38 results about "Deterministic system" patented technology

The invention discloses an optimal scheduling method for a comprehensive energy system, relates to the field of energy management, and is used for solving the problems that a traditional scheduling method is difficult to realize overall optimization, the traditional scheduling method is limited, the traditional scheduling method generally simplifies an energy system into a deterministic model, the uncertainty of energy supply and demand is ignored, and the energy consumption is low. And the scheduling scheme is poor in adaptability in actual operation. The method comprises the following steps of S101, data comprehensive acquisition and fine preprocessing, S102, precise system model construction, S103, uncertainty scene scientific generation, S104, optimal scheduling model elaborate construction, S105, model efficient solving and verification, and S106, scheduling scheme robust implementation and dynamic adjustment. The invention provides an optimal scheduling method for a comprehensive energy system, which can fully consider the energy supply and demand uncertainty and various energy coupling characteristic factors, realizes the economic and efficient operation of the comprehensive energy system, and reduces the energy cost and environmental pollution.

The invention discloses a self-adaptive micro-grid operation optimization method, system and device and a medium, and belongs to the technical field of micro-grid operation optimization, and the method comprises the steps: obtaining the operation state data of a micro-grid; performing depth feature coding and space-time attention processing on the micro-grid operation state data through a state sensing module to generate unified state representation; carrying out online strategy learning and optimization through a strategy evolution module based on unified state representation, and outputting a strategy optimization result; a new energy uncertainty model is adopted to generate multi-scene prediction data, the prediction data and a strategy optimization result are combined, and a prediction control problem is solved through a rolling optimization module; and according to a real-time risk assessment result, the decision weights of the strategy evolution module and the rolling optimization module are adjusted, and a micro-grid operation scheduling instruction is output. According to the method, a new state deep perception-dynamic strategy learning-robustness optimization normal form is constructed, and intelligentization and adaptive learning of the micro-grid are realized.

The invention discloses an approaching rainfall forecast post-processing method based on a frequency transformation diffusion model, and belongs to the technical field of meteorology. The method comprises the following steps: firstly, acquiring a radar echo signal predicted and output by a deterministic model; secondly, performing post-processing on the radar echo signal output by the deterministic model based on a rainfall forecast post-processing model of frequency domain transformation, and outputting a corrected radar echo signal; and finally, the corrected radar echo signal is utilized to carry out approaching rainfall forecasting. According to the post-processing model, high and low frequency components of radar signals are decoupled through a frequency decomposition module, high and low frequency component optimization is carried out through a high and low frequency diffusion model, and complete radar echo signal output is reconstructed through a frequency reconstruction module. According to the method, a high-fidelity radar echo prediction signal with a physical structure and local details can be generated, the problem of fuzzification of existing close rainfall deterministic prediction is solved, and the precision and accuracy of close rainfall prediction are remarkably improved.

The embodiment of the invention provides a thin interlayer and phase control physical property cause sand body configuration model construction method and system, and belongs to the technical field of reservoir modeling. The method comprises the following steps: identifying fluvial facies reservoir distribution characteristics based on exploration data, and constructing a corresponding cause sand body reservoir configuration model; obtaining a corresponding small-layer sand body deterministic model based on the cause sand body reservoir configuration model; on the basis of the deterministic model of the small-layer sand body, performing representation of an internal configuration interface of the sand body based on a lateral lamination model and an adaptive triangular mesh generation algorithm; and generating a distribution model of each lithofacies unit based on representation of a configuration interface in the sand body, and constructing a physical property model for fine modeling and heterogeneity description of high and low permeability sections in the configuration based on a phase control stochastic simulation method. According to the scheme, the limitations of insufficient precision, unreasonable transition and low calculation efficiency in traditional modeling are effectively overcome, and high-precision geologic model support is provided for reservoir management and oil reservoir development.

The application discloses a kind of improved logistic chaotic sequence hybrid measurement matrix construction method.The element of measurement matrix in the application is by the sequence with independent and identically distributed statistical properties generated by a modified logistic chaotic system, after two-level processing, it meets Bernoulli statistical distribution, satisfies the RIP criterion required to be obeyed by measurement matrix of compressive sensing technology, finally, the measurement matrix obtained by sequentially filling according to the toprizi matrix construction rule, with the randomness characteristics of chaotic deterministic system, and the measurement matrix has the advantages of low data amount required to be generated, easy to realize hardware resources etc., and the reconstruction accuracy of signal is better than random Gaussian measurement matrix.

The application provides a quality-uncertain waste power battery recycling and disassembling integrated optimization method, which firstly takes minimization of total operation cost as an objective function, considers coupling constraints, recyclable packaging related constraints, vehicle related constraints, demand satisfaction constraints, inventory related constraints, disassembling line related constraints and decision variable value range constraints, and constructs a stochastic programming model of waste power battery recycling-disassembling integrated optimization; a sample average approximation method is used to convert the stochastic model into an equivalent deterministic model, and a linearization treatment is performed on nonlinear terms to enhance the solving performance; finally, a two-stage heuristic algorithm is used to solve the proposed mathematical model, and the required optimal scenario number can be selected according to the actual problem. The application realizes the integrated optimization of the recycling and disassembling two-stage problems of waste power batteries in the quality-uncertain environment, and provides a scientific solution for the complex optimization problems in the waste power battery recycling field.

This invention discloses a method for optimizing the dynamic transmission error distribution characteristics of a harmonic reducer, comprising: establishing a static transmission error probability model to obtain the probability distribution of the overall static transmission error; constructing a dynamic transmission error mathematical model considering static transmission error and dynamic parameters; constructing a high-precision surrogate model of dynamic transmission error including the probability distribution of static transmission error and the range of dynamic parameters; obtaining the probability distribution of dynamic transmission error; and dynamically adjusting the range of dynamic parameters based on a particle swarm optimization strategy to find the parameter range that optimizes the dynamic transmission error distribution. By introducing static transmission error into the dynamic model of the harmonic reducer, considering the influence of the processing and assembly of the harmonic reducer on the transmission error, a probability-range hybrid uncertainty model is used to mathematically describe the probability distribution characteristics of static transmission error and the range of dynamic parameters, and a Chebyshev polynomial method is used to construct an approximate model of dynamic transmission error.

The invention discloses a scheduling method and device for a new energy power system containing small hydropower stations, equipment and a medium, and the method comprises the steps: constructing a hydropower space-time matrix model of the system based on a topological structure and water flow motion parameters between nodes of a watershed; substituting the load prediction value and the operation parameter prediction value of each unit into a deterministic model to obtain a reference value of a system optimization target; and according to the uncertainty model, the reference value and the certainty model of the system, constructing an IGDT scheduling model of the system and solving the IGDT scheduling model to obtain an output plan and an optimization target of each unit in the system. By constructing a hydropower space-time matrix model of the system, a topological structure and a water flow motion state between each small hydropower station and a conventional hydropower station are considered when a decision is made, and the accuracy and the reliability of a scheduling result are improved; the influence of water load uncertainty on a scheduling result is quantified through an information gap decision theory, a decision maker provides scheduling strategies under different risks and different effects, and the reliability and economical efficiency of a final scheduling scheme are guaranteed.

User speech or text data is received. A state from a plurality of states of a deterministic model is selected. At least some states are associated with a corresponding portion of a language model (LM)

The application discloses a kind of space-time graph prediction method and system based on diffusion model and space-time feature decoupling.It is determined that the present space-time prediction is in conflict with the problem of optimization of certainty and uncertainty, and a two-part network containing double-flow time-frequency encoder and conditional diffusion model is designed;The double-flow time-frequency encoder extracts the time-domain dynamics and frequency-domain periodic features of the space-time data in parallel through bidirectional Mamba-2 and adaptive spectral block, pre-trains the deterministic model, and obtains the fusion features using the pre-trained network;Its fusion features are injected into the diffusion model as a condition, and a token statistical self-attention mechanism is introduced to capture spatial dependencies and model probability distributions.The application belongs to the field of artificial intelligence and intelligent transportation technology, and can be used for high-precision prediction of urban traffic flow and air quality.

The invention discloses a high-resolution rainfall ensemble forecasting method, system and equipment and a medium. The method comprises the following steps: acquiring a historical atmosphere reanalysis data set and a multi-source fusion rainfall data set; the constructed deterministic neural network learning model is trained to output target duration accumulated average rainfall; training a probability precipitation model constructed by a variational auto-encoder model and a denoising diffusion probability model; acquiring meteorological data at a to-be-predicted moment, and inputting the meteorological data into the deterministic rainfall model to obtain an accumulated average rainfall prediction result within a target duration; sampling from Gaussian distribution to obtain noise, and performing denoising processing in a hidden space by using the trained denoising diffusion model; the denoising result is restored by the trained variational auto-encoder model and added with the output of the deterministic model to obtain the accumulated rainfall of the target time length; and constructing ensemble forecast based on the multiple groups of target duration accumulated rainfall forecast. According to the method, high-resolution rainfall distribution can be generated, the extreme rainfall prediction accuracy is improved, and the uncertainty of rainfall is evaluated.

The invention relates to a high-energy-load park affine optimization scheduling method considering wind and light correlation, and belongs to the field of energy optimization scheduling. The method comprises the following steps: acquiring prediction intervals of output and load demands of wind and light units in a park, constructing an initial parallelogram correlation model, and converting the initial parallelogram correlation model into a multi-constraint affine form model in combination with an improved adaptive polygon algorithm; a particle swarm optimization algorithm is used for dynamically adjusting constraint conditions, redundant intervals are eliminated, and irregular polygon affine constraints fitting the reality are generated; the method comprises the following steps: converting a deterministic model of electrolytic aluminum, energy storage and thermal power generating units into an affine form, and constructing an optimal scheduling model containing power balance, equipment operation and cross-time constraint; and solving by adopting a mixed integer linear programming solver in order to minimize the operation cost. According to the method, by dynamically correcting the wind-light correlation area, the conservative property of the uncertainty interval is reduced, the new energy consumption rate is improved, the starting and stopping cost of the thermal power generating unit is reduced, and collaborative optimization of economical efficiency and reliability is achieved.

The invention discloses a method for calculating the line loss of a high-proportion new energy access power distribution network, and belongs to the technical field of calculation of the line loss of the high-proportion new energy access power distribution network. The calculation method for the line loss of the high-proportion new energy access power distribution network comprises the following steps: S1, carrying out preliminary data acquisition and preprocessing by the calculation method for the line loss of the high-proportion new energy access power distribution network, and providing basic line parameters for all other modules by a module; according to the method for calculating the line loss of the high-proportion new energy access power distribution network, the change characteristics of the line loss of the power electronic power distribution network accessed by the high-proportion distributed power supply and the optimization mechanism are calculated; comprising the aspects of network loss change characteristics and a line loss optimization mechanism under a deterministic model, an uncertainty processing scheme, a line loss and electric energy quality regulation and control method and the like, and a line loss regulation and control scheme based on an evolutionary algorithm is established aiming at the electric energy quality problem caused by high-proportion distributed power supply and nonlinear load grid connection.

The disclosure provides an adaptive mechanism for controlling how digital state variables evolve within a distributed computing environment. In an example, the system maintains a cumulative quantity value stored in persistent, consensus-replicated storage. This value determines a dynamically adjustable linear response function that governs how subsequent state changes are calculated. When an authenticated instruction modifies the cumulative quantity, the system computes a transition amount using a quadratic accumulation process and then updates the response function to remain continuous and stable. The mechanism may maintain an aggregate stored value that increases under repeated state movements, preventing costless oscillation. The architecture operates deterministically, recalibrating from current state without, for example, reliance on external data. The result may provide an efficient, predictable, and scalable approach for managing adaptive state transitions within distributed deterministic systems while limiting value extractable from transaction reordering.

The invention provides a mining and selection collaborative plan intelligent optimization method considering grade uncertainty, and relates to the technical field of strip mine production plan optimization, and the method comprises the steps: introducing a budget parameter for constraining the cumulative deviation of all ore blocks, and calculating grade fluctuation; substituting the grade fluctuation into the deterministic model to obtain a linear mixed integer model; setting a rolling period, solving the linear mixed integer model at each decision moment, and generating a mining plan covering a plurality of days in the future; and in combination with actual grade data fed back by a mineral separation link, a subsequent plan is dynamically adjusted, and a mining plan covering several days in the future is generated again. Based on robust dual conversion, a budget parameter control model is introduced to conservative degree of grade fluctuation, a rolling optimization mechanism is established, and dynamic adjustment of short-term scheduling is realized by combining mineral separation feedback delay; mining and selection collaborative constraints are integrated, mineral processing technology requirements are directly embedded into the optimization model, and flexible balance of robustness and economical efficiency is achieved.

This invention relates to the field of power grid carrying capacity assessment technology, and discloses a method, system, equipment, and medium for assessing the photovoltaic (PV) carrying capacity range of a multi-voltage level distribution network. The method includes: first, constructing a simulation model of the multi-voltage level distribution network and performing power flow calculations; based on this, establishing a deterministic assessment model with the objectives of maximizing PV access capacity and minimizing overall system cost, and further constructing a two-layer robust assessment model considering the uncertainty of PV output; using a particle swarm optimization algorithm to solve the deterministic model to obtain the upper bound of the PV carrying capacity range; constructing multiple typical PV output scenarios using a scenario generation method based on kernel density estimation and Copula theory, transforming the robust model into deterministic sub-models under each scenario for separate solving, and taking the minimum result as the lower bound of the range. This method can provide a quantitative and robust decision-making basis for high-penetration PV access planning.

The invention provides a quality uncertainty-oriented waste power battery recycling and disassembling integrated optimization method, which comprises the following steps of: firstly, taking minimization of total operation cost as an objective function, and considering coupling constraint, recoverable packaging related constraint, vehicle related constraint, demand satisfaction constraint, inventory related constraint, disassembling line related constraint and decision variable value range constraint; constructing a stochastic programming model for recycling-disassembling integrated optimization of the waste power battery; converting the random model into an equivalent deterministic model by adopting a sample average approximation method, and performing linearization processing on a nonlinear term to enhance the solving performance; and finally, solving the proposed mathematical model by adopting a two-stage heuristic algorithm, and selecting a required optimal scene number according to an actual problem. According to the invention, integrated optimization of two stages of recycling and disassembling of the waste power battery in an environment with uncertain quality is realized, and a scientific solution is provided for a complex optimization problem in the field of recycling of the waste power battery.

The present application relates to the field of VPP multi-source collaborative scheduling, and discloses a virtual power plant optimization scheduling method based on information gap decision theory. Historical behavior data of EV, wind power and photovoltaic power generation data are preprocessed; EV travel load data is predicted by using a random sampling algorithm; wind and light output is predicted by using a deep learning time series network; a VPP model with the minimum operation cost as the target is established and solved, serving as a deterministic model of IGDT; a dichotomy method combined with cost margin and multi-start parallel search mechanism is used to find the optimal uncertainty level parameter and the corresponding scheduling scheme under the IGDT risk aversion model; a dichotomy method optimized by introducing chaos mapping and energy level decision mechanism is used to find the optimal uncertainty level parameter and the corresponding scheduling scheme under the IGDT opportunity pursuit model.

The invention discloses a vernier-multiphase clock time-to-digital conversion method. The method comprises the following steps: generating two groups of multiphase clocks with different frequencies; when an initial signal edge arrives, starting a counter Cnt1 and latching the state of the first group of multi-phase clocks; when the stop signal edge arrives, starting a counter Cnt2 and latching the state of the second group of multi-phase clocks; monitoring the edges of the two counting clocks, and stopping counting when the two counting clocks are aligned for the first time; according to the latched phase state, fine counting correction values of the starting signal edge and the stopping signal edge in respective clock periods are calculated; and finally, the accurate time interval is calculated by integrating the rough counting value and the fine counting correction value. According to the invention, through combination of multi-phase clock interpolation and the vernier method, a nonlinear synchronization error in a traditional vernier method is converted into a deterministic system error, and the resolution, absolute precision and linearity of time measurement are significantly improved.

The invention discloses a line loss calculation method for a distributed energy power distribution network, and relates to the technical field of power distribution network line loss calculation, and the method comprises the steps: S1, power distribution network modeling under energy permeability, S2, building a harmonic power flow calculation model, S3, building a power distribution network line loss optimization model solving process, and S4, building a power distribution network multi-objective optimization model. According to the line loss calculation method for the distributed energy power distribution network, aiming at the problem of power distribution network output power fluctuation caused by high-proportion distributed power supply grid connection, a power electronic power distribution network probability model is established on the basis of a deterministic model, and an established uncertainty processing module and a deterministic optimization model are integrated; establishing a power distribution network line loss optimization scheme of high-proportion distributed power supply access; in order to solve the problem that a single-objective optimization model cannot optimize the line loss and the electric energy quality at the same time, a multi-objective optimization model is established, a line loss and electric energy quality optimization scheme and strategy are established on this basis, and the established optimization model and related conclusions are verified based on example simulation.

This invention discloses a multi-objective robust optimization configuration method for integrated energy systems. The method comprises the following steps: First, establishing a multivariate load uncertainty model; then, establishing a multi-objective robust bi-layer joint optimization configuration model for the integrated energy system: the upper-layer model is the master model, and the lower-layer model is the sub-model. Through iterative optimization of the upper and lower layers, the optimal configuration strategy for the integrated energy system is obtained; finally, an intelligent optimization algorithm is used to solve the problem. For the multivariate load uncertainty model of the lower-layer model, a multi-scenario technique and a minimum-maximum regret criterion are used for joint solution, ultimately obtaining the Pareto solution set of the multi-objective robust configuration scheme for the integrated energy system. This invention proposes a multi-objective robust bi-layer joint optimization configuration method for integrated energy systems, which can provide optimized configuration schemes for integrated energy systems, improve energy utilization efficiency, reduce total costs during the planning period, and reduce pollutant emissions.

The present application provides a kind of for image registration based on neighborhood information and greedy search deterministic model fitting method, comprising: S1, the key point and descriptor of given image pair are extracted, and initial matching set is constructed according to descriptor similarity;S2, high probability inlier set is identified and obtained;S3, neighborhood set is constructed for each inlier;S4, the best model hypothesis is generated using the hypothesis optimization strategy based on greedy search;S5, the noise scale of best model hypothesis and inlier set I are obtained;S6, if the data volume of temporary inlier set is less than k, then it is equal to inlier set;S7, the residual vector between initial matching set and best model hypothesis is calculated, and affine matrix H is calculated;S8, if current sampling times is less than η, then return S4;Otherwise, S9 is executed;S9, distance matrix A is calculated by H and A is sparsified to obtain G;S10, cluster label is obtained using spectral clustering on G;S11, if only one model instance is contained in data, then label is updated by post-processing step.

The invention discloses a power distribution network distributed photovoltaic plannable capacity interval evaluation method considering soft switching fault interference factors, and relates to the technical field of power distribution network planning and operation. According to the method, for an SOP structure integrating energy storage, an E-SOP model is established, and the uncertainty of unplanned off-network of the E-SOP model is described. And then, respectively depicting the most optimistic value and the most pessimistic value of the installable capacity through a deterministic model and a double-layer robust optimization model so as to form plannable capacity interval evaluation of the distributed PV. The deterministic model is a linear programming problem and can be directly solved by a solver; the double-layer robust optimization model is converted into a mixed integer linear programming problem through a KKT optimization condition so as to be solved. Compared with a traditional method, the method has the advantages that the applicability is higher, the dependence on the data quality and the uncertainty description accuracy degree is low, and a reliable evaluation result can be obtained.

The invention discloses an electricity-gas energy system day-ahead scheduling method based on a natural gas quasi-steady-state model. The method comprises the following steps: step 1, collecting predicted output data of an electric power system, a natural gas system and wind power; 2, building a deterministic model of the electricity-gas integrated energy system; constructing an objective function and constraint conditions of the electricity-gas integrated energy system, and performing piecewise linearization on nonlinear terms existing in the model; 3, a two-stage robust optimization model establishment stage; step 4, Camp is adopted; a CG algorithm solving stage; and through alternate iteration of the main problem and the sub-problems, identifying the worst scene, and adding different constraints into the main problem according to whether the target function of the sub-problems is infinitely classified, so that the upper and lower bounds are continuously close to each other, and finally a target gap is achieved, and a result is obtained.

This invention provides a method, system, and device for detecting and locating leaks in water supply pipes based on a probabilistic model and nonlinear filtering. The method includes: acquiring information about the water supply pipeline, such as the length, diameter, friction, and elastic modulus of each pipe; constructing a pure hydraulic model based on the water supply network information; solving the continuity and momentum equations of the water supply network using the method of characteristics; establishing a probabilistic multiple leakage model based on the circumferential stress and yield stress of the pipes, combined with the solution of the differential equation system, to determine the location and quantity of leaks in the pipeline; and using nonlinear filtering to estimate the state space in a noisy environment to achieve real-time correction and prediction of leaks. This invention considers the situation of multiple leaks in the water supply network and the water pressure difference between horizontal and non-horizontal pipes. Starting from a pure hydraulic model, it effectively solves the original partial differential equation system using the method of characteristics, effectively reducing the high false alarm rate problem of deterministic models.

A safety device is configured to detect a hazardous situation in an industrial machine or plant. The safety device includes: at least one sensor for detecting parameters associated with the hazardous situation, a transmitter for safely transmitting a safety signal, and a processor having a deterministic portion for processing at least a first part of the parameters in accordance with a deterministic model and a probabilistic portion for processing at least a second part of the parameters in accordance with a probabilistic model.

A real-time artificial intelligence and / or machine learning (AI / ML) system for an aircraft can include an AI / ML module configured to receive one or more inputs and to calculate an AI / ML control output. The AI / ML module can include a non-deterministic model for processing the inputs and outputting the AI / ML control output. The system can include a deterministic module configured to receive one or more inputs and the AI / ML control output from the AI / ML module. The deterministic module can include a deterministic model for processing the inputs and / or AI / ML control output to calculate a deterministic condition. The deterministic module can be configured to check the AI / ML control output against the deterministic condition to determine whether to output the AI / ML control output.

The invention provides a security device and a security method. The safety device (1) is configured to detect a hazardous situation in an industrial machine or plant (100) and comprises: sensor means (11) for detecting a parameter (K) associated with the hazardous situation; a transmission device (12) for securely transmitting a security signal (S); a processing unit (13) having a deterministic part (131) for processing at least a first part (K1) of the parameter (K) according to a deterministic model and a probabilistic part (132) for processing at least a second part (K2) of the parameter (K) according to a probabilistic model.

The application discloses an unmanned ship trajectory tracking method based on a flow matching learning dynamics model, which comprises the following steps: generating a large amount of virtual trajectory data sets on a physical simulation platform; cutting the data sets into time sequence samples and inputting the time sequence samples into a flow matching dynamics network to train a continuous probability flow objective function by minimizing the difference between a predicted velocity field vector and a target velocity field vector; performing parallel inference by using the trained flow matching dynamics network to generate a multi-modal prediction trajectory set covering future various environmental uncertainties, and then integrating the multi-modal prediction trajectory set into a comprehensive cost evaluation system to perform cost integral evaluation and rollingly output optimal physical control instructions. The flow matching dynamics network accurately depicts the multi-modal probability distribution of trajectory evolution under complex wind and wave disturbance, completely breaks through the limitation that a traditional deterministic model cannot cope with high random sea conditions by means of 'point estimation', and enables the unmanned ship to maintain stable trajectory tracking control even when facing extreme harsh working conditions outside the distribution.

The invention relates to the technical field of industrial process quality control, in particular to a process industry oriented quality prediction method with time-varying random fluctuation dynamics. A traditional quality prediction method has the following technical bottlenecks: a deterministic model or a static fluctuation rate hypothesis is difficult to accurately describe time-varying random dynamics caused by raw material characteristic fluctuation and frequent production load adjustment; meanwhile, a traditional data processing method based on interpolation or deletion easily introduces estimation deviation or causes information loss, and the random missing phenomenon in process data cannot be effectively handled. The invention provides a novel quality prediction method aiming at the characteristics of frequent load change, multi-working-condition operation and the like in the process industry and the problems of multi-scale data and high mixing performance. Firstly, a probabilistic fluctuation state modeling method is put forward, a state equation of a PCIR model is constructed, time-varying mean regression characteristics of a hidden fluctuation state are described through a special probability structure, and an interpretable modeling framework is provided for system non-stationarity. Secondly, for the problem of data missing, a missing interval dynamic weight mechanism is designed, a weight vector is fused into an observation equation of a state space model, adaptive processing of random missing is achieved by dynamically adjusting the contribution degree of data of different missing durations to the model, and the limitation of a traditional method is overcome. Finally, according to the multi-working-condition operation characteristics of the process industry, a quality prediction framework suitable for the process is provided, and automatic screening of key state characteristics can be achieved.