114results about How to "Suppress noise interference" patented technology

The invention discloses a power equipment fault prediction method based on support vector regression, and aims to improve the prediction accuracy. The method comprises the following steps: firstly, collecting multi-dimensional operation parameter data of power equipment, and constructing an original monitoring data set; a data set is segmented through a sliding time window, wavelet transform is used for noise reduction, and then a normalization method is applied to generate a standardized data set. Time domain and frequency domain features are further extracted, a feature vector space is constructed, an optimal feature subset is screened through a mutual information method, and a training data set is generated; performing preliminary training on a support vector regression model by using the training data set to generate candidate support vectors, and screening a support vector set with the maximum contribution through an optimization algorithm; a grid search method is adopted to adjust kernel function parameters and penalty factors, and a support vector set is further optimized. And evaluating the performance of the model through cross validation, applying the optimized support vector regression model to power equipment fault prediction, and generating a fault occurrence probability prediction value.

The invention relates to a remote sensing image change detection method of a difference enhancement network based on edge guidance, and the constructed difference enhancement change detection network mainly comprises a backbone network, an edge enhancement module, a double-branch difference enhancement module, a multi-scale difference feature fusion module and a decoder. Edge information of the changed object is refined through an edge enhancement module, so that feature information with clear boundaries can be extracted for the changed object; a dual-branch difference enhancement module effectively learns the difference between the foreground and the background. The module captures global and local information of a dual-time image through space and channel branches, emphasizes a change area, inhibits noise interference, and enhances long-range dependence of difference. According to the method, the detection performance of the small object under the high-resolution remote sensing image is improved by using a multi-scale difference feature fusion method. Experimental results show that the detection effects of the proposed network on a plurality of public data sets are superior to those of several common remote sensing image change detection methods at present.

The invention discloses a cancer image contrast enhancement method and system based on deep learning, belongs to the technical field of medical image processing, and adopts an adaptive histogram equalization algorithm for preprocessing to enhance the contrast of a local region. Texture, shape and edge features are extracted through multi-scale analysis, and feature descriptors are constructed. And based on the feature descriptor, segmenting the image by using a region growing segmentation algorithm to obtain a focus region boundary. A self-adaptive contrast enhancement technology is adopted for the segmented focus area, and the focus display effect is highlighted. In consideration of noise interference, a denoising method based on wavelet transform is introduced, noise is suppressed in a high-frequency sub-band, and details are reserved in a low-frequency sub-band. Through the organic combination of the above technical schemes, the contrast and definition of the cancer image can be effectively enhanced, the focus area is highlighted, and noise interference is inhibited at the same time.

The invention relates to the technical field of air traffic control and voice recognition, in particular to an air traffic control instruction end-to-end voice recognition method under a high-noise condition, which comprises the following steps: preprocessing to-be-recognized voice, and extracting to obtain original voice frequency characteristics; carrying out noise reduction intensity control, frequency band gain control and fundamental tone tracking processing on the original voice frequency characteristics by adopting a self-adaptive attention noise reduction module to obtain noise-reduced voice; preprocessing the noise-reduced voice, and extracting to obtain noise-reduced voice frequency characteristics; a voice recognition module is adopted to carry out shared encoder coding, connection time sequence classification decoder decoding and attention-based encoder-decoder decoding on the noise reduction voice frequency characteristics, and an output voice recognition transcription text is obtained; according to the invention, the voice recognition precision of the air traffic control instruction under the high-noise condition can be improved.

The invention relates to the technical field of remote sensing data processing and earth surface parameter inversion, and discloses an earth surface matrix parameter hyperspectral data inversion method and system based on machine learning. Comprising the following steps: constructing a multi-source heterogeneous hyperspectral data set; performing feature screening on the preprocessed hyperspectral data set based on an adaptive band selection algorithm, constructing a dynamic weight matrix by calculating mutual information entropy and inter-class distance measurement between spectral bands to realize intelligent screening of key feature bands, and combining spectral derivative conversion and spectral index calculation to generate an enhanced feature vector; and a multi-task transfer learning neural network model is constructed, and an output layer realizes multi-parameter collaborative inversion based on a multi-task learning architecture. And performing preprocessing and feature enhancement operation which is the same as that of the training data on the hyperspectral image data of the to-be-inverted region, inputting the trained neural network model, and outputting a surface matrix parameter inversion result.

The invention discloses a hyperspectral image classification method based on frequency domain denoising and element gradient correction, and the method comprises the following steps: carrying out the preprocessing of all hyperspectral image data, and dividing an overall training sample set formed by the processed hyperspectral images into a training set and a verification set; constructing a sample weighting model based on frequency domain denoising and element gradient correction; in the training process, a parameterization frequency spectrum gating sensing transformation module is utilized to map features to a frequency domain through discrete Fourier transform, a learnable frequency spectrum response function is utilized to adaptively suppress spectrum jitter noise, and finally pure features are reconstructed. And automatically constructing a high-confidence pseudo-clean verification set based on a Gaussian mixture model and time domain consistency. According to the method, a time domain momentum updating mechanism is introduced, the variance of statistical estimation is effectively smoothed, random interference caused by training fluctuation is resisted, and the accuracy of pseudo clean set construction and the convergence stability of overall model training are further improved.

The invention relates to a structured light illumination confocal super-resolution measurement system and method, and relates to the technical field of optical measurement. The system comprises a digital micromirror device (DMD), a sample platform, an electric displacement platform, a microscopic imaging assembly, a complementary metal oxide semiconductor (CMOS) imaging assembly and computer equipment, and the DMD is used for adjusting the point light source array and the structured light field. According to the DMD-based confocal super-resolution measurement method, a structured light field and a signal-noise separation technology are combined, and high-precision and high-resolution three-dimensional surface reconstruction can be realized. By using the flexibility of the DMD and the advantages of the super-resolution technology, the spatial resolution and speed of confocal imaging are effectively improved, so that the measurement process is more accurate. In addition, low-rank decomposition and self-supervised deep learning methods are fused, the image reconstruction process is optimized, noise interference is suppressed, and the overall performance of the system is further improved.

The invention discloses a temperature control system of an immersion type freezing device and relates to the technical field of immersion type freezing temperature control. Temperature control in the dipping and freezing process is achieved, it is ensured that large ice crystals are reduced in the food freezing process, damage to the interior of food is reduced, and meanwhile energy consumption and cost are reduced. The system comprises a temperature acquisition module, a liquid pump driving module, an upper computer, a data transmission module, a control module and a refrigeration equipment control module, the upper computer performs data interaction with the control module through the data transmission module; the temperature acquisition module acquires the temperature of the frozen object and the external environment, converts the acquired temperature signal into an electric signal and sends the electric signal to the control module; the control module controls the power of the liquid pump driving module through a cascade control strategy and a PID (Proportion Integration Differentiation) control algorithm according to the temperature signal of the frozen object and the influence value of the external environment on the freezing liquid; and starting and stopping of the refrigeration equipment control module are controlled according to the real-time temperature of the frozen object. The method is suitable for the field of dipping type freezing temperature control.

The invention relates to the technical field of intelligent manufacturing, and particularly discloses a noise robust multivariable time sequence anomaly detection method for the field of intelligent manufacturing, which comprises the following steps: acquiring multivariable time sequence data; mapping the input data to a low-dimensional potential space, and restoring the input data to an original feature space to obtain reconstructed data; calculating a reconstruction error matrix of the original data and the reconstruction data, calculating a gradient matrix of the loss function relative to the input data, averaging and normalizing the gradient matrix along the time dimension to obtain a dimension importance score, and fusing the dimension importance score and the reconstruction error matrix to generate a weighted reconstruction error matrix; distinguishing the original data from the reconstructed data through a discriminator, and alternately optimizing parameters of the generator and the discriminator; and calculating an abnormal score based on the weighted reconstruction error matrix, introducing an abnormal diffusion mechanism to ensure the time sequence consistency, and judging that the sample with the abnormal score exceeding a threshold value is abnormal.

The application belongs to the field of integrated circuits, and specifically provides a low-power consumption enhanced reading type 11T CNFET SRAM cell circuit to solve the problems of slow speed, low stability, high power consumption and the like in the prior art; the application comprises N-type carbon nanometer field effect transistors MN1-MN9 and P-type carbon nanometer field effect transistors MP1-2, on the basis of a conventional 6T SRAM cell circuit, a dynamic load structure composed of transistors MN7, MN8 and MN9, and a high-resistance differential readout circuit composed of transistors MN3, MN4, MN5 and MN6; the adoption of P-type transistors realizes a bistable latch structure to reduce holding and writing power consumption, eliminate path competition phenomena caused by data flipping during writing, and improve writing speed; during reading, a resistive load tube is turned on to eliminate charge accumulation and ensure data storage stability; the high-resistance differential readout circuit reduces voltage disturbance of a storage node caused by bit line load; meanwhile, the differential writing / reading circuit can suppress common-mode noise and external interference on the bit line, reduce the probability of error sampling, and enhance overall anti-interference capability.

The application relates to the field of fault detection, and discloses a device fault detection method based on small sample learning and acoustic feature migration, a device and a medium, the method comprising the following steps: acquiring an acoustic signal of a target device; extracting a fusion feature vector; generating device-independent features based on a DANN network; generating a synthesized fault acoustic signal by using a Mel-CGAN; mixing the synthesized fault acoustic signal with an actual acoustic signal, and then training the DANN; and obtaining a fault detection result by using the trained DANN. The application can significantly reduce the dependence of fault diagnosis on labeled data, and can reduce the accuracy in a cross-device scene. The application can be widely applied in the fields of wind power and intelligent manufacturing.

The application provides a steam delivery pipeline leakage detection system, which comprises a metal conducting rod fixedly connected to the outer wall of a steam delivery pipeline, a sensor connected to the metal conducting rod, a signal analyzer electrically connected to the sensor, and a DCS control center electrically connected to the signal analyzer, and the sensor is used for collecting the vibration sound signal of the metal conducting rod. The steam delivery pipeline leakage detection system of the application can accurately and timely determine the leakage and the position of the leakage point by using the metal conducting rod connected to the steam delivery pipeline to transmit the vibration sound generated by the intense friction between the leakage medium and the pipeline wall, and the sound signal is not obviously attenuated in the transmission process due to the solid medium transmission, the transmission distance is long, and the sound signal is not easily interfered by other signals, the false positive rate of the leakage detection is effectively reduced, the operation and maintenance cost of the steam delivery pipeline is significantly reduced, and the industrial safety is improved.

The invention provides a cable joint partial discharge fiber bragg grating sound wave intelligent sensing and detection method, and relates to the technical field of cable partial discharge detection, and the method comprises the steps: obtaining multi-channel time-domain spectral response data of a detected cable joint through a fiber bragg grating sensing array, and demodulating the data to obtain a multi-dimensional feature vector group; decomposing the waveform signal into narrowband intrinsic mode components, extracting energy density through Hilbert transform and executing singular value decomposition, and screening dominant mode components to reconstruct the waveform signal to obtain stress wave time sequence characteristics; calculating a waveform high-order cross-correlation tensor as a hyperedge weight, extracting a time-frequency amplitude feature vector as an initial node feature, constructing a space-time hypergraph, and generating a node embedding vector through graph convolution; and initializing a Gaussian noise field based on a node embedding vector and setting condition information, executing reverse sampling denoising to generate a three-dimensional space probability density field to identify an initial positioning coordinate, and iteratively correcting the positioning coordinate and a wave velocity value until a residual error converges.

The present invention relates to a system and method for evaluating and automatically correcting a speckle image quality under a high temperature environment, and belongs to the field of digital image processing; the system and method comprise a speckle field preparation module and an evaluation and correction module; the speckle field preparation module comprises a support constraint component, a two-dimensional motion component, a speckle spraying component, and a host computer control component; the support constraint component comprises a test piece, a back support plate, a side clamping block, and a bottom support plate; the two-dimensional motion component comprises a motion support beam, a longitudinal motion component, and a transverse motion component; the evaluation and correction module comprises two CCD cameras, a data solving host computer, and a high and low temperature test chamber; a correction-improvement-correction automatic adjustment mode is adopted to realize clear and reliable speckle image acquisition, and the present invention realizes speckle image preparation, quality evaluation, and automatic correction under high and low temperature environments, further ensures accurate data collection in digital speckle detection, and realizes reliable solution of deformation of structural mechanisms under high and low temperature environments.

The invention discloses a wind speed measurement method and system based on a bistatic laser wind finding radar with separate transceiving, the wind speed in the atmosphere is accurately measured by transmitting a laser beam with a specific wavelength and using the Doppler frequency shift principle, the system integrates a plurality of modules, and the system is simple in structure and convenient to operate. Comprising an environmental parameter acquisition module, a Doppler frequency shift correction module, a signal processing module and an intelligent layout optimization module. By collecting environmental parameters in real time, the system dynamically adjusts Doppler frequency shift correction factors to compensate errors caused by environmental changes; the multi-stage signal processing module improves the signal-to-noise ratio and accuracy of wind speed measurement through multi-scale feature extraction, depth separable convolution and adaptive noise suppression technologies; the intelligent layout optimization module dynamically adjusts the position configuration of the base station based on a depth Q network algorithm, and ensures the measurement precision and stability in a complex environment; the method significantly improves the precision and reliability of wind speed measurement, and is suitable for the fields of wind power plant optimization layout, meteorological monitoring, aerospace flight plans and the like.

This application belongs to the field of visual compensation technology and provides a visual compensation method under starlight conditions. Through pre-sampling and grayscale variance statistics, it achieves the determination of starlight compensation intervals and the dynamic division of target and background regions in the imaging plane. It adopts a sampling method with different exposure time series for the target and background regions to obtain multiple frames of original sampled data and pixel integration time. Based on the pixel integration time, it constructs a spatially variable gain matrix and completes inter-frame registration and gain normalization processing to separate signal and noise components in the image. The signal component is used as a sparse sampling stream, and the pixel variance distribution of the noise component is used as a hyperparameter of the variational inference algorithm. Image reconstruction is completed through probability density gradient estimation, making the variational inference process match the actual noise distribution characteristics under starlight conditions, thereby improving the quality and reliability of visual imaging under starlight conditions.

This invention relates to the field of energy forecasting technology, and in particular to a method and system for short-term energy load forecasting based on an improved Transformer using SE-Block. The method includes reversible normalization preprocessing of acquired multivariate load sequence data; feature extraction and fusion of the preprocessed data using improved cross-scale interactive patching, including multi-scale feature extraction, cross-scale interactive alignment, residual correction, and dynamic fusion; and feature filtering of the fused features based on a channel attention mechanism, including feature response based on improved SE-Block and nonlinear interaction of context vectors. This invention addresses the non-stationarity of actual load caused by meteorological conditions and user behavior. By automatically eliminating noise interference among multiple variables, the model accurately depicts the fluctuation details of the load curve, demonstrating its robustness in multivariate load forecasting for integrated energy systems.

The invention discloses a total variation regularization depth expansion network method for hyperspectral image unmixing. The method comprises the following steps: inputting hyperspectral image data and an end member spectrum library; constructing a hyperspectral data unmixing model fusing a sparse constraint term and a total variation regularization term and an optimization objective function of the hyperspectral data unmixing model; converting the optimization objective function into an equivalent augmented Lagrangian form, and performing iterative solution on each variable; expanding an iteration process into a deep learnable network structure with a plurality of layers, and parameterizing hyper-parameters and a plurality of approximate operators in iteration into learnable network parameters; constructing a comprehensive loss function, and continuously optimizing network parameters by using back propagation and gradient descent methods; according to the method, total variation regularization prior is fused into a sparse unmixing model, the sparse unmixing model is expanded into a learnable end-to-end neural network through iterative solution, and the method has excellent unmixing precision and robustness for noise-containing data.

The application provides a temperature control system and an air conditioner; the temperature control system comprises a control unit, a power supply unit, an ambient temperature detection unit and an evaporator coil temperature detection unit; the ambient temperature detection unit comprises a ring temperature sensor assembly and a first inductor, the evaporator coil temperature detection unit comprises an inner coil sensor assembly and a second inductor, the control unit is used for acquiring the ambient temperature collected by the ring temperature sensor assembly and the evaporator coil temperature collected by the inner coil sensor assembly, and controlling the temperature of the air conditioner according to the ambient temperature and the evaporator coil temperature; the system structure not only realizes the temperature control of the air conditioner, but also plays a role in inhibiting common mode and differential mode noise interference by arranging the inductors in the ambient temperature detection unit and the evaporator coil temperature detection unit respectively, improves the EMC test qualified rate of the air conditioner, and has a simple structure and good practical value.

The application discloses a kind of based on acoustic wave FMCW's respiratory frequency detection method, belong to acoustic wave signal processing technical field;Method is: utilize mobile phone loudspeaker to generate a segment frequency continuous change acoustic wave signal, form continuous acoustic wave detection sequence;Mobile phone microphone receives the acoustic wave signal that is reflected back synchronously;To the acoustic wave signal that is reflected back received filtering processing;With reflected signal, the distance change curve of thoracic cavity with breathing movement is generated by autocorrelation operation to transmission signal;Distance change curve is carried out smoothing processing;The main frequency frequency is obtained by fast fourier transform, and respiratory frequency is calculated.The present application effectively suppresses environmental noise interference by using the advantages of FMCW technology, analyzes the autocorrelation coefficient of reflected signal, accurately extracts the thoracic movement information related to respiration, and realizes the accurate detection of human respiratory frequency;Using autocorrelation function is easier to find peak, more easily tracked nature, greatly increase the convenience of user.

This invention provides an enzyme turnover rate prediction method based on a dual-route hybrid expert mechanism, belonging to the field of bioinformatics technology. It solves the problems of low data utilization, insufficient information mining, and poor robustness in existing prediction methods due to missing values ​​and temperatures. The technical solution includes the following steps: S1: Constructing a unified standard enzyme turnover rate dataset; S2: Extracting multimodal embedding features; S3: Constructing intra- / inter-modal encoders; S4: Combining modalities into an expert hybrid module; S5: Designing an attention fusion mechanism. This invention can achieve high-precision prediction under complex in vitro environmental conditions.

This application provides a multimodal ultrasound intelligent identification method and precision diagnosis system for thyroid lesions. The method determines image data blocks with multimodal spatial registration and grayscale-contrast temporal alignment. Based on the grayscale ultrasound image sequence in the image data blocks, a first depth feature map of the target user's thyroid region is determined. A second depth feature map of the target user's thyroid region is determined based on the elastography map in the image data blocks. The hemodynamic spatial distribution characteristics of the parametric map in the image data blocks are captured to determine a third depth feature map of the target user's thyroid region. Cross-modal fusion is performed on the first, second, and third depth feature maps, and then the lesion segmentation mask, benign / malignant probability value, and risk classification of the target user's thyroid region are inferred and output in parallel. Using the scheme of this application, high-precision spatiotemporal alignment and cross-modal depth feature fusion of multimodal ultrasound images can be achieved to complete end-to-end intelligent identification of thyroid lesions.

The application discloses a color video restoration method based on a low-rank quaternion tensor and a space-time SVTV constraint, and comprises the following steps: acquiring a color video sequence to be restored, judging a video format and completing format analysis, converting YUV format into RGB format, constructing a corresponding pure quaternion tensor representation, and generating damaged video data; building a neural network parameterized low-rank quaternion tensor continuous function model, constructing a neural representation driven space-time SVTV regular term, and restraining a saturation gradient and a brightness gradient in different dimensions; fusing data consistency constraints and regular constraints, constructing a finite-dimensional solvable joint optimization model, and completing solvability verification; taking the damaged video as input, iteratively solving an optimal prediction tensor through a gradient descent algorithm or an Adam optimization algorithm, fusing a predicted pixel and an original known pixel, and outputting a complete restored video after data restoration and format adaptation. The application can realize high-precision, high-robustness and high-stability restoration of a missing area of a video, and is suitable for various video damage scenes.

The invention belongs to the technical field of information security, and particularly relates to an anti-wireless secret-stealing detection system based on antenna resonance-spectrum identification, which comprises a signal transceiving unit, a broadband antenna and an upper computer, and is characterized in that the signal transceiving unit is electrically connected with the broadband antenna, and the signal transceiving unit is connected with the upper computer. According to the method, the wireless secret stealing device embedded in high-level camouflage objects such as electronic equipment and cables can be identified more effectively, and effective detection can be performed no matter whether the secret stealing device is started or not. Based on an antenna resonance spectrum identification mode, the reflection echo signal of the wireless secret stealing device can be captured, noise interference in a complex electromagnetic environment is effectively suppressed through a pulse coherent integration technology, and the detection precision is improved.

The application discloses a fault detection method and device based on graph structure difference and electronic equipment, and relates to the technical field of intelligent monitoring. The method comprises the following steps: acquiring an observation data vector of a physical system to be monitored at time t, a linear mapping matrix and an upper state matrix vector, the observation data vector being determined by collecting sensing data of the physical system to be monitored in real time, and the linear mapping matrix and the upper state matrix vector being constructed based on a basic structure model, operating parameters and predictive input of the physical system to be monitored; constructing an optimization problem objective function at time t based on the observation data vector, the linear mapping matrix and the upper state matrix vector at time t; obtaining a sparse difference vector at time t by solving the optimization problem objective function at time t; comparing the sparse difference vector at time t with a threshold value to obtain a fault detection result, and sending a control instruction to a system controller. The method can improve the accuracy of fault diagnosis.

According to the non-stationary signal processing method and device, the electronic equipment and the storage medium, the non-stationary signal is accurately captured through the signal collector and converted into the discretized signal, and a high-quality data basis is provided for subsequent processing. And then the fractional Fourier transform algorithm is combined to effectively improve the resolution of the signal in the time-frequency domain and enhance the stability of the signal. And further mining the signal features by using an embedded track matrix algorithm, and constructing a track matrix reflecting the internal structure of the signal. Through singular value decomposition, effective extraction and dimensionality reduction of signal features are realized, and noise interference is effectively suppressed. And finally, based on processing of a singular value matrix algorithm, a processing result corresponding to the non-stationary signal is accurately determined, the accuracy and stability of non-stationary signal processing are remarkably improved, and the application effect of non-stationary signal processing is guaranteed.

The invention discloses an optical glass stress detection system and method, and relates to the field of optical measurement, and the system comprises the following components: a high-speed image processing module which carries out the image enhancement processing after an image processing unit receives image data, improves the contrast ratio and definition of an image, carries out the denoising processing of the enhanced image, and obtains a high-speed image; according to the invention, the Gaussian filtering algorithm of the high-speed image processing module effectively suppresses the noise interference existing in the image due to the influence of environment and equipment factors, improves the signal-to-noise ratio of the image, enables the subsequent feature extraction and stress analysis to be more accurate, and enables the image to be more accurate while smoothing the image through Gaussian filtering. According to the method, the edge information of the image can be well reserved, the system can identify and analyze the stress distribution condition more accurately, and meanwhile, the fuzzy degree of Gaussian filtering can be controlled by adjusting the standard deviation sigma of the Gaussian function.

This invention discloses a method and apparatus for audio line status identification in communication equipment rooms. The method includes audio data acquisition, acoustic physical constraint modeling, multi-region acoustic feature importance classification, audio line status identification model design, constraint loss dynamic balance optimization, acoustic feature loss function construction, and audio line status identification. This invention belongs to the field of data processing technology, specifically referring to a method and apparatus for audio line status identification in communication equipment rooms. This scheme models the sound wave propagation in the equipment room based on a three-dimensional acoustic wave equation to suppress non-fault noise interference; it divides the equipment room into zones, quantitatively allocates the number of monitoring points and regional loss weights, focuses on core areas, and significantly improves the fault detection rate of critical lines; it introduces the measured background noise sound pressure level of the equipment room to construct a noise correction coefficient, significantly improving the robustness of fault identification; and it designs an exponential dynamic relaxation coefficient, combined with benchmark weight allocation, to further enhance the early fault detection capability in critical areas.

The application discloses an irregular time sequence remote sensing image change detection method based on a space-time symbiotic matrix, and relates to the technical field of remote sensing image change detection. The irregular time sequence remote sensing image change detection method based on the space-time symbiotic matrix acquires a multi-temporal and multi-band remote sensing image sequence, normalizes and discretely quantizes pixels of each band; then, four types of space-time direction relations are defined, and a weighted space-time symbiotic matrix under each relation is calculated, which combines the time interval, intensity change and adaptive weight of each two time points; finally, the matrix is spliced into a feature tensor, and after the feature is extracted by a CNN, a pixel-level change detection binary classification result is output by a classifier. The application simultaneously captures the spatial texture and time evolution characteristics of the image by constructing the weighted space-time symbiotic matrix, and effectively distinguishes the real ground surface change from the pseudo change such as seasonal cycle by using the adaptive weight to process the irregular time sequence, so that accurate and reliable remote sensing image change detection is realized through deep learning.

This invention discloses a distributed intelligent decision-making and collaborative control system for unmanned aerial vehicle (UAV) swarms, relating to the field of UAV technology. The invention includes a data acquisition module, a situational awareness construction module, a distributed strategy negotiation module, a motion planning module, and a consistency control module. UAVs interact with neighboring nodes only to exchange decision parameters, reducing communication load and bandwidth requirements, avoiding the risk of single-node failure, and improving the robustness and operational reliability of the swarm system in complex maritime communication environments. It filters environmental information strongly correlated with its own motion, suppresses noise interference, and provides complete and reliable situational awareness data for swarm decision-making, improving the accuracy and anti-interference capability of maritime environmental situational awareness. Control parameters are adapted in real-time based on flight speed, attitude changes, and remaining battery power, balancing high-speed response accuracy and low-speed control stability, reducing low-power energy consumption, and enhancing the stability, endurance, and adaptability of swarm collaborative control to maritime operations.