1725 results about "Estimation result" patented technology

The invention provides an intelligent fault diagnosis method for a numerical control machine tool, which is applied to a control system of the numerical control machine tool. The method comprises the steps of collecting multi-source sensor data in the operation process of the numerical control machine tool, wherein the multi-source sensor data comprises at least two of a vibration signal, a temperature signal, a current signal and a sound signal; performing preprocessing and feature extraction on the multi-source sensor data to obtain preprocessed data; based on the preprocessed data, constructing and training a machine tool dynamic model to obtain a target machine tool dynamic model; when abnormal operation of the numerical control machine tool is detected, fault state estimation is carried out based on the target machine tool dynamic model and a model prediction control method, and a fault estimation result is obtained; and based on the fault estimation result, constructing a fault evolution path by adopting a Monte Carlo tree search algorithm, and outputting a fault diagnosis result. The accuracy and efficiency of fault diagnosis of the numerical control machine tool can be improved.

The invention discloses a classified processing pit-pond water storage amount remote sensing statistics joint estimation method, and relates to the technical field of water resource remote sensing monitoring, and the method comprises the steps: calculating the pit-pond density, and determining the grid width; calculating a normalized water body index, and extracting a pond water surface boundary; dividing the pit pond into an invertible pit pond and a non-invertible pit pond according to the water surface area and width of the pit pond; calculating the water storage amount of the invertible pit-pond by combining the area of the invertible pit-pond; calculating the water storage amount of the non-invertible pit pond by using the relationship between the water surface area of the non-invertible pit pond and the statistical model; and calculating the total water storage amount of the region, and generating a spatial distribution diagram of the water storage amount. According to the method, remote sensing inversion and a statistical model method are used for estimating the water storage amount, targeted processing can be carried out on different types of ponds, remote sensing images are combined with actually measured data, the water depth inversion precision of the invertible ponds can be effectively improved, and therefore a more reliable water storage amount estimation result is provided.

The invention belongs to the technical field of carrier frequency offset estimation, and particularly relates to a carrier frequency offset estimation method based on Newton orthogonal matching pursuit. The method comprises the following specific steps of: splitting CFO into two parts of frequencies, namely off-grid deviation and grid point frequency, and further modeling an observation signal as a parameter weighted compressed sensing problem; using a Newton orthogonal matching pursuit algorithm to iteratively optimize the off-grid deviation of the CFO and the estimation of the grid point frequency, and obtaining a CFO candidate frequency set; and obtaining a required CFO estimation result from the candidate set by using the lead code information of the target user. According to the method, the complexity requirement of an actual system is met, meanwhile, the precision of CFO estimation is remarkably improved, and good robustness is achieved.

The invention relates to the technical field of bolt tightening, and provides an intelligent bolt tightening control method and system based on axial force measurement. Comprising the steps of collecting bolt identification and process parameter data, collecting temperature measurement data, and obtaining sound velocity compensation parameters and elastic modulus compensation parameters. And zero-load preloading data are collected and processed to generate zero-load baseline data, and a self-calibration model is obtained. Axial force data and ultrasonic echo data are collected, and an axial force fusion estimation result is obtained. And comparing an axial force fusion estimation result with target axial force data. And when in the target interval, fine twisting control is carried out based on model predictive control and a micro-stepping strategy, and the controllable damping unit is driven to carry out energy absorption and stopping. And acquiring an axial force fusion estimation result when the target axial force is reached, processing the data in the holding stage to obtain an axial force rebound check result, and if the axial force rebound check result exceeds the limit, performing secondary twisting control. According to the scheme, refinement and traceability of the bolt tightening process are achieved.

The invention discloses an ecological product carbon sink value accounting method, belongs to the technical field of resource evaluation, and solves the problem that a regional carbon sink estimation result has deviation due to the fact that the existing method is insufficient in single remote sensing data coverage range and resolution and is difficult to adapt to spatial heterogeneity of a region to which a product belongs. The method comprises the following steps: acquiring multi-source heterogeneous carbon sink monitoring data associated with a sub-monitoring area in real time according to a preset acquisition period, preprocessing the multi-source heterogeneous carbon sink monitoring data, and performing data enhancement on a normalized data set based on a Mosaic-Mixup mixing method; executing the carbon sink value accounting model, wherein the carbon sink value accounting model outputs a carbon sink value accounting result; according to the method, a method of preprocessing the multi-source heterogeneous carbon sink monitoring data and performing data enhancement on the normalized data set is adopted, so that accurate and scientific data support can be provided for the carbon sink value accounting of the ecological product, and the carbon sink capacity of the regional ecological product can be accurately evaluated by the carbon sink value accounting model.

The invention provides a coherent signal arrival direction estimation method and device based on a deep convolutional network, and belongs to the field of array signal processing. The method comprises the following steps: receiving a to-be-detected signal containing a coherent signal by using a uniform linear array antenna to obtain an array receiving data matrix and extract a covariance matrix; forming an input feature vector by right upper triangular elements divided from a diagonal line in the covariance matrix, inputting the input feature vector into a covariance estimation model formed by a deep convolutional network, obtaining an estimation value of the right upper triangular elements under an ideal incoherent condition, and reconstructing the estimation value to obtain a covariance matrix estimation value; and performing characteristic decomposition on the covariance matrix estimation value, and generating a spatial spectrum by using a MUSIC algorithm to obtain an estimation result of the signal arrival direction. According to the method, the noise-containing mixed signal covariance matrix is mapped into the ideal incoherent noise-free signal covariance matrix through a physical constraint supervised learning framework, so that the estimation precision and robustness of the MUSIC algorithm in a coherent scene are improved.

The invention discloses a smart power grid state estimation method and system based on a space-time diagram convolutional network, and a medium, and the method comprises the steps: building a topological graph model of a power system according to the topological structure and measurement data of a smart power grid; an STGCN model containing a plurality of space-time convolution blocks is constructed, each space-time convolution block captures time dependence of time sequence data through a time gating convolution layer, and space features of the smart power grid are extracted through a space graph convolution layer; and establishing a power grid state estimation model based on the STGCN model to perform power system state estimation. According to the method, the accuracy and the reliability of an estimation result are ensured by fusing a space and time convolution technology. State estimation challenges under various topological structures can be effectively handled, stable estimation performance can be maintained, and a powerful guarantee is provided for safe operation of a smart power grid. And the operation efficiency and the response speed of the smart power grid are greatly improved.

The invention belongs to the technical field of underwater target detection, and relates to a multi-target detection method and system based on sparse reconstruction and expectation maximization clustering, and the method comprises the steps: carrying out the preprocessing of echoes received by a linear array in a detection region, and obtaining K independent sample vectors meeting the zero-mean multivariate complex Gaussian distribution; the method comprises the following steps: carrying out uniform and equal-interval sampling on a space sector of a detection area to obtain K theta alternative angles, constructing a K theta-dimensional airspace sparse vector based on the K theta alternative angles, establishing a sparse model for judging whether a target exists in an independent sample vector, and obtaining a joint probability density function; a joint method of expectation maximization clustering based on a sparse model is adopted, and classification and estimation results of parameters in the multi-target scene are obtained through loop iteration; and obtaining an adaptive detector based on likelihood ratio test according to classification and estimation results, and realizing accurate multi-target detection. The method can automatically adapt to different target conditions, and the accuracy and reliability of detection are improved.

The invention discloses an ecological carbon sink measurement and estimation data fusion platform for the thermal power industry, and belongs to the technical field of data fusion. The method is realized through five steps of a data acquisition module, a carbon sink measurement and calculation module, a prediction model construction module, a data fusion module and a decision support module. According to the invention, through butt joint with a thermal power enterprise energy management system, production data including generating capacity and coal consumption are acquired in real time, and the timeliness of the data is greatly improved. Ecological environment data and meteorological data around a thermal power enterprise are comprehensively collected by using a satellite remote sensing technology and a ground sensor, and the comprehensiveness and accuracy of the data are ensured. According to the method, a dynamic adjustment mechanism is adopted, and the pre-estimation model is continuously optimized according to real-time data feedback, so that the model can adapt to the change of the environment. And time sequence analysis and regression analysis are combined to form an integrated model, so that the accuracy and reliability of a pre-estimation result are greatly improved.

The invention discloses a real-time dynamic three-dimensional reconstruction method and system based on patch matching and lightweight monocular depth estimation fusion, and the method comprises the steps: carrying out the PatchMatch matching of an input multi-view image pair, obtaining sparse depth information, and fusing the sparse depth information with a monocular depth estimation result; and the fused depth map is used for constructing a three-dimensional Gaussian point cloud, and high-quality three-dimensional scene reconstruction and new view rendering are realized. In the whole process, an end-to-end differentiable training framework is adopted, and the reconstruction precision and rendering efficiency of the 3D Gaussian point cloud in a dynamic scene are improved by optimizing a pixel-level Gaussian parameter regression network and a lightweight depth estimation model. The method can complete training and depth inference in a short time, is suitable for real-time or near-real-time application scenes, can be widely applied to the fields of virtual reality, augmented reality, film and television production and the like, and is particularly suitable for efficient three-dimensional reconstruction and new view angle synthesis of a target in a multi-view-angle dynamic scene.

The invention discloses a DOA (Direction of Arrival) estimation method and system based on a deep complex value convolution attention residual network, belongs to the technical field of array signal processing, and solves the technical problems of low precision and poor robustness of the existing DOA estimation method under the severe conditions of low signal-to-noise ratio, limited snapshot number and the like. The method comprises the following steps: acquiring data by using a co-prime array and preprocessing to obtain SCM data as original input information of DOA estimation; constructing a deep complex value convolution attention residual network to directly process covariance matrix information of a complex field, and utilizing an initial two-dimensional complex value convolution layer, a cascaded complex value convolution block attention network and a cross-layer residual connection structure to deeply extract complex value features related to a space angle; the DOA estimation module is responsible for finally mapping the extracted high-dimensional complex value feature vector to a representation space directly related to a DOA estimation task, and the output module converts the internal feature representation output by the DOA estimation module into a DOA estimation result which can be explained by a user.

The invention discloses an interactive multi-model underwater maneuvering target tracking method and system based on a pure azimuth second-order EKF. The method comprises the following steps: establishing a discrete state space model of a pure azimuth target tracking system; performing second-order linearization on the pure azimuth measurement model; calculating mixed input of a sub-filter corresponding to each model in the model set of the interactive multi-model algorithm; estimating a target state through parallel filtering of the azimuth-pure second-order EKF sub-filter corresponding to each model in the model set; updating the probability of each model in the model set; and combining the estimation results of all the sub-filters to obtain a final estimation result of the target state. According to the method, the target tracking precision can be improved, the problem of highly nonlinear measurement of the UUV on a target detection and tracking system through a passive sonar is effectively solved, good real-time performance is achieved, and real-time tracking of the UUV on the motion state of the underwater maneuvering target is achieved through adaptive matching of the real motion of the target through the interactive multi-model algorithm.

The invention discloses a GNSS (Global Navigation Satellite System) / inertial navigation tightly integrated navigation method and system based on robust auto-velocity constraint factor graph optimization, and the method specifically comprises the following steps: extracting pseudo-range and Doppler observation data from original observation information of a GNSS satellite, and obtaining accelerometer and gyroscope data from an INS (Inertial Navigation System); constructing a pseudo-range residual block, a Doppler residual block, an inertial navigation pre-integration residual block and a speed constraint factor, and integrating into a sliding window estimator based on factor graph optimization; according to the size of a set sliding window, adding all residual blocks in the window to form an optimized objective function; performing a first round of factor graph optimization to obtain a preliminary state estimation result, and endowing the observed quantity containing gross error with a relatively low weight; and updating the prior weight matrix, and carrying out second round of optimization to obtain a joint optimal estimation result of a plurality of moment states in the current time window. According to the method, the influence of gross error-containing observation information on weight estimation and state estimation is suppressed, the positioning precision is high, the robustness is strong, and the reliability is high.

The invention discloses a mining equipment positioning system and method based on radar, ultra wide band and inertial navigation, and the system is characterized in that a multi-source sensor unit, a data synchronization and collection module and a preprocessing module are connected in sequence, and a state fusion module is connected with the preprocessing module, a UWB ranging error compensation module, a radar motion estimation module and a pose output module; the upper computer is connected with the pose output module; the method comprises the steps of collecting multi-source data; timestamp alignment and format conversion processing are carried out, and feature and speed constraint information extraction is carried out on the point cloud data; fitting correction is carried out on the ranging data by adopting a weighted least square method, and position information is obtained through an error weighted average algorithm; feature matching is carried out, and in combination with a KISS-ICP point cloud registration method, a speed estimation result is obtained; carrying out data fusion, and obtaining a pose estimation result by adopting an extended Kalman filtering algorithm; and outputting equipment position and attitude information. According to the invention, high-precision real-time estimation of the attitude and position information of the mining equipment can be realized.

The invention provides a nonlinear amplitude-phase error correction method and device suitable for broadband FMCW radar imaging. The method comprises the following steps: establishing a frequency modulated continuous wave radar difference frequency signal model containing a nonlinear error; correcting the amplitude error by adopting an envelope normalization correction method; estimating the high-order polynomial phase error by adopting a method of combining a phase unwrapping recursive least square method and an OShea optimization strategy to obtain a high-order polynomial phase parameter; estimating the periodic multi-sine phase error by adopting a method of combining a MUSIC algorithm and a trust region algorithm to obtain a periodic multi-sine phase parameter; and based on estimation results of the high-order polynomial phase parameters and the periodic multi-sine phase parameters, phase error correction is carried out by adopting a time domain resampling method. The device comprises a nonvolatile memory in which the executable code is stored and a processor, and is used for implementing the method. According to the technical scheme of the invention, the problem that the existing method is generally insufficient in noise immunity can be solved.

A machine learning device that trains a first encoding model for encoding first sensor data into first code, a second encoding model for encoding second sensor data into second code, and an estimation model for making estimation using the first code and the second code such that an estimation result from the estimation model conforms to correct answer data, trains a first adversarial estimation model that outputs an estimated value of the second code in response to the input of the first code such that the estimated value of the second code estimated by the first adversarial estimation model conforms to the second code outputted from the second encoding model, and trains the first encoding model such that the estimated value of the second code estimated by the first adversarial estimation model does not conform to the second code outputted from the second encoding model.

The invention discloses a method and system for estimating soil salinity based on feature extraction and machine learning, and relates to the technical field of agricultural water and soil engineering.The method comprises the following steps that data information is collected, vegetation canopy reflectivity data is calculated according to the collected data information, feature parameters are extracted from the data information, and the feature parameters are evaluated; according to an evaluation result, combining the evaluated characteristic parameters into different modeling schemes, and constructing different soil salinity estimation models according to the modeling schemes; verifying the different soil salinity estimation models to obtain a qualified soil salinity estimation model; and deploying the qualified soil salinity estimation model on a hardware platform, and analyzing the sample. According to the method, the soil salinity estimation model is established by collecting the canopy reflectivity, the remote sensing image and the salinity data in different growth periods under vegetation coverage, and compared with the precision of models established based on different data sources, the investment of manpower and material resource cost is greatly saved.

The invention provides a floating-point number calculation circuit, a floating-point number calculation method, a storage medium and electronic equipment, and relates to the technical field of computers. In the circuit, a mantissa calculation module is used for executing multiplication of two floating-point number mantissas to obtain a product mantissa when a floating-point number operation starting signal is received; the leading zero detection module is used for responding to the floating-point number operation starting signal and performing leading zero detection on the mantissa of each floating-point number to obtain a leading zero estimation result of the product mantissa; the normalization processing module is used for executing product mantissa displacement control and index adjustment based on the leading zero estimation result to obtain a mantissa operation result and an index operation result; and the result output module is used for outputting a floating-point number product result based on the mantissa operation result and the index operation result. The length of the floating-point number multiplication calculation path can be effectively shortened, and the floating-point number calculation efficiency is improved.

The invention relates to an intelligent measurement and control optimization method and device for dynamic parameter adaptive calibration, equipment and a medium. The method comprises the following steps: executing invariance causal test based on an external environment context to obtain a cross-environment invariant explanatory variable subset, and performing anti-fact simulation on each parameter in the cross-environment invariant explanatory variable subset to generate a causal attribution report; based on an under-excitation parameter subset and a cross-environment invariant explanatory variable subset in the baseline recognizable atlas, performing safe active excitation planning on the parameters to obtain a safe micro-perturbation excitation plan; and based on the residual error, the uncertainty estimation, the delay cross-correlation feature, the hysteresis loop area feature, the causal contribution score and the excitation-response fragment, performing calibration estimation on the target parameter by using a hierarchical estimator, and generating a calibration packet according to a calibration estimation result. By adopting the method, self-adaptive calibration of intelligent measurement and control dynamic parameters can be realized through residual attribution and cross-environment invariance test in combination with safe perturbation excitation.

Embodiments of the present application relate to the field of computer vision. Disclosed are a target six-degree-of-freedom pose estimation method and apparatus based on binocular vision and particle swarm optimization. The method comprises: obtaining a binocular image of a target, wherein the binocular image at least comprises a rotation matrix, a displacement vector and geometric features of the target; decomposing the rotation matrix into a rotation vector and a rotation angle by means of a rotation matrix decomposition method, and performing six-degree-of-freedom uniform sampling on the rotation vector and the rotation angle to obtain a data point set; establishing a mathematical relationship between the rotation matrix and the displacement vector on the basis of the geometric property of the geometric features and the data point set; and performing target pose estimation on the binocular image of the target by designing an objective function and an application optimization algorithm to obtain a pose estimation result. The present application solves the problems in the related art of low accuracy, poor universality and poor robustness.

The invention relates to the technical field of VR remote control, and discloses a VR remote control method and system based on gyroscope data fusion, and the method comprises the steps: obtaining current environment data, gyroscope three-axis data and accelerometer data; inputting the current environment data into a pre-trained error estimation model, and outputting an error estimation value and a prediction confidence coefficient; performing confidence coefficient judgment according to the error estimation value and the prediction confidence coefficient to obtain a zero offset estimation result; performing attitude analysis according to the gyroscope three-axis data and the accelerometer data to obtain a preliminary attitude estimation result; performing dynamic compensation according to the zero offset estimation result and the gyroscope three-axis data to obtain corrected gyroscope data; and performing fusion correction according to the initial attitude estimation result and the corrected gyroscope data, and outputting a final attitude estimation result. The method has the advantages that the accuracy of attitude estimation in VR remote control can be improved.

The invention provides an equipment running state optimization control method and device, electronic equipment and a storage medium. The method comprises the steps that historical data and real-time data of equipment operation are acquired, and key features related to the equipment operation state are screened out; preprocessing the historical data, dividing the preprocessed historical data into a plurality of categories, and generating a reference sample set of each category; performing weight calculation on the key features by using an analytic hierarchy process, and constructing a feature weight matrix; performing state estimation by using an improved multivariate state estimation algorithm; predicting the running state of the equipment according to the state estimation result, and judging whether the running state of the equipment is normal or not; and if the operation state of the equipment is normal, writing back an optimal control parameter generated based on the predicted operation state to the distributed control system. The parameter adjustment precision can be improved, the prediction precision can be improved, and the equipment operation efficiency can be improved.

The invention discloses an industrial process product quality estimation method based on a stacked ensemble learning model, and the method comprises the following steps: S1, carrying out the preprocessing and feature extraction of industrial evaporation operation data through a convolutional neural network model; s2, selecting and training a base learning device; s3, selecting a gradient boosting decision tree as a meta-learner of the stacked ensemble learning model, and estimating the concentration of the outlet sodium aluminate solution by taking a prediction result of the meta-learner and features extracted by the convolutional neural network model as input; and S4, calculating an error between the outlet solution concentration estimated value and an actual measurement value, and re-training the random forest model for error compensation to obtain a final outlet product quality estimated value. According to the industrial process product quality estimation method based on the stacked ensemble learning model, the estimation result has high accuracy, and the problem that the concentration of an outlet sodium aluminate solution in a multi-stage evaporation process in the aluminum oxide production process is difficult to estimate under the current industrial background can be solved.

The invention discloses a power distribution network topology state estimation method, electronic equipment, a medium and a product. The method comprises the steps of obtaining a topological structure and measurement data of a power system; constructing variable nodes and factor nodes according to the topological structure and the measurement data, and constructing a state-topological joint factor graph model according to the variable nodes, the factor nodes and the topological structure; based on the state-topology joint factor graph model, performing state estimation through a belief propagation algorithm to obtain an estimated value of state variable correction; if the estimated value of the state variable correction meets the convergence condition, correcting the topological state of the switch branch according to the active power and reactive power of the head end of the switch branch in the estimated value of the state variable correction; and outputting an estimation result of the topological state of the switch branch until the on-off state of the switch branch obtained according to the state variable is consistent with the original topological state of the switch branch. According to the method, asynchronous real-time updating of the topological state of the power system can be realized.

The invention discloses a sparse Bayesian parameter estimation method based on a Markov chain Monte Carlo (MCMC). The sparse Bayesian parameter estimation method is suitable for super-resolution target number detection and high-precision target parameter estimation in a communication perception integration (ISAC) scene. The method comprises the following steps: a base station transmits an orthogonal frequency division multiplexing (OFDM) signal and receives a target reflection echo; performing frequency domain transformation on the echo signal and constructing a high-dimensional observation vector; target parameters are modeled as random variables including path complex gain, time delay, Doppler frequency shift and angle, and a learnable parameterized sensing matrix is constructed; the sparse Bayesian framework is combined with MCMC sampling, the parameters are iteratively updated through random small-batch gradient optimization and the Metropolis-Hastings criterion, and finally the estimation result of the target parameters is output. According to the method, the precision limitation of a traditional gridding method is broken through, super-resolution parameter estimation is achieved, meanwhile, the calculation complexity and the storage overhead are remarkably reduced, and the method is suitable for the high-precision sensing requirement in a 6G communication system.

The invention discloses a high-precision surface rainfall deviation correction method based on space-time association. The method comprises the steps of evaluating satellite remote sensing rainfall data estimation precision and modeling a space-time association relationship; and space-time association relationship modeling: analyzing space-time error distribution of the long-time-sequence satellite remote sensing rainfall data, performing uncertainty modeling on a satellite rainfall estimation result by using a probability density distribution function, and providing data uncertainty information for rainfall deviation correction. And space-time association relationship modeling: researching a modeling method of the space-time association relationship of the satellite-ground data, and constructing space-time association models between the ground observation sites and the corresponding remote sensing pixels and between the pixels. Meanwhile, environment variables, precipitation deviation time sequence change characteristics and uncertainty information are introduced, and the surface precipitation deviation correction precision is improved. Through the research, the estimation error of the satellite remote sensing surface rainfall data can be effectively reduced, and the accuracy of deviation correction is improved.

A suspension system performs calculations using machine learning by mapping sensor information and suspension state information. The suspension system includes: a weighting parameter storage unit; a vehicle state estimation unit; a first vehicle behavior calculation unit that calculates a first physical value based on the estimation result; a second vehicle behavior calculation unit that calculates a second physical value based on the sensor information; an estimation accuracy verification unit that outputs an estimation accuracy of the suspension state by comparing the first and second physical values; and a driving data control unit that instructs weighting parameter learning based on an output result of the estimation accuracy.

The invention discloses an Internet of Things equipment real-time early warning method and system based on digital twinning, and relates to the technical field of equipment operation management, and the method comprises the steps: collecting multi-modal original data of Internet of Things equipment, constructing a graph structure, and generating a semantic graph vector; obtaining a digital twinborn model state corresponding to the target equipment, forming an extended state vector by the digital twinborn state, the physical equipment state and the semantic map vector, and inputting the extended state vector into an extended Kalman filter for state fusion to obtain a state estimation result; and performing Monte Carlo simulation according to the state estimation result, generating a plurality of abnormal state samples, calculating a residual mean value between the abnormal samples and the current estimation result, correcting a covariance parameter of the extended Kalman filter, and generating an early warning tag. Fine estimation and risk level early warning of the state of the Internet of Things equipment are realized, and the state fusion precision and the abnormal response timeliness are improved.

The invention discloses a single-view unknown object 6D pose estimation method based on segmentation and new view angle synthesis, and relates to the field of object pose estimation. The method comprises the following steps: designing a segmentation module and a new view angle synthesis module of a pose estimation network, and obtaining a segmentation result of a target object by using the segmentation module; generating a target object multi-view-angle embedding template by using a new view angle synthesis module; matching the embedding representation zquery'of the segmentation result with a multi-view embedding template of the target object to find out the embedding representation most similar to zquery ', and determining a preliminary rotation attitude estimation result R of the target object; and estimating the relative translation vector t of the target object, and combining the R to obtain the relative pose (R, t) of the target object. According to the method, robust 6D pose estimation of the unknown object in a complex visual scene can be realized only by taking a single reference image as input, and the pose estimation precision and robustness of the unknown object in the complex scene are improved while the real-time requirement is met.

The present invention is a parameter estimation method for compartment model based on physics-informed neural networks. Starting from a physical model, the method extracts information from an AIF and a small amount of measurement data to obtain kinetic parameters, thereby greatly improving the scanning efficiency of a measuring instrument, and reducing occurrence of inaccurate estimation results due to patient movement. In addition, the present invention has the robustness to AIF noise and measurement data noise, and can flexibly arrange the time of data acquisition, reduce an error of inaccurate estimation caused by long time 10 acquisition and the patient movement, and improve the efficiency of data acquisition of the instrument. Experimental results show that the present invention is more stable and has less errors. Meanwhile, the present invention does not require the setup of training datasets, and is superior to an end-to-end supervised reconstruction method U-net network with fewer samples.