Multi-line flow coordination calibration system

Abstract

The present application relates to the technical field of flow calibration, and discloses a multi-pipeline flow collaborative calibration system. The system comprises six modules, namely, flow characteristic acquisition, flow category classification, sequence analysis, trend analysis, correction and collaborative decision. The flow characteristic acquisition module acquires current flow data and real-time characteristic information of the multi-pipeline and indexes a historical flow data set; the flow category classification module classifies the historical characteristic information to obtain multiple flow categories, extracts relevant information to obtain a historical calibration score set; the sequence analysis module sequences the historical calibration score set and sorts it according to time to form a historical flow sequence; the trend analysis module analyzes the historical flow sequence to obtain a flow change rate and a stability deviation rate; the correction module matches the real-time characteristic information with the flow categories and corrects the above rate values according to the deviation; and the collaborative decision module makes a multi-pipeline collaborative calibration decision based on the corrected data, generates and executes a flow calibration scheme, and helps to accurately control the flow of the multi-pipeline system.

Description

Technical Field

[0001] This invention relates to the field of flow calibration technology, specifically a multi-pipeline flow collaborative calibration system. Background Technology

[0002] In numerous fields such as industrial production, energy transmission, and water treatment, the widespread application of multi-pipeline systems places extremely high demands on the accuracy of flow control. Whether it's the raw material transport pipeline network in chemical production, the multi-branch water supply network in urban water supply systems, or the coolant circulation pipeline group in power systems, the flow status of each pipeline directly affects the overall system's operating efficiency and safety performance. With the large-scale development of these fields, the number of pipelines is constantly increasing, and the interrelationships between pipelines are becoming increasingly complex. Traditional single-pipeline independent calibration methods are no longer sufficient to meet the needs of practical applications.

[0003] Traditional single-pipe calibration methods often only analyze and adjust the real-time flow data of a single pipe, ignoring the flow coupling effect between multiple pipes. In multi-pipe systems, flow changes in one pipe can affect the flow stability of other pipes through pressure transmission, media distribution, and other means. For example, when the flow in a production pipe suddenly increases, other pipes connected to the same main pipe may experience a sudden decrease in flow due to pressure drop. In this case, if independent calibration of a single pipe is still used, not only will the root cause of the flow anomaly not be accurately identified, but adjusting a single pipe may also lead to flow imbalance in the entire system, causing more serious operational problems.

[0004] Traditional calibration methods make limited use of historical flow data, often relying solely on short-term real-time data for calibration decisions, lacking analysis of long-term flow variation patterns in pipelines. In reality, pipeline flow is influenced by various factors such as medium temperature, pressure, pipeline aging, and usage frequency, exhibiting certain periodic or trend-like characteristics. For example, in water supply systems with significant seasonal variations, pipeline flow is generally higher during peak summer water consumption and relatively lower in winter. Ignoring this long-term trend and relying solely on real-time data for calibration can easily lead to short-term and unstable calibration results, failing to adapt to the dynamic changes in pipeline flow.

[0005] Traditional calibration processes lack detailed classification of flow characteristic information, making it difficult to develop targeted calibration strategies based on different flow operation scenarios. Different pipelines exhibit varying flow characteristics under different operating conditions, such as stable operation, fluctuating operation, and intermittent operation. The causes of flow deviation and calibration requirements differ significantly under each condition. Traditional methods fail to effectively distinguish these different types of flow characteristics, employing a uniform calibration standard and method, resulting in low calibration accuracy and an inability to meet the precise flow control requirements under various operating conditions. Furthermore, the calibration decision-making process lacks consideration of the matching degree between real-time characteristic information and historical flow categories, making it difficult to dynamically correct calibration parameters based on the current actual operating status of the pipeline. This further reduces the adaptability and effectiveness of the calibration scheme, severely restricting the improvement of the overall operational efficiency of multi-pipeline systems. Summary of the Invention

[0006] The purpose of this invention is to provide a multi-pipeline flow coordinated calibration system to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a multi-pipeline flow coordinated calibration system, the system comprising:

[0008] The flow characteristic acquisition module is used to acquire the current flow data of multiple pipelines and to acquire the real-time characteristic information of each pipeline. Based on the identifier of the multiple pipelines, the module indexes the historical flow data set of the multiple pipelines within the user's historical time period.

[0009] The traffic category classification module is used to classify the historical characteristic information in the historical traffic data set to obtain multiple traffic categories, extract the historical traffic deviation information set and multiple historical calibration data sets under the multiple traffic categories, and process them to obtain multiple historical calibration score sets.

[0010] The sequence analysis module is used to serialize the multiple historical calibration score sets to obtain multiple flow calibration sequences, and arrange them in chronological order to obtain multiple historical flow sequences.

[0011] The trend analysis module is used to perform flow trend analysis based on the multiple historical flow sequences to obtain the flow change rate and stability deviation rate.

[0012] The correction module is used to match the real-time characteristic information with the multiple traffic categories to obtain the matched traffic category, and to correct the traffic change rate and stability deviation rate based on the deviation between the real-time characteristic information and the standard matching characteristic information of the matched traffic category to obtain the corrected traffic change rate and corrected stability deviation rate.

[0013] The collaborative decision-making module is used to make multi-pipeline collaborative calibration decisions based on the corrected flow rate change rate and the corrected stability deviation rate, obtain a flow calibration scheme, and perform flow calibration adjustment.

[0014] Preferably, the flow characteristic acquisition module is used to acquire current flow data of multiple pipelines, and to acquire real-time characteristic information of each pipeline. Based on the identifiers of the multiple pipelines, it indexes the historical flow data set of the multiple pipelines within a historical time period, including:

[0015] Acquire current flow data from multiple pipelines, and collect flow velocity and pressure parameters for each pipeline as real-time characteristic information;

[0016] Based on the identifiers of the multiple pipelines, an indexing operation is performed within the user's historical calibration records to extract the historical flow data set of the multiple pipelines.

[0017] Preferably, the traffic category classification module is used to classify historical characteristic information within the historical traffic data set to obtain multiple traffic categories, extract historical traffic deviation information sets and multiple historical calibration data sets under the multiple traffic categories, and process them to obtain multiple historical calibration score sets, including:

[0018] Extract multiple historical characteristic information from the historical traffic data set, perform a classification operation, and generate multiple traffic categories;

[0019] Extract the standard flow values ​​of pipelines within the historical calibration data set under the multiple flow categories to obtain the historical flow deviation information set, and extract multiple historical calibration deviation information sets and multiple historical submission time sets submitted by users under the multiple flow categories;

[0020] Based on the degree of deviation between the multiple sets of historical calibration deviation information and the set of historical flow deviation information, multiple sets of historical basic calibration scores are generated.

[0021] Based on a preset time threshold and the proportional relationship between the multiple historical submission time sets, the multiple historical basic calibration score sets are corrected and calculated to generate multiple historical calibration score sets.

[0022] Preferably, the sequence analysis module is used to serialize the multiple historical calibration score sets respectively to obtain multiple flow calibration sequences, and arrange them in chronological order to obtain multiple historical flow sequences, including:

[0023] From the first set of historical calibration scores within the plurality of historical calibration score sets, a first reference calibration score is selected.

[0024] Based on the difference between other historical calibration scores in the first historical calibration score set and the first benchmark calibration score, weight probabilities are assigned to obtain a first weight distribution;

[0025] Based on the first weight distribution, the first historical calibration score set is simplified to obtain a first simplified calibration score set;

[0026] Based on the time stamp information of multiple historical calibration scores within the first simplified calibration score set, sort them in chronological order to obtain the first historical flow sequence;

[0027] Multiple historical calibration score sets are simplified and time-series arranged to generate multiple historical flow sequences.

[0028] Preferably, the trend analysis module performs flow trend analysis based on the multiple historical flow sequences to obtain the flow change rate and stability deviation rate, including:

[0029] Based on the sample calibration data of multiple pipelines, a set of sample flow rate sequences is collected, and a set of sample flow rate change rate and a set of sample stability deviation rate are obtained according to the calibration score change identifier within each sample flow rate sequence.

[0030] A flow trend analyzer is constructed by using the set of sample flow sequences as classification input and the set of sample flow change rates and sample stability deviation rates as classification output.

[0031] Based on the traffic trend analyzer, the multiple historical traffic sequences are classified into traffic trend categories to obtain multiple categories of traffic change rates and multiple categories of stability deviation rates.

[0032] The similarity between the real-time characteristic information and the multiple traffic categories is analyzed. Based on the magnitude of the multiple similarities, the traffic change rate and the stability deviation rate of the multiple categories are weighted and calculated to obtain the traffic change rate and the stability deviation rate.

[0033] Preferably, the correction module matches the real-time characteristic information with the multiple traffic categories to obtain a matched traffic category, and corrects the traffic change rate and stability deviation rate based on the deviation between the real-time characteristic information and the standard matching characteristic information of the matched traffic category, to obtain a corrected traffic change rate and a corrected stability deviation rate, including:

[0034] Select the traffic category with the highest similarity as the matching traffic category, and obtain the standard matching characteristic information of the matching traffic category;

[0035] Based on the deviation between the real-time characteristic information and the standard matching characteristic information of the matching traffic category, a calibration correction coefficient is set;

[0036] The calibration correction coefficients are used to correct the flow rate change rate and the stability deviation rate, thereby obtaining the corrected flow rate change rate and the corrected stability deviation rate.

[0037] Preferably, the collaborative decision-making module performs multi-pipeline collaborative calibration decision-making based on the corrected flow rate change rate and the corrected stability deviation rate, obtains a flow calibration scheme, and performs flow calibration adjustment, including:

[0038] Collect a set of sample corrected flow rate change rates and a set of sample corrected stability deviation rates, and set a sample flow calibration scheme based on the magnitude of each sample corrected flow rate change rate and sample corrected stability deviation rate to obtain a set of sample flow calibration schemes;

[0039] A collaborative decision-maker is constructed by using the set of sample corrected flow rate change rates and the set of sample corrected stability deviation rates as decision inputs and the set of sample flow calibration schemes as decision outputs.

[0040] The collaborative decision-maker is used to perform collaborative decision-making on the corrected flow rate change rate and the corrected stability deviation rate to obtain a flow calibration scheme.

[0041] Preferably, the system further includes a parameter optimization module, which is used to analyze the flow balance among multiple pipelines based on the flow calibration scheme, identify the relationship between flow transmission efficiency and load distribution among pipelines, optimize the pipeline flow control parameters, and generate a flow optimization parameter set.

[0042] Preferably, the system further includes an anomaly handling module, which is used to monitor the real-time flow fluctuation rate and pressure offset of multiple pipelines based on the flow optimization parameter set, analyze the impact of the fluctuation range on the pipeline calibration accuracy, dynamically adjust the flow distribution path and pressure ratio within the target range, and generate anomaly handling dataset.

[0043] Preferably, the system further includes a calibration output module, which is used to analyze the distribution ratio and calibration time of multiple pipeline target flow values ​​based on the anomaly processing dataset, adjust the parameters of the target calibration path, and generate a multi-pipeline flow calibration data table.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] By constructing a technical architecture that enables multiple modules to work together, many shortcomings in the traditional multi-pipeline flow calibration process are effectively solved, providing a brand-new technical path for precise flow control of multi-pipeline systems.

[0046] The system's flow characteristic acquisition module not only acquires current flow data and real-time characteristic information for multiple pipelines, but also indexes historical flow data sets based on pipeline identification, achieving comprehensive collection and integration of flow data. This data collection method breaks through the limitations of traditional single-pipeline calibration relying solely on real-time data, combining historical and real-time data to provide a richer data foundation for flow analysis and calibration decisions. By integrating historical data, a more comprehensive understanding of the flow operation history of each pipeline can be obtained, capturing long-hidden flow change patterns and avoiding calibration deviations caused by data bias, making analysis and decision-making more objective and reliable.

[0047] The traffic category classification module categorizes the characteristic information within the historical traffic data set, obtaining multiple traffic categories. It then extracts the corresponding historical traffic deviation information set and historical calibration data set, further processing them to obtain a historical calibration score set. This classification method accurately distinguishes the traffic operation characteristics under different operating conditions, grouping historical data with similar traffic characteristics and calibration requirements into the same category. This provides a basis for developing personalized calibration strategies for different traffic scenarios. By constructing the historical calibration score set, the effectiveness of past calibration schemes under different traffic categories can be clearly presented, providing a reference for optimizing calibration schemes and avoiding the insufficient calibration accuracy problem caused by the use of a uniform standard due to a lack of classification in traditional calibration methods.

[0048] The sequence analysis module serializes the historical calibration score set and arranges it chronologically to form multiple historical flow sequences. This processing method transforms discrete historical data into time-correlated sequence information, intuitively demonstrating the change process of flow calibration effectiveness over time. By analyzing historical flow sequences, key information such as the fluctuation period and trend of flow calibration effectiveness can be clearly identified, providing structured data support for trend analysis. This helps to delve deeper into the intrinsic relationship between flow changes and calibration operations, providing a more targeted basis for calibration decisions.

[0049] The trend analysis module performs flow trend analysis based on historical flow sequences to obtain flow change rate and stability deviation rate, enabling a macro-level understanding of the long-term variation patterns and stability of flow in multi-pipeline systems. Analysis of the flow change rate can predict potential abnormal flow trends, such as a continuous increase or decrease in flow, providing early warning for timely calibration measures. Meanwhile, the stability deviation rate analysis quantitatively assesses the stability of pipeline flow, identifying pipelines with poor stability and providing direction for focused calibration, avoiding the passive calibration situation caused by the lack of trend analysis in traditional calibration methods.

[0050] The correction module matches real-time characteristic information with multiple flow categories, and corrects the flow change rate and stability deviation rate based on the matching results and deviations, further improving the accuracy and relevance of the analysis results. Different real-time characteristic information corresponds to different flow categories. By matching, the specific scenario to which the current pipeline operating state belongs can be determined. Then, by combining the deviation between real-time characteristics and standard characteristics for correction, analysis errors caused by differences in operating conditions can be eliminated. This makes the corrected flow change rate and stability deviation rate more consistent with the actual operating conditions of the current pipeline, providing more reliable parameter support for collaborative calibration decisions.

[0051] The collaborative decision-making module performs multi-pipe collaborative calibration decisions based on the corrected flow change rate and stability deviation rate, breaking the limitations of traditional single-pipe independent calibration and fully considering the correlation and coupling effects between multiple pipelines. When formulating calibration schemes, it can comprehensively weigh the flow conditions of each pipeline, avoiding system flow imbalance caused by adjusting a single pipeline, and ensuring the positive impact of calibration operations on the entire multi-pipe system operation. Simultaneously, collaborative decision-making can formulate differentiated calibration strategies based on the actual conditions of each pipeline, taking corresponding adjustment measures for pipelines with different flow change rates and stability deviation rates, achieving overall optimization of the multi-pipe system flow, improving system operating efficiency and stability, and adapting to the complex and diverse operational needs of multi-pipe systems in different fields. Attached Figure Description

[0052] Figure 1 This is a timing diagram of the multi-pipeline flow collaborative calibration system described in this invention;

[0053] Figure 2 A flowchart illustrating the operation of the traffic characteristic acquisition module;

[0054] Figure 3 A flowchart illustrating the operation of the traffic category classification module;

[0055] Figure 4 A flowchart illustrating the operation of the sequence analysis module;

[0056] Figure 5 A flowchart illustrating the operation of the collaborative decision-making module. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] Please see Figure 1The present invention provides a multi-pipeline flow collaborative calibration system, the system comprising: a flow characteristic acquisition module, a flow category classification module, a sequence analysis module, a trend analysis module, a correction module, and a collaborative decision-making module.

[0059] The flow characteristic acquisition module obtains current flow data from multiple pipelines and collects real-time characteristic information for each pipeline, including flow velocity and pressure parameters. Based on the identifiers of multiple pipelines, this module indexes and extracts the corresponding historical flow data sets from the user's historical calibration records. The flow category classification module classifies the historical characteristic information in the historical flow data sets, generating multiple flow categories, and extracts the historical flow deviation information set and historical calibration data set under each category, further processing them to obtain multiple historical calibration score sets. The sequence analysis module serializes the multiple historical calibration score sets to form multiple flow calibration sequences, and arranges them in chronological order to generate multiple historical flow sequences. The trend analysis module performs flow trend analysis based on these historical flow sequences, calculating the flow change rate and stability deviation rate. The correction module matches the real-time characteristic information with multiple flow categories, determines the matching flow category, and corrects the flow change rate and stability deviation rate based on the deviation between the real-time characteristic information and the standard matching characteristic information of the matching category, obtaining the corrected flow change rate and corrected stability deviation rate. The collaborative decision-making module makes multi-pipeline collaborative calibration decisions based on the corrected flow rate change rate and stability deviation rate, generates a flow calibration scheme, and performs flow calibration adjustments.

[0060] Example 1: See Figure 2 The flow characteristic acquisition module acquires current flow data for multiple pipelines through a high-precision sensor network deployed at key nodes in the industrial site. This sensor network employs a distributed architecture, with each measurement point equipped with an electromagnetic flowmeter and a piezoresistive pressure transmitter, forming a comprehensive monitoring system for pipeline fluid parameters. The electromagnetic flowmeter operates based on Faraday's law of electromagnetic induction, accurately measuring the volumetric flow rate of conductive liquids. Its measuring tube is lined with polytetrafluoroethylene (PTFE) for corrosion resistance, and its electrodes are made of Hastelloy to withstand complex fluid environments. The pressure transmitter senses fluid pressure through an isolation diaphragm and converts the pressure signal into a standard electrical signal output. All sensors are connected to the data acquisition unit via a PROFIBUS-DP fieldbus for real-time data communication.

[0061] The data acquisition unit employs a modularly designed remote terminal device equipped with a 16-bit analog input module to sample and hold the current signal from the sensor and perform analog-to-digital conversion. The sampling frequency is set to 10 times per second, with 20 points continuously collected for each parameter within each sampling cycle. Random interference is eliminated using a median filtering algorithm. The converted digital signal undergoes preprocessing, including dimension conversion, engineering unit conversion, and data validity verification. The preprocessed real-time characteristic information is temporarily stored in a data buffer in a structured data format, containing fields such as timestamp, pipeline identifier, flow rate, pressure, and data quality identifier.

[0062] Historical calibration records are stored in a relational database management system, employing a distributed database architecture to achieve high-availability data storage. The database tables include key tables such as calibration record tables, pipeline information tables, and user operation log tables, which are interconnected via foreign keys to form a complete data relationship model. The calibration record table details the metadata for each calibration task, including the calibration task number, execution time, operator, pipeline equipment number, ambient temperature, fluid medium type, and parameter comparison data before and after calibration. All historical data is stored in time-partitioned databases, with each partition containing three months' worth of data to improve query efficiency.

[0063] Indexing operations are implemented by constructing compound query statements. The query conditions include a list of pipeline identifiers and a time range constraint. Pipeline identifiers use a globally unique encoding rule and consist of a device location code, pipeline type code, and serial number. The time range is set to the most recent thirty-six months by default, but can be adjusted flexibly according to user needs. During query execution, the system first searches the cache for recently accessed records; if no match is found, a database query request is initiated. After data integrity verification, the query result set is loaded into memory for further processing.

[0064] An outlier detection mechanism is implemented during data extraction, using statistical analysis methods to identify and remove records that significantly deviate from the normal range. For continuous data variables, the mean and standard deviation within a sliding window are calculated, and data points exceeding three times the standard deviation are marked as outliers. Simultaneously, the temporal continuity of the data is checked, and sequences with significant interruptions are interpolated or segmented. The extracted historical flow data set is then reorganized chronologically to form a complete historical data sequence for each pipeline.

[0065] Real-time characteristic information and historical traffic data sets require data format standardization processing before transmission. All numerical data is converted to International System of Units (SI), time information is uniformly converted to the ISO 8601 standard format, and text information adopts the UTF-8 encoding standard. Data transmission is completed through the Enterprise Service Bus and serialized in JSON format, consisting of a data packet header and a data body. The data packet header contains version information, timestamp, data source identifier, and checksum, while the data body contains the actual set of measurement data transmitted.

[0066] Data encryption measures are implemented during transmission, employing transport layer security protocols to ensure the confidentiality and integrity of data transmission. The receiving end decrypts the data and verifies it using a checksum to ensure the data has not been tampered with during transmission. Verified data is then sent to the data preprocessing pipeline for final data quality assessment and marking. The quality assessment includes range checks, logical consistency checks, and integrity checks. Data that passes the assessment is marked as usable and awaits module invocation.

[0067] A comprehensive logging mechanism is established throughout the entire data acquisition and indexing process, with detailed records of each operation step documented in the system log. Log information includes operation time, execution module, operation type, data processing volume, execution status, and exception information. This log data is used not only for system operation status monitoring but also provides raw data support for system performance analysis and optimization. The system is also equipped with a real-time monitoring interface that visually displays data acquisition progress and data quality indicators, allowing operators to easily monitor the system's operational status.

[0068] The persistent data storage employs a multi-replica mechanism, simultaneously backing up data to an off-site disaster recovery center while writing to the primary database. Backup data is compressed and encrypted before being transmitted to the backup storage system via a dedicated network channel. All historical data is indexed with a robust structure, including B-tree indexes for time fields, bitmap indexes for pipeline identifiers, and full-text indexes for memo information, to support rapid response to various complex query conditions.

[0069] The system periodically archives historical data, transferring data exceeding its retention period to nearline storage devices. The archiving process employs a tiered data storage strategy, determining the storage location and format based on data access frequency and importance. Frequently accessed "hot" data is stored on high-performance solid-state storage devices, less frequently accessed "warm" data is stored in hard disk drive (HDD) arrays, and rarely accessed "cold" data is archived to a tape library. All archiving operations ensure data integrity and traceability, guaranteeing that historical data can be fully recovered when needed.

[0070] Example 2: See Figure 3After receiving the historical flow data set from the flow characteristic acquisition module, the flow category classification module starts the data parsing program to perform structured processing on the input data. This data set contains calibration records accumulated by multiple pipelines over a historical period. Each record consists of fields such as timestamp, pipeline identifier, flow velocity measurement, pressure measurement, and environmental parameters. The parsing process first verifies data integrity, checks for null or outlier values ​​in required fields, fills in missing data using time series interpolation, and smooths outliers that significantly exceed reasonable ranges based on the trend of adjacent data points. The preprocessed data is then converted into a unified internal data format, where all numerical parameters are normalized to the [0,1] interval to eliminate the impact of dimensional differences on cluster analysis.

[0071] Cluster analysis employs an improved K-means++ algorithm to classify historical characteristic information. The algorithm randomly selects five initial cluster centers from the dataset, each representing a different flow state characteristic. The Euclidean distance from each data point to each cluster center is calculated, and the data point is assigned to the cluster of the nearest center. The formula for calculating the Euclidean distance is as follows:

[0072]

[0073] in: Representing data points With cluster center The distance between them Data points In the The values ​​taken on each feature dimension Cluster center In the Coordinate values ​​on each feature dimension This represents the total number of feature dimensions. After initial allocation, the algorithm recalculates the centroid position of each cluster by calculating the mean of each feature dimension for all data points in that cluster. The iterative process continues until the change in centroid position is less than a set threshold or the maximum number of iterations is reached, ultimately forming five stable traffic categories. Each category has a clear feature boundary and represents a traffic operation state.

[0074] Based on the generated flow categories, the module extracts calibration-related information from the historical data corresponding to each category. For each flow category, it first extracts the standard flow values ​​from all calibration records included in that category. These standard values ​​are derived from technical specifications provided by the equipment manufacturer or reference values ​​verified by authoritative institutions. By calculating the absolute deviation between the actual measured value and the standard value for each calibration, a historical flow deviation information set for that category is constructed. Simultaneously, it extracts historical calibration data submitted by users under that category, including manually entered calibration parameter adjustment values, calibration timestamps, and calibration result confirmation information. This user-submitted data is organized chronologically to form a complete historical operation trajectory.

[0075] The calibration scoring mechanism employs a multi-dimensional evaluation system. First, a baseline score is calculated for each calibration record. This baseline score is determined based on the degree of agreement between the actual deviation and the standard deviation of the calibration. The calculation formula considers both the absolute and relative values ​​of the deviation. Calibration records with smaller deviations receive higher scores, while those with larger deviations receive lower scores. All baseline scores are standardized and mapped to a score range of 0-100, forming an initial set of historical baseline calibration scores.

[0076] A time decay factor is introduced into the scoring correction mechanism to reflect the time validity of calibration records. The system sets a 30-day time threshold and calculates the time decay coefficient for each calibration record. Recently submitted records have higher weights, while older records have progressively lower weights. The decay coefficient is calculated using an exponential decay model, resulting in a non-linear decrease in weight over time. The time decay coefficient is weighted and calculated with a base score to obtain the final historical calibration score. This correction process ensures that the scoring results reflect both the technical accuracy of the calibration operation and changes in validity over time.

[0077] After calculating scores for all traffic categories, the module integrates the historical calibration score sets for the five categories. Each score set contains the timestamps and corresponding final scores for all calibration records within that category, arranged chronologically to form a time-series dataset. A final quality check is performed before data output to verify the reasonableness of each score value and the continuity of the sequence. Records with potential calculation errors are recalculated to ensure the accuracy and reliability of the output data.

[0078] A robust data traceability mechanism is established throughout the entire process, with intermediate results of each calculation step recorded in a process log. Log information includes the start and end times of data processing, the number of processing records, calculation parameter settings, and anomaly handling details. This log data is used for troubleshooting and process analysis, and also provides data support for system performance optimization. Finally, multiple sets of historical calibration scores are transmitted to the processing module via a data interface, providing input data for sequence analysis.

[0079] The system implements a dynamic monitoring mechanism during processing, monitoring the convergence of the clustering algorithm and the progress of score calculation in real time. Monitoring metrics include key parameters such as the movement trajectory of cluster centers, intra-cluster distance trends, and score calculation time. When an anomaly is detected, the system automatically activates emergency response procedures, adjusting or issuing warning signals according to preset strategies. All monitoring data is displayed in real time on the system monitoring interface for operator reference.

[0080] Data storage employs a hierarchical management strategy. Raw historical data is stored permanently in the core database, intermediate processing results are cached in a high-performance in-memory database for real-time querying, and final output data is pushed to downstream modules via a message queue. This storage architecture ensures both data security and meets real-time processing performance requirements. All data access operations are performed through a unified data interface, ensuring data consistency and integrity.

[0081] Example 3: See Figure 4 After receiving multiple historical calibration score sets from the traffic category classification module, the sequence analysis module initiates a serialization process. Each historical calibration score set contains calibration score records arranged chronologically under a specific traffic category, with precise timestamps. The process first analyzes the first historical calibration score set, selecting the earliest appearing calibration score in the time series as the first benchmark calibration score. This benchmark score represents the initial calibration level of this type of traffic state, providing a reference point for comparison. When calculating the differences between other historical calibration scores within the set and the benchmark score, an absolute value comparison method is used, covering both the numerical difference of the scores and the time interval. Weight probability allocation uses a non-linear mapping relationship based on the magnitude of the difference; smaller differences receive higher weights, and larger differences correspond to lower weights. The weight values ​​are distributed between zero and one, and their sum is a uniform value.

[0082] The simplified processing procedure applies a weighted average algorithm to compress the historical calibration score set, reducing the data volume while preserving the main characteristics of the sequence. Weight distribution is used as weighting coefficients in the calculation, with each historical calibration score contributing differently to the final simplified result based on its weight value. The simplified first set of calibration scores contains representative data points that reflect the overall trend and key characteristics of the original sequence. Time stamp information is extracted and retained from the original records to ensure that the simplified data points still possess temporal attributes. The data is sorted in ascending order to form continuous time series data, i.e., the first historical flow sequence. The processing of other historical calibration score sets follows the same logical flow, performing benchmark selection, difference calculation, weight allocation, simplification, and time-series arrangement operations one by one. Each set of simplified calibration score sets undergoes a consistency check to ensure data comparability between different sequences and the uniformity of the processing logic.

[0083] The trend analysis module constructs a training dataset using pre-prepared sample calibration data, which originates from typical operating condition cases in historical calibration records. The sample flow sequence set contains time-series data under various flow conditions, each sequence having undergone expert annotation and validation. When calculating flow change characteristic indicators based on the sample sequences, a difference operation method is used to obtain the change between adjacent time points, and then statistical methods are used to derive the change rate parameter. Stability assessment is achieved by calculating the dispersion index of the sequence data, reflecting the fluctuation of data points around the trend line. The sample flow change rate set and the sample stability deviation rate set serve as labeled data for supervised learning, forming complete training sample pairs with the sample sequence data.

[0084] The neural network classifier employs a multilayer perceptron architecture. The number of nodes in the input layer corresponds to the time step of the sequence data, and the output layer contains two nodes corresponding to the traffic change rate and stability deviation rate, respectively. The hidden layers are designed as three fully connected layers, with the number of nodes decreasing sequentially in each layer, using rectified linear units as the activation function. During training, the backpropagation algorithm is used to optimize the network weights, and the loss function comprehensively considers the weighted sum of the prediction errors for both the rate of change and the stability deviation rate. The number of training iterations is dynamically adjusted based on performance on the validation set, and an early stopping mechanism prevents overfitting. The trained traffic trend analyzer has the ability to extract trend features from historical traffic sequences and can output prediction results in continuous numerical form.

[0085] When classifying multiple historical traffic sequences, each sequence first undergoes standardization preprocessing to eliminate the influence of sequence length and numerical range. The analyzer analyzes each sequence independently, outputting the corresponding category traffic change rate and category stability deviation rate. These category-level indicators reflect the inherent variation characteristics and stability performance of various traffic states. The similarity calculation between real-time characteristic information and traffic categories uses a feature space distance-based metric, calculating the relative distance between real-time data points and the center points of each category in the multi-dimensional feature space. The reciprocal of the distance calculation result is normalized and used as a weighting coefficient for weighted fusion of category indicators. The weighting calculation process ensures that the final output traffic change rate and stability deviation rate reflect the correlation between the current real-time state and historical categories, enhancing the applicability of trend analysis results to current operating conditions.

[0086] A quality monitoring mechanism is established throughout the processing to analyze the integrity of the sequence data and detect any outliers or missing segments. When data quality issues are detected, a data repair procedure is initiated, using interpolation or trend extrapolation methods to supplement the complete data. All intermediate calculation results are recorded in the process log, including detailed information such as processing time, number of data points, calculation parameters, and output results for each sequence. This log data is used for audit trails and process analysis, and also provides data support for algorithm optimization. The final generated flow change rate and stability deviation rate are transmitted to subsequent modules via a standard data interface, with the data format including numerical results and confidence indices.

[0087] The system implements performance monitoring during operation, tracking the sequence processing progress and computing resource usage in real time. Monitoring metrics include parameters such as sequence processing time, memory usage, and computational accuracy. When processing anomalies or performance degradation are detected, the system automatically triggers optimization mechanisms, dynamically adjusting computational parameters or allocating additional resources. All monitoring data is displayed in real time on the system monitoring terminal for maintenance personnel to reference and make decisions. Data storage adopts a hierarchical management strategy: raw sequence data is archived in a long-term storage system, while processing results are cached on high-speed storage devices, ensuring a balance between data access efficiency and processing performance.

[0088] Example 4: See Figure 5The correction module receives flow rate change rate and stability deviation rate data from the trend analysis module, and simultaneously acquires a set of real-time characteristic information. This real-time characteristic information includes the current flow velocity and pressure measurements for each pipeline. These data have been preprocessed and standardized to have uniform dimensions and numerical ranges. The module first calculates the similarity between the real-time characteristic information and the central feature of each flow category, using a multi-dimensional spatial distance metric. The central feature of each flow category is derived from historical data and represents the typical operating state of that category. The distance calculation considers the comprehensive differences across all feature dimensions, ultimately selecting the flow category with the smallest distance to the real-time characteristic information as the matching flow category.

[0089] After obtaining the standard matching characteristic information for the matching flow category, the module executes the deviation calculation program. The standard matching characteristic information comes from historical data statistics for that category, including standard flow velocity and standard pressure values. The deviation calculation uses a relative percentage method to calculate the degree of deviation between the real-time flow velocity and the standard flow velocity, and the degree of deviation between the real-time pressure and the standard pressure, respectively. The two deviation values ​​are weighted and combined to obtain the overall deviation index, which reflects the degree of deviation between the current operating condition and the standard state. A calibration correction coefficient is calculated based on the overall deviation index; the larger the deviation, the higher the correction coefficient, reflecting the adjustment range of the trend analysis results. The calibration correction coefficient is calculated using a piecewise linear function relationship, specifically expressed as:

[0090]

[0091] in: Indicates the calibration correction factor. This is an index representing the overall deviation between real-time characteristics and standard characteristics. It is an amplitude adjustment parameter. For curvature control parameters, This is the hyperbolic tangent function. Amplitude adjustment parameter. The maximum adjustment range of the control correction coefficient, curvature control parameters The sensitivity of the correction factor to changes in deviation is affected. The hyperbolic tangent function ensures that the correction factor changes smoothly with increasing deviation and eventually tends to a stable value. This function is designed so that the correction process maintains appropriate sensitivity to small deviations while providing a saturation limit for larger deviations.

[0092] The flow rate change rate and stability deviation rate are corrected using calibration correction factors. The correction calculation employs a multiplicative model: the original flow rate change rate is multiplied by the correction factor to obtain the corrected flow rate change rate, and similarly, the original stability deviation rate is multiplied by the correction factor to obtain the corrected stability deviation rate. This correction method amplifies the trend analysis results when there are significant deviations between real-time characteristics and standard characteristics, thereby enhancing the relevance of calibration decisions. The corrected parameters more accurately reflect the flow characteristics under current actual operating conditions.

[0093] The collaborative decision-making module collects historical successful calibration case data to construct a training sample set. This sample data includes records of corrected flow rate change rates and corrected stability deviation rates under various operating conditions, along with corresponding calibration schemes. Each calibration scheme details the parameter adjustment instructions for each pipeline, including specific operational parameters such as valve opening adjustments and pump speed setpoint changes. The sample data has undergone expert verification and standardization to ensure accuracy and consistency. The training sample set covers various typical operating conditions and abnormal situations, providing comprehensive data support for the decision-maker training.

[0094] The decision tree model is constructed using the CART algorithm, with the corrected flow change rate and corrected stability deviation rate as input features, and the calibration scheme as the output label. The feature selection process calculates the information gain of the two input features to determine the optimal split point. Tree growth employs a recursive splitting method, where each node selects the feature that minimizes impurity for splitting. The Gini coefficient is used to measure impurity, and the splitting termination condition is set when the number of samples in a node is less than the minimum or the reduction in impurity is not significant. Pruning operations utilize a cost-complexity pruning method to optimize the generalization ability of the tree structure.

[0095] The trained collaborative decision-maker is capable of generating calibration schemes based on input parameters. Internally, the decision-maker contains multiple judgment rules and decision paths, each corresponding to a specific operating condition and calibration strategy. For the corrected flow rate change rate and corrected stability deviation rate of the current input, the decision-maker matches the results along the decision paths in the feature space, ultimately reaching a leaf node to obtain the corresponding calibration scheme. The scheme output includes specific parameter adjustment suggestions, execution order, and an evaluation of the expected results.

[0096] The execution mechanism translates the calibration plan into equipment control commands, which are then transmitted to field actuators, including control valves and variable frequency pumps, via industrial communication protocols. A security verification mechanism ensures accurate delivery and execution of control commands during transmission. Equipment feedback signals are monitored during execution to verify the actual effectiveness of the calibration operation. Dynamic adjustments are made as needed based on feedback information to ensure the calibration objectives are achieved.

[0097] The entire processing procedure establishes a comprehensive data recording and traceability mechanism. All correction calculation steps, decision-making processes, and execution results are recorded in detail in the system log. Log data includes key information such as timestamps, operation types, parameter values, and calculation results. This data is used for process analysis and system optimization, and also provides a basis for quality auditing. Data storage adopts a hierarchical management strategy to ensure data security and access efficiency. Continuous monitoring is implemented during system operation to track the real-time status of correction calculations and decision execution. Monitoring indicators include performance parameters such as calculation time, decision accuracy, and execution success rate. An anomaly detection mechanism monitors deviations and errors in the data processing process in real time and issues timely warning signals. Maintenance personnel can view the system's operating status through the monitoring interface and perform manual intervention or parameter adjustments when necessary.

[0098] Example 5: The parameter optimization module initiates a multi-pipeline flow balancing analysis program based on the flow calibration scheme generated by the collaborative decision-making module. This analysis process calculates the flow difference coefficient for each pipeline by comparing the current flow value of each pipeline with the target flow value set in the scheme. The flow difference coefficient reflects the degree of deviation between the actual flow and the target flow. The numerical calculation uses a relative percentage method, considering both the absolute and relative deviations of the flow. Transmission efficiency evaluation establishes a correlation model between flow and pressure to calculate the flow value that can be delivered per unit pressure; a higher value indicates better transmission efficiency. Load distribution analysis statistically analyzes the proportion of flow in each pipeline within the total flow to evaluate the system load balance. The optimization algorithm uses the gradient descent principle to iteratively adjust pipeline flow control parameters, including adjustable parameters such as valve opening and pump speed frequency. After each iteration, the flow difference coefficient and transmission efficiency index are recalculated until the parameter combination that minimizes the difference coefficient and maximizes the transmission efficiency is found. The optimization process generates a flow optimization parameter set containing the optimal control parameters for each pipeline, and the parameter values ​​are stored in a standardized control command format.

[0099] After receiving the flow optimization parameter set, the anomaly handling module initiates a real-time monitoring and dynamic adjustment mechanism. The monitoring system acquires real-time flow and pressure data for each pipeline through a high-frequency data acquisition unit, with sampling intervals set to milliseconds. Flow fluctuation rate is calculated using a sliding window statistical method, calculating the standard deviation and range of flow data within a fixed time window to comprehensively assess the degree of flow fluctuation. Pressure offset is calculated by the difference between the real-time pressure value and the target pressure value in the optimization parameter set, considering both the pressure change trend and rate of change. A mathematical model is established to analyze the impact of fluctuation range on calibration accuracy, describing the distribution law and variation characteristics of calibration errors under different fluctuation amplitudes. When the fluctuation rate or offset exceeds a preset threshold, the system initiates a dynamic adjustment program. Flow distribution path adjustment re-plans the fluid transmission path by changing pipeline connectivity and flow direction allocation. Pressure ratio adjustment uses a PID control algorithm to finely adjust the output value of the pressure control equipment. All adjustment operations are performed within the system's set safety range to ensure a smooth and reliable adjustment process. The anomaly handling dataset records detailed parameters for each adjustment, including adjustment time, adjustment reason, parameters before adjustment, parameters after adjustment, and adjustment effect evaluation value.

[0100] The calibration output module generates the final calibration scheme based on the anomaly handling dataset. When analyzing the distribution ratio of target flow values ​​for multiple pipelines, it calculates the weight of each pipeline's target flow in the total system target flow, evaluating the rationality of the flow allocation. Calibration time estimation comprehensively considers historical calibration operation times, current system complexity, and equipment response characteristics to establish a time prediction model. Parameter adjustments for the target calibration path consider equipment performance limitations and process requirements, optimizing the execution order and parameter adjustment step size of calibration operations. Calibration path planning uses graph theory algorithms, abstracting the pipeline system into network nodes to find the optimal calibration order and parameter transfer path. The parameter adjustment step size is determined based on equipment accuracy and sensitivity, maximizing adjustment efficiency while ensuring accuracy. The final multi-pipeline flow calibration data table uses a structured data format, containing complete fields such as pipeline identifier, target flow value, allowable error range, adjustment parameter type, target parameter value, calibration execution order, and estimated time. Before outputting the data table, consistency checks and logical verification are performed to ensure that all parameter values ​​are within technically permissible ranges and do not conflict with each other.

[0101] During calibration, the system performs adjustments step-by-step according to the sequence and parameters defined in the data table. Each adjustment step is accompanied by a real-time monitoring and feedback mechanism, comparing the actual adjustment effect with the expected target. When the deviation exceeds the allowable range, a correction procedure is triggered, making fine adjustments based on the magnitude and direction of the deviation. A complete execution log is established for the entire calibration process, recording detailed information such as the execution time, operator, equipment response, and actual effect of each step. The log data is stored in association with the calibration data table, forming a traceable calibration archive. After calibration, a summary report is generated, summarizing comprehensive information such as overall calibration effect, time consumption statistics, and deviation analysis. The report data is used to evaluate calibration quality and support optimization decisions.

[0102] Data transfer between all modules adopts a unified interface specification to ensure the integrity and consistency of data transmission. End-to-end monitoring is implemented during system operation to track data processing status and execution progress. Monitoring data is displayed in real-time on the user interface, providing a visual indication of the system's operational status. Anomaly handling mechanisms include multi-level response strategies such as automatic retries, alarm notifications, and manual intervention. System maintenance functions support operations such as parameter configuration adjustments, algorithm model updates, and historical data management, ensuring long-term stable operation and continuous optimization of the system.

[0103] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0104] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-pipeline flow rate collaborative calibration system, characterized in that, The system includes: The flow characteristic acquisition module is used to acquire the current flow data of multiple pipelines and to acquire the real-time characteristic information of each pipeline. Based on the identifier of the multiple pipelines, the module indexes the historical flow data set of the multiple pipelines within the user's historical time period. The traffic category classification module is used to classify the historical characteristic information in the historical traffic data set to obtain multiple traffic categories, extract the historical traffic deviation information set and multiple historical calibration data sets under the multiple traffic categories, and process them to obtain multiple historical calibration score sets. The sequence analysis module is used to serialize the multiple historical calibration score sets to obtain multiple flow calibration sequences, and arrange them in chronological order to obtain multiple historical flow sequences. The trend analysis module is used to perform flow trend analysis based on the multiple historical flow sequences to obtain the flow change rate and stability deviation rate, including: Based on the sample calibration data of multiple pipelines, a set of sample flow rate sequences is collected, and a set of sample flow rate change rate and a set of sample stability deviation rate are obtained according to the calibration score change identifier within each sample flow rate sequence. A flow trend analyzer is constructed by using the set of sample flow sequences as classification input and the set of sample flow change rates and sample stability deviation rates as classification output. Based on the traffic trend analyzer, the multiple historical traffic sequences are classified into traffic trend categories to obtain multiple categories of traffic change rates and multiple categories of stability deviation rates. Analyze the real-time characteristic information and the similarity of the multiple traffic categories. Based on the magnitude of the multiple similarities, perform a weighted calculation on the traffic change rate and the stability deviation rate of the multiple categories to obtain the traffic change rate and the stability deviation rate. The correction module is configured to match the real-time characteristic information with the plurality of traffic categories to obtain a matched traffic category, and to correct the traffic change rate and stability deviation rate based on the deviation between the real-time characteristic information and the standard matching characteristic information of the matched traffic category, thereby obtaining a corrected traffic change rate and a corrected stability deviation rate, including: Select the traffic category with the highest similarity as the matching traffic category, and obtain the standard matching characteristic information of the matching traffic category; Based on the deviation between the real-time characteristic information and the standard matching characteristic information of the matching traffic category, a calibration correction coefficient is set; Using the calibration correction coefficient, the flow rate change rate and stability deviation rate are corrected and calculated to obtain the corrected flow rate change rate and corrected stability deviation rate; The collaborative decision-making module is used to make multi-pipeline collaborative calibration decisions based on the corrected flow rate change rate and the corrected stability deviation rate, obtain a flow calibration scheme, and perform flow calibration adjustment.

2. The multi-pipeline flow collaborative calibration system according to claim 1, characterized in that, The flow characteristic acquisition module is used to acquire current flow data of multiple pipelines and collect real-time characteristic information of each pipeline. Based on the identifiers of the multiple pipelines, it indexes the historical flow data set of the multiple pipelines within a historical time period, including: Acquire current flow data from multiple pipelines, and collect flow velocity and pressure parameters for each pipeline as real-time characteristic information; Based on the identifiers of the multiple pipelines, an indexing operation is performed within the user's historical calibration records to extract the historical flow data set of the multiple pipelines.

3. The multi-pipeline flow collaborative calibration system according to claim 1, characterized in that, The traffic category classification module is used to classify historical characteristic information within the historical traffic data set to obtain multiple traffic categories, extract historical traffic deviation information sets and multiple historical calibration data sets under the multiple traffic categories, and process them to obtain multiple historical calibration score sets, including: Extract multiple historical characteristic information from the historical traffic data set, perform a classification operation, and generate multiple traffic categories; Extract the standard flow values ​​of pipelines within the historical calibration data set under the multiple flow categories to obtain the historical flow deviation information set, and extract multiple historical calibration deviation information sets and multiple historical submission time sets submitted by users under the multiple flow categories; Based on the degree of deviation between the multiple sets of historical calibration deviation information and the set of historical flow deviation information, multiple sets of historical basic calibration scores are generated. Based on a preset time threshold and the proportional relationship between the multiple historical submission time sets, the multiple historical basic calibration score sets are corrected and calculated to generate multiple historical calibration score sets.

4. The multi-pipeline flow coordinated calibration system according to claim 1, characterized in that, The sequence analysis module is used to serialize the multiple historical calibration score sets to obtain multiple flow calibration sequences, and arrange them in chronological order to obtain multiple historical flow sequences, including: From the first set of historical calibration scores within the plurality of historical calibration score sets, a first reference calibration score is selected. Based on the difference between other historical calibration scores in the first historical calibration score set and the first benchmark calibration score, weight probabilities are assigned to obtain a first weight distribution; Based on the first weight distribution, the first historical calibration score set is simplified to obtain a first simplified calibration score set; Based on the time stamp information of multiple historical calibration scores within the first simplified calibration score set, sort them in chronological order to obtain the first historical flow sequence; Multiple historical calibration score sets are simplified and time-series arranged to generate multiple historical flow sequences.

5. The multi-pipeline flow coordinated calibration system according to claim 1, characterized in that, The collaborative decision-making module performs multi-pipeline collaborative calibration decisions based on the corrected flow rate change rate and the corrected stability deviation rate, obtains a flow calibration scheme, and executes flow calibration adjustments, including: Collect a set of sample corrected flow rate change rates and a set of sample corrected stability deviation rates, and set a sample flow calibration scheme based on the magnitude of each sample corrected flow rate change rate and sample corrected stability deviation rate to obtain a set of sample flow calibration schemes; A collaborative decision-maker is constructed by using the set of sample corrected flow rate change rates and the set of sample corrected stability deviation rates as decision inputs and the set of sample flow calibration schemes as decision outputs. The collaborative decision-maker is used to perform collaborative decision-making on the corrected flow rate change rate and the corrected stability deviation rate to obtain a flow calibration scheme.

6. The multi-pipeline flow collaborative calibration system according to claim 1, characterized in that, The system also includes a parameter optimization module, which is used to analyze the flow balance among multiple pipelines based on the flow calibration scheme, identify the relationship between flow transmission efficiency and load distribution among pipelines, optimize pipeline flow control parameters, and generate a flow optimization parameter set.

7. The multi-pipeline flow coordinated calibration system according to claim 6, characterized in that, The system also includes an anomaly handling module, which is used to monitor the real-time flow fluctuation rate and pressure offset of multiple pipelines based on the flow optimization parameter set, analyze the impact of the fluctuation range on the pipeline calibration accuracy, dynamically adjust the flow distribution path and pressure ratio within the target range, and generate anomaly handling dataset.

8. The multi-pipeline flow collaborative calibration system according to claim 7, characterized in that, The system also includes a calibration output module, which is used to analyze the distribution ratio and calibration time of multiple pipeline target flow values ​​based on the anomaly processing dataset, adjust the parameters of the target calibration path, and generate a multi-pipeline flow calibration data table.

Images