9 results about "Continuous variable" patented technology

The application provides a real-time entropy evaluation post-processing quantum random number generation method. The quality of random numbers determines the privacy and security of a communication system. Quantum detection is inevitable in the quantum random number generation process, which will introduce side information. Therefore, the quantum noise entropy content of the system must be strictly evaluated, and the random numbers must be post-processed by using a security information theory extractor. The application proposes to reconstruct the quantum state phase space distribution in real time in a continuous variable quantum random number generation scheme and measure the deviation from the ideal value. If the deviation reaches the rejudgment threshold, the entropy evaluation is updated in real time, so as to adjust the size of the post-processing matrix in real time. The seed used to construct the random extractor is also updated in real time. On this basis, the information theory security generalized hash post-processing of multiple quantum random numbers is realized in parallel. The application provides a cost-intensive and expandable random number real-time entropy evaluation post-processing scheme, which can effectively improve the actual security and practicability of the quantum random number generator.

The application discloses an end-to-end automatic pipeline credit modeling method and system for a general financial scenario, and relates to the technical field of automatic modeling. The method is to first acquire a large amount of sample data and perform automatic cleaning and variable type determination, then for continuous variables, adopt a decision tree algorithm to perform automatic binning processing, then based on the binning results or discrete conditions, automatically calculate the information value of each variable and the correlation coefficient between variables, and complete the automatic screening of variables according to a preset threshold, finally, import the remaining variables obtained through screening and the original dependent variables into a machine learning model for model training, obtain a loan default prediction model and output the same, so that the whole process from the original data to the deployed model can be automated, and the modeling efficiency, objectivity and reproducibility in the multi-dimensional and big data scenario of the general financial field are significantly improved, and the method is convenient for practical application and popularization.

The application discloses a thermal network thermal power unit combustion optimization control system and method based on artificial intelligence in the technical field of thermal power unit control, which comprises the following steps: collecting multi-dimensional data including external meteorological parameters, system operation parameters, boiler key indicators and time mark variables, aligning with minute-level time stamps, constituting a historical time sequence sample set, and forming a sliding window input; adopting time series interpolation, sliding window mean and Z-score standardization methods to process missing values and abnormal points in the historical time sequence sample set, carrying out Min-Max normalization on continuous variables, carrying out one-hot encoding on category variables, and generating a historical multivariate sequence; inputting the historical multivariate sequence into an LSTM model to perform multi-step prediction on key indicators of the combustion process; and the application fully combines the field use of the existing DCS control system, optimizes modeling and closed-loop control on the combustion process, and achieves the control effect of improving efficiency, reducing emissions, being strongly adaptive and easy to deploy.

The application discloses an anaerobic ammonia oxidation activity prediction method and system based on a TabPFN model, and comprises the following working steps: step S1, collecting anaerobic ammonia oxidation system operation data, and constructing an original data set with anaerobic ammonia oxidation activity SAA as a prediction target, wherein the operation data comprises heterogeneous table data composed of numerical continuous operation parameters and discrete system information of a category; step S2, preprocessing the original data set, and sequentially completing missing value completion, abnormal value identification and cleaning, and category feature encoding conversion to obtain a standardized heterogeneous table data set; and step S3, dividing the standardized heterogeneous table data set into a training set and a test set. According to the heterogeneous table data characteristics of SAA, the application can simultaneously process continuous variables and category variables, and improve the adaptation capability to complex operation data; and the application can learn the nonlinear mapping relationship and interaction between SAA and multiple factors, so that the prediction precision and generalization capability are improved.

The application relates to the technical field of quantum communication transmission, and discloses a continuous variable quantum key distribution system parameter estimation method and system, wherein the method comprises the following steps: acquiring original data in an original data buffer pool; outputting sampling data by adopting an index sampling method; outputting multiplexing sampling data that passes authentication by adopting a message authentication code checking method; outputting grouping phase estimation values by adopting a grouping phase searching method; outputting phase compensation data by adopting a segmented linear interpolation compensation method; and performing channel parameter estimation and security code rate determination. Since the application discloses the public information of the multiplexing phase compensation process and the parameter estimation process, and performs message authentication on network interaction data, the original data utilization rate and the code rate are improved while the parameter calculation precision is ensured, and the accuracy and the security of the continuous variable quantum key distribution system are ensured.

The goal is to estimate solutions to mixed-integer nonlinear programming problems with practical accuracy. [Solution] The program is a program that enables a computer to implement the following functions using a neural network, wherein the neural network is a neural network that estimates continuous variables and binary variables for state variables of a mixed integer nonlinear programming problem, the neural network has a common layer connected to an input layer and a plurality of heads that branch off from the common layer and connect to each of a plurality of output layers, and has a function to estimate binary values ​​by switching the output layer of one of the heads of the neural network, which is a binary head, using a pre-given value as a threshold, and the program has a function to estimate the continuous variables using the continuous head, which is another head of the neural network, and a function to estimate the binary variables using the binary head.

The application discloses a key screening method, system and device for continuous variable quantum key distribution, and relates to the technical field of quantum communication security. The method comprises the following steps: initializing a model and saving training statistics; calculating an abnormal score for training data block by block and filling a sliding window; calculating a dynamic threshold value in a training stage; completing initial screening in the training stage; performing incremental LoOP calculation on each new key block; dynamically updating the threshold value of the sliding window and making a screening decision; and the method adopts incremental LoOP, so that each new key block only needs to perform kNN query once to obtain an abnormal score, thereby avoiding the problem of repeated training of the model and realizing real-time detection capability at the level of quantum key blocks.

The application discloses a method and device for solving a crude oil scheduling model. The method comprises the following steps: constructing an original mixed integer nonlinear programming (MINLP) model of original crude oil scheduling, and identifying key nonlinear terms in the original MINLP model; the original MINLP model comprises continuous variables, binary variables and key nonlinear terms; the key nonlinear terms comprise nonlinear mapping of physical properties of crude oil components after mixing and proportions of each component; according to the identified key nonlinear terms, the key nonlinear terms are designed for piecewise linear approximation; according to the design result of the piecewise linear approximation, an approximate MINLP model is constructed, and a solution of the approximate MINLP model is used as an initial solution of the original MINLP model; and according to the initial solution, the original MINLP model is solved to obtain a final solution of the original MINLP model. The method and device significantly reduce the nonlinear calculation burden, effectively improve the solving speed, and realize a large proportion of solving time shortening on the premise of ensuring feasibility and solution quality.

The application relates to a data binning method, device, computer equipment and storage medium. The method comprises the following steps: acquiring business data; dividing the business data into discrete variables and continuous variables; automatically binning each continuous variable to obtain a plurality of variable binning results corresponding to the continuous variable; in response to the number of categories of the discrete variables being less than or equal to a minimum category number threshold, outputting the discrete variables; in response to there being at least one discrete variable with a category number greater than the minimum category number threshold, automatically binning each discrete variable to obtain a plurality of variable binning results corresponding to the discrete variable; according to a preset evaluation index, screening the plurality of variable binning results corresponding to each continuous variable and / or discrete variable to obtain an optimal variable binning result; and outputting the optimal variable binning result and a parameter range of a corresponding binning method. The method can reduce the system calculation pressure, and can comprehensively obtain a plurality of variable binning results of data and an optimal variable binning result.