59 results about "Deterministic system" patented technology

The invention relates to the technical field of industrial process control, and discloses a digital twinning-based fermentation process temperature adaptive control system, which comprises a data acquisition and state representation module, a probability generation type digital twinning module, an adaptive risk management module, an opportunity constraint model prediction control module and a model iteration trigger module, according to the method, probability distribution of future states is predicted by constructing probability generation type digital twinning, and the uncertainty of the model is quantified in real time; the system can dynamically adjust the risk tolerance, drives the chance constraint model prediction controller, seeks an optimal control solution on the premise of ensuring the process safety probability, triggers the model adaptive update by means of an online evaluation mechanism, and realizes the optimal control solution on the basis of effectively managing the process uncertainty. The fermentation temperature is subjected to high-performance self-adaptive control considering safety and economy, and the problem that a traditional deterministic model is insufficient in control robustness is solved.

A computer-implemented method of controlling an output from a dialogue system, the method comprising:receiving, by way of an input, first input data relating to speech or text provided by a user;selecting a first state from a plurality of states of a deterministic model, at least some states of the plurality of states being associated with a corresponding portion of a language model prompt including an instruction to call a corresponding function;responsive to the first state being associated with a corresponding portion, generating a first language model prompt comprising at least part of the corresponding portion associated with the selected first state;providing the first language model prompt as input to a language model to generate a first language model output;determining whether to execute a function based on the first language model output;responsive to determining to execute a first function based on the first language model output, executing the determined first function to generate a first function output;selecting a second state from the plurality of states based on the first function output;responsive to the second state being associated with a corresponding portion of a language model prompt, generating a second language model prompt comprising at least part of the corresponding portion associated with the selected second state;providing the second language model prompt as input to the language model to generate a second language model output;determining whether to provide an output to the user based on the second language model output; andresponsive to determining to provide an output to the user based on the second language model output, outputting, by way of an output, speech or text to the user.

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 relates to the technical field of scene modeling, and solves the problems of multi-source heterogeneous data fusion distortion, uncertainty expression deficiency and insufficient static model self-adaption. According to the core scheme, physical sensing data and subjective description data are processed in a unified mode through a differential fuzzy processing mechanism (continuous quantities are mapped through trapezoidal membership functions, and discrete quantities are mapped through semantic rules); a dynamic weight superposition strategy is adopted, data collaboration is achieved based on a spatial incidence matrix, and dynamic optimization of an additional aging attenuation factor is achieved; synchronously outputting a deterministic model framework and a fuzzy boundary thermodynamic diagram during scene reconstruction; and establishing a real-time feedback closed loop optimization membership function parameter and weight strategy. The innovation points are as follows: semantic distortion is eliminated by a confidence conversion mechanism, an adaptive superposition operator switches an operation mode according to a confidence threshold, and three-dimensional semitransparent rendering visualization uncertainty distribution is realized. The method has the advantages that the multi-source fusion semantic distortion rate is reduced by 62%, the disaster early warning accuracy rate is improved by 55%, and the continuous operation precision attenuation rate of the model is controlled below 3%.

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.

Described herein are techniques for stacking machine learning models to better capture deterministic relations in a dataset. In some instances, a first machine learning model may not be capable of capturing all of the deterministic relations in a dataset due to the limitations of the model. Supplemental models may be trained so that the corrections generated by the supplemental models, when combined with the first machine learning model, perform better at capturing the deterministic models in the dataset. Techniques are described for training supplemental models to capture deterministic relations associated with ordinal data and nominal data and continuous data.

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.

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 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.

A system and method for stochastic optimization of manufacturing and supply chain processes are disclosed. The system comprises a processor and memory configured to receive a plurality of tasks associated with a process, along with data items for each task. The data is processed through various modeling modules, including yield, cycle time, throughput, on-time delivery, cost, and financial results modeling modules. The system analyzes the data to determine metrics and provides recommendations for optimization. The method includes steps for determining statistical distributions, identifying bottlenecks, calculating cycle times, and refining estimates to improve process efficiency. The system outputs metrics and recommendations via a user interface, enabling proactive management and optimization of manufacturing and supply chain metrics. This approach addresses the limitations of traditional heuristic and deterministic models by providing a comprehensive, integrated solution for process optimization.

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 application relates to a method and system for evaluating the degree of defects such as voids and debonding of a steel pipe concrete structure, which comprises the following steps: acquiring test detection data and engineering parameters of a steel pipe concrete arch bridge construction, wherein the detection data and engineering parameters comprise waveform and spectrum diagrams, ultrasonic wave velocity V, concrete 28d compressive strength f c , age t, void rate delta; qualitatively analyzing the defect type according to the waveform and spectrum diagram information, inputting the detection data and engineering parameters into a deterministic model to generate deterministic model parameters, and then inputting the detection data, engineering parameters and model parameters into a pre-built model; the built model is obtained by iteratively optimizing the model using an interactive parallel multi-process algorithm; the interactive model outputs results as dense, void and debonding according to the to-be-detected data, and the degree of voids and debonding is obtained through model inversion, which can further guide the treatment of voids and debonding grouting in the construction process; the method is simple in steps, convenient to operate and good in effect.

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.

A computer-implemented method includes receiving an input for a deterministic model, and determining whether the input and an output from the model corresponding to the input are stored in a cache. The method further includes, in accordance with the input and the output not being stored in the cache, computing the output from the model based on the input, storing the input and the output as a cache element, and storing raw scores respectively indicating a usage frequency and order of usage of the element, and a computation time for computing the output. The method further includes calculating a caching score for the element; the caching score includes a sum of a normalized scores each corresponding to a product of a raw score and a scaling factor.

The invention relates to the technical field of spacecrafts, in particular to an electric propulsion fault analysis method and system based on system simulation, and the method comprises the steps: classifying, organizing and storing test data of an electric propulsion vacuum bin, managing the test data through a database, carrying out null cleaning on the data, filtering the data, dividing working conditions, carrying out statistical analysis, constructing features, and carrying out dimensionality reduction. The method comprises the steps of building a system simulation model, building an anomaly detection model, mining threshold rules, building a trend prediction model and building a comprehensive judgment model. According to the method, a deterministic model in a design process, a ground ignition test and a large amount of on-orbit telemetry data are utilized, real-time accurate interpretation and state evaluation of on-orbit data are realized through logic simulation operation, and a potential fault mode is effectively identified. Compared with a traditional electric propulsion on-orbit state evaluation and fault diagnosis method, the electric propulsion on-orbit state evaluation and fault diagnosis method has higher accuracy and efficiency.

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 application discloses an automatic wharf import container bay allocation method embedded with deep reinforcement learning. The method comprises the following steps: firstly, for the container picking area, a target function is established based on the total processing time of the batch container and the total time of turning over all future containers; then, the established mathematical model is subjected to robust equivalence conversion, the uncertainty model is converted into a deterministic model, and a constraint of the bay layout selection is newly added; subsequently, the possible layout of the bay is counted and simplified, so that the number of constraint conditions in the model is greatly reduced; the upper limit value of the container turning in the target function after the robust equivalence conversion is solved by the deep reinforcement learning method; and finally, a container allocation scheme is obtained by using a commercial solver. On the basis of significantly improving the solution efficiency of the container allocation strategy, the application ensures that the wharf has higher stability and reliability when facing the import container operation with high uncertainty, and provides strong decision support for the wharf operation management.

The invention relates to the technical field of unmanned vehicle control, and discloses a speed control method and device for an unmanned vehicle and the unmanned vehicle, and the method comprises the steps: obtaining the multi-mode sensor data of the surrounding environment of the vehicle; constructing a dynamic vortex field model and Brownian motion parameter mapping relation based on the sensor data; dynamically adjusting a sensor acquisition strategy according to the vortex field intensity and compressing a prediction time domain; constructing a combined control equation of the coupled vortex field and Brownian motion parameters; and a speed control instruction is generated through variational optimization solution. According to the technical scheme of fusing the hydromechanics vortex field theory and Brownian motion random mapping, unified modeling of traffic flow microscopic disturbance and environment uncertainty is achieved, and the defect that a deterministic model is difficult to adapt to dynamic changes of complex traffic scenes is effectively overcome; therefore, the speed control of the unmanned vehicle has the physical interpretable response capability to sudden disturbance.

The application provides a new energy power system interval economic dispatching method based on a security limit definition method, relates to the power grid dispatching technical field, and comprises the following steps: S1, constructing an interval economic dispatching model, including an objective function and constraint conditions; S2, expressing line power flow by using power distribution factors and node injection power; S3, solving interval power flow in the interval economic dispatching model to determine a power flow interval radius; and S4, converting the interval economic dispatching model into a deterministic model by using a security limit definition and solving a dispatching plan, so that the technical problem that an existing interval dispatching method cannot simultaneously satisfy system power balance and line power flow safety constraints when large-scale new energy is accessed is solved.