12 results about "Steady state detection" patented technology

The invention discloses an air conditioner host load regulation and control parameter self-adaptive learning method. The method comprises the steps that multi-source real-time data of an air conditioner host is obtained and preprocessed; generating a steady-state identification sequence by adopting layered progressive steady-state analysis, wherein the short-term analysis adopts self-adaptive window variance calculation based on the instantaneous change state of the system; based on the steady-state identification sequence, constructing an event causal relationship graph by adopting an improved causal test algorithm, and determining causal association between regulation and response; integrating the causal atlas, and generating a self-adaptive regulation and control strategy library capable of online learning through density clustering and regression modeling; and responding to a power grid dispatching instruction, and matching and generating an optimal regulation and control instruction sequence from the strategy library. According to the method, through the technical means of hierarchical progressive steady-state analysis, improved causal test, density clustering and regression modeling, an online learning strategy library is constructed, the precision of steady-state detection and load prediction can be improved, and the generalization and self-adaption capability of the model under complex working conditions can be enhanced.

The application discloses a steady-state detection method based on two-fluid six-equation transient calculation, which comprises the following steps: before a detection time point, data required for local convergence judgment and TASS judgment are calculated and arranged; time local convergence of a system is judged according to enthalpy change of the system; after the convergence judgment is completed, TASS judgment is started on a historical state of the system, so that the system is ensured to completely enter a final stable state. The application improves the reliability under a low-energy and small-step working condition through a mixed relative convergence-time average judgment method, and meanwhile, the original steady-state detection efficiency is maintained.

Embodiments of the present application disclose a kind of SSD equipment, the steady-state detection device, method, medium and program product of SSD equipment, the device includes: acquisition unit, acquisition unit and determination unit, wherein acquisition unit collects the IOPS data of pre-set time period, obtains multiple IOPS data;Acquisition unit is divided into multiple time periods according to time sequence in pre-set time period, obtains the IOPS data corresponding to multiple time periods from multiple IOPS data, obtains multiple IOPS data sets;Determination unit determines the average IOPS of each time period in multiple time periods according to multiple IOPS data sets, obtains multiple average IOPS values;Determine current SSD steady-state evaluation value according to multiple average IOPS values;When current SSD steady-state evaluation value is greater than pre-set threshold, determine that SSD is in steady-state data state.

The application provides a wafer heating disc visual detection method and system. The steps of the detection method include detection environment parameter configuration, loading positioning, normal temperature cold state detection, hot state steady state detection, data processing and result determination, and are executed through a wafer heating disc visual detection system. The wafer heating disc visual detection system can obtain images of the upper surface, the lower surface and the side surface of the heating disc in multiple dimensions to realize full surface detection, can consider normal temperature cold state and hot state steady state working conditions, and realizes effective identification of cold state inherent defects and heat-induced newly added defects in combination with temperature distribution data collected by a fiber grating sensor. The whole system realizes automatic control from the detection environment parameter configuration, the mechanical arm loading positioning, image collection, data processing and result determination, and at the same time, the gray scale mutation review is integrated into the detection process to reduce manual intervention and manual subjective error and improve detection precision.

The invention relates to a minimum fuel consumption rate rotating speed optimization method and device for a diesel generating set and electronic equipment, and the method comprises the steps: collecting an operation parameter sequence of the generating set in real time, and carrying out the online steady-state detection and screening, so as to obtain effective steady-state working condition point data representing the current state of an engine; performing similarity comparison on the effective steady-state working condition point data and existing data in a steady-state database, and updating the steady-state database in a mode of dynamically replacing old data with new data; periodically or when the data updating amount of the steady-state database reaches a threshold value, retraining the oil consumption rate prediction model by utilizing all updated data in the steady-state database so as to calibrate model parameters and enable the model parameters to be matched with the actual oil consumption characteristics of the engine; and calculating the corresponding minimum fuel consumption rate rotating speed for the current generation power, and continuously updating a minimum fuel consumption rate rotating speed reference curve for closed-loop control of the rotating speed of the unit. According to the method, the problems of optimization lag and failure caused by dependence on fixed offline data are solved.

The application discloses a kind of dam engineering side slope steady detection method and device, comprising: target dam side slope region is equally spaced into at least one detection sub-region;Parameter acquisition is carried out to each sub-region, and side slope stability parameter, side slope safety parameter and side slope deformation parameter are obtained;Through stability data analysis model, combined with side slope stability parameter, construction stable coefficient is obtained by calculation, through safety data analysis model, combined with side slope safety parameter, construction safety coefficient is obtained by calculation, through side slope deformation data analysis model, combined with side slope deformation parameter, side slope deformation coefficient is obtained by calculation;Construction stable coefficient, construction safety coefficient and side slope deformation coefficient are carried out weight calculation, and comprehensive detection coefficient is obtained;Whether target dam side slope region exists safety hidden danger is judged by comprehensive detection coefficient, avoids the safety problem caused by not comprehensive detection, provides suggestion for dam side slope engineering steady.

To provide an article inspection device with which it is possible to reliably converge a temporal change of X-ray detection characteristic in a necessary and sufficient time, and to achieve sufficient inspection accuracy while using an X-ray detector of direct conversion type.SOLUTION: Provided is an article inspection device comprising: an image data generation unit 41 for generating image data Di on the basis of the output Lx of an X-ray detector 23, and an inspection control unit 40A capable of switching an X-ray source 21 and the X-ray detector 23 between an operating state and a non-operating state for starting or stopping, and executing inspection control for inspecting an article P by using the image data Di and determination criteria during the operating state. The inspection control unit 40A includes: steady state detection means 49 for detecting the steady state of outputs of the X-ray source 21 and the X-ray detector 23 after these have started operating; and a storage control unit 42 for storing the X-ray image data Di generated by the image data generation unit 41 on the basis of the output of sensor element on condition that a steady state is detected, in an image memory 42a which is usable for inspection control.SELECTED DRAWING: Figure 1

The invention provides a wafer heating disc visual detection method and system, and the method comprises the steps: detection environment parameter configuration, feeding positioning, normal-temperature cold-state detection, hot-state steady-state detection, data processing and result determination, which are executed through the wafer heating disc visual detection system. Images of the upper surface, the lower surface and the side face of the heating plate can be obtained in a multi-dimensional mode so as to achieve full-surface detection, normal-temperature cold-state and hot-state steady-state working conditions can be considered, and cold-state inherent defects and thermally-induced newly-added defects can be effectively recognized in combination with temperature distribution data collected by a fiber grating sensor; and the whole system realizes automatic control from environmental parameter configuration, mechanical arm feeding positioning, image acquisition, data processing and result judgment, and meanwhile, gray abrupt change rechecking is integrated in the detection process, so that manual intervention and manual subjective errors are reduced, and the detection precision is improved.

This invention discloses a chiller unit fault detection method based on local analysis using a contrastive residual autoencoder, belonging to the field of chiller unit fault detection technology. Addressing the problems of existing methods such as difficulty in effectively detecting early-stage minor faults in chillers, scarcity of fault samples, weak feature discrimination capabilities, and insufficient sensitivity to local anomalies, this invention adopts a two-stage architecture of offline modeling and online detection. In the offline stage, normal data undergoes steady-state detection and normalization preprocessing. Low-dimensional discriminative features are extracted using a contrastive learning network, and a residual autoencoder is constructed to reconstruct the features and calculate the principal features and residual statistics. Simultaneously, a local anomaly factor model is established to determine the anomaly threshold. In the online stage, real-time data is input into the model, joint statistics are calculated, and fault detection is achieved based on the rule that any exceedance of the principal feature, residual, or local density thresholds. This invention can significantly improve the detection rate and robustness of minor faults, reduce false alarms and false negatives, and adapt to complex operating conditions.

The present application relates to the technical field of intelligent temperature control, and discloses a water supply temperature control method, device, equipment and storage medium. The method comprises the following steps: acquiring actual water supply temperatures of a target area and actual end temperatures; performing steady-state detection on each actual water supply temperature according to a preset initial water supply temperature and referring to a time sequence relationship to obtain a steady-state detection result, and calculating an end reference temperature of the target area by using each actual end temperature; if the steady-state detection result is passed, calculating a temperature change trend and a temperature change deviation of the target area according to the end reference temperature and each actual end temperature and referring to the time sequence relationship; performing end temperature change compensation on the initial water supply temperature based on the trend type of the temperature change trend and the difference value of the temperature change deviation to obtain a target water supply temperature, and adjusting the water supply temperature according to the target water supply temperature. The present application improves the accuracy of dynamic temperature adjustment of a heating system during heating.

The invention relates to the technical field of material detection, and particularly discloses a building external wall thermal insulation material detection method and system.The method comprises the steps that standardized thermal excitation is applied to a standard thermal insulation material model subjected to heat conduction performance inspection through a thermal excitation device, and a thermal infrared image time sequence of the standard thermal insulation material model is synchronously collected; and performing time sequence analysis on the thermal infrared image time sequence of the thermal insulation material model by using a deep learning technology so as to construct thermal response characteristic representation of the thermal insulation material model. Furthermore, the same thermal excitation is performed on the to-be-tested materials in the same batch, the thermal response characteristic characterization of the to-be-tested materials is constructed, and similarity analysis is performed on the thermal response characteristic characterization of the to-be-tested materials and the thermal response characteristic characterization of the thermal insulation material model, so that the heat conduction performance of the to-be-tested materials is quickly and accurately evaluated. Compared with a traditional steady-state detection method, the method is based on unsteady-state thermal response characteristic analysis, the detection efficiency is remarkably improved, and the method is suitable for a large-scale quality detection scene of the building external wall thermal insulation material.

The present application discloses a data cleaning and steady-state detection method and system based on robust time series modeling, which relates to the field of time series analysis and steady-state detection technology. The method includes obtaining target time series data; constructing a linear trend model based on the target time series data; performing robust optimization on the linear trend model to obtain a robust model; performing nonlinear optimization on the robust model to determine robust parameters; judging whether the target time series data is in a steady state based on the robust parameters, and obtaining a preliminary steady-state detection result; based on the preliminary steady-state detection result, using a method combining data cleaning with a machine learning model to determine the final steady-state detection result. The present application provides a steady-state detection method with higher accuracy, stronger robustness, and stronger adaptability, which can adapt to various complex industrial process data and can effectively improve the accuracy and robustness of steady-state detection.