201results about How to "Strong complementarity" patented technology

The invention discloses a high-resolution remote sensing image change detection method based on multi-scale convolution decoding, electronic equipment and a storage medium, and belongs to the technical field of remote sensing image change detection. In order to meet the high-resolution remote sensing image change detection requirement, the method comprises the following steps: constructing a remote sensing image data set; a first stage and a second stage are formed based on a CNN module, a third stage and a fourth stage are formed based on a MambaVision module and a Transform module, and four dual-time-phase multi-scale feature maps are respectively obtained through processing of the four stages; constructing a time feature interaction module TFIM; a multi-scale convolution decoder EMCAD is constructed; and constructing a loss function, carrying out training, testing and evaluation by using the constructed remote sensing image data set, and applying the obtained trained optimal model to high-resolution remote sensing change detection. According to the invention, the detection capability of tiny changes in a high-resolution image is improved.

The invention belongs to the technical field of image processing, and discloses a transformer fault detection method based on an improved YOLOv8 model. Aiming at the problems of inaccuracy in feature extraction, low precision, poor real-time detection capability and the like existing in fault detection of a traditional transformer fault detection method, the invention establishes a transformer fault detection model based on an improved YOLOV8 model architecture, a self-calibration convolution defogging module is introduced, a generative adversarial network (GAN) and a multi-source data fusion network structure are added, a BiFPN structure is used, an ASPP module is replaced by an ASPFCSPC module, and a YOLOV8 model architecture is improved, so that the feature extraction capability of a transformer fault detection model is enhanced, the calculation complexity of the model is reduced, and the fault detection efficiency of the transformer is improved. The transformer fault detection precision, accuracy and real-time detection capability are improved, and the stability and adaptability of the model are enhanced.

The invention belongs to the technical field of three-dimensional simulation, and particularly provides a rock mass structural plane network simulation method fusing artificial intelligence and laser scanning, and the method comprises the steps: obtaining point cloud data; clustering the point cloud data by adopting a clustering algorithm to obtain an original structural surface; performing statistical analysis on the original structural plane according to the rock mass type and the structural partition to obtain a representative structural plane, and fitting to obtain a probability density function according to occurrence parameters of the representative structural plane; according to the development volume density of the representative structural plane, converting the probability density function into a volume density function, and according to the volume density function, generating a random structural plane by using a Monte Carlo simulation method; and constructing a BIM model of the rock mass structure, and performing three-dimensional space simulation on the rock mass structural plane network based on the BIM model and the random structural plane to obtain a simulation result. According to the method, the precision, comprehensiveness and authenticity of depicting the rock mass structural plane are greatly improved, and the spatial distribution characteristics of the structural plane network can be quantitatively evaluated.

The invention discloses a high-resolution remote sensing image double-branch segmentation method, which comprises the following steps: acquiring a high-resolution remote sensing image data set, preprocessing the high-resolution remote sensing image data set, and dividing the high-resolution remote sensing image data set into a training set, a verification set and a test set; constructing a semantic segmentation network model of the high-resolution remote sensing image, wherein the semantic segmentation network model comprises a detail branch, a semantic branch and an up-sampling part; inputting the training set into a high-resolution remote sensing image semantic segmentation network model for training, calculating a loss function, performing back propagation, updating network model parameters, obtaining an optimal parameter network model, and verifying the optimal parameter network model by using the verification set; and inputting the test set into the trained optimal parameter network model, and outputting an accurate segmentation image of the high-resolution remote sensing image. According to the method, feature extraction and fusion are carried out in a multi-level and multi-scale manner by capturing target information of different types and scales in the high-resolution remote sensing image, so that the segmentation precision of the high-resolution remote sensing image is improved.

The invention belongs to the technical field of marine surveying and mapping, and discloses an unmanned aerial vehicle laser / shipborne multi-beam data land and water topographic mapping method, which comprises the following steps of: coarsely extracting a data overlapping region through a virtual grid, finely extracting the overlapping region by using an alpha-shape algorithm, and obtaining boundary points; removing edge triangles in an irregular triangulation network constructed by the unmanned aerial vehicle laser sounding data and the shipborne multi-beam sounding data; calculating a vertex normal vector, taking a vertex tangent plane of the multi-beam triangular irregular network as a registration element, carrying out weight distribution according to the distance from a point to the vertex, and carrying out coarse registration through a point-to-plane ICP registration algorithm; and constructing a precise registration model based on a point-surface distance, and eliminating the elevation difference of the overlapping region by minimizing the normal distance from the unmanned aerial vehicle laser sounding point to the multi-beam sounding triangular surface element. By adopting the point cloud fusion model for processing, the phenomenon that different source point cloud elevations in the overlapping area are inconsistent is effectively improved, and underwater topographic data fusion is realized.

The invention belongs to the field of zero sample target detection, and discloses a deep learning-oriented zero sample target detection method and system. The method comprises the following steps: establishing a zero sample target detection model comprising a feature extraction module, a cross-modal alignment module and a course learning module; extracting image features and text features through a feature extraction module; inputting the image features and the text features into a cross-modal alignment module; carrying out cross-modal similarity calculation on image feature embedding and text feature embedding; the obtained cross-modal similarity is imported into a course learning module; fusion of image features and text features is enhanced through a course learning module, and the difficulty of course learning is dynamically adjusted through knowledge distillation in the back propagation process; and obtaining a zero sample target detection model and a detection result. According to the method, the advantages of high performance and high generalization ability of a zero sample learning large model are reserved, mutual complementation and enhancement among cross-modal features are realized, and the detection performance and robustness of the model are improved.

The invention discloses a personalized insurance scheme customization method and device, equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the steps of obtaining a to-be-processed text and a to-be-processed image; capturing high-resolution details of the image to be processed; capturing low-resolution details of the image to be processed; performing feature fusion on the image high-resolution features and the image low-resolution features to obtain image fusion features; performing text semantic recognition on the to-be-processed text to obtain text semantic features; carrying out feature fusion on the image fusion features and the text semantic features to obtain a multi-modal feature sequence; and importing the multi-modal feature sequence into a pre-trained insurance scheme generation model, and outputting a personalized insurance scheme. The invention further relates to the technical field of block chains, and the to-be-processed data is stored on the block chain node. According to the method, the information complementarity between the image and the text is enhanced through multi-modal feature fusion, and the accuracy and personalized matching degree of insurance scheme generation are effectively improved.

The invention discloses an unmanned aerial vehicle image detection method and system fusing visible light and infrared light, and relates to a target detection technology, and the method comprises the steps: starting a visible light sensor and an infrared sensor in groups, and carrying out the unmanned aerial vehicle detection of a target region according to a preset detection strategy; feature extraction is performed on the detected infrared image and visible light image by using independent branches, and a Res-CBAM attention mechanism module is arranged between the two independent branches; carrying out feature fusion on the image features extracted by the two branches by adopting a BiFPN structure; and based on the fused image features, performing target detection based on the classification network and the regression network to output a classification result. The target feature information in the infrared features and the scene detail information in the visible light features are fused, the complementarity of the features is enhanced, and the comprehensive performance of the model is improved.

The invention belongs to the field of electroencephalogram emotion recognition, and provides an emotion recognition method and system for a multi-scale convolutional network based on bidirectional attention, and the method comprises the steps: segmenting an electroencephalogram signal according to a time window, extracting differential entropy (DE) and power spectral density (PSD) features, introducing an electroencephalogram map to embed spatial information, and capturing a spatial dependency relationship in the signal. Then, the preprocessed features are input into an MSA-TCN model, and the model combines the attention in the bidirectional features, the attention between the bidirectional features, a multi-scale convolution network and a time sequence convolution network to effectively extract dynamic time features and cross-channel spatial features; the invention aims to overcome the limitation of the prior art in emotion recognition through an innovative bidirectional attention mechanism and a multi-scale convolutional network structure (MSA-TCN).

The invention relates to a tree painting projection data collection and analysis system and method, and the system comprises a data collection module, a data preprocessing module, a feature extraction module, a quantitative analysis module, a classification and prediction module, and a result evaluation module. The modules cooperate with each other to realize standardized processing, feature extraction, quantitative analysis, intelligent classification and prediction and result evaluation of data. According to the method, the holographic theory and the Ama pothole theory are combined, the drawing features are finely analyzed through the cross-modal comparative learning and graph neural architecture search combined algorithm, a scientific and objective psychological data acquisition and analysis method is provided, the diagnosis sensitivity and specificity are improved, a powerful tool is provided for clinical psychological assessment, and the method is suitable for clinical psychological assessment. The method has important clinical and research values, and has wide application prospects in the fields of psychological problem screening, social psychological services, judicial, public security, marriage matching, talent selection and the like.

The invention relates to the technical field of power generation prediction data processing, and discloses a photovoltaic power station power generation prediction method based on multi-model fusion, which combines a photovoltaic physical model, a power generation statistical model and a power generation prediction model, not only considers a physical rule, but also combines statistical learning and machine learning to improve the stability and accuracy of prediction, and improves the prediction efficiency. And predicting the generating capacity of the photovoltaic power station by using environmental meteorological data, electrical parameters of the photovoltaic power station and historical power generation data. Accurate photovoltaic power generation power prediction is beneficial to better arrangement of power generation plans and resource scheduling of the power grid. The prediction precision and stability are improved through multi-model fusion, so that power grid dispatching is more accurate and efficient, and power grid instability factors caused by photovoltaic power generation fluctuation are reduced. Therefore, photovoltaic power generation can be connected to a power grid more stably, the power demand is met, and wide application and development of clean energy are promoted.

The invention discloses an improved self-attention endoscopic polyp segmentation method based on PVT, and the method comprises the steps: collecting a plurality of gastrointestinal tract images, and carrying out the preprocessing and marking of the images, and obtaining image data and real labels; inputting the image data into a feature extraction network based on PVT and a space channel self-attention module to obtain multi-scale features; inputting the multi-scale features into a multi-scale cross fusion module to obtain four fused feature maps; inputting the fused feature map into a decoder to obtain two prediction maps; inputting the real labels into a decoder to obtain two real labels, and respectively carrying out loss calculation and iterative optimization on the two real labels and the prediction maps with the corresponding sizes; and recovering the two prediction images to the size of the input image, and adding the two prediction images to obtain an endoscopic polyp segmentation model. The invention relates to the technical field of medical image segmentation, and solves the technical problem that the detection precision of a small polyp region and a polyp with a highly fuzzy edge is not high in the existing method.

The invention relates to an intelligent building extraction method based on a remote sensing image. The method comprises the following steps: S1, obtaining a high-resolution data set; s2, designing a remote sensing image building semantic segmentation network based on a coding-decoding structure, wherein the remote sensing image building semantic segmentation network comprises a feature extraction skeleton network and a semantic segmentation decoder; s3, a remote sensing image building DSM estimation network based on the generative adversarial network is designed and comprises a DSM generator and a DSM discriminator, and the DSM generator comprises a DSM generator encoder and a DSM generator decoder; s4, designing a feature fusion and reinforcement module; s5, designing a loss function; s6, training and optimizing a remote sensing image building intelligent extraction network according to the high-resolution data set and the loss function; and S7, performing building intelligent extraction based on the remote sensing image through the trained remote sensing image building intelligent extraction network. According to the method, the problem of high DSM acquisition cost is solved, and the performance of extracting the building in the remote sensing image is effectively improved.

The invention relates to the technical field of code peculiar smell detection and reconstruction recommendation systems, in particular to an incremental code peculiar smell detection and reconstruction recommendation system and a method thereof.The incremental code peculiar smell detection and reconstruction recommendation system comprises a code extraction and static analysis module used for extracting a to-be-analyzed code snippet from a target project and preprocessing the code snippet to form a to-be-analyzed file; the runtime information acquisition and dynamic analysis module is used for operating the file to be analyzed in the sandbox environment, acquiring and summarizing runtime information and forming a dynamic analysis report; the learning module is used for receiving a static analysis result and a dynamic analysis report, comprehensively considering static information and an incremental model updating report, constructing vector representation of code potential features and updating an incremental learning model; and the reconstruction recommendation module is used for scoring each code snippet based on the code feature vector and an incremental learning model, recommending a corresponding reconstruction rule, and providing a more accurate peculiar smell detection result by fusing static code features and dynamic operation information.

The invention provides a short-term photovoltaic power prediction method and device, electronic equipment and a storage medium, and belongs to the technical field of new energy power prediction.The method comprises the steps that weather feature data are segmented based on a multi-scale pooling network, and seasonal feature data and trend feature data are obtained; performing feature extraction on the seasonal feature data to obtain first feature data, and performing feature extraction on the trend feature data to obtain second feature data; obtaining RMS feature data based on the first feature data and the second feature data; processing the weather characteristic data on the basis of a Conv1d-LSTM (Long Short Term Memory) network to obtain Conv1d-LSTM characteristic data; and obtaining photovoltaic prediction power based on the RMS characteristic data and the Conv1d-LSTM characteristic data. According to the short-term photovoltaic power prediction method and device, the electronic equipment and the storage medium provided by the invention, the prediction precision of the short-term photovoltaic power prediction model can be improved.

The invention discloses a hydrogen-light-electricity-heat coordinated integrated comprehensive energy system and an operation method, the system adopts a power controller to carry out energy management on the system, when the load demand fluctuates, according to the set priority principle of system operation, the operation mode of the system is converted in real time, and coordination control of energy balance is carried out, so that the load demand is optimized. The reasonable distribution of the power can reduce the problem of light abandoning, the maximum utilization of energy is realized, the overall efficiency of the system is improved, and the system can better cope with the change of the load demand and stably operate for a long time. The system integrates photovoltaic power generation, fuel cell power generation, water electrolysis hydrogen production, energy storage, hydrogen storage and heat storage, complementation and supply and demand collaboration of various energy sources are achieved, the comprehensive energy efficiency of the system is improved, and various load requirements of electricity, hydrogen and heat can be met at the same time. Wherein the hydrogen is prepared by utilizing electric energy generated by photovoltaics, and in the process of producing green hydrogen by utilizing renewable energy sources, zero carbon emission is realized, and the utilization of clean energy sources is realized.

The invention relates to a multi-modal human intention understanding system based on brain cognition inspiration, which comprises a multi-modal information acquisition unit, an intercrossing attention enhancement unit, a self-crossing attention enhancement unit, a source modal updating unit, a self-adaptive multi-modal information fusion unit and a continuous-dimension emotional intention understanding unit, and a fine-grained behavior intention understanding unit. Compared with the prior art, the method has the advantages that effective feature semantics from language, audio and video modalities are fully fused, efficient multi-modal modeling is realized by utilizing attention mechanisms in different strengthening and updating units, and human emotion and behavior intention understanding in a multi-task mode is further completed; the problems of low human intention understanding accuracy, insufficient multi-mode utilization rate, poor system robustness and the like in a real application scene are effectively solved.

The invention provides a target identification method and system based on multi-source information fusion, and relates to the technical field of low-altitude target detection. The method comprises the following steps: acquiring target radar track data, interception equipment track data and position area information data; based on target radar track data, extracting a first feature in an RCS form dimension, and extracting a second feature in a motion dimension; based on the target radar track data and the track data of the monitoring equipment, determining a frequency spectrum monitoring correlation factor and regional position information features; performing feature fusion on the first feature, the second feature, the spectrum interception correlation factor and the regional position information feature to obtain a target feature, and identifying a target type; and identifying a target threat level based on the target type and the target radar track data. The method and the device are used in a target identification process based on multi-source information fusion, and the technical problem that the target threat degree cannot be accurately identified in a complex environment in the prior art is solved.

The invention discloses a UAVD-Net-based unmanned aerial vehicle remote sensing image defogging method, system and device and a medium, and the method comprises the steps: constructing a non-uniform haze remote sensing image data set which comprises a training set, a verification set and a test set; an unmanned aerial vehicle remote sensing image defogging network based on UAVD-Net is constructed; training an unmanned aerial vehicle remote sensing image defogging network based on UAVD-Net by using the training set and the verification set to obtain an optimal training weight file Dehaze.pt; performing unmanned aerial vehicle remote sensing image defogging on the unmanned aerial vehicle remote sensing image defogging network based on UAVD-Net by using the test set and the optimal training weight file Dehaze.pt to obtain an unmanned aerial vehicle remote sensing image defogging result; the system, the equipment and the medium are used for implementing the method. The multi-level global information capture module, the self-adaptive local information enhancement module and the cross channel feature fusion module are introduced into the network, the global information and the local features of the image are effectively fused, complementation is achieved, the defogging precision of the unmanned aerial vehicle remote sensing image under the non-uniform haze condition is effectively improved, and the defogging accuracy of the unmanned aerial vehicle remote sensing image is improved. The method has the advantages of high adaptability and high defogging precision.

The invention relates to the technical field of green financial project financial feasibility evaluation systems, in particular to a green financial project financial feasibility evaluation system and method based on machine learning, and the system comprises a financial evaluation module, a market evaluation module and an environment evaluation module. The financial evaluation module comprises an analysis evaluation unit, a prediction analysis unit and a model simulation unit; the analysis and evaluation unit is used for collecting and sorting data of items and clients and processing the data; the prediction analysis unit predicts financial indexes under different market scenes by utilizing an optimization algorithm according to the customer data and the market data; the market assessment module provides key external environment information for financial assessment by deeply analyzing market trend and policy changes. Meanwhile, the environment evaluation module quantifies the environment influence of the project, and provides important environment indexes for market evaluation and financial evaluation. The complementary mechanism ensures that all key factors influencing the feasibility of the project are considered in the evaluation process.

The invention discloses a human body behavior recognition method based on multi-modal data and deep learning, and relates to the technical field of computer vision, and the method comprises the steps: carrying out the preprocessing of skeleton data and RGB data; carrying out feature extraction by adopting a Resnet3D 18 network RGB (Red, Green and Blue) video stream; inputting the proposed RGB features and skeleton features into a feature fusion network to obtain fusion features; the feature fusion module comprises a feature association module, a feature weighting module and a feature confidence module; the fusion features realize division of different types of actions through a softmax classifier; interference of noise and background on classification performance is reduced through preprocessing, a Resnet3D 18 network and an adaptive graph convolutional network double-flow feature extraction network are adopted to effectively integrate and extract feature information, and a fusion network performs dynamic confidence weighting on two heterogeneous features, so that a model can dynamically adjust importance of each modal according to actual conditions.

The invention discloses an air infrared weak and small target detection method based on space-frequency domain feature fusion. The method comprises the following steps: acquiring a plurality of infrared weak and small target images under a complex background; dividing the infrared weak and small target images under the plurality of complex backgrounds according to a preset proportion to obtain a training set and a test set of the infrared weak and small target images under the plurality of complex backgrounds; and inputting the training set of the infrared weak and small target image under the complex background into the pre-constructed target detection network with the space-frequency domain feature fusion, and training the pre-constructed target detection network with the space-frequency domain feature fusion to obtain the successfully trained target detection network with the space-frequency domain feature fusion. According to the method, the technical problems of low detection precision and relatively high false alarm rate and omission ratio when an existing convolutional neural network is used for detecting an infrared weak and small target under a complex background are solved.

The invention discloses a PLA modified material as well as a preparation method and application thereof, and belongs to the field of biodegradation. Aiming at the problems of short shelf life and insufficient performance of the existing PLA material product, the invention provides a PLA modified material which comprises the following components in parts by weight: 50-70 parts of PLA; 20 to 50 parts of an inorganic material; 3-5 parts of a plasticizer; 0.01 to 1 part of a compatilizer; 0.1 to 1 part of a lubricant; 0.1 to 1 part of an antioxidant; the invention relates to a PBST polymer, a preparation method of the PBST polymer is improved, raw materials for forming the PBST polymer are added in sequence, and one of the raw materials is recycled. According to the invention, the PBST polymer and the PLA are blended, and the complementarity of the PBST polymer and the PLA is fully utilized to obtain the composite material with high strength and good toughness; the PBST polymer is used for toughening the PLA, so that the use performance of the PLA material during freezing or cold storage is improved, the crystallization property and heat resistance of the PLA material are promoted, the overall performance of the PLA material is improved, and the shelf life of a PLA product can be remarkably prolonged.

The invention discloses a wind field detection method, device and equipment based on wind measurement data fusion and a medium, and relates to the technical field of wind measurement, and the method comprises the steps: carrying out the quality control of initial coherent wind measurement data and initial direct wind measurement data, and obtaining target coherent wind measurement data and target direct wind measurement data which meet preset quality requirements; performing space-time consistency matching on the target coherent wind measurement data and the target direct wind measurement data to obtain a target matching result, and splicing the target direct wind measurement data to the target coherent wind measurement data according to the target matching result to obtain spliced wind measurement data; performing data fusion on data in a to-be-fused height range in the spliced wind measurement data by using a real-time fitting prediction method to obtain fused wind measurement data, and determining a target wind field detection result by using the fused wind measurement data; the to-be-fused height range is a height range determined based on the maximum value and the minimum value of the height corresponding to the target coherent wind measurement data in the fusion time sliding window; therefore, the accuracy of wind measurement data can be improved.

The invention discloses an energy digital network-oriented data fusion discovery method, system and device and a medium. The method comprises the following steps: acquiring each energy digital object oriented to an energy digital network; combining the feature attributes extracted from the metadata of each energy digital object to obtain a text statement set of each energy digital object; utilizing a pre-training language model to extract semantic features of the energy digital object according to the text statement set, and generating a corresponding semantic feature vector; clustering the semantic feature vectors, performing pairwise similarity calculation on energy digital objects in each cluster in an optimal clustering result, and combining the energy digital objects with similarity to form a similar energy digital object combination set; fusing the energy digital objects in each combination in the similar energy digital object combination set to construct a new energy digital object; and complementation of cross-domain energy data is realized.

The invention provides a deep jump connection attention multi-modal sentiment analysis method based on image-text fine granularity alignment, and belongs to the field of multi-modal sentiment analysis. According to the method, by introducing an image-text fine-grained alignment and jump connection attention mechanism, a traditional multi-modal sentiment analysis method is improved, and the problems that modal alignment is not fine and important information is lost during feature fusion are effectively solved. According to the technology, firstly, feature extraction is carried out on an image and a text, the image is converted into a pixel block embedding sequence, the text is converted into a word embedding sequence, then deep interactive fusion is carried out through a cross-modal deep interactive network, and fine-grained alignment and feature deep fusion between modals are achieved. In the method, a jump connection attention channel controlled by a self-adaptive gate valve is designed to reserve important information, and the understanding of the model on the image-text relationship is improved. According to the method, the accuracy and interpretability of multi-modal sentiment analysis can be remarkably improved, and the cross-modal learning ability of the model is improved. The method is suitable for multi-mode sentiment analysis application scenes such as social media analysis and public opinion monitoring, and powerful support is provided for understanding the sentiment, attitude and viewpoint of the user.

The invention provides a bauxite resource prediction analysis method and system, and relates to the technical field of resource prediction. Performing distortion correction on the earth surface remote sensing image according to the digital elevation data of the target bauxite to obtain an earth surface remote sensing correction image, performing image masking on the earth surface remote sensing correction image to obtain an earth surface remote sensing mask image, and extracting earth surface resource distribution characteristics from the earth surface remote sensing mask image; obtaining geological exploration data of the target bauxite, generating an ore body three-dimensional distribution model through the surface resource distribution characteristics and the geological exploration data, obtaining a bauxite database, and constructing a metallogenic knowledge graph of the bauxite according to the bauxite database; the ore body grade is predicted according to the ore body three-dimensional distribution model and the mineralization knowledge graph of the bauxite, and ore body grade prediction information is obtained, the quality of remote sensing images can be improved, the complementarity of data is enhanced, and the reliability of bauxite resource prediction is improved.

The invention discloses a frozen soil hot melt collapse identification method, device and system, and a storage medium. The method comprises the following steps: S1, acquiring permanent frozen soil image information of a preset area; s2, training a dual-encoder RTS extraction network according to the permanent frozen soil image information of the preset area to obtain an RTS recognition model; and S3, inputting the permanent frozen soil image information of the target area into the RTS identification model to carry out frozen soil hot melt collapse identification. By adopting the technical scheme of the invention, the RTS and the small RTS in a complex environment can be identified more accurately.

The invention discloses an environment-friendly efficient cosmetic hose and a preparation method thereof.The cosmetic hose is prepared from, by weight, 60-80 parts of low-density polyethylene, 9-15 parts of high-density polyethylene, 15-20 parts of metallocene polyethylene, 30-40 parts of a PCR regenerated material, 1-3 parts of an antioxidant, 5-10 parts of a plasticizer, 1-3 parts of a coupling agent and 2-5 parts of a graphene auxiliary. The cosmetic hose is prepared by reasonably proportioning the raw materials, mixing and melting the raw materials at high temperature, extruding the raw materials into a hose shape, and then cooling, shaping, cutting, polishing and packaging the hose. Compared with the prior art, the prepared environment-friendly efficient cosmetic hose has excellent environment-friendly performance and also has excellent barrier property, tensile strength, antibacterial property and ultraviolet aging resistance, long-term use stability of the cosmetic hose is facilitated, and good quality control is guaranteed in the storage process of packaged contents.

The present invention discloses an intelligent fault diagnosis method and system for explosion-proof motors in the natural gas industry, the method comprising the following steps: S10: acquiring multi-source heterogeneous data of explosion-proof motors; S20: performing enhancement processing on the multi-source heterogeneous data of explosion-proof motors to obtain an enhanced multimodal data set of explosion-proof motors; S30: performing feature extraction on the enhanced multimodal data set of explosion-proof motors using a deep learning model to obtain a high-dimensional feature vector; S40: based on the high-dimensional feature vector, a multi-task learning network is used to perform fault classification and fault severity evaluation to obtain a fault type probability distribution and a fault severity index; S50: generating an intelligent diagnosis report of the explosion-proof motor including maintenance suggestions according to the fault type probability distribution and the fault severity index. The method can make full use of multi-source heterogeneous data and realize an explosion-proof motor fault diagnosis method of intelligent diagnosis, so as to improve the diagnostic efficiency and accuracy and realize preventive maintenance.