274 results about "Attenuation curve" patented technology

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 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 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 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 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 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.

The invention relates to the technical field of new energy automobile batteries, in particular to a new energy automobile battery state prediction method, optimization control method and system, and the method comprises the steps: collecting the real-time operation data of a new energy automobile battery; uploading the real-time operation data to a cloud processing platform in which historical operation data of the battery is stored, and dynamically predicting the state of the battery; fusing the charge state data driven by the real-time model and the charge state data driven by the historical data to carry out charge state estimation prediction, and outputting charge state estimation parameters; a circulation attenuation model is combined with reinforcement learning correction, historical circulation data is used for fitting a capacity attenuation curve, and a dynamic battery health state estimation value is output; a fusion thermal model and an internal resistance model are adopted, a safety voltage / temperature boundary is predicted through digital twin simulation, and a dynamic battery power state estimation value is output; and generating a battery control strategy based on the battery state parameters and issuing the battery control strategy to an onboard BMS module for execution.

The invention relates to the technical field of constructional engineering detection, in particular to a concrete strength detection method and system. The method comprises the following steps: placing a thermal reference block with known thermal physical characteristics in the center of a target area on the surface of to-be-detected concrete; taking the thermal reference block as a center, applying synchronous thermal pulses with the same energy to the surfaces of the concrete at the symmetrical positions on the two sides of the thermal reference block, recording the formed thermal response by using an infrared thermal imager, generating thermal response data, and forming two hot spots on the surface of the concrete; monitoring the environment temperature fluctuation by using the thermal reference block, and subtracting the quantity of the environment temperature fluctuation from the thermal response data in real time to obtain a thermal attenuation curve of the hot spots; and analyzing the isotherm contour form distortion degree of the thermal field corresponding to the thermal attenuation curve, and quantifying the isotherm contour form distortion degree as a thermal field structure polarization index. The problems of environmental interference and internal structure quantification in existing concrete strength detection are solved through a constructional engineering detection technology.

The invention discloses a processing process fault prediction method for essential oil production, and relates to the technical field of essential oil processing, and the method comprises the steps: constructing an environment equipment coupling model, and drawing an electrical equipment insulation resistance attenuation curve through a salt mist deposition experiment; environment disturbance variables are introduced into the generative adversarial network, and an anomaly detector capable of recognizing salt spray corrosion characteristics and humidity condensation characteristics is trained; capturing time-space correlation between temperature mutation and quality fluctuation through a self-attention mechanism; calculating a probability density function of the residual service life of the equipment in real time, and automatically correcting a temperature over-limit threshold value based on the residual service life distribution; calculating the contribution degree of each node to the fault through intervention simulation; and constructing a digital twinborn model containing thermodynamic and hydrodynamic characteristics. Through environment dynamic modeling, causal reasoning and digital twinning methods, the key problems of low fault prediction precision, poor traceability efficiency and lack of quantitative support in decision making in essential oil production are solved.

The invention discloses an intelligent control system and method for a wireless mouse, relates to the technical field of intelligent control of the wireless mouse, and solves the problems that in intelligent control of the wireless mouse, the polling rate of the wireless mouse is difficult to dynamically control according to the use habit of a user, and the polling rate of the wireless mouse is difficult to adjust when the polling rates of different time periods are adjusted. And smooth connection of the polling rate is difficult. According to the method, the click time point and the click frequency are analyzed to obtain the use time period of the current wireless mouse; inputting the use duration and the charging and discharging time point into a battery analysis model to obtain a battery attenuation curve, and obtaining an electric quantity available value based on the residual electric quantity, the battery attenuation curve and the environment temperature; a control instruction is sent based on the current time and the use time period, and the polling rate of the wireless mouse is dynamically regulated and controlled based on the control instruction and the electric quantity available value; according to the wireless mouse, the polling rate of the wireless mouse can be adjusted according to needs, unnecessary energy consumption is reduced, and the service life of a battery is prolonged.

The invention relates to the field of cooking control, in particular to a control method and device of a cooking utensil, equipment and a storage medium. The method comprises the following steps: identifying weight monitoring parameters of food materials in the cooking utensil, carrying out multi-time-point water content attenuation analysis on the food materials, and constructing a real-time water content attenuation curve; identifying real-time cooking environment state parameters, performing quantitative correlation analysis according to the real-time water content attenuation curve, and constructing a water content change quantitative correlation model; acquiring a monitoring image in the cooking utensil, and performing deep target visual identification and personalized taste preference mining to generate personalized taste features of a multi-food-material user; and performing real-time cooking state identification according to the real-time water content attenuation curve and the water content change quantitative correlation model, and performing rolling time window water attenuation prediction to obtain water attenuation prediction values of a plurality of time windows. The cooking utensil parameters are dynamically adjusted based on the moisture change in the cooking process, and therefore the cooking effect and quality are improved.

The invention relates to the field of urban water supply pipe network detection, and discloses a water supply pipeline leakage positioning method and system, a storage medium and computing equipment, and the method comprises the steps: collecting leakage sound source signals at a plurality of points of a pipeline, analyzing an attenuation curve, and obtaining the attenuation characteristics of the leakage sound source signals; noise filtering and normalization processing are carried out on the analyzed leakage sound source signal, a standardized leakage sound source feature library is constructed, and the sound source feature library can reflect the attenuation feature distribution rule of the leakage sound signal at the sound source when a leakage event occurs; after a leakage event sound source signal is collected at any point, corresponding different attenuation characteristic data in a leakage sound source characteristic library are matched according to information of pipelines around a leakage event; and based on the attenuation characteristics of the leakage sound signals, the distance between the acquisition point and the leakage source point is obtained by using the signal propagation distance and the signal intensity measured by the sensor so as to determine the estimation position of the leakage source. According to the invention, the leakage positioning precision can be effectively improved.

The invention discloses a clinical examination equipment remote calibration method and system based on edge calculation, and relates to the field of instrument calibration, the system is composed of a plurality of functional modules, and the system comprises: a multi-modal data acquisition module for acquiring historical calibration data of absorbance, including fluorescence images, cell fluorescence signal intensity and morphological characteristics; the edge calculation module is used for establishing a multiple linear regression model according to historical calibration data of the absorbance and predicting an attenuation curve of the optical detection unit; on the basis of a federated learning framework, a fluorescence signal intensity compensation matrix is optimized by utilizing shared parameters among edge nodes, and light path gain parameters are automatically adjusted; performing outlier analysis on each batch of detection data through a built-in Westgard rule engine, and triggering automatic redetection of morphological characteristics; analyzing absorbance time sequence data in combination with a 1D-CNN model, and identifying reagent pollution and optical assembly aging; and the drifting calibration module adopts a PID controller to dynamically adjust the power of the laser.