CN-122016018-A - Air loop detection method and system based on standard meter method

CN122016018ACN 122016018 ACN122016018 ACN 122016018ACN-122016018-A

Abstract

The invention discloses an air loop detection method and system based on a standard meter method, and relates to the field of gas flow detection. The method comprises the steps of collecting temperature, static pressure, humidity and geometric parameters, generating working condition benchmarks and calculating air density, carrying out time scale correction on differential pressure, temperature, humidity and reference signals, generating a compensation differential pressure sequence, calculating instantaneous volume flow according to the compensation differential pressure and the air density, calling a standard table conversion relation, updating Reynolds numbers to form a nominal flow sequence, carrying out section velocity distribution reconstruction based on the geometric parameters, correcting the flow sequence, generating a robust leakage index through intersegmental energy relation and pressure drop-flow balance, identifying abnormality and forming an abnormal mask, carrying out weighting and rejection on the flow sequence, finally generating a robust flow sequence and recording data to form traceable detection information. By means of dynamic working condition correction, anomaly identification and data tracing mechanisms, accuracy, reliability and data traceability of air loop detection are remarkably improved.

Inventors

ZHANG QIAN
Qian Shenlian
CHEN CHAOYANG
OU ZHENTING
XIANG LEI
YING HAIYAN
DONG XUXIN
JIANG XIN
GENG XUEMEI
YU RONG
CHEN ZHONGJIE
WANG HANTAO
Shou Xia
SHI HUA
WANG LINGJIE

Assignees

浙江省质量科学研究院

Dates

Publication Date: 20260512
Application Date: 20260416

Claims (10)

1. The air loop detection method based on the standard meter method is characterized by comprising the following steps of: collecting temperature, absolute static pressure, relative humidity and loop geometric parameters, constructing a working condition vector, setting a working condition consistency constraint coefficient, calculating air density according to the working condition vector, and generating a working condition reference; performing unified time scale correction on the differential pressure, the temperature and humidity pressure and the reference trigger signal based on the working condition standard, preprocessing the differential pressure sequence, calculating the dynamic fidelity of the differential pressure, and generating a compensation differential pressure sequence corresponding to the working condition standard; According to the compensation differential pressure sequence and the air density parameter in the working condition reference, calling a standard table conversion relation according to the standard table type and the geometric parameter, calculating the instantaneous volume flow, and updating and calculating the Reynolds number and the instantaneous volume flow through parameter self-consistency to form a nominal flow sequence; Reconstructing section speed distribution based on geometric parameter information in a nominal flow sequence and a working condition reference, extracting section correction coefficients, and carrying out section non-uniform correction on the nominal flow sequence to generate a corrected volume flow sequence; Establishing an inter-segment energy relation and a pressure drop-flow balance relation according to the corrected volume flow sequence, generating a robust leakage index, and identifying an abnormal time interval and a suspicious space segment to form an abnormal mask; weighting and removing the corrected volume flow sequence according to the abnormal mask to obtain a robust flow sequence, and calculating a representative value and an interval result in a target time window; And detecting working condition reference, differential pressure data, flow data and abnormal identification results in the whole process, and recording and correlating to form a detection data set and traceability information.
2. The method for detecting an air circuit based on a standard meter method according to claim 1, wherein constructing a working condition vector and generating a working condition reference comprises: Collecting temperature, absolute static pressure, relative humidity, loop geometric parameters and time stamps, establishing a unified time axis, correcting time offset and jitter of each measuring channel, and obtaining an aligned working condition sequence; Performing robust processing on the working condition sequence within a set time window, inhibiting outlier disturbance, forming robust temperature, robust static pressure and robust relative humidity, combining to obtain a working condition vector, and combining the geometric parameters of the loop to form a geometric information set; Calculating saturated water vapor pressure and water vapor partial pressure based on the working condition vector to obtain a wet air density base value, constructing a working condition deviation expression according to a reference working condition set, calculating a working condition consistency constraint coefficient, and carrying out consistency correction on the wet air density base value to obtain air density; and generating a working condition reference according to the unified time axis, the working condition vector, the air density and the geometric information set.
3. The method of claim 2, wherein generating a compensated differential pressure sequence corresponding to a condition reference comprises: performing unified time scale correction on the differential pressure, the temperature and humidity pressure and the reference trigger signal to ensure the alignment of signals of all channels; performing band-pass filtering on the differential pressure signal to remove low-frequency baseline drift and high-frequency noise, and decomposing and extracting an effective mode by adopting an empirical mode to obtain a denoised differential pressure sequence; calculating the dynamic fidelity of the differential pressure signal, evaluating the duty ratio of the effective components in the signal, and correcting the differential pressure sequence based on the dynamic fidelity; and performing differential pressure compensation according to the corrected differential pressure signal and the air density parameter in the working condition reference to generate a compensation differential pressure sequence.
4. A method of air circuit detection based on the standard meter method according to claim 3, wherein calculating the reynolds number and the instantaneous volume flow by means of self-consistent update of the parameters, forming the nominal flow sequence comprises: calculating the instantaneous volume flow according to the air density parameters in the compensation differential pressure sequence and the working condition reference, the standard table type and the loop geometric parameters; Calculating an instantaneous flow rate, obtaining a Reynolds number based on the flow rate and the air density, and carrying out self-consistent update; And according to the updated Reynolds number, calling a standard table conversion relation, correcting the flow coefficient and generating a nominal flow sequence.
5. The method of claim 1, wherein the step of performing a cross-sectional non-uniform correction on the nominal flow sequence to generate a corrected volumetric flow sequence comprises: reconstructing section speed distribution based on the geometric parameter information in the nominal flow sequence and the working condition reference, and calculating a full section speed field by adopting an interpolation method; extracting a section correction coefficient, and calculating the section correction coefficient through the ratio of the speed distribution to the theoretical uniform flow rate; And carrying out non-uniform correction on the nominal flow sequence according to the section correction coefficient, and generating a corrected volume flow sequence.
6. The method of claim 5, wherein generating a robust leakage index, identifying an anomaly time interval and a suspicious space segment, and forming an anomaly mask comprises: Based on the corrected volume flow sequence, establishing an inter-segment energy relation, calculating the pressure drop in the segment, and calculating the energy balance in the segment according to the relation between the flow and the pressure drop; Based on the energy relation between the sections, constructing a pressure drop-flow balance relation, and calculating a pressure drop correction coefficient through an exponential relation between flow and pressure drop; calculating residual errors in the segments according to the pressure drop-flow balance relation, and generating a robust leakage index; based on the time sliding window and the space positioning algorithm, an abnormal mask is generated, an abnormal time interval and a space segment are marked, and the abnormal mask is formed.
7. The method of claim 6, wherein obtaining a robust flow sequence, calculating a representative value and an compartmentalization result within a target time window comprises: Weighting the corrected volume flow sequence, weighting or eliminating abnormal data points, and generating a robust flow sequence; adopting an interpolation method to replace a flow value for the abnormal data points; Calculating the flow mean value, variance, maximum value and minimum value in the target time window to obtain the representative value and the compartmentalization result in the time window; And (3) compartmentalizing the flow in the target time window, wherein the compartmentalization comprises flow mean, variance, maximum value, minimum value and representative value.
8. The method of claim 1, wherein detecting the condition reference, the differential pressure data, the flow data and the anomaly identification result in the whole process, forming the record and the association comprises: Updating working condition references, and recording real-time temperature, static pressure, humidity and corresponding air density; Generating a dynamic storage strategy according to the differential pressure data, the flow data and the abnormal data, and forming a data set by using the time stamp and the data type to form a history record; based on synchronous processing, an accurate relation between working conditions and detection data is formed.
9. The method of claim 8, wherein forming the test data set and the trace back information comprises: performing time calibration on the differential pressure data and the flow data, and marking a time stamp corresponding to the stored data point; Recording occurrence of an abnormal event and generating an abnormal identification result; The detection data, the working condition information, the differential pressure flow data and the abnormal recognition result form a data set.
10. An air circuit detection system based on a standard meter method for implementing the air circuit detection method based on the standard meter method as claimed in any one of claims 1 to 9, comprising: The reference generation module is used for collecting temperature, absolute static pressure, relative humidity and loop geometric parameters, constructing a working condition vector, setting a working condition consistency constraint coefficient, calculating air density according to the working condition vector and generating a working condition reference; The preprocessing module is used for executing unified time scale correction on the differential pressure, the temperature and humidity pressure and the reference trigger signal based on the working condition standard, preprocessing the differential pressure sequence, calculating the dynamic fidelity of the differential pressure and generating a compensation differential pressure sequence corresponding to the working condition standard; the standard meter conversion module is used for calling standard meter conversion relation according to the standard meter type and the geometric parameter according to the air density parameter in the compensation differential pressure sequence and the working condition reference, calculating the instantaneous volume flow, and updating and calculating the Reynolds number and the instantaneous volume flow through parameter self-consistency to form a nominal flow sequence; The reconstruction and correction module is used for reconstructing section speed distribution based on the geometric parameter information in the nominal flow sequence and the working condition reference, extracting section correction coefficients, carrying out section non-uniform correction on the nominal flow sequence and generating a corrected volume flow sequence; the anomaly identification module establishes an inter-segment energy relation and a pressure drop-flow balance relation according to the corrected volume flow sequence, generates a robust leakage index, identifies an anomaly time interval and a suspicious space segment, and forms an anomaly mask; The abnormality processing module is used for carrying out weighting and eliminating processing on the corrected volume flow sequence according to the abnormality mask to obtain a robust flow sequence, and calculating a representative value and an interval result in a target time window; And the record association module is used for detecting working condition reference, differential pressure data, flow data and abnormal identification results in the whole process, recording and associating to form a detection data set and traceability information.

Description

Air loop detection method and system based on standard meter method Technical Field The invention relates to the field of gas flow detection, in particular to an air loop detection method and system based on a standard meter method. Background With the continuous improvement of the requirements of the industrial field on the detection precision and reliability of the air loop, the traditional detection method has difficulty in meeting the requirements of modern complex systems. The traditional air loop detection method generally relies on single detection means such as differential pressure measurement, flow conversion, temperature and humidity monitoring and the like, but the means have obvious problems. First, there may be a large error between the standard differential pressure and the flow conversion table due to non-uniformity of flow velocity distribution in the loop. Especially under different environmental conditions, the variation of air density directly affects the measurement accuracy, which is often neglected by conventional methods, resulting in deviations of the measurement results. Secondly, the existing method generally relies on static standard meter conversion to calculate flow, and cannot take dynamic change of air density into consideration, so that real-time correction cannot be realized. Thus, the accuracy of conventional detection methods may be greatly reduced as environmental conditions change. In addition, the conventional method has a disadvantage in abnormality detection. In the face of problems such as abnormal flow fluctuation or air leakage, an effective abnormality recognition and processing mechanism is generally lacking, so that the problem of distortion or omission of detection results is caused. The prior art often fails to find and feed back abnormal data in time, so that data is lost and misjudged, and the whole detection process is difficult to comprehensively reflect the real state of the loop. Furthermore, the recording and data association mechanism of the conventional detection method is weak. When an abnormality occurs, related working conditions and detection data cannot be traced back quickly, and subsequent analysis and decision making are affected. Therefore, the air loop detection method and the air loop detection system based on the standard meter method can dynamically calculate the air density by collecting the temperature, the absolute static pressure, the relative humidity and the loop geometric parameters in real time and combining the working condition reference and the standard meter conversion relation, and perform differential pressure correction and flow correction aiming at different working conditions. In addition, by introducing an abnormality recognition mechanism such as a robust leakage index, the method can discover and process the flow fluctuation or the occurrence of the abnormality such as leakage in real time. Finally, by recording all relevant data, a traceable data set is formed, so that the accuracy of the data is improved, and the transparency and auditability in the detection process are ensured. Disclosure of Invention Based on the above-mentioned drawbacks of the prior art, the present invention is directed to an air loop detection method and system based on a standard meter method, so as to solve the above-mentioned technical problems. In order to achieve the purpose, the invention provides the following technical scheme that the air loop detection method based on the standard meter method comprises the following steps: collecting temperature, absolute static pressure, relative humidity and loop geometric parameters, constructing a working condition vector, setting a working condition consistency constraint coefficient, calculating air density according to the working condition vector, and generating a working condition reference; performing unified time scale correction on the differential pressure, the temperature and humidity pressure and the reference trigger signal based on the working condition standard, preprocessing the differential pressure sequence, calculating the dynamic fidelity of the differential pressure, and generating a compensation differential pressure sequence corresponding to the working condition standard; According to the compensation differential pressure sequence and the air density parameter in the working condition reference, calling a standard table conversion relation according to the standard table type and the geometric parameter, calculating the instantaneous volume flow, and updating and calculating the Reynolds number and the instantaneous volume flow through parameter self-consistency to form a nominal flow sequence; Reconstructing section speed distribution based on geometric parameter information in a nominal flow sequence and a working condition reference, extracting section correction coefficients, and carrying out section non-uniform correction on the nominal flow sequence to generate a co