21 results about "Chi-squared distribution" patented technology

The invention discloses an intelligent segmental arc correlation method based on a minimum optical admissible domain, belongs to the field of intelligent calculation optimization of space target monitoring, solves the problem of large calculation amount of segmental arc correlation, and comprises the following steps: constructing the minimum optical admissible domain by a first observation segmental arc; uniformly sampling in the minimum optical tolerance domain, performing orbit propagation on each sampling point under a J2 perturbation model to obtain theoretical optical observation data of a second observation arc section, and calculating an angle root-mean-square error of the second observation arc section to obtain an angle loss function; using a DBSCAN clustering algorithm to identify wave troughs; selecting an initial value of a simplex method in each wave trough to obtain an angle optimal solution; a probability loss function is obtained through Bayesian theorem calculation; substituting the angle optimal solution into a probability loss function, and obtaining a probability optimal solution by using an LM algorithm; a threshold value is set according to the probability loss function obeying chi-square distribution, and when the probability optimal solution is smaller than the threshold value, the two arc segments are associated successfully; according to the invention, the calculation cost is saved, and the space target observation accuracy is improved.

The invention relates to a non-stationary industrial process anomaly detection method based on slow characteristic decomposition and Kupman high-dimensional space prediction, and belongs to the technical field of industrial process time sequence anomaly detection.The method comprises the steps that characteristic decomposition is conducted on industrial time sequence data through a slow characteristic analysis method, and the industrial time sequence data are divided into fast and slow change parts; respectively mapping the fast and slow features to a linearly observable Kupman high-dimensional space based on the Kupman theory, and constructing a depth prediction model to realize step-by-step prediction of a feature sequence; in combination with the deviation between a prediction result and an actual value, constructing an SPE statistical magnitude approximately obeying weighted chi-square distribution, and setting an SPE control limit of anomaly detection according to the SPE statistical magnitude; introducing KL divergence to measure a distribution difference between a current working condition and a normal working condition, and adaptively updating an SPE control limit according to the distribution difference; and in the model online deployment stage, an SPE value is calculated in real time and compared with a control limit, so that abnormal rapid identification and dynamic early warning under a non-stable working condition are realized.

This invention relates to the field of nuclear safety equipment testing technology. It provides a method for optimizing parameters in accelerated reliability testing of nuclear safety-grade valves. The method involves the following steps: establishing a correlation model between accelerated stress and life parameters; calculating the acceleration factor and formulating an initial test plan; deriving the theoretical sample size based on the success-failure test method combined with confidence requirements; constructing an extended coefficient table using the range-extended test method to optimize test parameters; generating multiple accelerated test plans through chi-square distribution verification; eliminating plans that do not meet the life characteristic threshold; determining the optimal sample size, number of failures, and test cycle through comprehensive test cost and risk analysis; and finally outputting a complete test plan including the action interval time. This method solves the problems of limited test samples, insufficient failure data, low confidence levels, and difficulty in adapting accelerated models in existing high-reliability valve technologies.

The present invention discloses an intelligent arc segment correlation method based on the minimum optical tolerance domain, belonging to the field of intelligent computing optimization for space target surveillance, which solves the problem of large computational volume of arc segment correlation, and includes: constructing a minimum optical tolerance domain from the first observation arc segment; uniformly sampling in the minimum optical tolerance domain, and propagating the orbit of each sampling point under the J2 perturbation model to obtain the theoretical optical observation data of the second observation arc segment and calculating the root mean square error of the angle of the second observation arc segment to obtain an angle loss function; using the DBSCAN clustering algorithm to identify the wave valleys; selecting the initial value of the simplex method in each wave valley to obtain the optimal angle solution; calculating the probability loss function by the Bayesian theorem; substituting the optimal angle solution into the probability loss function and using the LM algorithm to obtain the optimal probability solution; setting a threshold according to the chi-square distribution of the probability loss function, and when the optimal probability solution is less than the threshold, the two arc segments are successfully correlated; the present invention saves computational costs and improves the accuracy of space target observation.

This invention proposes a method for real-time pseudorange quality control in the BeiDou satellite-based augmentation system, comprising: calculating n pseudorange residuals at time t; determining whether the number of original pseudorange residuals n is greater than or equal to a preset parameter; subtracting the mean residual from the original pseudorange residual of each satellite; dividing the centered pseudorange residual of each satellite by the dual-frequency pseudorange noise; calculating the test statistic X; determining whether the test statistic X is less than the 95th quantile of the chi-square distribution with n-4 degrees of freedom; removing the satellite with the largest absolute value of the normalized pseudorange residual; completing the removal of pseudorange outliers; and obtaining the current pseudorange residual. This invention is real-time, meeting the high real-time requirements of differential integrity processing in the BeiDou satellite-based augmentation system. The embodiments of this invention can identify and remove small abnormal pseudoranges in the BeiDou interplanetary augmentation system, achieving good pseudorange quality control.

This invention discloses a method for constructing a statistical model of concrete dam deformation observation. The method includes preprocessing concrete dam deformation monitoring data; using a trial-and-error approach to determine the chi-square distribution degrees of freedom describing the weights of the influence of the dam surface temperature in the early stages; determining the optimal parameters of the LSTM network model using the GA algorithm based on the training sample set data; constructing a concrete dam deformation prediction model based on the optimal parameters obtained by iteratively solving the LSTM network model using the GA algorithm; and verifying the model's prediction accuracy. Based on the causal functional relationship between concrete dam deformation and its explanatory variables, this invention proposes a modeling method for concrete deformation prediction with good prediction accuracy by introducing the chi-square distribution and a GA-optimized LSTM network. This invention can effectively reduce the uncertainty in deformation prediction caused by upstream and downstream dam surface temperatures, quantitatively consider the influence of the lag effect of dam body temperature deformation, and fully explore the complex nonlinear functional relationship between concrete dam deformation and its explanatory variables, thus achieving good prediction accuracy.

The application provides a GNSS outlier detection method, system and storage medium based on residual multi-order chi-square distribution used on a satellite, comprising the following steps: step one, acquiring the position, velocity and satellite time in the earth-fixed system output by the GNSS; step two, converting the output data of the GNSS into orbital 14 plane roots; step three, calculating the residual and the residual rate of change to make a first heavy outlier judgment, then calculating one-dimensional chi-square test outliers, then calculating two-dimensional chi-square test outliers after the one-dimensional chi-square test is passed, calculating five-dimensional chi-square test outliers after the two-dimensional chi-square test is passed, if all the tests are passed, updating the parameters; if one test is not passed, determining that it is an outlier, and simultaneously counting the continuous invalid times, when the continuous invalid times are greater than a threshold value, the threshold value is adjusted along with the increase of the continuous invalid times, so that the system divergence is avoided. The application has the beneficial effects that the application can obviously improve the rejection rate of outliers, and provides correct data for the use on the satellite.

The invention discloses a vector retrieval method and device based on a neighbor graph, and relates to the field of vector retrieve.The method comprises the steps that according to a vector data set, an integral function of non-centrality chi-square distribution is adopted, and the centrality degree of each vector in the whole vector data set is determined; according to the centrality degrees of all the vectors, a centrality degree set # imgabs0 # is obtained; according to the similarity distance between the vectors in the vector data set and # imgabs 1 #, adopting a gradual vector adding method to construct a neighbor graph # imgabs 2 #; performing vector central degree traversal calculation on the # imgabs3 #; based on the central degree set # imgabs4 #, performing tuning and edge cutting operation on the neighbor graph to obtain a final result neighbor graph # imgabs5 #; and searching on # imgabs6 # by taking the feature vector of the query object as a query vector to obtain a vector retrieval result. According to the method, the precision and efficiency of vector retrieval based on the neighbor graph can be improved.

A coherent wind lidar echo signal spectrum simulation data generation method is provided, comprising: obtaining the bottom noise of the echo signal; removing the bottom noise from the echo signal spectrum data and counting the variance of the random noise; performing normalization processing on the random noise and performing Gaussian fitting to obtain the amplitude, Doppler shift and bandwidth of the echo signal; recording the Doppler shift difference between adjacent range gates to obtain the power spectrum peak energy of a single spectrum data and the correction amount of the distance; respectively counting the statistical parameters of the random variable, the power spectrum peak energy, the correction amount of the distance and the bandwidth; generating a group of wind speed profile graphs by using random walk and normal distribution characteristics of random variables, generating bandwidths under different range gates that satisfy the Lorentz distribution, power spectrum peak energies of a single simulated spectrum that satisfy the chi-square distribution, and distance correction amounts that satisfy the normal distribution; and generating a single simulated spectrum by a radar equation and repeatedly generating complete spectrum simulation data.

The present application provides a method, device and electronic equipment for determining the route protection zone of a low-altitude aircraft, and relates to the technical field of low-altitude traffic management. The method comprises: determining a first value based on the track error between the acquired nominal track data and the actual flight track data of the low-altitude aircraft; performing numerical integration on the collision probability algorithm of the non-central chi-square distribution to determine the risk probability of a collision of the low-altitude aircraft; and determining the height and width of the route protection zone of the low-altitude aircraft based on the gap between the risk probability and the configured safety level constant. The method calculates the collision probability by constructing a covariance matrix in the body coordinate system and combining it with the non-central chi-square distribution model, and uses an extended golden section search algorithm to quickly solve the route protection zone. The present application can effectively balance the efficiency of airspace resource utilization and operational safety, and lay the foundation for the intelligent upgrade of urban low-altitude traffic systems and the expansion of collaborative collision avoidance technology for multiple low-altitude aircraft.

The invention relates to the field of intelligent traffic flow regulation and control, in particular to an Internet of Vehicles intelligent traffic flow regulation and control method and system based on an ETC chip, and the method comprises the steps: predicting the driving direction of a vehicle through obtaining the position data of the vehicle and building the displacement vector of the vehicle; defining an included angle between the driving direction and the reference vector as a second included angle, constructing a Gaussian probability function of the second included angle within a first preset interval, performing integration within a preset distance from the center coordinate of the Gaussian probability function, and dividing the integral by the preset distance to obtain an overall driving direction as a reference factor for regulating and controlling the traffic light duration; obtaining a road vector in a preset range, defining an included angle between the driving direction and the road vector as a third included angle, calculating a standard deviation of the third included angle in a second preset time interval, and fitting the standard deviation into a chi-square distribution function; and integrating the chi-square distribution function to obtain a probability density, and screening out a road vector corresponding to the probability density exceeding a preset threshold value as a reference factor for regulating and controlling road planning.

This invention relates to a method for detecting anomalies in non-stationary industrial processes based on slow feature decomposition and Koopman high-dimensional space prediction, belonging to the field of industrial process time-series anomaly detection technology. The method includes: using slow feature analysis to decompose industrial time-series data into fast and slow changing components; mapping the fast and slow features to a linearly observable Koopman high-dimensional space based on Koopman theory, constructing a deep prediction model to achieve stepwise prediction of the feature sequence; constructing a SPE statistic that approximately follows a weighted chi-square distribution by combining the deviation between the prediction results and the actual values, and setting SPE control limits for anomaly detection accordingly; introducing KL divergence to measure the distribution difference between the current operating condition and the normal operating condition, and adaptively updating the SPE control limits based on its magnitude; and during the online deployment phase of the model, calculating the SPE value in real time and comparing it with the control limits to achieve rapid identification and dynamic early warning of anomalies under non-stationary conditions.

Methods, and systems of use, for sensor calibration. The method may comprise calibrating an attitude accelerometer of a sensor using an elliptical-fitting method, which may be based on calibration positions. The method may further calibrate a magnetometer of the sensor using a dot-product invariance (DPI) method, which may be based on the calibration positions. The method may comprise determining a magnetometer-calibration status of the magnetometer based on a chi squared distribution test method and a root mean square error (RMSE) method of dot-products generated by the DPI method. The method may further comprise calibrating an acoustic accelerometer of the sensor using the DPI method, which may be based on relative-values of a shape matrix. The method may comprise scaling an acoustic accelerometer of the sensor using a hydrophone. The system may comprise a sensor-error engine configured to perform steps of the disclosed methods.

The embodiment of the invention discloses a deception perception-based navigation management method, which comprises the following steps of: acquiring interested information which comprises signal power, attitude and direction of a GNSS (Global Navigation Satellite System) satellite; calculating a deception offset vector according to the interested information, and mapping the deception offset vector into eccentricity of non-central chi-square distribution; calculating a detection statistical magnitude, and calculating a missed detection probability according to the detection statistical magnitude; performing statistics on the detected deception signals according to a spatial direction to form a probability quality function, calculating spatial entropy according to the probability quality function, and calculating integrity weight loss according to the spatial entropy; calculating a position error expectation, and obtaining an over-limit probability according to the position error expectation; and determining a minimum protection level satisfying a constraint according to the integrity weight loss, the missed detection probability and the over-limit probability, and determining a navigation processing strategy according to the minimum protection level satisfying the constraint.

The present invention discloses a method for detecting abnormal ambiguities between reference stations based on tropospheric residual estimation, including: proposing to use the zenith tropospheric estimation residuals corresponding to each measurement equation obtained after ambiguity fixing and back substitution, and introducing statistical tests to screen for possible ambiguous outliers; since the original zenith tropospheric residuals exhibit a non-standard normal distribution with long tails and sharp peaks, the present invention proposes to use the Box-Cox transformation to transform the residuals into a standard normal distribution, that is, to make the sum of squared residuals satisfy the chi-square distribution; combining the altitude angle threshold and the chi-square critical value for judgment, preferably selecting satellite pairs with low observation data noise and correct ambiguity fixing, and re-estimating the zenith tropospheric delay; using the newly calculated zenith tropospheric delay to obtain the double-difference tropospheric delay reference value for each satellite, and re-performing ambiguity tests on the satellites excluded in the initial screening, effectively improving the discrimination ability of the satellite ambiguity fixing situation and improving the service quality of network RTK.

The present application provides a method and device for testing quantitative trading strategies, which can be used in the financial field or other fields. The method includes: obtaining real market quotation data; obtaining a strategy operation report according to the real market quotation data and the quantitative trading strategy to be tested, where the strategy operation report includes: a plurality of index information; determining a regression strategy verification model corresponding to the index information according to the index type of each index information; testing the index information according to the regression strategy verification model corresponding to each index information respectively. If all the index information passes the test, it is determined that the quantitative trading strategy to be tested is normal. The regression strategy verification model includes: a deviation measurement algorithm or a chi-square distribution algorithm. The present application can improve the accuracy and efficiency of quantitative trading strategy testing, and thus ensure the reliability of quantitative trading strategies.

The invention discloses a machine learning multi-dimensional data credibility prediction method based on block chain deterministic execution, and relates to the technical field of cross of block chains and artificial intelligence. By constructing a hierarchical random number generation mechanism, a random source in execution of a machine learning algorithm is eliminated from a bottom layer, so that when different block chain nodes are executed based on the same random number sequence, the consistency of calculation results is remarkably improved, and the problem of prediction result deviation caused by random seed difference is solved. A hardware error statistical verification system is introduced, floating point errors caused by heterogeneous hardware are controlled within an acceptable range through floating point operation log records and chi-square distribution verification, calculation result inconsistency caused by hardware architecture differences is avoided, and calculation resource consumption is greatly reduced. Besides, for multi-dimension and multi-source heterogeneous characteristics of multi-dimensional data, through an RLP coding serialization model and a Merkel tree verification mechanism, full-link credible evidence storage in a prediction process is realized, and a core pain point that a prediction result is not credible in a traditional scheme is solved.

The present application provides a method and device for obtaining the probability of aircraft collision based on non-central chi-square distribution, and relates to the field of aircraft collision technology. The method obtains the protection box radius of a spherical collision protection box with the center of mass of any aircraft as the sphere center based on the area radius of the collision area of ​​the two aircraft; for any aircraft, in a three-dimensional Cartesian coordinate system, the relative position vector of the center of mass position of the aircraft relative to the center of mass position of the other aircraft is determined, and the nominal track data and actual flight track data in the cruise phase are obtained; based on the error values ​​between the nominal track data and the actual flight track data corresponding to each aircraft, the relative position error matrix and the corresponding eigenvalues ​​are determined; the relative position vector, the relative position error matrix and the corresponding eigenvalues ​​are processed using a preset three-degree-of-freedom non-central chi-square distribution algorithm to obtain the collision probability between the two aircraft. The method improves the efficiency and accuracy of collision probability estimation.

The invention provides a GNSS outlier detection method and system based on residual multi-order chi-square distribution and a storage medium used on a satellite. The GNSS outlier detection method comprises the steps that 1, the position, the speed and the satellite time output by a GNSS under an earth-fixed system are acquired; step 2, converting output data of the GNSS into a track 14 mean element; 3, calculating a residual error and a residual error change rate to perform first outlier judgment, calculating a one-dimensional chi-square test outlier after passing the first outlier judgment, calculating a two-dimensional chi-square test outlier after passing the one-dimensional chi-square test, calculating a full chi-square test outlier after passing the two-dimensional chi-square test, and if all the two chi-square test outliers pass the first outlier judgment, updating parameters; if one test is not passed, the result is judged to be an outlier, meanwhile, continuous invalid times are counted, and when the continuous invalid times are larger than a threshold value, the threshold value can be adjusted along with increasing of the continuous invalid times, and system divergence is avoided. The method has the beneficial effects that the outlier elimination rate can be obviously improved, and correct data can be provided for the satellite for use.

This invention belongs to the field of radar technology, specifically relating to a dual-station passive positioning and target identification method. The method involves: acquiring radar coordinates; transforming the measured angles of each node radar in the northeast-sky coordinate system to a unified rectangular coordinate system referenced by the northeast-sky coordinate system of the first node radar; establishing four measurement parameter equations based on the triangulation formula, and randomly selecting three equations as a set of measurement subsets, through which the target position can be calculated; transforming the angle errors of each node radar in the northeast-sky coordinate system into angle errors in the unified rectangular coordinate system, and then obtaining the angle error covariance matrix; calculating the covariance matrix of the target position vector difference and its Mahalanobis distance based on the target positions obtained from different subsets, and performing hypothesis testing on the target based on the characteristic that Mahalanobis distance follows a chi-square distribution; and determining whether the target is a real target by comparing the Mahalanobis distances of different points, effectively eliminating false intersections.

The invention is suitable for the technical field of geochemical exploration and data processing thereof, and provides a geochemical comprehensive anomaly identification method and system for a complex lithologic region, and the method comprises the following steps: selecting a clustering index for clustering based on preprocessed geochemical data; clustering the preprocessed geochemical data by adopting an expectation maximization clustering algorithm based on a clustering index, and decomposing mixed normal distribution into a plurality of sub-bodies; for each daughter, calculating a robust mahalanobis distance of each daughter sample by adopting a fast minimum covariance determinant method; and according to the robust mahalanobis distance of each daughter sample, determining a threshold value for distinguishing the geochemical background and the anomaly based on chi-square distribution, and further identifying the geochemical comprehensive anomaly. According to the method, the statistical effectiveness of the mahalanobis distance of the geochemical sample in the complex lithologic region can be recovered to a certain extent, so that a geochemical comprehensive anomaly recognition result has statistical significance.