System and method for calibrating pipeline flow measuring instrument

Abstract

The invention provides a system and method for calibrating a pipeline flow measuring instrument, and the method comprises the steps: calculating difference index values between channels according to a flow distribution feature vector, carrying out the grouping of the difference index values through a clustering analysis method, and determining a channel set with unbalanced flow distribution for subsequent abnormality analysis; acquiring flow data of an abnormal channel from the abnormity identification result, and correcting the flow data by adopting a self-adaptive filtering method to obtain a corrected flow distribution diagram for recovering the balance state of the pipeline system; and recalculating an overall calibration parameter according to the corrected flow distribution diagram, adjusting the calibration parameter value through an iterative optimization method, judging whether a calibration error tends to be stable or not, and obtaining a final pipeline flow calibration model.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a calibration system method for a pipeline flow measurement instrument. Background Technology

[0002] Calibration systems for pipeline flow measurement instruments are a crucial research area, playing an indispensable role in industrial production and resource management; their importance is self-evident. Particularly in industries such as energy, chemicals, and water resources, the accuracy of flow measurement directly impacts production efficiency and cost control, and even affects the implementation of safety and environmental standards. However, despite years of research and application in this field, breakthroughs in its core technologies remain a key objective for the industry.

[0003] Currently, while calibration methods for pipeline flow measurement meet basic requirements to some extent, they generally suffer from insufficient adaptability. Many existing solutions often struggle to cope with dynamic changes under complex operating conditions, especially in multi-channel parallel calibration environments, where the system's ability to perceive and adjust for differences between different channels is inadequate. This limitation does not stem solely from simple technical flaws, but rather from a lack of comprehensive attention to the flow distribution characteristics and abnormal fluctuations in multi-channel environments, thus challenging the reliability of calibration results.

[0004] Focusing on the technical challenges, uneven flow distribution in multi-channel calibration environments becomes the primary issue. Due to differences in pipe structure or resistance, the flow distribution in different channels is often unbalanced. This imbalance can further lead to deviations in calibration accuracy and even cause complete data distortion in some channels. A deeper problem is that when this imbalance occurs, the system often fails to detect abnormal fluctuations in a timely manner, lacking the ability to quickly identify abnormal channels. These two issues are closely related: uneven flow distribution directly affects the generation of abnormal fluctuations, while the concealment of these fluctuations exacerbates the risk of calibration deviations. For example, in a multi-channel calibration scenario, if a pipe has a lower flow rate due to higher resistance, and the system fails to detect this anomaly in time, it may lead to the accumulation of errors throughout the calibration process, ultimately affecting the accuracy of the measuring instruments.

[0005] Therefore, how to accurately perceive the differences in flow distribution among channels and promptly identify abnormal fluctuations to avoid calibration errors in a multi-channel parallel calibration environment has become a key problem that this study urgently needs to solve. Summary of the Invention

[0006] This invention provides a calibration system method for pipeline flow measurement instruments, mainly including:

[0007] Real-time flow monitoring data is acquired by a sensor array deployed in a multi-channel pipeline system. The flow monitoring data is processed by a machine learning classification method to obtain the flow distribution feature vector of each channel, which reflects the fluid flow characteristics in the pipeline.

[0008] The difference index values ​​between channels are calculated based on the traffic distribution feature vector. Cluster analysis is used to group the difference index values ​​to determine the set of channels with uneven traffic distribution for subsequent anomaly analysis.

[0009] If there are significantly different index values ​​in the set of unbalanced channels, relevant time series data are extracted from them, and frequency domain analysis technology is used to process the time series data to obtain an abnormal fluctuation frequency spectrum for identifying pipeline flow anomalies.

[0010] The peak amplitude characteristics are evaluated by applying a peak detection method to the abnormal fluctuation frequency spectrum. If the peak amplitude exceeds a preset threshold range, the corresponding channel is marked as abnormal, and the abnormal identification result is used to guide subsequent correction.

[0011] The flow data of the abnormal channel is obtained from the anomaly identification results, and the flow data is corrected by an adaptive filtering method to obtain a corrected flow distribution map for restoring the balance state of the pipeline system.

[0012] The overall calibration parameters are recalculated based on the corrected flow distribution map. The calibration parameter values ​​are adjusted through an iterative optimization method. The calibration error is then judged to determine whether it tends to stabilize, and the final pipeline flow calibration model is obtained.

[0013] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0014] This invention discloses a multi-channel pipeline flow monitoring and calibration method, addressing flow anomalies and overall imbalances caused by uneven flow distribution in pipeline systems. This problem stems from amplified differences between channels, leading to abnormal fluctuations that affect system stability and efficiency. The invention acquires real-time flow data by deploying a sensor array, employs machine learning to extract distribution feature vectors, calculates difference indices, and uses cluster analysis to identify the set of unbalanced channels. If significant differences exist, time series data is extracted for frequency domain analysis and peak detection. Abnormal channels are marked, and adaptive filtering is used to correct the flow data. The calibration parameters are then iteratively optimized until the error stabilizes. This integrated monitoring, identification, and correction process enables precise anomaly localization and flow balance restoration, ultimately improving the stability and operational efficiency of the pipeline system and reducing potential failure risks. Attached Figure Description

[0015] Figure 1 This is a flowchart of a calibration system method for a pipeline flow measurement instrument according to the present invention.

[0016] Figure 2 This is a schematic diagram of a calibration system method for a pipeline flow measurement instrument according to the present invention.

[0017] Figure 3 This is another schematic diagram of a calibration system method for a pipeline flow measurement instrument according to the present invention. Detailed Implementation

[0018] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0019] like Figure 1-3 The calibration system method for a pipeline flow measurement instrument in this embodiment may specifically include:

[0020] Step S101: Real-time flow monitoring data is acquired by a sensor array deployed in a multi-channel pipeline system. The flow monitoring data is processed using a machine learning classification method to obtain the flow distribution feature vector of each channel, which reflects the fluid flow characteristics in the pipeline.

[0021] Based on the business content and extracted relevant attributes, the following business solution is generated, outlining the technical process around attributes such as sensor arrays, multi-channel pipelines, real-time flow, data acquisition, machine learning, classification methods, flow distribution, feature vectors, pipeline fluid, flow characteristics, data processing, and system deployment. The following steps are built upon interrelated attributes, discarding isolated content that cannot form a logical connection, and employing Support Vector Machine (SVM) as the sole machine learning algorithm for processing. A sensor array deployed in the multi-channel pipeline collects real-time flow data for each channel, recording the flow value at each time point to obtain an initial flow dataset. For the initial flow dataset, a preprocessing method is used to remove noise interference. If the flow value of a certain channel exceeds a preset threshold range, the value is smoothed to obtain a processed flow dataset. Based on the processed flow dataset, the flow change patterns of each channel over different time periods are extracted. The average flow and fluctuation amplitude of each channel are calculated using statistical methods to determine the flow distribution of each channel. The SVM algorithm is used to classify the flow distribution, and feature extraction is performed on the flow change patterns of each channel to obtain feature vectors reflecting the flow distribution. By analyzing the flow characteristics of fluid within the pipeline using eigenvector analysis, if the eigenvector of a certain channel shows abnormal fluctuations, that channel is marked as a potential abnormal region, and abnormal distribution information is obtained. Based on the abnormal distribution information, combined with the structural layout of the multi-channel pipeline, the flow correlation between each channel is determined, resulting in an overall flow characteristic description of the pipeline system. Using this overall flow characteristic description, flow control suggestion data for each channel is generated. If the flow correlation of a certain channel is lower than a preset threshold, it is recorded as an area requiring optimization, thus determining the final system optimization data.

[0022] For example, when deploying a sensor array in a multi-channel pipeline system, consider an industrial water pipeline network containing 10 parallel channels. Each channel is equipped with a high-precision flow sensor to collect flow data in real time. The sensor array records the flow value once per second. Assuming the flow value of a certain channel fluctuates between 5 and 15 cubic meters per hour within a certain minute, if it exceeds a preset threshold range, such as 3 to 20 cubic meters per hour, smoothing is used to adjust the outlier to the average value of nearby time points, thereby reducing noise interference and ensuring data reliability. In the data processing stage, for the processed flow dataset, statistical methods can be used to calculate the average flow and fluctuation range of each channel. For example, channel 1 has an average flow of 10 cubic meters per hour with a fluctuation range of 2 cubic meters per hour, while channel 2 has an average flow of 8 cubic meters per hour with a fluctuation range of 5 cubic meters per hour, indicating that channel 2 may have unstable flow characteristics. This flow distribution provides the basic data for subsequent classification.

[0023] For example, in feature extraction and classification methods, the support vector machine algorithm is used to analyze the flow change patterns of each channel. Feature vectors can be extracted, including indicators such as flow peaks and fluctuation frequencies. If the feature vector of channel 3 shows a fluctuation frequency more than twice that of other channels, it is marked as a potential anomaly area. This method helps to accurately locate problem areas and improve system diagnostic efficiency. When analyzing the fluid flow characteristics of a pipeline, if the abnormal fluctuations in channel 3 are related to the decreasing flow trend in the adjacent channel 4, it can be inferred that there is a flow correlation problem between the two. By combining the structural layout of the multi-channel pipeline, the overall flow characteristics can be judged. For example, it may be found that the valve between channels 3 and 4 is blocked, leading to uneven flow distribution. This allows for the generation of control suggestion data, proposing optimization schemes such as increasing the flow in channel 3 and checking the valve status.

[0024] For example, during system deployment and optimization, if the traffic correlation of channel 5 is lower than the preset threshold of 0.6, it is recorded as an area requiring optimization, and it is recommended to adjust its upstream diversion ratio to improve overall system stability. This control method based on real-time traffic data and machine learning classification not only improves the operating efficiency of the pipeline system but also reduces maintenance costs and extends equipment life. Through the above multi-faceted analysis, from data acquisition to final optimization recommendations, each step is closely linked, forming a complete technical process that significantly improves system performance.

[0025] Step S102: Calculate the difference index value between channels based on the traffic distribution feature vector, and use cluster analysis to group the difference index value to determine the set of channels with uneven traffic distribution for subsequent anomaly analysis.

[0026] By deeply processing the traffic distribution feature vectors, cluster analysis is used to group channel differences, obtaining a set of unevenly distributed channels. Based on this unevenly distributed channel set, traffic fluctuation data for each channel within a specific time period is extracted. Statistical tools are used to normalize the fluctuation data, resulting in a standardized fluctuation dataset. Using this standardized fluctuation dataset, the correlation of traffic fluctuations between channels is analyzed. If the correlation between a channel's fluctuation data and other channels is below a preset threshold, it is marked as an isolated channel, and a list of isolated channels is determined. For the isolated channel list, historical traffic records for the corresponding channels are obtained. Time series analysis is used to perform pattern mining on these historical traffic records, obtaining the periodic fluctuation patterns of each isolated channel. Based on these periodic fluctuation patterns, the periodic matching degree between isolated channels and other channels is calculated. If the matching degree of an isolated channel is below a preset threshold, it is classified as a key monitoring target, determining a set of key monitoring channels. From the set of key monitoring channels, information on abrupt changes in real-time traffic data is extracted. Anomaly detection tools are used to filter this abrupt change information, resulting in a list of potential abnormal time periods. Based on the list of potential abnormal time periods, and combined with the correlation data of flow fluctuations between channels, the impact range of abnormal time periods on the overall pipeline system is analyzed, and the propagation path of abnormal fluctuations is determined.

[0027] For example, when processing flow distribution feature vectors in depth, it's helpful to first understand the principles behind cluster analysis. Cluster analysis aims to group channels with similar characteristics, identifying sets with uneven flow distributions. Suppose an industrial water pipeline network has eight parallel channels. Cluster analysis reveals that the flow distribution of three channels significantly deviates from the average; these channels are grouped into an unevenly distributed set, laying the foundation for subsequent analysis.

[0028] For example, when extracting flow fluctuation data for a specific time period from an unevenly distributed set of channels, we can imagine recording the flow rate per minute for these three channels between 8:00 AM and 10:00 AM on a certain day. We might find that the flow rate of a certain channel frequently changes between 5 and 12 cubic meters per hour. After normalizing this data using statistical tools, the fluctuation data is standardized into a uniform range, facilitating subsequent comparison and analysis.

[0029] For example, when analyzing a standardized volatility dataset to determine the correlation between channels, if the correlation between the volatility data of a certain channel and other channels is lower than a preset threshold of 0.5, it is marked as an isolated channel. For instance, if the correlation between channel 2 and other channels is only 0.3, significantly lower than the threshold, it is included in the list of isolated channels, indicating a potential independent issue that requires attention.

[0030] For example, when analyzing historical traffic records for an isolated channel list, time series analysis can be used to uncover periodic fluctuation patterns. Suppose that historical data for channel 2 shows a significant traffic peak every 4 hours. This periodic pattern differs considerably from the fluctuation patterns of other channels, providing a basis for subsequent matching degree calculations.

[0031] For example, when calculating the periodic matching degree of an isolated channel with other channels, if the matching degree of channel 2 is only 0.2, which is far below the preset threshold of 0.6, it is classified as an object requiring key monitoring. This classification method helps to concentrate resources on high-risk areas.

[0032] For example, when extracting information on abrupt changes in real-time traffic data from a set of key monitoring channels, anomaly detection tools can be used to filter out the time points when the traffic value suddenly jumps from 10 cubic meters per hour to 18 cubic meters per hour, listing them as potential abnormal time periods. This filtering method can quickly pinpoint potentially problematic time periods.

[0033] For example, when analyzing the impact of abnormal time periods on the overall pipeline system, the correlation data of flow fluctuations between channels can be used to determine whether abnormal fluctuations will propagate to adjacent channels. Assuming that a sudden change in channel 2 causes a 30% decrease in flow in adjacent channel 3, it can be inferred that the propagation path of the abnormal fluctuations may involve multiple channels, providing a reference for subsequent adjustments. Through the above multi-faceted analysis and examples, each link is interconnected, forming a complete technical chain, which helps to accurately identify problems and optimize system operation.

[0034] Step S103: If there are significant difference index values ​​in the unbalanced channel set, relevant time series data are extracted from them, and frequency domain analysis technology is used to process the time series data to obtain an abnormal fluctuation frequency spectrum for identifying pipeline flow anomalies.

[0035] By extracting channel data with high difference index values ​​from an unbalanced channel set, frequency domain analysis is used to process the channel data, resulting in an abnormal fluctuation frequency distribution map. Based on the abnormal fluctuation frequency distribution map, channel identifiers for high-frequency abnormal fluctuations are extracted. A comparison tool is used to match these channel identifiers with a pre-defined abnormal pattern library to determine a list of potential abnormal channels. For the potential abnormal channel list, traffic change data for the corresponding channels over multiple time periods is obtained. Data smoothing tools are used to preprocess the traffic change data to obtain a smoothed traffic change sequence. If fluctuation points exceeding a preset threshold exist in the smoothed traffic change sequence, these fluctuation points are timestamped. Traffic details for the relevant time periods are extracted using these timestamped points to determine the duration of the abnormal fluctuation. Based on the duration of the abnormal fluctuation, the traffic change frequency distribution for the corresponding channel is obtained. Statistical tools are used to segment and analyze the frequency distribution to determine the periodicity of the abnormal fluctuation. Based on the periodicity of the abnormal fluctuation, historical traffic records related to the periodicity are extracted. If similar periodic fluctuations exist in the historical records, pattern comparison is performed on the fluctuations to confirm the repeatability of the abnormal fluctuations. If the repeatability confirmation results of abnormal fluctuations show that the fluctuation pattern is consistent, the real-time traffic data of the channel will be continuously monitored, and the evolution trend of abnormal fluctuations will be determined by comparing the monitoring data with historical records.

[0036] For example, when dealing with channel data with high difference index values ​​in an unbalanced channel set, one can start by understanding the principles of frequency domain analysis. Frequency domain analysis transforms time series data into a frequency distribution, revealing hidden periodic or abnormal frequency components in flow fluctuations. Suppose that in an industrial pipeline network, the difference index value of a certain channel reaches 8.5, far exceeding the average of 3.2. Frequency domain analysis yields an abnormal fluctuation frequency distribution map, revealing that its high-frequency fluctuations are concentrated around 15 times per hour, significantly deviating from the normal range.

[0037] For example, in the process of extracting channel identifiers for high-frequency abnormal fluctuations and comparing them with an anomaly pattern library, we can imagine identifying the aforementioned channel as C-7 and matching it with high-frequency abnormal patterns in the preset pattern library. If we find that its fluctuation frequency matches a certain pattern in the library with a degree of 85%, then it is added to the list of potential abnormal channels. This comparison method helps to quickly screen out channels of interest.

[0038] For example, when acquiring and smoothing traffic change data for channels in a potential abnormal channel list, suppose channel C-7 exhibits drastic fluctuations in traffic over the past 24 hours, ranging from 6 to 14 cubic meters per hour. After using a data smoothing tool, a smoothed sequence is obtained, where a fluctuation point in a certain time period exceeds the preset threshold of 2.5, reaching 3.1. By marking this fluctuation point with a timestamp and extracting detailed traffic data for the relevant time period, it is found that the abnormal fluctuation lasted for approximately 20 minutes, far exceeding the normal fluctuation duration.

[0039] For example, to analyze the duration of abnormal fluctuations and determine their periodicity, statistical tools can be used to segment the frequency distribution of flow changes in channel C-7, revealing that its abnormal fluctuations exhibit a periodicity of once every 6 hours. Extracting this periodicity provides direction for subsequent comparisons with historical records.

[0040] For example, when extracting historical records related to periodic characteristics and performing pattern comparisons, assuming that data from the past month for channel C-7 shows that the fluctuation pattern every 6 hours is highly similar to the current abnormal fluctuations, and the repeatability confirmation results show that the pattern consistency reaches 90%. This comparison result indicates that the abnormal fluctuations are not accidental but may be related to the system's operating rules.

[0041] For example, to continuously monitor and analyze the evolution trends of real-time traffic data, the real-time data of channel C-7 can be recorded every minute. This reveals that the fluctuation range is gradually increasing, and compared to historical records, the abnormal fluctuations show an intensifying trend. This continuous monitoring method helps to promptly grasp abnormal dynamics and provides a basis for subsequent adjustments.

[0042] Step S104: Apply a peak detection method to evaluate the peak amplitude characteristics of the abnormal fluctuation frequency spectrum. If the peak amplitude exceeds a preset threshold range, mark the corresponding channel as an abnormal state and obtain the abnormal identification result to guide subsequent correction.

[0043] Step 1: For channels marked as abnormal, obtain historical traffic data from the channels and segment the data using a time series segmentation tool to obtain segmented traffic data sequences. Step 2: Based on the segmented traffic data sequences, apply statistical analysis methods to calculate the traffic fluctuation amplitude within each time period, determining the fluctuation amplitude distribution characteristics of each time period. Step 3: Based on the fluctuation amplitude distribution characteristics, obtain the channel operating parameters related to the characteristics, and compare the operating parameters with preset standard values ​​using a parameter comparison tool to determine the degree of parameter deviation. Step 4: If the parameter deviation exceeds a preset range, collect the real-time operating data of the channel, and extract key operating indicators using a data filtering tool to obtain a set of key indicators. Step 5: Based on the set of key indicators, use a logical judgment tool to match the indicators with historical abnormal records. If the matching results show a similar pattern, mark the channel as high-risk and determine a list of high-risk channels. Step 6: For the high-risk channel list, obtain the real-time monitoring data of the corresponding channels, and continuously analyze the monitoring data using a data stream processing tool to determine whether there is a persistent abnormal fluctuation trend. Step 7: If the continuous abnormal fluctuation trend is confirmed, the operation log data of the channel is deeply mined, and the correlation analysis method is used to extract the potential relationship between abnormal fluctuations and operation parameters to obtain the abnormal correlation pattern.

[0044] For example, when dealing with channels marked as abnormal, one can start by acquiring and segmenting historical traffic data. Time series segmentation tools can divide traffic data over a long period of time into multiple smaller segments at fixed time intervals for subsequent analysis.

[0045] For example, suppose an industrial pipeline is marked as abnormal. After obtaining its flow data for the past 48 hours, a segmentation tool is used to divide it into segments of 6 hours each, resulting in a flow data sequence of 8 time periods, which lays the foundation for subsequent fluctuation analysis.

[0046] For example, for segmented traffic data sequences, statistical analysis methods can be used to calculate the traffic fluctuation range within each time period. The fluctuation range can be calculated by extracting the maximum and minimum values ​​within each time period, and its distribution characteristics can be observed. Suppose that the traffic value in a certain time period fluctuates between 2.5 and 5.8 cubic meters per hour, with a fluctuation range of 3.3, which is significantly higher than the average range of 1.8 for other time periods. This distribution characteristic suggests the possible existence of outliers.

[0047] For example, after analyzing the distribution characteristics of fluctuation amplitude, obtaining the relevant channel operating parameters is a crucial step. Using parameter comparison tools, current parameters such as pressure and temperature are compared one by one with preset standard values ​​to determine the degree of deviation. For instance, if the standard pressure value is 50 units, while the actual measured value is 58 units, the deviation is 16%, exceeding the preset range by 10%, indicating a need for further monitoring.

[0048] For example, if parameters deviate significantly from their range, real-time data collection becomes crucial. Data filtering tools can extract key operational indicators, such as flow rate and pressure change rate, forming a set of key indicators. Suppose that the flow rate in the real-time collected data is 7.2 cubic meters per hour, far exceeding the normal value of 4.5. After filtering, this flow rate can be included in the set of key indicators for subsequent matching and analysis.

[0049] For example, for a set of key indicators, logical judgment tools can be used to perform pattern matching with historical anomaly records. If the matching result shows that the current indicator has a similarity of 80% with a certain historical anomaly record, then the channel is marked as high-risk. Assuming that the indicator of channel D-3 matches the historical record, it is added to the list of high-risk channels, providing a key target for subsequent monitoring.

[0050] For example, continuous analysis of real-time monitoring data is essential for a list of high-risk channels. Data stream processing tools can update data every minute to determine if there are persistent abnormal fluctuation trends. For instance, if channel D-3's fluctuation range has been continuously increasing over the past two hours, rising from 2.1 to 3.9, it indicates a persistent abnormal trend and requires further processing.

[0051] For example, if a persistent abnormal fluctuation trend is confirmed, the next step is to deeply analyze the operational log data. By using correlation analysis methods, potential connections between abnormal fluctuations and operational parameters are extracted, forming abnormal correlation patterns. For instance, if the analysis finds that each increase in traffic fluctuation is accompanied by a synchronous increase in pressure values, with a correlation rate as high as 75%, this pattern can help in subsequently adjusting operational strategies in a targeted manner.

[0052] Step S105: Obtain the flow data of the abnormal channel from the anomaly identification result, and use an adaptive filtering method to correct the flow data to obtain a corrected flow distribution map for restoring the balance state of the pipeline system.

[0053] Step 1: Obtain the flow data of the abnormal channel from the anomaly identification results. Use a data filtering tool to initially clean the flow data, removing noisy data to obtain a cleaned flow dataset. Step 2: For the cleaned flow dataset, use an adaptive filtering method to smooth the data, generating a smoothed flow data sequence. Step 3: Based on the smoothed flow data sequence, construct a corresponding flow distribution map using a data mapping tool to determine the uniformity of the flow distribution. Step 4: If the uniformity of the flow distribution does not reach a preset threshold range, collect real-time monitoring data of the abnormal channel to obtain a real-time monitoring dataset. Step 5: For the real-time monitoring dataset, use a data comparison tool to match and analyze historical flow data to determine if there is a persistent deviation pattern. Step 6: If a persistent deviation pattern is confirmed, use a data adjustment tool to dynamically correct the flow parameters of the abnormal channel, generating a corrected parameter configuration. Step 7: Based on the corrected parameter configuration, use a data update tool to synchronously adjust the flow allocation mechanism of the pipeline system to determine the balanced state after system recovery.

[0054] For example, when processing flow data from abnormal channels, data cleaning can be the starting point, specifically targeting the removal of noisy data. Suppose an industrial pipeline channel is marked as abnormal, and its flow data contains invalid values ​​caused by sensor jitter. Data filtering tools can be used to remove these abnormal high-frequency fluctuation values, retaining data that falls within the normal fluctuation range. The cleaning process can set a fluctuation range threshold; for example, data with hourly flow variations not exceeding 10 cubic meters is considered valid data. The cleaned dataset will better reflect the true flow status.

[0055] For example, when using adaptive filtering to smooth a cleaned traffic dataset, the filtering parameters can be dynamically adjusted based on the data fluctuation characteristics. If a segment of traffic data experiences multiple small fluctuations within a short period, adaptive filtering will automatically select a smaller smoothing window to reduce the loss of detail while preserving the main trend. The smoothed data sequence can more clearly demonstrate the overall pattern of traffic changes, facilitating subsequent analysis.

[0056] For example, when constructing traffic distribution maps, data mapping tools can map smoothed data onto a two-dimensional chart along the time dimension, intuitively reflecting the uniformity of traffic distribution. Suppose a traffic distribution map for a certain channel shows that traffic values ​​are concentrated between 3 and 4 cubic meters per hour in some time periods, while reaching as high as 8 cubic meters per hour in other time periods. This uneven distribution suggests potential problems that require further attention.

[0057] For example, if the uniformity of traffic flow distribution is not up to standard, the collection of real-time monitoring data becomes particularly important. Traffic flow values ​​can be collected every minute by sensors to create a real-time monitoring dataset. Suppose the collected data shows that the traffic flow value of a certain channel suddenly increases from 5 cubic meters per hour to 9 cubic meters per hour in a short period of time; this abnormal trend needs to be recorded and analyzed promptly.

[0058] For example, in matching and analyzing real-time monitoring datasets with historical data, data comparison tools can be used to identify persistent deviation patterns. Suppose historical data shows normal flow rates are stable at 4 to 6 cubic meters per hour, while current data consistently exceeds 8 cubic meters per hour. This deviation pattern indicates a potential systemic problem requiring further investigation.

[0059] For example, after confirming a persistent deviation pattern, dynamically correcting flow parameters becomes crucial. Data adjustment tools can be used to adjust the opening of the flow control valve based on real-time data and historical benchmark values. For instance, if the current flow rate is too high, the tool will automatically reduce the valve opening by 10%, gradually bringing the flow rate back to the normal range and creating a corrected parameter configuration.

[0060] For example, based on the corrected parameter configuration, the flow distribution mechanism of the pipeline system is adjusted synchronously to ensure overall balance. Data update tools can apply the new parameters to the system in real time. Assuming that the flow rate in each channel stabilizes at around 5 cubic meters per hour after adjustment, it indicates that the system has returned to a balanced state. This approach helps improve system stability and operating efficiency.

[0061] Step S106: Recalculate the overall calibration parameters based on the corrected flow distribution map, adjust the calibration parameter values ​​through an iterative optimization method, determine whether the calibration error tends to stabilize, and obtain the final pipeline flow calibration model.

[0062] Step 1: Obtain key distribution data from the corrected flow distribution map. For this distribution data, use data extraction tools to separate the core parameters of the pipeline flow, obtaining a preliminary flow distribution feature set. Step 2: Based on the preliminary flow distribution feature set, use data processing tools to classify and organize the core parameters, determining the distribution pattern of pipeline flow in different intervals, obtaining a classified parameter set. Step 3: For the classified parameter set, use an iterative optimization method to adjust the calibration parameters layer by layer. If the adjusted calibration error exceeds a preset threshold range, use a data comparison tool to re-extract the distribution features, obtaining an adjusted parameter dataset. Step 4: Based on the adjusted parameter dataset, use a data mapping tool to construct a calibration framework for pipeline flow, determining whether the calibration error has reached a stable state, obtaining a preliminary calibration model. Step 5: For the preliminary calibration model, use a data verification tool to compare and analyze the calculated pipeline flow distribution. If there is a deviation between the calculated distribution results and the actual flow distribution, use a parameter correction tool for fine-tuning, determining the corrected calibration framework. Step Six: Based on the revised calibration framework, use data integration tools to perform final matching of the calibration parameters for pipeline flow, determine whether the parameter stability meets the preset standards, and obtain the final flow calibration model.

[0063] For example, when processing flow distribution data for a pipeline system, one can begin by acquiring key distribution data. Based on the revised flow distribution map, data extraction tools can be used to extract core parameters. Suppose a pipeline system's flow distribution map shows significant differences in flow values ​​across multiple time periods. The data extraction tool will prioritize selecting hourly flow peaks and troughs as core parameters, thus initially forming a flow distribution feature set. This approach helps to quickly focus on key points of pipeline flow changes, laying the foundation for subsequent analysis.

[0064] For example, based on the initial flow distribution feature set, data processing tools can classify and organize core parameters to determine the distribution patterns within different intervals. Suppose that in the extracted flow data, the flow rate is concentrated between 2 and 3 cubic meters per hour in some time periods, while in other time periods it is between 6 and 7 cubic meters per hour. The tool will classify the data into two categories: low flow and high flow, resulting in a categorized parameter set. This classification method facilitates the identification of pipeline flow distribution patterns, providing a basis for further calibration.

[0065] For example, when processing the categorized parameter set, iterative optimization methods can be used to adjust calibration parameters layer by layer. Assuming the initial calibration error is 15%, exceeding a preset 10% threshold, the data comparison tool will re-extract the distribution features and adjust them to form a new parameter dataset. This iterative process can gradually narrow the error range, ensuring that the calibration parameters more closely reflect the actual traffic distribution.

[0066] For example, based on an adjusted parameter dataset, a data mapping tool can construct a calibration framework for pipeline flow. Assuming that the calibration error stabilizes within 5% through mapping analysis, a preliminary calibration model can be formed. This framework construction method helps to intuitively assess the calibration effect and provides a reference for subsequent verification.

[0067] For example, for an initial calibration model, the data validation tool can compare the calculated flow distribution with the actual flow distribution. Suppose the calculated flow rate for a certain section of pipe is 4 cubic meters per hour, while the actual value is 5 cubic meters per hour, the parameter correction tool will fine-tune the calculation to determine the corrected calibration framework. This validation and fine-tuning mechanism effectively reduces bias and improves the model's applicability.

[0068] For example, in the final stage, the data integration tool matches the calibration parameters to determine if they meet preset standards. Assuming that after matching, the flow rate in each section of the pipeline stabilizes between 4.5 and 5.5 cubic meters per hour, the final flow calibration model is confirmed. This integration and matching process ensures parameter stability, providing reliable support for the long-term operation of the pipeline system.

[0069] The above embodiments are merely one of the preferred embodiments of the present invention and should not be used to limit the scope of protection of the present invention. Any modifications or refinements made to the main design concept and spirit of the present invention that are not of substantial significance, but solve the same technical problem as the present invention, should be included within the scope of protection of the present invention.

Claims

1. A calibration system method for a pipeline flow measurement instrument, characterized in that, The method includes: Real-time flow monitoring data is acquired by a sensor array deployed in a multi-channel pipeline system. The flow monitoring data is processed by a machine learning classification method to obtain the flow distribution feature vector of each channel, which reflects the fluid flow characteristics in the pipeline. The difference index values ​​between channels are calculated based on the traffic distribution feature vector. Cluster analysis is used to group the difference index values ​​to determine the set of channels with uneven traffic distribution for subsequent anomaly analysis. If there are significantly different index values ​​in the set of unbalanced channels, relevant time series data are extracted from them, and frequency domain analysis technology is used to process the time series data to obtain an abnormal fluctuation frequency spectrum for identifying pipeline flow anomalies. The peak amplitude characteristics are evaluated by applying a peak detection method to the abnormal fluctuation frequency spectrum. If the peak amplitude exceeds a preset threshold range, the corresponding channel is marked as abnormal, and the abnormal identification result is used to guide subsequent correction. The flow data of the abnormal channel is obtained from the anomaly identification results, and the flow data is corrected by an adaptive filtering method to obtain a corrected flow distribution map for restoring the balance state of the pipeline system. The overall calibration parameters are recalculated based on the corrected flow distribution map. The calibration parameter values ​​are adjusted through an iterative optimization method. The calibration error is then judged to determine whether it tends to stabilize, and the final pipeline flow calibration model is obtained.

2. The calibration system method for a pipeline flow measurement instrument according to claim 1, characterized in that, The process involves acquiring real-time flow monitoring data through a sensor array deployed in a multi-channel pipeline system, processing the flow monitoring data using a machine learning classification method, and obtaining flow distribution feature vectors for each channel to reflect the fluid flow characteristics within the pipeline, including: Based on the business content and the extracted relevant attributes, the following business solution is generated, which focuses on the technical process of sensor arrays, multi-channel pipelines, real-time flow, data acquisition, machine learning, classification methods, flow distribution, feature vectors, pipeline fluid, flow characteristics, data processing and system deployment. The following steps are based on interrelated attributes, discarding isolated content that cannot form a logical connection, and using support vector machine as the only machine learning algorithm for processing; By deploying a sensor array in a multi-channel pipeline, real-time flow data of each channel is collected, and the flow value at each time point is recorded to obtain an initial flow dataset. For the initial traffic dataset, a preprocessing method is used to remove noise interference. If the traffic value of a certain channel is detected to exceed the preset threshold range, the value is smoothed to obtain the processed traffic dataset. Based on the processed traffic dataset, the traffic change patterns of each channel in different time periods are extracted, and the average traffic and fluctuation amplitude of each channel are calculated by statistical methods to determine the traffic distribution of each channel. The support vector machine algorithm is used to classify the traffic distribution, and features are extracted for the traffic change patterns of each channel to obtain feature vectors that reflect the traffic distribution. By analyzing the flow characteristics of fluid in the pipeline using feature vector analysis, if the feature vector of a certain channel shows abnormal fluctuations, the channel is marked as a potential abnormal area to obtain abnormal distribution information. Based on the abnormal distribution information and combined with the structural layout of the multi-channel pipeline, the flow correlation between each channel is determined, and the overall flow characteristics of the pipeline system are described. By describing the overall flow characteristics, suggested data for flow control of each channel is generated. If the flow correlation of a certain channel is lower than the preset threshold, it is recorded as an area that needs to be optimized, and the final system optimization data is determined.

3. The calibration system method for a pipeline flow measurement instrument according to claim 1, characterized in that, The step of calculating the difference index value between channels based on the traffic distribution feature vector, and using cluster analysis to group the difference index value to determine the set of channels with uneven traffic distribution for subsequent anomaly analysis includes: By deeply processing the characteristic vector of traffic distribution, cluster analysis is used to group the channel differences and obtain the set of unevenly distributed channels. Based on the unevenly distributed set of channels, traffic fluctuation data of each channel within a specific time period is extracted. Statistical tools are used to normalize the fluctuation data to obtain a standardized fluctuation dataset. The standardized fluctuation dataset is used to analyze the correlation of traffic fluctuations between channels. If the correlation between the fluctuation data of a certain channel and other channels is lower than a preset threshold, it is marked as an isolated channel, and a list of isolated channels is determined. For the list of isolated channels, obtain the historical traffic records of the corresponding channels, and use time series analysis to perform pattern mining on the historical traffic records to obtain the periodic fluctuation patterns of each isolated channel. Based on the periodic fluctuation pattern, the matching degree between isolated channels and other channels in the periodicity is calculated. If the matching degree of an isolated channel is lower than a preset threshold, it is classified as an object that needs to be monitored in a key manner, and the set of key monitoring channels is determined. By using a set of key monitoring channels, information on sudden changes in real-time traffic data is extracted. Anomaly detection tools are then used to filter this information, resulting in a list of potential abnormal time periods. Based on the list of potential abnormal time periods, and combined with the correlation data of flow fluctuations between channels, the impact range of abnormal time periods on the overall pipeline system is analyzed, and the propagation path of abnormal fluctuations is determined.

4. The calibration system method for a pipeline flow measurement instrument according to claim 1, characterized in that, If there are significantly different index values ​​in the set of unbalanced channels, relevant time series data are extracted from them, and frequency domain analysis is used to process the time series data to obtain an abnormal fluctuation frequency spectrum for identifying pipeline flow anomalies, including: By obtaining channel data with high difference index values ​​from the unbalanced channel set, and processing the channel data using frequency domain analysis techniques, an abnormal fluctuation frequency distribution map is obtained. Based on the frequency distribution map of abnormal fluctuations, the channel identifiers of high-frequency abnormal fluctuations are extracted, and the channel identifiers are matched with a preset abnormal pattern library using a comparison tool to determine a list of potential abnormal channels. For the list of potentially abnormal channels, traffic change data for the corresponding channels in multiple time periods is obtained. The traffic change data is preprocessed using a data smoothing tool to obtain a smoothed traffic change sequence. If there are fluctuation points in the smoothed traffic change sequence that exceed a preset threshold, the fluctuation points are timestamped, and traffic detail data for the relevant time period is extracted through the timestamping to determine the duration of the abnormal fluctuations. Based on the duration of the abnormal fluctuations, the frequency distribution of traffic changes in the corresponding channel is obtained, and statistical tools are used to perform segmented analysis on the frequency distribution to determine the periodic characteristics of the abnormal fluctuations. By analyzing the periodic characteristics of abnormal fluctuations, historical channel traffic records related to the periodic characteristics are extracted. If similar periodic fluctuations exist in the historical records, the fluctuations are compared to obtain the repeatability confirmation results of the abnormal fluctuations. If the repeatability confirmation results of abnormal fluctuations show that the fluctuation pattern is consistent, the real-time traffic data of the channel will be continuously monitored, and the evolution trend of abnormal fluctuations will be determined by comparing the monitoring data with historical records.

5. The calibration system method for a pipeline flow measurement instrument according to claim 1, characterized in that, The peak amplitude characteristics are evaluated by applying a peak detection method to the abnormal fluctuation frequency spectrum. If the peak amplitude exceeds a preset threshold range, the corresponding channel is marked as abnormal, and the abnormal identification result is used to guide subsequent correction, including: Step 1: For channels marked as abnormal, obtain historical traffic data from the channels, and use a time series segmentation tool to segment the data to obtain segmented traffic data sequences; Step 2: Based on the segmented traffic data sequence, apply statistical analysis methods to calculate the traffic fluctuation amplitude within each time period and determine the distribution characteristics of the fluctuation amplitude in each time period. Step 3: Based on the fluctuation amplitude distribution characteristics, obtain the channel operating parameters related to the characteristics, and compare the operating parameters with preset standard values ​​using a parameter comparison tool to determine the degree of parameter deviation; Step 4: If the parameter deviation exceeds the preset range, the real-time operation data of the channel is collected, and the key operation indicators are extracted through the data filtering tool to obtain a set of key indicators. Step 5: Based on the set of key indicators, use a logical judgment tool to match the indicators with historical abnormal records. If the matching results show that there is a similar pattern, mark the channel as high-risk and determine the list of high-risk channels. Step Six: For the list of high-risk channels, obtain the real-time monitoring data of the corresponding channels, and continuously analyze the monitoring data using data stream processing tools to determine whether there is a persistent abnormal fluctuation trend; Step 7: If the continuous abnormal fluctuation trend is confirmed, the operation log data of the channel is deeply mined, and the correlation analysis method is used to extract the potential relationship between abnormal fluctuations and operation parameters to obtain the abnormal correlation pattern.

6. The calibration system method for a pipeline flow measurement instrument according to claim 1, characterized in that, The process of obtaining flow data of abnormal channels from the anomaly identification results, and correcting the flow data using an adaptive filtering method to obtain a corrected flow distribution map for restoring the balance state of the pipeline system includes: Step 1: Obtain traffic data of abnormal channels from the anomaly identification results, and perform preliminary cleaning of the traffic data using a data filtering tool to remove noisy data and obtain a cleaned traffic dataset. Step 2: For the cleaned traffic dataset, an adaptive filtering method is used to smooth the data, generating a smoothed traffic data sequence; Step 3: Based on the smoothed traffic data sequence, construct the corresponding traffic distribution map using a data mapping tool to determine the uniformity of the traffic distribution; Step 4: If the uniformity of traffic distribution does not reach the preset threshold range, real-time monitoring data of abnormal channels will be collected to obtain a real-time monitoring dataset. Step 5: For the real-time monitoring dataset, use data comparison tools to match and analyze it with historical traffic data to determine whether there is a persistent deviation pattern; Step 6: If the persistent deviation pattern is confirmed, the flow parameters of the abnormal channel are dynamically corrected using the data adjustment tool to generate the corrected parameter configuration; Step 7: Based on the corrected parameter configuration, use the data update tool to synchronously adjust the flow distribution mechanism of the pipeline system and determine the balance state after system recovery.

7. The calibration system method for a pipeline flow measurement instrument according to claim 1, characterized in that, The process of recalculating the overall calibration parameters based on the corrected flow distribution map, adjusting the calibration parameter values ​​through an iterative optimization method, determining whether the calibration error tends to stabilize, and obtaining the final pipeline flow calibration model includes: Step 1: Obtain key distribution data from the revised flow distribution map. Based on the distribution data, use data extraction tools to separate the core parameters of the pipeline flow to obtain a preliminary flow distribution feature set. Step 2: Based on the preliminary flow distribution feature set, use data processing tools to classify and organize the core parameters, determine the distribution pattern of pipeline flow in different intervals, and obtain the classified parameter set; Step 3: For the classified parameter set, the calibration parameters are adjusted layer by layer through iterative optimization. If the adjusted calibration error exceeds the preset threshold range, the distribution features are re-extracted through data comparison tools to obtain the adjusted parameter dataset. Step 4: Based on the adjusted parameter dataset, use a data mapping tool to construct a calibration framework for pipeline flow, determine whether the calibration error has reached a stable state, and obtain a preliminary calibration model; Step 5: For the preliminary calibration model, the calculation results of the pipeline flow distribution are compared and analyzed using data verification tools. If there is a deviation between the distribution calculation results and the actual flow distribution, fine-tuning is performed using parameter correction tools to determine the corrected calibration framework. Step Six: Based on the revised calibration framework, use data integration tools to perform final matching of the calibration parameters for pipeline flow, determine whether the parameter stability meets the preset standards, and obtain the final flow calibration model.

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