20results about How to "Explanatory" patented technology

The invention relates to the technical field of intelligent clamping control, in particular to an online prediction method and system for the clamping stability of a flexible manipulator, and the method specifically comprises the following steps: collecting a clamping operation signal in a simulation experiment platform through a sensor network, and constructing a data set marking the clamping instability probability; performing alignment processing on unequal-length signals by adopting dynamic time warping integrated with physical constraints; then constructing an online model including multi-modal feature adaptive extraction fusion, time sequence feature enhancement and key frame dynamic detection and stability probability prediction, and completing model training optimization by using mean square error loss and small-batch gradient descent; and finally, deploying the model to a manipulator system to realize real-time data processing and clamping instability probability online output. The method can effectively adapt to a biochemical vessel clamping scene, the time sequence signal alignment precision and the risk prediction reliability are improved, and a real-time guarantee is provided for the operation stability of the flexible manipulator.

This invention relates to the field of chromosome abnormality recognition technology, specifically to a chromosome abnormality detection method based on a multimodal large model. The method includes: constructing an image dataset and a text dataset; fusing image features and text features to obtain multimodal fusion features; assigning anomaly scores to image blocks belonging to band regions based on anomaly scoring rules formulated from the multimodal fusion features, and comprehensively processing the anomaly scores of all image blocks corresponding to the chromosome to determine whether the chromosome image is abnormal; and decoding and generating natural language text that meets the requirements of chromosome abnormality detection based on the multimodal fusion feature representation and anomaly scoring rules. This invention achieves accurate chromosome abnormality detection and band location positioning through multimodal fusion and dynamic text generation mechanisms, generating interpretable natural language text descriptions, and improving the practicality and interpretability of the detection results.

The embodiment of the invention provides a muscle relaxation depth prediction anesthesia system based on electromyographic signals, and relates to the technical field of medical monitoring technologies. The system comprises an electromyographic signal acquisition module used for acquiring electromyographic signals; the signal preprocessing module is used for preprocessing the electromyographic signals; the frequency spectrum state analysis module is used for performing windowing and frequency spectrum transformation on the preprocessed electromyographic signals to generate frequency spectrum characteristics of all time windows, and calculating frequency spectrum inertia reflecting dynamic changes of the frequency spectrum characteristics based on a preset frequency spectrum inertia model; wherein the spectrum features comprise a median frequency and a total power; and the trend prediction and display module is used for generating and displaying a muscle relaxation trend prediction result in combination with the current muscle relaxation depth and the frequency spectrum inertia. According to the invention, the method solves a problem of low reliability of muscle relaxation depth, and achieves the effect of improving the prediction precision.

The present application provides a kind of biomass cogeneration system combustion efficiency prediction method, the method obtains the historical data of biomass cogeneration system, clusters historical data, extracts the combustion rule under different working conditions;Adaptive adjustment of fuzzy area of prediction model, the combustion rule of system is optimized by particle swarm algorithm;Based on fuzzy system and the parameter training prediction model after optimization;Acquire the measured parameter data of biomass cogeneration system, to predict the combustion efficiency with the trained prediction model.The present application quantitatively and qualitatively analyzes the multi-condition of biomass cogeneration system;Under multi-condition, it can adaptively adjust the static parameters of model;Compared with traditional algorithm, it can improve the prediction accuracy under the premise of ensuring the running speed, and has strong interpretability.

The application relates to the technical field of data analysis and intelligent marketing, and particularly discloses a market trend prediction and precision marketing system based on deep learning. The system comprises a data interface module, an interlocking collaborative analysis engine and a strategy generation module. The interlocking collaborative analysis engine sequentially performs market trend prediction and user behavior analysis, and through a two-way coupling feedback step, converts the market prediction result into an adjustment signal for a user interest model, and feeds the emerging pattern identified in the user behavior to the market prediction model as a correction input; and then updates the two models based on the coupled new data, to realize collaborative evolution. The strategy generation module generates marketing instructions under resource constraints according to the collaborative analysis result. The application realizes the deep integration and co-evolution of market trends and user preferences by forcibly establishing and continuously optimizing a two-way interaction channel between macro prediction and micro analysis, so that the foresight, precision and self-adaptive ability of the marketing strategy are improved.

The present application relates to the technical field of carbon emission monitoring and calculation, and discloses a carbon emission calculation method based on distributed monitoring. The method deploys hierarchical edge monitoring units in the monitoring area, dynamically slices and separates the initial monitoring flow, obtains the original energy flow, activity event flow and source feature flow. The edge side generates a steady-state energy consumption feature sequence through a feature evolution engine and reconstructs the causal chain of the activity event flow to form a feature map. The two are fused into an edge fusion feature body. The cloud performs spatial topology fusion on each edge feature body to form a global collaborative feature field, and traces and quantitatively maps the source feature flow according to the feature field to generate a quantitative emission spectrum, and finally completes the calculation of the total regional carbon emission and the generation of the list. The method improves the efficiency and quantitative accuracy of carbon emission calculation through the collaborative processing of the edge and the cloud.

The invention provides an urban solid waste incineration process multi-controlled variable prediction method and system based on multi-modal depth feature fusion, and the method comprises the steps: collecting a flame image sequence and a process data sequence, and carrying out the preprocessing of a flame image, and obtaining a standard image set; respectively extracting channel features of the standard image set by using eight deep neural networks, and screening the channel features to obtain a flame image depth feature set; performing feature selection and depth feature extraction on the process data sequence to obtain a process data depth feature set; fusing the flame image depth feature set and the process data depth feature set to obtain a final fusion feature; and inputting the final fusion feature into a multi-controlled variable prediction network for prediction to obtain a multi-controlled variable prediction result. The method can effectively extract and integrate the flame image depth features and the important features of the process data, and achieves the precise prediction of multiple controlled variables in the urban solid waste incineration process, so as to improve the operation efficiency and the environmental protection effect.

The application relates to the technical field of encrypted traffic analysis, and particularly discloses an encrypted traffic classification method and system based on cross-modal contrast learning and a medium, the method comprising the following steps: obtaining encrypted traffic, and extracting a payload byte sequence and a packet sequence; using a content encoder to encode the payload byte sequence, so as to obtain a content vector; using a behavior encoder to encode the packet sequence, so as to obtain a behavior vector; wherein a time bias term is introduced into each attention head of a Transformer of the behavior encoder, so that different attention heads pay attention to different long-short time delays; and after the content vector and the behavior vector are fused, the fused vector is input into a classification head, so as to obtain a classification result. By introducing the time bias term, a part of the attention heads are focused on capturing high-frequency burst traffic details, and another part of the attention heads are focused on associating periodic heartbeat signals by spanning long-time silence periods, so that the recognition accuracy is improved.

This invention relates to the field of intelligent gripping control technology, specifically to an online prediction method and system for the gripping stability of a flexible robotic arm. The method involves: first, collecting gripping operation signals through a sensor network in a simulation experimental platform to construct a dataset labeled with the probability of gripping instability; then, using dynamic time warping incorporating physical constraints to align signals of unequal length; subsequently, constructing an online model that includes adaptive extraction and fusion of multimodal features, enhancement of temporal features, dynamic detection of keyframes, and prediction of stability probability; and using mean squared error loss and mini-batch gradient descent to complete model training and optimization; finally, deploying the model to the robotic arm system to achieve real-time data processing and online output of gripping instability probability. This invention can effectively adapt to biochemical vessel gripping scenarios, improve the alignment accuracy of temporal signals and the reliability of risk prediction, and provide real-time assurance for the operational stability of flexible robotic arms.

The invention provides a cross-scene active reasoning intelligent decision and consensus fusion navigation method and device, and the method comprises the steps: carrying out the preliminary fusion of the information of each navigation subsystem, inputting the information into an evaluation layer, employing a generation model constructed based on a neural network by the evaluation layer, achieving the credibility evaluation of the navigation subsystems, and outputting the likelihood probability; the decision-making layer is realized by adopting a neural network, training is carried out by adopting reinforcement learning, the likelihood probability of each navigation subsystem is input, and a decision is output as a fusion weight for carrying out re-fusion on the preliminary fusion pose; in training, a decision-making layer determines expected free energy based on credibility of a navigation subsystem and accuracy of a fusion track, and network parameters of the decision-making layer are optimized by taking minimization of the expected free energy as a reward target; and carrying out weighted fusion on the initial fusion poses of the navigation subsystems by adopting the optimal fusion weight to obtain a fusion track. By using the method of the invention, higher precision and robustness can be maintained in a dynamic and uncertain environment.

This invention provides a method, apparatus, device, and medium for detecting obstructive sleep apnea in children. The method includes: filtering an initial feature set using a recursive feature elimination method under nested cross-validation to obtain a target feature set; acquiring input data from a training sample set based on the names of the target features in the target feature set, and training N target machine learning models using the input data; extracting target feature data from the feature data of the object to be detected based on the names of the target features in the target feature set; inputting the target feature data into the N target machine learning models after training; and determining the detection result corresponding to the object to be detected based on the output results of the N target machine learning models. This invention can improve the sensitivity and specificity of detection, reduce the risk of missed or misdiagnosed cases, and lower data collection costs and clinical implementation barriers.

The application discloses a partial discharge source positioning method and system based on a physical constraint neural network and belongs to the technical field of electrical equipment state monitoring. The method comprises the following steps: collecting and pre-processing a partial discharge signal; and constructing a neural network model. A key step is to design a joint loss function which is fused with a coordinate prediction loss, a time delay consistency physical constraint loss and a geometric boundary constraint loss. The network is trained by optimizing the joint loss function, and a physical law is directly embedded into the learning process of the model. Finally, the end-to-end coordinate positioning is realized by using the trained model. The application overcomes the defects of a complex process of a traditional method and poor physical consistency of a pure data-driven model. By explicitly introducing a physical constraint into a loss function, the network simultaneously learns data features and physical laws in the training, so that the positioning precision and the model robustness are significantly improved on the premise of ensuring the physical rationality of output results.

The invention discloses a geological disaster hidden danger recognition method fusing phase gradient constraint and multi-mode enhancement, and relates to the technical field of synthetic aperture radar interferometry and geological disaster monitoring. The method comprises the steps that S1, a multi-temporal SAR image is preprocessed, interference is eliminated, a deformation related data field is generated, and a core phase information field is reserved; s2, constructing a multi-modal feature and physical reliability weight map based on the processed image data, and realizing weak signal enhancement and unreliable data screening; s3, constructing a weighted feature space based on the multi-modal features and the weight map, and screening candidate hidden danger areas through standardization and adaptive density clustering; s4, rejecting and screening false positive areas of the candidate hidden danger areas through three-level verification; and S5, optimizing the region boundary and quantifying the risk, and outputting a standardized result. The whole process is connected in series through physical constraints, manual sample labeling is not needed, the false alarm rate is remarkably reduced, weak signal recognition is enhanced, and accurate technical support is provided for geological disaster prevention and control.

This invention discloses a method, apparatus, device, and medium for measuring Cobb angle based on spinal curve detection. The method includes: acquiring a target bare back image of a target object in a natural standing state; inputting the target bare back image into a spinal curve detection model to obtain a target curve coordinate probability matrix and a target curve start-end point probability matrix; the curve coordinate probability matrix describes the probability that each pixel in the bare back image belongs to the spinal curve; determining the target spinal curve based on the expectation of the target curve coordinate probability matrix, and determining the target curve start-end point row coordinates based on the expectation of the target curve start-end point probability matrix; determining multiple target row coordinates on the target spinal curve based on the target curve start-end point row coordinates, and determining the Cobb angle of the target object based on the target curvature corresponding to adjacent target row coordinates. This solution can achieve high-precision calculation of the Cobb angle using optical images.

The application relates to the technical field of large language models, and discloses a multi-path legal reasoning engine based on an MCP protocol, which comprises a legal question input module, a legal question answering module and a question and answer result output module; the legal question answering module utilizes a large language model, combines a Monte Carlo tree search algorithm, a multi-path deduction mechanism, a retrieval enhancement mechanism and a user feedback optimization mechanism, and relies on an MCP unified management model to manage internal states, knowledge activation and reasoning path information, and generates an interpretable legal answer corresponding to a legal query question input by a user. The application integrates key information streams with the MCP protocol, outputs answers, reasoning chains, cited bases and credibility evaluations in a visual manner, and significantly improves the professionalism, interpretability and user trust of the whole legal reasoning process.

This invention relates to the field of environmental linkage alarm technology, and discloses a linkage alarm method and system based on environmental monitoring, including: dividing the parking area grid into multiple sub-areas and initializing the wind speed of the sub-areas as the environmental wind speed; correcting the wind speed of the sub-areas based on the building layout; executing an environmental vibration judgment strategy; estimating the equivalent wind speed experienced by the electric vehicle based on triaxial acceleration; and determining whether the environmental wind speed is a vibration trigger source based on the characteristics of the surrounding vibration; executing a single vehicle physical state assessment strategy; calculating the vibration amplitude of the acceleration and the tilt angle of the electric vehicle based on triaxial acceleration; executing an alarm and collaborative suppression strategy based on a threshold assessment of the physical state; setting different alarm levels according to the physical state; executing a theft detection and response strategy according to the alarm level; calculating the direction index of the average acceleration based on triaxial acceleration; calculating the theft confidence based on the kurtosis of the resultant acceleration; and improving the orderliness and intelligence of the alarm.

The invention belongs to the technical field of medical image analysis and artificial intelligence auxiliary diagnosis, and more specifically relates to a pancreatic cancer CT image prediction method and system based on iterative self-evolution. The method comprises the following steps: S1, constructing a segmentation data set and a fine tuning visual tool of the pancreatic cancer special disease; s2, utilizing a pre-trained vision-language large model to generate interactive image-text reasoning; s3, reasoning data screening and training set construction based on result consistency; s4, carrying out model iteration self-evolution training based on the screened data; and S5, performing deployment and reasoning output of a final diagnosis model after multiple iterations. A FastSAM tool for special disease fine tuning and an interactive reasoning architecture are introduced, and the focus detection rate and interpretability are improved through active operation; a self-training mechanism is constructed, a closed-loop framework is formed, and dependence on manual annotation is reduced; after deployment, a visual diagnosis report is generated, and an intelligent diagnosis and treatment scheme which is high in precision, high in interpretation and capable of continuously learning is provided for clinic.

This application discloses a novel algorithm for constructing a genetic map of a MAGIC population, comprising: collecting species data; segmenting chromosomes based on the collected data to form chromosome fragments; calculating the parental genetic similarity index of chromosome fragments to determine parental origin; obtaining a definite origin matrix and a fuzzy origin matrix; using definite fragments from a single parental origin in the definite origin matrix as anchor points; using the anchor points as starting points, examining adjacent fragments of fuzzy fragments in the fuzzy origin matrix to determine parental origin; merging fragments with the same parental origin and adjacent positions into a larger fragment to form a parental origin matrix; calculating recombination rate and genetic distance; constructing a genetic map; and using the novel algorithm to analyze the whole-genome resequencing data of a MAGIC population of a specified crop, thereby effectively detecting minor genes controlling complex quantitative traits, clarifying the parental origin of each variation site, and finding the required high-quality parents, thereby improving the breeding process.

This invention relates to the field of battery health state management technology and discloses a lithium battery internal state estimation method based on a physical information neural network. It utilizes an e-SPM model to obtain a loss function for optimizing the internal parameters of the neural network model. The neural network model is then trained using this loss function to optimize its internal parameters, thereby achieving lithium battery internal state estimation. This invention directly maps lithium battery current, voltage, and temperature to the internal electrochemical state of the lithium battery, making the output of the physical neural network more interpretable, easier to understand and analyze. Furthermore, it employs an unsupervised learning approach, substituting the output state variables into a simplified physical model of voltage, embedding them as constraints into the neural network's loss function. This training process does not require a large amount of labeled data, reducing the difficulty of obtaining experimental data.

The present disclosure relates to a method, device, medium and equipment for determining a sulfur content prediction model of wax oil, the method comprising: obtaining wax oil sample data, taking a plurality of parameter features in the wax oil sample data as input, taking sulfur content in the wax oil sample data as target output, training a preset model to obtain a trained sulfur content prediction model; for each candidate parameter feature, deleting the candidate parameter feature in the wax oil sample data to obtain wax oil sub-sample data corresponding to the candidate parameter feature; for each candidate parameter feature, taking the parameter features contained in the wax oil sub-sample data as input, taking the sulfur content in the wax oil sub-sample data as target output, training the preset model to obtain a sulfur content prediction sub-model corresponding to the candidate parameter feature; and determining a target sulfur content prediction model according to the sulfur content prediction model and the sulfur content prediction sub-model corresponding to each candidate parameter feature.