1072 results about "Informatics" patented technology

The invention provides a technical condition prediction method for a tunnel civil engineering structure, and relates to the technical field of traffic control monitoring systems.A biological acoustic composite sensing system comprising a distributed optical fiber acoustic sensing system and microorganism sample collection and analysis is constructed, and acoustic depth mode characteristics are mined by applying a deep learning model; the microbial dynamic biomarker is analyzed and identified by using bioinformatics; multi-modal information, a domain knowledge graph and a causal inference algorithm are fused, and a dynamic causal network model for revealing an internal driving relation in the degradation process is constructed; further, training and applying an adaptive neural network prediction model based on causal driving and fusing physical and biochemical mechanism constraints, and carrying out probabilistic prediction on future technical conditions of the tunnel; and executing anti-fact reasoning by using the causal network and the prediction model, and generating and optimizing an intervention strategy of cooperation of physical measures and biochemical measures. According to the method, the accuracy and advance of tunnel structure state prediction can be remarkably improved.

The invention relates to the field of medical informatics, in particular to an anesthetic dosage optimization method based on artificial intelligence. Comprising the following steps: collecting real-time physiological data and static data of a patient, converting the real-time physiological data and static data of the patient into standardized feature vectors through standardization processing, and predicting the initial infusion rate of an intraoperative anesthetic by using a long-short-term memory network; collecting and standardizing the intraoperative data of the patient to obtain the standardized intraoperative data; a personalized anesthetic infusion rate is generated through a dynamic metabolism risk and nerve sensitivity joint optimization algorithm; the maximum allowable infusion rate and the minimum effective infusion rate are determined based on a clinical guide, the remaining time of an operation is obtained, safety verification is carried out, and the final infusion rate is generated in combination with the remaining time of the operation. The technical problem that the prediction of the initial infusion rate of the preoperative anesthetic is inaccurate due to the fact that the initial infusion rate is determined by depending on the experience of an anesthetist in the prior art is solved.

The invention relates to a biological information MCP service calling method, system and device and a medium, and relates to the technical field of bioinformatics and artificial intelligence. The method comprises the steps of obtaining user request information; querying MCP service information matched with the user request information based on the user request information; building enhanced prompt information corresponding to the MCP service information which contains the user request information and is matched with the corresponding user request information, and sending the enhanced prompt information to the large language model to generate calling parameters; and calling the target biological information MCP service based on the calling parameter. The automatic discovery of the MCP service is realized, and the expandability and the flexibility of the system are improved; the user intention can be more accurately understood, and the tool discovery accuracy is improved; an MCP specification is used for assisting LLM to accurately generate calling parameters, and the use threshold of a user is lowered; interoperability and expandability are ensured based on an MCP protocol; and a large-scale biological information MCP service scene is effectively supported.

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 relates to the technical field of medical informatics, and discloses an intestinal flora associated disease risk prediction system based on multi-data set difference mutual identification, and the system comprises the steps: capturing the dynamic evolution characteristics of a network through constructing a species-genus-phylum three-level microbial interaction network, and employing a time sequence diagram convolution network; and designing a network disturbance experiment, introducing a network recovery force index to quantify the network recovery capability, constructing a network vulnerability map to identify key nodes and analyze a disturbance propagation path, and integrating stable and consistent network toughness characteristics in multiple data sets to establish an early warning mechanism. By means of the method, early and accurate early warning of disease risks is achieved, and a key time window is provided for clinical intervention.

The invention discloses a solar wind speed prediction method based on a fusion prediction mode and a collaborative attention mechanism. The method comprises the following specific steps: collecting and processing solar wind speed and related data within a certain time; dividing the data into a plurality of patches through a patterning segmentation method, learning a plurality of modes in prediction information, and guiding mode fusion by using historical information to obtain fused prediction mode information; learning historical information and fused prediction mode information by using an attention mechanism to obtain time sequence feature representation, aggregating global information through down-sampling and the attention mechanism, and learning inter-variable relation feature representation of variable dimensions for the global information by using a collaborative attention mechanism; mapping the relationship feature representation among the variables through a flat layer to obtain a solar wind speed prediction result; and training and optimizing the model. According to the method, prediction mode information can be fully mined so as to realize interaction between historical information and prediction information, meanwhile, the relation strength between variables can be captured, and the prediction precision is improved.

The invention relates to the technical field of medical care informatics, and discloses a laryngeal mask ventilation control method suitable for outpatient anesthesia, which comprises the following steps: before core analysis, injecting and monitoring a high-frequency signal to confirm the integrity of an original breathing waveform data stream; constructing a real-time ventilation waveform trajectory in a differential phase space only on the premise that a data stream is complete, and performing morphological comparison with an individualized baseline template; meanwhile, when a parameter adjustment event of the respirator is monitored, the baseline template is automatically re-calibrated, a collaborative analysis framework with front information source quality inspection and dynamic reference self-adaption capabilities is constructed, ventilation monitoring is converted from passive response to an isolated peak point into active insight to a system dynamics evolution trajectory, and the system dynamics evolution trajectory is optimized. Therefore, the risk of gradual deterioration of the ventilation state caused by tiny air leakage of the laryngeal mask or secretion accumulation can be clinically recognized in an early stage, and intervention time is won for anesthetists.

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.

To increase the amount of information to be utilized.SOLUTION: An information processing system includes an information processing apparatus, a first terminal device that holds first user characteristic information, and a second terminal device that holds second user characteristic information. The information processing apparatus includes: an information collection unit which collects, from the first terminal device, first anonymized information generated by anonymizing the first user characteristic information, and collects, from the second terminal device, second anonymized information generated by anonymizing the second user characteristic information; a learning unit which generates a model configured to learn, by machine learning, a relationship between the first user characteristic information and the second user characteristic information, using the first anonymized information and the second anonymized information, as learning data, and output, on receipt of the first user characteristic information, estimated user characteristic information estimated from the relationship between the first user characteristic information and the second user characteristic information; and a model output unit which outputs the model to the first terminal device.SELECTED DRAWING: Figure 3

The invention relates to the technical field of medical care informatics, and discloses an intelligent post-stroke cognitive impairment assessment method based on brain network dynamic reconstruction, which comprises the following steps: acquiring multi-modal brain image data of a patient, dynamically compensating the difference between haemodynamic delay and white matter conduction velocity through a nerve-blood vessel coupling model, and calculating the post-stroke cognitive impairment; precise space-time alignment of function and structure signals is realized; a pulse neural network dynamic entropy change model is constructed based on the alignment data, functional connection strength change is coded into a pulse distribution sequence, and Gamma wave band synchronization enhancement and other compensatory features are extracted; constraining rationality of brain network reconstruction by adopting an energy optimal transmission model, and generating a compensatory thermodynamic diagram; and real-time calculation of the edge end is realized through pulse time sequence compression coding. The limitation of traditional static analysis is avoided, the algorithm achieves microsecond response on embedded equipment through the pulse coding technology, and a reliable stroke rehabilitation evaluation tool is provided for primary hospitals.

The invention relates to a personalized scientific education system based on AI agent driving, and belongs to the technical field of informatics. The education system runs a main agent, a user twinborn agent, a learning planning platform and a graph structure database. The main agent is used for collecting knowledge states and learning behaviors of users, constructing a user model, and training one or more user twin agents based on the user model. And the user twinborn agent is used for simulating learning behaviors and results of the user under different learning paths. And the learning planning platform combines the user model and the knowledge graph stored in the graph structure database to generate a plurality of candidate learning paths for the twin intelligent agent of the user to perform analogue simulation. And the main agent selects an optimal path according to a simulation result and implements the optimal path to the personalized teaching process of the user. According to the technical scheme, high-adaptability learning path recommendation and dynamic adjustment are realized, and the intelligent level of individualized teaching is improved.

The invention relates to the technical field of bioinformatics, and provides a visual analysis method and system for a rice multi-tissue single cell expression profile. The method comprises the following steps: comparing sequencing data of an original single cell transcriptome of a rice tissue to obtain a standardized transcriptome data set; performing batch effect correction and integration on the standardized transcriptome data set to obtain a whole plant expression matrix; performing cell type annotation on the whole plant expression matrix to obtain a cell type annotation system; carrying out visual dimension reduction processing on the whole plant expression matrix fused with the cell type annotation system, and carrying out co-expression network construction to obtain a modular tissue correlation analysis model; and establishing an interaction end based on the module organization correlation analysis model, and realizing data visualization analysis through the interaction end. The invention provides a one-stop analysis platform for rice cell heterogeneity research, functional gene mining and molecular breeding.

The invention relates to an oncogene prediction method based on graph variation self-coding, and belongs to the field of bioinformatics. The method is based on a dual-path neural network framework: a main path processes an original network and features enhanced by a variational auto-encoder (VAE) by using a graph attention network (GAT) so as to capture a complex relationship between nodes; the auxiliary path generates an auxiliary network and features containing global information through an APPNP algorithm, and the auxiliary network and features are aggregated through GraphSAGE to retain structural information. The model introduces jump connection and residual connection to relieve gradient disappearance and enhance feature complementarity. And finally, integrating dual-path information output prediction through a linear layer. The method is verified on a plurality of biological network data sets, the prediction accuracy, robustness and hidden relation recognition capability are remarkably improved, and a reliable tool is provided for cancer 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 relates to the technical field of bioinformatics and computational biology, and provides an anti-cancer drug target screening method and system based on hypergraph-knowledge graph dual-channel, a hypergraph-knowledge graph dual-channel architecture is constructed through a feature extraction module, a self-adaptive multi-modal fusion module dynamically integrates multi-source heterogeneous data, and the anti-cancer drug target screening method and system based on the hypergraph-knowledge graph dual-channel architecture are obtained. The method comprises the following steps: adaptively fusing high-order interaction features and semantic association features of a gene, generating a high-quality negative sample by using a generative adversarial network to optimize a training process, finally optimizing feature representation through a discriminator, and calculating a synthetic lethal probability score between any gene pair. According to the application, firstly, the extracted multi-dimensional features are normalized through the feature extraction module, then the importance of different features is dynamically weighted based on the attention mechanism, finally, the synthesis lethal relationship prediction probability of the gene pair is output through the multi-layer sensor, and under the theoretical framework of the gene synthesis lethal effect, the synthesis lethal effect of the gene pair is predicted. And an innovative solution is provided for anti-cancer drug target screening.

The invention provides a spatial domain identification method based on data interpolation and cell type deconvolution, and belongs to the technical field of bioinformatics. In order to solve the problems that gap information between adjacent points cannot be utilized in low-resolution spatial transcriptome data and prior information of cell types in a tissue space structure level cannot be fully integrated in a traditional method, the method comprises the following steps: acquiring a spatial transcriptome data set and a single-cell RNA sequencing data set, and performing data preprocessing on the acquired data sets; and carrying out data interpolation on the preprocessed spatial transcriptome data, and carrying out cell type deconvolution in combination with single-cell RNA sequencing data. And constructing a deep learning model based on the graph convolutional network. And training a deep learning model according to gene expression information, spatial position information and cell type information of the spatial transcriptome data after cell type deconvolution by using a self-supervised contrast learning strategy. And performing spatial domain identification on the to-be-detected data based on the trained model.

A system and methods for adaptive bandwidth-efficient data encoding comprising: a sequence analyzer configured to analyze a received sequence dataset, maintain a count of unique characters, and identify positions where the unique character count increases by a power of two; an adaptive sourceblock optimizer that determines and dynamically adjusts optimal sourceblock sizes based on dataset characteristics; and a data deconstruction engine that deconstructs the dataset into sourceblocks and creates codewords for storage or transmission. The system analyzes sequence complexity, alphabet size, and character frequency distribution to optimize sourceblock sizes, and uses machine learning to improve decision-making over time. This adaptive approach enhances compression efficiency across varied genomic data types, including genome graphs, while maintaining data integrity and security. The system efficiently encodes, stores, and transmits complex genomic and bioinformatic datasets, addressing the growing challenges in data storage and bandwidth limitations.

The invention discloses a verification system and method for a material formula through a confidence interval, and relates to the technical field of material informatics and intelligent research and development decision. Comprising a data acquisition module used for acquiring candidate formula basic data, historical experiment basic data, environment associated data, material recessive data and equipment state data and preprocessing the acquired data; according to the method, the final error value is obtained through fusion, the adjusted final credible interval is constructed, the problems that an existing material performance prediction tool can only output a point prediction result and lacks a stable credible interval, and engineers are difficult to assess that performance reaches the standard and actually and successfully grasp are solved, the coverage rate is verified through the verification set, the error scale is scaled, and the reliability of the material performance prediction tool is improved. It is ensured that the credible interval meets the preset coverage requirement, successful mastering of performance standard reaching can be quantified, an engineer does not need to depend on experience judgment any more, and the accuracy of performance evaluation is improved.

The invention relates to the field of computer-aided drug design (CADD) and molecular informatics, in particular to a multi-modal representation learning method based on cyclopeptide molecules, which is used for predicting cell membrane permeability of cyclopeptide. The method mainly comprises the following steps: (1) data collection: integrating cyclic peptide permeability data from a ChEMBL database, a CycPeptMPDB database and a CyclicPepeda database and patent literatures; (2) multi-modal learning: for different modal data, a deep learning model is adopted to extract feature representations of the data; the method comprises the following steps of: encoding an SMILES sequence by using ChemBERTa (ChemBERTa); using Vision Transform to extract molecular image features, and learning a molecular image structure and 3D coordinate information based on GNN; (3) multi-modal feature fusion: adopting a self-adaptive extensible fusion mechanism, integrating SMILES feature information into image, graph and 3D coordinate features through a cross-modal feature fusion mechanism, and splicing all modal features to obtain multi-modal molecular representation; and (4) permeability prediction: sending the multi-modal molecular representation into a full connection layer for regression prediction so as to evaluate the permeability of the cyclopeptide.

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 discloses a multi-source data fusion and intelligent analysis method for an intelligent traffic enterprise, and relates to the technical field of intelligent traffic, and the method comprises the following specific steps: S100, collecting video frame data, radar track data and voiceprint data of the traffic enterprise in real time, S200, calculating a quantized video definition score, S300, calculating a radar signal-to-noise ratio, and S500, calculating a video definition score. According to the method, the video, radar and voiceprint data quality can be accurately judged through multi-modal data quality real-time evaluation and credibility quantification, and a cross-modal attention weight matrix is dynamically adjusted according to a quality evaluation result in a complex environment, that is, when the video quality is reduced, the video, radar and voiceprint data quality can be accurately judged; the system automatically increases the radar data weight, ensures that high-quality data source key information is always focused, learns more accurate and comprehensive abnormal characteristics, effectively reduces the traffic accident rate, and guarantees the road traffic safety.

The invention relates to the technical field of medical informatics, in particular to an ADL ability training data management system based on neurological disease patients. The system comprises a memory and a processor, and the processor executes computer programs stored in the memory so as to realize the following steps: acquiring attitude images and scales of normal personnel and neurological disease patients in the ADL ability training process; according to the position difference, the depth difference and the time difference between the joint points in the to-be-evaluated image and the joint points in the reference image, evaluating the possibility value that the to-be-evaluated image and the reference image are in the same action, and screening a contrast image; synthesizing the possibility value that the to-be-evaluated image and all the contrast images are in the same action and the acquisition time of the to-be-evaluated image to obtain a motion completion index; and determining an effective evaluation value of training according to the difference between the change characteristics of each type of indexes of the neurological disease patient at each moment and other indexes and each type of indexes. According to the invention, an accurate comprehensive result of ADL capability training data can be obtained.

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 relates to the technical field of medical informatics, in particular to a knee osteoarthritis traditional Chinese medicine treatment scheme optimization method based on big data analysis, and the method comprises the following steps: based on knee joint load distribution data, collecting knee joint shear stress data, monitoring cartilage stress gradient data, and calculating a stress peak value in a gait cycle; and obtaining a knee joint biomechanical load balance value. According to the method, by combining shear stress, cartilage stress gradient and gait cycle stress peak value calculation, individual biomechanical characteristics are accurately described, patellar tendon stretching and quadriceps femoris tendon displacement data are analyzed, the instantaneous elastic modulus is recognized, tendon adaptability adjustment measurement precision is improved, soft tissue elastic change is reflected, and by means of individualized stress characteristic data, the stability of the muscle tendon is improved. Abnormal data are eliminated, illness state analysis is optimized, the curative effect evaluation accuracy is improved, the rehabilitation training intensity is dynamically optimized according to the curative effect offset degree, the therapeutic scheme is more adaptive, and the curative effect predictability and the personalized level are improved.

Devices and techniques are generally described for unlearning information from large language models (LLMs). In various examples, a first language model (LM) trained on a first training corpus D may be determined. First data F that is a subset of D may be determined. A first auxiliary LM may be trained using the first training corpus D and a second auxiliary LM may be trained using a second training corpus D / F, where the second training corpus D / F represents the first training corpus D without the first data F. A first text input may be determined. The first LM may be updated based at least in part on a first prediction difference between predictions the first LM and the second auxiliary LM for a first set of inputs and a second prediction difference between the predictions of the first LM and the first auxiliary LM for the first set of inputs.

The invention provides a cell specific transcription factor regulatory network analysis method and a visualization platform, and relates to the technical field of bioinformatics, the method comprises the following steps: constructing a gene regulatory network through a GRNBoost2-cisTarget-AUCell-Cell GRN workflow based on a transcription factor in combination with a motif database; screening a direct regulation relationship in combination with the database, and calculating an activity score of a regulator in each cell; based on activity scores and cell type annotation results, grouping the single cell data by using a unified manifold approximation and projection (UMAP) dimensionality reduction method and a Leiden clustering algorithm, and displaying the following results through an interactive visualization tool: a cell clustering UMAP graph, performing color marking according to cell types; a UMAP graph and a heat map of transcription factor regulator activity; according to the visual map of the gene regulation and control network, transcription factors and target genes are distinguished through node shapes, and regulation and control relations are marked through line weights and colors. According to the invention, an accurate regulation and control network can be provided.

A system, method and apparatus for executing a bioinformatics analysis on genetic sequence data is provided. Particularly, a genomics analysis platform for executing a sequence analysis pipeline is provided. The genomics analysis platform includes one or more of a first integrated circuit, where each first integrated circuit forms a central processing unit (CPU) that is responsive to one or more software algorithms that are configured to instruct the CPU to perform a first set of genomic processing steps of the sequence analysis pipeline. Additionally, a second integrated circuit is also provided, where each second integrated circuit forming a field programmable gate array (FPGA), the FPGA being configured by firmware to arrange a set of hardwired digital logic circuits that are interconnected by a plurality of physical interconnects to perform a second set of genomic processing steps of the sequence analysis pipeline, the set of hardwired digital logic circuits of each FPGA being arranged as a set of processing engines to perform the second set of genomic processing steps. A shared memory is also provided.

The invention provides a knowledge graph completion method based on a large language model and a graph neural network. The method comprises the steps of obtaining definitions and descriptions of a head entity, a tail entity and a relation in a triple, and obtaining a problem instruction for triple classification; a multi-hop sub-graph with an entity as the center is obtained, a word embedding model is used as a feature extractor, the multi-hop relation between triples is analyzed, and therefore high-quality semantic features existing in multiple hops are obtained. Secondly, complex graph structure information is captured by using a graph neural network, and an interaction relationship between nodes is learned, so that graph structure data can be better understood. And finally, fine-tuning the large language model in combination with the question and answer instruction to help the large language model focus on the key task. According to the method, the characteristics of strong context perception capability and complicated sentence structure and context understanding of a large language model are combined with aggregated node structure information to generate embedded representation with better expression capability, so that the performance of knowledge graph completion is improved.

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.