9results about How to "Implement adaptive selection" patented technology

The invention relates to the technical field of power system fault detection, in particular to a factory power distribution system fault interval section determining method and system and terminal device.The factory power distribution system fault interval section determining method comprises the steps that when a power failure fault of a power distribution system is monitored, voltage and current data within a preset duration before the fault is obtained; comparing the phase current amplitude with a rated current threshold value, judging that the phase current amplitude is an external fault if the phase current amplitude does not exceed the limit, calculating a zero-sequence current if the phase current amplitude exceeds the limit, and comparing the zero-sequence current with a zero-sequence threshold value to distinguish a ground fault or an inter-phase fault; adaptively calling a phase difference analysis process according to an identification result, calculating a phase difference between a zero-sequence current and a zero-sequence voltage during a grounding fault, calculating a phase difference between a fault phase voltage and a fault phase current during an inter-phase fault, and judging whether a fault interval is internal or external according to a phase difference interval; and a structured message is automatically generated by integrating fault types, interval judgment and triggering criterion paths, so that quick and accurate fault positioning and handling are realized.

The invention discloses an ammeter relay resonance fault detection method and system based on harmonic analysis, and relates to the field of electrical variable measurement, and the method comprises the steps: obtaining a current signal and a voltage signal, and carrying out the preprocessing, and obtaining a current analysis signal and a power grid harmonic spectrum of a current period; quantifying the resonance risk of the power grid based on the power grid harmonic spectrum to obtain a resonance sensitivity index of the power grid in the current period; presetting a candidate wavelet basis set, and obtaining a time-frequency focusing capability evaluation index and a task suitability index of each candidate wavelet basis in the set; based on the three indexes, constructing a dynamic comprehensive cost function and selecting an optimal wavelet basis; and performing wavelet packet decomposition on the current analysis signal based on the optimal wavelet basis, extracting fault features, and identifying and judging the current working state of the relay. According to the method, adaptive selection of the wavelet basis can be realized, and the problems of inaccurate fault feature extraction, high false alarm rate and unreasonable computing resource utilization of an existing detection method are solved.

The invention relates to a laser SLAM fusion positioning method with environmental adaptability, and belongs to the technical field of autonomous mobile platform positioning. The method comprises the following steps: firstly, constructing a plurality of parallel sub-models comprising different laser SLAM positioning methods, then calculating the observation likelihood value of each sub-model in real time based on the matching quality of the current laser point cloud and a prior map, and updating the conformity of each sub-model in combination with the transfer relationship between the models; taking the conformity as a weight, carrying out weighted fusion on the pose estimation and covariance output by each sub-model, and generating a global positioning result; and finally, feeding back the posterior pose, the covariance and the conformity of each sub-model to the next period, and carrying out positioning information interaction to form closed-loop adaptive fusion positioning. According to the method, the robustness and the positioning precision of the laser SLAM system in a complex, dynamic or structure degradation environment are improved through adaptive fusion of a plurality of sub-models, and the problem of positioning drift or failure caused by incompatibility of the environment and the model is effectively avoided.

This invention provides an adaptive HP filtering method based on convex optimization of the spectral characteristic function. The method includes converting the numerical value of the fluctuation term coefficient λ in the HP filtering method into the value of the trend term spectral characteristic function, and calculating the trend term spectral characteristic function value using a downward convexity discrimination method based on binary tree traversal. Based on the continuous monotonicity property of the trend term spectral characteristic function value, the selection of the value of λ is transformed into a convex optimization problem of the trend term spectral characteristic function value, and the optimal λ is quickly solved using a bisection method, ultimately obtaining the optimal λ value. Adaptive HP filtering is then performed based on the optimal λ value. This invention effectively achieves adaptive selection of the HP filtering parameter λ and significantly reduces computational complexity, realizing fast adaptive filtering.

The application discloses a kind of electric meter relay resonance fault detection method and system based on harmonic analysis, it is related to the field of measurement electric variable, method includes: obtaining current signal and voltage signal and pre-processing, obtain the current cycle of current analysis signal and power grid harmonic spectrum;Based on power grid harmonic spectrum, the resonance risk of power grid is quantified, and the resonance sensitivity index of current cycle power grid is obtained;Pre-set candidate wavelet basis set, obtain the time-frequency focusing ability evaluation index and task adaptability index of each candidate wavelet basis in set;Based on three indexes, construct dynamic comprehensive cost function and select optimal wavelet basis;Based on optimal wavelet basis, wavelet packet decomposition is carried out to current analysis signal, and the current working state of relay is extracted fault feature and identified to judge.The application can realize the adaptive selection of wavelet basis, solve the problem that fault feature extraction is not accurate, false positive rate is high, and computing resource is not reasonably utilized in existing detection method.

The present application relates to a kind of multi-frequency impedance measurement methods based on frequency adaptive selection, it is related to power electronic converter impedance measurement technical field, including: according to measurement requirement given frequency range, and set iteration number and initial point number, generate initial impedance measurement frequency point set;Adaptive selection impedance measurement frequency point is through interpolation error calculation algorithm and dynamic average threshold method;All new impedance measurement frequency points are grouped according to odd-even, and the multi-sine signal of two groups of sub-frequency band amplitude is injected into the system to be measured;Based on the impedance data corresponding to all iteration cumulative impedance measurement frequency points, impedance curve is fitted by bilinear mapping and least square method.The present application can realize the adaptive selection of measurement frequency point, solve the problem that the peak or valley of impedance cannot be accurately observed in the traditional equal-frequency interval impedance measurement mode, and can provide important standardized tool and research background for system impedance acquisition and stability analysis.

The invention provides a thermal energy storage system pipeline vibration anomaly identification method, and belongs to the technical field of thermal energy storage systems. Vibration sensors are installed at key nodes of a thermal energy storage system pipe network, a topology model based on a graph theory is constructed, and wavelet packet decomposition and empirical mode decomposition technologies are adopted to extract multi-dimensional vibration feature vectors; a working condition identification code system is established, a longest common subsequence algorithm is utilized to match a historical working condition reference, an upper-layer game model with detection precision maximization as a target and a lower-layer game model with a false alarm rate minimization as a target are constructed to realize parameter collaborative optimization, and accurate positioning of an abnormal source is realized by combining finite element transfer function calculation. And according to the deviation measurement value, grading anomaly judgment is performed, and a self-adaptive monitoring frequency adjustment and sliding window database updating mechanism is adopted, so that the technical problems of insufficient pipeline vibration anomaly detection precision and relatively high false alarm rate of the thermal energy storage system are solved.

The invention provides a regional groundwater fluorine risk prediction method based on a machine learning model, and relates to the technical field of hydrogeology and environmental geoscience information. The method comprises the following steps: collecting and preprocessing predictive variable data, and constructing a sample data set; defining a model task according to the predictive variable data and the groundwater fluorine concentration exceeding condition; based on the sample data set, three machine learning algorithms are evaluated and screened through an entropy weight method to serve as base learners; performing hyper-parameter optimization on the base learner, generating out-of-fold prediction features by using cross validation, and constructing a meta learner input feature matrix; taking logistic regression as a meta-learner, constructing a stack integration model, predicting a global environment data set of a research area, and outputting an underground water fluorine exceeding probability distribution map and an underground water fluorine risk grading map; the SHAP method is adopted to analyze the feature contributions of the base learner and the meta learner, identify key driving factors and reveal the action mechanism. According to the invention, the groundwater fluorine risk prediction precision and robustness are improved.

The application discloses a lung CT image analysis method and electronic equipment based on deep learning, which comprises the following steps: constructing a data set; establishing an expert pool, including vectorizing CT image data and constructing an expert pool expert segmentation submodel containing multiple expert segmentation submodels for processing data queues with specific image distribution characteristics; constructing a feature extractor for gating, mapping the CT image into a high-dimensional semantic vector reflecting the confidence and distribution label of the abnormal area; using a gating network to adaptively segment the CT image, and selecting the optimal expert segmentation submodel from the expert pool according to the input mixed image feature vector. The application improves the segmentation accuracy of heterogeneous lesions, realizes adaptive selection of different image phenotypes, and outputs three-dimensional segmentation masks and attribute prediction vectors, supporting subsequent quantitative evaluation and clinical decision-making; and the application alleviates the sample imbalance problem and can accurately route rare lesion forms.