340 results about "Accurate estimation" patented technology

The invention discloses a compressed air energy storage optimization operation method based on multi-parameter cooperative control, and the method comprises the steps: multi-source data collection and preprocessing, thermodynamic state multi-dimensional parameter dynamic estimation, three-dimensional reconstruction and evolution prediction of a temperature field in a gas storage, and system loss analysis and efficiency evaluation based on an entropy generation theory. And multi-time scale energy flow dynamic tracking and state evaluation, and operation parameter optimization suggestion generation based on state evaluation. According to the method, accurate estimation of non-ideal gas parameters is realized through particle swarm-Bayesian hybrid inference, a temperature field in a gas storage is reconstructed, interlayer available energy transfer items are introduced to quantify temperature stratification influence, the operation efficiency and reliability of the system are improved, the service life of equipment is prolonged, and the operation cost is reduced.

The invention relates to the technical field of agricultural irrigation management, in particular to a method and system for estimating the agricultural irrigation water consumption of an irrigation area. The method comprises the following steps: collecting soil, weather and crop data through a sensor and a detector, processing the collected data through an edge computing node, and then selecting an optimal transmission path to transmit the data through an Internet of Things gateway based on an https protocol; an irrigation area agricultural irrigation water consumption prediction model is constructed based on an intelligent algorithm of deep learning, the model stores, preprocesses and analyzes received data, then modeling and prediction are carried out on soil humidity, meteorological conditions and crop growth conditions, and finally an irrigation water consumption estimation result is obtained; and updating the irrigation area agricultural irrigation water consumption prediction model to ensure the accuracy and timeliness of model prediction. According to the method, through an advanced sensor technology, the Internet of Things, big data analysis and a machine learning algorithm, accurate estimation and optimal management of the agricultural irrigation water consumption of the irrigation area can be realized.

The invention belongs to the technical field of image processing, and particularly relates to an object six-degree-of-freedom pose estimation method based on three-dimensional geometric information registration, which comprises a target detection module, a three-dimensional point cloud generation model and a geometric information registration pose estimation module. According to the method, geometric reconstruction, a geometric information high-precision registration technology and an object pose estimation task are combined, a geometric reconstruction module introduces object geometric feature extraction and feature decoding to realize deep understanding and modeling of an algorithm on object geometric features, the whole process accords with geometric disorder, and the accuracy of object pose estimation is improved. And the accuracy and scene adaptability of the algorithm are further improved. According to the method, accurate estimation of the six-degree-of-freedom pose of the object can be achieved, and powerful support is provided for the fields of industrial automation, robot vision, AR / VR, automatic driving, medical robots and the like.

The invention discloses a railway box girder full-section data monitoring method and system based on a three-dimensional scanner, and particularly relates to the technical field of data monitoring, and the method comprises the steps: collecting the data of the full section of a railway box girder through the three-dimensional scanner, scanning each part of the box girder for multiple times, and determining the data collection process of the full section of the railway box girder. Collected railway box girder full-section data are processed, point cloud data are spliced through an aerial triangulation calculation process based on dense point cloud data, key point circle center position accurate estimation and key plane fitting and optimization, and an operator uploads the spliced data and special mark point coordinates to an intelligent measurement integrated platform system. The point cloud information and the contour information of the prefabricated box girder are collected, the acceptance assessment model of the prefabricated box girder is constructed, the possible significant deviation between the box girder model and the design standard is quantified, and the railway box girder full-section acceptance efficiency can be improved.

The invention belongs to the technical field of highway tunnel ventilation control, and provides a highway tunnel ventilation control method and system. The method comprises the following steps of multi-source heterogeneous data acquisition, multi-source data fusion, state estimation, traffic flow prediction, dynamic pollution index calculation and multi-target weight generation, rolling optimization with constraints, control execution, model updating and feedback correction. The state sensing precision is improved through multi-source data fusion, real-time accurate estimation and error reduction of a pollutant concentration field and a traffic flow state are realized through a heterogeneous sensor and a Rayleigh fusion technology, the problem of sensing deviation caused by single data in a traditional method is solved, future traffic flow is predicted based on a graph neural network, and the traffic flow sensing accuracy is improved. The fan power is optimized through model prediction control, the ventilation intensity is adjusted in advance, pollutants are prevented from exceeding the standard, the response speed is increased compared with threshold value control, and energy consumption is reduced.

The invention discloses a security operation and maintenance management method and system based on a network security event, and relates to the technical field of network security, and the method comprises the steps: collecting and preprocessing multi-dimensional data, and calculating an attack state based on the preprocessed multi-dimensional data; the attack influence degree is calculated by using a weighted propagation model based on the attack state, and a fitness function is constructed and optimized based on the attack influence degree to obtain an optimal protection strategy set; and converting the optimal protection strategy set into a control instruction, executing the control instruction, and storing, collecting and analyzing generated multi-dimensional data. A non-linear ordinary differential equation is constructed through a non-linear reaction diffusion model, the predictability and the coping capacity of network security management and operation and maintenance are improved, the attack influence degree is calculated through a weighted propagation model, accurate estimation of the wide propagation effect of network security threats is achieved, and through construction and optimization of a fitness function, the network security threats can be accurately estimated. The flexibility and adaptability of the protection system are improved, and the requirement for manual intervention is greatly reduced.

The invention discloses an intelligent optimization system and method for track prediction. The method comprises the steps that 1, TLE data are preprocessed, and a data set is obtained; 2, training a machine learning model; 3, propagating the current state through aircraft dynamics by adopting an estimation algorithm, and then correcting the propagation state by utilizing a pseudo measurement method; 4, evaluating and optimizing the model; and 5, predicting and classifying. Machine learning prediction is used as UKF-based pseudo measurement prediction, a current state is propagated through an aircraft dynamics model, then the propagation state is corrected by using pseudo measurement data, and then an HMMS is used for predicting a pseudo measurement value driven by data, so that accurate estimation of a future state of an aircraft can be realized by effectively combining the prediction capability of machine learning. The method not only considers the similarity of the trajectory in the spatial dimension, but also can effectively capture the dynamic change in the time dimension, thereby more accurately predicting the flight trajectory.

A node positioning method for marine environmental monitoring wireless sensor networks is provided, which jointly estimates node positions and path loss factors by considering the node real-time movement and the path loss and absorption effect of underwater communication; transforms an original non-convex problem into a non-negative constrained least squares framework, and finds the optimal solution of marine node positions by two stages of interior point method and block coordinate update. In the first stage, the problem is re-expressed by using the penalty function according to the interior point method to obtain the approximate solution; and in the second stage, the original problem is transformed into a generalized trust region sub-problem, and the approximate solution obtained by the interior point method is used as the initial estimation, the accurate estimation values of the marine node position and path loss factor are obtained by iterative solutions combined with the block coordinate update.

The invention relates to the technical field of image processing, and discloses a non-contact vital sign detection method and device based on self-supervised learning. The method specifically comprises the following steps: generating a visual-text pair based on a visual sample and a text cue word, extracting visual features and text features by adopting a visual-language model, and aligning the visual features and the text features by using a contrast loss function; reconstructing a shielded video region in the visual sample based on the visual features and the text features, and optimizing the reconstructed video sample by using a reconstruction loss function; the predicted physiological signal is optimized using a frequency contrast loss function and a frequency ranking loss function. A self-supervised learning method is adopted, accurate estimation of the physiological indexes can be realized without using an external PPG signal as supervision, and the demand on computing resources is reduced; through the design of the model and the loss function, the accuracy of signal extraction and the generalization performance of the model are improved; no contact type sensor in any form needs to be worn, the economic cost is low, and the privacy is good.

The invention belongs to the technical field of aero-engine health management, and discloses an aero-engine remaining service life prediction method based on a diffusion model and a space-time attention mechanism. The invention provides a novel learning framework which is used for RUL prediction of an aero-engine. The framework fuses a diffusion process and an attention-based time module to achieve end-to-end accurate estimation from sensor signals to RUL. Aiming at the problem that the scarcity problem of aero-engine data limits the application of a deep learning network in the aspect of improving the RUL prediction precision, the method adopts a diffusion process to enrich the data diversity and improve the data quality. In order to improve the accuracy of RUL prediction, an attention mechanism is used to extract time features in samples and between the samples, and time information is effectively utilized, so that the capture capability of the aero-engine performance degradation process is enhanced. The RUL prediction method provided by the invention provides more reliable support for health management and maintenance decision of the aero-engine.

The invention relates to the technical field of data signal processing, in particular to a conductivity meter data processing method based on sage-husa self-adaptive Kalman filtering, which comprises the following steps of: sampling conductivity and temperature original data of a measured object through a conductivity sensor probe; an improved sage-husa adaptive Kalman filtering model of the conductivity meter is established; the method comprises the following steps of: transmitting original data measured by a probe of a conductivity sensor into a sage-husa self-adaptive filter, and performing filtering and noise reduction processing on an original signal by using an improved sage-husa self-adaptive Kalman filtering algorithm; the sage-husa adaptive filter can dynamically adjust filter parameters according to the change of the system state and the difference of noise characteristics, a system dynamic model can be well reflected without setting an accurate initial value so as to obtain more accurate estimation data, the robustness of the system is enhanced through outlier judgment introduced during innovation calculation, and the robustness of the system is improved. The measurement precision is effectively improved, the risk that parameters are affected by outliers is reduced, and the real-time performance of the system is enhanced.

The invention discloses a super-high-yield corn yield estimation method based on remote sensing time sequence curve characteristic parameters, belongs to the technical field of corn yield estimation, is used for corn yield estimation, and comprises the following steps: dividing an NDVI time sequence curve of corn into a growth stage and a decline stage, fitting the two stages into two Logistic curves respectively, and selecting parameters with high correlation in the two Logistic curves as input of a machine learning regression model, and predicting the yield. According to the method, the obvious difference rule of the spectrum saturation time not reached in the corn growth period is fully utilized, the saturation limitation of a yield estimation model is effectively overcome, and a stable and reliable method is provided for accurately estimating the super-high yield part of the corn.

The invention belongs to the technical field of automatic driving, and particularly relates to a 4D millimeter wave radar and camera fusion three-dimensional target detection method based on surface parameter estimation guidance. According to the method, information of an image and point cloud is effectively combined through an explicit target surface depth fitting model, the target surface depth is fitted, then accurate estimation of the three-dimensional space position of the target is achieved, and dense three-dimensional space information is obtained based on surface depth estimation in a subsequent module, so that accurate estimation of the three-dimensional space position of the target is achieved. And visual angle conversion of image features and feature extraction of false point cloud spatial position information are guided, so that the performance of a target detection algorithm is greatly improved.

The invention provides a wind power prediction error estimation method based on a meteorological prediction error, and the method comprises the steps: firstly analyzing the correlation between data, and determining main influence factors of the wind power prediction error according to the magnitude of the correlation; a multi-dimensional random variable kernel density estimation method is adopted to construct a weather forecast-forecast error condition probability distribution model; constructing an input feature data set based on the meteorological prediction error data set and additional feature correlation analysis, constructing a multi-neural network mixed error estimation model, and training by using the input feature data set and the wind power prediction error data set; and finally, randomly generating a large-sample weather forecast error sample according to the input weather forecast based on a Monte Carlo simulation method, constructing an input feature data set, inputting the input feature data set into the mixed error estimation model to obtain an error estimation sample data set, and finally fitting probability distribution of errors by using a kernel density estimation method. And error probability distribution estimation is completed. According to the invention, accurate estimation of wind power prediction error estimation is realized.

The invention discloses a forest land ecosystem carbon reserve accounting method and system, and the method comprises the steps: obtaining and screening feature variables, such as optical remote sensing data, vegetation indexes, texture factors, satellite-borne laser radar feature parameters, so as to determine features having the most influence on a target variable, and removing redundant and irrelevant variables; the method comprises the following steps of: screening characteristic variables, reserving variables which contribute to the model prediction effect, improving the precision and stability of the model, constructing a canopy height inversion model by adopting a random forest method based on the screened characteristic variables, realizing accurate estimation of the canopy height, and finally calculating the biomass of vegetation of the forest ecological system by using a different-speed growth equation. And finally, summarizing the biomass of the vegetation of the forest ecological system to obtain the total biomass of the vegetation, and multiplying the total biomass of the vegetation by the carbon-containing coefficient to obtain the vegetation carbon reserve of the forest ecological system, so that wide-area and efficient forest parameter monitoring is realized, the monitoring cost is reduced, the environmental adaptability is improved, and the problems of data continuity and standardization are solved.

The invention relates to a multi-parameter estimation algorithm combining a disturbance quantification model and a rooting method in interference measurement. In order to solve the problems that in the prior art, the disturbance degree of a sequence is difficult to quantify, and harmonic key parameters are influenced by spectrum leakage, the invention provides the following innovative scheme: firstly, a sequence multi-classification measurement entropy quantification model is constructed, and quantitative evaluation of the signal noise degree is realized; secondly, through a Hankel matrix construction and rooting parameter estimation method, realizing accurate estimation of the harmonic frequency of the real-value signal; meanwhile, an amplitude correction algorithm based on window function characteristics is designed, and the signal sampling condition is effectively judged; and finally, through matrix weighting operation of a phase demodulation function, efficient reconstruction of an optical surface shape is realized, the problems of spectrum leakage and disturbance quantification in a traditional method are solved, and the method has relatively high measurement precision.

The invention relates to the technical field of data acquisition, and more specifically discloses a data acquisition and analysis system and method for indoor decoration, and the method comprises the steps: extracting the decoration scheme information of a target indoor decoration project and pre-estimating the characteristics of parameter information, constructing a related feature map, and decoding the related feature map, according to the method, the material consumption of the indoor decoration engineering is accurately estimated, so that the problems of low accuracy, material waste or construction period delay existing in a single means based on manual estimation at present are effectively solved.

The invention discloses a transmission conductor three-dimensional attitude estimation method, system and equipment based on RTK and inertial measurement coupling and a medium, and belongs to the technical field of three-dimensional state monitoring, and the method comprises the following steps: fusing RTK absolute positioning and IMU inertial measurement data, and realizing accurate estimation of the transmission conductor three-dimensional attitude by using extended Kalman filtering; and abnormal identification and risk early warning are carried out based on an attitude calculation result, and finally real-time monitoring and intelligent warning of the state of the power transmission line are realized through a three-dimensional visual platform. According to the method, RTK and IMU data are fused, accurate sensing and continuous tracking of the three-dimensional attitude of the power transmission line are achieved through extended Kalman filtering, the defect that a single technology is insufficient in reliability in a complex environment is overcome, and finally a complete monitoring closed loop of the safety state of the power transmission line is achieved through intelligent early warning and three-dimensional visualization.

The invention belongs to the technical field of underwater integrated navigation. The invention provides a multi-navigation-state unmanned system underwater integrated navigation method based on global information fusion. According to the embodiment of the invention, different navigation combination modes under different navigation states are selected, and an SINS / DVL / GPS combined navigation mode is adopted offshore, so that the calculated amount is reduced, and the efficiency is improved; and the deep sea sneaking adopts an SINS / DVL / USBL combined navigation mode. Measurement deviation accumulative divergence caused by navigation state change in maneuvering characteristics of the underwater unmanned system is considered, t distribution is introduced in a federated Kalman filtering architecture for trailing characteristics to replace conventional normal distribution, thick-tail measurement noise is modeled, approximate solution of a posterior probability density function is carried out by using a variational Bayesian theory, and the probability density of the underwater unmanned system is calculated. Accurate estimation of the motion state of the unmanned system is realized, and updating and data fusion of a plurality of sub-filters are realized by using a federal structure.

The invention belongs to the technical field of flight control. The invention provides a predetermined time robust control method for an aircraft attitude angle under unknown disturbance. The method comprises the following steps: establishing an attitude angle and angular rate kinetic equation of an aircraft according to wind tunnel experiment data; in consideration of uncertainty and unknown disturbance existing in the kinetic equation, modeling the kinetic equation into a comprehensive disturbance form, and converting the kinetic equation into a ray-imitated form containing disturbance; a preset time disturbance estimator is designed to achieve accurate estimation of comprehensive disturbance, an attitude angle preset time backstepping controller is designed, disturbance is compensated in the controller, and preset time control over the attitude angle of the aircraft is achieved. The method disclosed by the embodiment of the invention has the advantages of simple structure and strong robustness, the parameter adjustment requirement for convergence time is simplified, and the preset time control on the attitude angle of the aircraft under the condition of unknown disturbance can be realized.

The invention discloses a rut depth estimation method and system based on pavement crack curvature feature inversion, and the method comprises the steps: collecting a high-resolution pavement image, carrying out the preprocessing and crack detection, extracting a crack contour, calculating the minimum curvature radius, curvature change rate and other parameters of a crack through difference geometry, and calculating the rut depth. And constructing a rut crack geometric response index R-CGRI to characterize the response strength of the crack to sinking deformation. An image is divided into M * N sub-regions, and the R-CGRI value of each region is calculated to form a two-dimensional response matrix. And performing regression fitting on each row of R-CGRI values, extracting a minimum point of a fitting curve as a rut depth estimation value, and finally reconstructing a rut longitudinal section form. The method does not need to depend on high-cost three-dimensional measurement equipment, accurate estimation and form modeling of the rut depth can be achieved only through two-dimensional image analysis, and the method is suitable for intelligent road inspection and deformation risk assessment.

The invention relates to the technical field of high-precision anti-interference satellite navigation, and particularly discloses a high-precision anti-interference navigation antenna array attitude autonomous estimation method, which comprises the following steps: designing a multi-antenna carrier tracking loop, and measuring a carrier phase in an interference environment; clock difference and ionosphere and troposphere delay are eliminated by means of carrier phase double difference between array elements. Solving a unique solution of each baseline vector from a carrier phase double-difference equation by utilizing the constraint that the array element spacing does not exceed the carrier half-wavelength; rough estimation of the array attitude is obtained according to the least square solution of the equation set satisfied by each baseline vector; compensating the phase error of each array element by utilizing rough attitude estimation; according to the sequence of inter-array element carrier phase double-difference calculation, multiple baseline vector solution, array plane attitude rough estimation and array element phase error compensation, performing iteration for three times to obtain array plane attitude accurate estimation; an attitude sensor, an INS antenna and a GNSS antenna do not need to be additionally installed, the method does not depend on other navigation sources, calibration is not needed, estimation errors are not accumulated, and the initialization time is short.

The invention relates to the technical field of forest total production value estimation, and discloses a forest ecosystem total production value automatic accurate estimation method based on a remote sensing technology, which comprises the following steps: acquiring a real-time positioning signal of remote sensing acquisition equipment, acquiring a to-be-estimated forest map from a geographic information database, and displaying the real-time positioning signal on the map; meteorological parameters and forest characteristic parameters of a to-be-estimated forest are acquired through remote sensing acquisition equipment, and the ground estimation model performs preliminary estimation on the total production value of the forest ecosystem through the meteorological parameters and the forest characteristic parameters; collecting landform characteristics of the map, judging whether the preliminary estimation result is corrected or not according to the landform characteristics, and adjusting the preliminary estimation result according to the landform characteristics if the preliminary estimation result needs to be corrected; and storing the adjusted estimation result, and displaying a dynamic fluctuation graph of the adjusted estimation result on a visual device. The method is accurate and reliable in estimation of the total production value of the forest ecosystem.

The invention belongs to the technical field of geotechnical engineering test and investigation, and provides a method for selecting an optimal shear wave velocity prediction model according to field demand evaluation, which can quantitatively evaluate the accuracy and rationality of a plurality of shear wave velocity prediction models and select the optimal prediction model. A reference value is provided for accurate estimation of the shear wave velocity and geotechnical engineering test and investigation; according to the technical scheme, the method comprises the following steps: evaluating and selecting a shear wave velocity optimal prediction model according to field requirements; the method comprises the following steps: determining a performance angle target area alpha'according to field requirements, respectively calculating mean values of center rates i and deviation angles alpha of various types of models for multiple types of shear wave velocity prediction models, performing comparative analysis to evaluate the accuracy and rationality of the shear wave velocity prediction models, and selecting an optimal shear wave velocity prediction model.

To provide an estimation device capable of precisely estimating the maximum oil temperature of a transformer.SOLUTION: An estimation device includes: an acquisition section for acquiring a measurement value of the maximum oil temperature of a transformer; a generation section for generating a relational expression showing a relationship between the current maximum oil temperature and the past maximum oil temperature; and a temperature estimation section for estimating the maximum oil temperature in time series by applying a Kalman filter to the relational expression. The temperature estimation section calculates a prediction value of the maximum oil temperature on the basis of the last estimation value of the maximum oil temperature, and calculates a current estimation value of the maximum oil temperature by updating the prediction value by using the measurement value as an observation value.SELECTED DRAWING: Figure 3

The invention discloses a high-pressure common rail system oil injection rule self-adaptive observation method considering structure degradation, and belongs to the technical field of engine fuel system control, and the method considers the influence of oil injector structure degradation on model parameters on the basis of an oil injection process dynamic model, and adds degradation factors into the model. Then selecting a state variable, and constructing a state space model containing two outputs of rail pressure and a degradation factor; on the basis of a sequential extended Kalman filtering algorithm, a double-time-scale and double-output high-pressure common rail system oil injection estimator is designed, self-adaptive iterative correction is conducted on model parameters through measured rail pressure and recognized degradation factors, and real-time accurate estimation of the oil injection rule under the structure degradation condition is achieved. And meanwhile, the calculation real-time performance of an actual application process is also improved.

The invention relates to the technical field of atmospheric science detection, in particular to a lightning radiation source positioning method based on multi-station synchronous observation. Comprising the following steps: synchronously acquiring lightning broadband electromagnetic signals by multiple stations and performing time-frequency analysis; by calculating indexes such as cross-station coherence and interference level, high-quality frequency points are intelligently screened out to suppress noise and interference; reconstructing a narrow-band signal for each high-quality frequency point and performing high-precision time difference (TDOA) estimation by adopting an improved generalized cross-correlation method; and carrying out weighted fusion on TDOA measurement values of a plurality of high-quality frequency points, carrying out optimization solution by adopting a Grid Search algorithm, and finally obtaining a three-dimensional coordinate of lightning. Through intelligent screening of high-quality frequency points, accurate time difference estimation, optimization of a positioning algorithm, multi-band information fusion and intelligent processing, the defects that a traditional single-band positioning method is prone to interference and limited in precision are effectively overcome, the lightning positioning precision, the anti-interference capability and the reliability are remarkably improved, and the method is suitable for popularization and application. And the lightning positioning requirements in various environments are met.

The invention discloses an accumulation detection and parameter estimation method for a high-speed target signal with unknown time information, which is applied to the field of radar signal processing and aims to solve the problems of poor accumulation detection performance, inaccurate parameter estimation and the like under the condition that the time information is unknown in the prior art. The method comprises the following steps: firstly, establishing a high-speed target echo signal model with unknown time information; thirdly, performing pulse compression on the echo signal and realizing coherent accumulation through RFT to obtain an initial radial distance coarse estimation value and an accurate estimation value of the radial speed; then, the pulse pressure echo signals are extracted based on the estimation parameters; then, SFT processing is carried out on the extracted signals, and estimated values of the starting time and the ending time are obtained; and finally, obtaining an accurate estimation value of the initial radial distance by using the obtained initial radial speed and the starting and ending time. And effective accumulation detection and high-precision parameter estimation of the target are realized through scaling Fourier transform.

The invention discloses a compressed quantum state chromatography method based on priori knowledge. The compressed quantum state chromatography method comprises the following specific steps: (1) setting proper parameters; (2) randomly sampling a Source operator; (3) traversing all sampled Fouli operators, constructing corresponding quantum measurement, and estimating an expected value of a target quantum state under the operator; and (4) processing the obtained expected value by using an optimization function containing priori knowledge, finally obtaining the estimation of the quantum state, and outputting the estimation. According to the compressed quantum state chromatography technology provided by the invention, based on the low-rank property of the target quantum state, the accurate estimation of the target quantum state is obtained by using the convex optimization function containing the prior knowledge of the target quantum state, and the quantum resources required by the quantum chromatography are further reduced.

The invention discloses a 3D human body posture reconstruction method and a lighting system based on feedback, and the 3D human body posture reconstruction method comprises the steps: carrying out the inverse projection of a predicted 3D position of a human body key point at a t moment, associating the predicted 3D position with an actual 2D position at the t moment to form a 2D joint point set of the human body key point, and calculating a plurality of 3D joint points corresponding to the human body key point according to the 2D joint point set; and fusing the 3D joint point clouds by using a fusion algorithm to obtain fused 3D positions of the key points of the human body, and optimizing the fused 3D positions by using a multi-view geometric parameter optimization method to obtain estimated 3D positions of the key points of the human body at the t moment. The 3D position estimation is more accurate and more reliable and is close to the real situation, the shielding problem can be relieved to a certain degree, and a more stable and more accurate estimation effect can be generated when the number of cameras is small.