5results about How to "Enables dynamic evaluation" patented technology

The invention provides a new energy storage consumption capability index prediction method, and relates to the field of power system operation, and the method comprises the steps: building a nonlinear mapping relation between solar radiation intensity, photovoltaic module conversion efficiency, a temperature coefficient, an aging attenuation rate factor and power generation efficiency for photovoltaic power generation, so as to dynamically correct a photovoltaic power generation potential output prediction value; based on a potential output prediction result of photovoltaic and wind power generation, solving an energy storage capacity configuration and operation strategy through an intelligent optimization algorithm to maximize the benefit of an energy storage system, and obtaining a dynamic prediction value of an energy storage consumption capability index; and combining prediction results of the statistical model based on the historical data, the dynamic model based on the physical mechanism and the data-driven model based on machine learning by adopting an adaptive weighted fusion strategy. According to the method, accurate prediction of potential output of new energy power generation and dynamic evaluation of the energy storage absorption capability are realized, and effective technical support is provided for new energy grid connection and optimal configuration of an energy storage system.

The invention relates to an environment energy management optimization method and system for a tag chip, and the method comprises the steps: obtaining the temperature data and environment electromagnetic data of a chip environment, and carrying out the spatial distribution analysis, and obtaining the corresponding environment parameter characteristics; acquiring real-time power consumption data of the tag chip, and performing correlation analysis on the real-time power consumption data and the environmental parameter characteristics to obtain corresponding energy consumption information; acquiring chip circuit acquisition data of the tag chip, and performing resonance frequency analysis on the chip circuit acquisition data according to a preset resonance optimization algorithm; performing fault diagnosis on the chip circuit acquisition data to obtain corresponding system state information and a corresponding system operation prediction scheme; and performing strategy optimization on the initial energy scheduling scheme according to the system operation prediction scheme to obtain a corresponding global optimization strategy. According to the invention, the system can flexibly cope with energy demand changes of different application scenes.

The invention relates to a deep-valley large-span bridge suspension pouring construction safety risk dynamic assessment method. The method comprises the following steps: S1, constructing a suspension pouring construction stage subjective and objective fusion analysis database; s2, preliminarily establishing a Bayesian network model, a network structure and network parameters, and carrying out adaptability optimization during target bridge risk assessment; s3, constructing a Bayesian network model adaptability module; s4, constructing a dynamic assessment guidance-time module, and establishing a risk dynamic assessment model without evidence guidance; s5, constructing a dynamic assessment guidance-evidence module, and establishing a risk dynamic assessment model with evidence guidance; according to the method, comprehensive safety evaluation of comprehensively considering the risk occurrence probability and the risk loss is realized, and the objectivity of safety evaluation is ensured; according to the method, the dynamic assessment of the risk is realized, so that the security risk assessment process is closely combined with the actual site construction, the reliability and accuracy of the assessment result are ensured, and finally a management basis is provided for a decision maker through a highly visual result.

The invention relates to the technical field of civil and mining engineering, and discloses a roadway surrounding rock mechanical parameter zoning dynamic inversion method, which comprises the following steps: S1, acquiring roadway section shape and roadway surrounding rock grade data, determining a surrounding rock inversion boundary and an inversion moment according to the roadway surrounding rock grade and section shape, and carrying out an in-situ test in the inversion boundary to obtain an inversion result; acquiring ground stress test data and surrounding rock strain monitoring data at an inversion moment; and S2, drawing a strain-hole depth fitting curve according to the strain monitoring data at the inversion moment, determining the characteristic depth of the surrounding rock, dividing a surrounding rock inversion region, and establishing an inversion calculation model. According to the method, the current inversion region range and the softening damage coefficient are inverted, so that the surrounding rock mechanical parameters in each inversion region are determined, zoned dynamic acquisition of the tunnel surrounding rock mechanical parameters is realized, and surrounding rock mechanical parameter evolution of different regions of the tunnel surrounding rock can be timely and accurately mastered; and thus, dynamic evaluation of roadway stability and scientific decision-making of support can be realized.

This invention relates to the field of distribution transformer technology and discloses an intelligent monitoring device and control method for distribution transformers, comprising: acquiring infrared thermal images of radiators, extracting brightness periodic features through spectrum analysis to determine fin pitch, and determining the effective height according to the target area size; preprocessing the thermal image brightness values, calculating the transverse and longitudinal second-order structure functions within the fin pitch-related scale range and fitting scaling exponents to obtain the span-to-span structure function exponent difference, and outputting the exponent difference estimate end-to-end through a convolutional neural network containing a structure function calculation layer; converting the exponent difference estimate into an allowable load tightening coefficient through monotonic mapping; calculating the nominal allowable load by combining operating parameters and a hot spot temperature rise model, and correcting it with the coefficient to obtain the actual allowable load; comparing the current operating load with the actual allowable load, and automatically issuing contact point and load adjustment control commands when exceeding the limit, thereby realizing adaptive load limiting control based on image data.