11 results about "Grey systems theory" patented technology

The embodiment of the invention discloses a GBIC-based multi-electric aircraft starting power generation system DBN model lightweight method, and relates to the technical field of reliability design and modeling of an equipment complex system.The GBIC-based multi-electric aircraft starting power generation system DBN model lightweight method comprises the steps that a DBN model of a starting power generation system is established, nodes in the DBN model correspond to key components and working states of the starting power generation system, and the nodes in the DBN model correspond to the key components and the working states of the starting power generation system; directed edges in the DBN model are used for describing the mutual relation between the nodes, a redundant structure in the DBN model is recognized, lightweight processing is carried out, and the fault state of the starting power generation system is recognized through the DBN model subjected to the lightweight processing. Aiming at the problems of complex reasoning calculation and low efficiency in the use process of the dynamic Bayesian analysis method of the equipment complex polymorphic system, the grey system theory and the Bayesian information criterion are introduced, the lightweight of the dynamic Bayesian network of the complex polymorphic system is realized, the redundant structure is eliminated, and the accuracy of the result is ensured.

The invention provides a depression prediction method of a grey prediction model and a related product. The depression prediction method comprises the following steps: acquiring a grey prediction model based on a grey system theory; acquiring multiple groups of historical diagnosis data and current diagnosis data of the target user; substituting multiple groups of historical diagnosis data and current diagnosis data of the target user into the grey prediction model, and calculating a prediction error of the grey prediction model; and in response to the prediction error meeting the preset requirement, predicting the future depression degree score of the target user according to the multiple groups of historical diagnosis data of the target user, the current diagnosis data of the target user and the gray prediction model. The method achieves the accurate prediction of the future depression degree change of the target user through the gray prediction model, and achieves the purpose of providing a diagnosis basis for the early intervention or the timely adjustment of the intervention method to achieve the optimal treatment effect.

The invention discloses a corona distribution prediction method for optimizing a gray model background value based on a Romberg algorithm, and the method comprises the steps: inputting corona data as an original sequence, and generating a first-order accumulation sequence through first-order accumulation according to a gray system theoretical model; establishing a grey differential equation and a whitening differential equation according to the grey system theoretical model; optimizing a background value formula in the grey system theoretical model by utilizing a Romberg formula; estimating model parameters through a least square method to obtain an original prediction sequence; dynamically correcting the original prediction sequence to obtain an optimized grey system theoretical model; and inputting corona data into the optimized grey system theoretical model to obtain a prediction result of corona distribution. According to the method, the background value of the traditional grey system theoretical model is optimized by utilizing the idea of variable step interpolation of the Romberg algorithm function, and the prediction sequence is dynamically corrected in combination with the residual model, so that the prediction model has the characteristic of achieving accurate prediction through a small amount of data.

The invention relates to the field of zero-carbon energy, and discloses a zero-carbon integrated energy system and an optimization method of the zero-carbon integrated energy system.The optimization method of the zero-carbon integrated energy system comprises the steps that a historical dependency relationship is accurately captured by constructing a multi-scale prediction model fusing fractional calculus and a grey system theory; decomposing the system into multi-scale optimization sub-problems by adopting wavelet transform, and establishing a collaborative optimization objective function system; performing global optimization solution by using a fractional gradient descent algorithm to ensure stable operation of the system; and a complete closed-loop management system is formed through an efficiency verification system and a parameter adaptive correction mechanism. According to the invention, autonomous optimization and continuous improvement of the system are realized, and the operation efficiency and stability of the zero-carbon comprehensive energy system are improved.

The invention relates to the technical field of deformation monitoring, in particular to a space-time self-learning early warning system for foundation settlement risks in the highway construction process, and the system comprises an information processing module, a weight construction module, a tensor analysis module, a risk calculation module and an early warning release module. According to the method, the distribution density is evaluated through the spatial position vector and the Gaussian kernel function, the coverage weight is generated, local deviation caused by uneven network distribution is eliminated, balanced expression of regional settlement characteristics is ensured, a lagging interval is deduced through the soil consolidation theory, and deformation direction consistency screening is executed. Environmental noise is eliminated to lock effective cumulative settlement caused by a specific process, a prediction model with self-adaptive capability is constructed in combination with a grey system theory, the dynamic growth rate of a settlement cumulative value is tracked in real time, conversion from static management and control to dynamic trend early warning is realized, the sensitivity of foundation instability precursor capture is improved, and the stability of a foundation is improved. And a scientific basis is provided for construction procedure adjustment.

The application relates to a kind of vehicle engine system electromagnetic vulnerability evaluation methods, belong to electromagnetic environment effect evaluation field.Establish the engine system bayesian network fault model, get the triangular fuzzy number corresponding to the fuzzy expression of the failure correlation between each node in the bayesian network fault model through semantic contrast table, calculate fuzzy conditional probability, convert it into interval grey number conditional probability;Determine electromagnetic stress probability density function and component sensitivity threshold probability density function, calculate component layer interval grey number failure probability, and calculate engine system interval grey number failure probability in combination with interval grey number conditional probability.Affirmative effect is: determine the weight of each sensor through fuzzy analytic hierarchy process, solve the shortcoming that traditional analytic hierarchy process is more subjective, combine grey system theory and fuzzy theory with bayesian network, can more accurately express the uncertain information in the whole evaluation process and carry out failure probability calculation.

The present application relates to the technical field of deformation monitoring, in particular to a time-space self-learning early warning system for foundation settlement risk in highway construction process, which comprises an information processing module, a weight construction module, a tensor analysis module, a risk calculation module and an early warning release module. In the present application, the spatial position vector and the Gaussian kernel function are used to evaluate the distribution density and generate the coverage weight, which eliminates the local deviation caused by uneven distribution and ensures the balanced expression of regional settlement characteristics. The lag interval is deduced by using the soil consolidation theory and the deformation direction consistency is screened, the environmental noise is eliminated to lock the effective cumulative settlement caused by specific process, the prediction model with self-adaptive ability is constructed by combining the grey system theory, the dynamic growth rate of the settlement cumulative value is tracked in real time, the change from static control to dynamic trend warning is realized, and the sensitivity of capturing the precursor of foundation instability is improved, which provides a scientific basis for construction process adjustment.

The invention relates to the technical field of temperature distribution monitoring, and provides a glacier internal temperature distribution monitoring system based on a temperature chain sensor, which comprises a ground penetrating radar, a micro base station, a central processing module and a wireless temperature chain sensing module. And summarizing coordinate temperature chains output by the wireless temperature chain sensing module into glacier three-dimensional temperature point cloud, optimizing the three-dimensional Gaussian radial basis fusion global polynomial interpolation model by using an interpolation parameter genetic algorithm to obtain a target adjustable parameter group, and inputting the target adjustable parameter group into the three-dimensional Gaussian radial basis fusion global polynomial interpolation model to obtain the glacier three-dimensional temperature point cloud. After a target adjustable parameter group is obtained by the three-dimensional Gaussian radial basis fusion global polynomial interpolation model, interpolation is carried out on the glacier three-dimensional temperature point cloud to generate a target glacier three-dimensional temperature model; according to the invention, the grey system theory, the ground penetrating radar feature extraction technology and the distributed sensor technology are combined to realize real-time monitoring of glacier internal temperature distribution.

The embodiment of the application discloses a DBN model lightweight method of a multiple-electric aircraft starting power generation system based on GBIC, relates to the technical field of reliability design and modeling of complex equipment systems, and comprises the following steps: a DBN model of a starting power generation system is established, nodes in the DBN model correspond to key components and working states of the starting power generation system, directed edges in the DBN model are used for describing mutual relations between nodes, redundant structures in the DBN model are identified and lightweight processing is performed, and a fault state of the starting power generation system is identified by using the DBN model subjected to the lightweight processing.The embodiment of the application introduces a grey system theory and a Bayesian information criterion aiming at the problems of complex and low efficiency of reasoning calculation in the process of a dynamic Bayesian analysis method of a complex polymorphic system, realizes lightweight of the dynamic Bayesian network of the complex polymorphic system, eliminates redundant structures, and guarantees the accuracy of results.

The present application provides an optimization method and system for reinforced concrete support design, wherein a multi-dimensional analysis of the load expected to be borne by a building is performed to determine the key stress points in the building structure; based on the key stress points, the behavioral characteristics of reinforced concrete materials at different building structural levels are simulated to generate a multi-level mechanical model; based on the multi-level mechanical model and data obtained from monitoring the building structure, an intelligent decision-making process is established using genetic algorithms, particle swarm optimization, and reinforcement learning frameworks to generate a reinforced concrete support design scheme; a comprehensive evaluation system is established using Bayesian networks and gray system theory to quantify the reinforced concrete support design scheme, obtain feedback information, and use the feedback information to optimize the reinforced concrete support design scheme to generate an optimized reinforced concrete support design scheme. The present application improves the scientificity and rationality of the reinforced concrete support design scheme.

The application discloses a shale gas drilling speed increasing method and system for rock drillability prediction, and the method comprises the following steps: collecting geological and rock mechanics parameters of a target stratum, establishing a rock drillability quantitative evaluation model based on a grey system theory analysis method; based on the rock drillability quantitative evaluation model and a PDC drill bit cutting tooth force balance equation, a mechanical interaction model of a PDC drill bit cutting tooth with rock containing a chamfer is established, and the mechanical interaction model is corrected through finite element analysis combined with experimental data; a rotating impact experiment is conducted based on a homogeneous isotropic rock sample, basic mechanical parameters of the rock are recorded, and the influence of different impact parameters on the rock breaking process is tested; and based on the corrected mechanical interaction model and the rotating impact experimental data, the best impact load and impact frequency range are determined to improve the shale gas drilling speed. The application can effectively reduce the unit rock breaking specific work and improve the shale gas drilling efficiency, and has wide engineering applicability.