839 results about "Feature transformation" patented technology

The invention relates to an electric hand drill wear state prediction and health management system, which belongs to the technical field of intelligent fault diagnosis and predictive maintenance of industrial equipment, and comprises a data acquisition and preprocessing unit used for acquiring and processing a multi-modal physical signal to generate a standardized data frame; the multi-domain feature transformation unit is used for receiving the standardized data frame and transforming the standardized data frame into a health feature vector and a load feature vector; the dynamic health baseline construction unit is used for reconstructing and generating a dynamic health baseline through a depth generation model according to the time sequence of the health feature vector and the load feature vector; and the residual error sequence generation and statistical monitoring unit is used for calculating the distance between the health feature vector and the dynamic health baseline, generating a residual error sequence, and performing statistical processing on the residual error sequence to obtain a statistical magnitude. According to the invention, the interference of working condition change on health state assessment is eliminated, and pure and reliable data input is provided for subsequent accurate monitoring.

The invention provides a cluster network flow prediction method based on multi-scale time feature fusion, and belongs to the technical field of computer network flow prediction. The method comprises the following steps: determining a multi-index prediction sequence based on traffic load characteristics of cluster IP instances, and constructing a high-quality time sequence data set; fourier transform and discrete wavelet transform are used for time-frequency feature analysis, and noise filtering and data dimension reduction are completed; projecting sequences of different time granularities to a unified model dimension, performing one-dimensional channel convolution merging, inputting the merged sequences into a time encoder and a cross-channel encoder, and capturing cross-scale long-term time dependence and a coupling relationship between variables; in the loss function design, time domain and frequency domain loss are fused, double-domain error calculation is carried out on a prediction result and a label through Fourier transform, and the robustness of a model to non-stationary fluctuation is enhanced; and through linear layer decoding and reverse normalization processing, the abstract feature is converted into an actual flow prediction value. According to the invention, the precision and reliability of cluster network flow prediction are significantly improved.

The invention relates to the technical field of industrial equipment state monitoring and intelligent fault diagnosis, and discloses an industrial fault diagnosis method and system based on natural language fault features and large model knowledge enhanced reasoning, and the method comprises the steps: obtaining a multi-source monitoring and state analysis result from detected equipment in an equipment operation stage; converting the features into a fault feature text of a structured natural language; carrying out vectorization coding on the fault feature text, executing similarity retrieval in a pre-constructed industrial fault knowledge base, and recalling knowledge fragments; and based on the fault feature text and the recall knowledge fragment, constructing a reasoning prompt word, inputting the reasoning prompt word into a large language model for knowledge enhanced reasoning, and generating a diagnosis result. According to the method, the problems that numerical evidence and semantic knowledge are difficult to unify, the knowledge coverage and updating cost is high, and cross-working-condition migration and conclusion consistency are insufficient in an existing intelligent diagnosis technology based on a rule base or a knowledge graph are effectively solved.

The invention discloses an injection molding process fault diagnosis model training method and system based on a large language model and a fault diagnosis method. The model training method comprises the following steps: collecting and cleaning process parameters under the fault working condition of the injection molding machine, converting the process parameters into a natural language text, combining the natural language text with a fault label to construct a textualized data set, and dividing the textualized data set into a training set and a verification set according to a proportion; and in combination with the text data dimension and the fault category number, loading the pre-trained large language model and configuring a diagnosis model structure in a quantitative mode. And inputting the training set into a model to extract semantic features, processing the semantic features by a feature conversion module to generate a high-order feature vector, and inputting a classification head to output a fault category probability. And back propagation is carried out by using a loss function, and model parameters are efficiently and finely tuned in combination with low-rank adaptation and a layered freezing strategy. And repeating training until the performance reaches the standard, and outputting a final diagnosis model. The method is efficient in training, and can effectively reduce the maintenance and use cost of the model.

The invention relates to the technical field of speech processing, can be applied to business scenes of medical health, financial science and technology and the like, and discloses a speech enhancement method based on multi-scale feature learning, which comprises the following steps: framing an input audio signal, extracting a Mel-frequency spectrum feature, extracting a frequency domain feature by using a multi-scale convolutional neural network, and carrying out multi-scale feature learning on the frequency domain feature; carrying out coding dimension reduction on the image; noise is suppressed through a deep residual network, enhanced audio features are generated, a non-autoregression generative model is adopted for feature conversion, and finally a generative adversarial network is used for reconstructing a target voice waveform. Voice frequency domain features are extracted through the multi-scale convolutional neural network, and the feature expression ability of different frequency bands is improved; noise suppression is carried out through a deep residual network, and the purity of the voice signals is enhanced; feature conversion is optimized through a non-autoregression generation model, and the modeling efficiency of speech enhancement is improved; the target voice waveform is reconstructed through the generative adversarial network, and the naturalness and definition of the generated voice are improved.

The invention relates to the technical field of wireless communication, in particular to a wireless communication anti-interference enhancement system based on multi-mode signal fusion. Comprising an intelligent signal transceiving module, a signal sensing and preprocessing module, an interference feature coding and classification module, an interference suppression decision module, an interference suppression module, a signal fusion module, an interference suppression performance evaluation module, an interference feature library and an interference suppression strategy library, the interference feature coding and classification module adopts a three-layer progressive architecture of multi-dimensional manifold feature extraction, feature transformation and coding inspired by a group theory, and deep learning classification based on chaotic dynamics, extracts interference features from three dimensions of time domain, frequency domain and nonlinearity, and realizes accurate interference identification; the signal fusion module adopts a multi-level fusion strategy, and dynamically adjusts the fusion weight according to the signal quality; the interference suppression module selects an optimal suppression strategy according to the interference type, and executes frequency domain cancellation, space domain cancellation and time domain cancellation; and performance evaluation feedback forms a closed-loop optimization mechanism.

The invention discloses a cross-modal target detection method based on learnable Fourier transform, and mainly solves the problem of insufficient fusion of a visible light image and an infrared image in a complex scene due to inter-domain difference in the prior art. According to the implementation scheme, the method comprises the steps that bimodal features are extracted through a double-flow CSPDarknet53 network; a target position guiding module is utilized to enhance target area representation and suppress background interference; the features are converted to a frequency domain, and amplitude texture information of the visible light image and phase contour information of the infrared image are adaptively enhanced through a learnable frequency domain feature enhancement module; suppressing noise through global filtering and then inversely transforming back to a spatial domain; and finally, outputting a target detection result of the multi-modal image by the detection head. According to the method, frequency domain physical characteristics are fully utilized, full complementation and adaptive fusion of cross-modal features are realized, the detection precision and robustness of vehicles, pedestrians and other targets under low-illumination and complex backgrounds are remarkably improved, meanwhile, high calculation efficiency is kept, and the method can be applied to the fields of automatic driving, intelligent monitoring and the like.

The present invention relates to the technical field of expression recognition, and in particular, to an expression recognition method and system based on multi-scale features and spatial attention. The method includes: performing feature extraction on acquired facial image data by using an HNFER neural network model to obtain an original input feature map; performing pooling and concatenation on extracted features based on a CoordAtt attention mechanism to obtain a feature map; performing deep convolution processing on the feature map to obtain an attention map, and then performing element-by-element multiplication to obtain a final feature map; and performing feature transformation and normalization on the final feature map to obtain an expression category probability and output the expression category probability. In the present invention, by integrating scale perception and spatial attention technologies, the model can recognize and classify different emotional states more accurately and maintain high performance even under complex environmental conditions.

The invention belongs to the technical field of agricultural machinery track classification, and particularly relates to an agricultural machinery track data classification method and device, electronic equipment and a storage medium. The method comprises the following steps: carrying out preprocessing and feature conversion operation on original agricultural machine GNSS trajectory data to obtain continuous and uniform time sequence trajectory data; the original agricultural machine GNSS trajectory data comprises a plurality of discrete trajectory points, and the category of the trajectory points comprises a field trajectory and a road trajectory; a trajectory classification model comprising a feature extraction module and a classification head is constructed, the time sequence trajectory data serve as training samples to train the trajectory classification model, the feature extraction module is trained in a comparative learning mode, and the classification head is trained through cross entropy loss; and deploying the trained trajectory classification model, and classifying agricultural machinery trajectory data by using the trained trajectory classification model. According to the invention, a contrast learning framework fused with the dynamic negative sample queue is designed, the classification accuracy and generalization ability are improved, and the method is suitable for track classification tasks in various complex scenes.

The invention discloses a data knowledge base management method and device for the egg industry and a medium, and relates to the technical field of intelligent breeding informatization, and the method comprises the steps: inputting a multi-modal fusion data set into a single deep learning framework, carrying out the multi-scale feature coupling of each modal feature through a self-adaptive nonlinear interaction function, and carrying out the multi-scale feature coupling; the feature weight is dynamically adjusted in combination with an enhanced feedback mechanism, and a multi-dimensional health assessment index set is generated by utilizing end-to-end joint feature extraction and nonlinear reasoning; performing entity recognition and relation extraction on the multi-dimensional health assessment index set, constructing an egg industry knowledge graph in combination with the breeding environment and the individual feature information, and performing structure optimization and dynamic evolution to obtain a dynamic evolution knowledge graph; and performing rule reasoning and data analysis on the dynamic evolution knowledge graph to generate a health management decision. Fine modeling and high-quality unified expression of multi-source data are achieved through piecewise nonlinear feature transformation and weighted fusion, and the accuracy of health state analysis input is ensured.

The invention provides a karst area pile foundation intelligent positioning and precise construction method which comprises the steps that a karst development characteristic data set of a target engineering area is obtained, and the karst development characteristic data set comprises a three-dimensional geological exploration data set and a karst attribute data set; and preprocessing the data set to obtain a karst preprocessed data set. And based on a feature extraction model, performing feature extraction on the preprocessed three-dimensional geological exploration data set to generate a three-dimensional geological feature map. And identifying the map through a karst cavity identification model to generate a karst cavity distribution thermodynamic diagram. And karst cave feature conversion processing is conducted on the karst preprocessing data set, and a pile foundation construction feature data set is obtained. And according to the karst cavity distribution thermodynamic diagram, constraint processing is conducted on the pile foundation construction characteristic data set, and a pile foundation constraint boundary diagram is generated. And outputting a pile foundation end hole positioning coordinate set according to a pile foundation positioning optimization algorithm and the pile foundation constraint boundary diagram. The construction safety and accuracy of the pile foundation in the karst area can be improved, and the construction risk and cost are reduced.

The invention discloses a lightweight underwater sonar target detection model based on Transform architecture, which comprises a backbone network, a lightweight hybrid encoder and a decoder, and is characterized in that the backbone network inputs the features of the last three stages of an input image into the encoder; the encoder converts the multi-scale features into a series of image features; the decoder fuses the loss functions WCIoU and NWDLoss while capturing the category and position information of the object, and generates the category and bounding box by iteratively optimizing the object query. According to the method, the backbone network adopts the depth separable convolution TConv to reduce the calculation complexity, and the feature processing and characterization capability of the backbone network is remarkably improved by fusing the index moving average and the gating attention mechanism; a query selection mechanism based on WCIoU perception is combined with an NWD loss function, collaborative optimization of classification information and position information is achieved, and the detection capacity for small targets is particularly enhanced.

The invention discloses an accurate analysis method for Internet user behaviors based on artificial intelligence, and the method belongs to the technical field of Internet, and comprises the steps: collecting user behaviors and basic attribute information through a multi-source data collection technology, and integrating data through a data fusion algorithm after data cleaning and preprocessing; designing and extracting a plurality of behavior features, screening key features by using a feature selection algorithm, and performing feature transformation processing at the same time; constructing a deep learning model fusing a recurrent neural network (such as LSTM and GRU), a convolutional neural network and the like, training the model by using a large-scale data set, and optimizing parameters; establishing a real-time analysis system, inputting new data into the model in real time, providing personalized service and recommendation according to an analysis result, and collecting feedback data to update the model online; and finally, evaluating the model by adopting multiple evaluation indexes, verifying the generalization ability through methods such as cross validation and the like, and further optimizing and adjusting the model.

The invention relates to the technical field of information security, in particular to a security baseline setting method and system equipment based on clustering decision and a medium, and the method comprises the steps: collecting original data, carrying out the construction of a feature vector through feature conversion, generating a sample, and marking and extracting the sample; constructing a classification model by using manifold learning, inputting the extracted feature vectors into the constructed classification model, optimizing the model through training, and outputting classification labels; processing the decision boundary by using constraint optimization and feature scaling to obtain a standardized feature vector converted by a decision boundary model, grouping based on the standardized feature vector, and extracting a baseline; the security policy is generated through data analysis, and the data is dynamically updated and continuously monitored, so that the unified configuration and centralized management of the security policy are realized, and the policy implementation efficiency and the system expansibility are remarkably improved.

A method for performing a computing task includes: extracting one or more features from a user content; converting the features to a floating point query vector; quantizing the floating point query vector; obtaining a database vector including one or more floating point feature vectors; determining a compensation vector based on a data distribution of the floating point query vector; quantizing the floating point feature vectors; determining an error function based on a difference between data distributions of i) the quantized query vector compensated with the compensation vector, and ii) the floating point query vector; determining, based on the error function, values of the compensation vector corresponding to the quantized feature vectors; combining the quantized query vectors and the values of the compensation vector to obtain one or more compensated query vectors; and performing the computing task using the compensated query vectors and the quantized feature vectors to obtain an output.

A multimodal metadata retrieval-augmented generation method and system, relating to the technical field of artificial intelligence. In the present invention, the method comprises: determining data modalities of query content, performing feature extraction on data of each modality on the basis of the determined data modalities, converting extracted features into feature vectors, and fusing the feature vectors of the modalities to generate a multimodal feature vector; extracting, from data of each modality in the query content, key metadata of the data of each modality, converting the key metadata into a key metadata vector and connecting same to the multimodal feature vector, and constructing an augmented vector; and performing retrieval by means of the augmented vector. The method helps improve knowledge coverage and information utilization efficiency, and enhances the accuracy, relevance, and credibility of generated content.

The invention relates to the technical field of artificial intelligence, and provides a quantum-enhanced multi-scale network intrusion detection method, which comprises the following steps: calculating a covariance matrix for an original traffic feature matrix, and obtaining a feature value and a feature vector through feature decomposition, mapping each sample xi to a quantum Hilbert space to generate an enhanced feature matrix, executing complex field transformation on the enhanced feature matrix to generate an entangled feature tensor, and realizing dynamic feature enhancement through a multi-head attention mechanism based on a quantum probability amplitude; performing space-time attention calculation and gating fusion on the feature tensor after dynamic feature enhancement to obtain a space-time fusion feature; converting the space-time fusion features into a time sequence form, extracting behavior features through a multi-scale convolution branch, and fusing the behavior features to obtain a three-dimensional feature tensor; and calculating a mean value of the three-dimensional feature tensor in a sequence dimension, generating a two-dimensional feature matrix, and performing classification prediction, uncertainty quantification and threat grading evaluation based on a classification network, an uncertainty network and a threat grading network.

A disease marker structure evolution characteristic change point determination method belongs to the field of disease markers, and comprises the steps of obtaining and preprocessing time sequence structure characteristic data of a disease marker, constructing a characteristic transformation image and calculating characteristic intensity distribution, establishing a structure characteristic fitting model and calculating a time evolution coefficient and an intensity evolution coefficient, determining a structural feature evolution trajectory and calculating a correlation index; establishing a structural feature piecewise function and identifying a feature mutation point; calculating a structural feature contribution value and generating a feature evolution matrix; calculating an evolution stability index and determining a structural feature change point; hierarchical clustering is carried out, main structural feature change points and secondary structural feature change points are determined, a feature weight distribution diagram is constructed, a change point time sequence table is generated, a time sequence corresponding relation is established, and a structural feature change point determination result is output; and fine analysis and accurate description of structural evolution characteristics of complex disease markers are realized.

The invention relates to the technical field of augmented reality, in particular to an AR real-time scene reconstruction and illumination matching system and method based on neural rendering, and the system comprises a real-time scene understanding module, a real-time neural radiation field module and a real-time reflection inference and enhancement module. Comprising a forward network, a feature transformation layer and a backward network, the forward network maps a high-dimensional rendering space to a feature manifold, the feature transformation layer executes mapping from the feature manifold to a rendering manifold, and the backward network performs rendering calculation based on a Riemannian metric driven adaptive ray tracing mechanism; the real-time reflection inference and enhancement module performs material prediction and illumination information calculation based on an illumination dynamic adaptation system on a differential manifold, real-time application of a neural radiation field technology in an AR scene is realized through an innovative differential geometry framework, and a real-time rendering frame rate of 30-60 fps is achieved while a high-quality rendering effect is kept.

The invention discloses a river water quality prediction method fusing quantum-like features and a graph-time sequence model. The river water quality prediction method comprises the following steps: acquiring multivariable water quality observation data, time sequence meteorological data and static basin attribute data of a plurality of river sites; preprocessing the data; generating a meteorological quantum feature vector and a static basin attribute quantum feature vector; after the meteorological quantum feature vector and the static watershed attribute quantum feature vector are spliced and fused with the spatial dependence features, inputting the spliced and fused data and the preprocessed multivariable water quality observation data into a graph-time sequence model for training; and performing reverse normalization processing on simulation output obtained by calculation of the trained graph-time sequence model to obtain continuous day-by-day river water quality simulation data, and evaluating model performance to predict day-by-day continuous river water quality data. According to the method, the corresponding space-time class quantum feature vectors are generated by executing high-dimensional class quantum feature transformation on the multi-source heterogeneous data, so that the characterization capability of the model on the nonlinear interaction relationship of the space-time features is enhanced.

The invention relates to the technical field of short-term load prediction in a power system, and discloses a short-term load prediction system based on a coder-decoder architecture, and the system is characterized in that a coder is used for extracting local features of a power load mode; and the decoder is used for converting the local features of the power load mode into predicted power load values and outputting the predicted power load values. The encoder is realized by a multi-scale expansion causal convolutional network MSDCC, and the decoder is realized by a bidirectional long short-term memory network BiLSTM. The prediction system construction method comprises the following steps: extracting related data from a historical database, preprocessing and analyzing the data, and constructing a predictor matrix; an MSDCC encoder is constructed; a BiLSTM decoder is constructed; and combining the encoder-decoder architecture to construct a short-term load prediction model. According to the method, the size of the feature map is effectively limited, model parameters are reduced, overfitting is avoided, calculation requirements are controlled, non-linear features are efficiently captured, meanwhile, time keeping complexity is low, and therefore the method has the advantages of being high in prediction efficiency and high in prediction precision.

The invention provides a deep steganography image secret information blind extraction method based on self-supervised learning. The deep steganography image secret information blind extraction method comprises a coding stage, a self-supervised learning task generation supervision signal, attention coupling through an attention coupling module and a decoding stage. The coding stage comprises the following steps of: respectively taking two secret-containing images as input, performing Haar transformation method processing, splitting input features generated by the processing of the Haar transformation method of the secret-containing images into two paths through a bidirectional coupling mechanism of a coupling layer, realizing feature decoupling and cross-path interaction by utilizing nonlinear transformation, different paths are enabled to focus different components of secret-containing images respectively, and meanwhile, information lossless in the feature transformation process is ensured through reversible design. According to the method, the self-supervised learning technology is innovatively utilized, the model can deeply mine the characteristics and rules of the secret-containing image, and therefore secret information blind extraction independent of a secret key is achieved.

The invention provides a method and a system for constructing a common financial business system based on artificial intelligence, and relates to the field of financial system construction. Comprising the following steps: converting financial rule data into analog modulation signal feature vectors containing value intensity, risk association degree and aging attenuation rate through a three-dimensional feature conversion module, and executing market behavior capture, rule space positioning and strategy generation operations in combination with a multi-stage state machine; the supervision text is converted into a continuous control signal injection feature generation logic through a dynamic constraint feedback loop, and a closed-loop adjustment mechanism of value gain calibration, risk attenuation compensation and aging synchronous correction is formed; a physical signal cross validation module is adopted to perform dual coupling verification of main channel waveform characteristics and auxiliary channel time sequence characteristics on the strategy; the time sequence deviation of characteristic conversion, state transition and constraint feedback is synchronously coordinated through a reference clock, and it is ensured that the time sequence deviation of the operation rhythm of all modules of the system is lower than a preset tolerance.

The invention belongs to the technical field of image processing, and particularly relates to a lightweight unmanned aerial vehicle target tracking method based on separable convolution. The method comprises the following steps: step 1, preparing two remote sensing image data sets which are respectively used for training and testing; 2, inputting a frame into a separable convolution block for feature extraction to obtain a feature sequence; 3, inputting the feature sequence into an inverted bottleneck block and a forward feedback network, carrying out feature transformation and information mixing, and enhancing the feature expression capability; 4, the extracted category number is input into a fusion module to be processed, and a fusion sequence is obtained; and step 5, obtaining a classification regression vector through loss calculation, and then outputting a result graph. According to the method, a lightweight remote sensing target tracking network architecture is innovated, an improved feature extraction module UIB-P and a fusion module ICA are added, and an innovative loss function is adopted, so that the model greatly improves the global representation capability and precision of target tracking while reducing the calculation amount.

The invention relates to a power load prediction system based on time sequence self-supervised representation learning, and the system comprises a data preprocessing module, an expansion time convolution network, a trend representation decoupler, a seasonal representation decoupler, a feature converter, and a predictor. The expansion time convolution network performs dynamic adaptive grouping on different variables in the same data processing unit, establishes an intra-group variable relationship by sharing a convolution kernel weight and stacking a plurality of expansion time convolution layers, and establishes an inter-group variable relationship through subsequent multi-group representation splicing operation and single-layer expansion time convolution; the trend representation decoupler is used for separating trend representation Z (T) from feature representation extracted from the expansion time convolution network based on a plurality of parallel one-dimensional causal convolution blocks with different scales; the seasonal representation decoupler adopts discrete Fourier transform to separate seasonal representation Z (S) from the feature representation extracted from the expansion time convolutional network; the feature converter maps a combined feature vector of trend representation and seasonal representation back to an original space from a potential space by stacking a plurality of deconvolution layers; and the predictor maps the feature representation of the original space into a power load prediction result by using a linear projection layer, and the adaptability, robustness and generalization ability of the model for modeling complex power load time series data can be improved.

The invention discloses a multi-degraded image recovery method based on frequency domain decomposition, and aims to solve the problems that a single model is difficult to deal with various image degradation and recovery processes of different frequency domains are mutually coupled in the prior art. According to the method, a degraded image is decomposed into a high-frequency space and a low-frequency space through fast Fourier transform, and a double-branch network architecture is adopted for targeted processing: for the high-frequency part, a high-frequency feature adaptive processing module HFPM is designed, and detail texture features are effectively extracted and interference is suppressed through feature enhancement and cross-layer fusion technologies; and for the low-frequency part, constructing a low-frequency feature conversion enhancement module LTEM, and capturing global context information by using cyclic convolution to improve the integrity of the structure contour. According to the method, decoupling processing of frequency domain features is realized, and the image restoration performance of the model in various degradation scenes such as rain removal, noise removal and defogging is remarkably improved through the synergistic effect of high-frequency detail enhancement and low-frequency structure optimization. Experimental results show that the method has excellent recovery effect and robustness when a plurality of image degradation tasks are processed at the same time, and can be effectively applied to visual tasks such as traffic accidents with high image quality requirements.

The present invention relates to the field of pipeline leakage monitoring. Disclosed is an oil and gas pipeline leakage wave identification and monitoring system. In the present invention, an mCNN is combined with LFLBs for performing feature extraction on an acoustic wave signal collected by a DFB, and the collected data improves information completeness; a three-way parallel one-dimensional CNN used in the present invention exhibits good temporal resolution and sensitivity to high-frequency feature transformations in signals; and the present invention integrates advantages of different scales, enabling the algorithm to learn more features, and incorporating the LFLBs to further extract high-level local features. An mCNN-LFLBs network model of the present invention exhibits significant innovation and advancement on the technical level, and also demonstrates extremely high value in actual application. The network model not only provides a novel and efficient technical means for critical fields such as natural gas pipeline inspection, but also introduces new ideas and methods to research fields related to deep learning and signal processing.

The invention relates to the technical field of computer vision and three-dimensional measurement, in particular to a three-dimensional point cloud semantic segmentation and measurement reference surface intelligent fitting system and a method thereof, which are mainly applied to scenes needing accurate three-dimensional space understanding, such as topographic survey, mining, urban planning, intelligent driving and industrial detection. The system comprises a data acquisition module, a preprocessing module, a feature extraction module, a reference surface acquisition module and a pixel feature conversion module. Three-dimensional point cloud data are collected through multi-sensor fusion, and the data quality and integrity are improved. A cascade filtering preprocessing process is innovatively designed, and the data quality is remarkably improved. The feature extraction module generates semantic tags, the reference surface acquisition module calculates a probability optimal fitting plane, and the pixel feature conversion module realizes multi-scale feature output. According to the method, the three-dimensional point cloud processing precision and efficiency are effectively improved, and powerful technical support is provided for the field of computer vision and three-dimensional measurement.

The invention provides a real-time monitoring method and system for the running health state of a pump set. The method comprises the steps of obtaining vibration data of a load end bearing seat in real time when a pump set operates; performing time-frequency feature transformation on the vibration data to obtain time-frequency feature data; using the time-frequency characteristic data to train an auto-encoder to obtain an auto-encoder model based on reconstruction; the time-frequency feature data after mask processing and the rebuilding-based auto-encoder model obtained through training are used for training a rebuilding generation task-based auto-encoder to obtain an anomaly detection model; inputting vibration data in an early health state as a sample into the anomaly detection model, and calculating an obtained anomaly score to obtain a health degree reference; and real-time vibration data is subjected to time-frequency feature processing and then input into the anomaly detection model to obtain an anomaly score, and the current health degree value of the pump set is obtained. The operation state of the pump set can be monitored in real time for a long time, and the manual inspection cost and the serious equipment fault occurrence risk are reduced.

The invention discloses a remote sensing image reasoning segmentation method based on a scene perception guide network, and the method is characterized in that the method comprises the following steps: 1, constructing a multi-scene reasoning segmentation remote sensing data set; step 2, constructing a scene perception guiding network based on an encoder-decoder structure, and processing the data set constructed in the step 1 to obtain a segmentation mask; 3, performing network training by adopting a self-adaptive scene perception loss function; according to the cross-scene generalization performance evaluation method, an efficient remote sensing image reasoning segmentation model is constructed through a scene perception guide mechanism and context-rich adaptive feature transformation, and the adaptation of semantic expressions in different scenes is guided by introducing scene cognition, so that the robustness of the remote sensing image reasoning segmentation model is improved. The problem that feature representation is inconsistent when a traditional method faces a complex geographical environment is effectively avoided.