24results about How to "High acceptability" patented technology

The invention provides an OPC UA information model automatic construction method based on a knowledge graph and a large language model. The method comprises the steps of obtaining an unstructured industrial text, performing semantic analysis on the unstructured industrial text, and extracting a main body entity, an object entity, a method entity and attribute information thereof according to a predefined hierarchical tuple structure; calling an industrial field knowledge graph in real time to perform semantic enhancement on an extraction result; performing similarity calculation and evaluation on an enhanced result, and performing information fusion and duplicate removal based on a similarity evaluation result to obtain an entity-attribute set; generating a tree-shaped hierarchical structure framework by using a large language model, and calling a knowledge graph to perform structural integrity verification; semantic association between the entity-attribute set and the tree structure framework is analyzed, attributes and methods are mapped to corresponding structure nodes, and a mapping reasoning process is output in a natural language form; converting the mapped model structure into a format of XML (Extensive Makeup Language) grammar, and executing automatic compliance check and repair; by designing a progressive task chain of information extraction, semantic fusion, model construction, interpretable mapping and standardized output, a set of complete understanding-enhancement-construction-verification automation system is constructed.

The present application relates to the technical field of short-circuit current calculation, and particularly relates to a flexible DC system short-circuit current calculation method based on a physical information neural network, comprising: setting a model input feature vector, the model input feature vector being used to represent a system operating state before a fault and fault information, and performing data preprocessing on the model input feature vector; constructing a hybrid driving calculation model, the hybrid driving calculation model comprising a physical calculation module and a neural network module, and being coupled based on a preset fusion architecture; performing end-to-end training and optimization on the hybrid driving calculation model by using a preset composite loss function; and performing flexible DC system short-circuit current calculation based on the optimized hybrid driving calculation model, so that the problems of poor precision, speed and convergence, poor interpretability and weak generalization ability in the prior art are solved.

The invention discloses a water area resource utilization grading and pricing method based on spatial heterogeneity correction. The method comprises the following steps: S1, evaluating the service value of a water area ecosystem; s2, determining a water area resource utilization standard price; s3, dividing water area space levels; and S4, correcting the water area resource utilization standard price. According to the method, the water area resource utilization standard price is determined by quantifying the influence of water area resource utilization on the ecological system service value, the hierarchical water area resource utilization coefficient reflecting spatial heterogeneity is introduced for spatial correction, and finally the water area resource utilization hierarchical guidance price is formed. According to the method, a complete technical process from ecological system service value profit and loss quantification to space differentiation pricing is constructed, the water area resource utilization grading guidance price is determined, the technical blank in the field is filled, a scientific basis is provided for a water area resource management decision, and the economic benefit of the water area resource management decision is improved. The method has good operability, acceptability and popularization and application prospects, and ecological protection and sustainable utilization of water resources can be promoted.

The invention relates to a parking lot pricing prediction system and method based on artificial intelligence and big data, and belongs to the technical field of big data analysis. The system comprises a three-layer state sensing and coding module, wherein a graph loop fusion network, an attention scoring model and a user clustering model are arranged in the three-layer state sensing and coding module; the graph loop fusion network is used for encoding the network game layer data into graph embedding vectors; the attention scoring model is used for carrying out dynamic weight distribution and fusion according to the importance of the influence of the current moment on the parking demand; the user clustering model is used for clustering individual heterogeneous layer data into a plurality of typical user groups, and extracting group features to obtain user heterogeneous state vectors. According to the method, a'three-layer perception-collaborative game 'framework is constructed, a regional Pareto improved pricing strategy is generated by utilizing a multi-agent dynamic game, meanwhile, the method has real-time response and decision interpretation capabilities, and social benefits and collaborative commercial and public benefits are embedded in a reward function of the method.

The present application relates to the biological field, and particularly to a weak acid buffer and its use in an immunopeptidome separation reagent, the weak acid buffer comprising amino acids and / or polypeptides, the mass-to-charge ratio of the amino acids and / or polypeptides being theoretically predictable and experimentally reproducible, or the mass-to-charge ratio being beyond the preset acquisition range of a mass spectrometer; the weak acid buffer further comprising a volatile acid, the volume concentration of the volatile acid being 0.01% to 10%; and the pH of the weak acid buffer being 3.0 to 3.5. The weak acid buffer of the present application has high compatibility, can maximally avoid pollution and signal interference to an analysis system, has a simple experimental procedure, and the overall operation can be completed within tens of minutes, thus having low technical threshold and high acceptability; can improve the number of immunopeptide groups identified, thus improving the efficiency of the experiment; and the reagent is cheap and easy to obtain, thus the method has low cost, is suitable for large-scale or even industrial use, has high universality, and the like.

The invention belongs to the technical field of biological information processing, and particularly relates to a method and system for constructing a risk prediction model from Kawasaki disease to huge coronary artery tumor. The invention provides a Kawasaki disease MGCAA risk prediction model construction method and system, and the method comprises the steps: building a prediction model based on six conventional clinical variables through employing a random forest (RF ranger) algorithm; an SHAP explanation mechanism is introduced, so that the contribution of each variable to a prediction result can be visually displayed in global and individual levels, and the transparency and clinical interpretability of the model are improved; through external verification and intercept-only recalibration, the reliability and the applicability of the model in different groups of people can be ensured; according to the method, an online webpage tool (Shiny App) is deployed, and a doctor can input clinical data of a patient in real time and immediately obtain individualized risk prediction and explanation, so that clinical early recognition of an MGCAA high-risk child patient is realized, and formulation of an individualized treatment scheme and early intervention measures is assisted.

The invention discloses an image generation model-oriented watermark generation and detection method and system, and the method comprises the steps: a model provider pre-embeds an invisible watermark in a diffusion model, and provides a high-quality non-perception image generation service for a legal user; when a malicious user generates and propagates illegal or harmful images by using an API, a detection request can be provided to a model party through a supervision party, and a model provider performs watermark detection on suspicious images; and the model provider extracts watermark information from the suspicious image, judges whether the watermark information is generated by the model or not, and submits a detection result to the supervisor to judge whether the watermark information is legal or not. According to the method, a watermarking mechanism which is only used for'detection 'instead of'traceability' is designed, the speech freedom and privacy of legal users are protected while the identifiability requirement of supervision on AI generation content is met, direct tracking of user identities is avoided, and therefore effective balance between content safety guarantee and user right maintenance is achieved, and user experience is improved. And the fairness and acceptability of AI content governance are obviously improved.

According to the multi-label automatic diagnosis and evaluation system and method for ankylosing spondylitis, pelvic X-ray film images and clinical data are combined, automatic diagnosis of ankylosing spondylitis is achieved through a multi-label classification model, and the severity degree of injury of sacroiliac joints and hip joints is evaluated. The system adopts a prior attention mechanism, can automatically focus a key area in an image, and further improves the diagnosis efficiency. Experimental results show that the system still has relatively high diagnosis accuracy in an environment with limited resources, and can be comparable with diagnosis results of experienced doctors. The multi-label classification model with the priori attention mechanism provided by the invention provides a promising and cost-effective tool for diagnosis and evaluation of ankylosing spondylitis, and has important practical application value.

This invention discloses a process optimization method, system, and equipment for a large-scale bio-fermentation model, applicable to pipeline network anomaly detection. The method includes: acquiring multi-source heterogeneous raw fermentation data; standardizing the multi-source heterogeneous raw fermentation data to obtain standardized data; constructing a cross-domain fermentation knowledge base based on the standardized data and publicly available industry information, wherein the cross-domain fermentation knowledge base includes constraint rules for at least one of four major domains: traditional Chinese medicine, food, pet food, and cosmetics; and constructing a fermentation domain knowledge graph based on the standardized data and the cross-domain fermentation knowledge base. Beneficial effects: By embedding prior knowledge from the fermentation knowledge base and knowledge graph into the model through a domain knowledge embedding layer, the model acquires professional domain reasoning capabilities.

This invention provides a multi-agent traceable decision-making system and method for environmental data mining, belonging to the interdisciplinary field of artificial intelligence and environmental informatics. The system includes: a task complexity assessment module, a multi-agent conditional collaboration module, an arbitrator triggering and adjudication module, an environmental knowledge graph module, a feedback learning optimization module, and a decision tracing and visualization module. The method includes the following steps: Step 1: Task complexity assessment and collaboration mode allocation; Step 2: Parallel processing and dynamic collaboration of two intelligent analysts; Step 3: Consistency check and arbitration triggering; Step 4: Arbitrator adjudication and weight integration; Step 5: Human expert correction and weight optimization feedback; Step 6: Decision tracing and weight visualization. This invention enables more accurate decision optimization, significantly improves the professionalism and accuracy of decision-making, and possesses the ability to proactively identify and prevent potential problems.

The application discloses a 3C vehicle-mounted contact network operation state intelligent analysis system and belongs to the technical field of cross of track traffic intelligent operation and maintenance and industrial artificial intelligence. The system associates multi-source heterogeneous observation data to specific equipment units through a device centralization data binding module. A multi-modal feature extraction and state management unit processes data and maintains equipment multi-dimensional state vectors by using a Kalman filter updating unit. A physical-data hybrid decision maker fuses data-driven rules and simplified physical model output diagnostic results. A topology analyzer performs global checking based on mechanical transmission rules. The system also includes a long-term health state prediction and feedback module, which predicts early failure risks by using a hidden Markov model and dynamically feedback adjusts Kalman filter process noise, so that long-term prediction and short-term estimation are coordinated. The application solves the technical problems of multi-source data fragmentation, lack of physical basis for diagnosis and inability to predictive maintenance.

The invention relates to the technical field of agricultural big data and intelligent traditional Chinese medicine planting evaluation, and discloses a traditional Chinese medicine planting site selection evaluation method based on big data. According to the scheme, sunshine and soil salt content collection devices are deployed in candidate places, sampling is carried out daily to form two types of sequences, and a mean value is solved; normalizing a global minimum value and a global maximum value into standardized sunlight and low salt indexes, and carrying out arithmetic averaging to obtain baseline suitability; a perturbation step length is set according to the range and the candidate number, positive and negative disturbance and amplitude limiting are carried out on sunlight and salt, and four types of disturbance baselines are reconstructed after normalization; the sunlight sensitivity and the salt-containing sensitivity are added according to the ratio of the actual standardized step length to the suitability change, a penalty term is generated by taking the maximum value of the whole, the robustness suitability is obtained through deduction and amplitude limiting, and then quantile grading is used; and finally summarizing the longitude and latitude to form a structured result table.

The invention discloses a clinical outcome machine learning prediction method and device based on multiple models and a storage medium, and the method comprises the steps: constructing a plurality of machine learning prediction models under a unified calculation framework, and combining with automatic parameter optimization, multi-dimensional performance evaluation, prediction probability calibration and model interpretation analysis. Systematic modeling and evaluation of a clinical outcome prediction task are realized, instability caused by dependence of a single model and manual parameter adjustment is effectively avoided, and the stability, generalization ability and repeatability of a prediction result are improved. Meanwhile, the reliability and practicability of the prediction result in risk assessment and clinical decision support are improved by performing calibration processing on the prediction probability and introducing clinical decision curve and clinical influence curve analysis. Through model explanation and visual output, the transparency and understandability of the prediction process are enhanced, so that the acceptability and application value of the model in medical research and clinical application are improved.

The application discloses a wastewater treatment artificial intelligence decision optimization method and system, and relates to the technical field of wastewater treatment decision, and solves the technical problem that different abnormal reasons are executed in a differentiated, safe and quantifiable hierarchical response strategy instead of one-size-fits-all control.The application diagnoses based on clear process mechanism indexes such as carbon-nitrogen ratio, sludge load and dissolved oxygen mismatch degree, each step of judgment and subsequent control action has clear physical and chemical meaning and process logic support, the credibility and acceptability of the system are improved, the priority diagnosis path of microbial inhibition state, high load impact and carbon source deficiency state is proposed, and specific, quantitative and executable control parameters and formulas are provided for each state and its subclass, realizing the leap from qualitative judgment to quantitative execution, and the system can automatically and gradually recover to normal working condition after impact relief, improving the self-recovery ability and long-term operation stability of the system.

The invention discloses a motor parameter staggered reasoning identification method based on verification driving and a related device, and belongs to the technical field of motor control. The method comprises the following steps: firstly, obtaining motor operation state parameters, dynamically selecting and executing an interlacing action from an extended action space through a strategy function, obtaining a motor target parameter reasoning value when the interlacing action is a reasoning action, and verifying the motor target parameter reasoning value and generating feedback when the interlacing action is a verification action; and based on a feedback updating strategy function, repeating the reasoning action and the verification action until a preset termination condition is met, obtaining a motor target parameter estimation value, and finally outputting the motor target parameter estimation value and a credibility index. According to the method, verification is embedded into a reasoning link, a closed loop of reasoning-verification-feedback-adjustment is achieved, parameter adjustment is verified through a motor fact common sense library, error accumulation is prevented, verification is dynamically scheduled through a strategy function, verification intensity and reasoning efficiency are balanced, and the method is adaptive to industrial multi-working-condition and small-data scenes.

The application relates to the field of intelligent medical treatment, and discloses an in-hospital integrated platform system based on artificial intelligence, which comprises the following steps: extracting current patient identification information, identifying a surgical patient in an anesthetic state in real time, and automatically associating a preoperative electronic medical record with an intraoperative anesthesia plan; based on an artificial intelligence semantic recognition model, image text extraction and semantic understanding are carried out on parameter content set in real time in a respirator equipment screen; according to the difference between the set parameters and the semantic recognition of the system, a cross-system risk factor atlas is constructed, and a risk linkage score is generated in real time for intraoperative operation behavior; the parameter evolution path under the same type of operation in the past is called, and analogy modeling is carried out in combination with the current patient anesthesia depth, physiological data and the intraoperative stage; when it is confirmed that there is a strategy-level abnormality in setting, the warning level and the display form are dynamically adjusted based on the operation equipment type, the setting personnel role and the current stage load intensity. The application has the advantage of improving the intelligent level of a hospital.

This invention discloses an intelligent review method for construction schemes based on adaptive multi-agent collaboration, belonging to the field of construction scheme review technology. It solves the technical problems of inability to deeply identify key technical points, lack of collaboration among different professional perspectives, absence of a supervisory layer and reflection mechanism, lack of quality monitoring during the review process, and the potential direct output of errors and logical contradictions. Through dynamic agent generation technology, the system can adaptively form agent review teams based on different scheme characteristics. Employing a multi-agent collaboration and shared blackboard mechanism, it simulates a multi-expert review scenario. By introducing a consensus decision-making algorithm based on confidence fusion, the system can autonomously negotiate and adjudicate conflicts of opinion between different agents or regulatory clauses, outputting a unified conclusion. Through the meta-agent layer, the system achieves real-time monitoring and reflection on its own review process, automatically verifying reasoning logic, identifying low-confidence conclusions, and triggering secondary reviews.

The present application relates to the use of a cyclic heptapeptide in the preparation of a medicament for reducing body weight. Specifically, the present application discloses a new use of a cyclic heptapeptide Cyclo-[TYVPKAF] or a pharmaceutically acceptable salt thereof in the preparation of a medicament for reducing body weight, preventing or treating a body weight related disease. The body weight related disease includes obesity, overweight, metabolic syndrome, type 2 diabetes, fatty liver, cardiovascular disease or osteoarthritis, etc. The present application first discovers that the specific cyclic heptapeptide has clear and convenient body weight control activity, and in particular, its orally effective characteristics provide a new choice for long-term body weight management.

This application discloses a hierarchical adaptive intelligent association and alignment method for multimodal flight data, comprising: acquiring multi-source airborne flight data and preprocessing it; mapping data of different modes in the preprocessed multi-source airborne flight data to a common feature space to obtain cross-modal alignment features, wherein an aircraft physical dynamic model is introduced as a constraint during the mapping process; performing local precise association and global semantic association in parallel based on the cross-modal alignment features; wherein the local precise association is used to obtain pixel-level or point-level matching results, and the global semantic association is used to obtain globally consistent semantic association results; and fusing the results of the local precise association and the global semantic association to obtain association information of corresponding granularity. This invention effectively solves the "data silo" problem caused by differences in format, spatiotemporal reference, and modality of multi-source flight data, significantly improving association accuracy, robustness, and system adaptability.

This invention discloses a weather-interference-adaptive point cloud 3D target detection method and apparatus, relating to the field of target detection technology. The method includes: constructing a weather-interference-adaptive point cloud 3D target detection model, comprising an interference sensing network and a hybrid expert target detection model; converting raw point cloud data into distance images and inputting them into the interference sensing network for interference level classification to obtain interference levels; matching the corresponding expert model in the hybrid expert target detection model using a sparse activation mechanism based on the interference levels; inputting the raw point cloud data into the matched expert model and outputting the corresponding detection results; based on the detection results and interference levels corresponding to the raw point cloud data, employing a staged training strategy, training the detection model through a constructed total loss function to obtain a trained model; inputting the point cloud data to be detected into the trained model and outputting the final 3D target detection result. Using this invention can improve detection accuracy.

A judgment prediction method based on dynamic legal knowledge graph and sequence-dependent multi-task learning, relates to the field of artificial intelligence prediction technology, through the time series attention mechanism to dynamically update the legal knowledge graph, so that the model can adapt to the changes of legal amendments and judicial policies. Design a sequence-dependent multi-task learning module, serialize the prediction of charges through the legal logic gating unit, and sort the candidate legal articles to generate legal article recommendation results, and then predict the sentence. Effectively solves the problems of static lag of knowledge graph, broken task logic chain, low distinguishability of confusing charges and insufficient explainability in the prior art, significantly improves the accuracy and compliance of judgment prediction. Among them, the legal article task adopts a recommendation form, that is, the legal article recommendation result is generated by sorting the probability distribution of the candidate legal articles, which essentially belongs to an implementation way of the legal article prediction task.

The invention discloses a multi-source data time sequence fusion and interpretability-based data missing reservoir water volume prediction method, which comprises the following steps of: 1, through reverse derivation of reservoir output flow and evaporation capacity of a reservoir, obtaining reduced water volume which only considers reservoir input flow and ignores loss water volume such as reservoir output flow, and taking the reduced water volume as an equivalent substitution variable of reservoir input runoff volume; 2, collecting meteorological data, and forming a data set with the reduced water volume; 3, building a time sequence fusion converter model, and meanwhile, designing an automatic parameter optimization module, so that the time investment of a parameter adjustment link is reduced, and the subjectivity and limitation of manual parameter adjustment are avoided; and 4, water volume prediction and variable importance analysis. According to the method, the index of the reduced water volume is used for replacing the reservoir flow, meanwhile, the weather forecast which is known data in the future is fully used for construction and prediction of the prediction model, the problem that part of reservoirs lack the reservoir flow is solved, high prediction precision is guaranteed, and meanwhile the defect of'black box 'of a traditional model is overcome.

Systems and methods for determining the concentration of a plant component in a developed food product prior to or during the manufacture of the food product are disclosed. The method may include: receiving, by a computing system, protein data of a plant component; retrieving at least one model, the at least one model having been trained to generate an output indicative of a desired concentration of a plant component to be used for developing the food product, the at least one model comprising at least one of a pH model, a temperature model, and a protein content model; providing the received protein data as input to the model; receiving an output from the model; and generating instructions for developing the food product, the instructions including a desired concentration range of the plant ingredient.

A multimodal clinical prognostic algorithm based on generative pre-training and gated attention is proposed. This algorithm utilizes the BioGPT pre-trained medical language model to semantically encode medical record text and encodes structured clinical data through linear mapping and self-attention mechanisms, obtaining textual and structured feature representations. A bidirectional cross-modal attention mechanism enables deep interaction between the two types of features, resulting in mutually reinforcing text-guided and structured-guided representations. A gated fusion layer dynamically learns modal weights and performs adaptive weighted fusion to generate a unified multimodal semantic representation. Finally, an adaptive risk projection head incorporating a fully connected network and Dropout is used to output the probability distribution of patient mortality risk categories within different time windows. This algorithm achieves deep semantic alignment and adaptive fusion of multimodal clinical data, improving the accuracy and interpretability of prognostic prediction.