73 results about "Weighted average method" patented technology

The application discloses a kind of rockfill dam deformation stability evaluation method based on multi-parameter analysis, it is related to water conservancy engineering technical field, and evaluation step is:S1, based on the data of monitoring point to rockfill dam is collected, data includes dam body deformation data and dam body contour data, data set is formed after data pre-processing;S2, dam body deformation data is converted into deformation time series according to time sequence, its fractal dimension is calculated by box counting method, and the mapping relationship between fractal dimension and rockfill dam deformation stability is established by combining historical data statistical analysis;S3, dam body contour data is imported into three-dimensional modeling software, and three-dimensional geometric model of rockfill dam is constructed.The application constructs comprehensive evaluation system, carries out correlation degree analysis and verification to fractal dimension, displacement strain index, obtains evaluation value based on weighted average method, intelligently divides stability grade, and provides intuitive decision basis for water conservancy engineering management.

The present application relates to the field of Bayesian networks, and discloses a social security case criminal behavior determination method, device, equipment and medium, which can construct an evidence element system and evidence structure attribute according to the characteristics of a social security case, and construct the correlation between the evidence and the behavior in the social security case. Based on the evidence element system, the evidence structure attribute and the correlation, a Bayesian evidence reasoning network of the social security case is constructed. The prior condition probability table of the Bayesian evidence reasoning network is generated based on the coordination coefficient method and the weighted average method. The evidence information obtained by investigating the scene of the social security case is input into the Bayesian evidence reasoning network, so that the Bayesian evidence reasoning network determines the target behavior information and the corresponding occurrence probability according to the prior condition probability table and the evidence information. The present application can effectively reduce the consumption of human resources of case investigators and enhance the intelligent investigation level of social security cases.

The application discloses a molten silicon liquid level estimation method based on a hybrid adaptive resampling particle filter, and comprises the following steps: firstly, processing laser spot images collected by a CCD sensor to obtain observation data of the liquid level; then, establishing a system state equation of the molten silicon liquid level according to a kinematic principle to describe the movement of the liquid level; performing state estimation on a laser centroid vertical coordinate by using a particle filter algorithm, and obtaining a new particle set by using a hybrid adaptive resampling method; after re-normalizing all particle weights, outputting an optimal estimation value; and finally, smoothing the filtered data by using a moving weighted average method, and the smoothed result is the estimation value of the molten silicon liquid level. The application solves the problem that in the prior art, the variance of Gaussian variation is too large, particles jump out of a sampling range, and it is difficult to accurately estimate a real liquid level.

The invention discloses a UWB single base station-based positioning method, system and device, a medium and a product, and relates to the technical field of positioning, and the method comprises the steps: comparing the position deviation of UWB initial positioning data and IMU positioning data with a set threshold value, and judging whether the UWB initial positioning data is abnormal or not; when the data is abnormal, performing strong compensation on the UWB initial positioning data by using IMU positioning data based on a Kalman filtering method; when the data is normal, performing weak compensation or non-compensation on the UWB initial positioning data by using IMU positioning data by adopting a weighted average method; when the UWB signal of the positioning tag is lost by the UWB single base station, the IMU positioning data is used as a positioning result, the UWB initial positioning data is corrected by using the IMU positioning data, the UWB single base station positioning data drift caused by factors such as multipath propagation and non-line-of-sight propagation can be effectively inhibited, and the positioning accuracy is improved.

The application discloses a kind of based on dual-camera width measurement's double-tower cylinder transport ovality on-line monitoring method, belong to wind power generation equipment state monitoring and intelligent operation and maintenance technical field.The method includes: after the side surface image of tower cylinder collected by camera is preprocessed, by edge extraction and least square method fitting, and further calculate the instantaneous projection pixel width;Scale factor and illumination compensation model are introduced, the actual physical width of the two sides of tower cylinder is obtained, and the weighted average method is used to obtain the short axis diameter of tower cylinder;According to the short axis diameter of tower cylinder and the ovality calculation model, the deformed long axis of ellipse is obtained, the real-time ovality of tower cylinder at present time is further calculated, and whether the real-time ovality meets the ovality upper limit requirement, the ovality change requirement and the ovality fluctuation amplitude requirement is judged, whether alarm signal is generated according to the determination result.The application method effectively solves the technical problem that tower cylinder is difficult to measure directly in double-tower cylinder parallel transport caused by tower cylinder shielding.

The application discloses a non-local mean-based adaptive SAR interferogram filtering method, and relates to the technical field of synthetic aperture radar. Firstly, two input SAR images are used to generate an interferogram and calculate the correlation coefficient of each pixel to form a correlation coefficient matrix. Phase information of the interferogram is extracted. Pixels that do not need filtering are screened. For pixels that need filtering, different search window radii are determined according to the correlation coefficient values, and the search window radius corresponding to the pixels that do not need filtering is set to 0. For each pixel that needs filtering, the weight of each pixel in the corresponding search window is determined, and the filtering result of each pixel is obtained by using a weighted average method to form a filtered SAR interferogram.

The invention relates to the technical field of artificial intelligence and big data, and discloses a vector compression reconstruction method for long text semantic retrieval, which comprises the following steps: firstly, segmenting a long text T into n text segments S1, S2,..., Sn according to semantic similarity or a fixed length; secondly, for each text segment Si, generating a local semantic vector li by using a BERT pre-trained language model, and converting the input text segment Si into a high-dimensional vector by the BERT model; then global vector generation is carried out, specifically, weighted average is carried out on the local vectors li, l2,..., ln of all the segments through a weighted average method, and a global semantic vector g is generated; carrying out explicit compression and key information storage; performing staged hybrid semantic retrieval; cross-segment semantic reconstruction and reasoning are carried out, and context information recovery is carried out; performing dynamic semantic reconstruction; and generating answers based on the reconstructed context. According to the method, deeper reasoning can be carried out, and more accurate and more comprehensive answers are generated.

The invention discloses an efficiency evaluation method and system based on air-ground integrated network analogue simulation confrontation, and relates to the technical field of network simulation and efficiency evaluation. The method comprises the following steps: constructing a space-ground integrated network topology simulation model by using a simulation engine; injecting a confrontation event, and simulating a confrontation scene; collecting network performance index data in the confrontation scene; on the basis of the collected index data, a weighted average method is adopted, and project weights included in all levels in a pre-constructed two-level performance evaluation system are utilized, a second-level score is firstly calculated, and then a first-level score is calculated on the basis of the second-level score; based on the score of the first level, utilizing a fuzzy evaluation method to obtain a fuzzy efficiency level of the space-ground integrated network; and taking the score of the first level and the fuzzy efficiency level as an efficiency evaluation result. According to the method, accurate reproduction of a multi-dimensional dynamic scene and closed-loop integration of simulation evaluation can be realized. And meanwhile, a multi-algorithm fusion method is adopted, so that the accuracy and robustness of evaluation are improved.

This invention discloses an intelligent oil pump oil quality monitoring method based on multi-source data fusion, belonging to the field of transformers. The method includes multi-source data acquisition, data processing, data fusion, result display, and long-distance data transmission: First, multi-dimensional raw data of the oil pump pipeline is collected through micro-water, pressure, and flow sensors and transmitted to a microcontroller control board; then, the raw data is denoised and filtered to obtain a standardized dataset; subsequently, a multi-source data fusion algorithm combining weighted average method and fuzzy logic is used to process the dataset, constructing an oil quality status assessment model to determine whether the oil quality has deteriorated; next, the monitoring results are visualized locally; finally, long-distance transmission of monitoring data and local-remote data synchronization are achieved. This invention improves the accuracy of oil quality monitoring through multi-parameter collaborative monitoring and data fusion, realizing local real-time display and remote real-time monitoring of oil quality status, adapting to the operational needs of transformer maintenance sites.

The invention belongs to the technical field of battery charging, and provides a charging remaining time estimation method and device and a medium, and the method comprises the following steps: calculating the charging current of each temperature and SOC interval according to a first charging data table and the charging current under a preset charging rate; at each temperature, calculating a weighted average current value of the whole charging process by using the obtained charging current according to an SOC (State of Charge) interval through a weighted average method; calculating the basic charging time at each temperature according to the weighted average current value and the battery capacity; and determining the remaining charging time based on the basic charging time and the compensation time at the initial charging temperature. The method has the advantages that the charging time of other temperature points is estimated by using the charging MAP table in the battery parameters and the actual charging time at a certain temperature point, and the calculation result is more accurate while the workload is reduced.

The invention belongs to the technical field of underground water numerical simulation, and provides an underground water pollution migration simulation method and device, electronic equipment and a storage medium, and the method comprises the steps: constructing a three-dimensional octree grid, a control equation and a definite solution condition of a hydrogeological structure of a target region; carrying out discrete solution on the control equation in the three-dimensional octree grid by adopting a finite volume method taking a node as a center; constructing a post-processing approximation function and a local posterior error indicator on octree leaf subunits of the three-dimensional octree grid; determining a unit set contributed by a global error by adopting a Drfler marking strategy, then performing splitting or merging operation on leaf units, and performing grid updating by adopting a high-order interpolation or volume weighted average method; and according to a preset simulation termination condition, repeatedly executing underground water pollution migration simulation and finally performing visual display. According to the method, the simulation accuracy of water pollution migration is improved in a posterior error estimation mode.

The invention provides a high-speed railway joint debugging and joint test evaluation method and device, and the method comprises the steps: determining a high-speed railway joint debugging and joint test detection index according to the operation data of a high-speed railway system; cloud processing is carried out on the detection indexes to obtain cloud feature parameters of the detection indexes; calculating the information entropy of each detection index based on an entropy weight method; determining the weight of each detection index according to the information entropy of each detection index; according to the cloud feature parameters of the detection indexes and the weight of each detection index, a high-speed railway joint debugging and joint test comprehensive evaluation cloud model is obtained through a weighted average method; according to the high-speed railway joint debugging and joint test comprehensive evaluation cloud model, determining a high-speed railway joint debugging and joint test evaluation result; according to the method, the transformation of the high-speed railway joint debugging and test from local evaluation to system overall comprehensive evaluation is realized, the fuzziness and randomness in the evaluation are effectively quantified through the combination of the cloud feature parameters and the weights, the reliability of the evaluation result is improved, and a data reference is provided for railway engineering acceptance.

The invention belongs to the field of teaching simulation, and particularly discloses an interventional nursing teaching simulation system based on multi-modal interaction, which comprises an organization simulation module, a multi-modal interaction module and an operation track analysis module. According to the scheme, through a biomechanical simulation method based on position dynamics and a multi-constraint mechanism, superficial fascia is subjected to two-stage solver, distance keeping constraint, dihedral angle constraint and linear and angular damping force modeling, and IMA blood vessels are subjected to combination of high-rigidity distance constraint and low-rigidity dihedral angle constraint and a high-low resolution grid balance strategy; the normal resistance and the tangential resistance are used for constructing multi-dimensional tactile feedback force, and accurate reduction of mechanical properties of different human tissues is achieved; through a deep learning algorithm, hyper-parameters are optimized through grid search, evaluation indexes are calculated through a weighted average method, the significance is verified by comparing frame number differences of simulation operation and standard operation in all stages and combining independent sample t verification, and a complete quantification system of operation track evaluation is constructed.

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 discloses a ship navigation risk early warning method based on unbalanced offshore meteorological data enhancement, which firstly determines the ship navigation risk grade, and divides the unbalanced data set of target sea area meteorological disaster-causing elements corresponding thereto. Secondly, an adversarial learning model composed of a deep neural network generator and an evidence reasoning discriminator is designed to balance the unbalanced data set under different risk grades, and a Gaussian distribution model is established to describe the feature distribution of meteorological disaster-causing elements under different risk grades. Then, the risk grade reliability distribution of meteorological disaster-causing elements is calculated through the Gaussian distribution model. Finally, the weighted average method is adopted to fuse the risk grade reliability distribution, and the risk mode with the highest reliability after fusion is selected as the navigation risk grade of the current target sea area. The application can generate a small number of meteorological data conforming to the real distribution, effectively solve the data imbalance problem, and improve the extreme weather risk warning capability.

The invention provides a photovoltaic hour generation power prediction method based on solar irradiation historical data mining, and relates to the technical field of photovoltaic generation power prediction. The method comprises the following steps: collecting local historical solar irradiation intensity data of a photovoltaic power station, introducing lunar calendar date as a time identifier, combining actually measured irradiation data of a preset backtracking time period, calculating a standard deviation, constructing a screening value domain, mining historical irradiation intensity data meeting conditions from historical data, and forming a data set to be processed. And further obtaining a solar irradiation intensity prediction value through mean value calculation, and finally generating a future hour-level photovoltaic generation power prediction value by using a weighted average method in combination with recent generation power data. The method gets rid of dependence on third-party meteorological data, has the advantages of data self-consistency, transparent algorithm, simplicity and convenience in implementation, high adaptability and the like, and can effectively improve the accuracy and practicability of prediction.

The invention provides a multi-level dynamic adaptive trajectory optimization method and device, a medium and a program product, and the method comprises the steps: obtaining a trajectory point set of a target vehicle, the trajectory point set comprising a plurality of trajectory points and first steering angles of the trajectory points; detecting noise points in the track points by using a median filtering method, performing de-noising processing, and adjusting a first steering angle of the noise points to obtain a second steering angle of the track points; smoothing the track point in the local area by using a weight average method to obtain a third steering angle of the locally smoothed track point; fitting a global trajectory trend of the trajectory points by using a curve fitting method to obtain a fourth steering angle of the trajectory points after global optimization; and dynamically updating the fourth steering angle of the track point by using an exponential smoothing method on the basis of a global track trend fitting result in combination with the previous track point to obtain a final steering angle of the track point after real-time optimization. According to the method, trajectory optimization is carried out through multi-method combination of hierarchical processing and dynamic adaptation, and the driving trajectory and state of the vehicle are reflected more truly.

The application relates to the technical field of computer vision, in particular to a visual SLAM method based on 3D Gaussian spatter in a dynamic environment, a system and a readable storage medium. By performing instance segmentation on a scene image and extracting a prior dynamic mask, a potential dynamic mask is identified and fused to obtain a comprehensive dynamic mask; the comprehensive dynamic mask is optimized by using residual entropy and depth consistency, and then dynamic features are accurately removed; then, in order to balance the stability of pose tracking and the efficiency of mapping, a two-stage key frame screening strategy is constructed; on this basis, an improved 3D Gaussian spatter technology is further introduced to render and construct a scene graph; for each Gaussian ellipsoid, the volume and opacity weight are estimated, the multi-view contribution weight is calculated, the maximum weight is obtained by using a weighted average method based on the multi-view contribution weight, and pruning is performed. The application aims to solve the problem of how to reduce the computing power consumption in the 3D GS-SLAM scheme while ensuring the rendering quality.

The invention relates to an agricultural drought grade prediction method and device fusing meteorological data and machine learning, and belongs to the technical field of disaster prediction. Machine learning and drought prediction are carried out by constructing a long short-term memory LSTM model and a random forest RF model and taking historical meteorological data and agricultural drought level observation values of corresponding years as training samples; distributing weights on the basis of the predicted root mean square error RMSE of each model, and deducing weight parameters of a weighted average method of each model; and carrying out weighted summation on the grade probabilities output by the two models according to weights to obtain a preliminary drought grade, and carrying out optimization correction according to errors. The problems that existing agricultural drought prediction depends on single meteorological data, the model generalization is poor, the grade cannot be dynamically updated, and an error correction mechanism is lacked are solved.

The application discloses a kind of based on data cleaning's electric meter data repair method, device, medium and equipment, belong to electric meter repair field, the present application is by obtaining the electricity consumption data of preprocessed intelligent electric meter, environmental temperature data and user profile data, based on user geographic location information Construction undirected weighted graph, and fusion user type, electricity consumption and temperature data generate node feature vector. Coding and decoding are carried out on node feature vector using graph auto-encoder, to generate reconstructed node feature vector, and calculate reconstruction error to determine abnormal threshold. Mark abnormal user nodes accordingly, and based on graph neighbor node information, repair abnormal data using weighted average method, finally obtain repaired intelligent electric meter data, realize the efficient cleaning and repair of electric power marketing data, the present application effectively solves the problem that prior art cannot accurately and efficiently repair the abnormal data of intelligent electric meter.

The application relates to the technical field of ancient building color style migration, in particular to an ancient building color main tone determination method based on a weighted average method, which comprises the following determination steps: obtaining a digital image after digital processing of an ancient building facade image; removing non-building wall color carriers in the digital image to obtain a wall image; extracting hue values, chroma values, lightness values of each color in the wall image and area ratios of each color in the wall image, and storing the values in a database; inputting data in the database into a contrast algorithm model to solve hue contrast, chroma contrast and lightness contrast in the wall image; and selecting a color meeting selected requirements from the database as an ancient building color main tone based on the hue contrast, the chroma contrast and the lightness contrast; the application can accurately determine the main tone in ancient building colors.

The invention discloses a pre-hospital emergency call volume prediction method and system based on an SSA-ARIMAX hybrid model, and relates to the technical field of pre-hospital emergency, and the method comprises the steps: layering historical emergency call data according to an event type and a geographic region, and dynamically determining an optimal window length through employing singular value entropy minimization; singular spectrum analysis is performed on the forward and reverse time sequences, and a trend term, a periodic term and a residual term are weighted and reconstructed according to forward and reverse singular value contribution rates; for the reconstructed trend term and residual term, an auto.arima algorithm and Bayesian optimization are combined to construct an ARIMAX model containing a nonlinear air temperature covariable; an improved weighted average method is adopted to predict a periodic term, a holiday effect is captured through a Prophet model, and a prediction result is obtained by dynamically adjusting the weight according to seasons. According to the method, the influence of extreme temperature, seasonal fluctuation and holiday effect on the emergency call volume can be accurately captured, and a scientific basis is provided for dynamic scheduling of emergency resources.

The invention belongs to the technical field of wind power plant wind speed prediction, and particularly provides a wind power plant virtual wind measurement point wind speed calculation method and a wind energy resource evaluation method based on a spatial data fusion method, and the method comprises the steps: collecting multi-source data related to a target wind power plant, and carrying out the preprocessing; quantifying the space-time correlation between the real anemometer tower and the virtual anemometer tower to generate a comprehensive space-time correlation index; constructing a regression model, taking the comprehensive spatial-temporal correlation index and data related to the wind speed in the multi-source data as independent variables, taking the observed wind speed of the real anemometer tower as a dependent variable to train the model, and predicting the initial wind speed of the virtual anemometer tower by utilizing the trained regression model; and fusing the initial wind speed output by the regression model and the wind speed values of other data sources by adopting a weighted average method to obtain an optimized wind speed calculation value. According to the method, the meteorological station is combined, the reanalysis data and the data of the surrounding real anemometer towers are analyzed, the problem caused by the deficiency of the anemometer towers is solved, and the wind speed of the virtual anemometer tower can be accurately calculated.

The application relates to a coarse-fine grid fast interpolation technology for wall turbulence calculation, aiming at solving the problems of large calculation amount and complex implementation process of an existing interpolation method. Half of the minimum grid spacing of a fine grid is used as a judgment standard to intelligently switch the interpolation mode. For each fine grid point, when the distance between the fine grid point and the nearest coarse grid point is less than the standard, the physical value of the nearest coarse grid point is directly assigned to the fine grid point; otherwise, if the distance is greater than or equal to the standard, the inverse distance weighted average method is used for interpolation. The method simplifies the complex calculation problem by taking half of the minimum fine grid size as the standard for judging whether the fine grid point is directly assigned by the physical quantity on the nearest coarse grid value point, under the premise of ensuring the distortionlessness of physical information. The method avoids the complicated calculation process in the traditional method, effectively reduces the algorithm complexity, and significantly improves the speed and efficiency of the interpolation calculation.

The application relates to the technical field of data fusion analysis, in particular to a city governance data fusion analysis method and system. The method comprises the following steps: acquiring pedestrian motion trajectory data in a city by using a sensor, and acquiring vehicle position information data in the city by using a GPS device; fusing the pedestrian motion trajectory data and the vehicle position information data by using a weighted average method; extracting pedestrian and vehicle trajectories from the fused data, and processing the pedestrian and vehicle trajectories by using a dynamic time warping algorithm; modeling a space-time mode of the pedestrian and vehicle trajectories based on a support vector machine model, identifying road traffic abnormalities, and introducing a traffic jam function in the modeling process in view of traffic jams. The application can better process the waiting conditions of pedestrians and vehicles in front of traffic lights by introducing a dynamic time warping (DTW) algorithm and optimizing a Euclidean distance calculation method, and unnecessary distance punishment caused by a stationary state is avoided.

The application provides an anchor rod supporting roadway surrounding rock strength estimation method and device, relates to the technical field of surrounding rock strength estimation method, and comprises the following steps: multiple monitoring sections are arranged at equal intervals along the axis of the roadway, two-dimensional data of wave velocity at the surrounding rock of each monitoring section is obtained, linear mapping is carried out according to the wave velocity value, an anchor rod monitoring area is set based on each monitoring section, and anchor rod parameters of the surrounding rock crack are obtained; a grayscale image is processed, a crack and anchor rod coupling algorithm is used to construct the surrounding rock strength coefficient of a single monitoring section; a time-dependent coupling is used to construct an anchor rod attenuation factor, the anchor rod attenuation factor is used as a correction term of the surrounding rock strength of a single monitoring section; and a spatial weighted average method is used to obtain the overall surrounding rock strength value. The crack features are automatically extracted by using a convolutional neural network, the problems of low efficiency and large error of the traditional method are solved, the anchor rod attenuation factor is dynamically corrected, the timeliness of early warning is significantly improved, and the roadway collapse risk is reduced.

The application provides a network intrusion model construction method based on meta learning, comprising the following steps: a global server constructs a plurality of client nodes based on a federated learning framework, and constructs a node detection model in each client node; the client nodes collect local multi-element heterogeneous data at a fixed time interval, generate simulated attack samples using a GAN network, and then form training data by combining the simulated attack samples and normal samples; the node detection model is iteratively trained using the training data; the node detection model after training is meta-optimized using an irrelevant meta algorithm; the global server dynamically allocates an aggregation weight to each client node, aggregates the models by a weighted average method, and generates a global detection model. The application solves the problem of class imbalance of model training samples in the prior art, which leads to insufficient detection capability of the detection model for minority class attacks and difficulty in quickly adapting to new network attacks.

The invention discloses a federated learning method and system based on low-precision local training and high-precision aggregation, and the method comprises the steps: obtaining a local training sample through a client, and carrying out the local model training of a round; and calculating an updated value of a model parameter, an activation value in forward propagation, an error signal in back propagation, a gradient and a momentum term in a stochastic gradient descent optimizer by adopting low-precision quantization. And the server receives the model parameters uploaded by the clients, generates global model parameters by adopting a weighted average method, and performs high-precision moving average smooth updating on the global model parameters to obtain updated global model parameters. According to the method, efficient training and accuracy of the model can be ensured while the computing overhead of the client side is reduced, and the method has more remarkable advantages especially in a scene with large data heterogeneity.

The invention relates to a landslide mass displacement monitoring method based on the Internet of Things, and the method comprises the steps: collecting multi-source data from a monitoring point through a multi-hop self-organizing network, carrying out the preprocessing of the multi-source data, and obtaining a stable data sequence; according to the transmission path of the stable data sequence in the complex environment, obtaining signal strength and delay indexes between network nodes; optimizing a transmission path according to the signal strength and the delay index, and obtaining a transmission link; acquiring a data stream of each monitoring point position in the transmission link, extracting space coordinate information, performing grouping association on the displacement information and the rainfall value through a clustering algorithm, and determining a clustered space association group to form an integrated data set of space matching; and carrying out feature fusion on the integrated data set subjected to space matching, calculating a comprehensive risk value of each group by adopting a weighted average method, and obtaining a risk assessment result. According to the method, the accuracy and the real-time performance of disaster early warning are remarkably improved, and reliable support is provided for disaster prevention and control in a complex environment.

The invention discloses a high-voltage parallel capacitor internal fault impedance characteristic monitoring method and a high-voltage parallel capacitor internal fault impedance characteristic monitoring system. The impedance characteristic monitoring method comprises the following steps: acquiring voltage and current signals; after anti-aliasing filtering and analog-to-digital conversion are carried out, a quasi-synchronous sampling algorithm is adopted for processing, and a sine amplitude and a cosine amplitude of a fundamental wave are calculated in a weight coefficient weighted average mode; fundamental waves and multiple main harmonic components are separated based on a fast Fourier transform algorithm, and complex impedance values under the fundamental waves and all harmonic frequencies are calculated; constructing a multi-state impedance characteristic criterion library, performing longitudinal trend comparison and transverse inter-phase comparison on the impedance value calculated in real time and the criterion library, and extracting fault characteristic parameters; according to the method, the random forest classifier after offline training is adopted to recognize the fault type, corresponding response is triggered according to the fault recognition result, and full-state, high-sensitivity and rapid online monitoring and protection of a high-voltage parallel capacitor branch from normal operation to various internal faults and resonance risks are achieved.