373 results about "Parametric identification" patented technology

Provided is a self-adaptive identification method for nonlinear dynamic parameters of a reducer, which belongs to the design field of a reducer. The method includes: modeling a harmonic reducer corresponding to a flexible joint as a concatemer of a rigid reducer and an elastic torsion spring, and carrying out dynamic theoretical modeling and parameter variable independence processing on the concatemer to form a dynamic equation for parameter identification; giving an optimized motion trajectory to each joint of a robot and controlling the robot to act accordingly, acquiring relevant data needed for parameter identification based on a built-in torque sensor and double encoders inside the joint; using an offline identification algorithm to accurately identify a plurality of dynamic parameters of a collaborative robot considering joint flexibility and friction, and obtaining a minimum parameter set.

The invention discloses a deep learning-based intelligent identification method and system for rock and soil mechanical parameters, and aims to accurately identify the elastic modulus, strength parameter and permeability coefficient of rock and soil and support the decision and design of engineering projects under complex geological conditions. And starting from data demand analysis, determining a parameter identification target and planning a field test layout. A basic data set is constructed by comprehensively collecting and integrating multi-source data including laboratory and field test data, remote sensing information and the like. The data is pre-processed and analyzed to remove noise and explore associations between the data. The method comprises the following core steps: constructing a rock-soil mechanical model by using a deep learning algorithm, and realizing high-precision inversion of parameters by defining and optimizing an objective function. And verifying an inversion result and carrying out uncertainty analysis. According to the method, an analysis result is fed back to engineering design adjustment and decision support, and scheme optimization is achieved. The model and strategy are dynamically adjusted through continuous monitoring and real-time data, and safety and high efficiency of engineering implementation are guaranteed.

The invention relates to a lightweight component surface defect quantitative evaluation method, system and device, and the method comprises the steps: obtaining a multispectral surface image, laser three-dimensional data and ultrasonic detection data of a lightweight component, and carrying out the fusion registration based on a preset multi-sensor synchronization protocol, and obtaining a component joint data set; performing dynamic pruning analysis on the component joint data set to obtain geometric contour features, and performing internal feature extraction on the component joint data set to obtain internal abnormal response features; stress gradient distribution data and volume ratio parameters are carried out on the geometric contour features and the internal anomaly features; and obtaining an original working condition data set of the lightweight component, carrying out service working condition coupling analysis on the multi-dimensional prediction matrix, and carrying out multi-dimensional quality l parameter identification to obtain component comprehensive quality information. According to the method, the morphology mechanical coupling feature extraction precision of composite defects such as cracks and holes can be improved.

The invention discloses a building outer wall leakage detection method and system, and belongs to the technical field of wall leakage detection. The method comprises the following steps: acquiring infrared thermal imaging, laser point cloud and multispectral imaging data, and generating a three-dimensional visual model with temperature information after environment temperature compensation correction, three-dimensional modeling and data fusion; and then extracting temperature characteristic parameters to identify an abnormal region, screening a to-be-verified region in combination with the building construction node coincidence degree and geometric parameters, and finally determining a leakage position through comparison of multispectral characteristic extraction and a preset leakage characteristic library. The system realizes full-process automatic detection by applying the method. According to the invention, temperature signal conduction limitation caused by insulation layer obstruction is broken through, the problem of misjudgment caused by a thermal bridge effect is solved through multi-source data fusion and spectral feature analysis, and the precision and reliability of leakage detection are improved.

The invention relates to an intelligent inspection control system and method. The system comprises an environment sensing module, a situation recognition module, a task management and scheduling module, a path planning module, an execution control module and a communication linkage module. The system can collect various environmental parameters of an inspection area in real time, dynamically adjust the priority of an inspection task after identifying an abnormal state, and generate an optimal path to drive a robot to execute response operation; meanwhile, abnormal information, images, videos, sensor snapshots and other contents are uploaded to a background management platform through a communication linkage module, and sound-light alarm equipment, broadcast equipment and other equipment are linked to carry out field response. The control method has task preemption and path reconstruction capabilities, and supports distributed sensing deployment and whole-process execution state monitoring. Compared with the prior art, integrated fusion of environment situation awareness, task dynamic scheduling and response control is achieved, and the adaptive capacity, the response speed and the safety guarantee level of the system to complex scenes are improved.

The invention discloses a driver controller detection system, which comprises an acquisition module, an acquisition module, a processing module, an identification module, a judgment module, an analysis module and a generation module, the acquisition module captures contact information and electromagnetic interference data of contacts in real time by means of a high-dynamic voltage sensor and a broadband EMC probe; the acquisition module is triggered by a locomotive signal and records dynamic working condition data; the processing module aligns the data, performs denoising and extracts fault parameters; the identification module scans the voltage waveform and judges the transient contact failure; the judgment module monitors EMC data and compares an instruction position, and judges misoperation; the analysis module analyzes fault association, judges a composite type and triggers an alarm; the generation module generates a detection report and a log. According to the system, a multi-dimensional monitoring network is constructed under a dynamic working condition, data reliability is guaranteed, faults are accurately identified, a compound fault source is positioned, and the safety and stability of locomotive operation are improved through risk grading early warning and automatic reporting.

The invention provides a method and device for detecting vehicle falling into water, a vehicle and a storage medium, the method is applied to the technical field of vehicle falling into water detection, the method comprises the steps that M falling into water detection parameters are obtained through M collection devices of different types in the vehicle, the falling into water detection parameters are used for representing the falling into water state of the vehicle, and M is a positive integer larger than 1; determining M basic probability distribution values corresponding to the M collection devices according to the M water falling detection parameters; according to the M basic probability distribution values, the falling-into-water confidence coefficient of the vehicle is determined; and when the falling-into-water confidence is greater than or equal to the preset confidence, determining that the vehicle is in a falling-into-water state. According to the method, whether the vehicle falls into water or not can be identified by fusing the falling-into-water detection parameters collected by multiple sensors, the reliability and robustness of vehicle falling-into-water detection can be effectively improved, and misjudgment caused by identification of single equipment is avoided.

The invention belongs to the technical field of parameter identification, and provides a thermal power generating unit electro-hydraulic servo system PID parameter identification method and system, and the method comprises the steps: obtaining the historical operation data of a steam turbine, carrying out the preliminary screening of the data, and constructing a parameter identification data set; according to the mean square error between the actual value and the calculated value of the opening degree of the valve, an objective function of PID parameter identification of the DEH electro-hydraulic servo system is established; setting population initial parameters, and generating a chaotic initial population; based on a parameter identification data set and a target function, introducing an initial population value of the generated chaotic initial population into an improved Bayesian optimization model, and obtaining a preliminary optimization result through Gaussian modeling and kernel function prediction; and taking the preliminary optimization result as input, and utilizing a whale optimization algorithm introducing a Levy flight disturbance mechanism to carry out iterative search for multiple times to obtain a final optimization result. According to the invention, the parameter identification precision and robustness are improved.

The invention relates to a bridge structure modal parameter identification method, which is based on a bridge structure tiny vibration video acquired by a camera. VMD (variational mode decomposition), PBVM (phase-based video motion amplification), Gabor filtering-based phase extraction method GBP (Gabor filtering), an improved clustering algorithm ICA (independent clustering algorithm) and an integrated covariance-driven random subspace recognition (SSI-Cov) and FDD (frequency domain decomposition) algorithm are fused to construct a set of complete non-contact structural modal parameter recognition system. According to the method, effective decoupling of multiple vibration modes of a bridge structure, frequency domain feature extraction of small-amplitude vibration and intelligent and automatic modal parameter extraction are achieved under the conditions that the target frequency bandwidth does not need to be known in advance, environmental noise interference exists and no obvious feature target exists, and the method belongs to the technical field of bridge structure health monitoring.

The invention relates to the technical field of hydrological data processing, in particular to a hydrological data treatment method and system. The method comprises the following steps: monitoring environmental parameters of hydrological equipment in real time, and recording the environmental parameters as equipment environmental data; performing environment abnormal feature detection on the equipment environment data, performing environment influence evaluation on the hydrological equipment according to environment abnormal features, and generating environment influence data of the equipment; performing operation parameter identification on the hydrological equipment according to the environmental influence data of the equipment; and performing deviation comparison on the operation parameters and preset standard operation parameters, and marking operation deviation parameters. Through a data processing technology, a time sequence interpolation technology and a spatial interpolation technology, the hydrological equipment is evaluated by environmental influence, so that the consistency, integrity and availability of hydrological data are enhanced; a distributed storage management mode is adopted, and a hydrological database is constructed, so that the hydrological data management efficiency is improved.

The invention discloses a power transmission tower modal parameter automatic identification method, device and system, and relates to the field of power transmission tower structure monitoring. According to the specific implementation scheme, a plurality of measurement points are arranged on a power transmission tower body in the height direction, and response data a are obtained; based on the response data a, a random subspace method is used for solving and drawing a first stability graph, through first modal data between each first stability point and a nearest point of an adjacent order, fuzzy C-means clustering (Fuzzy C-Means Clustering, abbreviated as FCM) is used for clustering the first modal data, false points are deleted, and a second stability graph is obtained; secondly, extracting stable points, meeting conditions, of each order by adopting two-time hierarchical clustering, and averaging to obtain the frequency, damping and vibration mode of each order; according to the method, the modal parameters of the power transmission tower are indirectly solved by using the data collected by the sensors arranged at the multiple measuring points, and the problems that the modal parameter identification efficiency of the power transmission tower is low and the reliability is poor are solved.

The invention belongs to the field of power distribution network parameter identification, and discloses a power distribution network line parameter identification method and system based on a graph neural network, and the method comprises the steps: employing a unified projection and attention weighting mechanism, obtaining the point features of a power distribution network line, and carrying out the self-adaption integration of multi-source heterogeneous observation data; initial edge features are explicitly constructed in neural network propagation of each layer of graph, node and edge representation is alternately updated, and the line parameter prediction precision is improved; power grid core laws such as Ohm's law and node power injection constraint are converted into a loss function to constrain a training process, consistency constraint of a parameter prediction result and a power system physical law is realized, and interpretability and credibility of the prediction result are improved; and inputting the power grid data into the graph neural network optimization model, and outputting a power distribution network line parameter identification result, thereby solving the problems that in the prior art, dynamic information interaction between nodes and edges is neglected, so that parameter identification is sensitive to network structure change, and physically incredible parameter output is easy to generate.

The invention discloses a ship motion attitude dynamic prediction method fusing CNN and BiLSTM, and the method comprises the steps: S1, obtaining ship navigation data, and carrying out the preprocessing of the ship navigation data; s2, establishing a ship navigation three-degree-of-freedom nonparametric model, and obtaining a regression function of the nonparametric model; s3, establishing a ship motion attitude non-parameter identification prediction model, training the ship motion attitude non-parameter identification prediction model, and optimizing a plurality of hyper-parameters by adopting an improved white shark optimization algorithm to obtain an optimized ship motion attitude non-parameter identification prediction model; and S4, dynamically updating the optimized ship motion attitude non-parameter identification prediction model by adopting an incremental learning method so as to realize dynamic prediction of the ship motion attitude. According to the method, a bidirectional long-short-term memory neural network BiLSTM is adopted as a basic model, a time sequence dependency relationship is more comprehensively captured in combination with past and future information, and an improved white shark optimization algorithm IWSO is adopted to perform hyper-parameter optimization on a non-parameter identification prediction model so as to select an optimal hyper-parameter combination, so that the WCBI-SAT can maintain relatively high prediction performance, and the prediction efficiency is improved. And the method has relatively high generalization ability and stability.

The invention provides a rubber bushing dynamic parameter identification method and device, and the method comprises the following steps: obtaining the test data of a driving end and a driven end of a rubber bushing through a test, and calculating the test dynamic stiffness and the test loss angle according to the test data; establishing a simulation model containing at least one dynamic characteristic parameter to be identified, and generating a result of the same caliber in the model according to a calculation caliber which is the same as that of the test so as to extract simulation dynamic stiffness and a simulation loss angle; aggregating differences between the test dynamic stiffness and the test loss angle and differences between the simulation dynamic stiffness and the simulation loss angle on a preset frequency domain evaluation set, and constructing a target function; and optimizing the dynamic characteristic parameters to minimize the target function to obtain the dynamic characteristic parameters of the rubber bushing. According to the invention, the accuracy and stability of parameter identification in the whole vehicle state are effectively improved.

The invention relates to the technical field of dispensing machine optimization, in particular to an adjusting method and system for self-adaptive optimization of a dispensing machine. The method comprises the following steps: identifying defective dispensing abnormal execution parameters by acquiring a dispensing machine operation log and mapping abnormal execution parameters; then, carrying out curing cross-linking abnormity derivation on defect parameters, quantifying interface bonding force deficiency, and further evaluating the sealing efficiency weakening degree; then, based on the sealing efficiency weakening degree data, interface multi-section curing requirements are analyzed, and then self-adaptive correction of the dispensing process is carried out; and finally, designing automatic firmware through the self-adaptive correction data, and embedding the automatic firmware into a control center to realize self-adaptive optimization of the dispensing machine. According to the invention, the optimization technology of the dispensing machine is optimized, so that the optimization technology of the dispensing machine is more perfect.

The invention discloses a power distribution network short-circuit parameter real-time measurement method, system and device based on PMU cooperation and a medium, and belongs to the technical field of power systems and automation, and the method comprises the steps: collecting PMU real-time measurement data, loading preset parameters, and constructing a PMU cooperation measurement network; carrying out a synchronous sampling algorithm, carrying out comprehensive preprocessing on real-time measurement data, and carrying out state reconstruction and inhibiting the influence of abnormal data according to a state estimator; establishing a short-circuit parameter identification model, and executing short-circuit parameter identification; performing multi-dimensional evaluation on the identification result, and evaluating the real-time performance of the algorithm; and based on the evaluation result, updating the key parameters, and executing adaptive adjustment of the system parameters. According to the invention, through high-precision synchronous acquisition, adaptive signal processing and closed-loop optimization mechanisms, comprehensive improvement of the real-time measurement precision, reliability and adaptability of the short-circuit parameters of the power distribution network is realized.

The invention discloses an aviation food truck docking identification method and system based on feature fusion, and the method comprises the steps: obtaining multi-sensor data of an aviation food truck, extracting cabin door positioning information, and carrying out the feature fusion processing to form a docking-pose feature matrix; performing pattern recognition based on the pose feature matrix to obtain track feature parameters, recognizing a cabin door geometric feature spectrum, generating a cabin door inversion feature library through inversion processing, and performing differential comparison to determine space focus features; identifying a cabin door center position and an obstacle interference position by using a space focus feature, constructing a docking failure preset condition to generate a reverse verification factor, and forming a composite space weight map; acquiring a butt joint gradient information identification position deviation area based on the weight map, and converting the butt joint gradient information identification position deviation area into a path optimization trigger area through deviation conversion parameters; adjusting pose information is formed through the trigger area, a recognition coordinate system is constructed for collision conflict detection, a butt joint recognition result is generated, and safe and accurate butt joint of the aviation food truck and the aircraft cabin door is achieved.

The invention relates to the technical field of electric power generation equipment monitoring, in particular to an intelligent multi-point monitoring method and system for a generator set. The method comprises the following steps: acquiring data of a generator set, and dividing the generator set into a primary monitoring sub-region and a secondary monitoring sub-region; deploying multiple types of sensors according to monitoring requirements of key components of each monitored sub-region, and collecting multi-dimensional parameters of the sub-regions in real time; the multi-dimensional parameters of the sub-regions are analyzed in real time according to the multi-dimensional parameters, power generation grid connection abnormal conditions are identified, and multi-level warning and abnormal positioning are carried out; the system comprises a multi-point region division module, a multi-dimensional data monitoring module and a power generation grid-connected abnormity warning module. By means of the mode, the abnormal recognition efficiency and adaptability are improved, and the operation safety and grid connection reliability of the generator set are improved.

The invention discloses a recommendation system-oriented privacy sensitive parameter identification and accurate deletion method, and relates to the technical field of recommendation system model optimization and privacy protection. In order to solve the problems that privacy sensitive parameters in an existing recommendation system model are difficult to accurately position and the model performance loss is too large after deletion, according to the evaluation thought of parameter importance and data feature relevance, importance scores of model parameters on two types of data sets are calculated respectively by dividing a forgetting set and a reservation set, and the privacy sensitive parameters are obtained. The privacy sensitive parameters with the forgetting set importance higher than the reserved set importance are screened out, and mask zero setting processing is carried out. According to the method, the model does not need to be retrained or subjected to weight updating, sensitive parameter recognition and deletion can be completed only through single-time forward propagation, and the original recommendation performance of the model is reserved to the maximum extent while the privacy security of a recommendation system is guaranteed. According to the method, accurate identification of privacy sensitive parameters is realized on a mainstream recommendation model, the reduction range of model recommendation accuracy after deletion is controlled within an acceptable range, and the privacy leakage risk is remarkably reduced.

The invention discloses a power transmission line external damage target Internet of Things information sensing detection method and system, and the method comprises the steps: analyzing whether an external damage target exists along a power transmission line or not based on the detection data of a monitor group; when the external damage target does not exist, the detection range of the monitor group is optimized; when the external damage target exists, tracking and early warning are carried out on the external damage target; the method can accurately grasp weak links of monitoring coverage by actively acquiring environment data and combining monitor parameters to identify blind areas, and can specifically reduce or eliminate the monitoring blind areas by dynamically adjusting detection parameters of monitors adjacent to the blind areas according to blind area maps, thereby improving the monitoring efficiency. Therefore, the overall monitoring coverage effectiveness along the power transmission line is improved, the capability of sensing the external damage target is remarkably improved, the risk of report failure caused by hidden activities of the external damage target in a blind area is reduced, and the adaptability and reliability of the power transmission line safety monitoring system are enhanced.

The invention relates to the technical field of construction management, and discloses a construction progress recognition method and system based on image recognition. The method comprises the steps that real-time image data of a construction scene are obtained, a construction progress feature monitoring database mapped with the real-time image data is established, and database monitoring indexes comprise image definition, feature matching degree and the like; configuring a directional recognition target, including key node recognition, construction stage division and the like, and then determining a target classification identifier; key identification parameters are selected through the main identification target and the auxiliary identification target, directional identification target influence analysis is carried out, parameter directional identification is executed after parameter identification constraints are established, and construction progress identification is completed. When parameter directional recognition is executed, a time efficiency and accuracy evaluation function is also established, and a balance adaptation function is constructed to screen recognition schemes. The system comprises a data acquisition module, a target configuration module and the like and can implement the method. Through multi-dimensional analysis and optimization, efficient and accurate recognition of the construction progress is achieved, and the construction progress management requirement is met.

The invention discloses a vertical superfinishing machine intelligent servo control method based on dynamic parameter identification, and relates to the technical field of servo control. Comprising the steps that first sensing equipment is arranged on a key motion axis of target equipment and used for obtaining vibration data of the motion axis, and vibration information items are obtained; and second sensing equipment is arranged on the oilstone unit, is pressure and temperature integrated sensing equipment and is used for acquiring real-time pressure data and temperature data of the oilstone to obtain a pressure information item and a temperature information item. According to the method, damage sorting is combined, low-damage combinations with different oscillation frequencies and feeding speeds are preferentially selected to serve as equipment execution, updating information of vibration information items, pressure information items and temperature information items is deeply obtained, secondary adjustment is conducted on initial information items, and the accuracy of the equipment is improved. Therefore, oilstone driving control based on dynamic parameter identification of vibration data, temperature data and pressure data combination is realized.

Systems and methods for active parameter identification. An example method includes applying a machine learning model to features extracted from each of at least one request to a computing interface, wherein the machine learning model is trained per value using a training set including a plurality of training values of a plurality of training requests, wherein the machine learning model is trained to output an indicator as to whether each portion of a request containing a respective value indicates a parameter when applied to the request; and identifying at least one parameter-indicating portion of each request to the computing interface based on outputs of the machine learning model.

The present invention discloses a material mechanical parameter identification method based on interval optimization and indentation testing, which specifically comprises the following steps: Step 1: performing multiple Berkovich indentation tests on the metal material to be tested, obtaining load and displacement curves of multiple loading processes of the nanoindentation test of the tested material, and obtaining the upper and lower limits of the loading curvature C; Step 2: establishing a design space for the material plasticity parameters, and establishing a simulated indentation displacement snapshot matrix X. sim Step 3: Establish an artificial neural network including an input layer, two hidden layers, and an output layer connected in sequence, and optimize the hyperparameters of the established artificial neural network; Step 4: Establish an uncertain optimization problem; Step 5: Based on the interval optimization and interval order relationship of the minimization problem, the uncertain optimization is converted into a deterministic optimization, and a two-layer genetic algorithm nested solution method is used to solve the uncertain optimization problem. The method of the present invention reduces the cost of parameter identification calculation and improves the efficiency of numerical calculation.

The invention discloses a hydroelectric generating set data acquisition and analysis method, system, equipment and medium, and belongs to the technical field of data acquisition and analysis, and the method comprises the steps: obtaining various original data of the operation of a hydroelectric generating set, and achieving the information collection of the set and the operation environment; performing multi-level structured modeling and node feature extraction on the acquired data by adopting a mode of combining a knowledge graph and a graph convolutional neural network, and analyzing an operation state and a future trend of a unit; according to a data analysis result, comparing a historical working condition with a scheduling task, judging whether the operation of the unit is in a reasonable interval, and triggering a corresponding regulation and control measure when the operation is abnormal; by monitoring the state and operation data of key equipment in real time, actual and reference parameters are compared, potential fault risks are identified, early warning information is generated, and data archiving and statistical analysis are carried out; and carrying out visual display on data acquisition, analysis, scheduling and early warning results. Collected data are effectively utilized, and scheduling decision and early warning maintenance are powerfully supported.

The invention discloses a temperature-driven structure parameter identification method based on uncertainty sampling, relates to the technical field of structure health monitoring, and aims to overcome the defects of sensitive environmental interference, low calculation efficiency and insufficient uncertainty quantization in an existing structure parameter identification method. By establishing an initial finite element model, structure surface temperature change and temperature-induced static response data are synchronously collected; after data preprocessing and steady-state uniform temperature field screening, high-sensitivity structural parameters are screened based on parameter perturbation analysis; adopting a transition expansion Markov chain Monte Carlo algorithm to sample posterior distribution, and reducing the calculation complexity of a high-dimensional parameter space through an intermediate distribution transition mechanism; and finally outputting a parameter posteriori expectation and a covariance matrix. The method does not need manual excitation, remarkably improves long-term monitoring efficiency and damage identification reliability, and is suitable for state evaluation and safety diagnosis of civil engineering structures such as bridges and space steel structures.

The invention provides a PINN-based bridge parameter identification method and device, equipment and a medium, and belongs to the technical field of bridge data analysis, and the method comprises the steps: obtaining vehicle vertical dynamic response data and bridge vertical dynamic response data when a vehicle passes through a bridge at a preset speed; constructing a motion differential equation of the axle coupled vibration system based on a preset pseudo bridge parameter, a vehicle basic parameter, a bridge basic parameter and an axle contact parameter, writing the differential equation as physical information into the initial physical information neural network model, and embedding the physical information into a loss function of the model to obtain a target loss function; and training the initial physical information neural network model based on the vehicle vertical dynamic response data and the bridge vertical dynamic response data, stopping training until the target loss reaches a preset loss threshold, obtaining a target physical information neural network model, and extracting target bridge parameters from the target physical information neural network model. According to the invention, accurate and efficient identification of medium and small span bridge parameters can be realized.

The invention discloses a parameter identification test system and method for a tuned mass damping inerter, and the method comprises the steps: generating a signal through a signal generator in a TMDI parameter identification process, converting the signal into mechanical vibration through a vibration exciter after amplification, and enabling the mechanical vibration to act on a main structure equipped with TMDI; a force sensor and a laser displacement sensor are used for obtaining the exciting force borne by the main structure provided with the TMDI and the displacement time sequence under the action of the exciting load; on the basis, based on a mathematical model of the TMDI, seven key parameters including the mass, the rigidity and the damping coefficient of the main structure with the TMDI and the mass, the rigidity, the damping coefficient and the system inertia coefficient of the TMDI are effectively recognized through a global optimization algorithm; the method is accurate in parameter identification, high in identification efficiency and suitable for performance analysis and optimization of various TMDIs.

The invention discloses a hole parameter measurement and identification method based on laser scanning, and belongs to the field of aeronautical manufacturing, and the method comprises the steps: obtaining part surface scanning data; carrying out noise reduction on part surface scanning data; performing three-channel space mapping on the denoised scanning data to obtain a hole surface scanning original data image; realizing hole region identification of the original data image based on a deep learning network model; local interference falling in a hole area is filtered out; hole parameters are obtained in combination with a contour line circle fitting mode, hole normal vector plane fitting is carried out, and then the obtained hole parameters are corrected based on a normal vector correction strategy; identifying, measuring and correcting hole parameters contained in all hole areas to obtain hole parameters of the complete image; and the recognition result of the hole parameters is reflected, and the result display of the global recognition strategy is realized. According to the method, a multi-step noise and interference removal mode and a surface normal vector correction strategy are included, the detection result is closer to a real numerical value, and better parameter precision measurement capability is shown.

The invention relates to a test tube positioning method and device based on an auxiliary mark. The method comprises the steps that calibration of internal parameters of a camera is completed, imaging distortion correction is conducted through the calibrated internal parameters, and the parameter sizes of a test tube and a test tube rack are determined. And performing id identification and feature angular point extraction on the square reference mark, solving the mark pose through a PnP algorithm, and determining the relative reference pose of the test tube rack. And solving a test tube orifice circle contour plane through homography matrix mapping, solving an ideal circle center coordinate based on a contour circle extraction algorithm, and taking the ideal circle center coordinate as a test tube index parameter. And identifying the tube orifice circle contour of the target test tube, calculating the Euclidean distance between the detected circle center coordinate and the test tube index parameter, and judging whether the test tube exists or not according to the distance. By introducing a square reference mark and a homography matrix mapping technology, points on an image plane can be accurately mapped to a test tube orifice circular contour plane. The problem of false detection caused by light source change is avoided, and the positioning stability and reliability are remarkably improved.