Air loop detection method and system based on standard table method

Abstract

The application discloses an air loop detection method and system based on a standard table method, and relates to the field of gas flow detection. The method comprises the following steps: collecting temperature, static pressure, humidity and geometric parameters, generating a working condition benchmark and calculating air density; correcting the time scale of differential pressure, temperature and humidity pressure and a reference signal to generate a compensated differential pressure sequence; calculating instantaneous volume flow according to the compensated differential pressure and the air density, updating the Reynolds number, and forming a nominal flow sequence by calling a standard table conversion relationship; reconstructing the cross-sectional velocity distribution based on the geometric parameters, and correcting the flow sequence; generating a robust leakage index through the inter-segment energy relationship and the pressure drop-flow balance, identifying abnormalities and forming an abnormality mask; weighting and removing the flow sequence to finally generate a robust flow sequence and record data, and forming traceable detection information. Through dynamic working condition correction, abnormality identification and data traceability mechanism, the accuracy, reliability and data traceability of the air loop detection are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of gas flow detection, specifically to an air circulation detection method and system based on the standard meter method. Background Technology

[0002] With the increasing demands for accuracy and reliability in industrial air loop monitoring, traditional methods are no longer sufficient to meet the needs of modern complex systems. Traditional air loop monitoring methods typically rely on single detection methods such as differential pressure measurement, flow conversion, and temperature and humidity monitoring, but these methods have several significant drawbacks. First, due to the non-uniformity of flow velocity distribution within the loop, there may be substantial errors between the standard differential pressure and the flow conversion table. Especially under different environmental conditions, changes in air density directly affect measurement accuracy, a factor often overlooked by traditional methods, leading to deviations in the measurement results. Second, existing methods usually rely on static standard table conversions to calculate flow rates, failing to consider the dynamic changes in air density and thus lacking real-time correction capabilities. Therefore, with changes in environmental conditions, the accuracy of traditional monitoring methods may decrease significantly.

[0003] Furthermore, traditional methods also have shortcomings in anomaly detection. When faced with problems such as abnormal flow fluctuations or leaks, they typically lack effective anomaly identification and handling mechanisms, leading to distorted or missed detection results. Existing technologies often fail to detect and report abnormal data in a timely manner, resulting in data loss and misjudgment, making it difficult for the entire detection process to fully reflect the true state of the circuit. Moreover, the recording and data correlation mechanisms of traditional detection methods are relatively weak. When an anomaly occurs, it is impossible to quickly trace back to relevant operating conditions and detection data, affecting subsequent analysis and decision-making.

[0004] Therefore, the air loop detection method and system based on the standard table method of this invention, by real-time acquisition of temperature, absolute static pressure, relative humidity, and loop geometric parameters, combined with the conversion relationship between the operating condition benchmark and the standard table, can dynamically calculate air density and perform differential pressure correction and flow correction for different operating conditions. Furthermore, by introducing anomaly identification mechanisms such as a robust leakage index, this method can detect and handle anomalies such as flow fluctuations or leaks in real time. Finally, by recording all relevant data to form a traceable dataset, not only is the accuracy of the data improved, but the transparency and auditability of the detection process are also ensured. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide an air passage detection method and system based on the standard table method to solve the above-mentioned technical problems.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an air passage detection method based on the standard table method, comprising: Collect temperature, absolute static pressure, relative humidity and loop geometry parameters, construct operating condition vector, set operating condition consistency constraint coefficient, calculate air density based on operating condition vector, and generate operating condition benchmark; Based on the operating condition reference, the differential pressure, temperature, humidity and pressure and reference trigger signal are uniformly time-scaled and corrected. The differential pressure sequence is preprocessed, the dynamic fidelity of differential pressure is calculated, and a compensated differential pressure sequence corresponding to the operating condition reference is generated. Based on the air density parameters in the compensated differential pressure sequence and operating condition reference, the instantaneous volumetric flow rate is calculated by calling the standard table conversion relationship according to the standard table type and geometric parameters. The Reynolds number and instantaneous volumetric flow rate are calculated through parameter self-consistency update to form the nominal flow rate sequence. Based on the geometric parameter information in the nominal flow series and the operating condition baseline, the cross-sectional velocity distribution is reconstructed, the cross-sectional correction coefficient is extracted, the cross-sectional non-uniformity correction is applied to the nominal flow series, and the corrected volumetric flow series is generated. Based on the modified volumetric flow rate sequence, establish the energy relationship between segments and the pressure drop-flow balance relationship, generate a robust leakage index, identify abnormal time intervals and suspicious spatial segments, and form an anomaly mask; The modified volumetric flow rate sequence is weighted and eliminated based on the anomaly mask to obtain a robust flow rate sequence. The representative value and intervalization result within the target time window are calculated. The operating condition baseline, differential pressure data, flow data, and anomaly identification results throughout the entire testing process are recorded and correlated to form a testing dataset and traceability information.

[0007] The present invention is further configured such that constructing the operating condition vector and generating the operating condition benchmark includes: Collect temperature, absolute static pressure, relative humidity, loop geometry parameters and timestamps, establish a unified time axis, correct the time offset and jitter of each measurement channel, and obtain an aligned working condition sequence; Within a set time window, robustness processing is performed on the operating condition sequence to suppress outlier disturbances and form robust temperature, robust static pressure and robust relative humidity. These are combined to obtain the operating condition vector and combined with the loop geometric parameters to form a geometric information set. Based on the operating condition vector, the saturated water vapor pressure and water vapor partial pressure are calculated to obtain the basic value of moist air density. The operating condition deviation expression is constructed according to the reference operating condition set, the operating condition consistency constraint coefficient is calculated, and the consistency correction is applied to the basic value of moist air density to obtain the air density. The operating condition baseline is generated based on a unified time axis, operating condition vector, air density, and geometric information set.

[0008] The present invention is further configured such that generating the compensated differential pressure sequence corresponding to the operating condition reference includes: Perform unified time-scale correction on differential pressure, temperature and humidity pressure, and reference trigger signals to ensure signal alignment across all channels; The differential pressure signal is bandpass filtered to remove low-frequency baseline drift and high-frequency noise. Empirical mode decomposition is used to extract the effective modes to obtain the denoised differential pressure sequence. Calculate the dynamic fidelity of the differential pressure signal, evaluate the proportion of effective components in the signal, and correct the differential pressure sequence based on the dynamic fidelity; Differential pressure compensation is performed based on the corrected differential pressure signal and the air density parameters in the operating condition reference to generate a compensated differential pressure sequence.

[0009] The present invention is further configured such that the step of calculating the Reynolds number and instantaneous volumetric flow rate through parameter self-consistent update to form the nominal flow rate sequence includes: The instantaneous volumetric flow rate is calculated based on the air density parameters in the compensated differential pressure sequence and operating condition reference, according to the standard gauge type and loop geometry parameters. Calculate the instantaneous flow velocity, obtain the Reynolds number based on the flow velocity and air density, and perform a self-consistent update; Based on the updated Reynolds number, the standard table conversion relationship is invoked to correct the flow coefficient and generate the nominal flow sequence.

[0010] The present invention is further configured such that the step of performing cross-sectional non-uniformity correction on the nominal flow rate sequence to generate a corrected volumetric flow rate sequence includes: Based on the nominal flow sequence and geometric parameter information in the operating condition reference, the cross-sectional velocity distribution is reconstructed, and the velocity field of the whole cross-section is calculated by interpolation method; Extract the cross-section correction factor and calculate the cross-section correction factor by the ratio of the velocity distribution to the theoretical uniform flow velocity; The nominal flow rate sequence is non-uniformly corrected based on the cross-sectional correction factor to generate the corrected volumetric flow rate sequence.

[0011] The present invention is further configured such that generating a robust leakage index, identifying abnormal time intervals and suspicious spatial segments, and forming an anomaly mask includes: Based on the corrected volumetric flow rate sequence, the energy relationship between segments is established, the pressure drop within segments is calculated, and the energy balance within segments is deduced based on the relationship between flow rate and pressure drop. Based on the energy relationship between segments, a pressure drop-flow balance relationship is constructed, and the pressure drop correction coefficient is calculated through the exponential relationship between flow rate and pressure drop; The residual within the section is calculated based on the pressure drop-flow balance relationship, and a robust leakage index is generated. Based on a time-sliding window and spatial positioning algorithm, an anomaly mask is generated, marking the anomaly time interval and spatial segment to form the anomaly mask.

[0012] The present invention is further configured such that obtaining the robust flow sequence and calculating the representative value and intervalization result within the target time window includes: The corrected volumetric flow rate sequence is weighted, and outlier data points are either weighted or removed to generate a robust flow rate sequence. Interpolation methods are used to replace flow values ​​for abnormal data points; Calculate the mean, variance, maximum and minimum flow rates within the target time window to obtain the representative value and intervalized results within the time window; The traffic flow within the target time window is ranged, including the traffic mean, variance, maximum value, minimum value, and representative value.

[0013] The present invention is further configured such that the operating condition baseline, differential pressure data, flow rate data, and anomaly identification results throughout the detection process are recorded and associated, including: Update the operating condition baseline and record real-time temperature, static pressure, humidity, and corresponding air density; A dynamic storage strategy is generated based on differential pressure data, flow data, and abnormal data. Timestamps and data types are combined to form a dataset, creating a historical record. Based on synchronous processing, a precise relationship between operating conditions and detection data is established.

[0014] The present invention is further configured such that the formation of the detection dataset and traceability information includes: Perform time calibration on differential pressure data and flow data, and mark the timestamps corresponding to the stored data points; Record the occurrence of abnormal events and generate anomaly identification results; The detection data, operating condition information, differential pressure flow data, and anomaly identification results form a dataset.

[0015] The present invention also provides an air passage detection system based on the standard table method, the system comprising: The baseline generation module collects temperature, absolute static pressure, relative humidity and loop geometry parameters, constructs operating condition vectors, sets operating condition consistency constraint coefficients, calculates air density based on the operating condition vectors, and generates operating condition baselines. Preprocessing module: Based on the operating condition reference, perform unified time-scale correction on differential pressure, temperature, humidity and pressure and reference trigger signal, perform differential pressure sequence preprocessing, calculate differential pressure dynamic fidelity, and generate a compensated differential pressure sequence corresponding to the operating condition reference; Standard meter conversion module: Based on the air density parameters in the compensated differential pressure sequence and operating condition reference, it calls the standard meter conversion relationship according to the standard meter type and geometric parameters, calculates the instantaneous volumetric flow rate, and calculates the Reynolds number and instantaneous volumetric flow rate through parameter self-consistency update to form a nominal flow rate sequence; Reconstruction and Correction Module: Based on the nominal flow rate sequence and geometric parameter information in the operating condition baseline, the module reconstructs the cross-sectional velocity distribution, extracts the cross-sectional correction coefficient, corrects the non-uniformity of the nominal flow rate sequence, and generates a corrected volumetric flow rate sequence. Anomaly identification module: Based on the corrected volumetric flow rate sequence, establish inter-segment energy relationships and pressure drop-flow balance relationships, generate robust leakage index, identify abnormal time intervals and suspicious spatial segments, and form anomaly masks; Anomaly handling module: Performs weighted and elimination processing on the corrected volumetric flow rate sequence based on the anomaly mask to obtain a robust flow rate sequence, and calculates the representative value and intervalization result within the target time window; Recording and correlation module: Records and correlates the operating condition baseline, differential pressure data, flow data and anomaly identification results throughout the detection process to form a detection dataset and traceability information.

[0016] This invention provides an air loop detection method and system based on the standard table method. The method collects temperature, absolute static pressure, relative humidity, and loop geometric parameters to construct a condition vector, sets a condition consistency constraint coefficient, calculates air density based on the condition vector, and generates a condition reference. Based on the condition reference, it performs unified time-scale correction on differential pressure, temperature, humidity, and pressure, and the reference trigger signal, performs differential pressure sequence preprocessing, calculates differential pressure dynamic fidelity, and generates a compensated differential pressure sequence corresponding to the condition reference. Based on the compensated differential pressure sequence and the air density parameters in the condition reference, it calls the standard table conversion relationship according to the standard table type and geometric parameters to calculate the instantaneous volumetric flow rate. Through parameter self-consistency update, it calculates the Reynolds number and instantaneous volumetric flow rate to form a nominal flow rate sequence. Based on the geometric parameters in the nominal flow rate sequence and operating condition baseline, the cross-sectional velocity distribution is reconstructed, the cross-sectional correction coefficient is extracted, and the nominal flow rate sequence is corrected for cross-sectional non-uniformity to generate a corrected volumetric flow rate sequence. Based on the corrected volumetric flow rate sequence, inter-segment energy relationships and pressure drop-flow balance relationships are established to generate a robust leakage index, identify abnormal time intervals and suspicious spatial segments, and form anomaly masks. The corrected volumetric flow rate sequence is weighted and eliminated based on the anomaly masks to obtain a robust flow rate sequence, and representative values ​​and intervalization results within the target time window are calculated. Operating condition baselines, differential pressure data, flow data, and anomaly identification results throughout the detection process are recorded and correlated to form a detection dataset and traceability information. The beneficial effects include: 1. Improved detection accuracy and real-time performance: By collecting temperature, static pressure, humidity and loop geometry parameters in real time, and combining dynamic operating condition benchmarks for differential pressure correction and flow correction, the measurement error problem caused by air density changes and flow non-uniformity in traditional methods is effectively solved, significantly improving the accuracy and real-time performance of the detection results. 2. Enhanced anomaly identification and robustness: The introduction of a robust leakage index and anomaly masking mechanism can accurately identify abnormal time intervals and suspicious spatial segments in the loop, and promptly detect problems such as gas leaks or flow fluctuations, thereby enhancing the anomaly handling capability of the detection system and improving its reliability. 3. Ensuring data integrity and traceability: All operating condition baselines, differential pressure data, flow data, and anomaly identification results during the testing process are recorded in detail and correlated to form a complete dataset and traceability information. This mechanism ensures the integrity and reliability of the data.

[0017] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 A flowchart illustrating an air passage detection method based on the standard table method is shown as an exemplary embodiment of the present invention. Figure 2 This is a schematic diagram of an air passage detection system based on the standard table method, which is an exemplary embodiment of the present invention. Detailed Implementation

[0019] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0020] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0021] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0022] Example 1:

[0023] An air passage detection method based on the standard table method, such as Figure 1 As shown, it includes: Collect temperature, absolute static pressure, relative humidity and loop geometry parameters, construct operating condition vector, set operating condition consistency constraint coefficient, calculate air density based on operating condition vector, and generate operating condition benchmark; Based on the operating condition reference, the differential pressure, temperature, humidity and pressure and reference trigger signal are uniformly time-scaled and corrected. The differential pressure sequence is preprocessed, the dynamic fidelity of differential pressure is calculated, and a compensated differential pressure sequence corresponding to the operating condition reference is generated. Based on the air density parameters in the compensated differential pressure sequence and operating condition reference, the instantaneous volumetric flow rate is calculated by calling the standard table conversion relationship according to the standard table type and geometric parameters. The Reynolds number and instantaneous volumetric flow rate are calculated through parameter self-consistency update to form the nominal flow rate sequence. Based on the geometric parameter information in the nominal flow series and the operating condition baseline, the cross-sectional velocity distribution is reconstructed, the cross-sectional correction coefficient is extracted, the cross-sectional non-uniformity correction is applied to the nominal flow series, and the corrected volumetric flow series is generated. Based on the modified volumetric flow rate sequence, establish the energy relationship between segments and the pressure drop-flow balance relationship, generate a robust leakage index, identify abnormal time intervals and suspicious spatial segments, and form an anomaly mask; The modified volumetric flow rate sequence is weighted and eliminated based on the anomaly mask to obtain a robust flow rate sequence. The representative value and intervalization result within the target time window are calculated. The operating condition baseline, differential pressure data, flow data, and anomaly identification results throughout the entire testing process are recorded and correlated to form a testing dataset and traceability information.

[0024] The present invention is further configured such that constructing the operating condition vector and generating the operating condition benchmark includes: Temperature, absolute static pressure, relative humidity, loop geometry parameters, and timestamps are collected to establish a unified time axis. Time offsets and jitter of each measurement channel are corrected to obtain an aligned operating condition sequence. Specifically, key operating condition data of the air loop are collected through multiple sensors, including temperature, static pressure, humidity, and loop geometry parameters. Each sensor generates a timestamp to identify the acquisition time of each data point. There are time offsets between the sensors. First, the sensor data is time-synchronized. By calculating the time offset between each sensor and the unified time axis, the collected data is corrected so that the data from each channel can be compared and analyzed under the same time reference. The corrected data forms a consistent time series. Within a set time window, robustness processing is performed on the operating condition sequence to suppress outlier disturbances and form robust temperature, robust static pressure, and robust relative humidity. These are combined to obtain an operating condition vector, which, in conjunction with the loop geometric parameters, forms a geometric information set. Specifically, the original collected operating condition data is robust to remove abnormal data caused by external interference or sensor errors. The robustness processing uses median filtering or weighted averaging to eliminate the influence of extreme values ​​on the data. Through median filtering, the value at each time point in the data is replaced with the median of all data within that time period, removing unreasonable fluctuations. Through weighted averaging, different weights are assigned to the values ​​at different time points to ensure that stable values ​​have a greater impact on the final result. The processed temperature, static pressure, and humidity data, combined with the loop geometric parameters (such as hydraulic diameter and cross-sectional area), constitute the operating condition vector, which includes various physical quantities reflecting the current operating condition status. Based on the operating condition vector, the saturated vapor pressure and partial pressure of water vapor are calculated to obtain the basic value of moist air density. An expression for the operating condition deviation is constructed based on the reference operating condition set, and the operating condition consistency constraint coefficient is calculated. Consistency correction is then applied to the basic value of moist air density to obtain the air density. Specifically, the density of moist air is calculated based on the temperature, humidity, and static pressure data in the operating condition vector. First, the saturated vapor pressure is calculated using temperature and humidity data. Then, based on the ratio of humidity to saturated vapor pressure, the partial pressure of water vapor is obtained. The partial pressure of dry air is calculated, and the partial pressure of water vapor is subtracted from the total pressure to obtain the partial pressure of dry air. The basic value of moist air density is obtained by calculating the density of moist air, dry air partial pressure, humidity, temperature, and gas constant. Consistency correction is then applied to the basic value of moist air density by comparing the deviation between the current operating condition and the reference operating condition (e.g., historical data or standard operating condition). The deviation is calculated, and a operating condition consistency constraint coefficient is generated based on the deviation. This coefficient is then used to correct the basic value of moist air density, resulting in a corrected air density value. The corrected air density more accurately reflects the flow characteristics under actual operating conditions. The operating condition baseline is generated based on a unified time axis, operating condition vector, air density, and geometric information set. Specifically, the operating condition baseline is generated by combining the robustened temperature, humidity, and static pressure data, the corrected air density value, and the loop geometric parameters with the unified time axis. The operating condition baseline includes real-time information such as temperature, humidity, static pressure, and air density, and an accurate timestamp is assigned to each data point to ensure the temporal consistency of all data points.

[0025] The present invention is further configured such that generating the compensated differential pressure sequence corresponding to the operating condition reference includes: The differential pressure, temperature and humidity pressure, and reference trigger signals are uniformly time-stamped to ensure signal alignment across channels. Specifically, differential pressure, temperature and humidity pressure, and reference trigger signals are acquired through multiple sensors. Each sensor's output data carries a timestamp, and the timestamps of different sensors may be offset or jittery. A time synchronization algorithm (such as cross-correlation or interpolation) is used to calculate the time offset between each signal channel and a unified time axis, and time correction is performed on each signal to ensure that the timestamps of each signal are consistent, enabling effective comparison and correlation of data acquired by different sensors. The differential pressure signal is bandpass filtered to remove low-frequency baseline drift and high-frequency noise. Empirical Mode Decomposition (EMD) is then used to extract the effective modes, resulting in a denoised differential pressure sequence. Specifically, the differential pressure signal undergoes bandpass filtering. Bandpass filters can filter out signals outside the frequency range, retaining the effective signals within the target frequency band. By selecting a suitable bandpass filter and setting appropriate low-frequency and high-frequency cutoff points, interference signals are removed. Empirical Mode Decomposition (EMD) is then used to decompose the differential pressure signal into several modes. By extracting the effective modes and removing noise modes, a smoother and more representative differential pressure signal is obtained. The dynamic fidelity of the differential pressure signal is calculated to assess the proportion of effective components in the signal, and the differential pressure sequence is corrected based on the dynamic fidelity. Specifically, dynamic fidelity measures the proportion of effective components (such as dynamic fluctuations in airflow) to noise components in the signal, and calculates the signal energy of the denoised differential pressure signal throughout the entire time series. By comparing it with the original signal (the differential pressure signal before denoising), the proportion of effective components in the denoised signal is assessed. The higher the fidelity, the more effective information is retained in the signal and the less noise components are removed. Based on the calculation results of dynamic fidelity, the differential pressure sequence is corrected to ensure that the final differential pressure sequence accurately reflects the true changes in the fluid. Differential pressure compensation is performed based on the corrected differential pressure signal and the air density parameters in the operating condition reference to generate a compensated differential pressure sequence. Specifically, differential pressure compensation is performed based on the air density parameters in the operating condition reference. Using the air density parameters calculated in the operating condition reference, the differential pressure signal is compensated based on the conversion relationship of the standard table. By applying the compensation coefficient to the corrected differential pressure signal, the compensated differential pressure sequence is obtained. The compensated differential pressure sequence can effectively eliminate the measurement error caused by changes in air density and can more accurately reflect the true flow state.

[0026] The present invention is further configured such that the step of calculating the Reynolds number and instantaneous volumetric flow rate through parameter self-consistent update to form the nominal flow rate sequence includes: Based on the compensated differential pressure sequence and the air density parameters in the operating condition reference, and according to the standard meter type and loop geometry parameters, the instantaneous volumetric flow rate is calculated. Specifically, based on the compensated differential pressure sequence and the air density parameters in the operating condition reference, combined with the loop geometry parameters (such as hydraulic diameter and cross-sectional area), the instantaneous volumetric flow rate is calculated through the conversion relationship of the standard meters. By combining the compensated differential pressure sequence with the air density parameters in the operating condition reference, and performing conversion according to the standard meter type and geometry parameters, and considering the nonlinear relationship between differential pressure and volumetric flow rate, the instantaneous volumetric flow rate is obtained. The instantaneous flow velocity is calculated, and the Reynolds number is derived based on the velocity and air density, followed by a self-consistent update. Specifically, the instantaneous velocity is derived from the ratio of the instantaneous volumetric flow rate to the cross-sectional area of ​​the annulus. The Reynolds number is calculated based on the instantaneous velocity and air density. The Reynolds number is a dimensionless quantity characterizing flow properties, involving velocity, fluid density, and annulus geometry (such as hydraulic diameter). After obtaining the preliminary Reynolds number, it is adjusted and corrected using the self-consistent update method, combined with other parameters from the operating condition baseline. This process iteratively corrects the initially calculated Reynolds number to ensure its consistency with the actual flow state. Based on the updated Reynolds number, the conversion relationships in the standard tables are used to correct the flow coefficients and generate the nominal flow series. Specifically, after the Reynolds number is self-consistently updated, the flow coefficients are corrected based on the corrected Reynolds number using the conversion relationships in the standard tables. The flow coefficients are provided by the standard tables and adjusted according to changes in the Reynolds number. By correcting the flow coefficients using the updated Reynolds number, the nominal flow series is finally calculated. The nominal flow series reflects the flow values ​​based on corrected differential pressure and air density. Combining the standard tables and correction coefficients, it can accurately represent the actual flow conditions.

[0027] The present invention is further configured such that the step of performing cross-sectional non-uniformity correction on the nominal flow rate sequence to generate a corrected volumetric flow rate sequence includes: Based on the nominal flow rate sequence and the geometric parameter information in the operating condition reference, the cross-sectional velocity distribution is reconstructed, and the full cross-sectional velocity field is calculated using an interpolation method. Specifically, based on the nominal flow rate sequence and the geometric parameter information in the operating condition reference, the velocity distribution of the annular cross-section is reconstructed. The cross-sectional velocity distribution is the velocity distribution of the fluid at different locations within the cross-section of the annular cross-section. Affected by flow inhomogeneity, the full cross-sectional velocity field is calculated using an interpolation method based on the nominal flow rate sequence and the geometric parameters of the annular cross-section. The interpolation method calculates the velocity at other unknown locations within the cross-section based on some known velocity values, thus obtaining the complete cross-sectional velocity distribution. The cross-section correction coefficient is extracted and calculated by comparing the reconstructed velocity distribution with the theoretical uniform velocity. Specifically, the cross-section correction coefficient is obtained by comparing the reconstructed velocity distribution with the theoretical uniform velocity. The cross-section correction coefficient represents the degree of non-uniformity of the velocity distribution in the cross-section. The larger the coefficient, the more significant the velocity difference and the stronger the need for cross-section correction. The nominal flow rate sequence is non-uniformly corrected based on the cross-sectional correction factor to generate a corrected volumetric flow rate sequence. Specifically, the correction process combines the nominal flow rate sequence with the correction factor to compensate for the non-uniformity of the velocity distribution within the cross-section. The nominal flow rate is multiplied by the cross-sectional correction factor to obtain the corrected volumetric flow rate sequence. This ensures that the calculated flow rate is closer to the actual situation under non-uniform velocity distribution and avoids flow rate errors caused by non-uniform velocity distribution within the cross-section.

[0028] The present invention is further configured such that generating a robust leakage index, identifying abnormal time intervals and suspicious spatial segments, and forming an anomaly mask includes: Based on the modified volumetric flow rate sequence, the energy relationship between segments is established, the pressure drop within each segment is calculated, and the energy balance within each segment is deduced based on the relationship between flow rate and pressure drop. Specifically, based on the modified volumetric flow rate sequence, the energy relationship between segments is established, which describes the relationship between flow rate and pressure drop. The pressure drop within each segment is calculated. By using the measured flow rate data and the geometric parameters of the loop, the pressure drop of each segment is deduced using fluid dynamics principles. The relationship between flow rate and pressure drop is non-linear. Appropriate conversion relationships or standard tables are used to determine the correspondence between pressure drop and flow rate. The energy balance within each segment is deduced by comparing the input flow rate and output pressure drop of each segment, ensuring the energy conservation of each loop segment. Based on the energy relationship between sections, a pressure drop-flow balance relationship is constructed. The pressure drop correction coefficient is calculated using the exponential relationship between flow rate and pressure drop. Specifically, this relationship describes the interaction and exponential relationship between pressure drop and flow rate. The relationship between flow rate and pressure drop is obtained through experimental data or standard tables. Using this relationship, a pressure drop correction coefficient is calculated to adjust the pressure drop data and compensate for errors caused by differences in flow characteristics in different parts of the loop. The pressure drop correction coefficient reflects the nonlinear effect between flow rate and pressure drop, changing with variations in flow velocity or flow rate. Based on the pressure drop-flow balance relationship, the residual within the segment is calculated to generate a robust leakage index. Specifically, the residual within the segment represents the difference between the actual measured pressure drop and the theoretical calculated value. The magnitude of the residual reflects the deviation between the flow measurement and the theoretical model. A larger residual indicates that there is leakage or flow anomaly in the system. The residual value is compared with the pressure drop and flow relationship of each segment, and a robust leakage index is generated through a weighted algorithm. The robust leakage index is a comprehensive index that takes into account flow, pressure drop, and residual. The larger the value, the higher the probability of leakage or anomaly. An anomaly mask is generated based on a time-sliding window and spatial localization algorithm, marking abnormal time intervals and spatial segments. Specifically, the anomaly mask is generated based on a robust leakage index, combined with the time-sliding window and spatial localization algorithm. The time-sliding window method is used to detect abnormal data within a continuous time period. By sliding the analysis window along the time dimension, changes in flow and pressure drop are monitored in real time to identify time intervals where anomalies may exist. The spatial localization algorithm, combined with the geometric information of the loop, identifies spatial segments where anomalies exist. Combining the time and spatial information, an anomaly mask is generated, marking abnormal time intervals and suspicious spatial segments. The resulting anomaly mask is a binary sequence, where a value of 1 indicates anomaly and a value of 0 indicates normal.

[0029] The present invention is further configured such that obtaining the robust flow sequence and calculating the representative value and intervalization result within the target time window includes: The corrected volumetric flow rate sequence is weighted to eliminate the influence of outliers, which are abnormal fluctuations in flow rate caused by instrument errors, environmental changes, or other unforeseen factors. To ensure the accuracy of the flow rate data, different weights are assigned to each data point to reduce the impact of outliers on the final result. Outlier data points are assigned smaller weights or are removed, while normal data points are assigned larger weights. This weighting process minimizes the impact of outliers on the data, resulting in a more stable and reliable flow rate sequence. For outlier data points, interpolation methods are used to replace flow values. Specifically, for data points marked as outliers, linear interpolation is used in the robust flow sequence to calculate the corresponding flow value using the values ​​of the two normal data points before and after the outlier, and this value is then used to replace the outlier. Interpolation ensures the continuity and stability of the flow sequence, avoiding calculation errors caused by missing or incorrect outliers. The mean, variance, maximum, and minimum flow rates within the target time window are calculated to obtain representative values ​​and intervalized results for that window. Specifically, the target time window is selected for statistical analysis within the corrected flow series. After weighting and outlier interpolation, the flow data within the target time window more accurately reflects the actual state of the system. Calculating the mean, variance, maximum, and minimum flow rates within the target time window helps analyze flow volatility, stability, and potential abnormal fluctuations. The flow mean represents the average flow level within the time window, reflecting the flow trend over the entire period. The flow variance indicates the degree of fluctuation in flow data within the time window, measuring the stability of the flow data. The maximum and minimum flow rates provide information on extreme values, identifying potential abnormal peaks or lows. Traffic flow within the target time window is intervalized, including the mean, variance, maximum, minimum, and representative values. Specifically, based on the mean, variance, maximum, and minimum values ​​of traffic flow calculated within the target time window, traffic flow intervalization is performed. The process of traffic flow intervalization involves dividing the traffic data into different intervals according to certain rules and generating corresponding representative values, including the mean, variance, maximum, and minimum values, to reflect the distribution characteristics of traffic flow within the time window.

[0030] The present invention is further configured such that the operating condition baseline, differential pressure data, flow rate data, and anomaly identification results throughout the detection process are recorded and associated, including: The operating condition baseline is updated by recording real-time temperature, static pressure, humidity, and corresponding air density. Specifically, operating condition data such as temperature, static pressure, and humidity are collected in real time and updated in combination with loop geometry parameters and timestamps. Operating condition data such as temperature, static pressure, and humidity are acquired in real time through sensors and the operating condition baseline is calculated in combination with real-time air density. Air density is calculated using a formula based on the current temperature, humidity, and static pressure and is updated as part of the operating condition baseline. All operating condition data and air density are recorded at each timestamp to ensure the accuracy and consistency of the operating condition baseline. A dynamic storage strategy is generated based on differential pressure data, flow rate data, and anomaly data. Timestamps and data types form a dataset, creating a historical record. Specifically, a dynamic storage strategy is generated based on differential pressure data, flow rate data, and anomaly data to ensure efficient data storage and management. Each time differential pressure and flow rate data are collected, a new data entry is generated based on the current timestamp and associated with the operating condition baseline. All differential pressure, flow rate, and anomaly data points are categorized and stored according to timestamps and data types, forming a dataset. Based on synchronous processing, a precise relationship between operating conditions and detection data is established. Specifically, synchronous processing technology ensures accurate correlation between operating condition baselines, differential pressure data, flow rate data, and anomaly identification results. All collected data, including operating condition data, differential pressure data, flow rate data, and anomaly data, have corresponding timestamps. Synchronization algorithms are used to synchronize the time of each dataset, ensuring time consistency across all data points.

[0031] The present invention is further configured such that the formation of the detection dataset and traceability information includes: Differential pressure and flow data are time-calibrated, and the timestamps corresponding to the stored data points are marked. Specifically, differential pressure and flow data are calibrated according to their respective timestamps. Differential pressure and flow sensors may have slight time offsets, therefore, a time synchronization algorithm is needed to correct the timestamps of the differential pressure and flow data. The time synchronization method used can be based on cross-correlation or interpolation to ensure that the differential pressure and flow data are aligned under the same time base. When the calibrated data points are stored in the database, the differential pressure and flow data are separately marked and associated with timestamps. This ensures that even in a multi-sensor environment, data collected by different sensors maintains temporal consistency, ensuring data accuracy and comparability. The system records the occurrence of abnormal events and generates anomaly identification results. Specifically, by comparing with preset anomaly detection thresholds, it identifies flow fluctuations or differential pressure changes that exceed the normal range. When the differential pressure or flow exceeds the preset threshold, the system automatically records the occurrence of the abnormal event and generates anomaly identification results. The anomaly event is tagged with the time of occurrence, anomaly type, and specific characteristics of the abnormal data. The generated anomaly identification results will contain detailed information about the event, such as the anomaly type (leakage, flow fluctuation, etc.), the time period of occurrence, and the magnitude of the anomaly. Detection data, operating condition information, differential pressure and flow data, and anomaly identification results form a dataset. After obtaining all differential pressure data, flow data, anomaly identification results, and operating condition information, the system will correlate these data to generate a complete detection dataset. The dataset contains the following information: differential pressure data, flow data, operating condition information, and anomaly identification results.

[0032] These data are linked through timestamps and relevant identifiers to form a complete historical record. Traceability information, through the association of identifiers and timestamps, ensures that each data point can be traced back to a specific time and operating condition. This information facilitates subsequent review, troubleshooting, and optimization.

[0033] Ultimately, the detection dataset and traceability information are stored in a secure database and can be queried and retrieved as needed. In this way, data from all detection processes is fully recorded and correlated, ensuring data integrity, traceability, and reliability.

[0034] Example 2:

[0035] Please see Figure 2 An exemplary air passage detection system based on the standard table method includes: The baseline generation module collects temperature, absolute static pressure, relative humidity and loop geometry parameters, constructs operating condition vectors, sets operating condition consistency constraint coefficients, calculates air density based on the operating condition vectors, and generates operating condition baselines. Preprocessing module: Based on the operating condition reference, perform unified time-scale correction on differential pressure, temperature, humidity and pressure and reference trigger signal, perform differential pressure sequence preprocessing, calculate differential pressure dynamic fidelity, and generate a compensated differential pressure sequence corresponding to the operating condition reference; Standard meter conversion module: Based on the air density parameters in the compensated differential pressure sequence and operating condition reference, it calls the standard meter conversion relationship according to the standard meter type and geometric parameters, calculates the instantaneous volumetric flow rate, and calculates the Reynolds number and instantaneous volumetric flow rate through parameter self-consistency update to form a nominal flow rate sequence; Reconstruction and Correction Module: Based on the nominal flow rate sequence and geometric parameter information in the operating condition baseline, the module reconstructs the cross-sectional velocity distribution, extracts the cross-sectional correction coefficient, corrects the non-uniformity of the nominal flow rate sequence, and generates a corrected volumetric flow rate sequence. Anomaly identification module: Based on the corrected volumetric flow rate sequence, establish inter-segment energy relationships and pressure drop-flow balance relationships, generate robust leakage index, identify abnormal time intervals and suspicious spatial segments, and form anomaly masks; Anomaly handling module: Performs weighted and elimination processing on the corrected volumetric flow rate sequence based on the anomaly mask to obtain a robust flow rate sequence, and calculates the representative value and intervalization result within the target time window; Recording and correlation module: Records and correlates the operating condition baseline, differential pressure data, flow data and anomaly identification results throughout the detection process to form a detection dataset and traceability information.

[0036] It should be noted that the air passage detection system based on the standard table method provided in the above embodiments and the air passage detection method based on the standard table method provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the air passage detection system based on the standard table method provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0037] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for detecting air passageways based on the standard table method, characterized in that, include: Collect temperature, absolute static pressure, relative humidity and loop geometry parameters, construct operating condition vector, set operating condition consistency constraint coefficient, calculate air density based on operating condition vector, and generate operating condition benchmark; Based on the operating condition reference, the differential pressure, temperature, humidity and pressure and reference trigger signal are uniformly time-scaled and corrected. The differential pressure sequence is preprocessed, the dynamic fidelity of differential pressure is calculated, and a compensated differential pressure sequence corresponding to the operating condition reference is generated. Based on the air density parameters in the compensated differential pressure sequence and operating condition reference, the instantaneous volumetric flow rate is calculated by calling the standard table conversion relationship according to the standard table type and geometric parameters. The Reynolds number and instantaneous volumetric flow rate are calculated through parameter self-consistency update to form the nominal flow rate sequence. Based on the geometric parameter information in the nominal flow series and the operating condition baseline, the cross-sectional velocity distribution is reconstructed, the cross-sectional correction coefficient is extracted, the cross-sectional non-uniformity correction is applied to the nominal flow series, and the corrected volumetric flow series is generated. Based on the modified volumetric flow rate sequence, establish the energy relationship between segments and the pressure drop-flow balance relationship, generate a robust leakage index, identify abnormal time intervals and suspicious spatial segments, and form an anomaly mask; The modified volumetric flow rate sequence is weighted and eliminated based on the anomaly mask to obtain a robust flow rate sequence. The representative value and intervalization result within the target time window are calculated. The operating condition baseline, differential pressure data, flow data, and anomaly identification results throughout the entire testing process are recorded and correlated to form a testing dataset and traceability information.

2. The air passage detection method based on the standard table method according to claim 1, characterized in that, Constructing the operating condition vector and generating the operating condition baseline includes: Collect temperature, absolute static pressure, relative humidity, loop geometric parameters and timestamps, establish a unified time axis, correct the time offset and jitter of each measurement channel, and obtain the aligned working condition sequence; Within a set time window, robustness processing is performed on the operating condition sequence to suppress outlier disturbances and form robust temperature, robust static pressure and robust relative humidity. These are combined to obtain the operating condition vector and combined with the loop geometric parameters to form a geometric information set. Based on the operating condition vector, the saturated water vapor pressure and water vapor partial pressure are calculated to obtain the basic value of moist air density. The operating condition deviation expression is constructed according to the reference operating condition set, the operating condition consistency constraint coefficient is calculated, and the consistency correction is applied to the basic value of moist air density to obtain the air density. The operating condition baseline is generated based on a unified time axis, operating condition vector, air density, and geometric information set.

3. The air passage detection method based on the standard table method according to claim 2, characterized in that, Generating the compensated differential pressure sequence corresponding to the operating condition baseline includes: Perform unified time-scale correction on differential pressure, temperature and humidity pressure, and reference trigger signals to ensure signal alignment across all channels; The differential pressure signal is bandpass filtered to remove low-frequency baseline drift and high-frequency noise. Empirical mode decomposition is used to extract the effective modes to obtain the denoised differential pressure sequence. Calculate the dynamic fidelity of the differential pressure signal, evaluate the proportion of effective components in the signal, and correct the differential pressure sequence based on the dynamic fidelity; Differential pressure compensation is performed based on the corrected differential pressure signal and the air density parameters in the operating condition reference to generate a compensated differential pressure sequence.

4. The air passage detection method based on the standard table method according to claim 3, characterized in that, The Reynolds number and instantaneous volumetric flow rate are calculated through parameter self-consistency updates to form a nominal flow rate sequence, including: The instantaneous volumetric flow rate is calculated based on the air density parameters in the compensated differential pressure sequence and operating condition reference, according to the standard gauge type and loop geometry parameters. Calculate the instantaneous flow velocity, obtain the Reynolds number based on the flow velocity and air density, and perform a self-consistent update; Based on the updated Reynolds number, the standard table conversion relationship is invoked to correct the flow coefficient and generate the nominal flow sequence.

5. The air passage detection method based on the standard table method according to claim 1, characterized in that, The nominal flow rate series is corrected for cross-sectional non-uniformity to generate a corrected volumetric flow rate series, including: Based on the nominal flow sequence and geometric parameter information in the operating condition reference, the cross-sectional velocity distribution is reconstructed, and the velocity field of the whole cross-section is calculated by interpolation method; Extract the cross-section correction factor and calculate the cross-section correction factor by the ratio of the velocity distribution to the theoretical uniform flow velocity; The nominal flow rate sequence is non-uniformly corrected based on the cross-sectional correction factor to generate the corrected volumetric flow rate sequence.

6. The air passage detection method based on the standard table method according to claim 5, characterized in that, Generate robust leakage index, identify abnormal time intervals and suspicious spatial segments, and form anomaly masks including: Based on the corrected volumetric flow rate sequence, the energy relationship between segments is established, the pressure drop within segments is calculated, and the energy balance within segments is deduced based on the relationship between flow rate and pressure drop. Based on the energy relationship between segments, a pressure drop-flow balance relationship is constructed, and the pressure drop correction coefficient is calculated through the exponential relationship between flow rate and pressure drop; The residual within the section is calculated based on the pressure drop-flow balance relationship, and a robust leakage index is generated. Based on a time-sliding window and spatial positioning algorithm, an anomaly mask is generated, marking the anomaly time interval and spatial segment to form the anomaly mask.

7. The air passage detection method based on the standard table method according to claim 6, characterized in that, Obtaining robust flow sequences and calculating representative values ​​and intervalization results within the target time window includes: The corrected volumetric flow rate sequence is weighted, and outlier data points are either weighted or removed to generate a robust flow rate sequence. Interpolation methods are used to replace flow values ​​for abnormal data points; Calculate the mean, variance, maximum and minimum flow rates within the target time window to obtain the representative value and intervalized results within the time window; The traffic flow within the target time window is ranged, including the traffic mean, variance, maximum value, minimum value, and representative value.

8. The air passage detection method based on the standard table method according to claim 1, characterized in that, The entire testing process includes recording and correlating operating condition baselines, differential pressure data, flow data, and anomaly identification results, including: Update the operating condition baseline and record real-time temperature, static pressure, humidity, and corresponding air density; A dynamic storage strategy is generated based on differential pressure data, flow data, and abnormal data. Timestamps and data types are combined to form a dataset, creating a historical record. Based on synchronous processing, a precise relationship between operating conditions and detection data is established.

9. The air passage detection method based on the standard table method according to claim 8, characterized in that, The formation of the detection dataset and traceability information includes: Perform time calibration on differential pressure data and flow data, and mark the timestamps corresponding to the stored data points; Record the occurrence of abnormal events and generate anomaly identification results; The detection data, operating condition information, differential pressure flow data, and anomaly identification results form a dataset.

10. An air passage detection system based on the standard table method, used to implement the air passage detection method based on the standard table method according to any one of claims 1-9, characterized in that, include: The baseline generation module collects temperature, absolute static pressure, relative humidity and loop geometry parameters, constructs operating condition vectors, sets operating condition consistency constraint coefficients, calculates air density based on the operating condition vectors, and generates operating condition baselines. Preprocessing module: Based on the operating condition reference, perform unified time-scale correction on differential pressure, temperature, humidity and pressure and reference trigger signal, perform differential pressure sequence preprocessing, calculate differential pressure dynamic fidelity, and generate a compensated differential pressure sequence corresponding to the operating condition reference; Standard meter conversion module: Based on the air density parameters in the compensated differential pressure sequence and operating condition reference, it calls the standard meter conversion relationship according to the standard meter type and geometric parameters, calculates the instantaneous volumetric flow rate, and calculates the Reynolds number and instantaneous volumetric flow rate through parameter self-consistency update to form a nominal flow rate sequence; Reconstruction and Correction Module: Based on the nominal flow rate sequence and geometric parameter information in the operating condition baseline, the module reconstructs the cross-sectional velocity distribution, extracts the cross-sectional correction coefficient, corrects the non-uniformity of the nominal flow rate sequence, and generates a corrected volumetric flow rate sequence. Anomaly identification module: Based on the corrected volumetric flow rate sequence, establish inter-segment energy relationships and pressure drop-flow balance relationships, generate robust leakage index, identify abnormal time intervals and suspicious spatial segments, and form anomaly masks; Anomaly handling module: Performs weighted and elimination processing on the corrected volumetric flow rate sequence based on the anomaly mask to obtain a robust flow rate sequence, and calculates the representative value and intervalization result within the target time window; Recording and correlation module: Records and correlates the operating condition baseline, differential pressure data, flow data and anomaly identification results throughout the detection process to form a detection dataset and traceability information.

Images