22results about How to "Reduce the need for computing resources" patented technology

The invention discloses a pre-training visual language model-based cross-country scene unmanned vehicle auxiliary decision-making method, and belongs to the technical field of unmanned driving. Comprising the following steps: constructing a simulation data set for training a three-dimensional target detection network; carrying out cross-modal consistency fusion and cue word design; and generating and mapping driver element actions and decisions. According to the invention, by fusing a three-dimensional target detection network and a pre-trained visual language model, introducing a thinking chain type reasoning and selective spatial information injection mechanism, and combining a trigger type reasoning process and meta-action sequence generation, high-level understanding and auxiliary decision making of a cross-country scene are realized; therefore, the defects of a traditional geometric programming algorithm in an unstructured environment are overcome.

This invention relates to a physical information neural network-based intelligent performance prediction method and system for automated multi-material fiber placement on complex curved surfaces in aircraft. The invention includes: collecting fiber placement process parameters; performing standardized preprocessing and physical constraint-enhanced sampling to obtain an enhanced training dataset; constructing a high-order nonlinear sparse regression candidate library based on the enhanced training dataset; jointly identifying piecewise smooth physical differential equation systems and their mode switching logic using sparse Bayesian regression and Hidden Markov Models to obtain an embeddable inverse mechanism model; constructing a Physical Information Embedded Generative Adversarial Network (PI-GAN); collaboratively optimizing the generator and discriminator through adversarial training to output surface defect indices and mechanical performance indices; and initiating incremental self-learning when introducing new process scenarios to achieve model self-evolution. This invention achieves end-to-end accurate prediction of multi-dimensional quality indices such as surface defects and mechanical performance.

The invention provides a method for rapidly detecting microbial species, and particularly, the method comprises the following steps: constructing a double-level database and a corresponding gene cluster; constructing a reference matrix X based on the database and the gene cluster; comparing to-be-detected third-generation sequencing data with the gene cluster to generate equivalence classes, and constructing a counting matrix Y based on the equivalence classes and the gene cluster; based on the counting matrix Y and the reference matrix X, determining the gene abundance in the to-be-detected sample; and determining microbial species in the sample to be detected based on the gene abundance. The rapid pathogen detection method provided by the invention is high in analysis speed, fully utilizes the advantages of the three-generation sequencing data, and can more directly obtain the microorganism composition information of the sample; the method is low in computing resource demand, easy to deploy and high in precision detection capability.

The application provides a method and system for predicting main steam parameters before steam warming of a combined cycle unit, the method comprising the following steps: obtaining historical operation data of a target power generating unit, the historical operation data comprising a plurality of observable input parameters and output parameters closely related to a main steam system; constructing an NARX neural network model, training the neural network model based on the historical operation data, and obtaining a trained main steam parameter prediction model; deploying the main steam parameter prediction model on a general computing device; obtaining initial state parameters under a current working condition before starting the power generating unit, and inputting the initial state parameters into the main steam parameter prediction model; and outputting a predicted trajectory of main steam key parameters before the high-pressure bypass system is put into operation from the main steam parameter prediction model.

This invention discloses a method and system for identifying innovative points in academic papers, relating to the field of artificial intelligence technology. The method first processes the paper abstract through a concept path extraction module, including: structured semantic segmentation, concept pair extraction and verification, constraint relation triple generation, hierarchical verification, and path optimization, to construct a complete set of concept paths for the paper. Then, in a rare path discovery module, the popularity index of each concept path in the global path frequency dictionary is calculated and compared with a threshold to identify rare paths as the scientific innovative points of the paper. This invention effectively suppresses the illusion of language models through a knowledge graph constraint mechanism, significantly improving the accuracy of concept path extraction and its coverage of long-tail concepts, achieving accurate and efficient identification of innovative points in academic papers at the path level.

The application discloses a scene generation method and device based on source-load uncertainty and equipment, and relates to the technical field of energy scheduling. The method comprises the following steps: acquiring real-time source-load data of a power system; processing the real-time source-load data based on a target prediction model to obtain a multi-time-scale source-load prediction result, wherein the target prediction model is obtained by compressing the weights of a long short-term memory network prediction model; generating a collaborative scheduling strategy of source-load resources under the multi-time-scale source-load prediction result based on a multi-agent scheduling decision model; and generating a hydropower source-load scene set of the power system at different time stages under the source-load uncertainty based on the collaborative scheduling strategy. In the foregoing manner, the prediction link and the decision optimization link are closely coupled, the multi-time-scale prediction result is used to dynamically guide the reinforcement learning decision, the adaptability of the system to the source-load uncertainty is enhanced, and the organic collaboration of the multi-time-scale and the accurate generation of the uncertainty scene are realized.

The application discloses a resource self-adaptive federated learning multi-modal knee osteoarthritis auxiliary diagnosis method, which comprises the following steps: a server initializes a multi-modal model for knee osteoarthritis diagnosis, and pre-deploys a low-rank adaptive plug-in in a trainable layer; after a client reports local hardware resources, the server allocates a resource echelon and issues a two-dimensional clipping ratio; the client clips the model in depth and width based on the clipping ratio, activates a LoRA module of a specified layer, loads local knee osteoarthritis X-ray images and clinical text data for multi-modal training, and establishes semantic mapping of image features and clinical descriptions; the client only uploads an activated LoRA parameter update amount, the server aggregates and optimizes a global model through weighted average aggregation and key layer compensation; and the learning ability of the model to key discriminative features of knee osteoarthritis is strengthened in combination with real-time resource monitoring and knowledge distillation. The application covers heterogeneous computing power devices through a resource self-adaptive mechanism, and improves the participation efficiency of primary medical institutions.

The application discloses a kind of multi-energy distribution network voltage control methods, comprising the following steps, define local voltage control rule;Analysis distributed power output causes voltage fluctuation upper and lower limit;Build global voltage control function;Algorithm is solved to objective function, the local voltage control rule is acquisition control point voltage u, determine whether to satisfy the following constraint, set the reactive power output of voltage compensation device.The multi-energy distribution network voltage control method, considering the uncertainty fluctuation of distributed power supply, improve the accuracy of voltage fluctuation analysis, formulate the localized voltage adjustment rule and global voltage optimization function, realize passive control and active control combination, reduce fluctuation rate, using interval number to express the uncertainty of distributed power supply, less computing resource demand, control reaction speed is fast.

The application discloses a multi-agent material calculation consistency calibration method, belonging to the cross field of material science, artificial intelligence and computational physics. The method adopts a master-slave structure multi-agent system, containing one master agent and at least three slave agents. The master agent is responsible for overall coordination, embedding physical mechanism constraints, consistency calibration and output results. The method steps are: constructing a calculation system, introducing physical mechanism constraints and embedding slave agent calculation models, collecting calculation data, performing bias analysis, adjusting parameters or correcting results through iterative calibration, and finally verifying and outputting the final results. The application introduces physical mechanism constraints, combines bias analysis and iterative calibration, and solves the problems of poor consistency of existing multi-agent material calculation results and easy violation of physical laws. Experiments show that the method reduces the deviation of the calculation results and the experimental measured values to below 3%, improves the calibration efficiency by more than 40%, and improves the calculation accuracy by more than 35% compared with the prior art, thereby improving the accuracy and engineering practicability of high-throughput calculation of material performance.

The invention discloses a method for detecting abnormal electricity consumption of an intelligent electric meter, which relates to the technical field of intelligent meter reading, and comprises the following steps of: 1, data acquisition and preprocessing, 2, data compression and dimension reduction, 3, lightweight data aggregation, 4, transfer learning and fine tuning, 5, self-supervised learning, and 6, model lightweight. According to the method, a lightweight aggregation algorithm is used, that is, data aggregation is carried out through a local weighted average method instead of a complex distributed data aggregation model, so that the calculation complexity is reduced, the aggregation efficiency is improved, and the dependence on hardware resources is reduced; before data aggregation, a data compression technology is used to carry out dimension reduction on power consumption data, so that the data volume is reduced, the aggregation efficiency is improved, and the information loss is reduced; transfer learning and fine tuning are adopted, a BERT pre-training model is utilized to accelerate training of the deep belief network, training data requirements are reduced, and model training efficiency is improved.

The application relates to the technical field of clean room air conditioner air outlet design, in particular to a clean room air conditioner air outlet optimization design method based on fluid dynamics; through construction of a multi-physical field coupling simulation model based on a fluid dynamics model, accurate prediction of a temperature field, a flow velocity field and a pollutant concentration field in the clean room is realized; the method takes Reynolds average NS equations and a discrete phase model as cores, combines a Standard k-epsilon turbulent flow model and a wall surface function method, effectively captures complex air flow organization and particle diffusion behavior in the clean room, provides a high-precision data basis for subsequent multi-target optimization, minimizes energy consumption under the premise of guaranteeing air quality, and has important engineering application value; and the technical problem that existing clean room air conditioner air outlet design methods mainly depend on experience and industry standards or focus on optimization of a single parameter and fail to fully consider the action of multiple factors is solved.

The invention provides an unplanned return ICU risk prediction method, system and device, and the method comprises the steps: fusing an MIMIC-IV public data set and local private data, and carrying out the standardization preprocessing of multi-source medical data through a rule module and a clinical knowledge base; constructing a partitioned feature system comprising basic features, laboratory indexes, hemodynamic parameters and respiratory metabolism dimensions, and optimizing feature expression by adopting a sliding window and a feature interaction method; performing medical field fine tuning based on a large language model, and converting the structured features into natural language description in combination with cue words; carrying out model training by adopting optimization strategies such as label smoothing and gradient cutting, and evaluating model performance in a mode of combining internal verification and external verification; and realizing interpretability analysis of model prediction by using an SHAP framework. According to the scheme, the accuracy and generalization ability of ICU return risk prediction and the applicability of an actual scene are remarkably improved.

The invention discloses a visual content generation method and a training method and device of a visual content generation model. The visual content generation method comprises the following steps: decoding discrete feature representation to obtain first visual data matched with target semantic content; obtaining a first continuous feature representation of the first visual data; and performing reconstruction processing on the first continuous feature representation through a visual content generation model to obtain a visual content generation result of the discrete feature representation. Compared with a method of directly performing reconstruction from discrete feature representation in related technologies, the method performs reconstruction from continuous feature representation, and the continuous feature representation contains richer spatial and semantic information, so that the complexity of executing a reconstruction process by the visual content generation model is remarkably reduced, and the reconstruction efficiency is improved. The efficiency of visual content generation based on discrete feature representation is improved.

The application discloses a small sample professional data knowledge graph growth method for low resource computing power, uses a full parameter version large model as an initial labeling tool to generate high-quality training data, solves the problems of training data scarcity and poor quality, and trains a lightweight large model based on the training data generated by the full parameter version large model and in combination with a LoRA fine-tuning method, realizes the migration of the extraction capability of the full parameter version large model to the lightweight large model, effectively overcomes the resource bottleneck caused by the full parameter version large model, and significantly reduces the computing resource requirement; meanwhile, aiming at the problem that entity relations are difficult to align, an entity alignment mode based on semantic embedding and large language model auxiliary judgment is provided, the semantic consistency of the knowledge graph is ensured, and the domain knowledge accumulation capability and the updating and expanding quality are improved.

The present application relates to the field of image processing and target image recognition technology of civil optical equipment such as intelligent traffic, city supervision and commercial monitoring, and particularly relates to a multi-target characteristic recognition method based on photoelectric image, which aims to improve the adaptability and accuracy of target recognition in target optical imaging, and the method obtains target motion image through photoelectric equipment, divides the target into relative static, straight line motion and turning state according to a preset threshold, extracts target motion trajectory, judges target state, respectively carries out straight line and arc line fitting on the target trajectory of straight line motion and turning state, analyzes the overall motion state of multiple targets in the imaging image based on the motion state trajectory, and adopts corresponding individual motion trajectory statistical analysis method for different states, the multi-state adaptive analysis capability of the method significantly improves the recognition accuracy, and the recognition accuracy of the target with frequently changing state in a complex scene is improved by more than 30%.

This invention provides a power amplifier predistortion method and system based on heterogeneous neural networks, belonging to the field of power amplifier predistortion technology. The method includes: constructing a predistortion neural network, including a backbone network and an auxiliary network. The backbone network is used to perform nonlinear fitting on the power signal of the power amplifier, and the auxiliary network is used to generate dynamic modulation parameters based on changes in the circuit state of the power amplifier. The power signal of the target power amplifier is used as the input signal of the predistortion neural network. The backbone network performs nonlinear fitting on the input signal to generate fitting features. Based on the dynamic modulation parameters output by the auxiliary network, the fitting features output by the backbone network are modulated to output a predistortion signal. Beneficial effects: By constructing a predistortion neural network including a backbone network and an auxiliary network, and dynamically modulating the fitting features, the system can dynamically adapt to changes in the operating state of the power amplifier, improving the accuracy and real-time performance of predistortion compensation, and reducing computational resource requirements.

The invention discloses a simulation method and device for plasma field distribution in a vacuum arc-extinguishing chamber. The simulation method comprises the following steps: establishing a three-dimensional simulation model of the vacuum arc-extinguishing chamber in a pole-mounted circuit breaker; the method comprises the following steps: importing a three-dimensional simulation model of a vacuum arc extinguish chamber in a pole-mounted circuit breaker into COMSOL Multiphysics software, and setting a plasma mass conservation equation, a momentum equation, a cathode magnetic field boundary condition function and an anode magnetic field boundary condition function in combination with an MUMPS solver; in COMSOL Multiphysics software, initial calculation conditions are set, the rotating angular speed of a moving contact and an arc current value serve as input variables, multi-physics field coupling is achieved, and simulation of plasma field distribution in a vacuum arc extinguishing chamber in a pole-mounted circuit breaker is completed. By establishing a multi-physics-field coupled vacuum arc extinguish chamber inner plasma field calculation model, the spatio-temporal evolution law of plasmas under the action of a strong electric field and a magnetic field is accurately represented, and a theoretical basis is provided for optimizing the arc extinguish chamber structure design, restraining breaking overvoltage and preventing restrike.

The invention discloses a Python-based aeronautical structure dynamics analysis error rapid evaluation method. The method comprises the steps of Taylor expansion-based error propagation theoretical model construction, progressive convergence criterion design and hierarchical credibility evaluation system implementation. According to the method, through a simplified Taylor expansion error propagation theory and a sensitivity-based credibility evaluation system, in combination with Python object-oriented programming, a rapid evaluation mechanism of a kinetic analysis result is established. The technical problems that in the prior art, error evaluation calculation is high in cost and low in efficiency, and reliability is difficult to guarantee are solved. The method is suitable for error evaluation in dynamic analysis of a large-scale aviation structure, and has a relatively high engineering application value.

The application discloses a breath methane and hydrogen synchronous detection method and system based on an improved gas sensor, and relates to the biomedical detection field.The method comprises the following steps: using the improved gas sensor to synchronously collect a breath flow signal and a sensor original electric signal at a set sampling rate; identifying a breath alveolar air plateau according to the breath flow signal, and intercepting a corresponding sensor original signal segment; performing filter denoising and baseline correction preprocessing on the sensor original signal segment of the plateau; based on the hydrogen and methane response time difference amplified by the gas diffusion delay structure of the improved gas sensor, extracting a first characteristic quantity related to the hydrogen concentration and a second characteristic quantity related to the methane concentration from the preprocessed plateau signal segment; and calculating and outputting the hydrogen concentration value and the methane concentration value according to the first characteristic quantity and the second characteristic quantity.The application can synchronously detect breath methane and hydrogen based on the sensor diffusion delay structure, and has the advantages of low cost, portability and high detection reliability.

The present application relates to a kind of vehicle driving path processing method, device, electronic equipment and storage medium, the vehicle driving path processing method, obtain the planning path of the reference line indicating vehicle driving, extract the straight path in planning path, so as to divide the straight path based on preset length, obtain segmented path, the length of segmented path is less than or equal to preset length, adjust the distance between segmented path and the road boundary of vehicle driving road, obtain target planning path, the distance between the reference line indicated by target planning path and road boundary is greater than or equal to preset distance threshold value;Therefore, by dividing the straight path into segmented path, segmented path is handled, solve the problem that the high demand of computing resource and low processing precision exist in the length process of straight path, and the effect of reducing the demand of computing resource and improving processing precision is realized by processing segmented path.

This invention relates to the field of wind energy forecasting technology, and more particularly to a method, system, device, and storage medium for early warning of extreme offshore wind power events based on typhoon forecasting. The method includes acquiring real-time satellite cloud images; identifying and marking regions of typhoons in the satellite cloud images based on their morphological characteristics; obtaining the pixel coordinates of the centroid of these regions and converting them into actual latitude and longitude coordinates to obtain the typhoon's eye coordinates; acquiring infrared satellite cloud images; obtaining the maximum wind speed radius based on the typhoon's eye coordinates; and estimating the current maximum wind speed using a preset wind speed model; calculating the typhoon's short-term trajectory, future maximum wind speed, and maximum wind speed radius using a preset typhoon short-term dynamics integrated prediction model; and predicting the typhoon's impact on a designated location within a future timeframe using a preset typhoon physical model, and calculating the wind speed and direction at that location.

This invention relates to the field of autonomous driving technology, providing a method and system for autonomous driving simulation testing based on functional design documents. The method includes: sending a simulation test form to the cloud and receiving an intermediate simulation test scenario file returned from the cloud; the simulation test form is obtained by sequentially parsing and classifying requirement management information; the requirement management information is extracted from the functional design document; the intermediate simulation test scenario file is obtained by running a form processing tool based on the Django web framework, using the simulation test form as input; the form processing tool is deployed in the cloud; a simulation test scenario is generated based on the intermediate simulation test scenario file, and autonomous driving simulation testing is performed based on the simulation test scenario. The simulation test scenario obtained by this invention can comprehensively cover the requirement management information in the functional design document, better meet testing requirements, and eliminates the step of secondary selection of scenario sets based on requirements in existing scenario construction methods.