An adaptive regulation control system and method for natural gas flow calibration

Abstract

The present invention provides an adaptive regulation control system and method for natural gas flow calibration, relating to the technical field of automatic control and regulation systems. The method comprises: collecting real-time pipeline operation data, performing real-time flow pattern identification to obtain real-time flow pattern data of a preset type; an instruction generation module: determining a trigger coefficient, determining whether a trigger condition is met, and generating a calibration trigger instruction if so; a data optimization module: receiving the calibration trigger instruction, obtaining current compensation parameters, generating preliminary correction coefficients, and then invoking reference data and combining it with a calibration data packet to determine a final set of optimized correction parameters; a calibration update module: updating a real-time compensation algorithm, determining the degree of deviation, and re-triggering the calibration process if the deviation exceeds a preset allowable range; and a process optimization module: extracting current key calibration data after the calibration process is completed, invoking historical calibration data, determining an optimal calibration cycle, and optimizing the calibration process based on the optimal calibration cycle. This improves the accuracy and efficiency of flow meter calibration.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control and regulation systems, and in particular to an adaptive regulation control system and method for natural gas flow calibration. Background Art

[0002] Due to the variability of gas flow patterns during operation, traditional calibration methods for natural gas flow meters typically rely on periodic manual calibration. This is not only time-consuming and labor-intensive, but also difficult to adapt to complex flow pattern changes and dynamic environments. Existing calibration methods are often rigid and lack real-time response to flow pattern changes, thus failing to effectively improve flow meter calibration accuracy and system stability.

[0003] At present, some existing calibration systems use calibration methods based on sensor data, but these systems mostly rely on static parameters and preset cycles, and lack the ability to dynamically adjust compensation parameters based on real-time flow data, resulting in certain errors in the calibration results, affecting the long-term use effect and accuracy of natural gas flow meters.

[0004] The present invention provides an adaptive regulation control system and method for natural gas flow calibration. Summary of the Invention

[0005] This invention provides an adaptive regulation and control system for natural gas flow calibration, addressing the existing issues of inability to adapt to flow pattern changes in real time, resulting in low calibration accuracy and efficiency. By collecting flow pattern data in real time and incorporating a dynamic optimization algorithm, the system automatically adjusts compensation parameters and dynamically optimizes the calibration process, thereby improving flowmeter calibration accuracy and efficiency. During the long-term use of natural gas flowmeters, real-time adjustment of compensation parameters and calibration strategies reduces human intervention and improves system stability and accuracy.

[0006] The present invention provides an adaptive regulation and control system for natural gas flow calibration, comprising:

[0007] Data acquisition module: collects real-time pipeline operation data through a multi-sensor array pre-placed on the natural gas pipeline, extracts real-time dynamic flow characteristics, and performs real-time flow pattern recognition based on the real-time dynamic flow pattern characteristics to obtain real-time preset flow pattern data;

[0008] Instruction generation module: determines the trigger coefficient, judges whether the trigger condition is met based on the real-time preset flow data and the trigger coefficient, and generates a calibration trigger instruction if it is met. The trigger coefficient includes: flow stability coefficient and error accumulation coefficient;

[0009] Data optimization module: Receives calibration trigger instructions, obtains current compensation parameters from the preset compensation parameter buffer, generates preliminary correction coefficients based on real-time dynamic flow characteristics, packages the collected real-time pipeline operation data, real-time dynamic flow characteristics, and preliminary correction coefficients into a calibration data package, calls reference data, and determines the final optimized correction parameter set based on the calibration data package;

[0010] Calibration update module: updates the real-time compensation algorithm based on the final optimized correction parameter set. After the update, the actual flow data is continuously collected within the preset calibration period and compared with the expected value to determine the deviation. If the deviation exceeds the preset allowable range, the calibration process is retriggered.

[0011] Process optimization module: After the calibration process is completed, the current key calibration data is extracted, the historical calibration data is called, the optimal calibration cycle is determined in combination with the current key calibration data, and the calibration process is optimized based on the optimal calibration cycle.

[0012] Preferably, the data acquisition module includes:

[0013] Data acquisition unit: collects real-time pipeline operation data through a multi-sensor array pre-placed on the natural gas pipeline;

[0014] Feature extraction unit: inputs the collected real-time pipeline operation data into the preset sliding time window processor to extract real-time dynamic flow characteristics;

[0015] Feature recognition unit: inputs the real-time dynamic flow state features into a preset lightweight flow state classifier for real-time flow state recognition to obtain real-time flow state data of a preset type.

[0016] Preferably, the preset types of flow state data include steady flow state data, pulsating flow state data and transient flow state data.

[0017] Preferably, the instruction generation module includes:

[0018] History calling unit: based on preset flow data of a type, calls corresponding historical flow data and historical error accumulation data from a preset historical database;

[0019] Stability threshold determination unit: determines the flow stability coefficient based on historical flow data;

[0020] Error threshold determination unit: determines the error accumulation coefficient based on historical error data;

[0021] Data comparison unit: compares the flow stability coefficient with the preset reference stability threshold, and at the same time, compares the error accumulation coefficient with the preset error tolerance limit;

[0022] Process triggering unit: If the flow stability coefficient is less than the preset reference stability threshold or the error accumulation coefficient exceeds the preset error tolerance limit, the calibration process is re-triggered.

[0023] Preferably, the stability threshold determination unit includes:

[0024] Feature analysis subunit: extracts frequency domain features from historical flow data within a preset number of consecutive calibration periods before the current moment, and determines the flow state duration ratio of pulsating flow and transient flow within each preset number of consecutive calibration periods before the current moment;

[0025] Index acquisition subunit: performing exponentially weighted moving average calculation on the flow state duration ratios of the pulsating flow and transient flow in each preset calibration period for a preset number of consecutive times before the current moment, respectively, to obtain the pulsating flow stability index and the transient flow stability index;

[0026] Coefficient determination subunit: determines the flow stability coefficient based on the pulsating flow stability index and the transient flow stability index.

[0027] Preferably, the data optimization module includes:

[0028] Parameter reading unit: reads the currently effective compensation parameters from the preset compensation parameter buffer;

[0029] Data processing unit: performs time series alignment processing on the currently effective compensation parameters and the real-time pipeline operation data collected later;

[0030] Matrix operation unit: According to the preset parameter adjustment rules, the real-time flow characteristics and the currently effective compensation parameters are matrix operated to obtain the initial correction coefficient;

[0031] Data packaging unit: packages the real-time data, initial correction parameters and real-time flow characteristics after time series alignment processing according to the preset standard protocol to obtain the current calibration data packet;

[0032] Data matching unit: Based on the current calibration data packet, it calls the historical calibration database on the cloud to perform a matching query and obtains the historical calibration data sets corresponding to several historical reference cases;

[0033] Data search unit: constructs a parameter optimization feasible domain for the historical calibration data set, and searches within the feasible domain through a preset planning algorithm until the optimal historical calibration data set is obtained;

[0034] Parameter update unit: Generates the current version number for the optimal historical calibration data set, and transmits the optimal historical calibration parameter set carrying the version number to the calibration update module through an encrypted channel, immediately triggering the preset verification process until the verification result meets the preset requirements and determines the optimal historical calibration data set as the final optimized correction parameter set.

[0035] Preferably, the calibration update module includes:

[0036] Calibration update unit: updates the real-time compensation algorithm based on the final optimized correction parameter set;

[0037] Strategy adjustment unit: sets the basic calibration cycle according to the preset standard, obtains the dynamic data of the natural gas pipeline working condition, and adjusts the sampling strategy based on the dynamic data of the natural pipeline working condition;

[0038] Data comparison subunit: based on the basic calibration cycle and the adjusted sampling strategy, it continuously collects actual flow data and compares it with the expected value;

[0039] Deviation acquisition unit: determines instantaneous deviation, continuous deviation and trend deviation based on comparison data;

[0040] Deviation analysis unit: determines the degree of deviation based on instantaneous deviation, continuous deviation and trend deviation;

[0041] Process trigger unit: If the deviation exceeds the preset allowable range, the calibration process will be re-triggered.

[0042] Preferably, the process optimization module includes:

[0043] Record retrieval unit: starts the historical data retrieval program and retrieves the calibration records of the latest preset number of times from the preset distributed time series database;

[0044] Data integration unit: constructs calibration data cube based on the retrieved calibration records;

[0045] Indicator determination subunit: determines several key indicators based on the feature importance of the data in the calibration data cube;

[0046] Record screening unit: determines several historical successful calibration records based on the calibration records of key indicators in the latest preset number of times;

[0047] Cycle recommendation unit: Analyze the interval cycles of all historical successful calibration records and determine several basic recommended cycles;

[0048] Data extraction unit: After the calibration process is completed, extract the current key calibration data;

[0049] Cycle screening unit: determines the optimal calibration cycle among all basic recommended cycles based on current key calibration data;

[0050] Process optimization unit: optimizes the calibration process based on the optimal calibration cycle.

[0051] An adaptive regulation control method for natural gas flow calibration, comprising:

[0052] Step 1: A multi-sensor array pre-placed on the natural gas pipeline collects real-time pipeline operation data, extracts real-time dynamic flow characteristics, and performs real-time flow pattern recognition based on the real-time dynamic flow pattern characteristics to obtain real-time preset flow pattern data;

[0053] Step 2: Determine the trigger coefficient. Based on the real-time preset flow data and the trigger coefficient, determine whether the trigger condition is met. If so, generate a calibration trigger instruction. The trigger coefficient includes: flow stability coefficient and error accumulation coefficient.

[0054] Step 3: Receive the calibration trigger command, obtain the current compensation parameters from the preset compensation parameter buffer, generate preliminary correction coefficients based on the real-time dynamic flow characteristics, package the collected real-time pipeline operation data, real-time dynamic flow characteristics, and preliminary correction coefficients into a calibration data package, call the reference data and combine it with the calibration data package to determine the final optimized correction parameter set;

[0055] Step 4: Update the real-time compensation algorithm based on the final optimized correction parameter set. After the update, continuously collect actual flow data within the preset calibration period and compare it with the expected value to determine the deviation. If the deviation exceeds the preset allowable range, the calibration process is retriggered.

[0056] Step 5: After the calibration process is completed, extract the current key calibration data, call the historical calibration data, combine the current key calibration data to determine the optimal calibration cycle, and optimize the calibration process based on the optimal calibration cycle.

[0057] Compared with the prior art, the present invention has the following advantages:

[0058] By collecting flow data in real time and combining it with a dynamic optimization algorithm, the compensation parameters can be automatically adjusted and the calibration process can be dynamically optimized, thereby improving the accuracy and efficiency of flow meter calibration. During the long-term use of the natural gas flow meter, the compensation parameters and calibration strategy can be adjusted in real time to reduce human intervention and improve the stability and accuracy of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0060] Figure 1 The diagram is a structural diagram of an adaptive regulation control system for natural gas flow calibration provided by an embodiment of the present invention.

[0061] Figure 2 It is a flow chart of an adaptive regulation control method for natural gas flow calibration provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0063] Example 1

[0064] The embodiment of the present invention provides an adaptive regulation control system for natural gas flow calibration, such as Figure 1 Shown, including:

[0065] Data acquisition module: collects real-time pipeline operation data through a multi-sensor array pre-placed on the natural gas pipeline, extracts real-time dynamic flow characteristics, and performs real-time flow pattern recognition based on the real-time dynamic flow pattern characteristics to obtain real-time preset flow pattern data;

[0066] Instruction generation module: determines the trigger coefficient, judges whether the trigger condition is met based on the real-time preset flow data and the trigger coefficient, and generates a calibration trigger instruction if it is met. The trigger coefficient includes: flow stability coefficient and error accumulation coefficient;

[0067] Data optimization module: Receives calibration trigger instructions, obtains current compensation parameters from the preset compensation parameter buffer, generates preliminary correction coefficients based on real-time dynamic flow characteristics, packages the collected real-time pipeline operation data, real-time dynamic flow characteristics, and preliminary correction coefficients into a calibration data package, calls reference data, and determines the final optimized correction parameter set based on the calibration data package;

[0068] Calibration update module: updates the real-time compensation algorithm based on the final optimized correction parameter set. After the update, the actual flow data is continuously collected within the preset calibration period and compared with the expected value to determine the deviation. If the deviation exceeds the preset allowable range, the calibration process is retriggered.

[0069] Process optimization module: After the calibration process is completed, the current key calibration data is extracted, the historical calibration data is called, the optimal calibration cycle is determined in combination with the current key calibration data, and the calibration process is optimized based on the optimal calibration cycle.

[0070] In this embodiment, the multi-sensor array includes a pressure sensor, a temperature sensor, and a basic flow sensor.

[0071] In this embodiment, the current compensation parameters are a set of correction coefficients stored after the system's most recent valid calibration, including a flow characteristic correction factor, a temperature drift compensation coefficient, and a pressure effect correction parameter;

[0072] In this embodiment, the final optimized correction parameter set not only includes the instantaneous correction coefficient calculated on the edge side, but also adds a stability correction factor based on long-term operation data, an equipment aging compensation coefficient, and an operating condition adaptation parameter.

[0073] The beneficial effects of the above technical solution are as follows: by collecting flow data in real time and combining it with a dynamic optimization algorithm, it is possible to automatically adjust compensation parameters and dynamically optimize the calibration process, thereby improving the accuracy and efficiency of flow meter calibration. During the long-term use of the natural gas flow meter, the compensation parameters and calibration strategy can be adjusted in real time, reducing human intervention and improving the stability and accuracy of the system.

[0074] Example 2

[0075] The embodiment of the present invention provides an adaptive regulation and control system for natural gas flow calibration, and a data acquisition module, including:

[0076] Data acquisition unit: collects real-time pipeline operation data through a multi-sensor array pre-placed on the natural gas pipeline;

[0077] Feature extraction unit: inputs the collected real-time pipeline operation data into the preset sliding time window processor to extract real-time dynamic flow characteristics;

[0078] Feature recognition unit: inputs the real-time dynamic flow state features into a preset lightweight flow state classifier for real-time flow state recognition to obtain real-time flow state data of a preset type.

[0079] In this embodiment, a multi-sensor array pre-placed on the natural gas pipeline collects parameter data such as flow, pressure, temperature, and gas composition in the pipeline in real time. For example, a pressure sensor measures the internal pressure of the pipeline, a temperature sensor collects the gas temperature, and a gas composition sensor obtains the specific composition data of the natural gas; these data will serve as the basic input for subsequent processing.

[0080] In this embodiment, the feature extraction unit includes: inputting the collected real-time pipeline operation data into a preset sliding time window processor, and the processor divides the data into multiple time periods according to the set time window size and extracts the dynamic flow characteristics within the time period. For example, according to the set 10-second sliding window, the data flow characteristics within every 10 seconds are extracted, such as the fluctuation amplitude and change rate of the flow; these features provide necessary input for subsequent flow identification.

[0081] In this embodiment, the feature recognition unit includes: inputting real-time dynamic flow state features into a preset lightweight flow state classifier for real-time flow state recognition, and obtaining real-time preset type of flow state data. For example, the flow state classifier identifies the flow state type according to different airflow characteristics, such as laminar flow, turbulent flow, etc., and generates corresponding flow state identification, and outputs corresponding flow state data to provide the system with a basis for determining the real-time flow state.

[0082] The beneficial effects of the above technical solution are: real-time collection of pipeline operation data through a multi-sensor array, and the use of a sliding time window processor and a lightweight flow state classifier to extract and identify real-time flow state features, thereby accurately judging the flow state type, enabling accurate flow meter calibration, improving the real-time performance and accuracy of the calibration process, and dynamically adjusting compensation parameters during pipeline operation, significantly improving the measurement accuracy and calibration efficiency of the natural gas flow meter, reducing errors, and optimizing system performance.

[0083] Example 3

[0084] An embodiment of the present invention provides an adaptive regulation and control system for natural gas flow calibration, wherein preset types of flow pattern data include steady flow pattern data, pulsating flow pattern data, and transient flow pattern data.

[0085] In this embodiment, the flow pattern classification results are stored in real time in a preset flow pattern characteristic database, which uses a time series data structure to store historical flow pattern characteristic data;

[0086] In this embodiment, steady flow state data includes: for example, in a stable natural gas pipeline, the flow rate and pressure of the gas change little, the fluid presents a uniform and stable flow state, and the flow meter can continuously and stably measure the flow data with little fluctuation; for example, if the flow change remains within a set small range during the measurement process and the reading of the flow sensor is stable, then the data is identified as steady flow state data.

[0087] In this embodiment, the pulsating flow state data includes: for example, the gas flow in the pipeline is affected by external factors (such as pipeline bends, pressure fluctuations, etc.), the flow velocity fluctuates periodically, and regular high and low fluctuations occur, and the flow data pulsates; for example, if the flow meter detects periodic changes in flow within a certain time interval, and such changes have a certain regularity, then the data is pulsating flow state data.

[0088] In this embodiment, transient flow state data includes: for example, in a natural gas pipeline, the flow rate fluctuates greatly due to certain sudden events (such as sudden opening or closing of the pipeline, airflow impact, etc.), and the flow velocity and pressure are in a state of rapid change, resulting in instantaneous and drastic fluctuations in the flow data; for example, if the flow meter detects that the flow rate fluctuates greatly in a short period of time and the fluctuation duration is short, then this data is transient flow state data.

[0089] The beneficial effects of this technical solution include: by pre-setting different types of flow pattern data, such as steady flow, pulsating flow, and transient flow, accurate flow pattern identification and calibration can be performed for different pipeline operating conditions. This diverse flow pattern classification method improves the system's adaptability, enabling it to cope with the changing conditions of natural gas pipelines. By identifying and classifying different flow pattern data, the system can more precisely adjust compensation parameters, thereby improving the calibration accuracy and stability of the natural gas flowmeter, ensuring the flowmeter maintains efficient and accurate performance under various operating conditions, and optimizing energy monitoring and management.

[0090] Example 4

[0091] An embodiment of the present invention provides an adaptive regulation and control system for natural gas flow calibration, including an instruction generation module, comprising:

[0092] History calling unit: based on preset flow data of a type, calls corresponding historical flow data and historical error accumulation data from a preset historical database;

[0093] Stability threshold determination unit: determines the flow stability coefficient based on historical flow data;

[0094] Error threshold determination unit: determines the error accumulation coefficient based on historical error data;

[0095] Data comparison unit: compares the flow stability coefficient with the preset reference stability threshold, and at the same time, compares the error accumulation coefficient with the preset error tolerance limit;

[0096] Process triggering unit: If the flow stability coefficient is less than the preset reference stability threshold or the error accumulation coefficient exceeds the preset error tolerance limit, the calibration process is re-triggered.

[0097] In this embodiment, the error accumulation coefficient is essentially a dynamically adjusted quality control boundary, which is implemented using a sliding average algorithm. First, the error sequence within the last K calibration cycles is extracted from the error accumulation database, and the short-term (last three times) and long-term (all K times) average absolute errors are calculated respectively.

[0098] In this embodiment, if the flow stability coefficient is less than the preset benchmark stability threshold or the error accumulation coefficient exceeds the preset error tolerance limit, these two indicators are compared with the preset error tolerance limit (stored in the compensation strategy knowledge base). When the short-term error exceeds a specific multiple of the long-term error or exceeds the tolerance limit, it is determined to be an accelerated error accumulation state. At this time, even if the flow stability has not deteriorated, the system will actively generate calibration instructions. This dual-criteria mechanism makes full use of the flow identification results and historical error accumulation data of step 1, so that the trigger decision takes into account both the real-time working conditions and the equipment performance attenuation trend, and realizes preventive maintenance. When any threshold condition is triggered, the system will encapsulate metadata such as the current flow characteristic parameters, sensor calibration status and compensation algorithm version to form a standard initialization parameter package, and transmit it to the cloud calibration system through an encrypted channel to provide a complete context environment for the subsequent calibration execution of step 3;

[0099] In this embodiment, when the flow stability coefficient is lower than the threshold, it indicates that the pipeline operation is in a high dynamic interference state. At this time, calibration must be triggered immediately to compensate for the measurement error caused by the sudden change in the flow state.

[0100] The beneficial effect of this technical solution is that by calling corresponding flow pattern data and error accumulation data from the historical database, a rich historical reference is provided for the instruction generation module. By determining the stability threshold and error threshold, the flow meter's operating status can be monitored in real time, and the flow pattern stability coefficient and error accumulation coefficient can be compared to ensure that they remain within a reasonable range. If the flow pattern stability is insufficient or the error accumulation exceeds the standard, the calibration process can be automatically triggered, effectively reducing calibration errors, improving the accuracy and stability of the natural gas flow meter, and ensuring long-term efficient operation.

[0101] Example 5

[0102] An embodiment of the present invention provides an adaptive regulation and control system for natural gas flow calibration, including a stability threshold determination unit, comprising:

[0103] Feature analysis subunit: extracts frequency domain features from historical flow data within a preset number of consecutive calibration periods before the current moment, and determines the flow state duration ratio of pulsating flow and transient flow within each preset number of consecutive calibration periods before the current moment;

[0104] Index acquisition subunit: performing exponentially weighted moving average calculation on the flow state duration ratios of the pulsating flow and transient flow in each preset calibration period for a preset number of consecutive times before the current moment, respectively, to obtain the pulsating flow stability index and the transient flow stability index;

[0105] Coefficient determination subunit: determines the flow stability coefficient based on the pulsating flow stability index and the transient flow stability index.

[0106] In this embodiment, the flow state duration ratio of the pulsating flow and the transient flow in each preset calibration period of a preset number of consecutive times before the current moment is determined. Specifically, the ratio of the cumulative duration of the pulsating flow / transient flow to the total duration of the window is calculated in each time window, and then the ratio sequence of N windows is weighted averaged.

[0107] In this embodiment, during the flow stability threshold calculation stage, pulsating flow and transient flow should be statistically analyzed separately. Pulsating flow is caused by periodic fluctuations (such as reciprocating pumps and compressor pulsations), and usually exhibits high-frequency oscillations near the steady-state mean with relatively stable fluctuation amplitudes. Transient flow is caused by sudden events (such as rapid valve opening and closing or pipeline damage), and manifests as a dramatic flow / pressure step or decay in a short period of time. It is a non-periodic, dynamic interference. The error of pulsating flow mainly comes from high-frequency noise interference, which can be corrected through bandpass filtering or periodic compensation. The error of transient flow is due to the hysteresis of nonlinear dynamic response and needs to rely on transient model for compensation. The specific statistical method (step-by-step description) is as follows:

[0108] Step 1: Calculate the proportion of each flow state independently within each time window (e.g. 5 minutes), and count them separately:

[0109] ;

[0110] in, is the pulsating flow ratio of the current window, is the transient flow ratio of the current window, is the duration of the pulsating flow in the current window, is the total duration of the time window (e.g. 5 minutes); is the duration of the transient flow in the current window;

[0111] Step 2: Dynamic weighted fusion, for a preset number of consecutive windows and Perform an Exponentially Weighted Moving Average (EWMA) calculation:

[0112] ;

[0113] ;

[0114] in, is the pulsating flow stability coefficient, is the transient flow stability coefficient, the weight coefficient and In order to dynamically adjust the error according to the influence of the flow state (for example, transient flow may be given a higher weight), and are the EWMA values ​​of the pulsating flow ratio and transient flow ratio in the window before the current window (the initial value can be set to 0 or the historical mean);

[0115] Step 3: Joint trigger logic: Final flow stability coefficient A weighted combination of the two:

[0116] ;

[0117] in, are the preset weights corresponding to the pulsating flow stability coefficient and the transient flow stability coefficient, Determines the final stability coefficient of the pulsating flow The degree of impact, Determines the final stability coefficient of transient flow The degree of impact, 、 It will be dynamically adjusted to ensure that the model is more sensitive to a specific flow state. For example, transient flow may have a greater impact on the stability of the system, so it can be given a higher weight, while pulsating flow has a smaller impact on the system, so it can be given a lower weight. When the set threshold is exceeded, calibration is triggered. For example, in the statistics of a natural gas transmission station: the proportion of pulsating flow is stable at 5% to 8% for a long time (caused by periodic exhaust of the compressor); the proportion of transient flow is usually 0.1%, but it suddenly rises to 2% in a certain event (related to the action of the downstream emergency shut-off valve). In this case, the model will significantly increase the weight of the transient flow. , thus triggering calibration first to deal with possible step errors.

[0118] The beneficial effects of the above technical solution are: by extracting the frequency domain features of historical flow data, accurately identifying the duration ratio of pulsating flow and transient flow, reflecting the dynamic process of flow state change, using the exponentially weighted moving average method to smooth the data, improving the accuracy of stability assessment, and calculating the flow state stability coefficient based on the stability indicators of pulsating flow and transient flow, ensuring efficient and stable operation during the calibration process, reducing calibration errors, and optimizing system performance.

[0119] Example 6:

[0120] The embodiment of the present invention provides an adaptive regulation and control system for natural gas flow calibration, and a data optimization module, including:

[0121] Parameter reading unit: reads the currently effective compensation parameters from the preset compensation parameter buffer;

[0122] Data processing unit: performs time series alignment processing on the currently effective compensation parameters and the real-time pipeline operation data collected later;

[0123] Matrix operation unit: According to the preset parameter adjustment rules, the real-time flow characteristics and the currently effective compensation parameters are matrix operated to obtain the initial correction coefficient;

[0124] Data packaging unit: packages the real-time data, initial correction parameters and real-time flow characteristics after time series alignment processing according to the preset standard protocol to obtain the current calibration data packet;

[0125] Data matching unit: Based on the current calibration data packet, it calls the historical calibration database on the cloud to perform a matching query and obtains the historical calibration data sets corresponding to several historical reference cases;

[0126] Data search unit: constructs a parameter optimization feasible domain for the historical calibration data set, and searches within the feasible domain through a preset planning algorithm until the optimal historical calibration data set is obtained;

[0127] Parameter update unit: Generates the current version number for the optimal historical calibration data set, and transmits the optimal historical calibration parameter set carrying the version number to the calibration update module through an encrypted channel, immediately triggering the preset verification process until the verification result meets the preset requirements and determines the optimal historical calibration data set as the final optimized correction parameter set.

[0128] In this embodiment, time series alignment is performed: sampled data from different devices, such as pressure sensors, temperature probes, and flow meters, are timestamped using a preset homologous clock signature. The system uses a weighted moving average algorithm to eliminate sampling time differences between devices, ensuring that all data is on the same time base. Due to differences in sampling frequency and latency among the various sensing devices in the pipeline (for example, a pressure sensor may sample at 100 Hz, while a temperature probe may only sample at 10 Hz), the system requires a specialized timing synchronization algorithm to align multi-source data. Specifically, a hardware clock synchronization protocol (using the PTPv2 Precision Time Protocol) is used to control clock deviations across all devices to within 50 microseconds. High-frequency data (such as pressure values) is downsampled, while low-frequency data (such as temperature values) is upsampled using cubic spline interpolation, ultimately aligning to a standard 20 Hz time base. To address packet out-of-order issues caused by network jitter, the system reorders data using a sliding time window mechanism, dynamically adjusting the window size to three times the typical transmission latency (default: 300 milliseconds). Crucially, this step detects and removes outliers caused by sensor failures using an adaptive threshold algorithm based on a moving standard deviation: five consecutive sampling points exceeding a 3σ range are considered an anomaly. After time alignment, the dataset is marked as "synchronized" and accompanied by metadata containing each channel's signal-to-noise ratio (SNR) metrics for use in weighted calculations during the subsequent optimization phase. The accuracy of this process directly determines the effectiveness of subsequent parameter optimization.

[0129] In this embodiment, a matrix operation is performed, using a standardized covariance matrix to calculate the dynamic adjustment weights for each parameter, generating an initial correction vector consisting of 12 dimensions. The correction value for each dimension is checked against preset boundary conditions to ensure that no over-range corrections occur. Based on a preset multi-parameter coupling model, real-time flow characteristics (including eight dynamic indicators, such as the pulsating flow intensity coefficient and velocity profile distortion) are collaboratively calculated with the current compensation parameters. In practice, a 24×24 standardized covariance matrix is ​​first constructed, with the matrix elements determined by the physical equations linking the parameters (for example, the cross term between the temperature compensation coefficient and the pressure correction value is derived using the van der Waals equation of state). The singular value decomposition (SVD) algorithm is then used to solve for the matrix's eigenvectors. Based on the modulus of the eigenvectors, the system automatically assigns dynamic adjustment weights to each of the 12 dimensions, with the first three principal component directions (typically corresponding to sensitive dimensions such as sudden flow changes, temperature gradients, and pressure fluctuations) receiving 70% of the correction weight. During the calculation process, the correction amplitude for each dimension is subject to pre-set physical constraints (for example, a single adjustment of the temperature correction coefficient must not exceed ±0.5%FS). These constraints are embedded in the optimization objective function as inequalities, and the boundary conditions are enforced using the Lagrange multiplier method. The resulting initial set of correction coefficients, containing the adjusted values ​​for 12 dimensions and their corresponding confidence interval estimates, serves as the initial population input for the subsequent global optimization.

[0130] In this embodiment, data is packaged according to a pre-set standard protocol: first, the time-aligned real-time data (including 12 raw measurement values ​​such as pressure, temperature, and flow) is converted into a fixed-length binary array according to the data frame structure defined by the ISO 31-20 standard. A 128-byte header containing information such as the data generation time, device ID, and data quality flag is then added. Finally, the initial correction coefficient and flow characteristic matrix are appended to the data body in the form of a structured array. To ensure data integrity, the system uses the SHA-256 algorithm to generate a hash digest of the entire data packet and embeds the digest value into the packet's trailing checksum. The total length of the data packet is strictly controlled within 4KB. If the length exceeds this limit, the Zstandard compression algorithm (with compression level set to 3 to balance speed and rate) is automatically activated. Each successfully encapsulated data packet is assigned a unique sequence number composed of a timestamp (42 bits), a node number (10 bits), and a packet counter (12 bits), providing global uniqueness. Before being sent to the transmission queue, the data packet undergoes a memory mapping check to verify that all pointer references and array boundaries comply with security specifications to prevent buffer overflow attacks.

[0131] In this embodiment, a matching query is performed against a historical calibration database in the cloud based on the current calibration data packet. The matching process consists of three steps: First, a rough screening of operating conditions is performed, filtering out 90% of irrelevant historical data based on static characteristics such as pipeline diameter (DN200-DN600) and medium composition (methane percentage ranges from 85%-99%). Next, a dynamic similarity calculation is performed, using an improved DTW (Dynamic Time Warping) algorithm to compare the morphological similarity of the current flow pattern characteristic curve with historical cases. The matching weights are assigned as follows: 40% for pressure fluctuation patterns, 30% for temperature gradient changes, and 30% for flow rate mutations. Finally, the results are sorted, retaining the top 50 cases with the highest similarity scores (with a minimum similarity threshold of 0.72). These cases are then grouped by operating environment (winter / summer) and age (new / old pipelines) to form a reference case set for subsequent optimization. The entire process adheres to the "no data out of domain" principle. All matching calculations are performed on edge computing nodes, returning only desensitized reference data indexes. The original historical data is always stored in the secure area of ​​the central database.

[0132] In this embodiment, a current version number is generated for the optimal historical calibration dataset. This optimal historical calibration parameter set, along with the version number, is then transmitted to the calibration update module via an encrypted channel. This immediately triggers a pre-defined verification process until the verification results meet pre-defined requirements. The optimal historical calibration dataset is then determined as the final optimized and revised parameter set. The selected optimal parameter set is first standardized and encapsulated within the version management system. The version number uses a semantic encoding scheme (major version, feature version, revision version, and build number), for example, v2.3.1_20240615 represents the third major improvement under the second-generation core algorithm architecture. The parameter set is then transmitted to all levels of the calibration update module via an encrypted channel based on the national secret SM4 algorithm. The transmission process utilizes a BitTorrent-like fragmentation verification mechanism to ensure data transmission integrity and anti-interference capabilities. Prior to deployment, the parameter set undergoes four levels of verification: basic verification (parameter range checking), simulation verification (digital twin system testing), physical verification (comparison with standard tables), and field verification (48-hour trial operation). Each level of verification includes 27 specific test items (such as sudden load increase and decrease testing and rapid start and stop testing). Only parameter sets that pass all verifications (with an overall score of 95 or higher) are marked as "approved" and officially written to the device's non-volatile memory. (The Flash memory uses a dual-bank alternating write mechanism to ensure data security in the event of unexpected power outages.) The entire optimization process (from parameter reading to final deployment) is completed within 150 seconds, meeting the real-time requirements of on-site calibration. All process data is archived in blockchain format, forming an immutable calibration traceability chain.

[0133] The beneficial effects of the above technical solution include: reading the currently effective compensation parameters from the compensation parameter buffer, ensuring the use of the latest compensation information during the calibration process; processing real-time data through time series alignment, improving data consistency and accuracy; generating preliminary correction coefficients according to preset rules, effectively reducing errors; and through the data packaging and data matching units, the system can efficiently match with the cloud-based historical database to further optimize the calibration results. Finally, the parameter update unit ensures the security and reliability of the calibration results through encrypted transmission of the optimal calibration data set. This optimization process improves the system's accuracy, reliability, and automation level.

[0134] Example 7:

[0135] An embodiment of the present invention provides an adaptive regulation and control system for natural gas flow calibration, including a calibration update module, comprising:

[0136] Calibration update unit: updates the real-time compensation algorithm based on the final optimized correction parameter set;

[0137] Strategy adjustment unit: sets the basic calibration cycle according to the preset standard, obtains the dynamic data of the natural gas pipeline working condition, and adjusts the sampling strategy based on the dynamic data of the natural pipeline working condition;

[0138] Data comparison subunit: based on the basic calibration cycle and the adjusted sampling strategy, it continuously collects actual flow data and compares it with the expected value;

[0139] Deviation acquisition unit: determines instantaneous deviation, continuous deviation and trend deviation based on comparison data;

[0140] Deviation analysis unit: determines the degree of deviation based on instantaneous deviation, continuous deviation and trend deviation;

[0141] Process trigger unit: If the deviation exceeds the preset allowable range, the calibration process will be re-triggered.

[0142] In this embodiment, the final optimized correction parameter set not only includes the instantaneous correction coefficient calculated on the edge side, but also adds a stability correction factor based on long-term operation data, an equipment aging compensation coefficient, and an operating condition adaptation parameter.

[0143] In this embodiment, dynamic data on natural gas pipeline operating conditions is obtained: by deploying high-frequency sensors (such as pressure transmitters, temperature sensors, and ultrasonic flow meters) at key nodes, the system can capture the changing trends of pipeline operating conditions in real time and generate dynamic data streams, including core indicators such as instantaneous flow rate, pressure gradient, and temperature distribution;

[0144] In this embodiment, the sampling strategy is adjusted based on dynamic data from natural pipeline conditions. A machine learning model is used to predict potential operating condition changes within the next six hours (such as flow rate changes caused by throttle valve adjustments). The sampling frequency is then optimized accordingly: a baseline sampling interval (e.g., every five minutes) is maintained under steady-state conditions, while the sampling frequency is automatically increased to seconds (e.g., every two seconds) during transient conditions (e.g., when flow rate increases exceed 5% / minute). This dynamic adjustment mechanism ensures that data meets statistical analysis requirements without compromising system performance due to oversampling. Furthermore, the system can identify special operating conditions (such as pipeline pigging and pressure regulating station maintenance) and intelligently adapt pre-set sampling plans to prevent abnormal data from interfering with calibration results. During steady-state operation, sampling is performed at equal intervals (recording a complete set of data every five minutes). During transient conditions, event-triggered sampling is initiated (increasing to 10 sets per second when a pressure change rate >0.5% / s is detected).

[0145] In this embodiment, actual flow data is continuously collected based on the basic calibration cycle and the adjusted sampling strategy. The data collection network adopts a layered architecture: the field sensor layer transmits the raw measurement values ​​via the PROFIBUS-DP bus, the regional collector performs preliminary filtering (using a zero-phase IIR filter with a cutoff frequency set to 0.8 times the Nyquist frequency), and the central processing unit finally integrates the synchronized data set of the entire pipeline network. The key innovation lies in the introduction of a redundant measurement mechanism: each measuring point is equipped with two sets of active and standby sensors. The real-time data is processed by the Byzantine fault-tolerant algorithm, which can automatically identify and isolate the faulty channel (the typical judgment criterion is that the deviation of three consecutive measurements exceeds 2σ). The system has a dedicated data quality assessment module, which scores data on a 100-point scale based on accuracy (error compared with the standard table), completeness (missing rate <0.1%), and timeliness (transmission delay <200ms). Data with a score below 80 will trigger automatic re-collection. All valid data is accompanied by a complete metadata description, including 18 types of auxiliary information such as the sensor calibration certificate number, ambient temperature compensation value, and power quality monitoring results, forming a traceable data lineage map. The first level of data collection is the theoretical benchmark value (theoretical flow calculated in real time based on the AGA8 state equation), the second level is the equipment benchmark value (independent measurement results of the ultrasonic standard meter), and the third level is the statistical benchmark value (moving average of the data from the same period of the past 30 days). The comparison engine uses a weighted difference analysis method to calculate the absolute deviation of the instantaneous measurement point, the root mean square error (RMSE) of the time series (such as a 15-minute segment), and a Spearman correlation test on the trend item. The system establishes a deviation classification model: random deviation (Gaussian distribution) only triggers data quality alarms, while systematic deviation (persistently large or small) activates dynamic fine-tuning of compensation parameters. Each comparison generates a structured evaluation report, which includes the deviation type (such as negative offset caused by insufficient temperature compensation), impact quantification (percentage deviation and cumulative error), and credibility score (based on sensor health status and flow complexity);

[0146] In this embodiment, the comparison with expected values ​​is based on a multi-reference analysis algorithm executed by the comparison engine. Three baselines are maintained simultaneously: a physical model baseline (theoretical values ​​calculated based on the AGA8 equation), an equipment baseline (ultrasonic reference meter readings), and a historical statistical baseline (the average of the past 30 days). The comparison process utilizes adaptive windowing technology: a 15-minute sliding window is used to calculate the root mean square error (RMS) during steady-state conditions, while a 3-second window is used to analyze extreme deviations during transient conditions. The system implements three levels of deviation assessment: primary detection (single-point threshold exceeding), intermediate verification (three consecutive threshold exceedings), and advanced confirmation (cross-checking against the equipment baseline). Only when all three levels are met is a deviation considered valid. For confirmed deviations, the system initiates root cause tracing: Granger causality analysis identifies the dominant factor (e.g., temperature compensation failure manifests as a lag correlation > 0.7). Frequency domain coherence testing is used to determine the accuracy of flow regime identification (a coherence coefficient < 0.6 is considered a flow regime misidentification). Each comparison generates a structured report, including the deviation level (graded according to the ISO 7066 standard), the weight of the influencing factors (the top three factors and their contribution), and the repair suggestions (mapped to the preset response strategy library). This information is pushed to the decision support system and the deviation heat map of the entire network is updated at the same time.

[0147] In this embodiment, instantaneous deviation, persistent deviation, and trend deviation are determined based on the comparison data. Three core deviation indicators are quantified through multi-timescale analysis: instantaneous deviation is defined as the difference between the most recent single measurement and the theoretical benchmark, expressed as a normalized percentage (e.g., -1.2% FS); persistent deviation is calculated through a sliding window integral (window length 1 hour), reflecting the long-term cumulative effect of deviation, and its statistics include maximum, minimum, and standard deviation; trend deviation is obtained through linear regression analysis, fitting the slope of the past 30 minutes of data and calculating its significance (a p-value < 0.05 is considered a valid trend). The extraction process of the three types of deviations uses an anti-interference design: the raw data is first subjected to wavelet denoising, then the different frequency domain components are separated through variational mode decomposition (VMD), and finally the deviation characteristics are calculated for each component.

[0148] In this implementation, the deviation degree is determined based on instantaneous, sustained, and trend deviations. The comprehensive deviation degree is calculated using a nonlinear aggregation algorithm: Each of the three types of deviations is first normalized (divided by their respective preset thresholds) and then fused using a weighted power average function (p=2). The final score is a range of 0 to 100. Weighting is adaptive to the operating conditions: in steady-state conditions, sustained deviations account for 50%, while transient and trend deviations each account for 25%. In transient conditions, the weight of instantaneous deviations is increased to 50%, and a correction factor for flow complexity is introduced (a 10% increase in the total score for turbulent conditions). The scoring model is trained and optimized using historical data to ensure that a score of 85 corresponds to the maximum allowable deviation in actual trade settlements (e.g., ±1.5%). The system updates the deviation degree every 5 minutes and uses trend extrapolation to predict score changes over the next 15 minutes. If the predicted value enters the critical range (>80 points), preventive measures (such as limiting the rate of change of the control valve opening) are initiated in advance. The comprehensive deviation is visualized in various ways: a dynamic radar chart displays the contribution ratio of the three types of deviations on the SCADA interface, and color codes (green / yellow / red) on mobile devices provide an intuitive overview of the overall status. A deviation evolution curve is also generated for offline analysis. The scoring results are also linked to the equipment maintenance system. If the associated deviation of a sensor exceeds the warning level for three consecutive days, a calibration work order for that device is automatically generated.

[0149] In this embodiment, if the deviation exceeds a preset tolerance, the calibration process is retriggered. When the combined deviation exceeds a preset threshold (default 75 points, adjustable in different levels), the system initiates an intelligent recalibration decision-making process. First, the system analyzes the network topology to determine the calibration impact domain (typically 10 pipe diameters upstream and downstream of the outlier point). The system then assesses the urgency of calibration (based on the rate of deviation growth and trade impact) and selects the optimal calibration mode (rapid local calibration or full-line precision calibration). Recalibration is triggered using a dual-channel confirmation mechanism: a hardware comparator monitors key indicators (such as pressure surges >15%) in real time, while a software decision engine comprehensively evaluates economic and safety considerations. The calibration process implements closed-loop management: compensation effectiveness is immediately verified after each calibration. If the new parameters fail to reduce the deviation to a safe range (<60 points), the calibration level is automatically upgraded (for example, from single-point calibration to multi-condition calibration). The system retains the three most recent calibration parameter versions, enabling rapid rollback to any stable state. A detailed report is generated for all recalibration events, including the triggering cause (such as "continuous deviation exceeding the limit"), execution process data (test operating points and results) and effect verification conclusions. The experience of handling similar events is linked through the enterprise-level knowledge graph to continuously optimize the adaptive capabilities of the calibration strategy.

[0150] The technical effect of the above technical solution is: by optimizing the correction parameter set to update the real-time compensation algorithm, the timeliness and accuracy of the compensation algorithm are ensured. The strategy adjustment unit adjusts the sampling strategy based on the dynamic pipeline operating data, which improves the pertinence and efficiency of sampling, continuously compares the actual flow data with the expected value, promptly detects deviations, and ensures that the flow meter always remains in the optimal working state by accurately evaluating the deviation. If the deviation exceeds the preset range, the process trigger unit automatically restarts the calibration process to ensure the stability and efficiency of the system. This method improves the accuracy and flexibility of the calibration process and optimizes the long-term performance of the flow meter.

[0151] Example 8:

[0152] The embodiment of the present invention provides an adaptive regulation and control system for natural gas flow calibration, and a process optimization module, including:

[0153] Record retrieval unit: starts the historical data retrieval program and retrieves the calibration records of the latest preset number of times from the preset distributed time series database;

[0154] Data integration unit: constructs calibration data cube based on the retrieved calibration records;

[0155] Indicator determination subunit: determines several key indicators based on the feature importance of the data in the calibration data cube;

[0156] Record screening unit: determines several historical successful calibration records based on the calibration records of key indicators in the latest preset number of times;

[0157] Cycle recommendation unit: Analyze the interval cycles of all historical successful calibration records and determine several basic recommended cycles;

[0158] Data extraction unit: After the calibration process is completed, extract the current key calibration data;

[0159] Cycle screening unit: determines the optimal calibration cycle among all basic recommended cycles based on current key calibration data;

[0160] Process optimization unit: optimizes the calibration process based on the optimal calibration cycle.

[0161] In this embodiment, the most recent preset number of calibration records are retrieved. A time-partitioned indexing strategy is employed to scan only the time period closest to the current operating conditions (e.g., the period matching the season or diurnal pattern). During data extraction, a triple check is performed: checking for completeness (ensuring that required fields are present), consistency (checking for timestamp continuity), and validity (eliminating anomalous records caused by sensor failures).

[0162] In this embodiment, a calibration data cube is constructed based on retrieved calibration records. After obtaining the original calibration records, the system performs multidimensional data integration operations to construct a calibration data cube, a specialized analysis structure. This cube uses calibration events as the basic unit and organizes data along three dimensions: time, equipment, and operating conditions. The time axis preserves the complete chronological chain of the calibration process, including the trigger time, execution timestamps of each stage, and total elapsed time. The equipment axis associates hardware information such as flowmeter serial numbers and sensor calibration records. The operating condition axis integrates environmental parameters such as pressure, temperature, and composition, as well as their derived characteristics. The data aggregation process uses a star schema, with the calibration event fact table at the core and linked to multiple dimension tables via foreign keys. The system automatically detects and resolves naming conflicts and unit differences across data sources, converting all values ​​to a standardized measurement system. To support efficient analysis, the cube establishes a multi-layered index structure, including an inverted index based on flow regime type and a B+ tree index based on time range. After construction is complete, the system generates a data quality report, which summarizes metrics such as field fill rate and value range compliance. When the quality score falls below a threshold, an alert is triggered and suspicious data is flagged.

[0163] In this embodiment, a two-stage feature selection method is used to determine several key indicators based on the feature importance of the data in the calibration data cube. In the first stage, a pre-trained random forest model is used to calculate the Gini importance score of each field, selecting the top 20% of features for inclusion in the candidate set. In the second stage, redundant features with fluctuation coefficients below a threshold or insignificant correlation with the calibration results are removed through time-series correlation analysis. The remaining key indicators are divided into three categories: performance indicators (such as deviation improvement rate and calibration time), operating condition indicators (such as pressure fluctuation amplitude and temperature gradient), and correction indicators (such as compensation parameter adjustment amplitude). The system dynamically maintains a list of feature importance and regularly retrains the selection model based on newly added data. For selected key indicators, the system automatically generates a visual distribution report, displaying their statistical characteristics and comparisons with historical baselines. Outliers outside the normal range are marked for manual review. All key indicators and their metadata (definition formula, calculation method, and reasonable range) are stored in an indicator library for standardized management.

[0164] In this embodiment, a number of historical successful calibration records are determined based on the most recent preset number of key metric calibration records. This process involves filtering valid samples from these historical calibration records according to predefined successful calibration criteria. These criteria are based on a combination of multiple conditions: the core condition is that the deviation improvement rate is no less than a preset threshold (e.g., 30%). Additional conditions include meeting the required stable operation duration after calibration (e.g., no alarms for four consecutive hours) and meeting the validation test pass rate (e.g., more than 90% of test items passing). The screening process utilizes a streaming processing architecture, evaluating calibration records one by one in reverse chronological order and performing complex condition matching via a rules engine. To eliminate random interference, the system additionally applies a sliding window consistency check, requiring that the key metric for successful calibration be stable within adjacent time windows. Records that pass the screening are marked as "historical successful calibrations," and their complete data (including original input and corrected output) is stored in a dedicated analysis pool. The system maintains real-time statistical analysis of the sample composition of the analysis pool to ensure a relatively balanced distribution of samples across flow regime types and operating conditions. If insufficient samples are available for a particular operating condition, the query time range will be automatically expanded or the screening threshold adjusted to supplement the data.

[0165] In this embodiment, the intervals of all historical successful calibration records are analyzed to determine several basic recommended intervals based on a sample set of historical successful calibrations. The system then performs period feature mining and analysis. The analysis process employs a hybrid approach: First, kernel density estimation techniques are used to plot the probability distribution curves of calibration intervals under different flow regimes, with the lower bound of the 90% confidence interval used as a conservative recommendation. Second, a hidden Markov model is established to identify the implicit state transition patterns between calibration performance and intervals. Finally, the theoretical minimum allowable interval is calculated based on the pipeline's fluid dynamics characteristics as a constraint. The analysis results generate multiple basic recommended interval scenarios, each corresponding to a specific flow regime and operating condition combination (e.g., 12 hours for steady flow and low temperature, 6 hours for pulsating flow and high load). The system verifies the statistical significance of each scenario and removes recommendations with insufficient sample support. All basic recommended intervals and their derivation basis are stored in a cycle knowledge base, which supports semantic search and allows filtering by device model, environmental parameters, and other criteria. Abnormal interval patterns detected during the analysis (e.g., two consecutive calibration intervals that are too short) automatically trigger a device health check.

[0166] In this embodiment, after the calibration process concludes, the system immediately initiates the data extraction process to extract key calibration data. This process captures the complete information flow from the real-time data bus. This extraction covers the original snapshot at the time of calibration trigger (millisecond-level sampling of operating condition data), the compensation parameter adjustment sequence (including coefficient changes for each iteration), and the multi-dimensional performance indicators from the verification phase. The data acquisition module employs a double buffering mechanism to ensure that high-frequency sampling data is not lost, while a time alignment algorithm addresses the time difference between multi-source data. The extracted core data items strictly correspond to the pre-set key indicator list, including required fields (such as initial / final deviation values) and optional fields (such as derived characteristics under specific operating conditions). The system performs real-time data quality verification, interpolating and repairing anomalous sampling points or marking them for exclusion. Upon completion of the extraction, a data summary report is generated, comparing the key indicator differences between the current calibration and the historical benchmark, highlighting items with significant changes (such as a sudden increase of more than 20% in the temperature correction coefficient). All extracted data is assigned a unified transaction ID and stored in conjunction with the calibration task metadata.

[0167] In this embodiment, the optimal calibration cycle is determined from all recommended basic cycles based on current key calibration data. The feature vectors of the key indicators of the current calibration are input into a cycle decision model pre-loaded with all recommended basic cycle options. The matching process consists of three steps: first, a flow state classifier is used to determine the mode to which the current operating condition belongs. Second, the Euclidean distance between the current indicator and representative samples of each mode is calculated to screen the five nearest candidate cycles. Finally, a multi-criteria decision algorithm is applied, comprehensively considering calibration costs, risk aversion coefficients, and operation and maintenance strategy preferences, to determine the optimal solution from among the candidate cycles. The decision model has a built-in conflict detection mechanism. If the recommended result deviates from the recent actual cycle by more than a preset tolerance, an expert rule review process is initiated. The final cycle determination result includes the main recommended value and a flexible range (e.g., "8 hours ± 1 hour"), along with a confidence score and a list of alternative solutions. The system records the complete reasoning path for this decision, including any excluded candidate cycles and the reasons for their elimination, for reference in subsequent optimization. The determined optimal cycle takes effect immediately and is synchronously updated to the calibration task scheduler.

[0168] In this embodiment, the calibration process is optimized based on the optimal calibration cycle. After obtaining the optimal calibration cycle, the system dynamically adjusts the entire process. At the scheduling level, the calibration trigger time window algorithm is restructured, replacing the fixed cycle with an adaptive sliding window based on the current cycle value. At the resource allocation level, computing power requirements are recalculated based on the new cycle parameters, and the task allocation strategy for edge nodes is dynamically adjusted. At the business rule level, the calibration compensation parameter adjustment algorithm is optimized to better adapt to parameter drift patterns under the new cycle. All optimization measures are validated through an A / B testing framework, initially piloted on 5% of calibration tasks and then gradually expanded to the full workload after confirming satisfactory results. The system continuously monitors optimized key performance indicators (such as calibration success rate and resource utilization) and automatically reverts to the previous stable configuration if any degradation exceeds a preset threshold. A version change report is generated for each process optimization, detailing the adjustments, expected benefits, and potential risks. This report is included in the unified version control of the configuration management system. The optimized new process takes effect at the start of the next cycle.

[0169] The beneficial effects of the above technical solution: Through the synergistic effect of multiple units of the process optimization module, the efficiency and accuracy of the natural gas flow meter calibration process are significantly improved. The record retrieval unit extracts the most recent calibration records from the distributed time series database, provides basic data for the data integration unit, and constructs a calibration data cube to facilitate comprehensive analysis. The key indicators are determined by the indicator determination sub-unit, and historical successful calibration records are screened to ensure optimization based on successful experience. The cycle recommendation unit analyzes the interval period of the calibration records and provides a basic recommended cycle for screening. Finally, the cycle screening unit determines the optimal calibration cycle based on the current calibration data, and the process optimization unit further optimizes the calibration process to ensure that each calibration is carried out under optimal conditions. This optimization method greatly improves the automation and accuracy of the calibration process, ensuring efficient and stable operation of the system.

[0170] Example 9:

[0171] The embodiment of the present invention provides an adaptive regulation control method for natural gas flow calibration, such as Figure 2 Shown, including:

[0172] Step 1: A multi-sensor array pre-placed on the natural gas pipeline collects real-time pipeline operation data, extracts real-time dynamic flow characteristics, and performs real-time flow pattern recognition based on the real-time dynamic flow pattern characteristics to obtain real-time preset flow pattern data;

[0173] Step 2: Determine the trigger coefficient. Based on the real-time preset flow data and the trigger coefficient, determine whether the trigger condition is met. If so, generate a calibration trigger instruction. The trigger coefficient includes: flow stability coefficient and error accumulation coefficient.

[0174] Step 3: Receive the calibration trigger command, obtain the current compensation parameters from the preset compensation parameter buffer, generate preliminary correction coefficients based on the real-time dynamic flow characteristics, package the collected real-time pipeline operation data, real-time dynamic flow characteristics, and preliminary correction coefficients into a calibration data package, call the reference data and combine it with the calibration data package to determine the final optimized correction parameter set;

[0175] Step 4: Update the real-time compensation algorithm based on the final optimized correction parameter set. After the update, continuously collect actual flow data within the preset calibration period and compare it with the expected value to determine the deviation. If the deviation exceeds the preset allowable range, the calibration process is retriggered.

[0176] Step 5: After the calibration process is completed, extract the current key calibration data, call the historical calibration data, combine the current key calibration data to determine the optimal calibration cycle, and optimize the calibration process based on the optimal calibration cycle.

[0177] The beneficial effects of the above technical solution are as follows: by collecting flow data in real time and combining it with a dynamic optimization algorithm, it is possible to automatically adjust compensation parameters and dynamically optimize the calibration process, thereby improving the accuracy and efficiency of flow meter calibration. During the long-term use of the natural gas flow meter, the compensation parameters and calibration strategy can be adjusted in real time, reducing human intervention and improving the stability and accuracy of the system.

[0178] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An adaptive regulation and control system for natural gas flow calibration, characterized in that: include: Data acquisition module: collects real-time pipeline operation data through a multi-sensor array pre-placed on the natural gas pipeline, extracts real-time dynamic flow characteristics, and performs real-time flow pattern recognition based on the real-time dynamic flow pattern characteristics to obtain real-time preset flow pattern data; Instruction generation module: determines the trigger coefficient, judges whether the trigger condition is met based on the real-time preset flow data and the trigger coefficient, and generates a calibration trigger instruction if it is met. The trigger coefficient includes: flow stability coefficient and error accumulation coefficient; Instruction generation module, including: History calling unit: based on preset flow data of a type, calls corresponding historical flow data and historical error accumulation data from a preset historical database; Stability threshold determination unit: determines the flow stability coefficient based on historical flow data; Error threshold determination unit: determines the error accumulation coefficient based on historical error data; Data comparison unit: compares the flow stability coefficient with the preset reference stability threshold, and at the same time, compares the error accumulation coefficient with the preset error tolerance limit; Process triggering unit: If the flow stability coefficient is less than the preset reference stability threshold or the error accumulation coefficient exceeds the preset error tolerance limit, the calibration process is re-triggered; Data optimization module: Receives calibration trigger instructions, obtains current compensation parameters from the preset compensation parameter buffer, generates preliminary correction coefficients based on real-time dynamic flow characteristics, packages the collected real-time pipeline operation data, real-time dynamic flow characteristics, and preliminary correction coefficients into a calibration data package, calls reference data, and determines the final optimized correction parameter set based on the calibration data package; Calibration update module: updates the real-time compensation algorithm based on the final optimized correction parameter set. After the update, the actual flow data is continuously collected within the preset calibration period and compared with the expected value to determine the deviation. If the deviation exceeds the preset allowable range, the calibration process is retriggered. Process optimization module: After the calibration process is completed, the current key calibration data is extracted, the historical calibration data is called, the optimal calibration cycle is determined in combination with the current key calibration data, and the calibration process is optimized based on the optimal calibration cycle.

2. The adaptive regulation and control system for natural gas flow calibration according to claim 1, characterized in that: Data acquisition module, including: Data acquisition unit: collects real-time pipeline operation data through a multi-sensor array pre-placed on the natural gas pipeline; Feature extraction unit: inputs the collected real-time pipeline operation data into the preset sliding time window processor to extract real-time dynamic flow characteristics; Feature recognition unit: inputs the real-time dynamic flow state features into a preset lightweight flow state classifier for real-time flow state recognition to obtain real-time flow state data of a preset type.

3. The adaptive regulation and control system for natural gas flow calibration according to claim 2, characterized in that: The preset types of flow data include steady flow data, pulsating flow data and transient flow data.

4. The adaptive regulation and control system for natural gas flow calibration according to claim 3, characterized in that: A stability threshold determination unit, comprising: Feature analysis subunit: extracts frequency domain features from historical flow data within a preset number of consecutive calibration periods before the current moment, and determines the flow state duration ratio of pulsating flow and transient flow within each preset number of consecutive calibration periods before the current moment; Index acquisition subunit: performing exponentially weighted moving average calculation on the flow state duration ratios of the pulsating flow and transient flow in each preset calibration period for a preset number of consecutive times before the current moment, respectively, to obtain the pulsating flow stability index and the transient flow stability index; Coefficient determination subunit: determines the flow stability coefficient based on the pulsating flow stability index and the transient flow stability index.

5. The adaptive regulation and control system for natural gas flow calibration according to claim 1, characterized in that: The data optimization module includes: Parameter reading unit: reads the currently effective compensation parameters from the preset compensation parameter buffer; Data processing unit: performs time series alignment processing on the currently effective compensation parameters and the real-time pipeline operation data collected later; Matrix operation unit: According to the preset parameter adjustment rules, the real-time flow characteristics and the currently effective compensation parameters are matrix operated to obtain the initial correction coefficient; Data packaging unit: packages the real-time data, initial correction parameters and real-time flow characteristics after time series alignment processing according to the preset standard protocol to obtain the current calibration data packet; Data matching unit: Based on the current calibration data packet, it calls the historical calibration database on the cloud to perform a matching query and obtains the historical calibration data sets corresponding to several historical reference cases; Data search unit: constructs a parameter optimization feasible domain for the historical calibration data set, and searches within the feasible domain through a preset planning algorithm until the optimal historical calibration data set is obtained; Parameter update unit: Generates the current version number for the optimal historical calibration data set, and transmits the optimal historical calibration parameter set carrying the version number to the calibration update module through an encrypted channel, immediately triggering the preset verification process until the verification result meets the preset requirements and determines the optimal historical calibration data set as the final optimized correction parameter set.

6. The adaptive regulation and control system for natural gas flow calibration according to claim 1, characterized in that: Calibration update module, including: Calibration update unit: updates the real-time compensation algorithm based on the final optimized correction parameter set; Strategy adjustment unit: sets the basic calibration cycle according to the preset standard, obtains the dynamic data of the natural gas pipeline working condition, and adjusts the sampling strategy based on the dynamic data of the natural pipeline working condition; Data comparison subunit: based on the basic calibration cycle and the adjusted sampling strategy, it continuously collects actual flow data and compares it with the expected value; Deviation acquisition unit: determines instantaneous deviation, continuous deviation and trend deviation based on comparison data; Deviation analysis unit: determines the degree of deviation based on instantaneous deviation, continuous deviation and trend deviation; Process trigger unit: If the deviation exceeds the preset allowable range, the calibration process will be re-triggered.

7. The adaptive regulation and control system for natural gas flow calibration according to claim 1, characterized in that: Process optimization module, including: Record retrieval unit: starts the historical data retrieval program and retrieves the calibration records of the latest preset number of times from the preset distributed time series database; Data integration unit: constructs calibration data cube based on the retrieved calibration records; Indicator determination subunit: determines several key indicators based on the feature importance of the data in the calibration data cube; Record screening unit: determines several historical successful calibration records based on the calibration records of key indicators in the latest preset number of times; Cycle recommendation unit: Analyze the interval cycles of all historical successful calibration records and determine several basic recommended cycles; Data extraction unit: After the calibration process is completed, extract the current key calibration data; Cycle screening unit: determines the optimal calibration cycle among all basic recommended cycles based on current key calibration data; Process optimization unit: optimizes the calibration process based on the optimal calibration cycle.

8. A control method for an adaptive regulation control system for natural gas flow calibration according to any one of claims 1 to 7, characterized in that: include: Step 1: A multi-sensor array pre-placed on the natural gas pipeline collects real-time pipeline operation data, extracts real-time dynamic flow characteristics, and performs real-time flow pattern recognition based on the real-time dynamic flow pattern characteristics to obtain real-time preset flow pattern data; Step 2: Determine the trigger coefficient. Based on the real-time preset flow data and the trigger coefficient, determine whether the trigger condition is met. If so, generate a calibration trigger instruction. The trigger coefficient includes: flow stability coefficient and error accumulation coefficient. Step 3: Receive the calibration trigger command, obtain the current compensation parameters from the preset compensation parameter buffer, generate preliminary correction coefficients based on the real-time dynamic flow characteristics, package the collected real-time pipeline operation data, real-time dynamic flow characteristics, and preliminary correction coefficients into a calibration data package, call the reference data and combine it with the calibration data package to determine the final optimized correction parameter set; Step 4: Update the real-time compensation algorithm based on the final optimized correction parameter set. After the update, continuously collect actual flow data within the preset calibration period and compare it with the expected value to determine the deviation. If the deviation exceeds the preset allowable range, the calibration process is retriggered. Step 5: After the calibration process is completed, extract the current key calibration data, call the historical calibration data, combine the current key calibration data to determine the optimal calibration cycle, and optimize the calibration process based on the optimal calibration cycle.

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