164results about How to "Enhance explanatory" patented technology

The invention discloses a water quality prediction method based on graph neural network and spatial-temporal feature fusion, and the method comprises the steps: obtaining hydrological data and water quality data of a plurality of monitoring stations in a preset drainage basin in a preset time period, representing the monitoring stations as nodes of a graph, and representing the adjacency relation between the monitoring stations as edges of the graph, a graph topology network mapping multiple monitoring station pollutant data is constructed, a spatial-temporal feature fusion module is adopted to perform feature extraction and fusion on the graph topology network to obtain water pollution spatial-temporal feature data, and the spatial-temporal feature fusion module is obtained by training a graph convolutional network, a gating circulation unit and a self-attention mechanism. And performing fusion processing on the extracted feature data to form a time sequence data set of a graph topology structure, and finally predicting the data set through a graph time sequence prediction model to obtain water quality prediction results of the plurality of monitoring stations in a target time period. The method can effectively deal with the spatial and temporal change process of water pollution, reduce the water quality prediction cost and improve the water quality prediction precision.

The invention discloses an AI-driven abnormal transaction intelligent monitoring and analysis system and method, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining structured features and unstructured features, carrying out the feature fusion, and constructing an account feature vector; constructing a cross-domain heterogeneous graph, learning an embedded vector of an account node in the cross-domain heterogeneous graph based on a time sequence heterogeneous graph neural network, and generating an account portrait vector; training an abnormal transaction detection model, and predicting the abnormal probability of the current real-time transaction record of the account by taking the account portrait vector and the transaction record as input data; dividing all accounts into account groups based on the account portrait vectors, calculating a group abnormal transaction threshold value, and calculating an adaptive abnormal transaction threshold value based on the group abnormal transaction threshold value and the similarity between the account portrait vectors and the center vectors of the account groups to which the account portrait vectors belong; and if the abnormal probability is greater than a self-adaptive abnormal transaction threshold, marking the transaction of the account as an abnormal transaction, triggering risk control measures, and regularly updating the self-adaptive abnormal transaction threshold.

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

The invention discloses a photovoltaic power prediction method based on seasonal weather classification and an RLHF large model, and the method comprises the following steps: 1, data acquisition: obtaining power generation data and historical meteorological data of a photovoltaic power station, and constructing a data set; 2, constructing a prediction model: constructing a weather classification and photovoltaic output prediction model, dividing a year into a plurality of seasons, dividing weather into a plurality of weather types, and constructing a photovoltaic output prediction model for each weather type of each season; an RLHF large model is constructed, a deep learning and reinforcement learning combined method is used, and the photovoltaic power generation power prediction precision and interpretability are improved through pre-training and human feedback; and step 3, constructing a fusion model: fusing prediction output of the weather typing and photovoltaic output prediction model and the RLHF large model to obtain a final prediction result of the photovoltaic power generation power. The invention also discloses a photovoltaic power prediction system based on seasonal weather typing and the RLHF large model.

The invention provides an esophageal cancer marker screening method based on enzyme transfer method methylation sequencing, which comprises the following steps: S1, collecting esophageal early cancer and para-carcinoma tissue samples, and carrying out enzyme transfer method whole genome methylation sequencing; s2, integrating methylation data of esophageal cancer and para-carcinoma tissues in databases of TCGA, GEO, MethMarker DB and the like; s3, screening differential methylation sites, preferably sites which are hypermethylated in esophageal cancer but hypomethylated in samples such as leukocytes, so as to reduce plasma background interference; s4, obtaining a candidate marker combination based on an integrated methylation feature screening model of LASSO regression and a random forest algorithm; s5, constructing a classifier model based on the candidate marker combination, carrying out training and reasoning test on the model, and evaluating the prediction performance of the model; and S6, obtaining the esophageal cancer marker combination. According to the method, enzyme transfer sequencing and machine learning are combined, the marker prediction accuracy is remarkably improved, and a new reference is provided for clinical research of esophageal cancer.

The invention relates to the technical field of biological information mining, in particular to a multi-omics data optimization method and system for intestinal flora matching and a medium. The method comprises the following steps: acquiring historical intestinal tract multi-omics data and integrating the historical intestinal tract multi-omics data to obtain host intestinal tract multi-omics data; performing multi-omics data fusion based on the host intestinal multi-omics data to obtain microbial flora fusion data; performing pattern classification according to the host intestinal tract multi-omics data to obtain host intestinal tract physiological pattern data; carrying out physiological pattern influence factor calculation on the microbial flora fusion data to obtain host intestinal tract physiological pattern influence factors; and performing host intestinal flora matching on the microbial flora fusion data and the host intestinal physiological data according to the host intestinal physiological pattern influence factor, thereby obtaining intestinal flora matching data. According to the method, the accuracy of flora matching prediction can be improved.

The invention relates to the related technical field of digital data processing, in particular to an intelligent ancient book information processing method and device based on artificial intelligence, and the method comprises the steps: scanning an ancient book, constructing a knowledge graph, dividing a domain database, and training a vertical processing model; introducing a multi-form historical carrier to form a multi-mode sensing set, establishing a multi-information network, and embedding a processing model; the method comprises the following steps: establishing a dialog box, realizing multi-round interaction and combining threshold optimization output, solving the technical problems of digitization and fragmentation limitation in ancient book information processing and incapability of effectively integrating and utilizing ancient book information due to diversity and complexity of ancient books and literatures, and realizing construction of a knowledge graph and division of a domain database. A knowledge system with a hierarchical structure and logic association is formed, deep learning and optimization are performed according to specific requirements in the field of ancient books by training a vertical processing model, a dialog box is established, multi-round interactive feedback with the user is performed, and the user is assisted to quickly and deeply explore the ancient books.

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

The invention relates to the technical field of temperature and humidity control, in particular to an intelligent temperature and humidity control method and system for a low-temperature fresh-keeping metal granary. The method comprises the following steps: firstly, acquiring various original data of the metal granary, predicting short-term environmental data in the granary in combination with a TFT model, and providing an accurate prediction basis for temperature and humidity control; secondly, multi-source data fusion is carried out on insect body monitoring images and audio frequency, image and audio frequency information is comprehensively analyzed, and the comprehensiveness and multi-dimensional analysis capability of insect pest monitoring are improved; finally, the accuracy and sensitivity of pest monitoring are improved through a multi-source data recognition model, a temperature and humidity control strategy is optimized in combination with a DQN reinforcement learning model, automatic and intelligent temperature and humidity adjustment is achieved, the environmental conditions in a granary are guaranteed, and the grain storage period is effectively prolonged.

The invention discloses an image super-resolution enhancement method for image segmentation, which combines super-resolution enhancement and image semantic segmentation by constructing a task-driven joint training framework, trains a segmentation network and a super-resolution enhancement network at the same time, and optimizes the super-resolution enhancement network through mutual collaborative optimization. The segmentation result and the super-resolution enhancement result are enhanced mutually, and image segmentation is completed. The method is different from an existing single training method, the overall performance of super-resolution enhancement and semantic segmentation tasks can be remarkably improved, the quality and semantic consistency of the enhanced image are improved by optimizing the loss functions of super-resolution enhancement and semantic segmentation at the same time and fully combining detail information and semantic information of the image, and the image quality is improved. By introducing a targeted loss function, high-frequency details and context information of the image are effectively integrated, the precision of semantic segmentation is improved while image enhancement is ensured, the explanation ability of the model is improved, and the robustness and adaptability of the model in a complex scene are also remarkably enhanced.

The invention, which relates to the automatic driving field, discloses a Transform-based networked automatic driving vehicle trajectory prediction method comprising the following steps: obtaining vehicle driving data and preprocessing the vehicle driving data to obtain time sequence information of each vehicle; a context vector of each vehicle is obtained by using a Transform encoder; inputting the context vector of each vehicle into a graph neural network to obtain an overall feature context vector I; obtaining a driving intention probability matrix of the current vehicle by using a random forest algorithm; splicing the driving intention probability matrix of the current vehicle and the overall feature context vector I, inputting the spliced driving intention probability matrix and the spliced overall feature context vector I into a decoder, and finally outputting the trajectory distribution probability of the vehicle; through combination of Transformer and GNN, not only can the dynamic state of a single vehicle be understood, but also the relative position and motion relation between vehicles can be understood, so that the vehicle trajectory can be predicted more accurately.

The invention provides a scene management and intelligent recommendation method and system based on a data space, and the method comprises the steps: selecting a system function in a preset database, and carrying out the matching of a corresponding data type according to the selected system function, making a corresponding data acquisition plan according to system functions and data types; collecting data based on the formulated data collection plan and selecting a data processing model matched with the collected data according to the collected data; processing the collected data to form a feature fusion layer, and introducing an attention mechanism and an inter-channel information transmission mechanism into the selected data processing model to optimize the selected data processing model; and training the optimized data processing model to obtain an attention mechanism weight matrix, and adjusting the obtained attention weight matrix to realize personalized recommendation. The technical problem that the heterogeneous data processing and recommendation ability is insufficient in the related technology is solved.

The invention relates to the field of artificial intelligence, and discloses a text processing method and device based on a hybrid expert model, equipment and a medium, and the method comprises the following steps: receiving a to-be-processed text input by a user; matching the to-be-processed text with professional fields corresponding to n expert models in the mixed expert model to generate a field matching result; m expert models are activated in the n expert models according to the field matching result and a preset activation strategy, the weights of the m currently activated expert models are configured, and m is smaller than or equal to n; and inputting a to-be-processed text into the currently activated m expert models for text processing, and then generating a corresponding final output text. The text is matched with the professional fields of different expert models, the corresponding expert model is selected and activated based on the matching result, and the weight is configured, so that the field where the activated expert model is skilled is matched with the text, the interpretation ability of the hybrid expert model in the professional fields is improved, and the interpretation efficiency of the hybrid expert model is improved. And the operation efficiency and the output accuracy of the model are improved.

The invention discloses a liver cancer post-transplantation muscle loss prediction method and device, and belongs to the technical field of biomedicine, and the method comprises the steps: constructing an unbalanced random forest model comprising a plurality of decision trees, independently predicting through the plurality of decision trees to obtain a plurality of high-risk or low-risk dichotomy prediction results, taking the proportion of the decision tree with the prediction result being high risk in all decision trees as the prediction probability of muscle loss after liver cancer liver transplantation finally output by the model; performing risk classification according to the prediction probability, and judging the postoperative muscle loss level of the patient as high risk, medium risk or low risk; constructing a decision tree model to layer prognosis of patients, and dividing the patients into a high-risk group, a medium-risk group and a low-risk group; and carrying out survival rate prediction in combination with liver transplantation standards and prognosis layering results of patients. According to the method, the muscle loss level after the liver transplantation can be accurately predicted, and a clinician can make a personalized treatment scheme and perform scientific management after the transplantation.

The invention relates to a fault plan determination method and device, computer equipment and a storage medium. A terminal obtains fault phenomenon data of a power system; and inputting the fault phenomenon data into a plan matching model obtained by training the fault phenomenon and the fault plan in the historical time period for matching processing to obtain a target fault plan. Based on the complex semantic processing and natural language generation capability of the plan matching model, the accuracy and interpretation capability of the fault plan matching of the power automation system are improved, so that the automatic matching of the fault phenomenon and the fault plan of the power dispatching automation system can be realized, and the fault processing speed of dispatching personnel is improved.

The invention discloses an optical fiber sensing precision enhancement method for high-resolution spectrum subdivision. According to the invention, the spectrum subdivision algorithm based on Fourier transform is applied, so that spectrum subdivision and resolution enhancement are realized, and the optical fiber sensing precision enhancement method based on high-resolution spectrum subdivision has remarkable beneficial effects on the whole optical fiber sensing precision enhancement method based on high-resolution spectrum subdivision. Fourier transform can convert a time domain signal into a frequency domain signal, high-frequency noise and low-frequency interference are effectively removed through frequency domain filtering, and frequency components in a target spectral range are reserved, so that the purity of the spectral signal is improved. And the filtered frequency domain signal is restored into a time domain signal through inverse Fourier transform, so that the integrity of the signal is kept. Furthermore, the time domain signals are subdivided through a linear interpolation algorithm, the data point density is increased, and the spectral resolution is remarkably improved. And a more accurate and detailed data basis is provided for recognition and analysis of subsequent characteristic spectrums, so that the measurement precision and reliability of the optical fiber sensor are enhanced.

The invention discloses an infectious disease prediction and transmission link speculation method and system based on a random forest, and belongs to the technical field of infectious disease transmission risk prediction, and the specific steps are as follows: data acquisition and preprocessing: collecting case information from official websites and social media platforms of a plurality of provincial and urban health departments throughout the country, cleaning and preprocessing the data set; predicting the illness time of the patient: constructing and training a model by using a random forest algorithm, and accurately predicting the infection time of the patient; evaluating an algorithm prediction result, including evaluating model performance through classification prediction accuracy and correction accuracy; the disease transmission relation speculation comprises the steps of constructing a dynamic transmission model based on the predicted infection time and generating an infection time sequence diagram, the transmission path of diseases in urban population is revealed, and a scientific basis is provided for infectious disease control and public health decision making.

The invention provides an interpretable knowledge tracking method and device based on a hypergraph convolution attention mechanism. According to the method, a two-parameter input mechanism of a 2PLM (two-parameter logic model) is introduced, and the challenge that the skill difficulty difference in the same learning item is obvious is effectively coped with by the HCAKT, so that the flexibility of the model is improved, and the interpretability of the model is enhanced. In addition, a hypergraph network is further added into the model and serves as a key tool for parameter balance, potential interference of redundant information on the performance of the model is effectively avoided, and the prediction accuracy is further improved by optimizing the mutual relation between parameters. And finally, a convolution attention mechanism is also integrated into the HCAKT to simulate and capture the forgetting rule of the student. The mechanism can deeply analyze the memory decline characteristics of the students in the learning process, so that the learning states of the students can be reflected more truly.

The invention discloses an electric tool fault type identification method and system based on data driving, which are used in the field of fault identification, and the method comprises the following steps: collecting operation data of an electric nail gun under different environment conditions and operation states; processing including time domain analysis and frequency domain analysis is executed on the operation data, and a waveform index and a rough principal component analysis method are used for extracting a feature set for distinguishing fault types in an analysis processing result; constructing a fault type identification model by using a support vector machine combination of K neighbors; performing fault type detection and recognition on the electric nail gun by using the trained fault type recognition model; and obtaining the fault type of the electric nail gun according to the analysis result. According to the invention, fault type detection and identification are carried out by using the model to quickly and accurately determine the fault type of the electric nail gun, the efficiency and accuracy of fault diagnosis are improved, and the cause and the feature of the fault are obtained by comparing the difference between different fault types.

The invention provides a comprehensive pipe gallery dynamic risk prediction method and system based on the Internet of Things, and relates to the technical field of data processing. The method comprises the following steps: acquiring historical comprehensive pipe gallery monitoring data; performing principal component analysis on the historical comprehensive pipe gallery monitoring data, and determining a characteristic value and a characteristic vector of the historical comprehensive pipe gallery monitoring data; determining the main axis direction and the main axis direction half axis length according to the feature vectors and the feature values, and establishing a minimum ellipsoid for describing the normal operation state of the comprehensive pipe gallery in combination with chi-square test and the law of great numbers; the minimum ellipsoid is issued to the edge end; acquiring current comprehensive pipe gallery monitoring data through a data acquisition terminal; the current comprehensive pipe gallery monitoring data are uploaded to an edge end, and under the condition that the current comprehensive pipe gallery monitoring data are abnormal, abnormal pipe gallery features are determined through a Shapley value; and outputting abnormal current comprehensive pipe gallery monitoring data and abnormal pipe gallery characteristics, and sending risk early warning. And the accuracy and timeliness of comprehensive pipe gallery risk prediction are improved.

The invention provides a user charging behavior modeling method and system, and the method comprises the steps: obtaining the global charging behavior data of a user, carrying out the analysis of the global charging behavior data of the user through a clustering method, and extracting the global charging behavior preference characteristics of the user; on the basis of global charging behavior preference characteristics, key factors influencing user behavior categories are analyzed by using information gain, a user behavior preference classification model is constructed, and charging behavior preferences of the user in each scene are determined; and obtaining historical charging behavior data of the user, and based on the historical charging behavior data and the key factors, utilizing the trained user charging behavior model to predict the user charging behavior of the target prediction day. By analyzing and modeling the charging behaviors of the user in different scenes, the efficiency of the power system and the charging experience of the user are improved.

The invention discloses an interactive load prediction method based on fusion of prior knowledge and data driving, and the method comprises the steps: reconstructing a load sequence into a trend load sequence and a disturbance load sequence through modal decomposition, and constructing a prior knowledge base of tag-feature weight according to SHAP interpretability analysis; and through a prior knowledge base and feature contribution analysis, channel-level weight learning is carried out by using an automatic correlation determination module, and key input features are obtained. And inputting the reconstructed load sequence and the key input features into an improved Transform model, mapping a priori knowledge base into a priori knowledge matrix, embedding the priori knowledge matrix into the model, and generating a load prediction result. And carrying out interpretability analysis on a prediction result, and feeding back and updating an analysis result to a priori knowledge base to realize dynamic collaborative updating of prediction and the knowledge base. According to the invention, organic combination of expert knowledge and a data-driven model is realized, and the precision and stability of load prediction are improved.

The invention belongs to the field of information management, and particularly relates to a cross-domain data set fault migration health state recognition method, which comprises the following steps: data acquisition: acquiring a cross-domain data set from a plurality of data sources for cleaning and feature extraction of the acquired data; a deep learning model is constructed, and faults in the cross-domain data set are identified; constructing a graph neural network model, and evaluating the health state of the cross-domain data set; monitoring the state of the cross-domain data set in real time, and triggering an alarm when a fault is detected; and determining the specific position and type of the fault through probability distribution output by the model.

The invention discloses a water-rich tunnel stress mechanism analysis method based on physical-data dual drive. A data driving model is introduced, and the model can capture various complex phenomena and change trends possibly occurring in the running process of the water-rich tunnel through analysis of actual observation data, so that the stress condition is predicted more accurately. By adopting the dual-drive method, the defects of a single model are overcome, stress analysis under complex working conditions can be coped, and a new method is provided for tunnel design and construction. According to the method, the theoretical basis of a physical model and the data capturing capacity of a data driving model are combined, and a more comprehensive and accurate analysis method is formed.

The invention discloses a nuclear power material purchase demand prediction method and system based on a time convolutional network, and relates to the technical field of time series data analysis, and the method comprises the steps: collecting a data set, carrying out the preprocessing of the data set, classifying the data set, setting a representation mode of the input data set, and constructing a time convolutional neural network model. A data set is divided, model training is performed by using the divided data set, time sequence data is input into a trained model, and a material purchasing demand prediction value is output through multi-layer convolution, residual connection and full connection layer processing. According to the method, the time sequence data features are deeply learned through expansion convolution and residual connection, the ability of capturing the complex time dependency relationship by the model is improved, gradient flow in a deep network is promoted through introduction of the residual connection, the gradient disappearance problem is avoided, and the training efficiency and the learning ability of the complex features of the model are improved.