22results about How to "Avoid excessive dependence" patented technology

This invention provides a method and system for UAV path planning based on a fusion algorithm, relating to the field of UAV autonomous navigation and path planning technology. The method includes the following steps: acquiring the starting coordinates, ending coordinates, and obstacle information in the flight environment of a flight mission, and constructing a flight environment spatial model; defining path constraints and optimization objectives, and generating an initial set of feasible paths based on the flight environment spatial model, path constraints, and optimization objectives; performing weighted fusion and guided enhancement on the initial set of feasible paths to obtain a weighted fused path and a guided enhanced path; employing a particle swarm optimization algorithm to construct an initial particle swarm based on the initial feasible path set, the weighted fused path, and the guided enhanced path, and iteratively optimizing the initial particle swarm to obtain a globally optimal particle; and decoding the globally optimal particle to obtain the final flight path. This invention significantly improves the efficiency and quality of UAV path planning in complex static obstacle environments.

The invention discloses a wireless network positioning method, server and system, and relates to the technical field of wireless communication and positioning navigation. The method comprises the following steps: receiving a signal sent by a to-be-positioned target node through a plurality of positioning reference nodes and extracting an original positioning parameter sequence; then, improved Z-score exception elimination based on median and median absolute deviation and adaptive attenuation coefficient smoothing based on average inter-frame hopping are carried out on the sequence, and a stable estimated value and a measurement variance are output; and finally, according to the number of the effective positioning reference nodes, adaptively switching a CHAN algorithm or a weighted least square method to carry out position calculation. The improved Z-score filtering and dynamic weight fusion technology is adopted, the defects in the prior art are overcome, the problem that a fixed algorithm is poor in robustness when the reference node topology change is positioned is solved through an algorithm self-adaptive switching mechanism, the precision and adaptability are remarkably improved in a complex scene, and meanwhile the deployment and maintenance cost is reduced.

The application relates to the field of artificial intelligence and computer vision, and provides a three-dimensional point cloud scene target positioning method based on a natural language instruction, which comprises the following steps: screening a relation triple containing an object category in a three-dimensional visual scene; determining a hierarchical attribute of the object category and constructing a hierarchical knowledge graph; analyzing a given natural language description to obtain a plurality of two-order semantic triples; adding the hierarchical attribute to the object in the two-order semantic triple to obtain a hierarchical two-order semantic triple; determining an initial visual feature of an object in a three-dimensional point cloud scene to be recognized; outputting a prediction score of the object; screening the objects corresponding to the prediction scores from high to low to obtain candidate objects; outputting a matching score of the candidate objects and a first high-order semantic triple; and determining a target object in the three-dimensional point cloud scene to be recognized. The application improves the prediction accuracy and has stronger robustness.

The application discloses a kind of industrial equipment's health degree analysis method without weight, belong to electric digital data processing technical field, comprising: in healthy state, according to the data index type for analyzing health degree, respectively collect at least one historical time window of industrial equipment industrial time series data, as reference data, collect the industrial time series data in current time window, as comparison data;According to reference data and comparison data, one by one through kernel density estimation, obtain probability density curve, to obtain probability distribution;According to data index type, the probability distribution of comparison data and the probability distribution of at least one reference data corresponding, obtain the JS divergence value for measuring data distribution similarity under data index type, to obtain the health degree of industrial equipment according to multiple JS divergence values.The application is based on kernel density estimation and JS divergence, quantifies distribution difference, without weight setting, improves health degree evaluation sensitivity and consistency.

The application belongs to the technical field of intelligent design and computing optimization, and provides a multi-agent-based cross-physical-field microstructure reverse design method and system, obtains a design intention, analyzes the design intention into a quantitative cross-physical-field attribute target vector; utilizes a pre-trained neural network model to map the design intention into an adjusting parameter in a latent representation space, decodes and generates a geometric configuration of an initial microstructure, and constructs an initial evolution population; performs cross-physical-field numerical simulation on the microstructure in the population; identifies an evolution direction of a parameter space through local gradient approximation, performs elite screening by using a non-dominated sorting driven by a Pareto, dynamically adjusts the adjusting parameter of the latent space, guides the evolution process to converge to a physically effective Pareto frontier, and obtains a design scheme according to an optimization result. The application realizes efficient and accurate design of a microstructure under complex constraints.

The application discloses a clock error drift modeling method and system based on statistical likelihood optimization. The method first acquires clock error data in a clock synchronization system and performs standardization processing; then, clock speed and clock drift are calculated, and the clock drift is modeled as a random process conforming to a Gaussian process; next, a neural network model is constructed, the negative log-likelihood of the clock drift is taken as a loss function for model training, and the loss function is optimized to minimize the clock error drift error; finally, the system generates correction instructions by using the trained model, and high-precision clock synchronization is maintained when GNSS signals are missing. The application effectively improves the robustness and precision of clock synchronization, and is particularly suitable for environments where GNSS signals are unstable or unavailable.

This invention belongs to the field of sensors and signal processing, specifically relating to a wavelet kernel-scale sensitivity-guided denoising method for abrasive particle induced voltage signals. The method includes: acquiring abrasive particle induced voltage signals and performing harmonic cancellation to obtain a preprocessed signal; constructing a wavelet kernel function and calculating a kernel-scale guided spectrum using it and the preprocessed signal; constructing a sparse joint denoising model based on the kernel-scale guided spectrum; processing the sparse joint denoising model to obtain a convex optimization objective function; solving the convex optimization objective function using an adaptive step-size gradient descent method and an adaptive iterative shrinking threshold method to obtain a weight vector characterizing the distribution of abrasive particle characteristic signals; binarizing the weight vector characterizing the distribution of abrasive particle characteristic signals to obtain a feature indicator vector; performing a Hadamard product between the feature indicator vector and the preprocessed signal, followed by low-pass filtering to obtain a denoised signal. This invention can adaptively and non-destructively enhance and denoise abrasive particle characteristic signals under strong interference environments.

The present application relates to a pathological whole slice image classification method based on multi-branch independent mask and Dirichlet evidence fusion, belonging to the cross field of biological information and artificial intelligence. In view of the defects of traditional multi-instance learning method in weakly supervised classification task of pathological whole slice image, such as excessive attention concentration and static fusion, the present application sets dynamic mask parameters through multi-branch independent setting, forces different branches to pay attention to different pathological regions, and solves the problem of insufficient feature diversity caused by attention concentration; combining the confidence and uncertainty of Dirichlet distribution quantization branch prediction, the branch fusion weight is dynamically adjusted based on evidence theory, and the fusion robustness of multi-branch prediction result is improved. The experiment is verified on the public pathological data set such as CAMELYON-16, compared with the MIL method, the present application improves the AUC index by 1.1-2.4%, and significantly enhances the accuracy and generalization ability of pathological WSI classification.

This invention relates to the field of identity verification technology, specifically an identity verification method, system, device, and medium integrating multimodal biometrics. The method includes acquiring user features of a target user and extracting corresponding user feature vectors based on these features. The user features include facial biometrics, dynamic signature trajectory features, employee ID features, and password features. Based on the confidence level of each user feature vector, a target weight is determined for each user feature. The target feature vector is obtained by weighting each user feature vector and its corresponding target weight. Finally, the identity information of the target user is verified based on the target feature vector. The beneficial effects of this invention are that it not only effectively improves the system's adaptability and anti-counterfeiting level in complex application scenarios but also ensures high security and high availability of identity verification.

The application discloses a kind of uncertainty field environment under multi-space-ground robot cooperative search method, it is related to multi-agent cooperative search technical field.The method includes: grid field task area, define space-ground robot state;Based on large language model, initialize the search value of task area cell;Design the total cost of cooperative search path planning including path cost and mode switching cost;Introduce communication distance constraint, adopt path decision method based on predictive control, select the minimum path point motion of cost, and dynamically update search value to carry out rolling optimization.The present application reduces invalid search area using large language model, improves search efficiency;Mode switching cost and different mode energy consumption are included in planning, avoid frequent switching and excessive dependence on high energy consumption mode, improve endurance.The present application is suitable for heterogeneous or homogeneous multi-agent system, significantly improves the practicability and efficiency of multi-space-ground robot cooperative search.

The invention provides a special-shaped household garbage classification method and system based on a knowledge graph, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting multi-dimensional parameter data of special-shaped household garbage, executing preprocessing and feature extraction to generate visual and material features, integrating and generating structured feature vectors, and converting the structured feature vectors into semantic feature representations, semantic feature representation is generated; constructing a junk knowledge graph, performing feature matching on the semantic feature representation and the knowledge graph, calculating an entity comprehensive matching degree, and screening to generate an instance candidate entity set; based on the instance candidate entity set and the knowledge graph, analyzing and reasoning an association path of the candidate entity and the garbage category, and calculating a credibility score of the association path; according to the method, the entity comprehensive matching degree, the association path and the credibility score are evaluated, and the garbage category is output, so that accurate classification of the special-shaped household garbage can be realized, the limitation of traditional matching based on a shape template is broken through, and the accuracy and adaptability of garbage classification are improved.

The invention discloses an AI-assisted vehicle evaluation scene library construction method and system, and relates to the technical field of vehicle testing. The method comprises the steps of obtaining user demand data; performing semantic analysis on the user demand data by using a pre-trained language processing model, and extracting a user demand tag; based on the user demand labels, constructing a label knowledge base; generating a vehicle evaluation scene description corresponding to the user demand based on the tag knowledge base; and screening the vehicle evaluation scene description, and constructing a vehicle evaluation scene library based on a screening result. According to the invention, intelligent, efficient and comprehensive construction of the vehicle evaluation scene library can be realized.

The invention discloses a knowledge graph reasoning method and system fusing GRU and multi-head attention, and belongs to the field of natural language processing, and the method comprises the steps of text sequence word vector conversion, GMKR model reasoning and hyper-parameter optimization; the GMKR model innovatively constructs a multi-head attention mechanism-GRU-structure filter collaborative system, carries out parallel mining of adjacent entity multi-semantic information, dynamically encodes historical paths and eliminates noise, innovatively designs a hierarchical reward mechanism, and matches a hierarchical Dropout strategy; according to the knowledge graph reasoning method and system fusing the GRU and the multi-head attention, the problems of insufficient attention of candidate entities, sparse rewards and overfitting in the prior art are effectively solved, the reasoning accuracy and generalization ability are remarkably improved, and large-scale complex knowledge graph reasoning requirements are met.

The application discloses a kind of steam turbine last stage moving blade water erosion state evaluation method, comprising the following steps: obtaining the flow passage geometric model of steam turbine last stage moving blade and the fluid thermodynamic parameter in last stage moving blade flow passage under operating full condition;Steam turbine last stage moving blade blade geometric model and steam turbine last stage stationary blade blade geometric model are obtained;For the flow passage geometric model and blade geometric model, introduce fluid mechanics control equation, momentum equation, solid mechanics control equation, fluid-solid coupling definition equation;And introduce boundary condition and assumption condition;The flow passage geometric model and blade geometric model are meshed;First, the large eddy simulation method is used to calculate the flow field thermodynamic parameter distribution, then the fluid-solid coupling calculation method is used, the flow field thermodynamic parameter distribution result is transferred to blade for further calculation, and numerical simulation is carried out, to obtain the stress distribution and deformation of last stage moving blade;Water erosion degree is calculated, and the steam turbine last stage moving blade water erosion state is obtained.

The invention discloses an equipment control method and system based on deep learning, and relates to the technical field of compressor control, and the method comprises the steps: obtaining a historical normal sequence and a current operation sequence, and carrying out the preprocessing to obtain a historical feature data set and a current feature data set; calculating the attention weight of each feature under each time scale based on the historical feature data set; dividing the historical feature data set and the current feature data set to obtain a historical fusion window and a current fusion window under each time scale; combining the attention weight to calculate a fusion value and construct a fusion sample, and obtaining a historical fusion sample set and a current fusion sample under each time scale; constructing and training a prediction model to obtain an effective prediction model under each time scale; and calculating a comprehensive valve opening value at the next sampling moment based on the current fusion sample and the effective prediction model. According to the method, the valve opening of the compressor can be accurately predicted, and the high-precision control requirement of the compressor under the complex working condition is met.

The invention discloses a multi-source data fusion type inventory prediction and regulation method for an intelligent storage environment, and relates to the technical field of intelligent storage management and logistics information processing. According to the method, the uncertainty boundary in the inventory evolution process is effectively described, excessive dependence on a single determined value is avoided, and the stability and reliability of inventory prediction are improved on the whole; by carrying out time slice-level envelope modeling on an inventory change process and automatically identifying a data missing interval, the continuity and consistency of an inventory evolution trajectory can be still kept under the condition that data is incomplete or fluctuates, and the prediction distortion risk caused by factors such as sensing abnormity and communication delay is remarkably reduced; furthermore, a future inventory prediction envelope interval is generated based on the inventory evolution envelope sequence, so that inventory regulation and control is converted into a prospective decision oriented to interval risks from a traditional afterward response, and the inventory safety and the storage efficiency can be considered at the same time.

The utility model discloses a kind of ground effect temperature difference collaborative driving's magnetic fluid regulation and control wind power device in the technical field of wind power generation equipment, it includes magnetic suspension generator, magnetic suspension generator has stator, rotor and motor shaft, the one end of motor shaft is connected with blade assembly, further include spiral guide tower pole and impeller, transmission shaft is fixed on impeller, transmission shaft upper portion is fixedly connected with motor shaft, transmission shaft lower portion is hinged with spiral guide tower pole, spiral guide tower pole outside is equipped with wind-assisted cylinder, magnetic suspension generator is embedded for adjusting the magnetic fluid regulation and control module of rotor speed. The utility model detects rotor speed by magnetic fluid regulation and control module, by changing the viscosity of iron-based nanometer magnetic fluid of magnetic field intensity, form flexible damping force, reduce the impact load and abrasion brought by braking, improve device reliability, ground effect temperature difference collaborative natural wind drive magnetic suspension generator, improve natural energy utilization rate and wind power generation stability.

The invention provides a parameter adjustment method for electromagnetic simulation, which comprises the following steps: analyzing curve data of a time domain reflectometer, and dividing to obtain a plurality of performance stages; extracting performance indexes from each performance stage, and establishing a correlation model among the performance indexes; the performance index at least comprises impedance; and adjusting the electromagnetic simulation parameters according to the correlation model and the mapping relation between the performance indexes and the electromagnetic simulation parameters. According to the method, the electromagnetic simulation parameters are adjusted according to the performance indexes after the performance stages are obtained through automatic division by analyzing the curve data of the time domain reflectometer, full-automatic optimization of the electromagnetic simulation parameters is achieved, and the efficiency of simulation design is improved.

This invention relates to a large language model reasoning method based on relational constraints and path guidance. The method receives an input question through a large language model and generates a set of relational constraints semantically related to the input question by combining preset prompts. Using a knowledge graph as the retrieval object, multi-hop relation retrieval is performed iteratively based on the generated set of relational constraints. After each hop, the semantic similarity between candidate relations and the input question and relational constraints is calculated, constructing a constrained multi-hop relation space. The multi-hop relation space is combined with the input question, and an interpretable relational path is generated through the large language model. Based on the generated relational path, corresponding candidate subgraphs are retrieved from the knowledge graph. Through hop-by-hop entity filtering and pruning operations, the relational path is instantiated into an entity-level reasoning path. The obtained reasoning path is combined with the input question, and an accurate and interpretable answer is generated through the large language model.

This invention discloses a 3D printing platform leveling and printing quality monitoring system and method based on vision and laser fusion detection. The system includes a vision detection module, a laser ranging module, a fusion calculation module, an AI recognition and decision-making module, and a control feedback module. Both the vision detection module and the laser ranging module establish bidirectional communication connections with the fusion calculation module to match the timing of image and distance information acquisition. This application effectively solves the problems of insufficient accuracy and instability in traditional technologies. By constructing a collaborative detection structure through the vision detection module and the laser ranging module, a triangulation subsystem of laser and vision detection modules is formed, completely overcoming the inaccuracy problem of traditional probes on highly reflective surfaces or magnetic plates. Simultaneously, it avoids the interference of environmental temperature, humidity, and material properties on measurement accuracy, significantly improving the stability and accuracy of the detection.

The invention discloses a flux linkage suppression-based low-voltage ride-through control method, device and system for a grid-forming doubly-fed motor, and a storage medium, and belongs to the technical field of power generation, power transformation or power distribution. The method comprises the following steps: firstly, locking a network construction control unit and obtaining an internal potential phase based on a virtual synchronous machine principle when a voltage drop occurs in a power grid; the proportionality coefficient of the excitation flux linkage ring is dynamically adjusted, so that the system damping is maximized, and the attenuation of the transient component of the stator flux linkage is accelerated; on the basis, a rotor current instruction is generated in real time according to voltage and current capacity limitation of a rotor-side converter, and a modulation signal is output to drive the converter in combination with a feed-forward compensation strategy and a resonance controller. According to the method, rotor overcurrent and overvoltage during a fault period can be effectively suppressed without adding any hardware circuit, the attenuation speed of a transient flux linkage is remarkably improved, meanwhile, the reactive power supporting capacity of a unit to a power grid is enhanced, and high-performance low-voltage ride-through of the grid-forming type doubly-fed motor is achieved.

The invention provides a grid-connected inverter harmonic suppression method based on double-vector model predictive control, and the method comprises the steps: building an inverter prediction model under an alpha-beta static coordinate system, separating an output current into a fundamental component and a harmonic component, and carrying out the prediction control of the fundamental component and the harmonic component; a dual-target optimization function containing a fundamental wave weight coefficient eta and a harmonic wave weight coefficient gamma is constructed, minimization of an error area between PCC harmonic current and a zero axis is taken as a harmonic suppression target, and a dual-vector model is combined to predict and control a dynamic optimal voltage vector and action time thereof, so that coordinated optimization of fundamental wave power transmission and harmonic suppression is realized. The method can effectively reduce the total harmonic distortion rate at the PCC, improves the electric energy quality of a multi-inverter parallel system, and is suitable for an alternating current bus system containing multiple harmonic sources.