330 results about "Attenuation curve" patented technology

The invention provides a digital twinborn-based park pipe network monitoring and early warning method and system, and relates to the technical field of pipe network monitoring and early warning, and the method comprises the steps: constructing a park pipe network domain knowledge graph, collecting real-time monitoring data and historical operation and maintenance data, and forming a knowledge enhanced pipe network data set; extracting pipe network performance attenuation characteristics by using a bidirectional gating recurrent neural network, and deeply fusing the pipe network performance attenuation characteristics with expert empirical rules in the domain knowledge graph to construct a performance attenuation analysis model; a performance degradation curve and remaining useful life are calculated based on the model, potential fault risks are predicted, and maintenance suggestions are generated. According to the invention, precise evaluation of pipe network performance degradation, early warning of fault risks and intelligent generation of maintenance suggestions are realized, and the intelligent level and reliability of operation and maintenance of the park pipe network are improved.

The invention discloses a wind power blade defect data automatic acquisition and analysis method based on an unmanned aerial vehicle, and particularly relates to the technical field of data acquisition and analysis. The method comprises the following steps: collecting blade surface heat distribution data, locally applying a heat source to a blade, collecting a temperature attenuation curve, calculating a heat diffusivity gradient difference value and a temperature attenuation time index, integrating a multi-parameter space, and classifying a heat distribution abnormal region into a normal region and a real defect region in combination with a deep learning model; cross validation is carried out through optical images and ultrasonic data, so that the detection accuracy is improved; dynamically adjusting a sensitivity threshold value and model parameters of the thermal imager according to a classification result, and generating a detection report; through data feedback iterative optimization model and task planning, the problems of misjudgment and missed judgment of the thermal infrared imager in complex material distribution are effectively solved, the accuracy, reliability and automation level of wind power blade defect detection are improved, and important support is provided for efficient wind power operation and maintenance.

The invention belongs to the technical field of AI large model evaluation, and particularly relates to an AI large model performance and risk automatic evaluation platform and method fusing multiple normal forms. According to the AI large model performance and risk automatic evaluation method fusing multiple normal forms, multi-dimensional evaluation and accurate repair of the AI large model in a complex scene are achieved, the performance index of the model is considered, the safety index and the reliability index are introduced, and therefore the actual situation of the model can be reflected more comprehensively, and the risk of the AI large model can be evaluated more accurately. By implementing a progressive pressure test and monitoring a performance attenuation curve and a failure critical point of the model in real time, potential problems of the model can be found in time, major faults in practical application are avoided, meanwhile, fragile links of the target model can be accurately positioned through backtracking analysis and iterative repair, iterative repair is performed according to a repair priority, and the fault detection accuracy of the target model is improved. Therefore, the stability and reliability of the model are effectively improved.

The invention relates to the technical field of material testing, and discloses a coating performance simulation method based on high and low temperature circulation and prestress loading. The method comprises the following steps: constructing a coating matrix composite geometric model containing a microporous structure by acquiring actually measured working condition data of a pipeline or a part, and quantifying surface roughness and interface curvature characteristics in combination with reverse engineering; a dynamic temperature-pressure coupling equation is generated based on the thermal expansion effect of a high-pressure medium, a micropore optical load is converted into equivalent heat flux density through a ray tracing algorithm, transient boundary conditions of a temperature field and a stress field are associated, a crack initiation path is simulated through an extended finite element method, and a life attenuation curve is predicted in combination with a Paris formula. And material parameters are dynamically corrected based on experimental data, and a multi-parameter collaborative optimization model is generated. According to the method, the problems of interface stress evaluation distortion and life prediction deviation under the combined load are solved, and multi-physics field co-simulation support is provided for coating compression resistance optimization.

The invention discloses an urban rainwater pipe network blockage risk early warning method and system based on edge calculation, and relates to the technical field of urban drainage monitoring. The problems that existing pipe network blockage detection lags behind, and the early warning precision is insufficient are solved. Dynamic hydraulic parameters and sediment migration state data of a pipe section are collected in real time through edge calculation equipment deployed at a pipe network node, the hydraulic state deviation rate is calculated, and the local blockage risk is rapidly recognized; when the deviation rate exceeds a threshold value, a dynamic sensing network is established by the trigger nodes, and a pipe network hydraulic topological relation is established by integrating the liquid level and flow velocity characteristics of the upstream and downstream nodes; executing distributed collaborative analysis based on the topological relation, identifying an abnormal attenuation area, calculating a sediment dynamic equilibrium index, and generating a blockage diagnosis parameter; and further combining real-time rainfall intensity to predict an overflowing capacity attenuation curve, generating a multi-stage early warning instruction according to an attenuation slope, and distributing the multi-stage early warning instruction to an operation and maintenance terminal, thereby realizing accurate early warning and active regulation and control of the blockage risk of the rainwater pipe network.

The invention provides an automatic intelligent defect analysis method and system based on power transmission and distribution line unmanned aerial vehicle image acquisition. The method comprises the following steps: acquiring infrared thermal imaging time sequence data and multispectral imaging data of the surface of a wire through an unmanned aerial vehicle carrying an infrared thermal imager and a multispectral camera; calculating a temperature gradient direction of a conductor main body area, screening an abnormal hot spot area whose axial included angle with the conductor is smaller than a preset angle, and positioning an oxidation area boundary by using a spectral absorption attenuation curve of an oxidation layer in the multispectral data; performing space matching on the hot spot migration direction and the oxidation boundary to generate a visual map; establishing a dynamic association rule according to an included angle variable quantity of a hot spot path in the map and a spectrum abrupt change point in the oxidation region; according to the critical angle threshold value and the sudden change point number threshold value, the defect grade is judged, and a defect analysis report is generated. According to the technical scheme provided by the invention, the automation degree and accuracy of defect detection are improved.

The embodiment of the invention provides an intelligent ward digital management method and system based on digital twinning. A ward digital twinborn body is constructed, a sensor array is used for collecting sickbed area heat radiation distribution, operation area airflow mode and medical equipment energy pulse data in real time, and field intensity superposition is carried out on airflow data in combination with a nursing track to form a dynamic airflow model. An energy pulse phase abrupt change point and a thermal radiation attenuation curve are analyzed by establishing an energy interference spectrum, and a negentropy compensation strategy is generated. When a medical equipment start-stop event is detected, topological distortion caused by a space overlapping area of dynamic airflow and a transfer path is predicted, vortex density distribution is reconstructed, a dynamic balance threshold value of an entropy increase gradient and a negentropy shunting rate is calculated based on updated parameters, and finally an intelligent regulation and control instruction set of a thermal stability coefficient and airflow purification efficiency is output. According to the technical scheme provided by the embodiment of the invention, the ward environment regulation and control precision and response speed are remarkably improved.

The invention relates to the field of battery monitoring and early warning, in particular to a battery health monitoring and early warning method and system. The method comprises the following steps: acquiring a historical charging and discharging monitoring log of a target battery; performing sliding window rolling charging and discharging identification on the historical charging and discharging monitoring log, and performing multi-level time sequence behavior mining to generate multi-level time sequence charging and discharging behavior characteristics; state deviation analysis between adjacent charging and discharging behaviors and adjacent charging and discharging efficiency attenuation quantification are carried out on the multilevel time sequence charging and discharging behavior characteristics, and a charging and discharging behavior efficiency attenuation curve is constructed; and carrying out attenuation trend law evolution on the charge and discharge behavior efficiency attenuation curve, and then carrying out normalized trend stability mining to obtain battery normalized attenuation trend stability. According to the invention, efficient and accurate battery health monitoring and early warning are realized, and the use efficiency and the management cost of the battery are optimized.

The invention relates to the technical field of electrochemical energy storage, and discloses a method and a system for predicting the residual life of a battery of an electrochemical energy storage system. According to the method, multi-dimensional operation data such as temperature distribution, current ripples and voltage attenuation curves in the charge-discharge cycle process of a target battery pack are collected, and feature weight parameters are generated in combination with real-time working conditions and historical database matching results; a degradation characteristic mode in a full life cycle is extracted as a reference template, and a dynamic degradation characteristic space is established; constructing a multi-scale coupled life prediction model, and constructing an adaptive evaluation function by iteratively updating model parameters and reference template physical field coupling parameters; triggering cross-cycle data verification, and carrying out working condition compatibility verification; and generating a feedback matrix based on the prediction error index and the verification result, and dynamically adjusting the mapping relation between the feature weight and the reference template. The method improves the accuracy and adaptability of battery residual life prediction, and is suitable for an electrochemical energy storage system under complex working conditions.

The variable excitation intensity time-resolved photoluminescence carrier lifetime test method comprises the following steps: S1, pulse excitation time-resolved photoluminescence response is carried out to obtain a time-dependent attenuation curve of the unbalanced carrier concentration of a to-be-tested sample, and S2, according to the attenuation curve, the time-dependent attenuation curve of the unbalanced carrier concentration of the to-be-tested sample is calculated. The carrier lifetime data under different excitation intensities are fitted to obtain the SRH lifetime and the surface composite lifetime of the sample, and the # imgabs0 # SRH composite lifetime, the # imgabs1 # radiation composite lifetime and the # imgabs2 # auger composite lifetime are obtained. The variable excitation intensity time-resolved photoluminescence carrier lifetime test method can accurately measure each component of the carrier lifetime.

The invention discloses a battery capacity analysis and prediction method and system of a storage battery, and relates to the technical field of data processing. The method comprises the following steps: acquiring operation data of a target storage battery in a preset time period, wherein the operation data comprises a voltage sequence, an internal resistance sequence, a temperature sequence and a charge-discharge cycle number sequence; determining a time interval corresponding to the plurality of operation conditions according to the operation data; calculating a temperature influence factor, a power loss factor and a cyclic aging factor of each time interval based on the sub-operation data in each time interval; receiving the battery capacity attenuation amount of the target storage battery in each time interval; generating a capacity attenuation curve of the target storage battery by combining the battery capacity attenuation amount, the temperature influence factor, the power loss factor and the cyclic aging factor of each time interval; and predicting the battery capacity in the next time period based on the capacity change curve. By implementing the technical scheme provided by the invention, the effect of improving the accuracy of battery capacity analysis and prediction of the storage battery is achieved.

The invention discloses a photovoltaic array life prediction method based on hybrid driving of a physical model and data. The method comprises the following steps: defining failure time and residual life of a photovoltaic array; preprocessing data; degeneration trend extraction: separating a trend term, a seasonal term and a residual term of output power through block median filtering and seasonal effect correction, and eliminating the influence of environmental fluctuation on degeneration analysis; residual life prediction based on data driving; parameter prediction based on a physical model: identifying degradation parameters of the double-diode model; a degradation parameter trajectory model is established, an output power attenuation curve is inverted, and the remaining life is predicted; and result fusion: comparing data driving and physical model prediction results, and outputting a final residual life value through weighted average or confidence interval fusion. According to the invention, a set of complete photovoltaic array residual life prediction system is constructed, a whole-process closed loop from data acquisition, mechanism analysis to life prediction is realized, and an efficient and accurate solution is provided for reliability evaluation of a photovoltaic system.

The invention relates to the technical field of pipeline safety, and discloses a hydrogen-doped pipeline hydrogen damage grading evaluation method based on fracture mechanics. Comprising the following steps: acquiring initial pipeline state data by establishing a hydrogen-doped pipeline digital twin model; then introducing a hydrogen concentration dependency term to correct the J integral expression, and performing stress analysis based on the J integral expression; generating a material toughness attenuation curve based on a stress analysis result; hydrogen diffusion and accumulation rate parameters are obtained based on a material toughness attenuation curve; predicting a damage development track in combination with real-time operation data; establishing a grading standard system of dynamic response based on the model for risk assessment; and finally, real-time monitoring and scientific grading of the hydrogen damage of the hydrogen-doped pipeline are realized. According to the invention, comprehensive dynamic evaluation of hydrogen damage of the hydrogen-doped pipeline is realized, and scientific decision support is provided for safe operation of the pipeline.

The invention relates to a temporary worker flexible employment information matching method, and the method comprises the steps: constructing a temporary worker skill portrait according to the historical employment information of a temporary worker; classifying the skills of the temporary workers according to the temporary worker skill portraits to obtain temporary worker subsets of different skill types; constructing a skill attenuation model for the temporary worker subset of each skill type; by analyzing the historical academic record and the training participation degree of the temporary worker, the learning ability coefficient of the temporary worker is calculated to serve as an input parameter of the attenuation model; according to the skill attenuation model, predicting the skill proficiency degree change condition of the temporary worker in a period of time in the future; dynamically acquiring latest skill use information of temporary workers, and updating input parameters; selecting a corresponding attenuation curve template to obtain a skill attenuation prediction result; combining various information to obtain a comprehensive matching degree; the prediction accuracy of the skill attenuation model is evaluated regularly; according to the invention, through the precise modeling skill attenuation process, the matching precision and timeliness of temporary workers and post demands are improved.

The invention discloses a tunnel monitoring method and system based on a vibrating wire sensor, and relates to the technical field of tunnel engineering construction safety monitoring, and the method comprises the steps: collecting the vibration frequency data and spatial position coordinates of the vibrating wire sensor in a monitoring region; the method comprises the following steps: calculating a time difference and an energy attenuation ratio between adjacent sensors by extracting spectrum energy density distribution, and constructing a stress wave propagation field; dividing grids in the propagation field to calculate node stress values, generating a stress field distribution cloud picture and extracting a stress concentration area; calculating a gravity center position coordinate change rate and a stress time sequence curve of the stress concentration area, and determining fracture extension parameters; and calculating a surrounding rock bearing capacity attenuation curve based on the parameters, and outputting an early warning signal when the slope exceeds a threshold value. According to the invention, real-time monitoring and early warning of the tunnel surrounding rock instability risk are realized.

The invention discloses a rural historical building protection evaluation system based on big data, and relates to the technical field of building protection. Comprising the following steps: a data processing module normalizes environmental data to generate an environmental degradation factor, extracts a crack depth parameter and a facade weathering parameter from structure detection data through a feature extraction algorithm, and generates a building life attenuation curve through correlation analysis; and the decision module establishes a maintenance optimization model according to the building life attenuation curve and historical maintenance data, generates a risk level through threshold comparison, evaluates expected effects of different maintenance modes on degradation delay, and outputs an optimal maintenance suggestion. According to the method, accurate prediction of the rural historical building degradation process and optimal recommendation of the maintenance strategy are realized, and the scientificity and efficiency of protection work are improved.

The invention discloses a method and a system for measuring damping parameters of a loudspeaker vibration component. The method comprises the steps of performing transient solution on a simulation analysis model of the loudspeaker vibration component according to a set initial damping value of the vibration component, calculating a free vibration process of the vibration component under a specified boundary condition, and obtaining a vibration displacement attenuation curve of the vibration component; reading the simulation attenuation time of the vibration part, and comparing the simulation attenuation time with the actual attenuation time of the vibration part to obtain a relative error; tolerance judgment is conducted, and if the relative error is smaller than a preset tolerance, the set initial damping value serves as the actual damping value of the vibration component; and if the relative error is greater than or equal to the preset tolerance, modifying the initial damping value, and repeating the steps until the relative error is less than the preset tolerance. According to the method, damping parameters of a vibration component material are calculated through back-stepping according to simulation and measurement results of attenuation time of a vibration displacement attenuation curve of a loudspeaker vibration component.

The invention relates to the technical field of power capacitors, and discloses a power capacitor explosion-proof test method, system, medium and equipment, and the method comprises the steps: constructing a composite stress field according to the actual working condition of a power capacitor, and injecting a multi-band harmonic current sequence; three-dimensional strain propagation characteristics of the power capacitor outer shell are captured in real time, a strain-discharge time-space correlation model is established, a structural integrity attenuation curve of the power capacitor outer shell is obtained accordingly, and when the slope of the attenuation curve breaks through a preset critical topological form, a multi-stage linkage protection device is triggered to implement energy discharge; and extracting a characteristic spectral line of a plasma spectrum formed by the self-healing characteristic of the metallized film in the energy discharge process, constructing a multi-stage evaluation index through the offset of the characteristic spectral line, and generating an explosion-proof performance grade evaluation result. According to the invention, accurate simulation of the composite stress field and dynamic monitoring and early warning of the power capacitor can be realized, and the accuracy of early warning and explosion resistance evaluation is improved.

The invention relates to the technical field of water, waste water, sewage or sludge treatment, and particularly provides a constructed wetland operation state monitoring method and system.The method comprises the steps that based on a distributed sensing node array, water flow permeation rates, medium temperature gradients and dissolved matter concentration distribution of different depths and areas of a constructed wetland are collected in real time; generating a three-dimensional dynamic thermodynamic-substance diffusion map; extracting an actual flux difference value of an input-output cross section, and calculating the deviation degree between the current actual load and the theoretical purification capacity by combining the actual flux difference value with the historical adsorption capacity of the wetland medium; and integrating the historical load data with a current early warning report corresponding to the deviation degree, constructing a medium metabolism efficiency attenuation curve, predicting the saturation adsorption periods of matrixes in different regions, and reversely deducing an optimal maintenance node. The system comprises a map generation module, a deviation degree calculation module and a period prediction module. According to the method, discrete physical-chemical-biological process parameters are converted into a continuous decision flow through a space-time coupling model.

The invention relates to a method for detecting surface cracks of a steel rail, which comprises the following steps of: controlling the environmental temperature difference change of the steel rail to enable thermal response to generate a hysteresis phenomenon, inducing slight asynchronous thermal fluctuation with discontinuous and asymmetric thermal diffusion in a local area of a material surface layer, and exposing a potential crack boundary; non-contact low-intensity laser thermal disturbance multi-angle scanning is used for executing a disturbance penetration test, non-structural thermal anomalies are removed, and a real crack area is determined; carrying out attenuation curve recording on the thermal response after multiple rounds of disturbance excitation, and reversely deducing the integrity degree of the microstructure below the crack according to the disturbance settlement time so as to judge the structural fatigue grade; environmental steady state comparison is carried out on the same detection area, through comparison of temperature and humidity, wind speed and radiation environment parameter changes before and after disturbance, if the crack area still continuously shows thermal response abnormity under a constant environment condition, it is confirmed that the thermal characteristic is caused by a structural crack, and otherwise, misjudgment caused by external accidental interference is eliminated.

The invention discloses a Raman spectrum fluorescence baseline correction method and device, and the method comprises the steps: collecting a time domain signal of a to-be-detected sample, and obtaining an IRF function of a to-be-detected sample collection system; pre-processing the time domain signal, and carrying out deconvolution on the pre-processed time domain signal and an IRF function to obtain a deconvolution result; fitting the second half data of the deconvolution result by using a support vector regression model to obtain a fitted curve; wherein a radial basis function is adopted as a kernel function, and hyper-parameters of the support vector regression model are optimized through a grid search and cross validation method; performing difference operation on the deconvolution result and the fitting curve to obtain a Raman attenuation curve; and windowing the Raman attenuation curve and averaging to obtain a Raman spectrum. According to the method, a fluorescence attenuation model and parameters thereof do not need to be preset, the IRF deconvolution result is processed and the time domain baseline is predicted by utilizing the characteristics of the time domain signal data and the support vector regression model, the signal-to-noise ratio of the extracted Raman signal can be greatly improved, a good effect is achieved, and the method is suitable for practical application.

The invention discloses a beer sealing cover defect identification method based on image intelligent identification, and relates to the field of image identification, and the method comprises the steps: extracting the brightness distribution, color histogram and edge gradient features of a picture shot by a camera; constructing a precise light type calculation model, and dynamically adjusting brightness transition curves of adjacent partitions; identifying a high-contrast edge in the picture, and dynamically adjusting a brightness decay curve of adjacent partitions according to an edge brightness difference; color calibration is carried out through a three-dimensional color lookup table; and performing defect identification on an actual sealing cover image by using a dual mechanism. The light rays of the light source emitter are regulated and controlled, so that under the contrast condition, a machine and human eyes are easy to identify, the halo diffusion radius is reduced, and color calibration is carried out through the three-dimensional color lookup table, so that the machine and the human eyes are prevented from being influenced by images during identification, and the overall identification accuracy is ensured.

The invention discloses a power distribution communication network fault positioning method and system based on deep learning, and the method comprises the steps: carrying out the fitting processing of obtained time series data of an optical fiber link, and obtaining an actual power attenuation curve; calculating an absolute difference value between the actual power attenuation curve and the theoretical power attenuation curve to obtain standardized difference data; inputting the standardized difference data into a nonlinear attenuation model to obtain nonlinear change characteristic data; inputting the topological characteristic data and the nonlinear change characteristic data of the power distribution network into a fault identification model to obtain a fault probability value of each optical fiber link; comparing the fault probability value with a preset fault probability threshold value to obtain a potential fault link, and performing feature extraction and abrupt change node identification on the potential fault link to obtain attenuation abrupt change node data; and processing the attenuation abrupt change node data, and determining fault point data based on the obtained stress value data. According to the method provided by the invention, the loss fault in the optical fiber network can be quickly positioned.

The invention relates to the technical field of batteries, in particular to a battery life prediction method for estimating attenuation curve parameters based on LSTM. Comprising the steps of collecting charging and discharging data of different batteries under each cycle, and establishing a battery pack database; on the basis of charging process data, screening charging fragments, and calculating tag capacity by using ampere-hour integral and an OCV-SOC correction method; according to the capacity attenuation curve track, fitting by using a parameterized model to obtain an aging coefficient set fitted by the capacity attenuation curve of the battery pack; extracting feature sets highly related to life prediction under early cycles of different batteries; taking the extracted feature set as the input of a model, taking the aging parameter set as the output of the model, and establishing a battery aging parameter estimation model based on LSTM; after the battery capacity aging parameters are obtained, the battery later cycle number is substituted into the parameterized model, and battery life prediction is obtained. According to the technical scheme, the battery life prediction precision under the condition of limited historical data can be improved.

The invention relates to a power battery performance degradation prediction method and system based on an electrochemical-thermal-mechanical-neural network model, and belongs to the technical field of battery management. According to the method, an enhanced single particle model considering liquid phase diffusion, an integrated thermal model, a particle mechanical stress model caused by concentration gradient and an aging mechanism model considering SEI growth, lithium precipitation and active material loss are coupled to establish ETMD, and based on the ETMD, the identification efficiency of aging model parameters is low aiming at the problem that a PSO algorithm is low. Under a circulating aging experiment of a limited path, the characteristic parameters of the aging model are obtained by using the feedforward neural network, and the parameters are substituted into the ETMD model again to predict the capacity, the power attenuation curve and the corresponding main attenuation mechanism under the current path, so that the prediction precision and the interpretability of the performance attenuation of the battery under the full path are improved. According to the method, the modeling capability of battery performance degradation under different working conditions can be effectively improved, and a theoretical basis and technical support are provided for residual life prediction and safety management of the power battery.

The invention relates to the crossing field of mechanical engineering and intelligent prediction technologies, particularly discloses a coal mill efficiency and service life prediction method and device, and aims to solve the problem that a traditional model is difficult to deal with nonlinear coupling prediction of equipment performance degradation under variable load and coal quality fluctuation. The method comprises the following steps: receiving a multi-source sensing data stream and constructing a structured feature matrix with aligned time sequences; efficiency degradation implicit features are extracted through a nonlinear dynamic encoder, and the interaction influence of grinding roller abrasion, lining plate fatigue and bearing degradation is quantified in combination with a multi-failure-mode coupling analysis module; and cooperatively predicting a network output efficiency attenuation curve and residual life probability distribution through a bidirectional attention mechanism. According to the method, through fusion of multi-source time sequence characteristics and multi-failure coupling modeling, limitation of a static threshold value and linear extrapolation is broken through, prediction precision and timeliness are remarkably improved, intelligent maintenance decision support is provided for a coal-fired power plant, non-planned shutdown risks are reduced, and operation economy and system reliability are optimized.

The embodiment of the invention discloses a battery aging detection method, electronic equipment and a storage medium. The method comprises the following steps: performing calendar aging test on a target battery; under different multiplying power, carrying out cyclic aging test on the target battery; on the basis of the calendar aging test result, calendar aging attenuation generated in the cyclic aging test result is filtered out, and a cyclic attenuation curve corresponding to the target battery is obtained; and detecting the cyclic aging attenuation of the target battery through the cyclic attenuation curve. According to the scheme, the accuracy of a battery aging detection result can be improved.

The invention discloses an attenuation analysis method and an attenuation analysis system for smoke component filtering performance, and relates to the technical field of performance detection.The method comprises the steps that a tracking medium is introduced, and a particle non-targeting rule of mainstream smoke components is determined; carrying out physical modeling on the filter assembly to construct a hydrodynamic model; smoke filtering dynamic simulation is carried out by taking the suction characteristics as simulation variables, and a first space section is monitored; determining particle distribution characteristics of the mainstream smoke components in the test period according to the second space section; and constructing a filtering performance attenuation curve based on particle distribution characteristics. According to the method, the technical problem that the design and operation parameters of the filter assembly are difficult to effectively optimize due to the lack of accurate analysis means for the attenuation law of the smoke component filtering performance in the prior art is solved, and the change law of the smoke component filtering performance under different conditions is comprehensively mastered; and the performance evaluation accuracy and reliability of the flue gas filtering system are effectively improved.

The invention relates to the technical field of tire evaluation, in particular to a method for evaluating the aging loss performance of a rubber engineering tire. The method comprises the following steps: determining tire attributes according to a tire vulcanization process database; constructing a multi-environment composite accelerated aging oven based on the tire vulcanization process database; segmenting a tire sample according to the tire attribute, inputting the tire sample into the multi-environment composite accelerated aging oven, and simulating accelerated aging; sampling according to a preset time interval to obtain an aging simulation result; determining a performance attenuation curve of each tire sample according to the aging simulation result; inputting the performance attenuation curve into an Arrhenius equation, and determining key failure time; and determining the tire performance according to the key failure time and the tire attribute.

The invention discloses a dam leakage channel detection and danger elimination quality evaluation method based on transient electromagnetism, and relates to the technical field of earth and rockfill dam danger elimination and reinforcement. The method comprises the following steps: A, leakage channel detection: A1, data acquisition: performing data acquisition on a dam by adopting pull-type small loop transient electromagnetic equipment to obtain original electromagnetic signal data; a2, forward modeling: performing forward modeling on the acquired data, and calculating a theoretical attenuation curve and apparent resistivity distribution by considering special boundary conditions of the dam; and A3, carrying out inversion calculation. According to the method, leakage signals can be accurately captured based on the high sensitivity of a transient electromagnetic method to a low-resistance body, the used pull-type small-loop transient electromagnetic equipment is convenient to operate, a dam can be comprehensively covered by combining reasonable measurement point arrangement, forward modeling and inversion calculation can comprehensively consider various factors, the position and depth of a leakage area are determined, and the detection accuracy is improved. Compared with a traditional method, the method has obvious advantages in detection depth and accuracy.