442 results about "Nonlinear transformation" patented technology

The invention discloses a multi-scale and mixed attention high-robustness motor imagery recognition method, which comprises the following steps: S1, acquiring motor imagery electroencephalogram signals, preprocessing the motor imagery electroencephalogram signals, dividing a training set and a test set, segmenting the training set, recombining the training set and expanding a training data set; s2, multi-scale feature extraction is conducted on the motor imagery electroencephalogram signals through a multi-scale convolution embedding module, and time dynamic and space cooperation features of different frequency bands are captured; s3, inputting the multi-scale features into LG-KAT, and respectively modeling a local fine-grained feature and a global time sequence dependency relationship through a local attention branch and a global attention branch; s4, features output by LG-KAT and low-layer embedded features are fused and flattened, a classification layer based on GR-KAN is input for nonlinear transformation and category mapping, model parameters are trained and optimized, and motor imagery task classification is achieved. The invention further discloses a multi-scale and mixed attention high-robustness motor imagery recognition system.

The invention provides a photovoltaic prediction method based on a data double decomposition and deep learning optimization model, and the method comprises the steps: collecting and preprocessing historical photovoltaic power data and meteorological associated data, carrying out the double decomposition of the preprocessed historical photovoltaic power data, and obtaining photovoltaic power component data; combining the photovoltaic power component data with meteorological associated data to construct a plurality of groups of photovoltaic-meteorological component data sets; an iTransform-KAN photovoltaic power prediction model is constructed, the photovoltaic-meteorological component data set is used to train and test the iTransform-KAN photovoltaic power prediction model, and the trained iTransform-KAN photovoltaic power prediction model is obtained; and determining a final predicted value through a linear superposition strategy based on the photovoltaic power component predicted value. According to the method, precise stripping of multi-scale features of photovoltaic power and adaptive learning of nonlinear transformation are realized, so that the capability of modeling a complex dynamic relationship is improved.

The invention discloses an acute pancreatitis complication assessment method based on artificial intelligence, and belongs to the technical field of medical information, and the method specifically comprises the steps: firstly, obtaining a clinical data set containing real-time clinical indexes and basic disease historical records; performing time sequence analysis on the historical records of the basic diseases, extracting long-term influence characteristics of the historical records of the basic diseases and generating basic disease influence factors; carrying out standardization processing on the real-time clinical indexes and the basic disease influence factors, and generating a comprehensive feature vector after calculating association weights among features; inputting the vector into a trained artificial intelligence evaluation model, and outputting a complication risk probability through layer-by-layer nonlinear transformation; and finally, mapping the risk probability value into a risk level, and integrating patient information to generate a structured risk assessment report. And an acute pancreatitis complication evaluation scheme integrating the acute stage index and the chronic basic disease influence is established.

The invention provides a full-text retrieval method and system fusing various types of documents, and relates to the technical field of information retrieval, and the method comprises the following steps: obtaining document representation through document content extraction and structure recognition, generating a cross-modal semantic vector by using word embedding and nonlinear transformation, constructing a hierarchical index and a cross-document association graph, and obtaining a full-text retrieval result; the basic correlation score is calculated after the query request is received, and the comprehensive score of the candidate content segments is calculated based on the association graph to determine the optimal retrieval result, so that unified representation and retrieval of heterogeneous documents are realized, the cross-document retrieval precision and relevance are improved, and the processing capability of a retrieval system on complex queries is enhanced.

The invention discloses a PCB defect real-time detection method based on multi-scale feature fusion, and relates to the technical field of PCB defect real-time detection methods, and the method comprises the steps: obtaining a to-be-detected PCB image, carrying out the size normalization and pixel value standardization processing of the image, and obtaining a standardized image meeting the input requirements of a model; inputting the standardized image into a backbone network of a teacher detection model, and extracting a multi-scale primary feature map containing texture information in different directions through a grouping convolution structure; transmitting the multi-scale primary feature map to a neck network of a teacher detection model, and performing weighted fusion on feature maps of different scales by using a learnable weight to generate a multi-scale fusion feature map; and in an up-sampling path of the neck network, generating channel description information after global pooling is performed on the deep fusion feature map, generating a channel attention weight through nonlinear transformation, acting the weight on a primary feature map of a corresponding level, and outputting an enhanced feature map.

The invention provides an infrared small target detection method and system based on depth-guided low-rank sparse decomposition, and relates to the technical field of remote sensing image processing, and the method comprises the steps: obtaining all original infrared images shot by remote sensing equipment, and sequentially stacking the original infrared images according to an obtaining time sequence, and obtaining an infrared original tensor; performing low-rank background and sparse target decomposition processing based on the infrared original tensor to obtain a low-rank sparse tensor decomposition model; a low-rank background tensor containing nonlinear transformation is obtained through processing of a constructed hierarchical nonlinear tensor ring background module; processing through a sparse target module fused with an attention mechanism to obtain a sparse feature tensor of the infrared small target area; and reconstructing a low-rank sparse tensor decomposition model guided by the deep neural network, and carrying out solving processing to obtain a final infrared small target detection result. According to the invention, accurate, robust and rapid detection can be carried out on a small target under a complex background.

The invention belongs to the technical field of load prediction in a low-voltage distribution area, and particularly relates to a time sequence prediction method for attention mixed multi-scale decomposition, which comprises the following steps: S1, preprocessing original time sequence data to obtain a standardized sequence X; s2, inputting X into MJDA, and outputting uniform high-dimensional representation U after feature enhancement; s3, inputting U into TCDA, and carrying out cross-dimension dependence modeling and deep nonlinear transformation to obtain a final enhanced feature U1; s4, inputting U1 output by the TCDA into a hybrid expert predictor group; the predictor group is composed of K parallel expert predictors, and a corresponding expert prediction result is obtained; meanwhile, U output by the MJDA is processed through a noise perception gating network, and weight distribution U used for expert predictor fusion is generated; and according to the U, carrying out weighted summation on the output of the K expert predictors to obtain a prediction result. According to the method, high-precision and high-stability load prediction can be realized in a low-voltage distribution area environment with limited resources.

The invention provides a facial skin flaw enhancement method based on a Lab color space. The method comprises the following steps: firstly, acquiring an RGB face image and converting the RGB face image into a CIE Lab color space with uniform perception; then, performing differentiation treatment according to the manually selected skin flaw type: for the vascular flaw, extracting statistical characteristics of a component and driving adaptive nonlinear transformation, and generating a grey-scale map which highlights the red flaw; for pigment flaws, nonlinear transformation is carried out on the component L, and then collaborative linear weighting and feature amplification are carried out on the component L, the component a and the component b, so that a grey-scale map with highlighted pigment spots is generated. And finally, coloring the grey-scale map in the Lab color space through adjustable parameters to generate a high-contrast color enhanced image. The method overcomes the dependence on hardware and training data in the prior art, can clearly and adaptively enhance various flaws such as acnes, couperose streaks and color spots, shows robustness under different illumination, and can be widely applied to clinical beauty, later photography and real-time video processing.

The invention provides a control method, system and control device based on a communication system, and the method comprises the steps: obtaining a current control state and a communication state of each terminal device, and carrying out the nonlinear transformation and structure splicing, so as to construct a joint state representation; obtaining the scheduling priority of each task, and selecting K tasks with the highest scheduling priority to form a task set; for each task in the task set, using a control trajectory prediction function to calculate a predicted action trajectory of a target task in future T steps; inputting the predicted action trajectory into a pre-constructed auto-encoder, and generating a low-dimensional control intention vector; in response to the control intention vector, decoding the control intention vector by using a decoder, generating a restored predicted motion trajectory, and minimizing a reconstruction error, so that the restored predicted motion trajectory approaches an original predicted motion trajectory as much as possible; and executing a control action corresponding to the restored predicted action track, and performing closed-loop feedback optimization.

The invention relates to an unmanned aerial vehicle trajectory tracking control method based on model prediction and preset performance constraint, and the method proposes to introduce Lyapunov stability constraint into a model prediction control framework and integrate a preset performance control mechanism, thereby achieving the unification of performance constraint and system stability analysis. Comprising the following steps: establishing a nonlinear system model based on unmanned aerial vehicle dynamics; position errors and attitude errors are defined, a preset performance function is constructed, and errors with performance constraints are converted into unconstrained errors through error normalization and nonlinear transformation; establishing a model prediction optimization problem on the premise of considering input saturation and stability constraints; designing an auxiliary control law based on the transformation error to construct a stability constraint; it is proved that the control strategy can ensure that errors meet preset performance constraints and system local asymptotic stability. According to the invention, stable and reliable trajectory tracking control of the unmanned aerial vehicle system can be realized, and the method has high tracking precision and good dynamic performance.

The invention discloses a multi-modal data joint embedding method based on a hierarchical progressive arithmetic interaction network, and the method comprises the following steps: obtaining text data and image data from the same semantic entity, and extracting a text local feature, a text global feature, an image local feature and an image global feature; inputting the text local feature and the image local feature into an atomic layer, and processing based on a Cartesian product to generate a first-order interaction feature; inputting the first-order interaction features into a combination layer, and carrying out nonlinear transformation processing to generate enhanced nonlinear interaction features; and inputting the nonlinear interaction features into the aggregation layer, and generating a multi-modal joint embedding vector in combination with the text global features and the image global features. The method effectively solves the problems of modal isomerism, single interaction level, lack of dynamic adaptability and the like in the prior art.

The invention discloses an electric power equipment safety long text knowledge retrieval method based on sparse and dense joint enhancement, which comprises the following steps of: encoding an electric power equipment safety long text and retrieval query by utilizing a pre-training language model for performing supervision and fine tuning on an electric power equipment safety field label data set, and generating a semantic word vector; a weighted sparse vector fusing semantic information and a dense semantic vector enhanced through nonlinear transformation are generated through fine-grained semantic enhancement branches; respectively calculating sparse similarity based on a common item weight product and dense similarity adopting a double-distance weighted fusion strategy combining a cosine distance and an Euclidean distance; and carrying out weighted summation on the two similarity scores through the learnable weight to obtain a final similarity, and realizing accurate sorting retrieval of the long text knowledge. The method gets rid of dependence on global features of sentence vectors, can accurately capture local key information in a long text, and has high retrieval precision and semantic robustness.

The invention discloses a cloud mobile phone equipment fingerprint disguising method and related equipment, and relates to the technical field of cloud mobile phone security, and the method comprises the steps: obtaining hardware entropy source data which comprises current timestamp data, a hardware unique identifier hash value and an environment noise sampling value; non-linear transformation is carried out on the hardware entropy source data based on a preset chaotic mapping algorithm, a virtual parameter set is generated, and the virtual parameter set meets a preset equipment parameter rule; covering the virtual parameter set to a multi-level system interface of the real equipment fingerprint through kernel-level injection operation; monitoring the use state of the virtual parameter set based on a preset life cycle model, and determining a parameter updating trigger condition; and when a parameter updating triggering condition is met, the nonlinear transformation is executed again to generate an updated virtual parameter set, and the updated virtual parameter set covers the multi-level system interface.

Disclosed are apparatuses, systems, and techniques that may use machine learning for implementing generative text-to-speech models. The techniques include identifying a mapping of speech characteristics (SC) on a target distribution of a latent variable using a non-linear transformation for at least a subset of the SC. Parameters of the non-linear transformation are determined using a neural network that approximates a statistics of the SC with a statistics predicted for the SC based on the identified mapping and the target distribution of the latent variable.

The invention discloses a high-fidelity three-dimensional Gaussian sputtering (3DGS) lightweight method for resource-constrained equipment. The method aims at solving the problems of high storage and computing resource consumption caused by the fact that a large number of parameters are stored in an existing 3DGS technology, and geometric distortion possibly occurring when details of a scene center are processed is overcome. The core of the method lies in a multi-stage progressive optimization framework, and the framework cooperatively applies four key technologies of Gaussian cutting and opacity regularization, dynamic spherical harmonic function adjustment, entropy constraint vector quantization and coordinate space shrinkage. Wherein in Gaussian clipping, redundant gauss are eliminated by quantifying the contribution degree of a Gaussian unit; the dynamic spherical harmonic function adjustment adaptively adjusts the order of color coding according to the scene complexity; the entropy constraint vector quantization is used for compressing a plurality of Gaussian attributes so as to realize more compact representation; and the coordinate space shrinkage is realized through nonlinear transformation, so that the rendering precision of details of the center of the scene is remarkably improved. According to the method, while the rendering precision and quality are kept, remarkable storage compression is realized, and the method is particularly suitable for deployment of resource-limited platforms such as mobile equipment.

The invention provides a multi-merchant hardware equipment supply chain demand intelligent matching method, and relates to the technical field of supply chain management, and the method comprises the steps: obtaining a demand order and supply capability data through a supply chain platform, and enabling the supply capability data to obtain production line test data in real time through an Internet of Things interface; performing preliminary screening based on equipment types and necessary authentication standards; calculating the comprehensive reliability of the optical fiber sensing system by adopting a comprehensive evaluation model fused with nonlinear transformation; calculating the multi-dimensional demand integrating degree of the supplier and the demander through multi-dimensional difference analysis and normalization processing; intelligent sorting is carried out based on weighted scores of configurable weight factors; implementing global productivity monitoring and conflict resolution optimization, and outputting an optimal matching pair; and continuously iteratively optimizing the evaluation model parameters according to the performance feedback data by adopting an incremental learning mechanism. According to the method, accurate matching and dynamic optimization under multi-target constraints are realized, and the supply chain resource configuration efficiency and the system autonomy capability are effectively improved.

The invention relates to an atomic clock frequency anomaly detection method, and belongs to the technical field of deep learning and the field of atomic clock anomaly detection. The method comprises the following steps: acquiring a frequency sequence set with a label generated by an atomic clock in a normal frequency sequence injection known abnormal mode; performing supervised training on the anomaly detection model by using the frequency sequence set; in the anomaly detection model, performing feature fusion, nonlinear transformation and classification on a first path of features extracted by performing multilayer decomposition on an input frequency sequence by adopting discrete wavelet transform and a second path of features extracted by adopting a CNN-Transformer network on the input frequency sequence, and then detecting an abnormal frequency sequence; and performing frequency anomaly detection on the atomic clock frequency sequence acquired in real time by using the trained anomaly detection model. According to the invention, high-precision detection of frequency anomalies with different characteristics is realized, the detection range is expanded, and the detection precision is improved.

The invention discloses an electric power production management system security situation awareness method based on a cryptographic algorithm. According to the invention, by deeply fusing SM2, SM3, SM4 and other national cryptographic algorithms and power system characteristics, a full-link autonomous and controllable security protection system is constructed. In a data acquisition stage, SM4 encryption transmission and SM3 hash evidence storage are adopted to ensure confidentiality and integrity of data from a source to processing, and eavesdropping and tampering risks in a transmission process are effectively resisted; in a core situation assessment link, point multiplication operation of SM2 elliptic curve cryptography is innovatively introduced into a node aggregation process of a graph neural network, and graph structure mapping of physical topology of a power system is combined, so that the model can accurately capture implicit association and cascade influence between equipment, and the situation assessment accuracy is improved. At the same time, the recognition capability of the hidden attack mode is enhanced by using the nonlinear transformation of SM4, and the perception depth and anti-attack toughness of the system to the complex threats in the power production scene are improved from the bottom layer of the algorithm.

The invention relates to the technical field of slope monitoring, in particular to a mine slope monitoring method based on unmanned aerial vehicle inspection. The method comprises the steps that a slope image is collected and preprocessed, a disturbance excitation expansion mechanism is introduced, time-space domain nonlinear transformation is carried out on the preprocessed slope image, disturbance excitation response is calculated, and a slope image after expansion transformation is obtained; introducing a residual energy diffusion modeling mechanism based on the slope image after expansion transformation, and constructing a residual energy tensor; based on the residual energy tensor, an entropy density splitting mechanism is introduced, a stack product space projection function is constructed, and nonlinear landslide information response intensity is obtained; and based on the nonlinear landslide information response intensity, constructing a stack product type discriminator, and carrying out landslide risk category discrimination to obtain a monitoring result. The problems that a traditional mine slope monitoring method is not accurate in processing of image data obtained by an unmanned aerial vehicle, so that the positioning and judgment accuracy of a landslide risk area is low, and self-adaptability and sensitivity are insufficient are solved.

The invention discloses an image fusion method and system based on multi-semantic guidance and mixed experts. The problems that in the prior art, robustness is insufficient, and visual fidelity and semantic integrity are difficult to balance are effectively solved. According to the method, infrared and visible light images to be fused and task identifiers are obtained, and firstly, a CLIP network is utilized to extract high-level semantic features as guide vectors; and then inputting the image and the guide vector into a pre-trained hybrid expert (MoE) fusion network. According to the network, intermediate features are extracted through an encoder, a gating network dynamically activates part of expert subnets according to task identifiers and semantic vectors and calculates routing weights, adaptive nonlinear transformation and weighted fusion are carried out on the features, and finally a high-quality fusion image is reconstructed through a decoder. The method can adapt to different task requirements, and the calculation efficiency is remarkably improved while the image fusion quality and the semantic consistency are improved.

The invention belongs to the field of hyperspectral image classification, and discloses a hyperspectral image classification method and system based on multi-scale spatial-spectral joint representation and dynamic context modeling, and the method comprises the steps: carrying out the feature dimension reduction processing of a hyperspectral image through principal component analysis; spatial spectrum collaborative information of hyperspectral data is deeply mined through a multi-scale spatial spectrum joint characterization module, and adaptive fusion and enhancement of spatial spectrum characteristics under different scales are realized; a dynamic context modeling strategy is introduced, and the perception ability of the model to context information is optimized by establishing a long-range dependency relationship between features; advanced feature integration and nonlinear transformation are carried out through a multi-layer perceptron, and precise classification of hyperspectral image ground objects is completed. According to the method, the performance superior to that of a current mainstream method is obtained on three public data sets, and the effectiveness and generalization ability of the method are verified.

The invention discloses a mechanical arm state limited preset time point control method and system, and relates to the technical field of robot control, and the method comprises the steps: obtaining a joint position tracking error and a joint speed tracking error based on a joint motion state and an expected trajectory of a mechanical arm; based on constrained system states corresponding to the joint position tracking error and the joint speed tracking error, nonlinear transformation is carried out on the joint position tracking error and the joint speed tracking error through an unconstrained state function, and an equivalent unconstrained virtual error variable is constructed in combination with coordinate transformation; based on the unconstrained virtual error variable, designing a sectional sliding mode variable; and designing a second-order reaching law based on the sectional sliding mode variable to form a closed-loop dynamic state, and solving based on the closed-loop dynamic state to obtain a control torque so as to control the mechanical arm joint. According to the method, the trajectory tracking error of the mechanical arm can be converged at the moment specified by a user, the system state is ensured to meet strict physical constraints in the whole process, and the method can be applied to accurate trajectory control in a complex task scene.

The invention discloses a multi-meteorological factor mode forecast temperature correction method based on deep learning, and relates to the technical field of meteorological data processing, and the method comprises the steps: collecting multi-meteorological factor data and observation temperature field data, and outputting a meteorological chart structure; inputting the meteorological chart structure into a graph convolutional network for deep feature extraction, and outputting a multi-meteorological factor feature tensor; performing global and local space-time characteristic analysis on the multi-meteorological-factor characteristic tensor through a dynamic gating fusion network, and outputting a fusion characteristic tensor; inputting the fusion feature tensor into a full-connection neural network for nonlinear transformation and dimension mapping to form a temperature correction model of deep learning; performing different model parameter disturbances on the temperature correction model to generate an accuracy index; inputting into a quality detection model for training, and judging the quality of each temperature correction model. According to the invention, through dual-mechanism cooperation of meteorological chart structure construction and dynamic gating fusion, deep feature extraction and adaptive weight fusion of multiple meteorological factors are realized.

The invention provides a vibration signal identification method and system based on waterfall plot enhancement, and relates to the technical field of signal identification, and the method comprises the steps: obtaining a to-be-identified vibration signal, and generating an initial waterfall plot; calculating a gradient matrix to construct an edge feature map, and extracting a vibration feature region; feature sub-regions are divided, and an enhanced waterfall plot is obtained through non-negative matrix factorization and nonlinear transformation; extracting scene nodes based on the instantaneous energy value, and constructing a scene topological structure to generate a vibration mode descriptor; and carrying out vibration signal identification by using a preset classifier. According to the method, the distinguishability of weak vibration signal features can be effectively improved, and the recognition accuracy is improved.

The invention specifically discloses a high-precision remote sensing image semantic segmentation method based on a pyramid decoder and a multi-scale feature interactive attention module. The method comprises the following steps: firstly, extracting three complementary level features of low-level details, high-level semantics and an original image through a lightweight backbone network; and then inputting the multi-scale features into a network taking an encoder-decoder structure as a core, introducing a pyramid residual context module in a decoding stage, and explicitly enhancing high-level semantics by using pyramid pooling and residual nonlinear transformation to suppress redundant information. The multi-scale feature interactive attention module adaptively calculates space-channel weights of low-level details, high-level semantics and original features, so that differential fusion is realized, and feature conflicts are reduced. And after feature fusion is completed, compensating coding compression loss by optimizing jump connection, and finally outputting a 1024 * 1024 pixel-level semantic segmentation result. The method can be widely applied to high-resolution remote sensing scenes such as urban planning, disaster assessment and environment monitoring.

The invention relates to a black-box signal anti-attack method and device based on spectrum characteristics, and the method comprises the steps: S1, carrying out the preprocessing of an input IQ signal, converting a time domain signal into a frequency domain characteristic through Fourier transform, calculating an optimal frequency band according to the length of the input signal, and uniformly dividing the signal into a plurality of frequency bands; s2, performing nonlinear transformation on the spectrum features by using an adaptive spectrum disturbance generator, extracting key feature representation in the spectrum, and constructing frequency domain disturbance through the extracted amplitude and phase parameters; s3, performing time domain reconstruction on the frequency domain disturbance by using inverse Fourier transform, and superposing original signals to generate a target confrontation sample; s4, designing a loss function according to the attack effect, gradient guidance and disturbance constraint to optimize a spectrum disturbance generator; and S5, selecting a sensitive frequency band based on a dynamic mask mechanism, and iteratively optimizing countermeasure disturbance based on the selected frequency band. And when the query resources are consumed up, returning a final confrontation sample. The method only depends on input and output of a model, a self-adaptive spectrum disturbance generator and a dynamic mask mechanism are used for dynamically updating frequency domain confrontation disturbance, the frequency domain confrontation disturbance is converted into a time domain through inverse Fourier transform, and finally a confrontation sample with higher concealment is generated.

The invention relates to the technical field of unmanned aerial vehicle communication encryption, and discloses a data encryption transmission system based on unmanned aerial vehicle communication. The system comprises an airspace situation awareness module, a channel feature coding module, a hierarchical encryption engine module, a key dynamic derivation module and an anti-interference relay module. The airspace situation awareness module collects electromagnetic spectrum characteristics through a multispectral sensor array to generate a dynamic spectrum fingerprint spectrum; a channel feature coding module extracts multi-dimensional channel parameters, and generates a matched chaotic mapping sequence in combination with a quantum random number generator; the hierarchical encryption engine module segments the original data stream and executes differential round hierarchical nonlinear transformation; the key dynamic derivation module generates a composite session key drifting along with time according to the flight path coordinates; the anti-interference relay module is embedded with a frequency spectrum fingerprint watermark, and a multi-hop relay link is established through adaptive beam forming. The system can adapt to the dynamic flight scene of the unmanned aerial vehicle, and guarantees the safety and stability of communication data transmission.

The invention provides a text-to-image multi-target generation method and system based on an evidence diffusion model, and relates to the technical field of text graphics, and the method comprises the steps: obtaining a text prompt; inputting the preprocessed text cue into an evidence diffusion model, firstly extracting a noun cross-attention graph, inputting the noun cross-attention graph into a multi-layer perceptron evidence network, mapping the noun cross-attention graph to an evidence space through nonlinear transformation, outputting an evidence value of each pixel about each semantic category, constructing Dirichlet distribution, and obtaining an evidence diffusion model; the Dirichlet distribution is optimized by introducing pixel evidence loss; and converting all noun cross attention maps into a basic belief distribution function by using a D-S evidence theory, calculating a conflict coefficient of an overlapping region of the noun cross attention maps based on the basic belief distribution function, constructing Token conflict loss, and carrying out joint optimization calculation on pixel evidence loss, Token conflict loss and model self loss until an image is generated. According to the invention, the accuracy and logic consistency of multi-target image generation are improved.

The invention relates to a safety monitoring method for an irrigation and drainage project operation state and an intelligent system, and belongs to the technical field of artificial intelligence. The method comprises the following steps: collecting and marking irrigation and drainage project operation monitoring data through a sensor, and constructing a training data set; completing data normalization by combining quantile and median robust scaling with adaptive nonlinear transformation, and mining and screening high-order interaction features by combining a mutual information theory and a gradient boosting decision tree; constructing a deep classification network fusing physical prior and adaptive feature interaction, introducing physical constraint and multi-scale feature fusion, and optimizing a model through adaptive marginal classification loss and physical feature manifold alignment loss; real-time data is preprocessed and then input into the model, and operation state grade classification and graded alarm are achieved. According to the method, data noise can be inhibited, a multi-index coupling relationship can be mined, the interpretability and robustness of the model can be improved by integrating a physical rule, irrigation and drainage project abnormity can be accurately identified and early warned, and the method is suitable for intelligent safety monitoring of an irrigation area.

The embodiment of the invention provides a sewage draining exit state identification method and system, and can solve the problems that the traditional sewage draining exit monitoring mainly depends on manual inspection, the efficiency is low, the coverage is limited, and a hidden sewage draining exit is difficult to find in time. The method comprises the following steps: acquiring image data of a to-be-identified area through an unmanned aerial vehicle; image data of the to-be-recognized area is input into a sewage draining exit state recognition model, the sewage draining exit state recognition model is obtained through training based on sewage draining exit image data under different marked sewage draining states, different shooting angles and environment conditions, and a model input channel is internally provided with a water color feature branch; the RGB extraction module is used for extracting RGB values of a target area and a surrounding water body in the image, and converting the RGB values into hue H and saturation S in an HSV color space through nonlinear transformation; and based on the sewage draining exit state identification model, determining a sewage draining exit position, a sewage draining state and a confidence coefficient corresponding to the sewage draining state in a to-be-identified area.