345 results about "Information gain" patented technology

The invention belongs to the technical field of artificial intelligence and medical information, and discloses a multi-round inquiry method and system based on a large language model and session state tracking. According to the method, extraction and synonym normalization are carried out for key medical elements, and high-confidence filling and conflict resolution are continuously completed in multiple rounds of conversations; and fusing the red flag symptom rule and model prediction, and carrying out hierarchical scoring and security constraint generation on individual risks. The information gain maximization serves as a target, and the next round of clarification problem is generated in a self-adaptive mode under the risk constraint; and through cooperation of a large language model and a knowledge base / knowledge graph, sorting and gate type calibration are carried out on candidate diseases and matched departments, and doctor-seeing suggestions, examination suggestions and medication precautions are generated. Finally, efficient understanding and multi-round reasoning of the unstructured symptom information are realized through joint supervision of the session state, the slot confidence and the risk hierarchy.

The invention discloses an anti-shielding three-dimensional scanning measurement field construction method for large-size measurement, and belongs to the technical field of optical three-dimensional measurement. The method comprises the following steps: firstly, performing adaptive sampling based on geometric features of a workpiece to generate a candidate mark point set; secondly, constructing a virtual measurement scene, simulating a dynamic scanning process by using a ray tracing technology, and calculating the visibility and observation quality of each point under multiple viewpoints; then measuring precision is quantified by using a Fisher information matrix, and a layout optimization model which takes maximization of information gain as a target and meets coverage rate and engineering constraint at the same time is established; and finally, solving the model to obtain an optimal mark point subset, and verifying and outputting the optimal mark point subset. According to the method, the problems of measurement interruption, low point distribution efficiency and difficulty in guaranteeing precision caused by shielding of a large-size complex component in scanning are solved, and automatic construction of a high-robustness and high-precision measurement field is realized.

The invention relates to the technical field of data classification, in particular to a hospital intelligent inquiry log analysis method and system.The method comprises the following steps that medical concept depth is counted, symptom keywords are extracted, statistical information gain and semantic consistency scores are calculated, split decision-making indexes are generated by combining the gain and the scores to construct an intention classification tree, and an intention classification result is obtained; inputting a log to generate an inquiry intention identifier, recognizing an error node based on diagnosis and treatment correction feedback and updating a punishment value, establishing a virtual error correction link, calculating a path confidence fusion node, and establishing a physical branch to generate a reconstruction classification tree when a guide weight exceeds a limit. According to the method, keyword statistics is upgraded into three-dimensional feature analysis by introducing medical concept hierarchies, a classification tree following a medical system is constructed, a reverse penalty and virtual link mechanism is constructed by using feedback, node fusion and reconstruction are executed based on confidence, classification logic self-adaptive evolution is realized, and intention recognition interpretation and robustness are improved.

The invention discloses a spatial non-cooperative target form reconstruction-oriented perception planning control integration method, which comprises the following steps of: extracting local semantic features of a target component in a single-view observation image through a pre-trained semantic segmentation network, coding the local semantic features and RGB (Red, Green and Blue) information into an MLP (Markup Language Protocol) of NeRF, perceiving a target geometric form and component-level semantics, and obtaining a component-level semantic feature of the target component; the perception result is optimized along with fly-around observation, evaluation is carried out, and an uncertainty thermodynamic diagram is generated; based on the uncertainty thermodynamic diagram, a space observation value function is constructed, spacecraft dynamics and view field constraints are combined, and an initial fly-around trajectory is generated by adopting an information gain weighted three-dimensional A * algorithm and optimized in real time; and designing a trajectory tracking control law and an attitude stability control law based on a time synchronization stability theory, and controlling the spacecraft to execute the optimized fly-around trajectory. According to the invention, a perception-planning-control closed-loop execution system is realized, and a closed-loop collaborative process of perception-evaluation-planning-control-re-perception is formed.

The invention discloses an SEO content detection and release gating method and system based on information gain, and the method comprises the steps: carrying out the paragraph-level viewpoint sentence decomposition of a collected search engine result page document and a candidate document for a target query, and constructing a consensus semantic field; performing semantic alignment and screening on the viewpoint sentences of the candidate documents and the consensus semantic field, and performing multi-source evidence verification and reliability aggregation on the screened effective newly-added viewpoint sentences to obtain macroscopic semantic novelty measurement of the candidate documents; constructing a consensus knowledge graph to identify the structural hole and calculating the gain of the structural hole; and carrying out joint scoring and issuing decisions, and outputting a structured evidence packet. According to the method, evaluation of novelty and information gain is quantified by constructing a ranking-weighted consensus semantic field; the reliability of the measurement gain is ensured through evidence conditional macroscopic divergence calculation and viewpoint sentence-level multi-source verification; and a structured evidence packet organized according to the viewpoint sentences is generated, so that the decision is well documented and auditable.

The invention relates to the technical field of biological information, and discloses a messenger RNA stability prediction method, model, device, equipment and medium, the method comprises the following steps: obtaining target sequence information of a target messenger RNA; acquiring at least two of the following target feature information based on the target sequence information by using a feature extraction module in the messenger RNA stability prediction model: first sequence feature information, Kozak sequence feature information, Motif attention feature information and manual feature information; and predicting the stability of the target messenger RNA based on the target feature information by using a prediction head in the messenger RNA stability prediction model. According to the method, the mRNA stability is predicted by fusing the universal sequence feature of the mRNA, the Kozak sequence feature of the learnable position weight, the Motif attention feature based on the hash k-mer and the manual feature, and the accuracy of mRNA stability prediction is improved.

The invention provides a traffic accident scene reconstruction method and device, and relates to the technical field of three-dimensional reconstruction, and the method comprises the steps: obtaining a video frame sequence of a traffic accident scene collected by an unmanned plane, carrying out the information gain evaluation of the video frame sequence, and constructing a key frame set; according to the key frame set, macroscopic geometric reconstruction and microscopic normal recovery based on airborne light source dynamic change are carried out on the traffic accident scene, and a macroscopic depth map and a microscopic normal field are obtained; constructing a fusion optimization model with the macroscopic depth map as low-frequency constraint and the microscopic normal field as high-frequency gradient guidance, and fusing the macroscopic depth map and the microscopic normal field by using the fusion optimization model to obtain a fused depth map; and constructing an accident scene reconstruction model according to the fused depth map. By adopting the traffic accident scene reconstruction method and device, key details of the accident scene can be captured, the sensitivity to micromorphology is increased, and the accuracy of traffic accident scene reconstruction is improved.

The invention discloses an uncertainty guided few-sample harmful speech detection method (U-GIFT). According to the method, a pre-training language model is finely adjusted based on a small number of labeled samples, and a semi-supervised self-training and uncertainty guiding strategy is combined. Monte Carlo Dropout is started in the reasoning stage, multiple times of random forward propagation are carried out to obtain sample posterior distribution, prediction entropy and information gain are calculated, pseudo-label samples are sorted and screened, and only high-confidence samples are selected to be added into a training set. And in order to reduce the influence of a pseudo labeling error, designing a stability weighting mechanism, giving a sample weight according to a prediction variance, and constructing a joint loss function, so that the model preferentially learns a stable sample to improve the detection performance. According to the method, the semantic and attention mechanism of the pre-training model is utilized, the detection effect is remarkably improved under the conditions of few samples, imbalance, multiple languages and cross domains, models such as BERT, RoBERTa, XLM-R, LLaMA2 and DeepSeek-R1 are compatible, and the method is suitable for content auditing and risk prevention and control.

The invention relates to a drug interaction prediction method and system based on multi-view comparative learning, and belongs to the technical field of natural language processing. According to the method, two channels of a drug molecular map and a biomedical knowledge map are constructed in parallel, structural and semantic features are extracted by using a pre-trained heterogeneous map neural network, and multi-view comparative learning guided by information gain is introduced for joint optimization, so that the generalization ability and robustness of the model to unknown drug pairs are enhanced. According to the method, system evaluation is carried out on the performance of the system in two types of prediction tasks (multi-type and multi-label) and three prediction scenes. Experimental results show that the method has excellent performance in all tasks and scenes. Further case analysis also verifies the effectiveness of the system in predicting the interaction type of the unseen drug pair.

The invention relates to the technical field of wind turbine generator fault early warning, and discloses a wind turbine generator fault early warning method and system based on multi-modal data fusion, and the method comprises the steps: collecting the data of a multi-modal sensor, and carrying out the time-space alignment preprocessing; multi-modal features are extracted through variational mode decomposition, STL decomposition and other methods, and cross-modal fusion is achieved through dimension adaptive projection and a multi-head attention mechanism; calculating a dynamic weight based on three factors of data quality, fault type correlation and information gain, and carrying out weighted fusion; constructing a dynamic unit topological graph, and capturing cross-unit association features by using a space-time diagram convolutional network; long-time early warning with confidence is realized through double-branch gating fusion in combination with a Bayesian neural network; a multi-label classification identification multi-fault mode is adopted, and an operation and maintenance decision is optimized through an adaptive large neighborhood search algorithm. According to the method, the long early warning window of the offshore wind turbine generator can be realized, and uncertainty quantification and intelligent operation and maintenance decision support are provided.

The invention provides a dynamic knowledge graph construction and diagnosis reasoning method oriented to intelligent inquiry, and relates to the technical field of knowledge graphs, comprising the following steps: acquiring multi-source medical data, performing entity recognition and semantic annotation, establishing a causal probability graph based on a structured entity set, and establishing a dynamic knowledge graph; and selecting an optimal questioning problem according to the information gain in the inquiry process, dynamically updating the causal probability by using the Bayesian rule, and finally propagating the conditional probability along the causal path to generate a diagnosis conclusion. According to the invention, personalized inquiry decision and accurate diagnosis reasoning are realized, and the diagnosis accuracy and efficiency of the intelligent inquiry system are improved.

The embodiment of the invention provides a vehicle control method and device, equipment, a medium and a program product, and relates to the technical field of intelligent driving. The method comprises the steps that driver identity information is obtained based on information collected by a biological feature recognition module, a driving file corresponding to the driver identity information is obtained, the driving file is used for indicating driver driving preference, a source model is adjusted based on the driving file, a target model is obtained, and the target model is made to adapt to the driver driving preference. Multi-modal sensor data are obtained, the multi-modal sensor data comprise steering wheel holding state information and vehicle dynamic state information, and the intention recognition result is recognized based on the multi-modal sensor data and the target model; according to the method, the intention recognition model based on big data training is adjusted in a personalized manner by fusing the biological characteristics of the driver and the multi-modal sensing data, so that the individual driving preference is accurately adapted, and the accuracy and adaptability of driving intention recognition are improved.

The invention discloses a stroke motion cognition evaluation system and method based on spatial perception nerve enhancement. The stroke motion cognition evaluation system comprises a data acquisition module, a preprocessing module, a feature extraction module, a weight coefficient generation module, a motion cognition evaluation module and a visualization module. Extracting information of different frequency bands of the brain through a feature extraction module, and obtaining power spectral density, a phase locking value and cross-frequency coupling features; meanwhile, a weight coefficient generation module is used for obtaining weight coefficients for performing weight fusion on different features, and the features extracted by a feature extraction module are fused through a motion cognition evaluation module to completely describe complex brain network interaction, so that cognition and spatial perception state evaluation of the subject in the motion imagination process is completed. In addition, threshold adjustment and personalized feedback are performed based on the real-time change of the evaluation result, so that the quality and efficiency of motor imagery training can be remarkably improved.

The invention discloses a new energy station safety inspection management method and system, and relates to the field of inspection management, firstly, an airborne Bayesian network model is introduced, uncertainty reasoning is performed on real-time image flow, and a visual uncertainty map is generated, so that the system can go deep from what judgment to where uncertain quantitative perception. Furthermore, a blind detailed investigation strategy is abandoned, the optimal detailed investigation viewpoint capable of obtaining the highest-quality diagnosis information is autonomously decided by evaluating expected information gains of different candidate viewpoints based on the uncertainty map, and the problem that viewpoint value evaluation is missing is solved. And finally, through dynamic trajectory generation and adaptive camera parameter adjustment, a sensing-decision-planning-action closed-loop intelligent inspection system is constructed, an execution barrier between a traditional flight platform and an intelligent load is broken, the problems of slow response and rigid decision of the system are solved, and autonomous and accurate detailed inspection in a real sense is realized.

This invention discloses an AI-based method and system for monitoring the entire project process, relating to the field of project monitoring technology. It constructs a document citation graph of the entire project process and identifies each complete citation path from the graph as a cross-reference link. Based on the document content corresponding to the cross-reference link, it calculates the content completeness value and the information gain value of the cited content, thereby calculating the authenticity value of the cross-reference link. The authenticity value of the cross-reference link is used as a credibility correction factor for task completion status identification in project monitoring, assisting in determining whether the task has been truly completed. This allows for a quantitative assessment of the authenticity of cross-reference links, increasing the accuracy of automatic identification of the true status of the entire project. It also enables existing AI monitoring systems to accurately judge and clearly identify risks when processing cross-referenced text, ensuring that the results of the entire project process monitoring match the actual situation and guaranteeing accurate monitoring results.

The invention relates to a method (100) for transmitting information in a distributed system, comprising - estimating (101) information available at at least one sink node of the distributed system, - evaluating (102) a relative information gain (30) of information (40) available at one or more source nodes of the distributed system in relation to the estimated (101) information available at the at least one sink node on the basis of a function, wherein the function depends on at least one probability distribution of the information (40) available at the at least one sink node and / or at the at least one source node, - requesting (103) an allocation of resources in a network on the basis of the evaluated (102) relative information gain (30) in order to prioritise the transmission of the information (40) and thus to maximise an expected information gain for the at least one sink node.

Embodiments of the present application disclose a decision tree model generation method and a data recommendation method based on the decision tree model. The method comprises: obtaining a training data set formed by feature information of a plurality of training samples, the training samples having known category attributes; in a process of generating a decision tree model according to the training data set, iteratively calculating information gain of each feature attribute under each node, and dividing a data set contained by a current node according to a feature attribute corresponding to maximum information gain until a category attribute can be determined according to the data set contained by the node; if information gains of a plurality of feature attributes under the current node are equal and are maximum information gain, then calculating respective correction information gains of the plurality of feature attributes, and determining a feature attribute for dividing the data set contained by the current node according to the calculated correction information gains; and outputting the decision tree model formed according to the training data set. The decision tree model generated by the present application has more accurate classification effect.

A display method for outputting a result of scoring for a performance of a scoring competition, the display method includes obtaining input information regarding to the scoring competition, obtaining a recognition result of a plurality of elements included in the performance, determining a difficulty level of each of the plurality of elements based on the recognition result and a first rule, limiting a target to be used for the scoring to a part of the plurality of elements according to the difficulty level of each of the plurality of elements, and outputting a scoring screen that includes a first display area displaying the plurality of elements and the difficulty level, a second display area for an aggregate result, third display area for a score of the performance directed to the limited part of the elements, and a fourth display area for a 3D model of the athlete.

The invention provides a training method and device of a simulation test model, and relates to the technical field of nuclear power simulation. The method comprises the following steps: acquiring historical test operation sequence information and historical test result information in a historical test procedure; acquiring feature information of the nuclear power plant simulation system; the historical test operation sequence information and the historical feature information are subjected to feature fusion and then input into a simulation test model, and first test result information is obtained; and training the simulation test model according to the historical test result information and the first test result information. Through training, the simulation test model learns to obtain the capability of predicting the test result corresponding to the test operation sequence information based on the characteristic information of the system, the method can adapt to different states of the nuclear power plant simulation system, and the test result is accurately predicted. The efficiency, accuracy and adaptability of simulation testing of the nuclear power plant simulation system are improved, the intelligent level of testing is improved, and therefore the workload of manual testing is reduced.

The invention relates to the field of medical equipment risk management, in particular to a medical equipment risk management system and method based on data analysis, and the system comprises an information acquisition and storage module, a data preprocessing module, an internal and external factor association module, an anomaly detection module, a risk assessment module and a decision management module. A total data set is obtained by collecting and storing medical equipment and operation data, and data preprocessing is performed to reduce noise interference; according to the preprocessed medical equipment data information, constructing an anomaly detection neural network model, and outputting a medical equipment anomaly detection result; calculating the correlation degree of the influence of the internal factors and the external factors of the medical equipment on the risk; risk conditions existing during operation of the medical equipment are evaluated in real time, abnormal behaviors of the equipment are found in time, and measures are taken to avoid equipment faults and accidents according to the risk evaluation conditions. According to the invention, effective data analysis and risk assessment can be carried out, and the safety of medical equipment is improved.

The application belongs to the technical field of natural language processing, and particularly relates to an aspect-level sentiment classification method based on a graph attention network, which comprises the following steps: obtaining word embedding representation of context text in which an aspect word is located; dynamically adjusting the weight of a context word according to the relative position of the context word and the aspect word, and obtaining context semantic features; aggregating syntactic information through an improved graph attention network to obtain syntactic features of the text; using a deep cross network to fuse the syntactic features of the text and the context semantic features to obtain final feature representation; and performing sentiment prediction on the final feature representation through a full connection layer to obtain the sentiment polarity distribution of the aspect word in the text. The application solves the problem of feature information loss that may occur in a multilayer network of the graph attention network, and considers the position information of the context word when extracting semantic features, so that the syntactic features and the context semantic features are fully fused, thereby improving the accuracy of aspect-level sentiment classification.

The application provides a drought risk identification and early warning method based on multi-source satellite fusion NDVI and machine learning, acquires NDVI time series data corresponding to a research area, and extracts feature information of a disaster-bearing body corresponding to the research area based on the NDVI time series data; acquires water resource supply data corresponding to the research area, and extracts water resource supply features corresponding to the water resource supply data; through a machine learning model obtained by pre-training, a disaster-bearing body NDVI prediction value of the research area at a future time is predicted based on the disaster-bearing body feature information and the water resource supply features; the disaster-bearing body NDVI prediction value is used for identifying the drought risk of the research area in combination with precipitation forecast data, or dynamically adjusting the anti-drought irrigation strategy data. The application can significantly improve the accuracy of drought risk prediction and effectively improve the dynamic adjustment effect of the anti-drought irrigation strategy.

The invention belongs to the field of robot control, and provides a robot path planning method and system based on a target attention point, and the method comprises the steps: carrying out the environment field modeling based on a Gaussian process according to laser data obtained by a robot; based on a leading edge detection and clustering algorithm, generating target attention candidate points at known-unknown boundaries of an environment field, and taking prediction variances at the candidate points as measurement of field uncertainty; constructing a utility function fusing map information gain, field information gain and path cost, and selecting a target attention point from the candidate points by maximizing the utility function; and generating an optimal path by using a TEB path planner. According to the method, autonomous sampling of the robot can be effectively guided, information acquisition is maximized while the advancing path is optimized, and the precision and efficiency of environment field reconstruction are remarkably improved.

The application provides a cross-scene cognitive ability evaluation method based on fine-grained migration, which comprises the following steps: taking cognitive data of a user in a first scene as source domain data and taking cognitive data of the user in a second scene as target domain data; taking a source domain data set as a training set, training a random forest classifier, and generating a source domain model; obtaining a test accuracy rate of the individual classifier on a target domain data set and an information gain difference from source domain features to target domain features; clustering all the individual classifiers into multiple clusters according to the test accuracy rate and the information gain difference; updating the individual classifiers in each cluster by using a corresponding growth mechanism to obtain a target domain model; and evaluating the cognitive ability of the user in the second scene through the target domain model. The application also provides a cross-scene cognitive ability evaluation system based on fine-grained migration and a data processing device.

The invention relates to an autonomous data acquisition method and system based on an unmanned aerial vehicle, and belongs to the field of data processing. The method comprises the following steps that: the unmanned aerial vehicle performs macroscopic scanning and candidate area discovery, and outputs a candidate area set; performing causal reasoning and target confirmation based on the candidate region set to obtain a target region set; performing adaptive fine acquisition based on the target region set to obtain a fine data set; performing three-dimensional semantic reconstruction based on the refined data set, and outputting a final semantic tag and a three-dimensional semantic scene model; and carrying out risk deduction and decision report based on the final semantic label and the three-dimensional semantic scene model. According to the method, an autonomous data acquisition method fusing geometric and semantic significance analysis, causal reasoning, information gain optimization and three-dimensional semantic reconstruction is realized, and an efficient and reliable technical means is provided for disaster assessment and decision support.

The application discloses an image optimization method and device for a tower climbing robot and a storage medium, and belongs to the technical field of image processing. The optimization method comprises: performing step-by-step feature extraction and information gain on a to-be-processed image to determine a plurality of first representation results and a plurality of second representation results; performing first coupling processing on the plurality of first representation results to determine a first domain connection result from the plurality of first representation results; performing second coupling processing on the plurality of second representation results to determine a second domain connection result from the plurality of second representation results; and determining a target image based on the first domain connection result and the second domain connection result. The application performs step-by-step feature extraction and information gain on the to-be-processed image, so that the neural network focuses on positive information in the image, and the feature correlation between the positive information is strengthened, the quantity and intensity of interference information are squeezed from the negative side, and therefore, a higher image optimization effect is achieved.

This invention relates to the field of shield tunnel segment inspection technology, and discloses a visual inspection-based shield tunnel segment assembly machine assembly quality inspection device and method, including: achieving autonomous perception and rapid recovery of calibration parameters when they fail due to vibration or temperature drift through time-efficiency monitoring and sliding window optimization technology; overcoming calibration bottlenecks through texture perception and active exploration trajectory planning technology; solving the calibration instability problem caused by the lack of natural textures by artificially creating multi-view geometric constraints using micro-motion trajectories; achieving intelligent decision-making and adaptive adjustment in the active exploration process through information gain-driven convergence judgment and global optimization technology; significantly enhancing parameter estimation confidence through global optimization integrating multi-source data; and constructing an unattended autonomous calibration management system through result encapsulation and closed-loop feedback technology, enabling persistent storage of calibration parameters, accumulation of prior knowledge, and real-time timeliness monitoring.

The interface receives the patient's physiological information. The processor obtains contribution information corresponding to the contributions of N first parameters (N being an integer greater than or equal to 2) to the patient's state based on the physiological information. The processor enables the visualization device to visualize the contribution information under the condition that M of the N first parameters (M being an integer greater than or equal to 2 and less than N) are replaced by L second parameters (L being an integer less than M).

The present application relates to the field of data processing, and more particularly to a sewage treatment equipment fault diagnosis method and system. The method comprises the steps of: obtaining a training data set, each sample in the training data set is a correlation feature vector of all kinds of diagnostic feature categories at each time with a fault label, and the correlation feature vector is composed of statistical data of diagnostic feature data of the diagnostic target sewage treatment equipment fault and statistical data of corresponding diagnostic feature data of the upstream equipment; constructing a comprehensive split gain, the comprehensive split gain is equal to the weighted sum of the information gain of the random forest algorithm and the propagation correlation gain; based on the comprehensive split gain, a random forest model is constructed using the training data set to realize fault diagnosis of the target equipment. The accuracy of the sewage treatment equipment fault diagnosis is improved.

The invention discloses a data analysis method and system based on questionnaire investigation, and particularly relates to the technical field of data analysis, and the method comprises the following steps: S1, collecting and preprocessing questionnaire data, and obtaining original questionnaire data; s2, questionnaire feature engineering: extracting core features from the standardized questionnaire data set based on a joint screening mechanism of semantic similarity and information gain, and obtaining low-dimensional feature vectors through a dimension reduction algorithm; s3, subject mining and user group division are carried out on the low-dimensional feature vectors; and S4, outputting a result based on the analysis model, and generating a targeted questionnaire analysis report which comprises topic distribution, group difference and potential demand association conclusions. According to the data analysis method and system based on questionnaire investigation, through a multi-strategy fusion preprocessing mechanism, a combined screening feature engineering method, a fusion modeling analysis strategy and a self-adaptive model optimization module, the accuracy and efficiency of questionnaire data analysis are improved.