69 results about "Haar wavelet" patented technology

The invention relates to the technical field of digital image security and authentic identification, and provides an image tampering detection method and system based on mixed features and RGB features, and the method comprises the steps: obtaining a to-be-detected input image, and carrying out the preprocessing of the to-be-detected input image; respectively extracting a Haar wavelet high-frequency component, a discrete cosine transform frequency domain feature and a Bayer convolution noise feature, and carrying out matrix level fusion to obtain a mixed feature; extracting RGB (Red, Green and Blue) features for the preprocessed input image; the mixed features are connected through cross-layer residual errors, and mixed feature learning features are obtained; and integrating the mixed feature learning features and the fused RGB features by using a cross-modal feature interaction architecture to obtain a prediction probability graph. Multi-modal features are fused, high-frequency response is enhanced, and the accuracy of image tampering detection is improved by adopting a dynamic fusion mechanism. The technical problems that an existing tampering detection method is insufficient in feature characterization capacity in a complex scene, low in tampering trace detection sensitivity and the like are solved.

The invention discloses an ancient textile image restoration system based on artificial intelligence. The system comprises a multi-source image acquisition module, a damaged area detection module, a pattern generation module, a color restoration module, a texture synthesis module and a multi-scale fusion module. The system introduces a wavelet guidance-frequency domain attention mechanism and a rotation invariant Haar wavelet basis function to realize accurate identification and classification of a damaged area; a saliency-guided wavelet decomposition control and self-adaptive threshold denoising method is combined, so that the perception capability of slant textures and edge details is improved; the texture synthesis module constructs a hierarchical modeling strategy fusing Gram style loss, Wasserstein style loss and a total variation regular term, and realizes generation of high-quality textures with unified styles and smooth edges; the system can be widely applied to cultural relic digital repair and display scenes.

The invention relates to an epilepsy prediction method based on adaptive sparse attention and a hierarchical graph convolution network, and the method comprises the steps: carrying out the time domain convolution, spectrum transformation and Haar wavelet down-sampling of an electroencephalogram signal, respectively generating time domain, spectral domain and fidelity down-sampling features, and fusing the features into a low-level feature set; on the basis of a sparse attention mechanism, constructing and applying a multi-level sparse mask to adaptively screen and weight-aggregate key discriminative features in the feature set to obtain screened features; on the basis of the feature, by constructing a local channel graph and a global frequency band graph and respectively executing graph convolution, capturing local spatial correlation of each channel in a single frequency band and global cross-frequency-band spatial dependence among different frequency bands, and fusing the local spatial correlation and the global cross-frequency-band spatial dependence into an embedded feature; and inputting the embedded features into a classifier to obtain a state probability, and triggering an alarm based on the state probability. Therefore, the problems of key information loss, insufficient time-space spectrum dependent modeling and feature redundancy are solved, and the accuracy, stability and real-time performance of epilepsy prediction are improved.

The invention relates to an underwater single target tracking method based on a wavelet token and a space-time Transform. The method comprises the following steps: firstly, constructing a reference frame sequence, a search frame and a previous frame historical token into a space-time input sequence, and extracting cross-frame features through a Transform encoder; then, Haar wavelet decomposition is carried out on the historical token, and a low-frequency component representing a target structure and a high-frequency component capturing motion details are separated out; then, adaptively fusing the global features and the historical components of the current search frame by using a gating mechanism, and generating a wavelet token; and finally, inputting the wavelet token and the global feature into a prediction head, and outputting a target classification confidence map and a bounding box regression map to determine the position and the scale of the target. According to the technical scheme of the invention, the interference of underwater low-illumination noise can be effectively suppressed through the wavelet token, and the space-time continuity of target motion modeling is maintained in combination with a gating strategy, so that the tracking robustness of an underwater complex scene is effectively improved.

The invention relates to the technical field of precision manufacturing and intelligent control, and particularly discloses an intelligent spark machine machining parameter optimization method based on effect feedback, which comprises the following steps of: calculating an abnormal characteristic value of an electrode feeding speed by using Haar wavelet transform, and analyzing time sequence data of conductivity of a working solution by using fast Fourier transform; according to the method, abnormal characteristic values are obtained, the characteristic values are constructed into a comprehensive characteristic vector, the comprehensive characteristic vector is input into a random forest model for comprehensive analysis, an electric spark machining effect score is generated, the system can dynamically adjust the electrode feeding speed and the working fluid conductivity based on the effect score, and it is ensured that machining parameters are always in the optimal configuration; according to the method, the machining efficiency is improved, the rejection rate is reduced, the surface quality and the size precision of the product are greatly improved, and the requirement of precision manufacturing is met.

The invention relates to the technical field of intelligent environment recognition, and particularly discloses a water and soil conservation measure intelligent query method and system based on multi-dimensional parameterization. Temperature and humidity data are collected in real time through a multi-source sensor deployed in water and soil, and a temperature stability characteristic value is calculated by adopting zero-sequence processing and fast Fourier transform; a humidity change rate characteristic value is extracted by combining first-order difference processing and Haar wavelet transform; the feature values are constructed into a comprehensive environment state feature vector, and the comprehensive environment state feature vector is input into a trained random forest model for environment recognition result evaluation; according to the method, the sensing technology, signal analysis and machine learning prediction are fused, the sensing ability, the regulation and control precision and the self-adaptability of the system are improved, the method is suitable for complex and changeable environment scenes, and the method has the advantages of being high in adaptability, high in adaptability and the like. Good application prospects are realized.

The invention provides a high-fidelity anti-compression image watermarking method and system based on spectrum-airspace decoupling, and belongs to the field of information security. Firstly, the watermark information is mapped and remodeled; a watermark encoder based on multi-granularity spectrum-spatial domain feature decoupling is constructed, discrete cosine transform is introduced to filter out high-frequency components, Haar wavelet transform is adopted to realize lossless downsampling, a fast Fourier transform dynamic filter is combined to capture global semantic features, and local texture details are combined through multi-scale spatial domain volume accumulation; designing a physical perception and visual self-adaptive dual embedding strategy, and anchoring watermark energy to an anti-compression brightness channel; constructing an anti-attack layer containing differentiable JPEG compression simulation and mixed noise simulation, and participating in network training; and constructing a decoder and designing a loss function to carry out network optimization. According to the method, the robustness of the watermark under strong compression and complex black box attacks is improved, and extremely high visual imperceptibility is realized through physical and visual constraints.

The invention discloses a medical image segmentation method and system based on feature extraction optimization. The medical image segmentation method comprises the following steps: performing target region segmentation on a segmented image through a constructed image segmentation model; an image segmentation model in the invention adopts a U-Net structure and comprises an encoder and a decoder; a Haar wavelet transform and KAN convolution module is introduced into the encoder to carry out feature extraction on the input feature map; meanwhile, a channel cross attention module and a space weighting module are connected in series on jump connection of the U-Net structure, the channel cross attention module captures complex dependence of cross-stage features through channel attention, and the space weighting module generates dynamic weights by using KAN and space attention, optimizes a space-channel relation of the features, and obtains the cross-stage features. The two are combined to relieve the problems of multi-scale information loss and semantic inconsistency, and the global semantic integration capability is remarkably improved.

The invention discloses a line fault detection method and device based on morphological wavelets, relates to the technical field of line detection, and solves the problem that in the prior art, when a single-phase grounding fault occurs in a small-current grounding system, the fault current is small, the characteristics are not obvious, and when line fault detection is carried out by using time domain, frequency domain and time frequency analysis methods, the fault current is not obvious. And the technical problems of low fault line selection accuracy and poor reliability exist. The method comprises the following steps: acquiring zero-sequence current signals in a period before and after a line fault occurs; pre-processing the zero sequence current signal; decomposing the preprocessed zero sequence current signal by using a morphological Haar wavelet; calculating modulus maxima of the plurality of lines according to the decomposition result; correcting the modulus maxima of the plurality of lines based on the line length; screening fault lines according to the corrected modulus maxima; the accuracy and reliability of single-phase earth fault line selection can be improved.

The invention discloses a power data desensitization method based on multi-granularity dynamic sensitivity grading, terminal equipment and a storage medium. The power data desensitization method comprises the following steps: dividing power data into four sensitive grades of identity information, transaction records, power consumption behaviors and equipment data; full life cycle protection of data acquisition, storage and transmission is realized through a collaborative desensitization mechanism of field data national secret SM4 encryption / dynamic mask and recorded data Laplacian noise injection / behavior mode generalization; according to the system, Haar wavelet decomposition is adopted to separate user power consumption time sequence data into a low-frequency approximation coefficient and a high-frequency detail coefficient, Paillier homomorphic encryption aggregation is carried out on a low-frequency component, Gaussian noise is added to a high-frequency component, then equalization processing is carried out, and an aggregation power consumption sequence is reconstructed through wavelet inverse transformation.

The invention discloses a hidden space adversarial sample generation method and system based on multi-scale feature separation, and the method comprises the steps: employing a neural network quantization training method based on straight-through estimation, and training a hierarchical vector quantization variational auto-encoder; carrying out differentiable Haar wavelet transformation on the input image by adopting a wavelet packet transformation algorithm, decomposing the input image into a low-frequency component and a high-frequency component, and realizing multi-scale feature separation; inputting the high-frequency component into a hierarchical vector quantization variational auto-encoder, and extracting and quantizing global high-frequency features and local high-frequency detail features; in the potential space, a learnable disturbance variable is introduced, a potential vector after disturbance is constructed, and the potential vector is reconstructed into an adversarial sample through a decoder; and based on a preset disturbance target, carrying out iterative optimization on the disturbance vector until a confrontation sample which satisfies an attack success condition and is optimized in visual quality is generated. According to the method, a wavelet domain variational auto-encoder and a hidden space iterative attack algorithm are fused, and an adversarial sample with high fidelity and clear interpretation is generated.

The invention discloses an open vocabulary multi-target tracking method based on confidence coefficient adjustment and wavelet convolution, belongs to the technical field of computer vision and target tracking, and can enhance the adaptive modeling capability of a target motion mode by designing a confidence coefficient weighted Kalman updating mechanism and developing a confidence coefficient noise adaptive Kalman filtering algorithm. And meanwhile, the Haar wavelet transform is introduced, so that the receptive field of convolution operation is remarkably expanded, and the capturing capability of high-frequency motion details and the maintaining capability of low-frequency contour information are effectively improved. And in combination with the joint cost matrix of the motion features and the appearance features, a more adaptive target tracking model is formed, and the tracking accuracy and stability are effectively improved. The method can effectively deal with rapid movement and temporary shielding of the target, accurately tracks various types of targets with undefined types, and is suitable for open vocabulary multi-target tracking tasks in various complex environments.

The invention relates to the technical field of deep learning and load identification, in particular to a deep learning load identification method and system based on bilateral filtering denoising and multi-wavelet feature fusion and a medium, and the method comprises the steps: firstly converting an acquired training data set into an image, and carrying out the preprocessing of bilateral filtering denoising; graying the de-noised image, extracting low-frequency and high-frequency components by using Haar wavelet transform, and extracting low-frequency approximation and high-frequency information in horizontal, vertical and diagonal directions by using Daubechies wavelet transform; pixel unification and normalization are carried out on the feature map, a training set and a test set are divided after category label integers are coded, and a convolutional neural network containing two branches is constructed to extract depth features and splice and fuse the depth features; and finally, extracting fusion features through a full connection layer, and taking sparse classification cross entropy as a loss function to train a CNN model in an off-line manner to obtain a load identification model. The method can improve the accuracy and stability of load identification, and is suitable for various electric equipment load identification scenes.

The invention relates to the technical field of digital image processing, in particular to an SAR image denoising method and system. The method comprises the following steps: acquiring a noisy SAR image; obtaining a pre-trained diffusion model; the diffusion model comprises de-noising processes of T time steps; uniformly sampling K time steps from the T time steps; wherein K is equal to 1 / 40 T to 1 / 20 T; performing a de-noising process of K time steps on the SAR image with noise to obtain a de-noised SAR image; the diffusion model comprises a de-noising network established based on a U-Net architecture, and the de-noising network is used for predicting a noise component and variance required by reverse sampling; the denoising network comprises an encoder, and the encoder is used for extracting a multi-scale feature map from a noisy SAR image and decomposing any feature map into a low-frequency approximate sub-band and three high-frequency detail sub-bands based on Haar wavelet transform. By adopting the scheme, the de-noising precision, de-noising efficiency and reasoning stability of the diffusion model can be improved.

The invention discloses an image defogging method based on a wavelet domain diffusion model, which is used for solving the problems of overlong reasoning time, large calculation amount, unstable restoration effect and the like in the prior art. The method comprises the following steps: constructing a training set and a test set; performing wavelet domain feature enhancement processing on all foggy weather images and clear images in the training set to obtain corresponding second-level components and second-level wavelet domain enhancement features; loading random Gaussian noise to a second-level low-frequency approximate component in the second-level component of each clear image, inputting the second-level low-frequency approximate component and the second-level low-frequency approximate component of the corresponding foggy day image into a noise prediction network at the same time, and outputting prediction noise; de-noising the second-level low-frequency approximate component of each foggy day image based on the predicted noise, and then carrying out inverse discrete Haar wavelet transform processing to obtain second-level recovery features of the second-level low-frequency approximate component; training the noise prediction network by using a loss function to obtain a wavelet domain diffusion model; a defogged image can be obtained based on the model.

The invention relates to the technical field of filling, in particular to a filling barrel cover segmentation positioning method, device and equipment, an adopted filling barrel cover segmentation positioning model uses a Haar wavelet-based down-sampling module to replace standard down-sampling operation, spatial dimension information is encoded to channel dimensions through wavelet transform, and the channel dimensions are subjected to channel division and positioning. High-frequency detail features are reserved while the resolution is reduced, and the target segmentation precision of the barrel cover is improved; an EffectiveSE attention mechanism is embedded in a neck network Neck, a channel compression-dynamic re-calibration strategy is adopted, and interference noise such as barrel cover target edge area feature response is adaptively enhanced through a learnable gating unit, light reflection of a filling barrel and surrounding metal is inhibited, so that barrel cover edge features with clear targets can be obtained, and the fusion effect of multi-scale features is improved; details of different scales of barrel cover targets can be better captured; a C2F-PC lightweight module is adopted, part of PConv convolution and cross-stage features are fused, redundant operation is reduced through channel selective calculation, and the segmentation speed of the model is increased.

The invention provides a feature fusion method, system and device based on a frequency domain and a storage medium, and the method comprises the following steps: 1, separating low-frequency features and high-frequency features: carrying out the separation of the low-frequency features representing a global structure and the high-frequency features representing detail textures through a Haar wavelet decomposition guidance feature map; 2, generating a low-frequency weight: processing the low-frequency features, and generating the low-frequency weight which can be used for modulating a target feature map; 3, generating a high-frequency weight: processing the high-frequency feature, and generating the high-frequency weight adaptive to the spatial size of the target feature map; and 4, feature fusion: fusing the low-frequency weight, the high-frequency weight and the target feature map, and outputting the fused enhanced features. According to the method, global and detail information separation of cross-scale features is realized through Haar wavelet decomposition, and adaptive fusion is realized in combination with double-branch weight learning, so that the problems of insufficient feature fusion and poor generation quality of the generative adversarial network in image generation are solved.

The invention relates to the technical field of computer vision, in particular to a video causal intervention and physical noise decoupling method and system oriented to edge calculation. According to the invention, through multi-modal video acquisition and time domain alignment, CLAHE equalization, dynamic illumination compensation and pixel color value normalization are carried out on an original video stream, so that the video quality is improved, and high-quality data are provided for subsequent processing; 3D convolution and Haar wavelet transform are utilized to decouple and normalize a frame sequence, cause and effect features and noise data are separated, orthogonality is ensured by a loss function, accurate analysis of a cause and effect relationship is facilitated, and interference is reduced; according to the invention, a noise path is cut off based on a Do operator, calculation is accelerated by using an FPGA, and precise control, including adjustment of camera parameters and control of a stepping motor, is realized through control signal generation and feedback.

The invention discloses a multi-scale parallax calculation module, method and device based on Haar wavelet transform and a medium, and the method comprises the steps: firstly constructing a feature pyramid through the Haar wavelet transform, extracting high-frequency and low-frequency details, and then carrying out the parallax calculation through employing a Coarse-to-Fine strategy. According to the method, the problem that random initialization is slow in convergence in a weak texture area or falls into local optimum can be effectively solved, and meanwhile, the wavelet detail component can provide richer texture and edge information for cost calculation. The method has the advantages of being high in precision, high in robustness and high in speed in dealing with parallax calculation tasks of weak-texture, non-texture or repeated-texture scenes.

The invention belongs to the technical field of image processing, and particularly relates to a traffic scene image defogging method based on bidirectional Haar wavelet transform and AOD-Net, comprising the following steps: constructing a data set; performing frequency domain decomposition on the image by using Haar wavelet transform, separating low-frequency and high-frequency features, and realizing display decoupling of structure and fog information and texture and detail information; a multi-head self-attention mechanism is introduced, a low-frequency self-attention enhancement module and a high-frequency self-attention enhancement module are constructed respectively, and global structure modeling and detail feature self-adaptive enhancement are achieved; according to the method, on the premise that the color naturalness and the structure consistency of the restored image are kept, the common problems of over-enhancement, artifact generation, edge breakage and the like in a traditional defogging network are effectively reduced, and the overall visual quality and stability are remarkably improved.

The invention relates to the technical field of electric energy quality disturbance detection, and particularly provides an electric energy quality disturbance detection method and device based on a Haar wavelet and an extreme random tree, and electronic equipment, and the method comprises the steps: carrying out the multi-scale decomposition of a first electric energy quality disturbance signal according to the discrete wavelet transform based on the Haar wavelet, and obtaining a second electric energy quality disturbance signal; obtaining a first detail coefficient and a first approximation coefficient; according to the first detail coefficient and the first target approximation coefficient, calculating a target characteristic quantity of power quality disturbance to obtain a first characteristic vector; and inputting the first feature vector into a trained extreme random tree detection model, and outputting a first classification detection result of the first power quality disturbance signal through the extreme random number classification model. According to the power quality disturbance detection method and device based on the Haar wavelet and the extreme random tree, the accuracy of the detection result is effectively improved.

The invention relates to a river flow prediction method, which comprises the following steps of: 1, dividing a daily runoff data set, and performing multi-stage decomposition on a training set by using Haar wavelets to obtain de-noising parameters; 2, multiplexing the verification set and the test set to obtain data; step 3, carrying out first-order differential transformation, and zooming to an interval of [0, 1] by using MinMaxScaler to obtain a normalized value of a differential sequence; 4, mapping the data to a high-dimensional space through a linear embedding layer, and then carrying out position coding to obtain data; step 5, inputting the data obtained in the step 4 into a DTransformer encoder, so that a catastrophe point obtains a higher attention weight so as to strengthen difference characteristics of adjacent time steps and obtain encoder output; 6, inputting the data obtained in the step 5 into an LSTM decoder, and performing feature recombination to obtain encoder output; 7, mapping the data obtained in the step 6 to 7-dimensional output through a full connection layer; and 8, performing inverse normalization and inverse difference transformation on the data obtained in the step 7 to obtain final output.

The invention discloses a dispensing robot motion control system based on template matching, and belongs to the technical field of industrial automation and robot control. According to the invention, the problems of inaccurate positioning, wrong and leaked glue and track optimization existing in the existing micro object glue dispensing are solved. The method specifically comprises the following steps: preprocessing a collected image, carrying out denoising and feature enhancement on the collected image by adopting an improved bilateral filtering algorithm in combination with Haar wavelet transform, and then realizing accurate positioning by fusing color matching, gray mode matching and an improved dung beetle optimization algorithm; and finally, through D-H parametric method modeling and forward and inverse kinematics analysis of the robot, the motion trail of the robot is optimized. According to the invention, the dispensing positioning precision and matching efficiency can be effectively improved, the phenomena of glue mistake and glue leakage are reduced, and the method can be applied to high-precision dispensing operation of micro objects.

This invention discloses an extended attention visual processing method for unmanned power grid inspection, comprising: acquiring and preprocessing UAV inspection images; inputting the preprocessed images into an encoder-decoder segmentation network to extract shallow detail features and deep semantic features; in the shallow encoding stage, using a hybrid frequency domain downsampling module to extract high-frequency edge information using Haar wavelet transform and fusing it with spatial domain structural features; in the deep encoding stage, introducing an extended alternating perception mechanism to model local detail associations and global topological dependencies; in the decoding stage, using a feature reconstruction module to complete upsampling, feature fusion, and context refinement to progressively restore the spatial structure of power lines; training the network using a depth-alignment-connectivity loss function and outputting a power line segmentation mask; this method can reduce power line segmentation breaks and missed detections in complex backgrounds, and improve the continuous segmentation capability and structural integrity of weak targets.

The invention relates to a high-compression-ratio low-cost image compression method, which comprises the following steps of: segmenting an input image into macro blocks with the height of 2 and the width of 128 or 256, and converting RGB (Red, Green, Blue) to YUV (Yttrium, Ultraviolet) to reduce component correlation; the macro block is pre-coded, and a pre-coding bit number is obtained through up-sampling, vertical Haar wavelet transformation, horizontal transformation, quantization and entropy coding; calculating an optimal quantization parameter based on the precoding bit number to realize code rate control; and formally coding according to the optimal parameter, updating the buffer and outputting a code stream. According to the method, through multi-stage transformation and dynamic quantization control, the high compression ratio is achieved, vision is lossless, the algorithm is simple, the hardware implementation cost is low, the method is suitable for multiple platforms, and the scene requirements of ultra-high-definition videos and the like are met.

A medical image segmentation system and a segmentation method, the method comprising the steps of: constructing an encoder-decoder segmentation network, introducing a frequency-space dual-flow decoupling module DualWaveNet in the shallow coding stage, extracting local anatomical structure features through the spatial branch, and using fixed Haar wavelet decomposition to construct the frequency branch to explicitly separate and recombine the low-frequency structure information and the high-frequency boundary texture in the horizontal, vertical and diagonal directions. In the deep bottleneck stage, a global graph relationship reasoning module GraphRM is introduced, the deep channel response is constructed into a potential semantic node, the long-range topological dependence between the lesion area and the similar background tissue is modeled and calibrated by combining the local graph perception branch and the global semantic relationship branch. After the decoder is gradually up-sampled, the segmentation result of the prostate cancer MRI image is output. The system comprises a processor which can run the image segmentation method. The present application can improve the ability of micro-lesion positioning, fuzzy boundary recovery and background mis-segmentation suppression under the condition of single modality MRI input.

The application provides an efficient information fidelity down-sampling method and a down-sampling module, and is a new effective down-sampling method in a segmentation task; the module uses Haar wavelet transform to reduce the spatial size, and simultaneously increases the number of feature channels, so that the information of the space is not lost; a 1*1 pixel-by-pixel convolution operation is used to learn representative features, so that the features of the image are extracted with a small number of learning parameters, and the segmentation quality of semantic segmentation is improved; the performance of the segmentation model is enhanced, and the function of evaluating the features after down-sampling is realized. The general down-sampling module provided by the application directly replaces the pooling layer or the stride convolution layer without increasing the calculation amount, and is integrated into the current architecture of semantic segmentation; the application provides a new metric index feature entropy, which is used for measuring the quality of the features after sampling, and evaluating the size of the uncertainty of the output feature map.

The invention discloses a behavior detection method and system based on a frequency domain perception and gating attention mechanism, and the method comprises the steps: constructing a behavior detection model based on a YOLOv10n lightweight architecture, introducing a frequency domain perception layer based on Haar wavelet transform into a backbone network, and combining an extrusion excitation mechanism (SE) attention mechanism, thereby achieving the behavior detection of a frequency domain. Constructing a frequency domain sensing feature module; the module decomposes an input feature map into low-frequency (LL) and high-frequency (LH, HL, HH) sub-bands through wavelet decomposition, and respectively captures structure information and detail features. The SE mechanism dynamically adjusts the weight of each sub-band and enhances the sensing ability of the model to key frequency components. Meanwhile, a gating separation attention enhancement module is introduced into the neck network for feature fusion, a shielding confidence map is estimated through the gating separation attention enhancement module, the weight of a shielded area in a feature map is dynamically adjusted, and adaptive gating and enhancement of the features are achieved.

This invention relates to the field of network security technology, specifically disclosing an adaptive dynamic network security strategy intelligent control method. By real-time monitoring of the qubit error rate and communication channel signal strength in network traffic, abnormal feature values ​​of the communication link and channel disturbance feature values ​​are extracted respectively. Specifically, Bayesian inference is used to achieve high-sensitivity identification of quantum noise interference, and Haar wavelet transform is used to improve the robustness of external interference detection. The two types of features are fused into a comprehensive security risk feature vector, which is input into a gradient boosting tree model for training. With the goal of minimizing the prediction scoring error, a network security score is output, and threat levels are classified accordingly. The system automatically adjusts the protection strategy according to the security level and forms a closed-loop optimization control through continuous feedback, improving the adaptability and proactive defense capabilities of the network system. This solves the problems of lagging security response and inaccurate strategy adjustment in existing technologies under complex environments.

The invention relates to the technical field of environmental parameter monitoring of aquaculture, and particularly discloses an optimal environmental parameter analysis method and system based on aquaculture, and the method comprises the steps: collecting the flow velocity and water temperature data of a water body in real time, and analyzing the data through Haar wavelet transform and fast Fourier transform. The method comprises the following steps: calculating a water body flow velocity abnormal index and a water temperature abnormal index, constructing a comprehensive feature vector by using the two abnormal indexes, taking the comprehensive feature vector as input of a machine learning model, evaluating the influence degree of environmental parameters on the culture quality, performing dynamic adjustment by applying a PID control principle based on a serious influence result, and automatically optimizing the water body flow velocity and the water temperature to an optimal state. An efficient and reliable intelligent management scheme is provided for modern aquaculture, the survival rate, the growth speed and the product quality of cultured organisms are improved, and sustainable development of the industry is promoted.