84results about How to "Easy to explain" patented technology

The invention provides a brain parenchyma MRI image-based infarction change prediction method and system, and relates to the technical field of image recognition, and the method comprises the steps: obtaining a brain parenchyma MRI image, constructing a multi-modal image set, carrying out the screening according to a screening standard, carrying out the artifact recognition, calculating a quality score, carrying out the preprocessing, obtaining standard image data, carrying out the feature extraction, and carrying out the prediction of the infarction change of the brain parenchyma MRI image. Obtaining a brain region segmentation mask; performing feature extraction to obtain multi-modal feature data, constructing a topological relation among different brain regions, extracting high-order semantic features, obtaining brain region feature data, and performing selection to obtain high-quality feature data; constructing a time sequence characteristic matrix, distributing weights, generating residual output, performing prediction through a full-connection neural network, generating an infarction change prediction result, generating a prediction prognosis result, and generating a comprehensive prediction report.

The invention relates to the technical field of geological surveying and mapping, in particular to a multi-source data fusion geological surveying and mapping system which comprises a multi-dimensional data collection module, a data calibration and synchronization module, a geological image enhancement module, a data fusion and model generation module and a visualization and interaction module. The data calibration and synchronization module carries out timestamp calibration and geographic position processing on data, the geological image enhancement module enhances geological images, the data fusion and model generation module fuses the processed data and constructs a geological model by using a modeling technology, and the visualization and interaction module provides a multi-dimensional data interaction platform. Through the geological texture recovery sub-module, visual quality of images is improved, reliability and accuracy during geological data and resource evaluation are enhanced, polarized light data are collected through the polarized image processing sub-module, key information such as surface material roughness is revealed, and surface and underground geological processes can be better understood and explained.

The invention discloses a numerical analysis method for predicting speed distribution of downwash airflow of an unmanned aerial vehicle in a rice canopy based on a fluid-solid coupling method. The numerical analysis method comprises the following steps: measuring physical parameters of rice plants based on physiological characteristics of rice; establishing a three-dimensional rice plant model based on three-dimensional geometric modeling software; dividing a three-dimensional rice plant canopy grid based on grid division software; establishing a fluid-solid coupling model of the lower washing flow field of the unmanned aerial vehicle and the rice canopy; setting an initial boundary condition and performing spatial discretization; full-coupling finite element numerical simulation of the fluid-solid coupling model is carried out; performing post-processing on the numerical simulation result based on flow field post-processing software to obtain an evolution process and spatial distribution of the downward washing flow field of the unmanned aerial vehicle and dynamic behaviors of rice plants; and analyzing the influence of the dynamic behavior of the rice plant on the downwash airflow of the unmanned aerial vehicle, and determining the distribution rule of the downwash airflow speed in the rice canopy. The problems of high time consumption, influence of external conditions, difficulty in copying, limitation and the like are effectively solved.

The invention discloses a differential pressure time series data prediction method and device based on an improved ARIMA model, and the method comprises the steps: collecting and packaging voltage data of a single battery of energy storage equipment, obtaining time series data, carrying out the cleaning processing, and obtaining the cleaned time series data; carrying out ADF stability verification on the cleaned time sequence data; after the time sequence data passes the cleaning, carrying out Ljung-Box white noise detection on the cleaned time sequence data; generating prediction data after checking; constructing an ARIMA model and determining parameters; training the ARIMA model by setting a training data set; performing performance evaluation on the trained ARIMA model by setting a verification data set to generate an evaluation index; when the evaluation index reaches a set standard, training is completed, and a trained ARIMA model is obtained; and inputting the prediction data into the trained ARIMA model for prediction processing to obtain differential pressure prediction data. The accuracy of predicting the voltage difference of the single battery is improved, the voltage abnormity can be monitored, and then the stability of energy storage equipment is maintained.

The invention provides a psychological semantic scene image generation method, which comprises the following steps of: 1, processing data according to a psychological scale filled by a user to obtain a three-level level score of a psychological factor; 2, constructing a basic semantic concept group through hierarchical semantic mapping; 3, constructing structured cue words, wherein the structured cue words comprise a text cue word project and an image cue word project; 4, generating an image note through a large language model according to the text cue word, and generating a background image and page elements through a text-to-graph model according to the image cue word; 5, generating a semantic scene graph from the background picture and the page elements generated in the step 4 through a differentiable graph synthesizer; and step 6, generating a final psychological report in combination with the generated graph annotation and the semantic scene graph.

The invention discloses a medical image classification auxiliary diagnosis system which is based on a deep learning model and interpretability design and aims to improve the accuracy and efficiency of medical image classification. For the significant difference between a medical image and a natural image in the aspects of imaging principle data quality and the like, the generalization ability of a traditional model is limited. The invention designs a residual multi-layer perceptron network based on a sparse attention mechanism, and aims to mine a key focus area in a medical image and enhance the interpretability of a model. Finally, an auxiliary diagnosis system comprising a data enhancement and image classification module is constructed, convenient diagnosis support is provided for medical staff, and the misdiagnosis rate and the missed diagnosis rate are reduced. The system has a good application prospect, the medical image classification performance can be effectively improved, and the development of intelligent medical treatment is promoted.

The invention discloses a potato waxiness evaluation model as well as a construction method and application thereof, and belongs to the technical field of food detection. The model construction method comprises the following steps: firstly, analyzing the waxy quality of potatoes in chewing and swallowing processes through sensory evaluation; then, stress relaxation testing is combined with a Maxwell model, and the mechanical property of the sample in the solid state in the chewing stage is measured; measuring the mechanical properties of the fluid phase sample in the swallowing stage by adopting frequency scanning and amplitude scanning in combination with an Ostwald-de Wavele power law model; and according to the correlation between the mechanical indexes and the taste scores, establishing a mechanical analysis-based potato waxiness evaluation model. According to the method, dynamic phase change and mechanical property analysis are combined for the first time to evaluate the waxy quality, so that waxy evaluation is more accurate and conforms to actual eating experience.

The invention discloses a mixed expert model-driven medical large model discharge settlement generation method and system, and the system comprises an acquisition module, a labeling module, a model training module, and a generation module, and the acquisition module is used for data acquisition and preprocessing; through rule and expert model review, the consistency and high quality of a data labeling result are ensured; the model training module is used for training a Chinese medical large model, and integrates a plurality of field expert networks in a large-scale pre-training language model by adopting a hybrid expert model technology; and the generation module is used for generating a discharge nodule, and comprises the following steps: the system receives the input of the patient, processes the input data of the Chinese medical large model in the model training module, and generates a personalized discharge nodule. By adopting the method and the system, the generation efficiency and the quality of the hospital discharge notes are remarkably improved, the personalized hospital discharge notes can be generated according to the specific condition of each patient, and nursing suggestions and follow-up visit plans which better meet the requirements are provided.

The invention relates to a visualization processing method and device based on an attention mechanism and a storage medium. The visual processing method based on the attention mechanism and applied to the electronic equipment is characterized in that the visual processing method based on the attention mechanism is applied to the electronic equipment and comprises the steps that an original text C and a question Q are spliced and combined, and first input information is obtained; inputting the first input information into a question and answer model to obtain answer information A, output result information T of the question and answer model and an attention matrix Attenm; giving a focusing range, and calculating an attention matrix in the focusing range; based on the attention matrix in the focusing range, a heat map is obtained through calculation, and visualization processing is carried out on output result information T of the question and answer model.

The application discloses a method for predicting brain task activation patterns by combining structural and functional magnetic resonance imaging, and belongs to the cross field of biomedical engineering and pattern recognition. First, the method obtains brain function features, structural features and Prompt text through data preprocessing, and then respectively establishes a Prompt text coding model for embedding the text of the Prompt into a text feature space, extracts high-level semantic features of the brain, and fuses a backbone network model of features of two modalities of functions and structures, and a Prompt interaction module for fusing Prompt information so that the prediction result of the model can be more easily explained and understood. The model is optimized based on a classification-contrast loss objective function, and the brain task activation patterns in a task state are predicted from the brain task activation patterns in a non-task state. The application improves the prediction accuracy of the model, and is helpful for predicting the brain task activation patterns of patients with mental diseases, the elderly or infant groups.

This invention belongs to the field of nonlinear data classification technology and discloses a nonlinear data classification method based on deep transfer learning and visual recognition capabilities. This invention transforms high-dimensional numerical features into structured images, enabling convolutional neural networks to leverage their powerful visual recognition capabilities to handle tabular data classification tasks. This image-based representation naturally reveals the implicit nonlinear relationships within the data. By further integrating deep transfer learning, this method effectively alleviates the data sparsity problem in small-sample scenarios and improves generalization performance through a series of training optimization strategies. The framework proposed in this invention achieves an effective connection between the numerical feature space and the image-based deep learning architecture, providing theoretical innovation and practical value for complex nonlinear data classification tasks.

The utility model relates to the technical field of simulation terminals, and discloses an intelligent simulation terminal suitable for a marketing scene, comprising an intelligent simulation terminal body, the front surface of the intelligent simulation terminal body is provided with a control panel, and the upper surface of the intelligent simulation terminal body is rotatably connected with a display panel and an adjusting mechanism. The intelligent simulation terminal comprises an intelligent simulation terminal body, a display panel, an adjusting mechanism and a cooling mechanism, the adjusting mechanism is arranged on the intelligent simulation terminal body, the adjusting mechanism is used for adjusting the angle for conversation, the adjusting mechanism comprises two hydraulic devices, the two hydraulic devices are rotatably connected to the upper surface of the intelligent simulation terminal body, and the cooling mechanism is arranged on the rear surface of the display panel. By arranging the adjusting mechanism, the angle of the display panel can be adjusted, a user can conveniently adjust the angle to a proper angle for marketing work of video calls, the adjustable camera module is used for expanding the shooting angle, explanation of more marketing work can be conveniently carried out, and more convenience is achieved.

The invention discloses a big data visualization modeling method, device and equipment based on machine learning, and relates to the technical field of big data modeling, the method comprises the following steps: accessing an external data source to an initialized multi-source data directory, and registering external data in the multi-source data directory to a public data directory; training the established initial machine learning model by using the data in the registered public data directory to obtain a target machine learning model; constructing a visual big data model based on the data in the registered public data directory, a data processing tool operator and the target machine learning model; and performing preview debugging on the visual big data model to determine operator parameters of the visual big data model. According to the method, the operator parameters of the model are determined by previewing and debugging the visual big data model, the model prediction result can be previewed, and data from different sources and formats can be processed by supporting access of a multi-source data directory.

This application discloses a data-aware intelligent analysis method, system, storage medium, and computer device. The method includes: receiving a target problem and initializing state information, wherein the state information includes key sub-conclusions, a reasoning state logic diagram, and key data descriptions; a language model-based agent iteratively performs the following operations: obtaining input information for the current step; updating the state information of the current step based on the action and environmental feedback information of the previous step, and making decisions based on the state information of the current step to obtain the action and tools to be invoked for the current step; invoking the tools to execute the action of the current step, and obtaining environmental feedback information for the current step after the action is completed; checking whether the target problem has been analyzed; if not, proceeding to the next loop; if completed, generating analysis results. This method can improve the reasoning ability, response consistency, and interpretability of deep reasoning models.

The invention relates to the technical field of data integration analysis, and provides an intelligent copper wire bonding data integration analysis method and system based on the Internet of Things, and the method comprises the steps: S1, collecting parameters of bonding copper wire equipment through a sensor, and obtaining original data; s2, selecting an RFID communication protocol as a gallery road for copper wire bonding data transmission; s3, preprocessing the original data to obtain preprocessed data; s4, merging the preprocessed data together and providing a unified view to obtain integrated data; s5, analyzing the integrated data by using grey correlation analysis, analyzing the relation between the copper wire bonding data, and describing the relation between the bonding copper wire data; and S6, calculating a bonding process capability index, judging whether the bonding process capability index is within a preset bonding quality standard range or not, if so, determining that the bonding process is stable, and otherwise, adjusting the process. And the comprehensive influence of the copper wire bonding data comprehensive evaluation factors on the bonding effect is analyzed by using the grey correlation, the grey correlation analysis result speed is high, and the working efficiency is improved.

The application provides an open vocabulary semantic segmentation method and system, comprising: decomposing target category text into a set of attribute texts; using a visual feature extractor to encode a target picture into visual features; using a text feature extractor to encode the attribute texts into attribute features; feeding the visual features and the attribute features into an attribute aggregation network to obtain a segmentation probability map, and converting the segmentation probability map into a segmentation result using a threshold method; calculating the loss of the predicted segmentation result and a real segmentation mask, and training using a gradient descent method; and using the trained model to infer test images and test attribute texts to obtain test image segmentation results. The application proposes a decomposition-aggregation strategy, which greatly improves the accuracy of open vocabulary semantic segmentation.

The invention discloses a star sensor simulation teaching system, which comprises a star map simulation device, the star map simulation device generates a simulation star map by a star map simulation computer, projects a star point in a view field pointed by a star sensor visual axis to a star sky simulation device, and simulates a real star sky environment; the star sensor simulation equipment simulates a star sensor through an industrial camera, shoots a star map on the star sky simulation equipment by adjusting the distance of the bracket, and simulates the shooting process of the star sensor; the device has the beneficial effects that the working principle and the working process of the star sensor are visually displayed by simulating the real star sky environment and the working process of the star sensor, so that students can understand and master the working principle and the working process conveniently; practical operation links such as star map acquisition, processing, recognition and attitude calculation are provided, and the operational ability and practical ability of students are enhanced.

The present application provides a method for predicting the efficacy of umbilical cord blood natural killer cell treatment of neuroblastoma, belonging to the technical field of medical prediction and evaluation. The present application solves the clinical need for early and accurate prediction of the efficacy of immunotherapy for neuroblastoma. The method comprises: obtaining peripheral blood test data of a neuroblastoma patient before multiple reinfusion treatment nodes of umbilical cord blood natural killer cell treatment; based on the processed node-level peripheral blood test data, at least one dynamic linkage feature reflecting the dynamic linkage relationship of the indicators in the patient's body is calculated; at least one static feature index is obtained; the dynamic linkage feature and the static feature index are input into a pre-trained efficacy prediction model to obtain the response probability of the patient to umbilical cord blood natural killer cell immunotherapy. The present application accurately predicts the efficacy of umbilical cord blood natural killer cell treatment of neuroblastoma by analyzing the peripheral blood test data of the patient at multiple time points and constructing dynamic linkage features.

The application provides a geological radar data fan imaging method, which performs boundary extension on two-dimensional rectangular profile data of a geological radar, and then performs third-order interpolation between channels and triangular projection transformation. Through conversion of the two-dimensional rectangular profile to the fan profile, the morphological characteristics of the geological radar measuring line in the three-dimensional space can be more clearly and accurately described, and target positioning and interpretation in the three-dimensional space can be realized.

The invention discloses a three-dimensional VSP seismic data interpolation method based on weighted matching pursuit, comprising the following steps: S1, respectively performing static correction, deconvolution and VSP dynamic correction processing on original seismic data collected in the field, eliminating the time difference between traces of different offset distances, and obtaining the three-dimensional VSP seismic data before interpolation; S2, obtaining the spatial sampling position of the three-dimensional VSP seismic data according to the shot point coordinates of the three-dimensional VSP seismic data before interpolation; S3, encrypting the three-dimensional VSP seismic data before interpolation in the spatial direction with blank data, and obtaining a data body with spatial sampling rules; S4, performing F-K transformation on the data body with spatial sampling rules obtained in step S3, and obtaining an F-K spectrum; S5, performing linear Radon transformation on the F-K spectrum, and obtaining a weighting operator according to the Radon transformation result; S6, processing the F-K spectrum with the weighting operator obtained in step S5 to obtain a new F-K spectrum; S7, performing F-K inverse transformation on the new F-K spectrum to obtain the three-dimensional VSP seismic data after interpolation.

The utility model discloses a particulate matter calculation microorganism simulation display device which comprises a base, a first motor is fixedly installed on one side of the top of the base, the output end of the first motor is fixedly connected with a transmission shaft, one end of the transmission shaft is fixedly connected with a first bevel gear, and the top of the base is rotationally connected with a rotating rod through a bearing. The outer surface of the lower end of the rotating rod is fixedly connected with a second bevel gear, the second bevel gear is in meshed connection with the first bevel gear, the top of the rotating rod is fixedly connected with a connecting rod, the two sides of the connecting rod are fixedly connected with adjusting frames, the outer surfaces of the tops of the two adjusting frames are both provided with sliding grooves, and the inner walls of the two sliding grooves are both slidably connected with sliding blocks; the outer surfaces of one sides of the two adjusting frames are fixedly connected with second motors, the output ends of the second motors are fixedly connected with reciprocating lead screws, the display screen body can be adjusted to different angles and directions, the practicability of the device is improved, and the device is convenient to use.

The application discloses an indoor design picture performance method and a display device thereof and relates to the technical field of display equipment. The technical scheme is as follows: a rectangular storage box and a rectangular collecting box are arranged, a display unit is arranged between the rectangular storage box and the rectangular collecting box, a treading lifting mechanism is arranged below the rectangular storage box, a plurality of picture frames arranged linearly are stored in the rectangular storage box, and the treading lifting mechanism is used for driving the picture frames in the rectangular storage box to ascend. The application has the advantages that the device is designed ingeniously and is easy to operate. The picture frames can be adsorbed by the treading lifting mechanism, the adsorbed picture frames can be turned to the front side by the groove wheel mechanism, the collection frame for adsorbing the picture frames is turned over by the rotation of the second motor, the device is convenient for explanation and observation, the collection frame for adsorbing the picture frames is attached to the rectangular collecting box when the collection frame is turned over, the picture frames are collected, and the practicability of the device is improved.

The invention provides a galaxy spectrum processing method, device and equipment, which can be applied to the technical field of data processing. The method comprises the following steps: acquiring a to-be-processed galaxy spectrum; performing feature extraction on the to-be-processed galaxy spectrum by using a self-attention model to obtain galaxy features, the galaxy features representing physical state and chemical composition information of a galaxy corresponding to the to-be-processed galaxy spectrum; detecting the galaxy features based on the historical galaxy features to obtain a detection result; and under the condition that the detection result represents that the galaxy features are not abnormal, classifying the galaxy features to obtain a classification result representing galaxy types and a red shift prediction value corresponding to the galaxy.

The invention relates to the technical field of nano materials and analysis and detection, in particular to a reagent for rapidly detecting aureomycin and a rapid aureomycin detection method. After the 3-mercaptopropionic acid modified AgInS2 quantum dots are added into aureomycin, the fluorescence intensity and color of the AgInS2 QDs are changed due to electrostatic interaction and band gap transition, so that a fluorescence colorimetric sensor is constructed to realize ultra-sensitive detection of the aureomycin. In order to realize on-site detection of aureomycin, a portable device equipped with a smart phone and an AgInS2 QDs sensor integration are also designed. A cloud server data analysis system based on a machine learning algorithm is integrated into the smart phone, so that color data collection, correction, explanation and display are facilitated. According to the invention, a user-friendly field detection solution is provided for aureomycin detection by utilizing the functions of capturing and processing fluorescence color signals of a modern smart phone.

The application provides a spatial noise annoyance modeling method based on binaural psychoacoustic parameter difference, and the method uses the binaural psychoacoustic parameter difference as a bridge, establishes a spatial noise annoyance model based on noise self annoyance by using multivariate linear regression, and realizes correction of noise self annoyance in the sound source direction, so that the annoyance of noise in different directions is more reasonably and accurately predicted, the use range of the noise annoyance model is widened, the physical mechanism of the spatial noise annoyance is explained, and the application value of the noise annoyance model is enhanced.

A reliable flow sensor (100) having a housing (110, 410) with predictable thermal variations and reduced mechanical tolerances for more consistent fluid flow and more consistent flow measurements. The thermal variations can be made predictable by using etched structures in the silicon block (110, 410). The mechanical tolerances can be reduced using photolithography and high precision semiconductor manufacturing equipment and techniques.

A method for predicting the remaining life of an engine based on clustering and LSTM. The invention relates to the field of aviation engine health management, and specifically to a method for predicting the remaining life of an engine based on clustering and LSTM. A method for predicting the remaining life of an engine based on clustering and LSTM is proposed, which uses clustering and LSTM to monitor the health status of an aircraft engine, predict the remaining life of the engine, and thus ensure the normal operation of the flight. The prediction is performed in the following steps: 1) standardization; 2) cluster analysis; 3) mutation analysis; 4) LSTM model prediction; 5) calculation of weight values; 6) multi-stage prediction. Experimental analysis shows that the ILSTMC method we proposed is effective in predicting the remaining life of an aviation engine, and enables operators to better manage the health of the engine based on the prediction results of this case.

The utility model provides a teaching display stand for language training, and belongs to the technical field of display stands. Comprising a panel, a connecting mechanism fixedly installed on the lower surface of the panel, supporting legs movably connected with the panel through the connecting mechanism and a connecting rod, the connecting mechanism comprises a connecting frame fixedly installed on the lower surface of the panel and a connecting cylinder fixedly connected with the supporting legs and the connecting rod, and the inner wall of the connecting cylinder is slidably connected with a mounting rod; and one end of the mounting rod is fixedly connected with a limiting plate. According to the utility model, the supporting legs and the connecting rods are used as components for supporting the panel, and the panel is horizontally placed on the ground to be used as a table, so that students can learn and write at the same time, and the second use mode is that the pulling plates in the connecting mechanisms on the supporting legs and the connecting rods are pushed towards the limiting plates, and the springs are compressed at the same time; and the first square rod and the second square rod are pushed away from the square hole.

The invention provides a transformer mechanical state original taste sensing method based on spatial magnetic field modal evolution, which comprises the following steps: extracting a steady-state magnetic field component and a vibration magnetic field component in a transformer, and obtaining a first detection feature and a second detection feature according to the steady-state magnetic field component, diagnosing whether the magnetic circuit state of the transformer core is normal based on the first detection feature and the second detection feature; and diagnosing whether the axial and radial vibration modes of the transformer winding are normal or not according to the vibration magnetic field component. According to the invention, the internal magnetic field space modal characteristics and the vibration frequency characteristics of the transformer are fused, and the external measurement signals of the transformer are strongly correlated with the change of the internal mechanical state, so that the fault types of the winding and the iron core can be effectively distinguished, the fault area can be preliminarily positioned, and the diagnosis accuracy is greatly improved.

The invention relates to the technical field of computer networks, and discloses a multistage CDN (Content Delivery Network) traffic scheduling method based on linear constraint, which comprises the following steps of: receiving an access request analyzed by a local DNS (Domain Name System) to form a scheduling instance to be optimized; carrying out complexity discrimination on the scheduling instance, and determining an applicable scheduling category of the scheduling instance; establishing a performance upper limit model related to server hardware resources; functional linear constraints are introduced, and Big-M parameters related to the constraints are finely set; and server cluster information, server information, quality constraint information and region hijacking information are obtained, joint solving is performed on the performance upper limit model and the functional constraint, a target cache node IP address meeting all constraint conditions is determined and fed back to a local DNS, and response and scheduling of an access request are completed. According to the invention, through a multi-stage scheduling framework and refined Big-M parameter setting, the computational efficiency and resource utilization precision of CDN scheduling in a complex network environment are improved.