10 results about "Logistic distribution" patented technology

This invention provides a method and system for detecting impurities in flunarizine hydrochloride raw material. The detection method includes: acquiring the original chromatographic signal, performing empirical mode decomposition for denoising and baseline optimization to obtain a baseline-corrected and denoised chromatogram; determining the signal-to-noise ratio based on the energy relationship between noise and signal in preprocessing, determining the optimal analytical scale through a preset function, determining the number of chromatographic peak components by screening ridges through continuous wavelet transform, and extracting initial model parameters; establishing a mathematical model for the convolution of a standard Gaussian function and a modified Logistic distribution function for each component peak, using a trust-region reflection algorithm for nonlinear iterative fitting, with the objective function containing a least-squares residual term and a weighted regularization term; after the objective function converges, obtaining the optimal model parameters, and calculating the integral area of ​​each impurity component peak to determine its content.

The invention relates to an air temperature and cold wave index evaluation method for the ice condition of a water conveyance canal in a needle cold region. The method comprises the following steps: collecting and collecting air temperature data along a canal; calculating the probability distribution of the daily average minimum temperature change; calculating a temperature cold wave index TCI; defining cold wave classification grade indexes and threshold values; and TCI analysis. According to the method, a three-parameter Log-logistic distribution function is adopted to describe the change of the air temperature, then normalization processing is carried out, and finally the cumulative frequency of the air temperature is introduced into river and canal line cold wave classification. On the basis of researching climate and air temperature characteristics and distribution rules along the canal, an air temperature and cold wave index method and a cold wave grade classification standard are established, and corresponding relations among different grades of cold waves, negative accumulated temperature thresholds and ice condition occurrence and development conditions are analyzed. The accumulated negative accumulated temperature threshold value and the cold wave grade classification method proposed by the air temperature and cold wave index method are more suitable for judgment and prediction of the ice condition development process, and a powerful scientific basis is provided for prediction of disaster prevention and reduction of river and canal ice conditions.

The invention relates to the technical field of logistics distribution, in particular to a logistics distribution method and system based on the Internet of Things, and the method comprises the steps: calculating the predicted passing time cost of each road section in a road network in a plurality of time windows in the future through a space-time prediction model; using a Bayesian inference model to generate discrete sign-in success probability distribution for each order in the plurality of time windows; a multi-objective optimization algorithm is applied to solve and obtain a group of Pareto optimal distribution scheme sets; and selecting a final delivery route from the Pareto optimal delivery scheme set according to a preset risk preference or cost benefit strategy, and issuing the final delivery route to a vehicle terminal for execution. According to the invention, scientific basis for selection based on different business strategies can be provided for decision makers, so that the final distribution decision is more flexible and strategic.

The application discloses a logistics distribution multi-objective combination optimization method based on graph filtering and a cross attention mechanism, and comprises the following steps: a neural model GF-MOCA for multi-objective combination optimization is built; instance data of a logistics distribution multi-objective combination optimization problem is acquired, including characteristic vectors of n distribution nodes; after decomposition processing, a plurality of scalarized sub-problems are output; the characteristic vectors of the distribution nodes and weight vectors in the instance data are input into the neural model GF-MOCA for training; the neural model GF-MOCA iteratively predicts the distribution node selection probability distribution at each time in a self-recurrence mode, and samples or selects nodes according to the distribution node selection probability distribution, gradually constructs the visiting sequence of the distribution nodes, and finally forms a solution sequence representing a complete distribution scheme. The application can quickly generate a high-quality Pareto distribution scheme set covering different target preferences by using a single model, and meets the needs of real-time decision-making of logistics distribution.

The application discloses a method for quantitatively predicting premature creep failure of high-temperature alloy based on error distribution, comprising the following steps: predicting creep rupture performance to obtain predicted creep rupture time and stress; obtaining predicted time error and predicted stress error by using the predicted creep rupture performance and experimental data; fitting the predicted error by using logarithmic-logistic distribution and establishing improved logarithmic-logistic distribution; obtaining statistical data of the predicted error distribution, including the width of the distribution, the probability and position of outliers, etc., by using the improved logarithmic-logistic distribution to fit the predicted error; according to the effectiveness of the distribution fitting graph and the regression graph evaluation method, determining the best fitting method according to the width of the distribution, and predicting the probability of premature creep failure of the material according to the position of the outliers. The method is simple and efficient, can quantitatively predict premature creep failure of most austenitic steel, high-chromium steel and nickel-based alloy, and has the characteristics of stable and reliable results.

The invention relates to the technical field of hydrology and meteorology, and discloses a summer corn drought detection method and system.The method comprises the steps that day-by-day precipitation and evaporation capacity data in a certain area are obtained, and the effective precipitation and the actual water demand of summer corn are obtained; obtaining a difference value between the effective precipitation amount and the actual water demand amount of the summer corn within a certain period of time; performing trend fitting on the difference value D < t > by using a smooth spline so as to replace a position parameter of Log-logistic distribution, and obtaining a time-varying position parameter; acquiring Log-logistic distribution of a time-varying time sequence according to the time-varying position parameters, estimating the parameters by using a probability weighted distance method PWMs based on empirical frequency, and standardizing drought detection indexes; the invention provides a new non-stationary drought index of the summer maize, which considers the requirements of different crops in growth stages on moisture and the difference of drought tolerance, and can realize day-by-day fine detection of the drought of the summer maize.

The invention provides a transaction network carding method and device based on a graph theory and a graph algorithm, and a medium, and the method comprises the steps: calculating the total number of transactions and the total amount of transactions between accounts according to the collected transaction records of a plurality of accounts, and forming an initial transaction network; calculating the initial transaction network based on a community discovery algorithm to obtain a plurality of sub-networks in the initial transaction network; ranking all accounts of an initial transaction network based on a PageRank algorithm, and dividing the importance degrees of the accounts according to a ranking result; ranking transaction behaviors among all accounts of the initial transaction network based on Logistic distribution, and dividing transaction behavior levels according to a ranking result; based on an intermediary centrality algorithm, calculating an initial transaction network, and searching for intermediary accounts among communities; based on a proximity centrality algorithm, an initial transaction network is calculated, accounts which are effectively spread in a community are searched, and carding and regularization of the whole transaction network are achieved.

The invention relates to the technical field of logistics distribution, and discloses a distribution time determination method and electronic equipment, and the method comprises the steps: firstly constructing a prediction model, modeling a distribution time prediction process as a probability statistical model, selecting a Bayesian regression model as a core algorithm, and defining a feature space and a target variable; secondly, collecting and cleaning historical distribution data, extracting features and preprocessing the features to ensure data quality; then, training a Bayesian regression model by using the preprocessed data, so that the Bayesian regression model can accurately reflect the relationship between the feature space and the target variable; and finally, collecting feature data in real time in the delivery process, and inputting the feature data into the trained model to obtain predicted delivery time. According to the method, multiple influence factors are comprehensively considered, the real-time prediction capability is realized, the prediction accuracy of the distribution time can be remarkably improved, the logistics distribution efficiency and the service quality are improved, and the operation cost of an enterprise is reduced.

The invention relates to the field of garbage collection, in particular to a control method in a garbage can bag breaking mechanism, and a stepping motor adopts the following control method: step 1, an S-shaped control curve is adopted to divide a total stroke into an acceleration section, a constant speed section and a deceleration section; 2, constructing a curvilinear equation by adopting transformation of a logistic distribution function; 3, stretching change adjustment is conducted on an S-shaped curve obtained through the curvilinear equation, and a curve oscillogram of the curvilinear equation is obtained; 4, a curvilinear equation corresponding to the curve oscillogram with the appropriate curvature is selected, the curvilinear equation is operated to obtain the frequency of the single-chip microcomputer in each step of the stepping motor, and a shaping data table of the frequency of the single-chip microcomputer is generated; and 5, when the equipment runs, the bag rolling mechanism clamps the garbage bag, and the single chip microcomputer calls the parameters in the shaping data table to control the stepping motor to run. According to the bag lifting control method, on one hand, the calculation amount of an S-shaped control method can be simplified, and on the other hand, a more appropriate control curve can be selected.

The present application relates to the technical field of supply chain management, in particular to a medicine supply chain scheduling method and system based on reinforcement learning, comprising the following steps: obtaining the temperature difference inside and outside the carriage and the heat conduction coefficient to calculate the heat penetration rate and the thermal safety time limit, planning the path and correcting the speed based on the congestion index to obtain the predicted passing time, comparing the time limit with the deadline to generate the action mask vector, inputting the environmental parameters into the neural network to output the score, and using the action mask to correct and generating the scheduling instruction through the Softmax function. In the present application, the thermal safety time limit is accurately derived, the thermal safety time limit and the delivery deadline are used as double constraints to construct the action mask to eliminate invalid nodes, multi-dimensional parameters are input into the neural network and the probability distribution is generated under the mask constraint, and it is ensured that the vehicle scheduling instruction follows the temperature control safety threshold, effectively avoiding the risk of drug heat failure and improving the accuracy of cold chain logistics distribution.