17 results about "Experimental data processing" patented technology

The application discloses a double-machine clutching type testing device and method for mechanical loss measurement of a variable-frequency motor, which comprises two identical variable-frequency motors, a rotating speed measurement unit, a power measurement unit and a driving control unit, wherein any one of the variable-frequency motors is used as a driving motor, and the other one is a load motor and is always powered off during the whole testing process; the driving motor is separated from or connected with the load motor through a shaft coupling to realize no-load or load; when the driving motor is connected with the load motor, the input active power of the driving motor under different rotating speeds is measured through the rotating speed measurement unit and the power measurement unit; when the driving motor is separated from the load motor, the input active power of the driving motor under different rotating speeds is measured; and the change of the input active power measurement value of the driving motor under the same rotating speed and no-load operation is the mechanical loss of the corresponding rotating speed. The application has the advantages of relatively simple measurement process and experimental data processing process, high measurement efficiency and high measurement precision.

This invention relates to an experimental data processing method, apparatus, AI analysis module, and computer equipment, belonging to the field of data processing. The method includes: segmenting multi-dimensional raw data by type and unifying the format; generating a similarity matrix based on temporal and spatial neighborhood information to mark abnormal fluctuation points; separating effective signals by matching a noise library with time-frequency features; extracting multi-layer features of basic statistics, time-series correlation, and trend changes; and dynamically selecting core features to update a tracking experimental model. The accompanying AI analysis module integrates hardware circuits for data segmentation, similarity calculation, anomaly marking, noise matching, feature extraction, and model updating, achieving full-process acceleration. This method significantly improves the efficiency of experimental data processing and the reliability of conclusions through multi-dimensional data compatible processing, accurate anomaly detection, multi-layer feature fusion, and adaptive model optimization, and is suitable for real-time analysis in various scenarios such as scientific research and industry.

The invention discloses a flow field reconstruction and PIV (particle image velocimetry) measurement method based on a physical information neural network, which belongs to the technical field of fluid mechanics, deep learning, computer vision and PIV crossing and comprises the steps of low-cost experimental system construction, experimental data processing and period identification, simulation synchronization and data set construction and physical information neural network flow field reconstruction. According to the invention, the problems of high cost and non-real time of the traditional PIV technology are solved, a novel low-cost solution is provided for flow field measurement, and the popularization and application of the PIV technology in basic scientific research and teaching are promoted.

The application provides a kind of space science experimental data online collaborative analysis method and system, it is related to scientific experimental data processing and analysis technical field.The method comprises: in response to the project creation request sent by the first device, create space science data online research project;In response to the analysis request of space science data online research project sent by the second device, according to the running environment information scheduling environment mirror and computing resources, create running environment;In response to the online analysis operation sent by the second device, based on the plurality of functional components configured by running environment, determine target sub-result, in response to the result update request sent by the second device, the target sub-result identification information is carried in result update request, according to the identification information of target sub-result, update target result.The application can quickly and accurately complete the online processing and analysis of complex scientific tasks in the scene of multi-person collaborative processing, efficiently utilize space science experimental data, effectively improve data processing and analysis efficiency.

This invention claims protection for an improved wavelet thresholding denoising method. The method decomposes a noisy signal through several layers of wavelet decomposition, obtaining the corresponding high-frequency wavelet coefficients for each layer. The amplitude of the corresponding wavelet coefficients is analyzed; those for useful signals are lower, while those for noise are higher. An improved threshold function is applied to process the wavelet coefficients at each layer, retaining coefficients below the threshold and discarding those above. These wavelet coefficients are then reconstructed using inverse wavelet transform, outputting the denoised signal. This invention constructs a new threshold function to address the shortcomings of traditional wavelet denoising algorithms in noise reduction. Experimental data processing results show that the new threshold function effectively suppresses the influence of random errors in the inertial measurement unit. Compared to the original compromise threshold algorithm, the signal-to-noise ratio is improved, the root mean square error is reduced, and the scheme of this invention achieves better signal smoothness and excellent denoising effect compared to traditional denoising schemes.

The application discloses a fireproof mortar pyrolysis reaction mechanism obtaining method and system based on a multi-peak pyrolysis curve, and comprises the following steps: S1, collecting non-isothermal pyrolysis experimental data of the fireproof mortar, i.e. thermogravimetric experimental data; S2, processing the thermogravimetric data to obtain a differential thermogravimetric curve of the fireproof mortar, and determining the number of reaction peaks; S3, fitting the differential thermogravimetric curve based on a deconvolution method to obtain a sub-reaction peak; S4, optimizing the sub-reaction peak by using a particle swarm algorithm to determine the combined kinetic parameters thereof; and S5, obtaining the pyrolysis reaction mechanism and kinetic parameters of each reaction peak. The application processes the multi-peak pyrolysis conversion rate data of the fireproof mortar by using the deconvolution method, overcomes the difficulty in obtaining the pyrolysis mechanism function of the sub-reaction peak by using the combined kinetics and the particle swarm algorithm, solves the problem that the mechanism function needs to be assumed in the traditional method, and obtains the reaction mechanism function based on the combined kinetics, which is more suitable for the actual pyrolysis process of the fireproof mortar.

The invention relates to the field of aero-engine heat transfer and mass transfer analysis, and discloses a method and a system for processing cooling effect heat exchange experimental data under a curved surface structure of an aero-engine. The method comprises the following steps: expanding a whole curved surface cloud picture into a two-dimensional image containing real arc length coordinate points, reconstructing the expanded two-dimensional image into a grid matrix, performing identification assignment operation on the grid matrix by using a respe function, and accurately mapping physical quantity data to corresponding positions of the grid matrix, thereby forming a two-dimensional image type grid cloud picture. According to the method, data are displayed in a more intuitive and ordered manner, a polygonal region is drawn to select an invalid data range, and a createMask function is used to generate a binary mask region, so that the range of invalid data is clearly identified, corresponding physical quantity data is marked as invalid data, and interference is eliminated for subsequent deep analysis of valid data.

This invention relates to the fields of structural engineering experimental data processing and machine learning technology, and discloses a hysteresis curve parameter recognition method based on machine vision phenomenology. It obtains initial estimates of hysteresis model parameters using traditional parameter recognition methods. These initial estimates serve as a benchmark for applying physical constraints to perform adaptive intelligent sampling in the parameter space, generating multiple parameter combinations. The force-displacement curves corresponding to each parameter combination are converted into standard grayscale images. A Siamese convolutional neural network model is constructed and trained to learn the mapping relationship between image similarity and curve fitting goodness. The similarity between the generated image and the original experimental image is evaluated using the trained model, and the parameter combination corresponding to the highest score is selected. Further multi-layer physical rationality verification is then performed to select the optimal hysteresis model parameters. This invention integrates the physical basis of traditional methods with the visual shape recognition capabilities of CNNs, significantly improving parameter recognition accuracy and robustness while ensuring that the recognition results conform to physical laws.

The invention discloses a digital processing method and system for Zeeman effect experiment data, and relates to the technical field of atomic physics experiment data processing, and the method comprises the following steps: S1, image reading and preprocessing: obtaining a Zeeman effect interference image, carrying out the self-adaptive zooming according to the image size, converting the Zeeman effect interference image into a gray image, and carrying out the self-adaptive zooming according to the image size; enhancing the contrast ratio through self-adaptive histogram equalization, and removing salt and pepper noise by adopting 3 * 3 median filtering; s2, interference ring identification and measurement: carrying out edge detection by adopting a Sobel operator, detecting an interference ring in the image through Hough transform, screening two maximum concentric circles closest to the center of the image, automatically calculating the diameter of the ring, and converting the diameter of the ring into a physical size; s3, key physical quantity calculation: calculating a wavenumber difference, substituting the wavenumber difference into the optimized formula to calculate an electron charge-to-mass ratio, and performing error propagation analysis. Through automatic image processing and a precise algorithm, manual measurement errors are avoided, and the relative error of the electron charge-to-mass ratio can be reduced to 1.07% or below.

This invention specifically relates to a method for testing and analyzing the noise of a mechanical hydraulic steering system. The method includes: S1, placing microphones at the driver's inner and outer ears in the vehicle under test, simultaneously reading the engine speed, and measuring the in-vehicle noise level and corresponding engine speed under typical operating conditions; S2, warming up the vehicle's engine; S3, measuring typical noise under idling conditions; S4, measuring typical noise under driving conditions; S5, analyzing the measured data according to experimental data processing methods to determine whether noise problems exist. This invention describes a method for testing and analyzing typical NVH problems that may exist in mechanical hydraulic steering systems under vehicle conditions, proposing a method for testing and analyzing the noise of mechanical hydraulic steering systems, and solving the problem of insufficient testing methods in this field for the high-quality development of commercial vehicles.

This paper relates to the field of automated data processing and provides an experimental data processing method, apparatus, and system. The method includes: sending a task preparation sequence instruction to a data computing system, causing the data computing system to create task-related computing processes according to the task preparation sequence instruction, create data objects within the computing processes, and assign initial values; sending a data acquisition instruction to a device control terminal, receiving data acquired by the device control terminal through operation of the experimental equipment; generating a data modification instruction based on the acquired data and sending it to the data computing system; sending a data calculation instruction to the data computing system, causing the data computing system to perform calculations on the data of the relevant task computing processes according to the data calculation instruction; and sending a data output instruction to the data computing system, causing the data of the relevant computing processes to be stored in a data object database according to the data output instruction. This paper can realize the entire business process and has the advantages of ease of use, ease of learning, and versatility.

This application discloses a method, apparatus, device, and storage medium for high-resolution phase information extraction. The method includes: deconstructing and separating overlapping positive and negative phase images of a sample using a deep convolutional neural network model based on an attention mechanism; establishing a differential phase image based on the deconstructed and separated positive and negative phase images; generating network training data using an X-ray phase difference microscopy physical model; and directly using the trained model for experimental data processing to extract the absolute and differential phase information of the sample. The above-mentioned solution provided by this application can quickly and accurately extract the absolute and differential phase information of a sample from overlapping positive and negative X-ray phase microscopy data. Furthermore, this invention uses numerical simulation to generate network training data, eliminating the need to collect training data on an experimental system, greatly reducing experimental difficulty and saving experimental time.

The application discloses an intelligent auxiliary system for medical scientific research training based on an artificial intelligence large model, and particularly relates to the field of artificial intelligence and scientific research data processing, and is used for solving the problems of multi-source dispersion, non-uniform structure, missing steps difficult to identify and dependence on manual verification of experimental records in the existing medical scientific research process; a digital experimental record file is constructed by image acquisition on an experimental table record carrier and in combination with target detection and text recognition, logical breakpoints are recognized based on numerical jump and semantic co-occurrence, and jump record recognition is realized by using phenotype feature distribution and optimal transport calculation; missing steps are further reasoned and completed by dynamic programming, and finally, a complete experimental process draft is generated in combination with a structured template, automatic analysis, abnormal identification, process completion and multi-modal data fusion processing of scientific research data are realized, and the intelligent level of experimental data processing and the standardization of the scientific research process are improved.

The application provides a medical experiment data processing method and system, which realizes intelligent integration and efficient processing of multi-modal medical experiment data by introducing an adaptive mixed model, has the characteristics of data preprocessing automation, multi-dimensional feature extraction, adaptive model selection and strong visual interaction. The system can automatically identify and adapt to different types of data processing algorithms, effectively improving the accuracy and efficiency of data processing; through multi-modal feature fusion and time alignment technology, cross-data source correlation analysis is realized; at the same time, user-defined view and real-time feedback optimization are supported, which significantly enhances the practicability and flexibility of the system, provides comprehensive, intuitive and efficient data analysis support for medical researchers, and effectively promotes the deep mining and scientific discovery of medical experiment data.

The embodiment of the application belongs to the field of big data, and relates to an AB experiment data processing method, comprising the following steps: acquiring AB experiment data through a spring service; adding attribute labels to candidate events in the AB experiment data according to an attribute list configured in a TiDB database to obtain to-be-processed events; storing the to-be-processed events in a clickhouse table of a clickhouse database; and executing a preset SQL task to perform data processing on the clickhouse table to obtain an AB experiment data processing result, wherein the attribute labels are used to extract data in the to-be-processed events. The application further provides an AB experiment data processing device, a computer device and a storage medium. In addition, the application also relates to blockchain technology, and the AB experiment data can be stored in a blockchain. The application improves the processing efficiency of AB experiment data.

The application relates to a method for exploring the vibration response of the floor of the cab of a wheel excavator based on Hyper Mesh simulation, and belongs to the technical field of related parameter testing of engineering machinery. In the method, an acceleration sensor is arranged on the floor of the cab of the wheel excavator, and meanwhile, all experimental equipment is arranged; then, the wheel excavator is started to begin testing, the vibration signal of the floor of the cab under an idling condition is measured, and after the experimental data are processed, the vibration acceleration spectrum diagram of the floor of the cab is obtained through drawing software; a three-dimensional model of the cab with the same size as the appearance size of the actual vehicle is established, is simplified and is imported into Hyper Mesh software to perform geometric cleaning; after the three-dimensional model of the cab is processed, the finite element model of the cab is obtained, the finite element model is calculated, and finally, the structural modal vibration mode diagram of the cab is obtained; by comparing the vibration signals of the floor of the cab in the actual vehicle test and the Hyper Mesh simulation, the accurate vibration response of the floor can be determined.

This disclosure provides an experimental data processing method, apparatus, storage medium, and electronic device. The method includes acquiring experimental group service data of a target strategy; extracting a preset identifier from the experimental group service data; and generating first experimental data based on the preset identifier; wherein the preset identifier indicates whether the target solution determined by the target strategy is transmitted to the user terminal for execution. By identifying the preset identifier in the experimental group service data, it can be determined whether the target strategy reaches the user, i.e., whether the target solution determined by the target strategy is transmitted to the user terminal corresponding to the experimental group service data for execution. Generating the first experimental data based on the experimental group service data that reaches the user reduces interference from experimental group service data that cannot reach the user, thus improving the accuracy of the first experimental data.