726 results about "Informatics" patented technology

The invention belongs to the field of bioinformatics, and relates to a molecular property prediction method based on multi-modal gating and comparative learning, which comprises the technologies of comparative learning, graph neural network, cross-modal alignment, gating attention and the like. Firstly, data standardization and graph construction are carried out, and molecular fingerprint embedding is extracted; secondly, a heterogeneous dual-channel graph coding architecture is adopted, one path captures atom short-range interaction through an attention mechanism, the other path integrates a molecular global structure and long-range dependence, and complementary molecular representation is generated; then, a cross-modal attention mechanism is introduced, bidirectional association of graph and fingerprint features is achieved, and modal weights are adaptively and dynamically distributed through a gating fusion module; and finally, a comparison pre-training strategy is adopted, a molecular graph and fingerprints are utilized to construct a sample pair, and discriminative molecular representation is learned on unlabeled data. According to the method, the accuracy of molecular property prediction is remarkably improved, and an efficient and reliable calculation tool is provided for virtual drug screening and lead compound optimization.

The invention discloses a metabonomics data batch correction method based on multi-kernel learning, and belongs to the cross technical field of bioinformatics and analytical chemistry. According to the method, the multi-kernel learning technology is utilized, the advantages of different kernel functions are fused in a self-adaptive mode, a model conforming to data reality is constructed, complex drift characteristics of metabolite signals are accurately captured, and efficient and accurate normalization processing of metabonomics data is achieved. Compared with traditional data standardization methods such as SVR and LOESS, the method has the advantages that the performance is excellent in the aspect of reducing the metabolite peak intensity variability, and the data stability is remarkably improved. In the subsequent multivariate statistical analysis, the classification accuracy is greatly improved, the comparability among different batches of data is also remarkably enhanced, reliable data support can be provided for discovery of disease biomarkers, and the method plays a key role in large-scale metabonomics research.

The invention belongs to the field of bioinformatics, and relates to an improved integrated deep learning cell communication ligand-receptor interaction prediction method. The method comprises the following steps: firstly, carrying out extraction and dimensionality reduction on biological sequence features of a ligand and a receptor, and constructing multi-modal feature input; secondly, constructing an improved deep neural network branch, introducing a batch normalization layer and a Leaky ReLU activation function, solving the problems of gradient disappearance and neuronal necrosis, and improving regularization strength to prevent overfitting; meanwhile, an enhanced heterogeneous graph auto-encoder branch is constructed, the graph embedding dimension is remarkably expanded to improve the feature capacity, and full convergence of the model is ensured by increasing training rounds; thirdly, fusing the improved deep network with the prediction probability of a heterogeneous graph auto-encoder by adopting a weighted integration strategy; and finally, outputting a potential interaction relationship based on the fusion probability. By optimizing the architecture and the strategy, the prediction accuracy and robustness are remarkably improved, and a reliable tool is provided for analyzing a complex cell communication network.

The invention discloses a method and system for predicting juvenile depression based on intestinal flora, and relates to the technical field of bioinformatics and artificial intelligence, and the method comprises the steps: firstly, obtaining an original sequence of a microbiome, carrying out the preprocessing of the original sequence of the microbiome, and obtaining a feature matrix; and screening core flora characteristics with stable trans-folding by adopting characteristic importance evaluation and interpretability analysis based on a gradient boosting decision tree. A mixed weighted graph is constructed based on Spearman correlation and a proximity relationship, and an absolute value of a correlation coefficient is taken as an edge weight and an edge density is adjusted through a threshold adaptive strategy. And finally, through an improved graph attention neural network, based on edge weight attention, layer normalization and random inactivation, enhancing robustness, and adopting adaptive optimization to complete parameter learning. And determining a dynamic classification threshold according to the AUC of the target patient, and outputting a sample discrimination result and confidence. According to the method, the accuracy, stability and biological interpretability of juvenile depression recognition are remarkably improved.

The invention belongs to the crossing field of artificial intelligence and material informatics, discloses a multi-layer material knowledge graph construction method and system based on a large model, and solves the problems of low utilization rate of multi-modal information, flat knowledge representation, uncontrollable quality and insufficient entity alignment precision in the prior art. The method comprises the following steps: analyzing material science and technology literatures, extracting multi-modal information of texts, tables and images, and preprocessing the multi-modal information into structured data; constructing a three-layer semantic framework of a concept term layer, a material entity layer and a material data layer; extracting knowledge by a large model layer by layer to generate a triple, and performing multi-dimensional quality evaluation and dynamic threshold filtering; entity fusion is realized by adopting hybrid alignment of semantic embedding and literal similarity and large model discrimination, and an atlas is maintained by combining incremental updating and version management. According to the method, knowledge integrity and accuracy are improved, entity heterogeneous scenes in the material field are adapted, whole-process automation is achieved, and high-quality knowledge support is provided for material research and development and data sharing.

The invention discloses a tumor personalized drug recommendation method and system based on fusion of multiple clinical guidelines, and belongs to the technical field of bioinformatics and precision medicine. The method comprises the following steps: acquiring gene variation data and clinical feature information of a patient; generating a preliminary drug candidate list based on drug recommendation rules of a plurality of clinical guidelines; calculating the weight of each guide by adopting a dynamic weight distribution algorithm; obtaining drug-gene-disease associated information through multi-hop reasoning of the knowledge graph; calculating a drug evidence score by adopting a multi-dimensional scoring algorithm; carrying out personalized score adjustment in combination with individual features of the patient; and outputting a personalized drug recommendation result. According to the method, through multi-guide dynamic fusion, multi-dimensional evidence scoring, knowledge graph reasoning and personalized adjustment, the problems of incomplete guide coverage, lack of personalization, low response speed and the like in the prior art are solved, the method has the advantages of high accuracy, high clinical applicability, quick response and the like, and the clinical real-time decision-making requirement can be met.

A control system for a network of quantum systems operates the network to perform quantum informatics processing. The control system includes an entanglement scheduler configured to identify operations for which entanglement of two or more of the quantum systems is desired, identify plural potential entanglement attempts for generating the entanglement; and schedule at least some of the potential entanglement attempts to be performed to generate the entanglement for at least one of the operations. The entanglement scheduler may use direct entanglement, entanglement swapping and / or quantum teleportation approaches to generate the entanglement. The entanglement scheduler may schedule probabilistic entanglement attempts based at least in part on the probability of success of the entanglement attempts. The entanglement scheduler may generate entanglement to support plural branches of a quantum informatics process and / or to respond to events occurring while the quantum informatics process is executing.

The invention relates to the technical field of bioinformatics and clinical medicine, and discloses a multi-omics data association analysis-based tumor early screening and typing early warning system, which comprises a data acquisition and preprocessing module for integrating standardized longitudinal multi-omics data; the dynamics and topology analysis module is used for generating topology fingerprints representing dynamic behaviors of the system through state space reconstruction and persistent coherence analysis; the causal inference and risk assessment module is used for calculating critical moderation indexes in parallel to synthesize risk indexes and constructing a dynamic causal network; and a collaborative diagnosis and report generation module. According to the system, risk indexes derived by critical moderation, topological fingerprints and a dynamic causal network are creatively combined, multi-modal information fusion is carried out through a collaborative diagnosis unit, and finally a comprehensive early warning report is generated. According to the invention, the accuracy and reliability of early risk early warning of tumors can be obviously improved, and a mechanism-level traceability basis is provided for clinical intervention.

The invention relates to the technical field of big data management, in particular to a bioinformatics data classification method and system based on big data, and the method comprises the following steps: obtaining time sequence recognition trend reversal and positioning fragments, extracting recognition difference positions inside and outside a frequency band data division region, screening samples with consistent features, and rearranging path labels; connecting nodes are cut off to generate fracture indexes, and label states are updated and written into sample fields to form a classification result set. According to the method, a labeling area is constructed by extracting trend inversion points in a time sequence, sample fragments are divided by combining data fluctuation positions in a disturbance frequency band, label numbers are arranged according to the fluctuation sequence of samples in a path, a corresponding sequence of a label chain connection relation and the sample positions is established, and label section boundaries are positioned and limited by fracture nodes. And the updated label state is synchronously written into a sample field, and the path label is bound according to a chain sequence, so that the sample identifier is kept coherent in sequence change, and the continuous coverage capability of the path information in classified output is improved.

The invention discloses a multi-component catalyst active site prediction system and method fused with quantum embedding, and relates to the technical field of catalysis and material informatics, and the system comprises a structure and site enumeration module which generates candidate sites; the adaptive quantum embedding calculation module obtains key reaction microcosmic parameters; the unified site fingerprint and feature engineering module constructs and fuses standard site fingerprints; the physical consistency machine learning module predicts adsorption energy and other parameters and uncertainty thereof; the active learning and sample selection module selects a high-value sample optimization model; the microdynamics evaluation module calculates index values such as activity; and the multi-objective optimization and sorting module generates an optimization sorting list. According to the method, the unification of calculation precision and efficiency is realized, the problem of non-unification of locus characterization is solved, the model interpretability and extrapolation reliability are improved, and the comprehensive evaluation and optimization sorting of multi-target performance are completed.

The invention specifically relates to a clinical deterioration risk prediction and early warning system and method based on machine learning, and relates to the technical field of medical artificial intelligence and clinical informatics, and the method comprises the steps: obtaining multi-dimensional time series data in real time; constructing a dynamic feature engineering vector; machine learning risk prediction; judging a risk threshold value and triggering early warning; and interpretation and suggestion generation driven by the large language model. According to the method, multi-dimensional time sequence data is continuously acquired in real time, multi-sliding window statistical features and standardized clinical deterioration scores are extracted in combination with dynamic feature engineering, and accurate quantitative risk prediction of multiple disease deterioration types such as sepsis and respiratory failure is realized by means of machine learning models which are specifically trained by XGBoost, LSTM and the like. The problem that traditional early warning depends on manual judgment and is high in hysteresis is effectively solved; medical staff can be helped to quickly grasp the core inducement of disease deterioration, and a standardized and landing action scheme is provided.

The invention relates to the technical field of bioinformatics data processing, in particular to a genome sequence compression method and system. The method comprises the following steps: acquiring genome sequencing data; comparing the sequencing data with a reference genome to determine a difference site; differentiating the difference sites as sequencing errors or real variations through a time sequence difference neural network model; performing differential compression coding according to an identification result; a friendly variation detection format is constructed, and rapid variation query is supported through a multi-level index structure and a variation metadata table. According to the method provided by the invention, the sequencing error and the real variation can be accurately distinguished through the time sequence differential neural network model, and important biological variation information is protected while the compression efficiency is improved by adopting the differential compression coding strategy.

Example systems and techniques are disclosed that may determine at least one treatment strategy for a lesion. An example system may include memory configured to store clinical guidance and / or informatics for a PCI procedure and processing circuitry communicatively coupled to the memory. The processing circuitry may be configured to determine the plurality of treatment pathways. The processing circuitry may be configured to obtain angiogram imaging data of a coronary vasculature of a patient. The 2024 / 058837 processing circuitry may be configured to determine the clinical guidance and / or informatics based at least in part on the angiogram imaging data. The processing circuitry may be configured to output for display the angiogram imaging data and at least a portion of the clinical guidance and / or informatics, wherein the at least a portion of the clinical guidance and / or informatics is overlaid onto the angiogram imaging data.

The invention discloses an automatic construction system for a biological information analysis process, and the system comprises an intention understanding and semantic analysis module which is used for analyzing a natural language text inputted by a user into a structured task description meeting the requirements of a biological information analysis task; the knowledge graph and retrieval module is responsible for constructing a knowledge graph special for the bioinformatics field so as to provide knowledge retrieval and recommendation services; the process generation core module is used for receiving the structured task description, actively associating the knowledge graph with the retrieval module so as to supplement the field large model, and generating a biological information analysis target process language code; and the execution and monitoring module is used for guaranteeing workflow execution, full-life-cycle state monitoring, real-time fault diagnosis and intelligent self-healing decision making of target process language codes. According to the system, the executable analysis process can be directly generated according to the natural language requirement of the user, the tool compatibility and parameter validity are verified through the borrowed knowledge graph before execution, the dependency on the programming ability of the user is greatly reduced, and the process operation reliability is improved.

The invention relates to the technical field of medical informatics and artificial intelligence technology cross application, in particular to a dynamic intelligent prediction system for HIV infected person immune reconstruction insufficiency. Comprising a data acquisition and management module, a data preprocessing and feature engineering module, a Bayesian joint modeling core module, a dynamic prediction calculation module, a multi-dimensional verification and evaluation module and a clinical application service module. According to the method, on the basis of large-sample multi-center longitudinal follow-up visit data, a Bayesian shared parameter dynamic joint model is adopted, the dependency relationship between dynamic trajectories of immune indexes such as cell counting and the like and IIR occurrence time is captured, real-time risk prediction is achieved through a sequential Bayesian updating mechanism, and through multi-dimensional verification, the risk prediction accuracy is improved. The method has the advantages that the performance is better than that of an expert-driven model, expert experience pre-judgment and 19 mainstream machine learning algorithms, an individualized conclusion with a confidence interval can be output, clinical decision is assisted, prediction is promoted to be clinical from scientific research, and the method has important application value and popularization prospect.

The invention provides a cell development process dynamic modeling method and device based on time sequence single cell transcriptome data and a medium, and relates to the crossing field of bioinformatics and computational biology. The method comprises the following steps: constructing a Shenchang differential equation learning framework; adjusting parameters of the single cell development state change model based on the Shenxuan differential equation learning framework so as to construct a population cell development state change model; obtaining a cell specific gene regulation network and a population cell gene regulation network based on the population cell development state change model so as to predict occurrence opportunity of cell lineage differentiation and a molecular decision mechanism of cell differentiation; therefore, the problems of incomplete modeling mechanism, insufficient noise processing and lack of energy principle in the existing cell development process are solved.

The invention discloses an expert intelligent recommendation method and system based on CTR estimation and multi-feature fusion. The method comprises the following steps: acquiring multi-source heterogeneous case data through data acquisition and standardization; case semantic features are extracted through text preprocessing and LDA topic modeling; feature engineering and quantification are carried out on expert basic information, academic backgrounds, historical behaviors and matching degrees with cases; a GBDT + LR fusion model is adopted to realize multi-feature fusion and CTR estimation; generating an expert recommendation list based on CTR scores in combination with rigid filtering and post-processing strategies, and constructing a continuous learning mechanism through user feedback; the system comprises a function module corresponding to the method to realize accurate and fair expert recommendation. The problems that in the prior art, recommendation accuracy is low, the model structure is single, the feature utilization rate is low, and labor-dependent efficiency is low are solved, expert recommendation accuracy, efficiency, fairness and universality are improved, and the method is suitable for college teaching competition, course review, project acceptance and other scenes.

The present invention relates to the technical fields of human-computer interaction and brain informatics, and in particular to a closed-loop neurofeedback method and device based on haptic memory electroencephalogram signal conditioning. The method comprises: collecting electroencephalogram signals of a subject, and performing real-time online data preprocessing; designing a haptic memory stimulation task, activating the haptic memory of the subject, and recording a corresponding electroencephalogram signal response; performing feature extraction and analysis on a corresponding electroencephalogram signal to obtain an electroencephalogram feature related to haptic memory; and on the basis of a haptic memory electroencephalogram feature signal, designing a closed-loop neurofeedback system, the closed-loop neurofeedback system monitoring the electroencephalogram signals of the subject in real time, performing real-time stimulation on the basis of a preset haptic stimulation task, and recording a corresponding electroencephalogram response. In the technical solution of the present invention, the haptic memory electroencephalogram feature signal is combined with the closed-loop neurofeedback system, thus providing new ideas and methods for the diagnosis and treatment of haptic memory-related diseases.

The invention discloses an esophageal cancer prognosis risk analysis method and system based on machine learning, and a medium, and relates to the technical field of artificial intelligence technology and bioinformatics, and the method comprises the steps: 1, collecting multi-modal data of a patient, and carrying out the standardization processing, so as to construct a stable feature set; 2, constructing a plurality of machine learning models, realizing high and low risk group prediction of each machine learning model based on stable feature set modeling, and determining the machine learning model and an optimal feature set according to the high and low risk group prediction; and step 3, calculating SHAP interaction values among the features in the optimal feature set, drawing an interaction value curve according to the SHAP interaction values to obtain TopA interaction feature pairs, taking the interaction feature pairs as newly constructed features to be included in the original feature set in the step 1, and repeating the feature screening process in the step 1 and the step 2 to obtain an optimal machine learning model. Predicting high and low risk groups of patients; according to the prognosis risk analysis method, the detection specificity of high and low risk groups of local advanced esophageal cancer patients is greatly improved.

The invention provides an artificial intelligence grading classification-regression combination prediction method for multi-class materials, and belongs to the technical field of material informatics and artificial intelligence. According to the method, a historical sample data set of material chemical composition and crystal structure information is obtained, a band gap value is divided into a plurality of intervals based on an equal width principle, and interval labels are generated. Key features are extracted, an SVM classification model is trained to predict a band gap interval, and then a multi-layer neural network regression model is utilized to predict a band gap value. If the highest confidence coefficient is higher than the threshold value, directly calling the multi-layer neural network regression model of the corresponding interval; and if the value is lower than the threshold value, multi-interval parallel regression and weighted fusion prediction are carried out. According to the artificial intelligence grading classification-regression combination prediction method for the multi-class materials, the precision and robustness of band gap prediction can be effectively improved, particularly, the performance is prominent when high and low band gap samples are distributed imbalanced, and a precise and reliable solution is provided for material band gap prediction.

The invention discloses a psychological state dynamic evaluation and early warning system based on multi-modal behavior data, which belongs to the field of medical care informatics and comprises a multi-modal behavior data hierarchical coding module, a time sequence causal atlas construction and reasoning module, a double-stage self-adaptive early warning decision module and a context awareness intervention strategy generation module. A cross-modal association mode is extracted through a double-layer coding mechanism, a time sequence graph containing a causal relationship is constructed, causal reasoning is performed, a double-stage mechanism of short-term mutation detection and long-term trend prediction is adopted to generate graded early warning, and an optimal intervention strategy is selected based on a deep Q network according to a user situation. According to the method, the accuracy, timeliness and intervention effectiveness of psychological health assessment are improved, and dynamic monitoring and early warning of the psychological state are realized.

The invention discloses a multi-omics cancer subtype recognition method, system and device based on density sensing cluster structure guide contrast learning and a medium, and belongs to the technical field of bioinformatics and artificial intelligence crossing. The method comprises the following steps: acquiring and preprocessing multi-omics data; constructing an omics specific auto-encoder and learning potential representation; constructing a density sensing cluster block in the potential space; constructing a cross-omics positive and negative sample pair based on cluster block sample overlapping; difficult negative sample mining; constructing a cluster block level cross-omics contrast learning target, and training and updating; a self-supervised soft refinement mechanism is introduced to dynamically enhance a cluster structure; and carrying out multi-loss joint optimization and model iteration training. According to the method, the robustness and the stability of a cancer subtype recognition result can be improved, high-dimensional, multi-source and multi-noise multi-omics data can be efficiently modeled and analyzed, good generalization ability and application potential are achieved, and reliable technical support can be provided for cancer subtype research, patient stratified analysis and precise medical aid decision making.

The invention belongs to the technical field of artificial intelligence algorithm and bioinformatics crossing, and relates to a drug interaction prediction method based on multi-modal molecular characterization. According to the method, through the edge perception GCNII architecture and the Hop2Token multi-hop coding mechanism, effective modeling of atomic-level and bond-level local environments and a cross-substructure high-order dependency relationship in drug molecules is realized, and the accuracy and robustness of drug interaction prediction are improved. According to the method, Mol2Vec and MolT5 cross-modal molecular characterization is integrated, fusion of molecular overall semantics and substructure grammar semantic association is achieved, and the generalization ability of the model to complex molecules and unknown medicine combinations is remarkably improved. According to the method, the dynamic feature screening algorithm driven by the SHAP value of the artificial intelligence technology is adopted for biological verification, the feature redundancy problem is effectively solved, the molecular biological information analysis processing calculation efficiency and the model transparency are improved, and the traceability of the prediction process is guaranteed.

The invention relates to the technical field of medical informatics, in particular to a method for determining a drug code, which comprises the following steps of: acquiring a specification text, a chemical structure, an active component and target information, and integrating into task input; respectively extracting a molecular topological vector and a semantic topological vector based on topological coherence, injecting the molecular topological vector and the semantic topological vector into a sparse high-dimensional super-vector, and mapping the sparse high-dimensional super-vector into a continuous vector through binding transformation; generating a preliminary code by using a generative neural network combining diffusion denoising and a converter, and calculating a super-dimensional residual error; constructing a causal model in the pharmacological knowledge graph, and outputting a correction code after residual error calibration; the joint probability variance is evaluated through Monte Carlo discarding, and automatic confirmation, manual recheck or anti-fact search are realized through two-stage threshold values; and writing the combined super vector, the final code and the variance into an experience library, and optimizing the generation network and the causal model at the same time by using a hierarchical Bayesian near-end strategy. According to the invention, the extrapolation capability and interpretability of new drugs are considered, the coding accuracy is obviously improved, and the labor cost is reduced.

The invention relates to the technical field of bioinformatics, particularly discloses a metabolite target interaction prediction method and system for myocardial injury, and aims to solve the problems that complex heterogeneous data processing is insufficient, and dynamic changes of a metabolic network are difficult to capture and fine time sequence prediction is difficult in the prior art. According to the method, multi-source heterogeneous data is integrated, a myocardial injury risk factor and metabolite target knowledge base is constructed, and machine learning and deep learning technologies are utilized to realize risk factor weighting and static and sequential dynamic risk assessment. By fusing the static risk level and the dynamic adjustment factor, the system can predict the metabolite target interaction related to the individual high-risk state and generate personalized prevention and intervention strategy suggestions according to the metabolite target interaction. The system comprises a plurality of functional modules including data acquisition and preprocessing, knowledge base construction, feature engineering, risk modeling, time sequence optimization, target prediction and the like, so that early, dynamic and accurate prediction of myocardial injury risks and recognition of personalized intervention targets are realized.

The invention discloses a mental stress assessment and intelligent dredging method and system, and belongs to the technical field of digital medical treatment and health informatics. Multi-modal physiological signals are continuously collected through the wearable device, medical diagnosis level pressure state evaluation is carried out based on the personalized physiological baseline, and personalized pressure indexes and levels are generated; secondly, when it is diagnosed that the pressure level exceeds the standard, the system serves as an intelligent decision support system, the environment and schedule information after privacy protection processing is fused, and an optimal grooming action is dynamically selected from a predefined intervention action library and executed; finally, the system serves as a continuous learning system, the intervention efficiency is evaluated in real time according to feedback data of the pressure index after execution, the decision model is updated, and collaborative self-optimization of the diagnosis strategy and the intervention strategy is achieved. According to the method, medical diagnosis, personalized treatment decision and adaptive learning are integrated, and the systematicness, accuracy and intelligent level of mental stress related health problem management are improved.

The invention discloses a neural network calculation method and device for gene expression regulation and control analysis, and relates to the technical field of bioinformatics, and the method comprises the steps: obtaining first feature data and second feature data; constructing an input feature comprising a plurality of regulation and control hierarchies; and inputting the input features of the plurality of regulation levels and the second feature data into the target neural network model, and outputting a prediction result of the gene expression state. According to the neural network calculation method provided by the invention, chromatin accessibility and three-dimensional space interaction data are deeply fused through a dynamic routing module, so that the problem of'black box 'which is inaccurate in prediction and difficult to explain in a traditional deep learning model is solved in a mode of explicitly simulating a real biological regulation mechanism; and a key gene regulatory pathway can be accurately identified.

The invention discloses a rapid progression type nasopharyngeal carcinoma risk prediction method based on an artificial neural network, and relates to the field of medical informatics crossing. The invention provides a rapid progressive nasopharyngeal carcinoma risk prediction method based on an artificial neural network, and aims to solve the problem that a rapid progressive nasopharyngeal carcinoma patient is difficult to recognize in time by depending on TNM staging and experience judgment in the prior art. According to the method, historical case data collection, missing value filling and standardization preprocessing, core feature determination through feature screening, class imbalance correction, feature coding and feature matrix construction are sequentially carried out, an artificial neural network model is trained and optimized under a cross validation framework, and performance and threshold values are determined on a validation set. During clinical application, patient features are input, and the model outputs a rapid progress risk probability and a risk level. Compared with a conventional staging or linear model, the method can improve the prediction accuracy, and achieves the early recognition and individualized treatment of a high-risk patient.

The invention provides a near-beta titanium alloy multi-performance prediction method based on physical information driving, and relates to the technical field of material informatics. Retrieving related literatures of the near-beta titanium alloy, and establishing an original data set; converting components and process features in the data set, embedding element physical attributes and phase change dynamics into feature engineering, performing data preprocessing and standardization, and dividing a test set and a training set by using stratified sampling; performing parameter tuning on the XGBoost model by adopting an Optuna hyper-parameter optimization framework in combination with five-fold cross validation, training and verifying the model by using a training set and a test set, and constructing a regression prediction machine learning model based on physical information driving; and inputting the physical characteristic parameters of the new material components into the optimized learning model for prediction, and outputting prediction results of the tensile strength and the ductility. The multi-objective performance is collaboratively optimized through physical characteristics, process parameter extrapolation is supported, and a high-precision and low-data-dependence solution is provided for near-beta titanium alloy design.

The invention belongs to the field of wastewater treatment and industrial automatic control, and particularly relates to an intelligent dosing control method based on water quality online monitoring. The method comprises the following steps: capturing a water body spectrum fingerprint spectrum by using a full-spectrum scanning module to obtain a characteristic signal sequence; a chemical informatics molecular algorithm is introduced for preprocessing and feature enhancement, and molecular fingerprint information is recognized; a convolutional neural network model is constructed to decouple chemical oxygen demand, ammonia nitrogen, total phosphorus and water toxicity indexes from the spectral signals, and multi-parameter parallel inversion is achieved; and based on the inversion index and the spectral dynamic trend, the dosage is calculated through an intelligent decision-making algorithm, and accurate dosing of the medicament is driven. According to the invention, through fusion of full spectrum scanning and deep learning, high-precision anti-interference water quality perception is realized, potential risks can be identified and preventive intervention can be carried out, and the intelligent level and economical efficiency of dosing control are improved.