1120 results about "Calibration result" patented technology

The invention relates to the technical field of water pollution detection, in particular to a water quality pollution multi-dimensional analysis system which comprises the following steps: synchronously measuring an ultraviolet-visible absorption spectrum of a water sample to be detected and scattered light intensity in an orthogonal direction, and deducting spectrum baseline interference caused by suspended particulate matters in the water sample through a correction algorithm; the interferents in the water sample are selectively converted and degraded by applying a programmed potential to the inflowing water sample to be detected; the reverse pulse is periodically applied, so that the online regeneration function of the electrode is realized; analyzing the spectral data by adopting a multivariate correction algorithm, and calculating the concentration of the target pollutant; the calculation residual error of the multivariate correction algorithm is analyzed, new interference and system performance drift of current measurement are diagnosed, and a corresponding system state code is generated; and responding to the system state code, automatically starting and carrying out state checking and multi-point calibration on the instrument, and using a calibration result to update the analysis model. Through multi-dimensional perception and closed-loop operation, accuracy and reliability of water quality monitoring are guaranteed.

The invention discloses a laboratory analytical instrument automatic calibration system based on artificial intelligence, and belongs to the technical field of instrument automatic calibration. Comprising the following modules: an intelligent hardware interaction module for realizing plug-and-play of different instruments and accurate conveying of standard substances; the instrument digital twinning module automatically identifies the characteristics of the instrument and simulates the behaviors of the instrument; the multi-mode AI calibration module is used for optimizing calibration parameters and adjusting the calibration process in real time; the environment intelligent compensation module calculates and generates a compensation coefficient in real time, and eliminates the influence of environmental factors on a calibration result; the instrument health management module is used for evaluating the health state of the instrument and predicting the service life of a core component, and realizing conversion from passive maintenance to predictive maintenance; the calibration knowledge graph module is used for realizing automatic extraction, reasoning and application of calibration knowledge through a graph neural network and continuously optimizing a calibration strategy; and the intelligent scheduling and management module is used for generating an optimal calibration plan, coordinating the work of each module and realizing reasonable distribution and efficient utilization of calibration resources.

The invention discloses a flowmeter-oriented embedded multi-parameter fusion calibration control method and system, relates to the technical field of industrial measurement and embedded control, and is used for solving the problem of unstable calibration caused by misalignment of multi-link flow signals, compensation lag and fusion oscillation. The method relates to synchronous acquisition, pretreatment and calibration of differential pressure type, ultrasonic wave and thermal type mass measurement links and environment parameters such as temperature, pressure and density. All-channel unified time scales are established through ternary binding, filtering and denoising, zero snapshot and alignment rules, a stability scoring mechanism is constructed in combination with offset behavior recognition, event classification and feature aggregation, and dynamic switching of a compensation path and a fusion strategy is driven. In a double-path fusion structure, table look-up compensation, model estimation, multi-algorithm parallelism and fusion track recording are adopted, version traceability and calibration result closed-loop control are achieved, and the stability and rechecking performance of mass flow output under complex disturbance are effectively improved.

An adaptive calibration method and system for an electric energy meter, relating to the technical field of electric energy meter calibration. The method comprises: acquiring basic data and additional data of an electric energy meter (S1); performing time series data alignment, and performing period division on an environment data set to generate a time series segmentation result (S2); acquiring device information of the electric energy meter, performing key feature analysis within a period, and establishing an interference weight factor for each feature (S3); building a performance degradation model (S4); acquiring usage environment data of the electric energy meter by means of a monitoring sensor (S5); then generating an adaptive calibration result on the basis of the performance degradation model (S6); and finally performing electric energy meter calibration management (S7). The present invention solves the technical problem in the prior art where the accuracy of electric energy metering is affected due to untimely calibration caused by an inability to monitor performance degradation of an electric energy meter in real time, thereby implementing effective calibration management for the electric energy meter, ensuring the long-term accuracy and reliability of the electric energy meter, and improving the efficiency of calibration work.

The invention provides a pipe thread positioning and measuring method and system based on three-dimensional vision, and belongs to the technical field of industrial automation and three-dimensional vision. According to the method, a measuring system composed of a robot measuring system and a three-dimensional visual locating system is built, and hand-eye calibration is completed; the method comprises the following steps: acquiring a three-dimensional point cloud model of the end part of a pipe thread workpiece, and extracting an end surface normal vector, a tangent vector and a circle center coordinate through preprocessing, cylinder fitting, near-end surface point cloud extraction and end surface circle fitting so as to establish a measurement reference coordinate system; and converting the reference coordinate system to a robot base coordinate system based on a hand-eye calibration result and the tail end pose of the robot, generating a robot moving trajectory, guiding the robot to be positioned to a measurement position, and completing preparation before measurement. According to the invention, automatic high-precision extraction of the pipe thread measurement reference is realized, the problems of non-uniform reference, large error and low efficiency in traditional measurement are solved, the method is suitable for various pipe thread workpieces, and a reliable basis is provided for subsequent high-precision measurement.

The invention relates to wide-range multi-component sensor calibration equipment and a wide-range multi-component sensor calibration method. The method comprises the following steps: firstly, checking equipment parts and setting a target temperature range and a compensation model of each temperature interval; then, dynamic temperature control is started, jig deformation is monitored in real time, a transient temperature gradient suppression technology is applied, multi-physical-quantity calibration is executed, and a compensation measurement value is output; then setting multi-environment coupling calibration test conditions to simulate actual working conditions, and monitoring and recording calibration data of each physical quantity; and finally, calculating a calibration error by combining a calibration result and a reference standard value. According to the process, by precisely setting parameters and combining dynamic control, real-time monitoring and multi-physical-quantity calibration, the transient temperature gradient influence is effectively inhibited, and the measurement precision of the sensor in a complex environment is ensured; the temperature control performance is improved through the micro soaking film and the intelligent temperature control strategy, the actual working condition is simulated through multi-environment coupling testing, the accuracy and reliability of the calibration result are remarkably improved through compensation and error calculation, and guarantee is provided for precise application of the sensor.

The invention discloses an AI-based tendering and bidding full-process intelligent generation and review method and system. The method comprises the steps of tendering full-process intelligent generation, bidding full-process intelligent generation and bidding review. An AI scoring model is adopted for review, the technical scheme of each bidding document is deeply analyzed through a natural language processing technology, and real-time cross validation is carried out with a knowledge graph, so that the score of each sub-item is provided with detailed comments and a definite evidence chain, the objectivity, transparency and credibility of review are greatly improved, and the review efficiency is improved. The review quality is improved from experience judgment to a data and knowledge driven level; secondly, potential violation behaviors are identified through a surrounding bidding risk report generated through multi-dimensional correlation analysis, and the risk prevention and control capability is effectively enhanced; and finally, based on comprehensive data analysis, automatically generating a bid-winning candidate sorting and comprehensive review report, and ensuring that the calibration result is optimal.

The invention discloses an electromechanical equipment distributed control system based on the Internet of Things, which belongs to the technical field of distributed control, and specifically comprises the following steps: splitting a control task into task units, registration dependencies, data entries and task levels at an edge gateway, and generating a candidate scheduling range according to registration items; a calibration heartbeat with a reference segment is issued, and a driving node executes and returns a measurement record in an idle window for calibrating hardware fingerprints and link overhead and dynamically updating matching parameters; when computing power saturation is triggered, splitting a low-level task unit into suspensible fragments, setting a de-duplication execution identifier, opening a state interface, and completing migration check; selecting a target control node and a transmission path according to a calibration result, issuing fragment and state interface information, driving a target node to continue fragments according to an execution template, and guiding a source node to release occupied resources in sequence; and checking and comparing the execution result, and when the rule is triggered, recovering to the previous stable state according to the mapping rollback table.

The invention relates to the technical field of agricultural remote sensing measurement, and discloses an unmanned aerial vehicle hyperspectral online calibration measurement method for agricultural remote sensing, which comprises the following steps: acquiring hyperspectral data of a target crop and reference vegetation in the same scene in real time through an unmanned aerial vehicle, and extracting a characteristic wave band reflectivity difference value delta R, according to the comparison result of the time sequence change rate of the delta R and the preset threshold value, crop physiological changes and environmental interference are dynamically distinguished, and online calibration is achieved. A self-consistent calibration mechanism is constructed by means of the time sequence stability of crop spectral characteristics, the illumination mutation influence can be eliminated without depending on an external reference substance, and the accuracy of the calibration result is improved. And meanwhile, a lodging scene is effectively identified through synchronous analysis of a gravity vector and spectrum drift, so that the system can still keep accurate tracking of a real physiological state of a crop under an extreme working condition.

The invention discloses a health monitoring management system based on a multi-modal index, and belongs to the technical field of health monitoring, and the system specifically comprises a data collection unit which synchronously collects physiological signals at the same anatomical position of a user through a professional medical device and a wearable device; the calibration model construction unit is used for extracting reference waveform characteristics of professional medical equipment, analyzing a signal offset mode of the wearable equipment, fitting a dynamic correction curve and generating a calibration comparison table; the dynamic correction unit is used for inputting the real-time data of the wearable equipment into a calibration comparison table for correction, generating a reconstructed data stream aligned with the time domain of the reference signal, and ensuring the synchronization of waveform key points; the calibration verification unit is used for calling high-precision data to verify the calibration result in a preset period, and updating the calibration rule base when the matching error of a plurality of continuous calibration windows is lower than a threshold value; according to the method, the wearable device is periodically calibrated through the high-precision professional medical device, and the daily health monitoring precision of the user is improved.

The invention provides a method for automatically calibrating collected data of high-precision measurement and control deformation monitoring equipment, and belongs to the technical field of wind tunnel inner profile measurement. Multi-equipment clock synchronization is realized by establishing a unified time reference system, and measurement data of different frequencies are processed by adopting a CPU-GPU heterogeneous parallel processing architecture, so that the measurement accuracy is improved. The method comprises the following steps: performing three-level alignment on time, space and precision dimensions by using a multi-level clustering algorithm, eliminating clock differences among equipment by using a clock drift correction equation set, establishing a space coordinate system conversion matrix to unify a global coordinate system, and performing adaptive data fusion according to a precision weight by using a Kalman filtering fusion algorithm. And a data quality evaluation system is established to ensure the accuracy of a calibration result, a high-precision calibration data set is finally output, and the technical problem that the measurement precision is reduced due to space-time inconsistency of heterogeneous data of multiple devices is solved.

The invention discloses a power transmission line fault positioning method and system based on ground potential end transient information perception, and relates to the technical field of line fault positioning, and the method comprises the steps: obtaining a voltage traveling wave signal and a current abrupt change rate signal detected by a trunk line and branch node sensor array in a power transmission line; processing the voltage traveling wave signal and the current abrupt change rate signal to obtain traveling wave components and polarity-amplitude calibration results of different modes; according to traveling wave components and polarity-amplitude calibration results of different modes, in combination with topological parameters of the power transmission line and spatial distribution of a ground potential end sensor, adjusting edge weights or connection relationships in the graph structure to obtain a composite feature matrix and an edge weight matrix of each node; and inputting the composite feature matrix and the edge weight matrix of each node into a graph neural network positioning model, and outputting a power transmission line fault positioning result. According to the invention, through ground potential end information fusion and the dynamic graph neural network, accurate fault positioning under a complex power transmission topology is realized.

The invention discloses a distributed flood forecasting scheduling model construction method based on sub-basins, particularly relates to the technical field of flood forecasting model optimization, and is used for solving the problem of local prediction failure due to spatio-temporal differences caused by neglecting parameter sensitivity of an existing flood forecasting model. A target drainage basin is divided into sub-drainage basins, hydrological and geographic feature parameters are extracted, and a cross-drainage-basin water conservancy facility topology network is constructed to dynamically correct river channel evolution boundary conditions; quantifying parameter sensitivity spatial diversity based on a Sobo index method, identifying a sensitive abnormal response region in combination with historical flood event distribution, and applying spatial lag correction through a river topology network to generate parameter sensitivity grade classification; constructing a dynamic weight matrix, and performing partition parameter calibration by adopting global optimization and local adjustment strategies respectively; and the hydraulic equilibrium constraint of the calibration result is verified based on the river topological relation, and a partition and grading flood discharge scheduling scheme is generated after iterative correction, so that the forecasting capability in a sudden flood scene is remarkably improved.

The invention relates to the technical field of power system relay protection, and discloses a relay protection device mutual inductor adjustment system and method, and the system comprises a system connection initialization module, a data collection reference data generation module, a power frequency background signal offset module, a pilot frequency current injection error calibration module, and a calibration result verification output module. The method comprises the following steps: initializing sampling frequency of a to-be-calibrated mutual inductor and calibration source output precision of a calibration system, collecting amplitude and phase data of real-time power frequency current of the mutual inductor, generating offset current with equal amplitude and opposite directions through power electronic compensation based on reference data, optimizing offset current parameters through a closed-loop adjustment algorithm, and adjusting the offset current parameters through a closed-loop adjustment algorithm. Power frequency background signal offset is completed, pilot frequency calibration parameters are set, a difference value between a mutual inductor pilot frequency output signal and a calibration source standard signal is extracted by adopting a zero pointing technology, an optimal calibration coefficient is automatically calculated, verification of a calibration result is completed, and accuracy and reliability of the calibration result are ensured.

The invention provides an ultrasonic detection method and system for inverting residual stress by using sound wave phase velocity, and belongs to the field of air coupling ultrasonic guided wave detection. The method is based on an air coupling ultrasonic guided wave sound field propagation finite element model, and guides optimization design of air coupling ultrasonic transducer parameters. The air coupling ultrasonic guided wave detection technology is adopted for conducting non-contact excitation and receiving of guided wave signals on the composite material component with the preset stress through the stretcher. And calculating the phase velocity of the air-coupled ultrasonic guided wave in the composite material component based on a geometric positioning and time difference cooperative gradual difference method. And calibrating the phase velocity in the target composite material and preset stress to obtain a corresponding relational expression, performing phase velocity detection on a composite material component with unknown stress based on a calibration result of the target composite material, and performing inversion to obtain composite material construction residual stress. By adopting the method and the system, the residual stress detection of the composite material component is realized.

The invention provides an electricity meter running state online calibration method, which relates to the technical field of electric power metering, and comprises the following steps: acquiring voltage signals and current signals of an electricity meter to be calibrated under different load conditions, and extracting instantaneous phase characteristics and instantaneous amplitude characteristics of the voltage signals and the current signals; a voltage feature sequence and a current feature sequence are obtained, and a deep residual neural network model is constructed based on a load feature vector to output a dynamic compensation parameter matrix, so that online calibration is performed on a measured value of the electric meter. Therefore, the accuracy and the real-time performance of ammeter calibration under the complex load condition can be remarkably improved, the influence of environment change and nonlinear load characteristics on the calibration result is effectively avoided, and the long-term metering reliability of the ammeter and the operation efficiency of a smart power grid can be improved.

The invention relates to the technical field of sensor calibration, and discloses a multi-sensor external parameter calibration system and method based on a mechanical arm and an adjustable sliding rail, and a calibration target object unit comprises a calibration rack and a target object fixed on the calibration rack; the mechanical arm sliding rail unit comprises a mechanical arm, and a sliding rail with scales is arranged at the tail end of the mechanical arm; the mechanical arm is controlled to drive the sliding rail to a plurality of poses; the sensor unit comprises at least two sensors which are connected through a sliding rail, the relative positions of the sensors are adjusted along the sliding rail, the sensors are locked at preset scales so as to determine preset external reference values, and the sensors collect target object observation data corresponding to all poses. And the data processing unit calculates a corresponding external reference estimation value by using the observation data of the target object, optimizes to obtain a final external reference value, and compares the final external reference value with a preset external reference reference value to calculate an error. According to the invention, the external reference value is preset, the multi-view observation data is automatically acquired and fused and analyzed, and the verifiability of the calibration result and the intuitiveness of practical teaching are realized.

The invention discloses an electrochemical sensor calibration method and equipment. The method comprises the following steps: acquiring probe data, temperature and humidity data and gas characteristic data of a sensor to be calibrated; according to the probe data, constructing a dynamic updating model for dynamically updating the zero data of the sensor to be calibrated; according to the temperature and humidity data, constructing a temperature and humidity compensation model for performing temperature and humidity compensation on the to-be-calibrated sensor; according to the gas characteristic data, a multi-gas correction model is constructed, and the multi-gas correction model is used for correcting output data when the sensor to be calibrated is in different gas types; the dynamic updating model, the temperature and humidity compensation model and the multi-gas correction model form a calibration model of the sensor to be calibrated; and calibrating the to-be-calibrated sensor according to the calibration model to obtain a calibration result which is used for indicating the to-be-calibrated sensor to be calibrated and then output. The output data of the sensor to be calibrated in different gas types can be corrected, and the problem that accurate measurement cannot be achieved in a multi-gas interference scene is solved.

The invention discloses a forest fire risk prediction method based on multi-source data, and belongs to the technical field of forest fire prevention. Aiming at the problem of low prediction precision caused by one-sided information of a single data source and insufficient multi-source data fusion in the prior art, the method is realized by the following steps: acquiring micrometeorological data including temperature and humidity, wind speed and air pressure, and image data including an infrared image and a visible light image; the data of the mountain fire-prone area comprises historical fire frequency, vegetation type and topographic information; uTC + 8 time synchronization and WGS84 coordinate system space calibration are carried out on the data, and missing values and abnormal values are processed; carrying out feature layer fusion by adopting an attention mechanism, and extracting core features such as a temperature and humidity coupling index and vegetation dryness; spatial correlation features are captured through CNN, a time sequence trend is captured through LSTM, a mountain fire occurrence probability is output by using a Sigmoid function after decision-making layer fusion, and a result is calibrated in combination with sub-region features. Through multi-source data deep fusion and spatial-temporal feature collaborative analysis, the accuracy and timeliness of forest fire risk prediction are improved, a new data source can be expanded and accessed, and the method is suitable for a complex forest fire prevention scene.

The invention discloses a definition calibration method and device, a storage medium and computer equipment. Comprising the following steps: acquiring a to-be-tested camera module shooting an image card to obtain an image card image; identifying a plurality of statistical areas with equivalent texture features in the graphic card image; calculating the definition of the statistical area; and comparing the definition of the statistical area at the same preset test position with the reference value, and / or comparing the definition of the statistical area at different preset test positions, and determining a calibration result of the to-be-tested camera module. According to the method, quantitative evaluation of camera performance definition and uniformity can be realized through multi-region definition statistical comparison on the same graph card image, test errors during cross-module, cross-region and cross-time sequence detection are avoided, accurate definition verification is realized, additional arrangement of an independent work station and a module disassembly and assembly process are avoided, and the detection efficiency is improved. One-button four-corner definition quantitative comparison is realized, and time, manpower and space costs are remarkably reduced.

The invention belongs to the field of highway traffic geometric quantity measurement, and discloses a highway traffic structural body three-dimensional geometric quantity multilayer calibration method and system based on target reference, and the method comprises the steps: collecting point cloud data of a highway infrastructure structural body through a three-dimensional laser scanner, and carrying out the denoising preprocessing; on the basis of the point cloud data after denoising preprocessing, plane errors of the three-dimensional laser scanning equipment are calibrated by using a standard plane target; on the basis of the denoised point cloud data, a standard spherical target is used to calibrate the stereo error of the three-dimensional laser scanning device; based on the calibration results of the plane error and the stereo error of the three-dimensional laser scanning equipment, the coordinates of different stations are converted through a four-parameter method, and the same-name area registration is carried out by using the maximum geometric error. The reliability, the accuracy and the adaptability of the highway infrastructure scanning equipment are improved.

The invention discloses a dynamic adaptive black level correction method and device, and relates to the technical field of image processing. According to the dark current space distribution difference, an effective pixel area in each frame of original image is dynamically divided; determining an initial black level calibration coefficient of each space block based on the dark pixel noise level; obtaining the current temperature and the temperature change rate of each space block, and obtaining the level calibration coefficient of each space block after temperature self-adaptive calibration based on the initial black level calibration coefficient corresponding to each space block; fusing the calibration information of the previous frame and the level calibration coefficient of the current frame to obtain a cross-frame stable calibration result; and correcting the effective pixels in each space block based on a cross-frame stable calibration result to obtain a corrected pixel value, and reconstructing the corrected pixel value into an output image. The problems that an existing black level calibration technology is poor in space uniformity, insufficient in temperature change adaptability and unstable in calibration coefficient are solved.

The invention relates to the technical field of model parameter calibration, and provides a hydrodynamic model calibration method and system based on an improved particle swarm optimization, and the method comprises the steps: S1, obtaining the corresponding relation between the section information of a hydrodynamic model and the roughness; s2, defining a roughness parameter range and an allowable error range of each section; s3, initializing a particle swarm; s4, performing parallel scheduling hydrodynamic model calculation according to the matrix number of the particle swarm to obtain a simulation result data set; s5, performing time sequence comparison on a simulation result and an actual measurement result, and calculating an error value; s6, screening a calibration optimal solution, finding out a particle with the minimum error, and recording a corresponding roughness parameter; s7, judging whether the optimal solution is within an error range or not, if yes, directly executing the step S8, and if not, repeatedly executing the steps S4 and S5 until the optimal solution meets the condition; and S8, outputting a calibration optimal solution, and displaying a calibration result, an actual measurement result process line and optimal parameters. According to the invention, the calibration efficiency can be effectively improved.

The invention discloses a distributed hydrological model parameter two-stage calibration method based on remote sensing data and actually measured flow. The method comprises the following steps: collecting and preprocessing basic data required for constructing a distributed hydrological model, and remote sensing data and actually measured flow data for calibrating model parameters; constructing a distributed hydrological model, determining hydrological parameters needing to be calibrated in the model, and setting a value range of each parameter; performing grid-by-grid parameter calibration by utilizing remote sensing data to obtain model parameters with spatial heterogeneity characteristics; performing optimization adjustment on grid-by-grid model parameters based on remote sensing data calibration by utilizing actually measured flow data; and finally selecting a parameter combination with the optimal simulation precision as a finally determined model parameter for researching the drainage basin. Spatial distribution information of remote sensing data and time sequence precision of actually measured flow data are fused, and a multi-target calibration scheme considering parameter spatial heterogeneity and calibration result reliability is constructed.

The invention discloses an electric carbon coupling carbon flow calculation method, system and device and a medium. The method comprises the steps of obtaining a real-time power curve of a power transmission line and corresponding current, resistance and power factor multi-source operation data; segmenting the real-time power curve according to a time window, and extracting a power dynamic feature set in each time period based on a current change trend; calculating the energy loss of the power transmission line in each time period according to the multi-source operation data, and generating a carbon emission factor calibration result in combination with the difference between a reference carbon emission factor and the energy loss; determining a carbon flow attribution result of each power generation node in each time period based on the carbon emission factor calibration result and a power factor curve of the power generation node; and calculating a comprehensive carbon flow value of each power generation node, and summarizing to obtain a carbon flow calculation result of the power system. According to the method, the real-time performance, the accuracy and the physical consistency of carbon flow calculation are remarkably improved.

The invention relates to a calibration method of CIS probe card measuring equipment, which comprises the following steps: step 1, before testing, debugging a product bracket for fixing a product to be tested to a preset height; 2, obtaining a plurality of calibration blocks consistent with the sizes of the to-be-tested products respectively, obtaining a plurality of calibration seats matched with the to-be-tested products respectively, and installing one calibration block in the product support through the corresponding calibration seat; 3, a light source is arranged in front of the calibration block, a light sensor is arranged behind the calibration block, and the horizontal angle and the vertical angle of the light sensor are adjusted, so that the planes where the light source, the calibration block and the light sensor are located are parallel; 4, dismounting the calibration block, replacing the calibration block with a corresponding product to be tested, and starting to test; and 5, after the test is completed, compensating the output value of the optical sensor based on a compensation algorithm to obtain a calibration result. Deviation of parameters such as light intensity and illumination of the light source at different positions can be eliminated, and optical parameter detection of the lens module is achieved.

The invention relates to the technical field of toxic gas detection and calibration, in particular to an artificial intelligence toxic gas detection alarm joint calibration method based on multi-sensor data fusion, which comprises the steps of data acquisition, data preprocessing, data fusion, calibration model establishment, real-time calibration and calibration result evaluation. According to the artificial intelligence toxic gas detection alarm joint calibration method based on multi-sensor data fusion, data of a plurality of toxic gas sensors are integrated through a weighted fusion algorithm, environmental parameters such as temperature, humidity and air pressure are introduced for correlation fusion, and errors and environmental interference of a single sensor are effectively eliminated. For example, sensor weights are dynamically distributed based on historical detection precision and stability, so that data errors after fusion are reduced, and the problem that traditional single sensor calibration cannot cope with complex environments is solved.

The embodiment of the invention discloses a three-dimensional tennis track real-time reconstruction method and system based on multiple cameras, internal and external parameters and calibration results of the multiple cameras are utilized, respective two-dimensional pixel coordinates are converted into three-dimensional rays, and nodes are positioned in combination with a minimum distance algorithm. According to the embodiment of the invention, the real-time reconstruction of the three-dimensional track of the tennis ball is realized by virtue of the synchronous multi-camera two-dimensional image through the steps of camera accurate calibration, two-dimensional to three-dimensional ray conversion, intersection point calculation and screening, data output and the like. Compared with the prior art, the reconstruction precision is improved through accurate calibration and ray intersection screening; by means of multi-camera data synchronization and simple calculation process, data delay is reduced, and real-time performance is prominent; by means of multi-camera data redundancy and a placeholder mechanism, the robustness in a complex environment is enhanced; the output three-dimensional point data can directly support the functions of ball speed calculation and the like, and the expansibility is good.

The invention relates to a calibration method of a binocular camera and a laser radar based on a hollow ellipse calibrator, and belongs to the field of robot vision. Hamming code graticule marks are respectively arranged at four corners of one side face of a square aluminum plate of the hollow elliptical calibrator in the clockwise direction, an elliptical through hole is formed in the middle of the hollow elliptical calibrator, and the center of the elliptical through hole coincides with the center of the square aluminum plate; and the long axis of the elliptical through hole is positioned in the vertical direction. A hardware system of the calibration method comprises a binocular camera, an upper computer, a 16-line laser radar and a hollow ellipse calibrator. An image processing program, a three-dimensional point cloud processing program and a mathematical calculation program are arranged in the upper computer; the calibration method comprises the following operation steps: (1) carrying out image feature recognition on the hollow ellipse calibrator; (2) carrying out point cloud feature recognition on the hollow ellipse calibrator; (3) calculating external parameters of the camera and the 16-line laser radar; and (4) determining a calibration result. According to the calibration method, the calibration accuracy and robustness are improved, and the calibration method is suitable for practical application.

Provided are a method and apparatus for calibrating contact parameters of electrode materials, and a computer storage medium. The method includes the following operations. A discrete element simulation test is performed on an electrode material based on a calibration model and calibration ranges of contact parameters of the electrode material when an axial force-displacement curve of a target pellet of the electrode material meets an adaption condition of the contact model; then a simulated axial pressure-axial strain relationship of the electrode material is acquired based on a target discrete element simulation parameter of the electrode material; whether the contact parameters are calibrated successfully is judged based on this relationship; and when the contact parameters are calibrated successfully, calibration results of the contact parameters are determined.