31results about How to "Improve build precision" patented technology

The invention provides a power grid equipment standard knowledge graph generation method and system, power grid equipment knowledge can be intelligently integrated and optimized, and if a target power grid data node needing to be integrated currently is an existing data node in a reference power grid equipment standard knowledge graph, the target power grid data node needs to be integrated through a power grid equipment feature extension mechanism. According to the method, knowledge vectors are inferred by utilizing the characteristics of forward data nodes or dependent data nodes, so that the reference power grid equipment standard knowledge graph is optimized, the accuracy of the reference power grid equipment standard knowledge graph is improved, and standardized and intelligent management of power grid equipment knowledge is realized; the construction of the optimized power grid equipment standard knowledge graph is taken as a powerful service engine, namely a Zhongtai capability semantic network, so that subsequent standard intelligent recommendation and association are realized, and an efficient and intelligent solution is provided for power grid equipment management.

The invention relates to a target detection performance curved surface progressive sampling method based on Gaussian process multi-core fusion. The invention relates to the technical field of target detection, in a target detection process, multi-level search space construction is carried out, and efficient kernel function combination and optimization are realized in a complex performance curved surface scene; a progressive sampling optimization strategy of a multi-scale collaborative adaptive kernel system is established, a kernel function combination structure and parameters are optimized step by step, and the fitting precision of a Gaussian process model to a target detection performance curved surface is improved. The method is mainly used for solving the problem of model performance evaluation under a multi-dimensional interference condition, and the performance evaluation efficiency and accuracy of the target detection model in a complex environment are improved by optimizing kernel function combination and parameter tuning.

The invention relates to the technical field of tunable semiconductor lasers, and discloses a mode-free hopping wavelength tuning method, device and equipment for a tunable laser, and the method comprises the steps: carrying out the scanning of a reflection region current of the tunable laser based on a preset scanning strategy, and obtaining a plurality of ladder data; extracting effective ladder data, and dividing the data belonging to the same wavelength tuning region into one group; for each effective data set, performing midpoint fitting on the effective ladder data, obtaining a smooth wavelength tuning path, and then performing scanning verification and screening feature points; phase current positive and negative loading is carried out at each feature point, a phase region tuning curve is obtained, and a target wavelength is retrieved; and in each smooth wavelength tuning path, splicing all target wavelengths end to end, obtaining an initial current tuning curve, obtaining a target current tuning curve by using an interpolation method, constructing a current tuning curve in a full wavelength range, and taking the current tuning curve as a wavelength current lookup table, thereby realizing the mode-free hopping wavelength tuning method of the tunable laser.

The invention discloses an end-to-end SLAM (Simultaneous Localization and Mapping) method based on a deep cyclic convolutional neural network. According to the method, an image sequence frame is collected in real time through a depth camera, converted into a set image size and then output; creating a convolutional neural network model to receive and process an image sequence frame output by the depth camera, and outputting extracted feature point cloud coordinates and descriptor information and camera pose information for establishing topological information of an image geometric structure; creating a recurrent neural network model to receive and process an image sequence frame output by the depth camera, and outputting a credible semantic region and object-level road sign information of which the credibility is greater than a set first threshold value; according to the method, the three-dimensional map with semantic recognition and perception is constructed based on the deep learning model, and the parameter indexes such as the positioning precision, the map construction precision, the loopback detection precision, the system efficiency and the robustness are remarkably improved.

The invention relates to the technical field of object scanning, in particular to a polarized light scanning method and system based on LED virtual shooting digital assets, and the method comprises the steps: obtaining each alternative feature point in a non-highlight polarization image in different cross polarization directions corresponding to different shooting angles, and the SIFT vector of the alternative feature point; according to the local area of each alternative feature point, analyzing a neighborhood gray scale difference condition, a diagonal feature obvious degree and a polarization characteristic similar condition to determine a texture feature, and further selecting all target feature points; and according to the gray value, the texture feature and the SIFT vector of each target feature point, target feature point matching between different polarization images is carried out to obtain a fusion image, and then the LED virtual shooting digital assets of the object are constructed. According to the method, feature point selection and feature point matching in the process of scanning the object by the polarized light are improved, so that the construction accuracy of the LED virtual shooting digital assets of the object is improved.

The invention discloses a three-dimensional model construction method and device, a storage medium and computer equipment, and relates to the technical field of financial science and technology and the technical field of digital medical treatment. Comprising the following steps: acquiring a two-dimensional image, and dividing the two-dimensional image into a plurality of panes; predicting a local approximate plane normal in a view angle range corresponding to each pane, determining a three-dimensional modeling starting point, determining a three-dimensional coordinate of the three-dimensional modeling starting point, taking the pane where the three-dimensional modeling starting point is located as a starting point pane, and taking the three-dimensional coordinate of the three-dimensional modeling starting point as a starting point center coordinate of a starting point local approximate plane corresponding to the starting point pane; determining a neighborhood pane adjacent to the starting point pane, and determining a neighborhood center coordinate of a neighborhood local approximate plane corresponding to the neighborhood pane based on the starting point center coordinate, a local approximate plane normal of the starting point pane and a local approximate plane normal of the neighborhood pane; and based on the starting point center coordinate and the neighborhood center coordinate, splicing the starting point local approximate plane and the neighborhood local approximate plane to obtain a three-dimensional model.

This invention provides a method and system for constructing a negative obstacle risk map, relating to the field of robot environmental perception technology. The method includes: acquiring multimodal sensing data of a legged robot in the current operating environment to obtain the types of negative obstacles, multiple candidate regions, and the corresponding confidence and uncertainty of each candidate region; using a risk diffusion algorithm, performing ink blurring processing based on the type, confidence, and uncertainty to generate a continuous risk field corresponding to each candidate region; dividing the continuous risk field to generate a continuous risk gradient distribution corresponding to each candidate region; performing terrain modeling based on laser point clouds, updating the continuous risk gradient distribution using Bayesian fusion to obtain a multi-level grid map, and then performing tactile closed-loop correction on the multi-level grid map to generate a risk cost map. This invention improves the accuracy of constructing a negative obstacle risk map.

The invention discloses a construction method and device for a mobile robot map and a storage medium, and belongs to the technical field of positioning and mapping. The construction method comprises the following steps: acquiring a current measurement point cloud and a current prediction pose of the mobile robot at a current moment; performing normal distribution transformation matching on the current measurement point cloud and a point cloud corresponding to the current prediction pose on a pre-constructed global voxel map to obtain a point cloud global matching residual error; performing iterative nearest point matching on the current measurement point cloud and a point cloud corresponding to a current prediction pose on a pre-constructed local dense map to obtain a current observation pose and a point cloud local matching residual error; determining a current actual pose according to the current observation pose, the current prediction pose and the point cloud local matching residual error, and obtaining a current actual point cloud matched with the current actual pose; and constructing a mobile robot map according to the current actual pose and the current actual point cloud. The method is used for improving the construction precision of the mobile robot map.

The invention discloses a geosteering method based on interactive seismic forward modeling establishment, and relates to the technical field of geosteering in oil-gas exploration and development. The method comprises the steps of firstly establishing a work area and constructing a seismic wave velocity field through an interactive interface, then generating a depth domain seismic section, then interactively drawing a stratigraphic position line and a fault line based on the section, discretizing the stratigraphic position line and the fault line into a space point set with geological attributes, then constructing a triangular mesh model based on the point set, identifying a stratigraphic framework, and finally, constructing a seismic wave velocity field based on the triangular mesh model. Then dynamically estimating shale content and porosity based on measured data of a drilled section, obtaining longitudinal wave velocity through rock physical forward modeling, extrapolating and interpolating the longitudinal wave velocity by combining a stratigraphic framework, establishing a velocity model for seismic forward modeling, and performing seismic wave field forward modeling to obtain a seismic wave field; and finally, carrying out multi-dimensional feature comparison on the forward modeling response and the actual seismic section, if a matching criterion is met, making a guiding strategy, otherwise, reversely adjusting model parameters and carrying out forward modeling iteration again until matching.

The present application provides a method and system for generating a standard knowledge graph for power grid equipment, which can intelligently integrate and optimize power grid equipment knowledge. If the target power grid data node that needs to be integrated is an existing data node in the reference power grid equipment standard knowledge graph, the knowledge vector is inferred by using the characteristics of the forward data node or the dependent data node through the power grid equipment feature expansion mechanism, thereby optimizing the reference power grid equipment standard knowledge graph. This not only improves the accuracy of the reference power grid equipment standard knowledge graph, but also realizes the standardization and intelligent management of power grid equipment knowledge, so as to construct an optimized power grid equipment standard knowledge graph as a powerful service engine, namely, a middle-end capability semantic network, so as to realize subsequent intelligent recommendation and association of standards, and provide an efficient and intelligent solution for power grid equipment management.

The invention relates to the technical field of data processing, in particular to a BIM model-based multi-party cooperative construction management and control method and system, and the method comprises the steps: obtaining the point cloud data of a real-time construction project, and determining the optimal thick side length of a voxel block according to the down-sampling rate of the point cloud data and the unit side length of the spatial distribution of the point cloud data; voxelization is carried out on the point cloud data based on the optimal coarse side length to obtain a plurality of voxel blocks, and the average density of point cloud distribution of the point cloud data in the voxel blocks is determined; determining the optimal side length of the voxel block according to the average density, the down-sampling rate, the optimal coarse side length and the unit side length, and performing down-sampling on the point cloud data based on the optimal side length to obtain a down-sampling project point cloud set of the real-time construction project; and constructing a BIM model of the real-time construction project by using the down-sampling project point cloud set, and managing and controlling the real-time construction project by using the BIM model. Therefore, the construction accuracy of the BIM three-dimensional model is improved.

The invention provides a method, a device and equipment for constructing a suspension system model, the method comprises the following steps: constructing an initial model of a target suspension system based on a first target model type and an initial sample set, and carrying out iteration updating on the initial model to obtain a target model of the target suspension system, a sub-model corresponding to a to-be-optimized space of the target suspension system in a target design space is constructed, the current model and the sub-model are superposed, an optimization result of an initial model in current iteration is obtained, the target design space comprises a preset design space of the target suspension system or a to-be-optimized space determined in previous iteration, and the target suspension system is optimized according to the optimization result. The current model comprises the initial model or the optimization result of the initial model in the last iteration, so that the construction precision of the suspension system model can be greatly improved.

The invention discloses a comparison template generation method, device and equipment and a computer readable storage medium, and belongs to the field of image detection, and the method comprises the steps: analyzing a target file of a to-be-modeled product, so as to obtain a pre-modeled image and primitive contour information from the target file; a station image of the to-be-modeled product and the pre-modeling image are calibrated and aligned, and the station image is an image collected by a camera on the to-be-modeled product at a production station; marking a difference area and attribute information of the difference area based on the station image and the pre-modeling image after calibration and alignment; and repairing the difference region in the pre-modeling image based on the primitive contour information according to the attribute information of the difference region so as to generate a comparison template file fitting the actual state of the to-be-modeled product. The comparison template constructed by the method can meet the detection requirements of high precision and high efficiency.

The invention discloses a deep tight reservoir crustal stress field boundary condition inversion method integrated with machine learning, and belongs to the field of petroleum and natural gas engineering. The method comprises the following steps: firstly, constructing a fine three-dimensional heterogeneous modeling model through high-precision scanning and image processing; then, multi-source data such as hydraulic fracturing, acoustic emission and logging are fused, and the real ground stress of a target point is determined; an integrated machine learning model trained by an optimization algorithm is utilized to intelligently invert and generate a high-precision boundary load required by the crustal stress site; and finally realizing reliable prediction of the global crustal stress field. The method is used for providing key and reliable geomechanical basis and boundary condition input for well location trajectory optimization, hydraulic fracturing scheme design and development scheme adjustment of a deep tight reservoir, and effectively supporting safe and efficient drilling and effective utilization of reserves.

The present invention discloses an end-to-end SLAM method based on a deep recurrent convolutional neural network. The method collects image sequence frames in real time through a depth camera, and converts them into a set image size before outputting; creates a convolutional neural network model to receive and process the image sequence frames output by the depth camera, and outputs the extracted feature point cloud coordinates and descriptor information and camera pose information for establishing topological information of the image geometric structure; creates a recurrent neural network model to receive and process the image sequence frames output by the depth camera, and outputs credible semantic areas and object-level landmark information with a credibility above a set first threshold; creates a grid map with semantic information. The present invention constructs a three-dimensional map with semantic recognition and perception based on a deep learning model, and has significant improvements in parameter indicators such as positioning accuracy, map construction accuracy, loop detection accuracy, system efficiency and robustness.

This invention discloses an error compensation method and system for sheet metal. The method includes: acquiring measured deviation data of sheet metal and theoretical part digital models; generating an adaptive parametric mesh based on local curvature characteristics; mapping the deviation data and weighted fitting to construct an error field; synthesizing a set of deviation vectors and extracting the dominant deformation mode; inputting mold deformation and material fluctuation parameters; generating a predicted deviation field through finite element simulation; obtaining an optimized predicted deviation field through residual optimization; extracting compensation vectors and inversely mapping them to the mold surface; determining the spatial distribution of compensation amount; generating adjustment instructions; and obtaining a mold error compensation and correction scheme. The method and system of this invention can achieve accurate and efficient compensation of sheet metal forming errors, meeting the high-precision and high-consistency production requirements of sheet metal processing in the field of industrial control technology.

The invention discloses a map generation method and device of a robot, equipment, a medium and a product. The method comprises the steps of determining a motion state and a rotation angle of a target robot in a motion process of the target robot; determining a target collection frame number, and collecting data of the target collection frame number by adopting a preset sensor under the condition that the motion state is an in-situ spinning state and the rotation angle is smaller than or equal to a standard angle so as to obtain target collection data; and performing coordinate system conversion processing on the target collection data to obtain final collection data, and performing matching on the candidate construction map according to the final collection data based on a violent matching algorithm, so as to update the candidate construction map according to a matching result, and generate a target construction map. According to the technical scheme, under the condition that the robot is in the in-situ spinning state, the data collected by the sensor can be analyzed based on the violent matching algorithm so as to complete rapid and accurate robot mapping.

The invention discloses a medicine formula adjusting method, and belongs to the technical field of intelligent medical treatment, and the adjusting method comprises the steps: obtaining a reactant component type and a reactant demand concentration based on a medicine formula effective component expected to be obtained; based on reactant component type requirements, constructing a medicine formula group; preparing an alternative formula according to the medicine formula group, and obtaining a by-product type; obtaining the accumulation speeds of all by-product types in different decoction environments, and establishing an association relationship between the decoction parameters of the traditional Chinese medicinal materials in the medicine formula group and the alternative formula; obtaining available formulas from the alternative formulas based on the effective components and the decoction parameters of the medicine formula expected to be obtained; when the actual effective component concentration is not lower than the effective component concentration in the effective components of the expected medicine formula, the available formula with the minimum by-product accumulation amount is obtained, and the medicine formula is obtained. The problem that the medicine performance guarantee effect is insufficient, especially the problem that the content of by-products is difficult to control is solved, and the medicine performance is effectively improved.

The present application relates to a method, device, equipment and storage medium for trajectory planning of an unmanned ship. The method comprises: obtaining electromagnetic environment monitoring data, the number of planned monitoring points and the starting position of the unmanned ship to be planned in a preset area; using Kriging estimation based on the monitoring data to obtain the Kriging variance, and screening the location information that meets the preset threshold conditions based on the Kriging variance; using clustering to calculate based on the number of planned monitoring points and the location information that meets the preset threshold conditions to obtain the location information of the planned monitoring points; using a genetic algorithm to design a navigation route with the shortest total distance for the unmanned ship to be planned based on the starting position of the unmanned ship to be planned and the location information of each planned monitoring point. This method determines the planned monitoring points through Kriging variance estimation and clustering methods under limited resources, thereby improving the accuracy of electromagnetic spectrum map construction; the genetic algorithm is used to adaptively propose trajectory planning, which can effectively save monitoring efficiency and comprehensively improve the efficiency of electromagnetic space perception.

The invention discloses an automatic generation method and system for a stratigraphic partition map. The method comprises the steps that a unified stratigraphic sequence is established, and all drill hole layers are unified; acquiring distribution of all sampling points and boundary points of each stratum, and determining whether the attribute information of each point is used for marking whether the current stratum is missing or not; carrying out triangulation on the distribution of the sampling points and the boundary points of each stratum, judging whether a current triangle needs to be extinguished or not according to attribute information of three points forming the triangle, and if the current triangle needs to be extinguished, carrying out extinguishment position calculation to determine a extinguishment position point; and adding the pinching position points to the sampling point and boundary point distribution of the current stratum, assigning the attributes of the pinching position points according to the condition that the current stratum is not missing, deleting the points with the missing current stratum in the sampling point and boundary point distribution, and carrying out triangulation on the left points again. The invention provides a set of practical stratum pinching position algorithm, and the algorithm is used for tracking the stratum subareas, so that the construction precision of the three-dimensional geologic model can be improved.

The present invention discloses a gradient information driven dimensional perturbation mutation method for high-dimensional expensive optimization problems, including: (1) determining the design space and optimization target, initializing the population and key iteration parameters, and providing convergence conditions; (2) constructing a global radial basis function proxy model according to a similar screening criterion; (3) constructing a gradient information driven dimensional perturbation mutation operation to generate a candidate offspring pool; (4) constructing a double-layer offspring selection strategy to determine the true offspring individuals; (5) using an autoencoder to achieve dimensionality reduction and dimensionality increase, and designing a local search strategy; (6) updating the population and key iteration parameter information, judging whether the algorithm has reached the convergence conditions, and if converged, outputting the optimized solution obtained by the algorithm, otherwise going to step (2) until the algorithm reaches the convergence conditions. The present invention effectively combines the advantages of both fast convergence of gradient mutation and global search of dimensional perturbation, can accelerate the convergence speed, and has strong applicability and good flexibility for high-dimensional problems.

The present application provides an intelligent question-answering method based on the collaboration of a large language model and a knowledge graph, which relates to the technical fields of artificial intelligence and natural language processing, including: decomposing a complex problem into multiple simple problems, analyzing the degree of correlation between the simple problems and basic functions to form a multi-hop reasoning path; automatically extracting structured information from the simple problems based on a multi-task learning framework of a large model to construct a knowledge graph; constructing a cumulative reasoning learning framework based on a large model of logical reasoning, iteratively verifying the process results of the knowledge graph formed based on the multi-hop reasoning path, so as to correct the reasoning path until the correct answer is inferred.

The invention provides a profile map generation method and electronic equipment. The method comprises the following steps: controlling a determination module in the electronic equipment to determine position information of a drilling track and position information of a plurality of first geological boundaries based on the drilling track according to drilling data of each first drilling hole in a plurality of first drilling holes; controlling a reconstruction module in the electronic equipment to reconstruct the position information of the plurality of first geological boundaries according to the data of the abnormal body closed line and the data of the fault structure line corresponding to the drilling track so as to obtain the position information of a plurality of second geological boundaries; and controlling a generation module in the electronic equipment to generate a profile map based on the drilling track according to the position information of the plurality of second geological boundaries and the position information of the drilling track. According to the invention, the drawing efficiency and precision of the profile map can be improved, and the cost is reduced.

The invention discloses a diffusion model assisted channel map rapid construction method and system. The method comprises the following steps: firstly, acquiring channel information of a target area, and constructing channel map paired data; secondly, establishing a Markov chain between a fine-grained channel map and a coarse-grained channel map by using channel map residual transfer based on a latent diffusion model architecture, and constructing a channel map residual estimation network embedded with time steps; combining the channel map residual error estimation network with a pre-trained fine-tuned generative adversarial network to construct a latent diffusion model based on channel map residual error transfer; then training the constructed channel map residual error estimation network by adopting a supervised training mode to obtain an optimal parameter; and finally, quickly reconstructing a fine-grained channel map of a target area in the wireless communication system by using the trained model. Compared with a traditional generation diffusion model, the channel map construction method provided by the invention has the advantages that the fine-grained channel map can be constructed with fewer reasoning steps, and meanwhile, higher construction precision is realized.

The invention relates to the technical field of metal additive manufacturing, in particular to a laser coaxial powder feeding multilayer deposition contour rapid prediction method. The method comprises the following steps: acquiring spatial distribution of powder concentration; obtaining laser intensity distribution after powder shielding by utilizing the spatial distribution; obtaining a substrate temperature field according to the laser intensity distribution; obtaining a contact angle and a sectional area of a deposition layer through the substrate temperature field; based on the contact angle and the cross-sectional area, constructing a prediction model of the single-channel single-layer deposition contour; according to the prediction model, a single-channel multi-layer deposition contour is obtained; and according to the single-channel multi-layer deposition contour, rapid prediction of the laser coaxial powder feeding multi-layer deposition layer contour is realized. According to the method, the component surface topography prediction efficiency under different process parameter combinations is greatly improved, the problems of complex experimental process and high cost are effectively solved, and the problems of high spatial resolution, large calculation scale and high time cost of numerical simulation are also solved.

The invention relates to the technical field of geological exploration, in particular to a primary wave and multiple combined full-waveform inversion method, which comprises the following steps of: based on an initial velocity model, performing numerical simulation by adopting a virtual surface source to obtain multiple simulation records of different orders; distinguishing according to a multiple order in the analog data, and constructing a multiple full-waveform inversion objective function of the corresponding order; an inversion gradient is calculated based on the objective function, and only the same-order multiples are matched and inversion is executed; sequentially carrying out inversion according to multiple orders from high to low, and taking a result of a previous order as an initial model of a next order; taking a multiple wave inversion result as a primary wave inversion initial model, and executing primary wave full-waveform inversion; and when a convergence condition is satisfied, outputting a final underground velocity model. According to the method, the dependence on the initial velocity model is reduced, and the stability and convergence of the inversion process and the construction precision of the underground velocity model are improved at the same time.

The present invention relates to a three-dimensional image-guided correction planning method, system, device and medium. The method includes: obtaining a three-dimensional CT image and a two-dimensional X-ray image of a patient's oral cavity, integrating and processing the three-dimensional CT image and the two-dimensional X-ray image to obtain a corresponding comprehensive image set; extracting features from the comprehensive image set to obtain a tooth image and a jaw image, performing three-dimensional construction based on the tooth image and the jaw image to obtain an initial three-dimensional model; aligning and fusing the tooth image and the jaw image to obtain a corresponding oral fusion image; registering the oral fusion image with the initial three-dimensional model based on a preset registration method to obtain a corresponding oral model; performing trajectory analysis on the patient's oral cavity to obtain an initial trajectory path, iteratively optimizing the initial trajectory path to obtain an optimized trajectory scheme; simulating the trajectory of the oral model based on the optimized trajectory scheme to obtain a corresponding simulated trajectory result. The present invention can achieve refined management of oral details.

The application discloses a kind of based on twin neural network's rock block section three-dimensional modeling method and system, belong to geologic modeling technical field, can solve the problem of lower modeling precision and modeling efficiency of existing method.The method comprises the following steps: S1, obtains the first image and the second image of rock block section under different viewing angles;S2, based on feature normalization method, the loss function of twin neural network is improved, and the first feature representation of first image and the second feature representation of second image are determined using the improved twin neural network;S3, according to the first feature representation and the second feature representation, a feature matching cost function is constructed, and the disparity information of first image and second image is determined based on the feature matching cost function;S4, according to the depth information of disparity information, the depth information of first image and second image is determined, and a digital three-dimensional model of rock block section is constructed based on the depth information.The application is used to construct the digital three-dimensional model of rock block section.

The invention provides a scene understanding-oriented time sequence audio-visual knowledge graph automatic generation method, and belongs to the field of computers. According to the invention, video and audio data are used as input for scene understanding and are segmented into continuous audiovisual segments based on time synchronization. Next, a background time period without an audio event is identified by segmenting the audio signal, and segments in which an event occurs are sampled. Subsequently, an audiovisual knowledge graph is generated at each sampling time using an entity and relationship detection technique. And finally, performing relation completion on the generated time sequence audio-visual knowledge graph to improve the integrity and accuracy of the generated time sequence audio-visual knowledge graph. According to the method, the integrity and practicability of the knowledge graph are improved, and the system can automatically deduce and complement related audio-visual relationships, so that scene understanding and intelligent development of application are better supported.

This invention relates to the field of electrophysiological catheter technology, and particularly to a catheter assembly and system for constructing a cardiac internal model. The catheter assembly for constructing a cardiac internal model includes a catheter body, an ultrasound transducer, and a positioning sensor. The ultrasound transducer includes a first ultrasound generator and a second ultrasound generator. The first ultrasound generator is disposed at the end of the catheter body, and the second ultrasound generator is disposed circumferentially along the catheter body. The positioning sensor is disposed on the catheter body. This arrangement increases the ultrasound imaging range of the catheter assembly, improves the accuracy of constructing the cardiac internal model, and simultaneously determines the relationship between the ultrasound image presented by the ultrasound device and the positional information provided by the positioning system, thereby improving the accuracy of constructing the cardiac internal model.