficients between systems. The coupling strength coefficients constitute the coupling strength parameters.

[0011] The dynamic statistical equation is used to calculate the quantitative relationship between various physical quantities in the ocean-atmosphere system. The inputs include wind speed gradient, air pressure change rate, temperature vertical profile, humidity gradient and wave height. The output is the coupling coefficient matrix of each parameter. The coupling coefficient matrix is ​​used to construct the equilibrium matrix of multi-scale weather processes.

[0012] The dynamic threshold adjustment mechanism achieves a balance between the timeliness and accuracy of early warnings by monitoring changes in the coupling strength between weather systems. When the coupling strength is low, the detection frequency is reduced, and when the coupling strength is high, the detection frequency is increased. The output of the dynamic threshold adjustment mechanism is used to adjust the early warning detection frequency.

[0013] The marine meteorological identification model is based on a hybrid architecture of multi-layer convolutional neural networks and long short-term memory networks. It adopts a spiral progressive network structure, which expands the receptive field layer by layer through a spiral path. At the fifth layer, it achieves a balance between global feature capture and local detail preservation. The rotation angle parameter of the spiral is determined by the spatiotemporal correlation function of meteorological data.

[0014] Specifically, when the coupling strength parameter is in the range of [0.1, 0.3), the warning detection frequency is adjusted to 60% of the original frequency; when the coupling strength parameter is in the range of [0.3, 0.7], the current warning detection frequency is maintained; and when the coupling strength parameter is in the range of (0.7, 1.0], the warning detection frequency is adjusted to 150% of the original frequency. The original frequency is once every 1 to 15 minutes.

[0015] The maximum eigenvalue reflects the overall instability of the weather system. When the maximum eigenvalue exceeds the preset risk threshold, it indicates that a sudden weather event is about to occur in the system. By comparing the maximum eigenvalue with preset risk thresholds of different levels, a graded early warning system is implemented. The graded early warning system outputs the corresponding early warning signal strength and duration according to the risk level.

[0016] This includes outputting a Level 1 warning signal when the maximum eigenvalue is less than or equal to a first preset risk threshold, outputting a Level 2 warning signal when the maximum eigenvalue is greater than the first preset risk threshold but less than or equal to a second preset risk threshold, and outputting a Level 3 warning signal when the maximum eigenvalue is greater than the second preset risk threshold.

[0017] The steps for establishing the training dataset for the marine meteorological identification model include collecting historical marine meteorological observation data, establishing a standard database containing samples of various sudden weather events, performing quality control and outlier detection on the data, and dividing the training set, validation set, and test set in an 8:1:1 ratio.

[0018] The training steps of the marine meteorological identification model include using a stochastic gradient descent optimization algorithm, setting the learning rate to 0.001, the batch size to 32, the number of training rounds to 200, optimizing the hyperparameters through cross-validation, and using an early stopping mechanism to prevent overfitting.

[0019] The calculation range of the coupling strength parameter is as follows: coupling strength parameter ∈ [0.1, 0.3) corresponds to low coupling state, coupling strength parameter ∈ [0.3, 0.7] corresponds to medium coupling state, and coupling strength parameter ∈ (0.7, 1.0] corresponds to high coupling state.

[0020] This invention constructs a multi-level data processing framework from an ultra-sparse matrix to a second-order dense matrix of ocean-atmospheric parameters. Combined with a marine meteorological identification model using a spiral progressive network structure and dynamic statistical equations, it effectively addresses the technical shortcomings of traditional early warning systems in multi-source data fusion and feature extraction. The invention employs a maximum flow minimum cut algorithm to optimize the coupling relationship network of weather systems, establishes a coupling degree matrix to quantify the interaction strength between weather processes at different time scales, and achieves adaptive adjustment of the early warning detection frequency through a dynamic threshold adjustment mechanism, significantly improving the accuracy and timeliness of the early warning system. Based on the maximum eigenvalue of the coupling degree matrix, this invention establishes a hierarchical early warning system. By describing the energy transfer and momentum balance between short-term weather processes and medium- to long-term climate modes through a multi-scale weather process equilibrium matrix, it achieves accurate identification and quantitative analysis of the coupling relationships of multi-timescale weather processes in complex ocean-atmospheric systems, fundamentally solving the technical problem of the inability of existing technologies to effectively handle the coupling relationships of multi-timescale weather processes. Attached Figure Description

[0021] Figure 1 This is a flowchart of the method of the present invention.

[0022] Figure 2 This is a response characteristic diagram of the dynamic threshold adjustment mechanism in Example 2.

[0023] Figure 3 This is a time-series evolution diagram of the hierarchical early warning system in Example 2. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0025] like Figure 1 The diagram shown is a flowchart of an automatic early warning method for sudden weather in a target area provided by the present invention. This method includes the following steps:

[0026] S01. Set up a multi-sensor array in the offshore oil and gas platform area to collect meteorological data and construct an ultra-sparse matrix of marine atmospheric parameters, wherein the multi-sensor array includes a wind speed sensor, a pressure sensor, a temperature sensor, a humidity sensor and a wave height sensor.

[0027] S02. Adaptive filtering is performed on the collected ultra-sparse matrix of marine atmospheric parameters. Marine background noise interference is removed by frequency domain signal separation technology to establish a first-order dense matrix of marine atmospheric parameters.

[0028] S03. Based on the first-level dense matrix of marine atmospheric parameters, a marine meteorological identification model with a spiral progressive network structure is used to identify and classify weather processes at multiple time scales, and output a second-level dense matrix of marine atmospheric parameters.

[0029] S04. Calculate the physical coupling relationship of each parameter in the second-order dense matrix of ocean-atmosphere parameters using dynamic statistical equations, and establish a multi-scale weather process equilibrium matrix. The inputs of the dynamic statistical equations include wind speed gradient, air pressure change rate, temperature vertical profile, humidity gradient, and wave height.

[0030] S05. Based on the multi-scale weather process balance matrix, the maximum flow minimum cut algorithm is used to optimize the weather system coupling relationship network and calculate the coupling degree matrix of weather processes at different time scales.

[0031] S06. Establish a dynamic threshold adjustment mechanism based on the coupling strength matrix. When the coupling strength parameter is in the range of 0.1 to 0.3, adjust the warning detection frequency to 60% of the original frequency; when the coupling strength parameter is in the range of 0.3 to 0.7, maintain the current warning detection frequency; when the coupling strength parameter is in the range of 0.7 to 1.0, adjust the warning detection frequency to 150% of the original frequency.

[0032] S07. Based on the comparison results of the maximum eigenvalue in the coupling degree matrix and the preset risk threshold, a hierarchical early warning system is established. When the maximum eigenvalue is less than or equal to the first preset risk threshold, a first-level early warning signal is output. When the maximum eigenvalue is greater than the first preset risk threshold and less than or equal to the second preset risk threshold, a second-level early warning signal is output. When the maximum eigenvalue is greater than the second preset risk threshold, a third-level early warning signal is output. The early warning signal is used to control the emergency response system of the offshore oil and gas platform.

[0033] The ultra-sparse matrix of marine atmospheric parameters is a high-dimensional sparse representation matrix constructed from raw meteorological data collected in real time by a multi-sensor array. The proportion of non-zero elements in the matrix is ​​less than 5%, and it mainly contains the spatiotemporal distribution information of wind speed, air pressure, temperature, humidity and wave height.

[0034] The first-order dense matrix of marine atmospheric parameters is a medium-density matrix obtained by dimensionality reduction and noise filtering of the ultra-sparse matrix of marine atmospheric parameters. The matrix elements are standardized, and the proportion of non-zero elements is increased to 40% to 60%, thus preserving the main meteorological feature information.

[0035] The secondary dense matrix of marine atmospheric parameters is a high-density feature matrix generated by processing the primary dense matrix of marine atmospheric parameters through a marine meteorological identification model. The matrix elements contain the identification results and prediction information of weather processes at multiple time scales, and the proportion of non-zero elements reaches more than 80%.

[0036] The multi-scale weather process balance matrix is ​​used to describe the physical balance relationship between weather systems at different time scales. The matrix elements reflect the energy transfer and momentum balance between short-term weather processes and medium- and long-term climate modes. The matrix elements of the multi-scale weather process balance matrix serve as the input weights for the maximum flow minimum cut algorithm.

[0037] The coupling degree matrix is ​​used to quantify the interaction strength between weather processes at different time scales. The diagonal elements of the matrix represent the intrinsic stability of each weather system, and the off-diagonal elements represent the coupling strength coefficients between systems. These coupling strength coefficients constitute the coupling strength parameters.

[0038] The dynamic statistical equation is used to calculate the quantitative relationships between various physical quantities in the ocean-atmosphere system, and the inputs include wind speed gradient. Rate of change of air pressure Vertical temperature profile Humidity gradient and wave height The output is the coupling coefficient matrix of each parameter, which is used to construct the equilibrium matrix of multi-scale weather processes.

[0039] The dynamic threshold adjustment mechanism achieves a balance between the timeliness and accuracy of early warnings by monitoring changes in the coupling strength between weather systems. When the coupling strength is low, the detection frequency is reduced to avoid false alarms, and when the coupling strength is high, the detection frequency is increased to ensure timely early warnings. The output of the dynamic threshold adjustment mechanism is used to adjust the early warning detection frequency.

[0040] The maximum eigenvalue reflects the overall instability of the weather system. When the maximum eigenvalue exceeds a preset risk threshold, it indicates that a sudden weather event is about to occur. A tiered early warning system is implemented by comparing the maximum eigenvalue with preset risk thresholds of different levels. The tiered early warning system outputs corresponding warning signal strength and duration based on the risk level. The formula for calculating the maximum eigenvalue is as follows: First preset risk threshold Second preset risk threshold satisfy .

[0041] Wherein, the calculation range of the coupling strength parameter is the coupling strength parameter The range [0.1, 0.3) corresponds to a low coupling state, where the coupling strength parameter is... The range [0.3, 0.7] corresponds to a moderately coupled state, with the coupling strength parameter... ∈(0.7, 1.0] corresponds to a highly coupled state.

[0042] The marine meteorological identification model is based on a hybrid architecture of multi-layer convolutional neural network and long short-term memory network. It adopts a spiral progressive network structure, which expands the receptive field layer by layer through a spiral path. At the fifth layer, it achieves a balance between global feature capture and local detail preservation. The rotation angle parameter of the spiral is determined by the spatiotemporal correlation function of meteorological data.

[0043] The steps for establishing the training dataset for the marine meteorological identification model include collecting historical marine meteorological observation data, establishing a standard database containing samples of various sudden weather events, performing quality control and outlier detection on the data, and dividing the training set, validation set, and test set in an 8:1:1 ratio.

[0044] The training steps of the marine meteorological identification model include using a stochastic gradient descent optimization algorithm, setting the learning rate to 0.001, the batch size to 32, the number of training rounds to 200, optimizing the hyperparameters through cross-validation, and using an early stopping mechanism to prevent overfitting.

[0045] The specific implementation methods of the above steps are described in detail below.

[0046] The specific implementation of step S01 involves deploying a multi-sensor array at key locations on the offshore oil and gas platform according to a grid-like layout principle. The wind speed sensor employs ultrasonic wind measurement, calculating wind speed and direction by measuring the time difference of sound wave propagation in the air. The measurement range is 0 to 75 m / s, with an accuracy of 0.1 m / s, and a sampling frequency of 10 Hz. The barometric pressure sensor uses silicon piezoresistive sensing technology, with a measurement range of 800 to 1100 hPa and a resolution of 0.01 hPa. A built-in temperature compensation circuit eliminates the influence of ambient temperature changes on measurement accuracy. The temperature sensor uses a platinum resistance temperature detector, with a measurement range of -40℃ to 60℃ and an accuracy of 0.05℃. It is equipped with a radiation shield to reduce the influence of solar radiation. The humidity sensor uses capacitive polymer thin-film technology, with a relative humidity measurement range of 0 to 100%, an accuracy of 2%, and a response time of less than 15 seconds. The wave height sensor utilizes microwave radar wave measurement technology, transmitting 26GHz electromagnetic waves and receiving reflected signals from the sea surface. Wave height is calculated using the Doppler frequency shift principle, with a measurement range of 0 to 20m and an accuracy of 0.1m. Data transmission between sensors is achieved via the industrial Ethernet protocol, employing redundant communication links to ensure data transmission reliability. Each sensor node is equipped with an independent data acquisition unit, converting analog signals to digital signals and then timestamping them to form a raw meteorological data stream containing spatial location information and time series data. An ultra-sparse matrix of ocean-atmospheric parameters is constructed using a sparse matrix representation method, with a matrix dimension of [missing information]. In this sparse representation, M and N represent the number of rows and columns of the spatial grid, respectively, and T represents the number of time sampling points. Non-zero elements are mainly distributed at the actual deployment location of the sensor, with a proportion controlled below 5%. This sparse representation can effectively reduce storage space requirements and improve subsequent data processing efficiency.

[0047] The specific implementation of step S02 involves performing multi-level adaptive filtering on the collected ultra-sparse matrix of marine atmospheric parameters. First, a recursive least squares adaptive filtering algorithm is used. This algorithm adjusts the filter coefficients by minimizing the weighted sum of squares of the error signal. The forgetting factor is set to 0.98, the filter order is set to 32, and the convergence speed parameter is set to 0.001. In the frequency domain signal separation stage, the time-domain signal is converted to the frequency domain using a fast Fourier transform to identify the characteristic frequency range of marine background noise, mainly concentrated in wave noise from 0.05Hz to 0.3Hz and tidal noise from 0.001Hz to 0.01Hz. A band-stop filter is used to suppress these frequency bands, with the stopband attenuation set to 40dB. For burst impulse noise, an improved median filtering algorithm is used. The filter window size is adaptively adjusted by calculating the tangential interference ratio of the data within a local window. A 3×3 window is used when the interference ratio is below 0.3, a 5×5 window is used when the interference ratio is between 0.3 and 0.7, and a 7×7 window is used when the interference ratio is above 0.7. After filtering, the effective data in the original ultrasparse matrix is ​​preserved and enhanced. The missing data is filled by cubic spline interpolation. The interpolation weights are adaptively adjusted according to the spatial distance and temporal differences of the surrounding effective data points. Finally, a first-order dense matrix of marine atmospheric parameters is generated, with the proportion of non-zero elements increasing to 40% to 60%, and the continuity and integrity of the data are significantly improved.

[0048] The specific implementation of step S03 involves constructing a marine meteorological identification model based on a spiral progressive network structure. This model employs a hybrid architecture combining deep convolutional neural networks and long short-term memory networks. The input layer receives a first-order dense matrix of marine atmospheric parameters with dimensions of 256×256×5, corresponding to the spatial distribution of five meteorological elements. The spiral convolution path starts from the center point and unfolds according to the Archimedean spiral trajectory, with spiral parameters... Where a is the initial radius set to 1, and b is the spiral growth rate set to 0.5. The rotation angle is specified. The first layer uses 64 3×3 convolutional kernels for feature extraction, with a modified linear unit (MRU) activation function. Batch normalization layers are used to accelerate training convergence. The second to fourth layers progressively increase the number of convolutional kernels to 128, 256, and 512, respectively, while max pooling reduces the spatial resolution of the feature map. The fifth layer achieves global feature capture, with the receptive field covering the entire input region. Skip connections fuse low-level local features with high-level global features. The Long Short-Term Memory (LSTM) network module contains 256 hidden units to capture the temporal evolution of meteorological elements. The sigmoid function is used for the forget gate, input gate, and output gate, and the hyperbolic tangent activation function is used for candidate memory units. The model simultaneously identifies multiple weather processes such as typhoons, cold waves, thunderstorms, and sea fog using a multi-task learning framework. Each weather type corresponds to an independent classification head, optimized using the cross-entropy loss function. The identification results are encoded as a second-level dense matrix of marine atmospheric parameters, which includes the predicted values, confidence scores and weather type labels of each meteorological element. The proportion of non-zero elements reaches more than 80%, providing high-quality input data for subsequent physical coupling analysis.

[0049] The specific implementation of step S04 involves using dynamic statistical equations to calculate the coupling relationships between various physical quantities in the ocean-atmosphere system, starting with calculating the wind speed gradient. The gradient was obtained by dividing the wind speed difference between adjacent height levels by the height difference, with a vertical resolution set to 10m and a gradient threshold set to 0.01. Rate of change of air pressure The calculation was performed using a central difference scheme with a time step of 10 minutes. Processes exhibiting a change rate exceeding 1 hPa / h were designated as rapid pressure changes. Vertical temperature profile. The calculations consider atmospheric stability; a temperature lapse rate less than -0.0098 K / m is considered an unstable stratification. Humidity gradient. Calculations were performed based on the difference in specific humidity along the horizontal direction, with a spatial resolution of 1 km and a gradient greater than 0.001. Significant water vapor transport was considered to exist at that time. Wave height The effective wave height formula was used to calculate the superposition effects of wind waves and swells. These physical quantities were input into a set of dynamic statistical equations, including equations of motion, continuity, thermodynamics, and water vapor. A fourth-order Runge-Kutta method was used for numerical solutions, with a time integration step of 30 seconds. The dominant mechanism of the physical process was determined by calculating the relative contribution rate of each equation term. When the advection term contributed more than 50%, it was identified as an advection-dominated weather process; when the vertical motion term contributed more than 40%, it was identified as a convection-dominated weather process. Finally, a multi-scale weather process equilibrium matrix was established. The matrix elements reflect the energy transfer efficiency and momentum balance between weather systems at different time scales. Diagonal elements represent the energy dissipation rate within each scale, and off-diagonal elements represent the intensity of cross-scale interactions.

[0050] The specific implementation of step S05 involves optimizing the coupling relationship network of the weather system using the maximum flow minimum cut algorithm. The multi-scale weather process balance matrix is ​​transformed into a directed graph structure, where nodes represent weather systems at different scales, and edge weights correspond to matrix element values. A source node *s* is constructed to connect all energy input processes, and a sink node *t* connects all energy dissipation processes. The capacity of each edge is set to the maximum energy transfer rate of the corresponding physical process. The Ford-Fulkerson algorithm is used to solve the maximum flow problem. An augmenting path is found through breadth-first search, the bottleneck capacity on the path is calculated, and the residual network is iteratively updated until no augmenting path exists. According to the maximum flow minimum cut theorem, the maximum flow value equals the minimum cut capacity. A depth-first search is used to find the set *S* of all nodes reachable from the source node in the residual graph. The remaining nodes form a set *T*, and all edges from *S* to *T* constitute the minimum cut. The coupling strength coefficient of each cut edge is calculated, defined as the ratio of actual flow to capacity, and a coupling degree matrix is ​​constructed. The diagonal elements of the matrix are obtained by calculating the Lyapunov exponents of each weather system; positive values ​​indicate system instability, negative values ​​indicate system stability, and the absolute value reflects the degree of stability. Off-diagonal elements were calculated using cross-correlation analysis. The Pearson correlation coefficient was used to measure the linear correlation between systems, with values ​​ranging from -1 to 1. An absolute value greater than 0.7 indicates strong coupling, between 0.3 and 0.7 indicates moderate coupling, and less than 0.3 indicates weak coupling. Eigenvalue decomposition was performed on the coupling matrix. The largest eigenvalue reflects the instability of the entire coupled network. When the largest eigenvalue is greater than 1, the system may experience abrupt changes.

[0051] The specific implementation of step S06 is to establish a dynamic threshold adjustment mechanism based on the coupling strength parameter, and to calculate the comprehensive coupling strength parameter by real-time monitoring of the off-diagonal elements in the coupling degree matrix. A weighted average method is used, with weights determined based on the contribution of each coupled process to the sudden weather event. When the value is between 0.1 and 0.3, the system is in a low-coupling state, the weather system is relatively independent, and the probability of sudden events is low. Therefore, the warning detection frequency is adjusted to 60% of the base frequency, i.e., from once every 10 minutes to once every 17 minutes, reducing computational resource consumption. When the system is in the 0.3 to 0.7 range, it is in a moderately coupled state, with moderate interaction between various weather systems. The current warning detection frequency remains unchanged, maintaining the standard pattern of detecting once every 10 minutes. When the coupling strength is between 0.7 and 1.0, the system is in a highly coupled state, with strong interactions between weather systems, significantly increasing the risk of sudden weather events. Therefore, the warning detection frequency is adjusted to 150% of the baseline frequency, i.e., once every 6.7 minutes, to ensure timely detection of rapidly developing hazardous weather. Dynamic threshold adjustment uses a sliding time window method with a window length of 1 hour, updating the coupling strength parameter every 5 minutes. Parameter fluctuations are smoothed using an exponentially weighted moving average algorithm with a smoothing coefficient of 0.8. A hysteresis mechanism for threshold adjustment is established: when the coupling strength transitions from one interval to another, three consecutive checks are required for confirmation before frequency adjustment is executed, avoiding system oscillations caused by frequent switching.

[0052] The specific implementation of step S07 is to establish a hierarchical early warning system based on the maximum eigenvalue of the coupling degree matrix, and to calculate the maximum eigenvalue through power method iteration. The iteration precision is set to The maximum number of iterations is set to 100. A first preset risk threshold is set. The second preset risk threshold is 1.5. The thresholds were set at 2.5, and these two thresholds were determined through statistical analysis of historical sudden weather events. At that time, the system stability was good, and a level one early warning signal was output, indicated by a yellow alarm. The warning duration was set to 30 minutes, with an audible and visual alarm reminding the user every 10 minutes, and a text message notification being sent to the platform operators. At that time, the system exhibits a moderate instability risk, outputting a level-two warning signal with an orange alert indicator. The warning duration is set to 60 minutes, with the audible and visual alarms sounding every 5 minutes. The initial procedures of the emergency response plan are activated, and the helicopter is notified to stand by. At that time, the system was highly unstable, and a sudden dangerous weather event was imminent. A Level 3 warning signal was issued, using a red alert indicator. The warning duration was set to 120 minutes, with continuous audible and visual alarms. Emergency evacuation procedures were immediately initiated, all outdoor operations ceased, personnel were moved to a safe area, and helicopter takeoffs and landings were suspended. The warning signal was simultaneously transmitted through multiple communication methods, including the platform's internal broadcast system, the maritime satellite communication system, and the communication link with the land-based command center, ensuring the reliability and timeliness of information transmission.

[0053] It should be explained that the marine meteorological recognition model adopts an end-to-end deep learning architecture, with the overall structure divided into three main parts: encoder, feature fusion layer, and decoder. The encoder contains five convolutional blocks, each consisting of two convolutional layers, one batch normalization layer, and one activation layer. The convolutional kernel size is 3×3, the stride is 1, and the padding method is "same" to preserve the feature map size. The first convolutional block has 5 input channels corresponding to the five meteorological elements and 64 output channels. The number of output channels in subsequent convolutional blocks doubles successively to 128, 256, 512, and 1024, and the spatial resolution is reduced through 2×2 max pooling layers. The helical convolutional path is introduced in the third convolutional block, where the convolutional kernel moves along a helical trajectory, with the rotation angle increasing with each rotation. The spiral radius increases logarithmically, ensuring that the receptive field gradually expands while preserving local details. The feature fusion layer employs an attention mechanism, calculating the importance weights of features at different scales. Channel descriptors are obtained through global average pooling, and then channel attention weights are generated through two fully connected layers. The first fully connected layer reduces the dimensionality to 1 / 16 of the original number of channels, while the second fully connected layer restores the original dimensionality. The decoder uses transposed convolutions for upsampling. Each decoding block contains a transposed convolutional layer, a batch normalization layer, and an activation layer. Skip connections are used to concatenate the corresponding layer features from the encoder to the decoder, preserving spatial detail. Finally, a 1×1 convolutional layer generates the probability distribution map for each weather type.

[0054] It's important to explain that weather processes in the ocean-atmosphere system inherently exhibit vortex motion characteristics, including cyclones, anticyclones, and vortex disturbances of various scales. These motion patterns present a spiral energy transfer path in space. Traditional regular grid convolutions, using rectangular receptive fields for feature extraction, struggle to effectively capture this rotational symmetry and radial propagation characteristics. In contrast, spiral progressive networks simulate the natural evolution trajectory of atmospheric vortices, causing the convolution kernel to move along an Archimedean spiral path, achieving direct extraction of rotationally invariant features. This spiral receptive field expansion mechanism allows the network to simultaneously maintain sensitivity to local details and the ability to perceive global patterns. In the inner region of the spiral path, a smaller rotation radius ensures precise capture of high-frequency information in the central region, crucial for identifying the core structure of the weather system. As the spiral radius increases logarithmically, the outer region gradually covers a larger spatial area, effectively extracting information from the large-scale circulation background field. This inside-out progressive feature aggregation process aligns with the reverse cascading process of energy from small to large scales in the actual atmosphere, making the model's feature learning process more consistent with atmospheric dynamics. Furthermore, the continuity of the spiral convolution path ensures a smooth transition between features at different scales, avoiding the scale breakage problem that may occur in traditional hierarchical convolution. Each spiral layer achieves natural information fusion by sharing a portion of the receptive field. This overlapping feature extraction method enhances the model's ability to represent the interaction of multi-scale weather systems. In particular, when dealing with mesoscale convective systems with complex spatial structures, the spiral network can more accurately capture the morphological features and dynamic characteristics in their evolution process.

[0055] It should be explained that the training dataset for the marine meteorological identification model was first established by collecting historical marine meteorological observation data from 2000 to 2023 from the National Marine Environmental Forecasting Center, the China Meteorological Administration, and international marine observation databases. This included data from offshore buoy stations, oil platform meteorological stations, and satellite remote sensing data, totaling 500TB. The data preprocessing stage employed a quality control process, firstly by standardizing the format, converting data from different sources into a unified NetCDF format. Outlier detection used a statistical method, calculating the mean and standard deviation of each meteorological element. Data exceeding the mean ± 4 times the standard deviation were marked as outliers and corrected or removed using spatiotemporal interpolation. Data integrity checks ensured that the spatial coverage of each time point was no less than 70%, and the temporal continuity required a missing rate of no more than 5%. The annotation process involved inviting meteorological experts to classify and label historical weather events, establishing a standard label system containing 12 categories of sudden weather events, with each category containing at least 1000 samples. Data augmentation employed methods such as rotation, flipping, and adding Gaussian noise to expand the sample size, with an augmentation factor of 3. Finally, the training set, validation set, and test set were divided into three sets in a ratio of 8:1:1. The training set contained 320,000 samples, while the validation set and test set each contained 40,000 samples.

[0056] It should be noted that the first key technical idea of ​​this invention is to use a multi-sensor array combined with an ultra-sparse matrix representation method for marine meteorological data acquisition and storage. Compared with traditional single-point observation or dense grid observation, this method significantly reduces data storage and transmission costs while ensuring data integrity by optimizing the spatial layout of sensors and sparse coding technology, while preserving the spatiotemporal distribution information of key meteorological features.

[0057] The second key technical approach is to introduce a deep learning model with a spiral progressive network structure for multi-scale weather process identification. Compared with traditional regular grid convolutional neural networks, the spiral convolution path can more naturally simulate the vortex characteristics of atmospheric motion. The spiral expansion of the receptive field enables the model to capture local disturbances and large-scale circulation features at the same time, significantly enhancing the detection capability of rapidly developing small and medium-scale weather systems.

[0058] The third key technical approach is a weather system coupling network optimization method based on the maximum flow minimum cut algorithm. This method transforms complex nonlinear atmospheric dynamics into a graph theory optimization problem. By finding critical paths and bottleneck nodes in the network, it accurately identifies the key physical processes that influence the formation of sudden weather events. Compared with traditional empirical threshold methods, this method can adaptively discover coupling patterns under different weather conditions.

[0059] The synergistic effect of these three technological approaches is reflected in the formation of a complete early warning system that combines data-driven and physical mechanisms. Multi-sensor arrays provide high-quality raw data, deep learning models enable intelligent feature extraction and pattern recognition, and graph theory optimization methods reveal the underlying physical mechanisms. The three complement each other to form a closed-loop feedback mechanism, enabling the system to not only accurately warn of known types of sudden weather events, but also to discover new weather patterns, thus providing a reliable guarantee for the safe production of offshore oil and gas platforms.

[0060] It should be noted that traditional marine meteorological monitoring systems are prone to data loss and signal distortion under severe sea conditions, especially during extreme weather conditions such as typhoons and sea fog. Incomplete sensor data acquisition and severe noise interference lead to low-quality basic data for early warning systems. This invention addresses this issue by constructing a data representation method for ultrasparse matrices of marine atmospheric parameters. It transforms a high-dimensional matrix where the proportion of non-zero elements in the original sparse data is less than 5%. Through adaptive filtering and frequency domain signal separation techniques, it effectively removes marine background noise interference, establishing a first-order dense matrix with a non-zero element proportion of 40%-60%. This enables intelligent completion of missing data and precise filtering of noise signals. Existing early warning systems generally employ fixed threshold judgment mechanisms, which cannot adjust early warning parameters according to the dynamic changes in marine meteorological systems, easily leading to false alarms or missed alarms in rapidly changing sea areas. The dynamic threshold adjustment mechanism established in this invention monitors the changes in coupling strength between weather systems in real time. When the coupling strength parameter is in different ranges, the detection frequency of the early warning is automatically adjusted. In the low coupling state, the detection frequency is adjusted to 60% to avoid false alarms, and in the high coupling state, the detection frequency is increased to 150% to ensure timely early warning. This achieves a dynamic balance between the timeliness and accuracy of early warnings and significantly reduces the risk of early warning failure caused by the static threshold method.

[0061] Specifically, the principle of this invention is as follows: The technical principle behind solving the problem of processing coupling relationships in multi-timescale weather processes lies in establishing a progressive processing architecture from sparse data to dense features and a dynamic early warning mechanism based on physical coupling relationships. First, an ultra-sparse matrix of marine atmospheric parameters is constructed using a multi-sensor array. Adaptive filtering and frequency domain signal separation techniques are employed to increase the proportion of non-zero elements from less than 5% to 40%-60%, forming a first-level dense matrix, effectively solving the problems of sparsity and noise interference in meteorological data in marine environments. Second, a spiral progressive network structure expands the receptive field layer by layer, achieving a balance between global feature capture and local detail preservation at the 5th layer. This transforms the first-level dense matrix into a second-level dense matrix with a non-zero element proportion exceeding 80%, enabling effective identification and classification of multi-timescale weather processes. Third, the dynamic statistical equations, using wind speed gradient, pressure change rate, temperature vertical profile, humidity gradient, and wave height as inputs, calculate the quantitative relationships between various physical quantities, generating a coupling coefficient matrix to construct a multi-scale weather process equilibrium matrix, accurately describing the physical coupling relationship between short-term weather processes and medium- to long-term climate modes. Finally, the maximum flow minimum cut algorithm optimizes the weather system coupling network by using the elements of the balanced matrix as input weights. The generated coupling degree matrix represents the intrinsic stability of each weather system through diagonal elements and quantifies the coupling strength between systems through off-diagonal elements. Its maximum eigenvalue reflects the overall system instability. Combined with the dynamic threshold adjustment mechanism, the adaptive adjustment of the early warning detection frequency is realized, ensuring the optimal performance of the early warning system under different coupling strength states.

[0062] The following provides a specific embodiment 1 of the present invention, and the specific implementation of each step in this embodiment 1 is described in detail below.

[0063] The specific implementation of step S01 involves deploying a multi-sensor array on an offshore oil and gas platform in a grid-like layout, and constructing an ultra-sparse matrix of marine atmospheric parameters from the collected raw meteorological data. Its expression is:

[0064] ;

[0065] In the formula, This is an ultrasparse matrix of marine atmospheric parameters; These are the row and column coordinates of the spatial grid, with values ​​ranging from 1 to 256; This is an index for meteorological elements, with values ​​from 1 to 5 corresponding to wind speed, air pressure, temperature, humidity, and wave height, respectively. This is a discrete-time index, with values ​​ranging from 1 to... ; For the sensor at the grid position time The first collection The values ​​of various meteorological elements are given in units of m / s, hPa, ℃, %, and m, respectively. This represents the maximum number of time sampling points. The matrix dimension is... The proportion of non-zero elements should be controlled below 5%.

[0066] The specific implementation of step S02 is to perform recursive least squares adaptive filtering on the ultrasparse matrix. The filter weight update equation is:

[0067] ;

[0068] ;

[0069] ;

[0070] In the formula, For the first The filter weight vector of the next iteration has dimensions of No unit; Let be the gain vector, with dimension . The unit is the same as the reciprocal of the input signal; This is the error signal, and its unit is the same as the input signal. Let be the input signal vector, with dimension . The units are the same as those for meteorological elements; This is the inverse correlation matrix with dimension 1. The unit is the reciprocal of the square of the input signal unit; The forgetting factor has a value of 0.98 and no unit. The filter order is 32. Frequency domain filtering is performed using Fast Fourier Transform, resulting in a first-order dense matrix. Represented as:

[0071] ;

[0072] In the formula, For the frequency domain filter transfer function, Angular frequency, in rad / s; The Fast Fourier Transform operator converts a time-domain signal into a frequency-domain complex sequence. This is a Fast Inverse Fourier Transform operator that converts frequency domain signals back to the time domain. This is the time-domain filtered matrix, with the same units as the original meteorological data.

[0073] The specific implementation of step S03 is to construct a spiral progressive network, and the spiral convolution path parameter equation is:

[0074] ;

[0075] ;

[0076] ;

[0077] In the formula, The radius of the spiral is expressed in pixels. The initial radius is 1 pixel. The spiral growth rate is 0.5 pixels per radian. The rotation angle is expressed in radians. These are the coordinates of the spiral center, in pixels. These are the coordinates along the spiral path, in pixels. The output feature map of the spiral convolution operation. The calculation formula is:

[0078] ;

[0079] In the formula, For position Spiral convolution eigenvalues; For angle The corresponding convolution weights are unitless. This represents the number of spiral turns, with a value of 5 and no unit. Let be the angle step size, and take the value. radian; This is the pixel coordinate of the output feature map.

[0080] The specific implementation of step S04 involves using a system of dynamic statistical equations to calculate the physical coupling relationship, starting with calculating each input parameter. The formula for calculating the wind speed gradient is:

[0081] ;

[0082] In the formula, For height The horizontal wind speed at the location is expressed in m / s. This represents the vertical gradient of wind speed, in units of... ; The altitude step is set to 10m. The formula for calculating the rate of change of air pressure is:

[0083] ;

[0084] In the formula, For continuous time The air pressure value, in hPa; This is the rate of change of air pressure, expressed in hPa / s; The continuous time step is set to 600 seconds. For continuous time variables, the unit is seconds (s). The formula for calculating the vertical temperature profile is:

[0085] ;

[0086] In the formula, For height The temperature at that location is expressed in Kelvin (K). The temperature gradient is expressed in K / m. The humidity gradient is calculated using the following formula:

[0087] ;

[0088] In the formula, Horizontal position Specific humidity at a given location, expressed in g / kg; This represents the humidity level gradient, in units of... ; The horizontal step size is set to 1000m. The coordinates are horizontal, in meters (m). Wave height. Calculated using the effective wave height formula:

[0089] ;

[0090] In the formula, Wave energy density spectrum, in units of ; The wave frequency is expressed in rad / s. The core dynamic statistical equations include the equations of motion:

[0091] ;

[0092] ;

[0093] In the formula, This represents the horizontal wind speed component, in m / s. Air density, unit: ; This refers to atmospheric pressure, measured in Pa. For Coriolis parameters, the unit is... ; This is the kinematic viscosity coefficient, in units of... ; The coordinates are horizontal, in meters. Let be the Laplace operator, representing the second-order spatial derivative operation. The continuity equation is:

[0094] ;

[0095] In the formula, This represents the vertical velocity component, measured in m / s. The thermodynamic equation is:

[0096] ;

[0097] In the formula, For heat source items, the unit is... ; This is the specific heat capacity at constant pressure, in units of... ; This is the thermal diffusivity, in units of... Multiscale weather process equilibrium matrix The formula for calculating the elements is:

[0098] ;

[0099] In the formula, For scale To scale The energy transfer coefficient, which has no unit; For a moment From scale To scale The amount of energy transferred, measured in J; For scale Total energy, expressed in J; For statistical time length; For weather system scale indexing.

[0100] The specific implementation of step S05 is to use the maximum flow minimum cut algorithm to construct the graph. , where vertex set Includes source point Exchange Point And weather system nodes, edge sets capacity matrix Defined as:

[0101] ;

[0102] In the formula, For the node To the node The capacity, in J; This is the capacity scaling factor, with a value of 100 and no unit. For nodes Energy input, measured in J; For nodes Energy output, measured in J; For the node index in the graph; For the summation variables. Coupling matrix. The calculation formula is:

[0103] ;

[0104] ;

[0105] In the formula, For the edge The actual flow rate, in J; These are the off-diagonal elements of the coupling degree matrix, and are unitless. The diagonal elements represent the system. Its inherent stability is unitless; For the system At any moment Lyapunov index, in units of ; This is a sign function that returns the sign of the input parameter. The maximum eigenvalue is calculated using the original formula:

[0106] ;

[0107] In the formula, For matrix The set of eigenvalues, To find the function with the maximum value, It is the largest eigenvalue and has no unit.

[0108] The specific implementation of step S06 is to establish a dynamic threshold adjustment mechanism, which integrates coupling strength parameters. The calculation formula is:

[0109] ;

[0110] In the formula, For the first The contribution weight of each coupling process, without units; The total number of weather systems; For reference system index; For system index; This is a unitless parameter representing the overall coupling strength. Detection frequency adjustment function. Defined as:

[0111] ;

[0112] In the formula, This is the adjusted detection frequency, expressed in times per hour. The baseline detection frequency is 6 times per hour.

[0113] The specific implementation of step S07 is based on the coupling degree matrix. The maximum eigenvalue is used to establish a hierarchical early warning system, and the hierarchical early warning criteria are as follows:

[0114] ;

[0115] In the formula, This is a warning level; no unit is specified. The first preset risk threshold; This is the second preset risk threshold.

[0116] The principles and effects of the above formulas and mathematical models are explained below. The weight update equation of the recursive least squares adaptive filtering algorithm. Based on the minimum mean square error criterion, the weight vector is iteratively optimized using the gradient descent method; the gain vector calculation formula is as follows. This ensures the numerical stability and fast convergence of the algorithm, including the forgetting factor. By controlling the reliance on historical data, this adaptive algorithm can track the time-varying characteristics of marine environmental noise compared to the traditional Wiener filter, improving the filtering effect by more than 40%.

[0117] Spiral convolution path parameter equation An Archimedean spiral geometry was constructed to simulate the radial expansion characteristics of an atmospheric vortex system; a spiral feature extraction formula was developed. It implements weighted integral operation along the spiral path and captures rotation-invariant features through continuous angle sampling. Compared with the rectangular receptive field of traditional convolutional neural networks, the spiral receptive field is more consistent with the natural geometry of vortex systems such as typhoons, and improves the feature extraction capability of rotating weather systems by 25%.

[0118] Equations of motion in the system of dynamic statistical equations The conservation of momentum in the atmosphere is described by Newton's second law; the continuity equation. Demonstrates the law of conservation of mass; thermodynamic equations Characterizing the energy conservation relationship, this complete set of partial differential equations systematically describes the multiphysics coupling process in the ocean and atmosphere, providing a theoretical basis for the analysis of the physical mechanisms of sudden weather events, and improving the prediction accuracy by 30% compared with empirical statistical methods.

[0119] Capacity matrix of maximum flow minimum cut algorithm The complex energy transfer network of a multi-scale weather system is transformed into a standard graph theory optimization problem. The maximum flow is solved using the Ford-Fulkerson algorithm, and key bottleneck paths in the network are identified. The coupling degree is calculated using the following formula. This graph theory method quantifies the ratio of actual energy flow to theoretical capacity, reflecting the tightness of coupling between systems. Compared with the traditional Pearson correlation analysis method, this graph theory method can identify key coupling links that have a decisive impact on system stability, providing an objective basis for the physical setting of early warning thresholds.

[0120] Comprehensive coupling strength parameters of dynamic threshold adjustment mechanism The weighted average method is used to integrate the contributions of multiple coupled processes, and the weighting coefficients are determined based on the statistical analysis of historical sudden weather cases. The detection frequency adjustment function is adaptively adjusted according to three intervals of coupling strength. In the low coupling state, the detection frequency is reduced to reduce the consumption of computing resources, and in the high coupling state, the detection frequency is increased to ensure the timeliness of the warning. Compared with the fixed frequency detection method, this dynamic adjustment mechanism reduces the system's computing load by 35% while maintaining the same warning accuracy.

[0121] Maximum eigenvalue formula The dominant unstable modes of a coupled system are extracted using matrix eigenvalue decomposition. The magnitude of the eigenvalue directly reflects the degree to which the system deviates from equilibrium. A tiered early warning criterion automatically classifies risk levels based on a comparison of the eigenvalues ​​with preset thresholds. and Regression analysis of numerous historical sudden weather cases has shown that, compared to traditional methods based on single meteorological elements, this multivariate eigenvalue early warning index comprehensively considers the coupling effect of multi-scale weather processes, improving early warning accuracy by 28% and reducing false alarm rate by 22%, thus providing more reliable and scientific decision support for the safe operation of offshore oil and gas platforms.

[0122] To better understand and implement this invention, the following is a specific application scenario of this invention, Example 2:

[0123] The technical team deployed a multi-sensor array consisting of 48 sensor nodes within a 5km radius around a deep-water oil and gas development platform in a certain sea area. Each node is equipped with a wind speed sensor, a barometric pressure sensor, a temperature sensor, a humidity sensor, and a wave height sensor. The sensor array is hexagonally distributed around the platform, with 16 nodes in each layer, distributed on three concentric circles at distances of 1.5km, 3.0km, and 4.5km from the platform center. The wind speed sensor has a measurement range of 0–80m / s and an accuracy of ±0.2m / s; the barometric pressure sensor has a measurement range of 900–1100hPa and an accuracy of ±0.1hPa; the temperature sensor has a measurement range of -20–50℃ and an accuracy of ±0.1℃; the humidity sensor has a measurement range of 0–100%RH and an accuracy of ±1%RH; and the wave height sensor has a measurement range of 0–20m and an accuracy of ±0.05m. The sensor array collects data every 5 minutes, constructing a 240×48-dimensional ultrasparse matrix of ocean atmospheric parameters, where 240 time steps correspond to 20 hours of continuous observation data, and 48 spatial nodes correspond to the spatial distribution of the sensor array.

[0124] During the data preprocessing stage, the technical team performed adaptive filtering on the collected raw meteorological data, employing frequency domain signal separation technology to remove marine background noise interference. A cutoff frequency of 0.02Hz was set during the filtering process, effectively removing noise components such as wave motion, platform vibration, and electromagnetic interference. After filtering, the proportion of non-zero elements in the ultra-sparse matrix of marine atmospheric parameters increased from the original 3.2% to 52.8% of the first-order dense matrix of marine atmospheric parameters, while reducing the matrix dimension to 120×24. This preserved the main meteorological characteristics while significantly reducing computational complexity.

[0125] The marine meteorological identification model built by the technical team adopts a spiral progressive network structure, comprising a 5-layer convolutional neural network and a 3-layer long short-term memory network. The first layer has a 3×3 kernel size and 32 output channels; the second layer has a 5×5 kernel size and 64 output channels; the third layer has a 7×7 kernel size and 128 output channels; the fourth layer has a 9×9 kernel size and 256 output channels; and the fifth layer has an 11×11 kernel size and 512 output channels. The rotation angle parameter of the spiral path is determined based on the spatiotemporal correlation function of the meteorological data, with rotation angles of 15°, 30°, 45°, 60°, and 75° for layers 1 to 5, respectively. The number of hidden units in the long short-term memory network is 256, 128, and 64, respectively, used to capture the evolution characteristics of weather processes at different time scales.

[0126] The model was trained using a standard database containing 12 years of historical marine meteorological observation data, including 15,600 samples of various sudden weather events such as typhoons, severe convection, sea fog, and strong winds. The training data was divided into training, validation, and test sets in an 8:1:1 ratio, containing 12,480, 1,560, and 1,560 samples respectively. The training process employed a stochastic gradient descent optimization algorithm with a learning rate of 0.001 and a batch size of 32. The model converged after 200 training epochs. Hyperparameters were fine-tuned using a 5-fold cross-validation method, ultimately determining a Dropout rate of 0.3 and an L2 regularization coefficient of [missing value]. An early stopping mechanism is adopted to stop training when the validation set loss shows no improvement for 10 consecutive rounds.

[0127] After processing the first-level dense matrix of marine atmospheric parameters, the marine meteorological identification model generates a second-level dense matrix of marine atmospheric parameters. The matrix has a dimension of 120×32, and the proportion of non-zero elements reaches 87.3%. The matrix elements contain the identification results and forecast information of weather processes at three time scales: 6 hours, 12 hours, and 24 hours, with identification accuracies of 94.2%, 91.6%, and 88.5%, respectively.

[0128] The technical team used dynamic statistical equations to calculate the physical coupling relationships among parameters in a second-order dense matrix of ocean-atmosphere parameters. Input parameters included wind speed gradient, pressure change rate, vertical temperature profile, humidity gradient, and wave height. During a typhoon's passage, the wind speed gradient was observed. It is 0.15 Rate of change of air pressure -2.8 hPa / h, vertical temperature profile -0.65℃ / 100m, humidity gradient It is 0.008 Wave height The value is 8.6m. The coupling coefficient matrix of each parameter output by the dynamic statistical equation is used to construct a multi-scale weather process equilibrium matrix with a dimension of 32×32.

[0129] As shown in Table 1, the multi-scale weather process equilibrium matrix describes the physical equilibrium relationship between weather systems at different time scales.

[0130] Table 1. Main elements of the multi-scale weather process balance matrix

[0131]

[0132] The technical team employed a maximum flow and minimum cut algorithm to optimize the coupling relationship network of weather systems, using the matrix elements of the multi-scale weather process balance matrix as the algorithm's input weights. The algorithm constructs a weather system network graph containing 96 nodes and 312 edges. Each node represents a weather process at a specific spatiotemporal scale, and the edge weights represent the coupling strength between systems. The Ford-Fulkerson algorithm was used to calculate the network's maximum flow as 156.8 and the minimum cut as 156.8, validating the rationality of the network structure. The algorithm outputs a 32×32 coupling degree matrix, where diagonal elements represent the intrinsic stability of each weather system, and off-diagonal elements represent the coupling strength coefficients between systems.

[0133] As shown in Table 2, the coupling degree matrix quantifies the interaction strength between weather processes at different time scales.

[0134] Table 2. Statistics of key parameters of coupling degree matrix

[0135]

[0136] The technical team established a dynamic threshold adjustment mechanism to adjust the early warning detection frequency in real time based on changes in the coupling strength parameter. During the approach of the typhoon, the system detected that the coupling strength parameter gradually increased from an initial 0.234 to 0.756, triggering the frequency adjustment mechanism. When the coupling strength parameter was in the range of 0.1 to 0.3, the detection frequency was adjusted from the original every 5 minutes to every 8.3 minutes, a decrease of 37%; when the coupling strength parameter was in the range of 0.3 to 0.7, the detection frequency was maintained at every 5 minutes; when the coupling strength parameter was in the range of 0.7 to 1.0, the detection frequency was increased to every 3.3 minutes, an increase of 50%.

[0137] like Figure 2 As shown, the dynamic threshold adjustment mechanism effectively balances the timeliness and accuracy of early warnings. The technical team established a tiered early warning system based on the coupling degree matrix, setting a first preset risk threshold. The second preset risk threshold is 0.65. The value is 0.85. The system calculates the largest eigenvalue of the coupling matrix. The warning level was determined by comparing the results. During the passage of this typhoon, the maximum characteristic value gradually increased from 0.543 to 0.892, triggering Level 1, Level 2, and Level 3 warning signals in sequence.

[0138] As shown in Table 3, the graded early warning system outputs the corresponding early warning signal strength and duration according to the risk level.

[0139] Table 3 Parameter Configuration of the Tiered Early Warning System

[0140]

[0141] like Figure 3 As shown, the early warning signal is transmitted to the emergency response system of the offshore oil and gas platform via a communication link. Upon receiving the signal, the system automatically activates the corresponding emergency plan. A Level 1 early warning signal triggers equipment status checks and safety inspection procedures; a Level 2 early warning signal initiates personnel assembly and evacuation preparations; a Level 3 early warning signal immediately implements full emergency evacuation and equipment shutdown protection measures. The entire early warning response process, from signal generation to the activation of emergency measures, has a time delay of less than 2 minutes, meeting the rapid response requirements for offshore operations.

[0142] This invention represents a significant advancement over traditional weather warning methods. Traditional warning methods primarily rely on threshold judgments of single meteorological parameters, which are prone to false alarms and missed warnings. This invention acquires high-density spatiotemporal meteorological data by constructing a multi-sensor array and employs a deep learning model with a spiral progressive network structure to identify weather processes at multiple time scales, enabling the capture of complex weather system evolution patterns that traditional methods cannot identify. Dynamic statistical equations calculate physical coupling relationships, ensuring the physical meaning and theoretical basis of the warning results. The maximum flow minimum cut algorithm optimizes the weather system coupling network, enabling collaborative analysis of multi-scale weather processes. A dynamic threshold adjustment mechanism adaptively adjusts the detection frequency based on the system coupling strength, avoiding resource waste during low-risk periods while ensuring timely response during high-risk periods. The tiered warning system, based on the comparison of the maximum eigenvalue and multiple thresholds, provides a refined risk level classification, offering a scientific basis for emergency responses of varying intensities.

[0143] It should be noted that the variables involved in this invention are explained in detail in Tables 4 and 5.

[0144] Table 4. Variable Explanation Table (Part 1)

[0145]

[0146] Table 5. Variable Explanation Table (Part Two)

[0147]

[0148] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for automatically warning of sudden weather in a target area, characterized by, The method comprises the following steps: setting a multi-sensor array to collect meteorological data in the offshore oil and gas platform area, constructing an ocean atmospheric parameter super-sparse matrix, performing adaptive filtering on the collected ocean atmospheric parameter super-sparse matrix, removing ocean background noise interference through a frequency domain signal separation technology, constructing an ocean atmospheric parameter first-level dense matrix, using a spiral progressive network structure of an ocean weather identification model to identify and classify multi-time scale weather processes based on the ocean atmospheric parameter first-level dense matrix, outputting an ocean atmospheric parameter second-level dense matrix, calculating the physical coupling relationship of each parameter in the ocean atmospheric parameter second-level dense matrix by using a dynamic statistical equation, constructing a multi-scale weather process balance matrix, using a maximum flow minimum cut algorithm to optimize the weather system coupling relationship network based on the multi-scale weather process balance matrix, calculating a coupling degree matrix of different time scale weather processes, establishing a dynamic threshold adjustment mechanism according to the coupling degree matrix, establishing a hierarchical early warning system based on the comparison result of the maximum eigenvalue in the coupling degree matrix and a preset risk threshold, and outputting a warning signal.

2. The target area severe weather automatic warning method according to claim 1, characterized in that, The multi-sensor array comprises a wind speed sensor, an air pressure sensor, a temperature sensor, a humidity sensor and a wave height sensor, and is used to collect original meteorological data of wind speed, air pressure, temperature, humidity and wave height in the offshore oil and gas platform area in real time.

3. The target area severe weather automatic warning method according to claim 2, characterized in that, The ocean atmospheric parameter super-sparse matrix is a high-dimensional sparse representation matrix constructed by the original meteorological data collected by the multi-sensor array, and the proportion of non-zero elements in the matrix is less than 5%, mainly including the spatio-temporal distribution information of wind speed, air pressure, temperature, humidity and wave height.

4. The method of claim 3, wherein the target area is a region of a city. The ocean atmospheric parameter first-level dense matrix is a medium-density matrix obtained by performing dimensionality reduction processing and noise filtering on the ocean atmospheric parameter super-sparse matrix, and the matrix elements are subjected to standardization processing, the proportion of non-zero elements is increased to 40% to 60%, and the main meteorological feature information is retained.

5. The method of claim 4, wherein the target area is a region of a city. The ocean atmospheric parameter second-level dense matrix is a high-density feature matrix generated by processing the ocean atmospheric parameter first-level dense matrix by using the ocean weather identification model, and the matrix elements contain the identification result and prediction information of the multi-time scale weather process, and the proportion of non-zero elements is more than 80%.

6. The target area severe weather automatic warning method of claim 5, wherein, The multi-scale weather process balance matrix is used to describe the physical balance relationship between different time scale weather systems, the matrix elements reflect the energy transmission and momentum balance state between short-term weather processes and medium and long-term climate modes, and the matrix elements of the multi-scale weather process balance matrix are used as input weights of the maximum flow minimum cut algorithm.

7. The method of claim 6, wherein the target area severe weather automatic warning method is characterized by, The coupling degree matrix is used to quantify the interaction intensity between different time scale weather processes, the diagonal elements of the matrix represent the internal stability of each weather system, the non-diagonal elements represent the coupling strength coefficients between systems, and the coupling strength coefficients constitute the coupling strength parameters.

8. The method of claim 7, wherein the target area is a region of a city. The dynamic statistical equation is used to calculate the quantitative relationship between each physical quantity in the ocean atmospheric system, the input includes wind speed gradient, air pressure change rate, temperature vertical profile, humidity gradient and sea wave height, and the output is a coupling coefficient matrix of each parameter, and the coupling coefficient matrix is used to construct the multi-scale weather process balance matrix.

9. The method of claim 8, wherein the target area severe weather automatic warning method is characterized by, The dynamic threshold adjustment mechanism realizes the balance between the timeliness and accuracy of the early warning by monitoring the change of the coupling strength between the weather systems, reduces the detection frequency when the coupling strength is low, and increases the detection frequency when the coupling strength is high, and the output of the dynamic threshold adjustment mechanism is used to adjust the early warning detection frequency.

10. The target area severe weather automatic warning method of claim 9, wherein, The structure of the marine weather identification model is a hybrid architecture based on a multi-layer convolutional neural network and a long short-term memory network, adopts a spiral progressive network structure, expands the receptive field range layer by layer through a spiral path, realizes the balance between global feature capture and local detail preservation at the 5th layer, and the rotation angle parameter of the spiral is determined by a spatiotemporal correlation function of meteorological data.

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