69 results about "Continuous variable" patented technology

The invention provides a sampling method suitable for continuous-discrete mixed variable design optimization of an advanced reactor. The sampling method comprises the following steps: (1) screening an effective discrete variable combination; (2) constructing a cumulative probability distribution function of the discrete variable combination; (3) hierarchically designing a space based on a cumulative probability distribution function interval; (4) distributing the number of samples according to the interval width proportion of the cumulative probability distribution function of each layer; (5) Latin hypercube sampling is carried out on the continuous variables in each layer; (6) integrating a global sample set and verifying the quality of the global sample set; and (7) constructing an agent model and evaluating precision. According to the method, the probability characteristic of the discrete variable and the space filling characteristic of the continuous variable are integrated, efficient exploration of the nuclear reactor design space is achieved, and compared with a traditional method, the sample quality and the calculation efficiency are remarkably improved.

The invention discloses a roll angle measuring method for a continuous variable roll test of a transient wind tunnel, and belongs to the technical field of wind tunnel test. Comprising the following steps: step S1, early-stage preparation: selecting two angle measurement sensors to carry out measurement work, and installing the two angle measurement sensors and an installation platform according to a preset angle; s2, carrying out field calibration operation; carrying out field calibration on the coefficient of the angle measurement sensor by adopting a fixed attack angle and variable roll angle mode; and S3, data interception, discretization and application during a step variable attack angle continuous variable roll angle test: in a wind tunnel test process, continuously collecting output voltage values of the two angle measurement sensors, calculating roll angles corresponding to the output voltage values, intercepting roll angle range values corresponding to the two angle measurement sensors according to needs, and calculating the roll angle range values of the two angle measurement sensors according to the intercepted roll angle range values. Splicing into a target measurement range; compared with a traditional step variable rolling and fixed rolling variable attack angle test mode, the scheme has the advantage that the test efficiency is greatly improved.

The invention relates to a self-adaptive information negotiation method applicable to a continuous variable quantum key distribution system, which comprises the following steps of: estimating an optimal code rate that an LDPC (Low Density Parity Check) code should reach under a current quantum channel, converting an initial code rate into the optimal code rate by a receiver, and obtaining a low-reliability bit number l which needs to be disclosed in conversion calculation, the method comprises the following steps of: decoding an LLR (Local Library Ratio) by using Bob, sending the LLR to Alice through a classic channel in combination with an encryption technology, sorting absolute values of the LLR by Alice in a decoding iteration process, selecting l bit positions with low reliability, sending the bit positions to Bob, determining a bit value by Bob according to the bit positions with low reliability sent by Alice, and feeding back and sending the bit value to Alice, and correcting the LLR value by Alice according to bit information sent by Bob. According to the method disclosed by the invention, the code rate of the LDPC code can be flexibly adjusted along with the change of the state of the time-varying channel, and the negotiation efficiency is improved, so that the key rate of the system is improved. The method is excellent in performance in a time-varying channel environment, negotiation efficiency under a low signal-to-noise ratio condition can be remarkably improved, and an efficient and reliable information negotiation scheme is provided for practical application of a quantum secret communication system.

The invention relates to a cable tunnel bridge fire prediction method and device, electronic equipment and a storage medium. The cable tunnel bridge fire hazard prediction method comprises the following steps: establishing a cable tunnel bridge fire hazard numerical simulation model, establishing a cable tunnel bridge fire hazard simulation model based on FDS software, obtaining key parameters such as heat release rate, temperature distribution and flame spread boundary under different working conditions, and constructing a multi-time sequence sample data set; performing state discretization on continuous variables such as temperature and a spreading range, and estimating a node prior probability in combination with a statistical frequency; constructing a dynamic Bayesian network topological structure according to a causal relationship among a heat source, temperature and spread, and setting a cross-time slice node dependence path to realize time sequence modeling; inputting a training sample and performing parameter learning to generate a conditional probability table; in the prediction stage, multi-step reasoning is achieved through forward propagation, flame spreading range probability distribution and interval estimation of multiple time steps in the future are obtained, and the specific position where the flame arrives is predicted.

The invention discloses a causal graph inference method and a causal graph inference system which are oriented to strong nonlinear continuous variable distribution and based on RealNVP and micro NOTEARS constraints. According to the method, a structure parameter matrix is constructed to represent candidate causal connection, parent variable condition input is constructed for each variable based on structure parameters, and accurate likelihood modeling is performed on the condition density of each variable by adopting a condition RealNVP normalization flow model. By constructing an objective function containing a conditional log-likelihood item, a sparse regular item and a NOTEARS style differentiable acyclic constraint item, a sparse causal structure meeting acyclic constraint is obtained by utilizing gradient optimization, and a causal graph is output. Further, based on the learned causal graph and a conditional RealNVP model, anti-fact inference is executed under a given intervention condition, and an anti-fact result is output. The method is suitable for strong nonlinear relation and complex continuous distribution scenes, and has the advantages of being stable in structure inference, high in interpretability and capable of supporting anti-fact analysis.

The invention discloses a token stream guide variable code rate image compression method oriented to unification of perception and understanding. The method comprises the following steps of: 1, acquiring a two-dimensional image, and processing the two-dimensional image to acquire a one-dimensional token sequence; 2, processing the one-dimensional token sequence through a variable token mask to generate a binary compressed bit stream; 3, decoding the compressed bit stream to obtain an unmasked token sequence, and carrying out dynamic prediction on the unmasked token sequence to recover a complete token sequence; and 4, realizing human perception through the receiving end I after the complete token sequence is recovered, realizing machine perception through the receiving end II after the complete token sequence is recovered, and finally realizing image compression. The method has the capability that a single model supports the continuous variable bit rate, supports a large language model to directly perform semantic understanding based on the compressed code stream, and has the characteristics of low calculation complexity and low delay.

The invention belongs to the field of continuous variable quantum key distribution, and particularly relates to a spline coordination method suitable for discrete modulation continuous variable quantum key distribution, which can effectively reduce the data coordination difficulty and improve the operation speed of data coordination by using constellation points to replace a quantization mode of mapping a binary sequence by a single orthogonal component. The method is an effective scheme easy to realize high-speed quantum secret communication. And the high-performance error correction code with a fixed code rate is obtained by using the characteristics of the RL-LDPC code, so that the difficulty in obtaining the error correction code is reduced. The method mainly comprises the steps of continuous variable quantization and multi-level coding and multi-level decoding (MLC-MSD). In the continuous variable quantization process, the continuous variables are mapped into the multi-level binary sequence through the constellation point division interval, the number of coding and decoding levels in spline coordination is reduced, and then the complexity of a data coordination model is reduced. In the MLC-MSD, the characteristic that the code rate is moderately modified while the high error correction performance of the RL-LDPC code is maintained is utilized, and the error correction code with the required code rate is obtained.

A method for optimization of multidimensional continuous functions using a quantum processor (50) is disclosed. The method comprises initializing (S400) quantum circuit parameters ({right arrow over (ω)}) to set an initial guess |ψ) of a function ƒ({right arrow over (x)}), running (S410) a variational quantum circuit U({right arrow over (ω)}) over n qubits of the quantum processor (50), implementing (S420) a quantum state tomography function of the individual qubits to estimate qubit parameters [θ, φ, r] of the function ƒ({right arrow over (x)}) for each qubit. The method further comprises estimating (S430) a vector of a continuous variable ({right arrow over (x)}) from the qubit parameters [θ, φ, r], estimating (S440) a value of the function ƒ({right arrow over (x)}) and a gradient ∀ƒ({right arrow over (x)}) of the function ƒ({right arrow over (x)}) at the point of the vector of the continuous variable ({right arrow over (x)}), updating (S450) the quantum circuit parameters ({right arrow over (ω)}) and iterating (S460) the steps S410-S450 until optimization of the function ƒ({right arrow over (x)}).

Detects errors that occur in a continuous variable state. [Solution] The quantum computing device comprises an initialization unit that prepares a continuous quantity state which is the target of a desired processing and a check qubit and initializes the check qubit, and a calculation unit that performs a coherent parity check on the continuous quantity state using the check qubit to detect errors that have occurred in the continuous quantity state.

The invention provides a fault diagnosis method for open set domain generalization under a continuous variable working condition. The method comprises the following steps: firstly, constructing a training sample with time domain data, a working condition index and a fault category index; converting the time domain data into a time-frequency domain and extracting semantic features; a class-specific semantic reconstruction module is adopted to classify the semantic features; a cross-domain alignment module is adopted to estimate mutual information of the semantic features of the training samples and the working condition indexes, and the smaller the mutual information is, the lower the working condition dependence degree is; constructing joint loss including cross-domain alignment loss; the cross-domain alignment loss constrains the feature distribution consistency between continuous domains based on mutual information minimization; back propagation training is carried out by adopting joint loss, so that the class-specific semantic reconstruction module learns and extracts the working condition invariant feature extraction capability; and performing fault classification by using the trained feature extraction module and the class-specific semantic reconstruction module. According to the method, fault diagnosis can be realized under a cross-continuous change working condition, and meanwhile, the method has the capability of identifying unknown fault types.

The invention discloses a code rate adaptive data coordination method suitable for continuous variable quantum key distribution, and belongs to the technical field of continuous variable quantum key distribution. Aiming at the problem that the existing method cannot realize stable and efficient extraction of a key in a key distribution system, high coordination efficiency under a specific code rate is realized by using a protograph LDPC code, and then code rate compatibility is realized through a specific matrix puncturing technology, so that an error floor is effectively reduced, and the key distribution efficiency is improved. And stable and efficient data coordination is realized in a relatively large signal-to-noise ratio range. Through simulation tests, the self-adaptive range of the signal-to-noise ratio is 0.028-0.252, the coordination efficiency can reach more than 95%, the average frame error rate is about 30%, the robustness of the system is effectively improved, and the application of the continuous variable quantum key distribution technology in actual scenes is promoted.

The invention provides a scheduling method and system of an integrated energy system and a storage medium, and the method comprises the steps: constructing a mixed integer nonlinear programming model with the minimum total operation cost as a target, carrying out the iterative solving of the model until the upper and lower bounds converge, solving a main problem to obtain a tentative solution of a unit start-stop scheme and the lower bound of the current iteration, and carrying out the scheduling of the unit start-stop scheme. The main problem comprises a regularization item and is constrained by a cut plane, and a penalty coefficient of the regularization item is adjusted according to a distance between a tentative solution and a historical optimal solution and a lower bound gain rate; fixing a tentative solution of the discrete variable, setting solution precision according to a current upper and lower bound gap and a convergence rate, and solving an energy flow sub-problem to obtain a continuous variable solution and an upper bound; if the sub-problem is feasible, weighted averaging is carried out based on dual solutions in historical iteration to generate an optimal cut plane, and the optimal cut plane is added to the main problem; and if the sub-problem is not feasible, identifying a key constraint cluster causing infeasibility, generating a feasible cut plane and adding the feasible cut plane to the main problem.

The invention provides a switching power supply system-oriented efficient global optimal multi-physics co-simulation method and system, and the method comprises the steps: defining a hybrid design space containing discrete variables and continuous variables, and constructing a composite objective function; generating a small number of parameter combinations of the discrete variables and the continuous variables by adopting a mixed horizontal orthogonal array to form an initial sample set; performing multi-physical field simulation on the initial sample set to obtain a performance observation value; constructing an initial Gaussian process proxy model based on the initial sample set and the performance observation value; a discrete forced continuous random sampling strategy is adopted to generate a large number of global uniform samples, iteration is carried out through a Bayesian optimization framework based on the initial Gaussian process proxy model, and optimal candidate points are obtained; and when the composite objective function converges or reaches the maximum number of simulation times, outputting a global optimal design parameter combination.

The application discloses a kind of based on formalization method's trusted machine learning model explanation method and device, it is related to artificial intelligence technical field, the method includes obtaining target machine learning model and its input characteristic value;Input characteristic value is compiled using formalization method, to obtain continuous variable;Continuous variable is discretized, to obtain the input characteristic value after discretization;The logic variable compilation of characteristic value after discretization is carried out, to obtain the variable after formalization compilation;According to the decision rule of model, the logic expression after formalization compilation is simplified, to obtain the rule reason of the expression of implication, that is, decision;Rule reason is obtained using natural language representation, to obtain the explanation result of model.The application uses formalization method to explain trusted machine learning model, compared with traditional explanation method, can more accurately reveal the internal logic of model decision, to improve the reliability of model explanation.

The invention discloses a continuous variable quantum secret sharing method based on a state distinguishing detector. The method comprises the following steps: a user generates a binary key sequence; a QPSK state signal is generated; sequentially coupling the QPSK state signals through an asymmetric beam splitter; the Dealer uses the SDD to estimate the received signal; repeating the steps, and extracting an original combined key; obtaining an original key of each user based on a key sequence combination principle; and calculating a key rate, and extracting a key to complete encryption message decoding. According to the method, quantum state distinguishing is completed through a state distinguishing detector (SDD), and the SDD is allowed to directly determine bit information directly transmitted by different users by identifying mixed state categories, so that each user does not need to declare and discard respective original key subsets, a mapping rule is directly restored, key loss is reduced, and the user experience is improved. And the key rate performance of the SDD-CVQSS protocol is improved. In addition, compared with the traditional CVQSS, the SDD-CVQSS shows obvious advantages in key performance indexes of the key rate and the maximum transmission distance.

This optimization device is configured to comprise: a variable transformation part (1) that acquires a first continuous variable, which is represented as either a real number value or an integer value, and transforms the first continuous variable into a candidate solution, which is a binary variable; a variable inverse transformation part (2) that inversely transforms the candidate solution into a second continuous variable; an evaluation value calculation part (3) that calculates an evaluation value of the second continuous variable; and a transformation rule update part (4) that updates, on the basis of the evaluation value, each of the rule of transformation into a candidate solution performed by the variable transformation part 1 and the rule of inverse transformation into a second continuous variable performed by the variable inverse transformation part 2.

The invention discloses an optimal communication defense resource allocation method based on two-stage hybrid optimization. According to the technical scheme, the method is characterized by comprising the steps that in the first stage, global configuration rough search is conducted, global exploration is conducted on the AC-ODRA problem under an alternating current model through a myxobacteria algorithm, the deployment position (discrete variable) of defense resources is rapidly determined, and a high-quality candidate solution is obtained; in the second stage, resource input fine adjustment is conducted, specifically, for the candidate solutions in the first stage, the deployment positions of the candidate solutions are fixed, and high-precision local optimization is conducted on the specific resource input amount (continuous variables) under the augmented Lagrange framework through an adaptive torque estimation optimization method of multi-starting-point random disturbance guided by instrument importance. According to the method, global search and local fine tuning are combined, the solving precision and robustness of the AC-ODRA problem are remarkably improved, a defense resource allocation scheme with lower cost can be obtained on the premise of guaranteeing the safety of a power grid, and the method has important theoretical value and application prospects.

The application discloses a linear guide rail SOMC design method, equipment, medium and program product, including: (1) based on the linear guide rail structure characteristics and static load analysis, the simulation model and SOMC design model are constructed with static stiffness as the target, weight and maximum contact stress as the constraint; (2) the design space is constructed, the initial population is generated and the database is established, the high influence continuous variable set and the high influence discrete variable set are obtained through SRC, CEA sensitivity analysis method; (3) the corresponding candidate set is generated based on the two types of variable sets, and the complete candidate guide rail set is obtained through individual pairing; (4) the random forest prediction model is established, the excellent subset is screened and the best offspring guide rail is selected; (5) the database and the prediction model are updated after simulation evaluation, and the step (3) is returned until the index reaches the standard, and the optimal parameter value is output. The application can effectively balance the SOMC process with stiffness as the optimization target, weight and maximum contact stress as the constraint, and realize higher precision and better comprehensive performance.

The invention discloses an all-working-condition risk quantification method applied to a new energy civil aircraft power system, which comprehensively considers the influence of discrete factors and continuous factors, quantifies the performance risk of the power system under all working conditions into a probability density distribution function, and comprises the following steps: defining a discrete variable and continuous variable distribution set; establishing a power system performance change degree probability density function PDFcon under the influence of continuous variables; establishing a power system performance probability density function PDFsys under the common influence of the continuous variable and the discrete variable; and step 4, quantifying the system risk, namely quantifying the system risk under the common influence of the discrete variable and the continuous variable into a probability density distribution function for evaluating the risk of the system at the moment. According to the method, quantitative analysis of all-working-condition risks can be realized, and reliable support is provided for airworthiness verification, safety evaluation and engineering application of a new energy power system.

The application discloses a multi-dimensional time sequence anomaly detection method based on standard Gini index composition, and belongs to the technical field of time sequence anomaly detection. In view of the problem that the existing time sequence anomaly detection method cannot learn the relationship between discrete values and continuous values, when measuring the similarity of sensors, the continuous variable is discretized, and the similarity value between the sensors is obtained by calculating the standard Gini index between the two sensors. The traditional Gini index has a multi-value bias problem, that is, it is biased to give a score to a value with more values and higher attributes, and the standard Gini index can effectively alleviate this problem. The standard Gini index can effectively depict the similarity between sensors with different value types; and the application gives each sensor an embedding vector, which can represent the unique characteristics of each sensor. When reconstructing the data, the embedding vector and the corresponding transformation feature are connected, so that the original data can be better reconstructed.

The invention relates to the technical field of LED driving chips, and discloses an LED driving chip design management method and system based on deep learning, and the method comprises the steps: collecting chip design parameters, application scene data, user behavior data and environment parameters, and obtaining original data; adopting a KNN interpolation algorithm to fill missing values in the original data, and using an isolated forest algorithm to identify and remove abnormal samples to obtain preprocessed data; mapping category variables in the preprocessed data into low-dimensional vectors by adopting embedded codes, performing Z-score standardization on continuous variables, and extracting dynamic control features in a sliding window for user behavior data to obtain a feature data set; inputting the feature data set into a FlashLED-Net hybrid model, dynamically adjusting chip design parameters and a multi-flash mode control strategy, and outputting optimized chip design parameters and control codes; according to the method, manual intervention and trial and error links are greatly reduced, and the chip design period is shortened.

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 invention provides an optimal design method and system for a material preparation formula and a medium. The optimization design method of the material preparation formula comprises the following steps: acquiring experimental data including different formula combinations and corresponding performance indexes thereof; performing formula variable space design on the basis of the category and value rule of formula variables in the experimental data and formula constraint conditions to determine a formula search boundary comprising discrete variables and continuous variables; based on a target acquisition function, performing search optimization in the formula search boundary by adopting particle swarm optimization in combination with a branch and bound method so as to determine a plurality of candidate formula combinations, the target acquisition function being defined based on performance prediction values and prediction uncertainty of the candidate formula combinations determined by a support vector regression model; and in response to a termination condition meeting search optimization, determining the candidate formula combination with the optimal target acquisition function value as a formula design scheme.

The invention discloses an engineering structure multi-objective intelligent optimization method based on discontinuous variables. The method comprises the steps that a multi-objective optimization model is constructed, and the discontinuous variables in the multi-objective optimization model are selected; selecting representative sample points in the discontinuous variable feasible region based on central point composite test design, and fitting second-order polynomial response surface model parameters through a least square method to obtain an agent model; a population is initialized through real number coding, single-point crossover operation and single-gene random reset mutation operation based on discontinuous variables are designed, and a solution set is ensured to be matched with a feasible region of a project; based on an elitism strategy and a fast non-dominated sorting algorithm, sorting is carried out, a Pareto optimal solution set is determined, and a balanced optimal structure design scheme of the Pareto optimal solution set is selected. According to the method, test design, an agent model and an intelligent algorithm are integrated, the optimization efficiency is remarkably improved, the precise application problem of discontinuous variables in engineering is solved, collaborative optimization of performance and economy is achieved, and the method is suitable for lightweight and long-life design of engineering structures such as bridges and buildings.

The application discloses a satellite optimal collision avoidance control method and system integrating multiple factors and belongs to the technical field of spacecraft orbit control. The application establishes an orbit differential equation of both collision targets based on a Gauss perturbation equation; establishes a continuous variable direction thrust direction angle function based on Fourier series fitting; establishes an optimal control model of collision avoidance-return orbit integrating multiple factors; sets an optimization target based on weight distribution, solves the continuous thrust direction angle and switching time through an optimization algorithm, and realizes mission satellite avoidance control. The application can solve the problem that the existing technology only considers safety factors for collision avoidance of non-cooperative targets, which leads to difficult subsequent consecutive space operation and reduced satellite mission efficiency, and the orbit control scheme is simple and reliable, meets the actual engineering needs, and effectively reduces the satellite orbit deviation risk caused by satellite collision avoidance.

The present disclosure discloses a method and a system for generating verifiable quantum random numbers. The present disclosure uses continuous variable systems and homodyne detection to produce verifiable quantum random numbers. A plurality of analog to digital converter circuits are used to generate sequences of random number, which can be verified for violation of a Bell-CHSH inequality. The Bell-CHSH inequality is used to ensure true quantum nature of the generated random numbers.

The invention discloses a continuous variable quantum state generation, recovery and training method and model, and the method comprises the steps: converting an input quantum state into a thermal state through a forward diffusion process of thermal loss channel modeling; generating the thermal state into a target quantum state through a trainable reverse denoising process; constructing a coherent state data set; obtaining a diffusion quantum state according to a sample quantum state in the coherent state data set; based on the diffusion quantum state, recovering the sample quantum state to obtain an output coherent state; and training the continuous variable quantum state recovery model according to the sample quantum state and the output coherent state. According to the method, the common environmental noise effect in continuous variable quantum communication is effectively simulated, so that the generation method is more flexible, the fidelity of generation and recovery results, the response speed to unpredictable noise and the robustness of quantum state recovery are improved, and the blank left by a model taking generation as a center is filled.

The invention discloses a hybrid model predictive control method and system for controlling a motor, and relates to the technical field of machine learning, and the method comprises the following steps: S1, employing a machine learning technology to construct a hybrid dynamic model; s2, performing multi-time-domain state prediction by using the hybrid dynamic model; s3, performing real-time cost quantification by using the prediction state sequence; s4, performing control parameter solving by using the optimal state response path; and S5, performing model error compensation by using the execution parameter set. Multi-time-domain state prediction constructed based on a machine learning technology is set, continuous variable evolution and discrete mode conversion of a motor system are described in a unified state space at the same time, the structure is based on a unified hybrid dynamic function, recursive prediction of continuous-discrete variables is achieved, and the reliability of the motor system is improved. And a Kalman prediction correction technology is combined to carry out error updating on a continuous state, and a logic constraint is applied to a discrete state, so that the prediction precision and the dynamic response capability of a machine learning system under a complex working condition are improved.

A processing unit determines a first solution to a surrogate problem represented by a second evaluation function obtained by adding a product of an auxiliary variable and a weight coefficient corresponding to a constraint to a first evaluation function of a mixed-integer programming problem including integer and continuous variables, using the branch and bound method with linear relaxation of the integer variable. The processing unit determines a second solution to the mixed-integer programming problem by local search using an initial solution where the integer variable in the first solution is set to an integer value while fixing the continuous variable value, determines a third solution to the mixed-integer programming problem while fixing the integer variable value included in the second solution, decreases the weight coefficient corresponding to the constraint satisfied by the third solution, and then repeats determining the first, second, and third solutions and decreasing the weight coefficient.