145 results about "Accurate estimation" patented technology

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 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 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 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.

To provide a technique capable of accurately estimating heat energy consumption of a heat utilization facility without requiring an expensive flowmeter and without stopping a factory.SOLUTION: An estimation device (1) includes an estimation unit (11) configured to acquire a first measurement value from each of a plurality of heat utilization facilities (21 to 22) and calculate an estimation value of thermal energy consumption of each of the plurality of heat utilization facilities (21 to 22) from the first measurement value, a correction value calculation unit (12) configured to calculate a correction value from an analysis value of a drain flow rate of an entire plant (2) including the plurality of heat utilization facilities (21 to 22) calculated by a simulator (3) and an actual measurement value of the drain flow rate of the entire plant (2), and an estimation unit (13) configured to estimate the thermal energy consumption of each of the plurality of heat utilization facilities (21 to 22) by correcting the estimation value using the correction value.SELECTED DRAWING: Figure 4

The invention relates to the technical field of ocean engineering construction automation, and discloses a hinge sinking mattress construction real-time monitoring control system based on multi-sensor fusion, which inverts a near-field hydrodynamic environment acting on a hinge sinking mattress through a hydrodynamic field inversion module by using strain measurement data and a parameterized forward dynamical model, and performs real-time monitoring and control on the hinge sinking mattress. The state fusion estimation module fuses sparse absolute position measurement data and high-density form measurement data in an extended Kalman filtering framework, introduces form manifold projection correction, and accurately estimates the position, attitude and form of the sinking mattress. And the predictive control decision module takes the estimated system state and the inverted near-field hydrodynamic environment as input, and solves an optimization problem to generate an optimal control sequence under multiple constraints of considering structural safety and physical limitation of an execution mechanism. According to the method, active suppression of hydrodynamic disturbance is achieved, the robustness and precision of state estimation are improved, and the automation level and efficiency of hinge sinking construction are remarkably improved.

The invention relates to a Lamb wave frequency dispersion characteristic sparse reconstruction method based on non-convex optimization, and belongs to the technical field of high-end equipment perception and test.The method comprises the steps that excitation is conducted on a U-shaped suspension arm, and time domain sampling signals at the junction of a U-shaped suspension arm structure are obtained; the time domain sampling signals are preprocessed; constructing a non-convex sparse frequency dispersion curve estimation model based on the preprocessed signal; and converting the non-convex sparse frequency dispersion curve estimation model into an optimization problem, searching a convex preserving condition of a target function, selecting a forward and backward splitting algorithm for solving, obtaining frequency-wave number distribution, and further extracting a frequency dispersion curve. The method solves the problems that a traditional convex regularization method is limited in induction sparse capability and underestimates signal amplitude. Through a non-convex optimization method, the purpose of accurate estimation of the frequency dispersion curve based on fewer measurement points is achieved.

The invention relates to the technical field of new energy power generation, and discloses a power prediction deviation estimation method based on an intelligent combination model. The method comprises the steps of collecting historical generation power data and prediction data, calculating a power deviation sequence and analyzing error characteristics; introducing a subtraction average optimizer algorithm with an elitist strategy, and performing iterative optimization on the bandwidth constraint factor, the modal decomposition number and the initial learning rate by taking the minimum average absolute error as a fitness function; decomposing the error sequence into a plurality of intrinsic mode functions (IMF) by using variational mode decomposition; for each IMF component, a CNN-BiGRU-AM neural network model is constructed, and modeling and prediction are carried out; and finally, reconstructing the prediction result of each sub-component to obtain the accurate estimation of the overall error. By fusing error decomposition, deep modeling and parameter optimization, the estimation precision and stability of the power prediction deviation are remarkably improved, and the method is suitable for error correction and scheduling optimization of new energy power generation systems such as wind power, photovoltaic and the like.

The invention discloses a self-adaptive precision polishing control system and method based on multi-modal perception and artificial intelligence decision. According to the system, a multi-mode sensing network is constructed through an acoustic emission sensor, a force / torque sensor, a vibration sensor, a visual sensor and a temperature sensor, and physical signals in the polishing process are collected in real time. The system adopts a deep learning model to carry out feature fusion and space-time analysis on multi-source sensing data, realizes accurate estimation of the material removal rate and comprehensive scoring of the surface quality, further integrates a reinforcement learning optimization engine, takes a process state as input, takes control parameter adjustment as action, and takes a multi-target reward function as guidance, and realizes comprehensive evaluation of the surface quality. And the autonomous optimization decision of the polishing parameters is realized. The invention further comprises a digital twinning system for offline training and online verification, and a transfer learning-based small sample rapid adaptation mechanism, so that the polishing quality consistency, the processing efficiency and the process autonomy can be remarkably improved.

The present invention enables more accurate estimation of a three-dimensional structure including an internal structure of a target without requiring a large number of captured images. A three-dimensional structure estimation device (10) includes: an acquisition unit (111) that acquires a plurality of fluoroscopic images obtained by imaging a target from mutually different directions; a learning unit (112) that, using the plurality of fluoroscopic images, learns a learning model to which input data indicating three-dimensional coordinates of an arbitrary point inside the target is input and which outputs first output data indicating a signed distance of the arbitrary point with respect to a surface of an internal structure possessed by the target and second output data indicating a first attenuation coefficient at the arbitrary point; a calculation unit (113) that inputs, to the learning model, input data indicating respective points inside the target and, by correcting the first attenuation coefficient indicated by the second output data on the basis of the signed distance indicated by the first output data, calculates second attenuation coefficients at the respective points inside the target; and an estimation unit (114) that estimates a three-dimensional structure of the target on the basis of the second attenuation coefficients at the respective points inside the target.

The invention discloses a satellite phase deviation resolving method considering signal distortion deviation, and relates to the technical field of satellite navigation and positioning. The method comprises the following steps: selecting a GNSS base station network to obtain double-frequency observation data; classifying receiver types and unifying same-kind signal distortion delay; signal distortion modeling is introduced based on an MW combined observation equation, and wide-lane satellite phase deviation is obtained through adjustment and ambiguity fixed solution in combination with a precise orbit and clock correction; and based on the ionosphere-free combination observation equation, combining the solved wide-lane deviation, and obtaining the narrow-lane satellite phase deviation and the ionosphere-free combination signal distortion deviation through adjustment and ambiguity fixation. According to whether the receiver type is calibrated or not, the signal distortion deviation is directly applied or estimated in real time, more comprehensive error compensation is achieved in an observation equation, and the positioning precision and the convergence speed are improved. According to the method, synchronous separation and accurate estimation of the wide-lane and narrow-lane phase deviation and the signal distortion deviation of the satellite are realized, and the estimation accuracy and physical consistency of the phase deviation are effectively improved.

The invention discloses a GNSS multipath parameter estimation method based on multi-correlator output, and belongs to the field of satellite signal multipath resistance. The invention discloses a GNSS multipath signal parameter estimation method, and aims to provide a novel GNSS multipath signal parameter estimation method which comprises the following steps: performing correlation operation on a local spreading code and a received signal through a GNSS correlator, and mining internal correlation between a correlator interval and a multipath error based on a correlation output result of a composite signal; the method comprises the following steps: acquiring multipath error data of multiple groups of correlators to construct a relation curve by combining an analytical formula of correlator spacing and multipath errors, reversely deducing multipath parameters by solving coordinates of turning points of the curve, and finally realizing accurate estimation of the multipath parameters. According to the method, the time delay, phase and amplitude parameters of the short multipath can be accurately identified, and the capability bottleneck of a traditional algorithm in the aspects of short multipath suppression and estimation is effectively broken.

The invention discloses a virtual power plant baseline load estimation method and system based on deep learning, and the method comprises the steps: carrying out the preprocessing of missing data in an original data set, extracting the energy consumption characteristics of multiple types of users, constructing a load baseline estimation model of the multiple types of users, and carrying out the calculation of the load baseline estimation model. Inputting the user load historical data set and the meteorological factor data set into a trained user load baseline estimation model, outputting various types of user load baseline prediction results, obtaining a fitting weight through analysis of various types of user historical power consumption data, fitting prediction values of a plurality of user load baselines, and obtaining a plurality of user load baseline prediction results; the accurate estimation of the load base line of the multi-type user virtual power plant in multiple scenes is realized; the system comprises a data preprocessing module, a user load baseline estimation module and a virtual power plant load baseline estimation module. The method can achieve the estimation of the virtual power plant load base line under multiple users and multiple scenes, and is higher in estimation precision.

The invention relates to the technical field of SAR satellite load parameter setting, and provides an RCS dynamic estimation method and device based on Bayesian optimization. According to the method, peak detection is carried out on RCS equivalent values in sample data, the sample data are divided into different types of data sets based on a detection result, Bayesian dynamic estimation objective functions and estimation intervals are set for the different types of data sets respectively, Bayesian dynamic estimation models of the different types of data sets are trained respectively, and the RCS equivalent values in the different types of data sets are calculated. The method comprises the following steps: firstly, acquiring to-be-estimated data, determining a data type based on an RCS equivalent value peak detection result of the to-be-estimated data, and calling a trained Bayesian dynamic estimation model of a corresponding type to determine an RCS estimated value of the to-be-estimated data, thereby solving the problem that artificial parameter setting cannot meet adaptive accurate estimation in a complex scene; the estimation accuracy and reliability of the RCS in a complex scene are ensured, adaptive adjustment of the RCS estimation is realized, and the precision of the RCS estimation is remarkably improved.

The invention discloses a conventional unit installed capacity estimation method and system based on a load characteristic index, and belongs to the technical field of power system planning. The method comprises the following steps: acquiring historical load information, and calculating daily load and annual load characteristic indexes; deriving and planning annual total power consumption based on indexes, and calculating the minimum installed capacity of a conventional unit through annual and typical daily power balance; and energy storage peak regulation parameters are further calculated, and climbing verification and power receiving correction are carried out. The system comprises a data module, an index module, a capacity module and a correction module. The time sequence characteristics are quantified through the key load indexes, the complex power source planning process is simplified, rapid and accurate estimation of the installed capacity is achieved, the method is suitable for a new energy high-permeability system, and the planning efficiency and economical efficiency are improved.

The application discloses a ground reconnaissance radar CFAR detection method and system, the method comprises the following steps: based on the minimum description length criterion, model selection and clutter edge identification are carried out on reference window samples, and a reference sample set homogeneous with a to-be-detected unit background is dynamically selected; bilateral quartile range is used for bilateral truncation on the selected reference samples, and abnormal interference samples on both sides of the reference window are removed; based on the effective samples after truncation, accurate estimation of the background clutter power is completed through maximum likelihood estimation of the truncated Weibull distribution, and an adaptive detection threshold is calculated according to a preset false alarm probability. The application has both environmental adaptability of regional decision and anti-interference robustness of truncated statistics, and can effectively balance target detection probability and false alarm rate stable control in complex clutter scenes such as uniform background, multiple targets, clutter edge and the combination of the two, and significantly improves the target detection performance and environmental adaptability of the ground reconnaissance radar in a complex working environment.

To provide a technique capable of estimating a part to be preferentially processed with higher accuracy.SOLUTION: Acquiring binary data for classifying whether an arbitrary position of a processing target surface is a contact position or a non-contact position with respect to a reference surface when the processing target surface is rubbed against the reference surface with a workpiece in a predetermined assembled state in a processing apparatus that automatically performs scraping; A processor that acquires measured height data in which height information at an arbitrary position on a machining target surface of a workpiece that is not in a predetermined assembled state is a point group, acquires correction amount data in which a correction amount of the height information at the arbitrary position on the machining target surface is a point group based on the binary data and the measured height data, acquires assumed height data in the predetermined assembled state by superimposing the correction amount data on the measured height data, specifies a convex portion of the machining target surface based on the assumed height data, and generates machining instruction data for cutting the convex portion.SELECTED DRAWING: Figure 9

This invention discloses a method for estimating the speed and position of a permanent magnet synchronous motor (PMSM) based on a third-order switched extended state observer. Specifically, based on the mathematical model of the PMSM, a back-EMF observer based on a sliding mode observer is constructed to obtain the estimated back-EMF. Further, the estimated back-EMF signal is processed using a low-pass filter and amplitude normalization. The processed back-EMF is then used as the input signal to a position estimation scheme based on the third-order switched extended state observer, achieving accurate estimation of the PMSM's speed and position under different operating conditions. This invention is simple in principle and easy to implement. It combines the advantages of both third-order linear and third-order nonlinear extended state observers, improving estimation accuracy and dynamic performance while reducing the adverse effects of noise on the estimation scheme. This solves the technical problems of low estimation accuracy, high noise sensitivity, and difficulty in parameter tuning in existing estimation schemes.

The invention provides a personnel positioning method and device, electronic equipment, a storage medium and a program product, relates to the technical field of data processing, combines the machine room characteristics of a data center, constructs a positioning tracking model, promotes a three-dimensional model to a three-dimensional space, realizes three-dimensional acceleration triple consistent distribution, and is beneficial to solving the blind spot problem in UWB positioning. Through the motion data and the acceleration at the current moment, the motion data at the next moment is calculated, and the accurate estimation of the motion state of the person at the next moment is realized. According to the invention, through positioning the tracking model, calculating the acceleration, calculating the motion data of the personnel at the next moment according to the motion data and the acceleration at the current moment, and correcting the motion data at the next moment, accurate positioning of the personnel in the machine room is realized.

The application discloses a kind of guiding control method and brain-like force sense glove based on speed map, the method includes constructing mixed vector field and virtual potential field;Combined with mixed vector field and virtual potential field, construct global convergent vector field as speed map;Obtain guide coordinates, and input speed map to obtain guide information;Based on guide information and the current speed of robot, obtain error information;Substitute it into controller to realize robot control.The application designs a global convergent vector as guide map in advance, guides system state to tend to be stable, so that robot is attracted to desired trajectory when there is disturbance, compensates the uncertainty and interference of control system with minimum calculation amount, significantly reduces the transient overshoot in flexible interaction process;Design three-dimensional force accurate estimation based on brain-like force sense glove, combined with guide control method and admittance relationship, realize flexible interaction of any type and any number of robots.

The application discloses a railway box girder full-section data monitoring method and system based on three-dimensional scanning, and particularly relates to the technical field of data monitoring, which comprises the following steps: collecting the full-section data of the railway box girder through a three-dimensional scanner, determining the full-section data collection process of the railway box girder by scanning each part of the box girder for multiple times, processing the collected full-section data of the railway box girder, splicing the point cloud data by means of the aerial triangulation calculation process based on the dense point cloud data, the accurate estimation of the key point circle center position, the key plane fitting and optimization, uploading the spliced data and the special marker point coordinates to an intelligent measurement integrated platform system by an operator, collecting the point cloud information and the contour information of the prefabricated box girder, constructing an acceptance evaluation model of the prefabricated box girder, and quantifying the possible significant deviation between the box girder model and the design standard. The application is helpful to improve the efficiency of the full-section acceptance of the railway box girder.

This invention discloses a method for estimating atmospheric errors in bistatic satellite-to-ground radar target detection using high- and low-frequency joint reception. The method first obtains a large-scale atmospheric delay estimate through multi-source data fusion and ray path integration to achieve coarse compensation; then, it extracts the residual atmospheric phase based on echo self-calibration to achieve fine compensation; finally, it combines high- and low-frequency constraints to achieve phase deambiguity and extract the absolute total atmospheric delay. From global modeling to local correction and cross-band consistency, it achieves accurate estimation of atmospheric delay errors along the propagation path.

The invention discloses a drainage basin scale snow melting runoff estimation method and system, electronic equipment and a storage medium, and the method comprises the steps: collecting reanalysis meteorological data, remote sensing accumulated snow monitoring data and digital elevation model data in a target drainage basin range, and carrying out the preprocessing to generate a drainage basin scale surface average data sequence; dividing the target drainage basin range into a plurality of calculation units; determining a critical temperature threshold value of the whole drainage basin based on a response relationship between the drainage basin average accumulated snow coverage rate and the drainage basin average temperature; on the basis of comparison of the basin average temperature and the critical temperature threshold value, the basin overall snow melting period is recognized by combining basin average accumulated snow coverage rate changes; an improved degree-day factor method is executed in parallel on each calculation unit, and the snow melting rate is calculated; and confluence calculation is carried out on runoff generated by the snow melting rate of each calculation unit, and the snow melting runoff of the drainage basin outlet section is output. According to the method, dynamic and accurate estimation of the drainage basin snow melting runoff is realized, and the simulation precision and practicability are remarkably improved.

A method, system, and computer program product for improving accuracy and efficiency of quantum error mitigation / correction techniques. A model (e.g., noise model) of a quantum gate set (collection of quantum gates) that accounts for SPAM and gate errors is built by running learning circuits at different depths with different initial states and measurements to extract information about the quantum system. A gauge transformation is then applied to the quantum gate set to transform both the SPAM and gate errors together. A gauge transformation enables a different perspective of the quantum system while still preserving the underlying physics. A quantum error mitigation / correction technique is then tailored to avoid interpreting errors (e.g., SPAM, gate errors, etc.) inconsistently if treated individually using the transformed SPAM and gate errors thereby requiring fewer additional circuit runs for accurate estimations which significantly reduces the computational overhead associated with quantum error mitigation / correction.

The invention discloses a fast and accurate estimation method for instantaneous torque of a switched reluctance motor based on joint interpolation, and belongs to the field of motor control. According to the invention, the data volume required by the torque estimation model of the switched reluctance motor is mainly reduced, the estimation precision is improved, and the real-time performance of a motor control strategy and the accuracy of the torque estimation model are ensured; according to the method, only a small amount of flux linkage data at the key position of the motor is needed, the flux linkage saturation characteristic of the motor is taken as a reference, fitting is carried out on the flux linkage-current relational expression through the segmented Lagrange interpolation function, and then the flux linkage characteristic is expanded to the full position through the Fourier series interpolation function; and finally, the torque estimation method based on the segmented Lagrange-Fourier joint interpolation is formed. According to the method, on the premise of ensuring enough estimation precision, the data demand and the calculation amount are greatly reduced, and the torque can be accurately and quickly estimated.

The application relates to a satellite collision probability estimation method, device and readable storage medium. The satellite collision probability estimation method comprises the following steps: forward propagation of a non-Gaussian relative velocity probability density function to obtain a reference velocity histogram, so as to determine an initial parameter set of a Gaussian mixture model; every first preset time interval, a plurality of sets of distance residuals and angle residuals of a first satellite and a second satellite in a running state are updated, so as to update the parameter set of the Gaussian mixture model; every second preset time interval, the relative positions of the first satellite and the second satellite are updated, and the instantaneous collision volume of the first satellite and the second satellite is determined by using the separation axis theorem; the mean values of Gaussian components in the updated parameter set are substituted into a static spherical harmonic coefficient table for query, so as to obtain the relative position probability density of the first satellite and the second satellite; and the collision probability is obtained based on the instantaneous collision volume and the relative position probability density, so that fast and accurate estimation is realized.

The application discloses an estimation method of an unknown failure tumbling target angular velocity based on an electromagnetic induction torque, which is composed of the following steps: obtaining historical data of the unknown failure tumbling target, wherein the historical data comprises angular velocity historical data and electromagnetic induction torque historical data; performing dimension increasing on the historical data through nonlinear transformation to obtain dimension-increased data; performing dynamic mode decomposition on the dimension-increased data to obtain a system matrix, an output matrix and a prediction matrix; constructing a linear predictor model of the unknown failure tumbling target in the electromagnetic despinning process according to the system matrix, the output matrix and the prediction matrix; and solving the linear predictor model as a constraint condition of an optimal estimation problem to obtain a real-time accurate estimation value of the angular velocity of the unknown failure tumbling target. In the electromagnetic despinning process of a close-range space target, the application realizes real-time accurate estimation of the target angular velocity by using electromagnetic despinning torque data, thereby solving the problem that the target angular velocity is difficult to measure in a close-range despinning task.

The application provides a two-dimensional DOA estimation method for arbitrary array single-base MIMO radar based on data rearrangement, and relates to the technical field of array sensor direction finding. The rank structure of a coherent signal covariance matrix is recovered by rearranging a received data matrix through arbitrary array element positions of a transmitting array and a receiving array; S2: eigenvalue decomposition is performed on the signal covariance matrix to obtain a signal subspace; S3: reliable rough estimation is obtained through rotation invariance; S4: an integer ambiguity vector corresponding to spatial phase ambiguity caused by array element spacing greater than half a wavelength is solved; S5: the phase of an array flow pattern with phase ambiguity is compensated, and an accurate estimation value of the 2D-DOA of the target is calculated; the high-precision 2D-DOA estimation problem in a complex scene of a coherent signal source, arbitrary array geometry and phase ambiguity caused by part of array element spacing greater than half a wavelength is solved, unambiguous and high-precision angle parameter estimation is realized, and the estimation accuracy and algorithm reliability are improved.

Provided is an analysis method enabling accurate estimation of the types, amounts, and mixing ratios of fillers present in an elastomer composition, including whether the sample contains two or more fillers. The present disclosure relates to an analysis method including estimating the type, amount, and mixing ratio of at least one filler in an elastomer composition containing an elastomer component and the filler, the method including estimation based on the property, particle size, and particle size distribution of the filler.

The invention discloses a grade estimation method for associated cobalt in skarn type iron ore. The method comprises the following steps: collecting a sample, analyzing element content, and storing an analysis result into a data set; preprocessing the analysis data; element correlation analysis is carried out, and strong correlation elements are selected; a linear fitting formula between the cobalt grade and the strong correlation element grade is obtained, a residual sum of squares and a decision coefficient between a linear fitting line and the strong correlation element grade data point are calculated, the residual sum of squares is minimized, the closer the decision coefficient is to 1, the optimal linear fitting formula in the strong correlation element grade variable range is selected, and the optimal linear fitting formula is calculated; taking as a cobalt grade estimation formula; and estimating the cobalt grade of the main ore body through a cobalt grade estimation formula. According to the method, accurate estimation of the cobalt element grade in the mining area is realized, and basic data is provided for spatial distribution and grade grading of cobalt elements in the mining area.