23 results about "Statistical error" patented technology

This invention discloses an accelerated safety assessment method and system for low-altitude navigation systems based on alternating cross-entropy and importance sampling, relating to the fields of computer simulation and safety assessment technology. The method includes: constructing a probabilistic risk assessment model and initializing a proposal distribution; entering an alternating iterative loop: first, performing importance sampling assessment, calculating accident rate estimates and statistical errors; if the error does not meet accuracy requirements, initiating cross-entropy optimization, updating the proposal distribution parameters using simulation data; the new distribution is fed back to the next round of assessment. This loop continues until the results converge, and an assessment report is output. This invention, through a closed-loop feedback mechanism of alternating assessment and optimization, enables the sampling distribution to adaptively approximate the optimal distribution, intelligently focusing simulation resources on high-risk scenarios, solving the computational efficiency bottleneck of assessing extremely low probability events, achieving an order-of-magnitude improvement in assessment efficiency, and possessing high adaptability and engineering practical value.

The application provides a frequency standard measurement method, device, computer equipment and storage medium, the method comprises: frequency source output measurement signal is carried out frequency division processing, obtains low frequency pulse signal; using high-precision time interval counter to low frequency pulse signal is carried out delay measurement and time interval counting, and is output in the form of time stamp; by setting a sliding window, using sliding average algorithm to time stamp is carried out sliding average filtering processing, all time stamps are carried out sliding difference to obtain time difference, time difference is grouped according to order and is taken average in group to obtain average time difference; by increasing the length of the sliding window, the number of samples is increased to reduce the statistical error; using average time difference, calculate frequency measurement value, relative frequency deviation and allan variance. The method of the application increases the sliding window, and the frequency measurement result obtained by the algorithm of grouping and averaging the time difference has higher measurement precision and stability.

The invention discloses an intelligent top pipe splitting method based on a BIM technology. The method comprises the following steps that 1, a first Revit self-adaptive pipe joint family is manufactured; 2, generating an overall model of the pipeline according to a projection path of the pipeline by using the first Revit self-adaptive pipe joint family; 3, by means of Dynamo software, the splitting fixed length and the splitting distance of the pipeline are preset, segmentation is conducted, and multiple segments of segmented curves are obtained; 4, processing all the segmentation curves through Boolean operation by utilizing Dynamo software to obtain corresponding segmentation curves segmented into accurate lengths; 5, by utilizing Dynamo software, generating all pipe joints after the pipeline is segmented through the segmentation curve; 6, according to a preset corner threshold value of the adjacent pipe joints, the corner between the adjacent pipe joints is calculated; and 7, exporting a detailed list of all the pipe joints. According to the method, design and machining can be quickly joined, the material statistical error is reduced, and the design and machining efficiency is improved.

The application discloses a kind of based on statistical error analysis's gate group timing flow abnormal diagnosis method, comprising the following steps, S1, flow data pre-processing based on filtering method;S2, flow error distribution analysis;S3, based on k up / down Reasonable flow error range determination of σ rule;S4, over gate flow coefficient relationship curve fitting;S5, theoretical gate station flow calculation;S6, abnormal flow data diagnosis and correction.The advantage is: the method can make full use of the specific monitoring error distribution characteristics and monitoring variable mapping relationship of different gate stations, reduce the missed detection or false detection rate.Through introducing the real error distribution of flow monitoring data in the abnormal data diagnosis model, the specific monitoring error range of specific project is determined, and the reliability of flow abnormal diagnosis process is improved.

The invention discloses a converter blanking automatic statistical system based on intouch configuration software development, relates to the technical field of steelmaking process analysis, and is used for automatically collecting and counting converter blanking weight data and generating a report so as to improve the accuracy and analysis efficiency of steelmaking process data. The display module is used for selecting bin blanking types and displaying statistical results; the data acquisition and storage module is used for acquiring the weighing data of the stock bin through a PLC (Programmable Logic Controller); the data processing and association module adopts an enumeration comparison method to merge the weights of similar materials; and the dynamic report generation module dynamically generates a header and updates the statistical table. According to the method, under the condition that the blanking types of the stock bin are changed, automatic statistics of the blanking types, weight and other data can be achieved through simple operation, the data are displayed on an operation picture in a table form, the problems that in the prior art, manual statistics errors are large, and table updating efficiency is low are solved, and powerful support is provided for data analysis of the steelmaking process.

The invention is suitable for the technical field of charging piles, and provides a charging pile availability determination method and a charging pile monitoring system. The method comprises the steps of obtaining target state data and target fault data of a target charging pile based on an identifier of the target charging pile and a target time interval; state data correction is performed according to an acquisition result of the target state data and the target time interval, and an online time interval of the target charging pile is determined according to a correction result; de-duplicating the obtained target fault data, and obtaining a fault time interval of the target charging pile according to a de-duplicating result; and determining the availability rate of the target charging pile based on the online time interval and the fault time interval. According to the method, statistical errors of the online time interval and the fault time interval can be reduced, the availability rate of the charging pile can be accurately obtained, the state of the charging pile can be timely and accurately mastered subsequently, the charging pile is reasonably used and maintained, meanwhile, the determination process of the availability rate of the charging pile is simplified, the calculation amount is reduced, and the efficiency is improved.

The invention discloses a robust sub-sampling method based on an influence function. The method comprises the following steps: constructing a model and a robust sub-sampling problem; based on a robust iteration sub-sampling algorithm, data points which are close to a clean subset as much as possible are screened out from a data set through step-by-step iteration, it is ensured that after finite iteration, sub-sampling data are from unpolluted samples, and the optimal statistical error boundary of parameter estimation is obtained; calculating a gradient by using a robust sub-sampling method in each iteration based on a robust small-batch stochastic gradient descent algorithm, and updating parameters based on the gradient; the sub-sampling method provided by the invention realizes an approximately optimal error rate and provides a strong robustness guarantee.

The utility model discloses an organoid culture pore plate convenient to count and position, which belongs to the technical field of cell culture and comprises a culture pore plate, a plurality of pore grooves are arranged on the culture pore plate, scale marks are arranged at the bottoms of the pore grooves, and the scale marks comprise a plurality of transverse lines and a plurality of longitudinal lines which are uniformly distributed outwards by taking the circle centers of the pore grooves as centers. According to the utility model, orientation coordinates in the pore plate are arranged, so that the dynamic growth process of the organoid can be conveniently positioned and observed, regular counting of each organoid in each pore is facilitated, the budding number is calculated, and the accuracy of statistical analysis is improved; by setting the grids, the organoid can be located in a grid with a fixed area, so that the area can be conveniently calculated without being limited by different shooting machines or software, the efficiency of experimental data analysis is improved, and calculation or statistical errors are reduced; the same type of organs are continuously observed through scale line positioning, the efficiency of organoid culture experiment operation and data analysis and the data reliability are improved, and the device has very high practicability and wide application prospects.

The invention belongs to the field of radio detection, particularly relates to a signal and data processing method for a multi-base incoherent scatter radar, and aims to solve the problem of failure of a conventional signal and data processing method for the multi-base incoherent scatter radar. The method comprises the following steps: dividing a detection distance range of a multi-base incoherent scatter radar beam into a plurality of detection distance doors, and calculating a signal autocorrelation function of each detection distance door; after time correction is carried out on the signal of the receiving station, an observation system equation constant is determined based on antenna gains of the transmitting station and the receiving station and the detection co-body height in combination with radar transmitting power; fitting the signal autocorrelation function according to the spectrum fuzzy function and the plasma scattering theory power spectrum function in combination with the observation system equation constant and the statistical error of the signal autocorrelation function, and performing inversion to obtain the ionosphere parameter of each detection range gate. The method is applied to a multi-base incoherent scattering radar, and an effective ionosphere multi-parameter result is obtained.

The application relates to a dynamic frequency and voltage adjustment method, device, equipment, storage medium and program product. The method comprises the following steps: determining the value of the utilization time length under the upper and lower limits of the utilization time length of a video decoder according to the current decoding information of the video decoder; the upper and lower limit conditions comprise a frame loss limit condition and a statistical error condition of the video decoder; and the dynamic frequency and voltage adjustment strategy parameters of the video decoder are determined according to the value of the utilization time length, and the dynamic frequency and voltage adjustment strategy parameters are used for dynamically adjusting the frequency and voltage of the video decoder. The method can more accurately dynamically adjust the frequency and voltage of the video decoder.

The invention relates to the technical field of grape nondestructive testing, in particular to a grape yield and quality evaluation method and system based on image recognition, and the method comprises the steps of image data set acquisition and processing, model training and verification, model recognition and report output. Correcting the plane grain number in the image into an actual grain number through a linear regression model; the maturity of a single fruit grain is accurately identified through expert labeling; a variable coefficient is calculated through the coordinates of the fruit grains and the occupied pixel area and is used for judging the fruit grain uniformity of the grape bunch; the evaluation system is used for realizing the method, and seamless connection of the whole process from original image input to report generation is realized. According to the method, the problem that the existing actual grain number statistical error is large is solved, and meanwhile quantitative evaluation on the maturity and the uniformity is achieved; an algorithm model used by the evaluation system is simple in form, few in parameter, efficient in calculation and suitable for being deployed on lightweight computing equipment, and the requirements for practicability, robustness and generalizability of the method are met.

The invention discloses a vehicle operation condition prediction method, device and equipment, a medium and a product. The method comprises the following steps: acquiring sensor data and spatial positioning data of a vehicle, and constructing a space-time sequence tensor based on the sensor data and the spatial positioning data; inputting the space-time sequence tensor into a target prediction model to obtain a prediction output result; the prediction output result comprises an operation state identification result, a transportation task starting point and a transportation task ending point; and determining the running state of the vehicle according to the running state recognition result, and determining the transportation times of the vehicle according to the transportation task starting point and the transportation task ending point. According to the technical scheme of the invention, the method can solve the problems that the operation state of the mine card is difficult to recognize, the statistical error of the number of times is large, and the data is discontinuous due to the lack of GPS signals in the mine transportation operation, achieves the recognition of the operation state of the mine card, and automatically calculates the number of times of transportation every day.

This invention relates to a method, system, and computer program product for performance evaluation of diffractive optical elements based on measured morphology and simulation analysis. It pertains to the field of diffractive optical element design, testing, and performance evaluation technology, addressing the problem of existing technologies lacking effective analysis methods and evaluation models for diffractive optical elements. The method includes the following steps: Step S1, for a reflective phase mask, constructing a corresponding etching depth distribution based on the designed phase distribution; Step S2, based on the designed phase distribution, constructing a corresponding etching depth distribution and establishing a statistical error model; Step S3, measuring the actually processed diffractive optical element to obtain its surface three-dimensional morphology data and obtaining the actual processing error distribution; Step S4, based on the statistical error model described in Step S2 and the actual processing error distribution described in Step S3, constructing a light field propagation model; Step S5, based on the light field propagation model, performing performance analysis on the diffractive optical element.

The invention belongs to the technical field of impact toughness evaluation, and particularly relates to a low alloy steel impact energy intelligent prediction method based on transfer learning, which comprises the following steps: step 1, selecting an impact energy prediction position of a material, step 2, shooting a microstructure optical microscope image of a target position, and carrying out an impact toughness test. The method comprises the following steps of: 1, acquiring image data and impact energy data, 3, sorting the image data and the impact energy data, and dividing training and testing data sets, 4, building a transfer learning model, and completing model parameter adjustment and optimization selection, and 5, inputting an optical microscope image at any position, and realizing impact energy prediction. According to the low alloy steel impact energy intelligent prediction method based on transfer learning, the impact energy value prediction result can be obtained only by shooting the light microscope image of the concerned position and inputting the light microscope image as the prediction model, the manpower time cost and possible statistical errors caused by manual statistics of microstructure parameters are avoided, and intelligent impact energy prediction is achieved.

The application belongs to the field of radio detection, and particularly relates to a signal and data processing method of a multi-base incoherent scatter radar, and aims to solve the problem that the conventional signal and data processing method is invalid for the multi-base incoherent scatter radar. The application comprises: dividing the detection distance range of the multi-base incoherent scatter radar beam into multiple detection distance gates, and calculating the signal autocorrelation functions of the detection distance gates; after time correction of the signals at the receiving station, determining the observation system equation constant based on the antenna gains of the transmitting station and the receiving station, the detection common height, and the radar transmitting power; fitting the signal autocorrelation functions according to the spectral ambiguity function, the plasma scattering theory power spectrum function, the observation system equation constant and the statistical error of the signal autocorrelation functions, and inversely obtaining the ionospheric parameters of the detection distance gates. The application is applied to the multi-base incoherent scatter radar, and effective ionospheric multi-parameter results are obtained.

We provide a pronunciation learning support system that improves the efficiency, acceptability, and continuity of pronunciation learning by acoustically quantitatively evaluating learners' pronunciation errors, visually presenting their directionality and magnitude, and individually optimizing teaching materials based on error history. [Solution] This invention processes speech on a phoneme-by-phoneme basis, extracts formants F1 and F2 from each phoneme, and performs error evaluation based on Z-scores and normalized distance Z_norm. Error information is visually displayed as points, vectors, and ellipses in F1F2 space, intuitively presenting the statistical error structure to the learner. Error information is recorded as a history and analyzed over time, and error trends are classified using clustering processing. The teaching material presentation means calculates presentation priority based on scoring based on the amount of error and frequency of recurrence, and is equipped with a mechanism for gradually controlling the difficulty of the teaching material according to the recurrence rate and improvement trend.

The invention belongs to the technical field of industrial automation control, and particularly relates to a textile processing progress monitoring method and system, and the method comprises the steps: collecting the original operation linear speed data of textile equipment, and carrying out the preprocessing of the data, and obtaining an operation speed time sequence; carrying out scale permutation entropy analysis and phase space reconstruction to obtain an operation microscopic disorder degree and a phase space trajectory deviation degree; calculating an effective man-hour correction coefficient based on the operation microcosmic disorder degree and the phase space trajectory deviation degree; and correcting the instantaneous measurement speed by using the effective man-hour correction coefficient to obtain the residual processing time of the textile equipment, uploading the residual processing time to a management system, and comparing the operation microcosmic disorder degree with a preset disorder degree threshold to carry out abnormal early warning. According to the method, the man-hour statistical error caused by micro shutdown or low-speed creep of equipment is reduced, and the accuracy of textile processing progress prediction is improved.

The invention provides a real-time classroom behavior detection method based on edge information and a decoupling shared detection head. On one hand, an efficient edge information convolution module is provided to extract edge information of classroom body behaviors, the performance of model detection is better improved, a global-to-local Transform module is added to enhance the feature extraction capability, and then context anchor attention is utilized to make up for the deficiency of global information perception when only dependence on local multi-scale convolution is performed, so that the accuracy of model detection is improved. And on the other hand, a decoupling sharing detection head with light weight and efficiency merging is adopted, all convolution layers share the same weight, and each corresponding normalization layer carries out independent calculation, so that numerical value normalization among different level features is ensured, cross contamination of statistical errors of batch normalization layers is also avoided, and the detection accuracy is improved. And meanwhile, additional group normalization calculation overhead is not introduced. According to the technical scheme, the requirements of high real-time performance and easy deployment are met, and an innovative method is provided for solving the problem.

The application discloses a kind of based on circuit board's wiring length determination method, it is related to circuit board technical field.According to the current layer of circuit board, the coordinate corresponding to the auxiliary area of the target device connected with the starting point of the current wiring when the current wiring reaches is determined to the wiring length of the current layer, considering the actual coordinates of the vicinity of the target device reached by the starting point of the current wiring, avoid the problem of statistical error caused by artificial statistics to determine the wiring length of the corresponding target device.In the case of via, the starting point of the corresponding wiring of the next layer needs to be tracked by the via tracking, to avoid the omission of artificial statistics.In the case of pin, the fact that the corresponding pin device includes two pins in the conventional artificial statistical process will ignore the statistical error caused by the wiring length between the pins.Considering the wiring length determination of the wiring length mapping relationship between the inner layer and the outer layer of the circuit board, the calculation accuracy of the wiring length is improved.

The application discloses a kind of quick calculation methods of rain radar partition rainfall data, it is related to meteorological technical field.The method is by constructing multistage basic rule grid system and stratified lossless compression weight matrix, complex geometric operation is converted into static look-up table;In real-time calculation stage, analysis radar data dynamic parameter, based on rainfall gradient threshold triggers adaptive grid refinement, uses coarse grid and nearest neighbor interpolation to gentle area of rainfall, uses fine grid and high-precision interpolation to dramatic change area and boundary area;Finally, through stratified compression weight, realizes the quick accumulation of partition statistics, outputs each partition surface average rainfall.The application reduces dimension from look-up table and weighted accumulation to full-amount geometric operation in real-time calculation load, under the premise that guaranteeing boundary statistical error is less than 1%, the computing time efficiency is improved by more than 90%, memory occupation is reduced by 90%, and can automatically adapt to different scanning modes of radar, provides efficient and accurate data support for flood forecasting and waterlogging early warning.

The present invention discloses a method, system, device and storage medium for determining the source distance of density logging, which belongs to the technical field of pulsed radioactive source logging in oil and gas exploration. The method comprises: establishing a formation detection model using Monte Carlo simulation software; obtaining the relationship between the count rate and the long source distance and the short source distance from the formation detection model, and preliminarily screening the value ranges of the long source distance and the short source distance; further screening the value ranges of the long source distance and the short source distance based on the statistical error and measurement accuracy of the formation detection model; and determining the final value range of the short source distance using the short source distance compensation function and sensitivity requirements. The present invention can quickly determine the value ranges of the long source distance and the short source distance, and has higher accuracy. A reasonable source distance confirmation method for a density logging instrument can help improve the accuracy of density instrument data, obtain high-precision formation density parameters, make subsequent logging interpretation and analysis more accurate, and save a certain amount of logging costs.

This application provides a testing apparatus, method, and storage medium, relating to the field of equipment testing technology. The apparatus includes a first fixing member, a second fixing member, a counting module, and a motion module connected to either the first or second fixing member. The sensor to be tested is fixed to the second fixing member, and the counting module is connected to the sensor. The motion module drives the first or second fixing member to reciprocate, causing the sensor to repeatedly sense the first fixing member and generate a sensing signal. The counting module counts the sensing signals to obtain the sensor's test result. Therefore, this application eliminates the need for manual testing and statistics, automatically controlling the sensor to generate sensing signals and counting the number of times, reducing testing time, improving testing efficiency, and minimizing statistical errors, thus improving the accuracy of the test results.

The application discloses a method and system for determining magnification and minimum sample number in micro-component content analysis, and relates to the technical field of microscopic image quantitative analysis. The method comprises the following steps: constructing a standardization simulation model, excluding the interference of natural rock heterogeneity, and simulating the actual microscopic observation process by adopting a double-mode sampling method; using a double statistical stability determination method to objectively determine the minimum observation sample size under different magnifications, establishing a quantitative correlation between the magnification and the minimum sample number, and determining the optimal observation magnification by taking into account the analysis accuracy and efficiency. The application realizes the standardization of sample selection and the controllability of statistical error in micro-component quantitative analysis, effectively eliminates subjective bias caused by artificial experience, significantly improves the accuracy, stability and repeatability of the component content analysis results, and can be widely applied to rock and mineral identification, reservoir evaluation and microscopic image panoramic stitching scenes, thereby providing a standard and reliable technical support for rock multi-scale micro-component quantitative analysis.