Data management method for a gas flowmeter

Abstract

The application discloses a data management method of a gas flowmeter, and belongs to the technical field of gas metering; in view of the problems of lack of full-dimension health assessment and early warning and insufficient correction precision of medium working conditions in the prior art, the method collects zero drift sequences, signal-to-noise ratio time sequences and circuit board temperature sequences in real time, extracts degradation characteristics and obtains a comprehensive health index through weighted fusion, divides flowmeter states into a health state and a degradation state, executes layered adaptive flow correction in the health state, completes primary correction through a zero drift mean value and a circuit board temperature index, and then completes secondary correction by combining gas-specific ideal reference parameters and medium comprehensive temperature and pressure to calculate a medium influence coefficient; in the degradation state, degradation levels are quantitatively judged and graded early warnings are sent, and untrustworthy data are marked; the application realizes the change from passive fault alarm to active early warning of the flowmeter, and significantly improves the metering precision and operation reliability under complex working conditions.

Description

Technical Field

[0001] This invention relates to the field of gas metering technology, and more specifically, to a data management method for a gas flow meter. Background Technology

[0002] As a core instrument for industrial metering, gas flow meters are widely used in key sectors of the national economy such as petrochemicals, long-distance natural gas pipelines, power energy, and metallurgical manufacturing. Their metering accuracy is directly related to the fairness of energy trade settlement, energy efficiency control and safe operation of industrial production processes. With the improvement of industrial automation, higher requirements are placed on the long-term stability, data reliability and fault early warning capabilities of gas flow meters.

[0003] The existing data management technology for gas flow meters still has the following shortcomings: Lack of a comprehensive health status assessment and early warning mechanism; Existing technologies mostly only have fault alarm functions and cannot identify early degradation states before structural failures occur in the flow meter; a multi-dimensional health assessment system covering zero drift characteristics, signal quality, and electronic unit status has not been established, nor has a quantitative classification of degradation levels been achieved, which makes it impossible for managers to arrange maintenance in advance and only deal with the problem passively after it occurs. The accuracy of media condition correction is limited; Gas volume is highly sensitive to changes in temperature and pressure. Existing correction methods mostly use single-point instantaneous values ​​for calculation, which are easily affected by instantaneous operating condition fluctuations such as valve regulation and compressor start-up and shutdown, resulting in data jumps. At the same time, no ideal reference parameters are customized for the physical properties of different gases.

[0004] Therefore, there is an urgent need to develop a gas flow meter data management method that integrates real-time acquisition of multi-source data, comprehensive health assessment, graded degradation early warning, and adaptive precise flow correction, in order to solve the above-mentioned problems existing in the current technology. Summary of the Invention

[0005] To overcome the above-mentioned deficiencies of the prior art, embodiments of the present invention provide a data management method for a gas flow meter.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A data management method for a gas flow meter includes: Multi-source data acquisition: Real-time acquisition of multi-dimensional status parameters of the flow meter, including zero drift sequence, signal-to-noise ratio time series, and circuit board temperature series; Health assessment: Degradation features are extracted for each state parameter, and zero drift health, signal-to-noise ratio health and electronic unit health are calculated respectively. After weighted fusion of the health scores of each group, the comprehensive health index of the flow meter is output. Health status determination: The overall health index is compared with the preset warning threshold to determine the status of the flow meter; the status includes deterioration status and healthy status. Based on different states, the corresponding evaluation mode is executed, and the data evaluation results are output. The data evaluation results include degradation level assessment and corrected flow data; When the flow meter is in a degraded state, the evaluation mode is executed as the degradation evaluation mode; Based on the comprehensive health index of the flow meter, the degradation level of the flow meter is determined and a graded warning is issued, marking the flow data measured at the current time point as unreliable data; the degradation levels include slight degradation, moderate degradation and severe degradation; When the flow meter is in a healthy state, the evaluation mode is flow correction mode; Using the current zero drift compensation value and circuit board temperature as a benchmark, the original flow measurement value is corrected by combining the medium temperature and pressure data of the measured flow medium, and the corrected final flow data is output.

[0007] Specifically, the logic for establishing multidimensional state parameters; Identify the most recent stop-flow window, automatically read the output value of the flow meter at the theoretical zero flow, and record the output value at each time point within the stop-flow window to form a zero-drift sequence; When the flow meter is in normal working condition, the original high-frequency signals at each time point are subjected to fast Fourier transform to separate the effective signal and noise, calculate the signal-to-noise ratio, extract the signal-to-noise ratio at each time point, and form a signal-to-noise ratio time series. The circuit board temperature was recorded at various time points when the flow meter was in normal working condition, forming a circuit board temperature sequence.

[0008] Specifically, the calculation logic of the comprehensive health index; The comprehensive health index is obtained by weighted fusion of zero drift health, signal-to-noise ratio health, and electronic unit health.

[0009] Specifically, the zero-drift health score is obtained by weighted fusion of the zero-drift mean, zero-drift volatility, and zero-drift trend rate in the zero-drift sequence. The signal-to-noise ratio health score is obtained by weighted fusion of the signal-to-noise ratio mean and signal-to-noise ratio volatility in the signal-to-noise ratio time series. The health status of electronic units is obtained by weighted fusion of the average temperature, maximum temperature, and temperature fluctuation rate in the circuit board temperature series.

[0010] Specifically, the logic for determining the current state; If the overall health index is higher than the preset warning threshold, the flow meter is determined to be in a degraded state; otherwise, it is in a healthy state.

[0011] Specifically, the implementation process of the degradation assessment model; When the flow meter determines that the condition is degraded, the degradation assessment mode is executed. The difference between the calculated comprehensive health index and the warning threshold is calculated, and then the pre-edited mapping rules are used to convert it into a degradation level and send it to the management personnel.

[0012] Specifically, the execution process of the traffic correction mode; Extract the mean of zero drift from the zero drift sequence After correcting the original flow measurement value based on the zero-drift mean, the zero-drift corrected flow rate is determined. ; Extract the average temperature from the circuit board temperature series and highest temperature The influence coefficient of the electronic unit is then comprehensively converted, and the zero drift correction flow rate is multiplied by the influence coefficient of the electronic unit to obtain the first correction flow rate value. Based on the ideal reference parameter set, the medium influence coefficient is output after weighted fusion processing of the medium comprehensive temperature and medium comprehensive pressure. Using the pre-established mapping rule between the medium influence coefficient and the correction factor, the current medium influence coefficient is transformed into the correction factor, and then multiplied with the first correction flow value to obtain the final flow data.

[0013] Specifically, zero-drift correction flow The computational logic; The corrected formula is: ; The original flow measurement value. For flow deviation; via Where K is the conversion coefficient.

[0014] Specifically, the comprehensive transformation process of the electronic unit influence coefficient; Comprehensive conversion through temperature average and highest temperature Combined with the preset optimal operating temperature of the electronic unit The circuit board temperature index is obtained through weighted fusion processing, and the conversion is completed using the pre-edited mapping rules between the circuit board temperature index and the influence coefficient of the electronic unit.

[0015] Specifically, the process of obtaining the integrated temperature, integrated pressure, and ideal reference parameter set of the medium; The time point at which the original flow measurement value was measured is identified as the target time point, and the medium temperature and medium pressure at each time point within the set time zone before the target time point are retrieved. For each group of medium temperature and medium pressure, the arithmetic mean is taken to output the comprehensive medium temperature and comprehensive medium pressure; according to the type of gas transported by the pipeline, the ideal reference parameter set corresponding to the gas type is configured in the pre-established gas database. The ideal reference parameter set includes ideal reference temperature and ideal reference pressure.

[0016] The technical effects and advantages of this invention are as follows: This invention constructs a multi-dimensional, multi-parameter flow meter health status assessment and hierarchical early warning system. By real-time acquisition of zero drift sequence, signal-to-noise ratio time series and circuit board temperature series, degradation features are extracted and weighted and fused to obtain a comprehensive health index. This realizes the transformation from post-fault alarm to early degradation warning. It identifies early degradation status before the flow meter experiences structural failure, guides managers to arrange maintenance in advance, and avoids production interruption and economic losses caused by sudden failures. This invention establishes a hierarchical adaptive precision flow correction system, which significantly improves the metering accuracy under complex operating conditions. The first-level correction integrates the superimposed effects of the average temperature, the highest temperature, and temperature fluctuations. By matching the influence coefficient of the electronic unit with the circuit board temperature index, it solves the problem of insufficient accuracy of compensation with a single fixed temperature coefficient. The second-level correction uses the sliding window arithmetic average to obtain the comprehensive temperature and pressure of the medium. Combined with the exclusive ideal reference parameters of different gas types, the medium influence coefficient is calculated, which effectively eliminates the correction error caused by instantaneous operating condition fluctuations and differences in gas characteristics. Attached Figure Description

[0017] Figure 1 This is a flowchart of a data management method for a gas flow meter according to the present invention. Detailed Implementation

[0018] 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.

[0019] like Figure 1 As shown, a data management method for a gas flow meter is as follows: Multi-source data acquisition: Real-time acquisition of multi-dimensional status parameters of the flow meter, including zero drift sequence, signal-to-noise ratio time series, and circuit board temperature series; Specifically: Identify the most recent stop-flow window, automatically read the output value of the flow meter at the theoretical zero flow, and record the output value at each time point within the stop-flow window to form a zero-drift sequence; Additional explanation: The zero-drift sequence is the non-zero output value of the flow meter under the theoretical zero-flow condition. It reflects the degree of drift of the sensor zero point over time and is one of the main sources of measurement error. Triggering conditions: The fluctuation of the flow measurement value is less than 0.05% of the full scale within 10 consecutive minutes, and the upstream and downstream valves are closed (valve position signals are read via Modbus protocol). Window duration: The standard shutdown window is 30 minutes, which can be adjusted to 15-60 minutes depending on the on-site working conditions.

[0020] When the flow meter is in normal working condition (flow rate between 10% and 90% of full capacity), the original high-frequency signals at each time point are subjected to fast Fourier transform to separate the effective signal and noise, calculate the signal-to-noise ratio, extract the signal-to-noise ratio at each time point, and form a signal-to-noise ratio time series. To clarify, the signal-to-noise ratio (SNR) is the ratio of the effective signal power to the noise power in the flowmeter's output signal, reflecting the sensor's signal quality and anti-interference capability.

[0021] The circuit board temperature was recorded at various time points when the flow meter was in normal working condition, forming a circuit board temperature sequence. Additional information: Circuit board temperature refers to the operating temperature of the core chip of the flow meter's electronic unit (such as an AD converter or microprocessor), reflecting the aging degree of the electronic components and the working environment. An NTC thermistor can be installed on the surface of the AD converter chip, the microprocessor chip, and the power module; the maximum value of the three sensor temperatures can be taken as the circuit board temperature at the target time point.

[0022] Health assessment: Degradation features are extracted for each state parameter, and zero drift health, signal-to-noise ratio health and electronic unit health are calculated respectively. After weighted fusion of the health scores of each group, the comprehensive health index of the flow meter is output. Specifically: Based on the zero-drift sequence, the zero-drift mean, zero-drift volatility, and zero-drift trend rate are extracted. After weighting and fusing the mean of zero drift, the volatility of zero drift, and the trend rate of zero drift with a preset zero drift standard, the zero drift health score is output. Additional explanation: mean zero drift N represents the total number of recorded time points in the stop window, and i represents the number. Output values ​​for each time point in the zero-drift sequence; Zero drift volatility ; The zero-drift trend rate is determined by fitting the sequence using linear regression to determine the slope, and the slope is used as the trend rate. ; Zero drift health ; , as well as These are preset weighting coefficients, and their sum is one.

[0023] , , The maximum allowable value for zero drift, the maximum allowable value for zero drift volatility, and the maximum allowable value for zero drift trend rate are preset in the zero drift standard.

[0024] Based on the signal-to-noise ratio time series, the signal-to-noise mean and signal-to-noise ratio volatility are extracted. After weighting and fusing the mean signal-to-noise ratio (SNR) and the SNR fluctuation rate with a preset SNR standard, the SNR health score is output. Additional explanation: The mean signal-to-noise ratio (SNR) is obtained by averaging the values ​​of each group in the SNR time series. ; The signal-to-noise ratio volatility is obtained by calculating the standard deviation of the signal-to-noise ratio time series. ; Signal-to-noise ratio health ; , , The preset signal-to-noise ratio (the factory-calibrated benchmark signal-to-noise ratio), minimum allowable signal-to-noise ratio value, and maximum allowable signal-to-noise ratio fluctuation value are defined in the signal-to-noise standard. and These are preset weighting coefficients, and their sum is one.

[0025] Based on the circuit board temperature sequence, the average temperature, the highest temperature, and the temperature fluctuation rate are extracted. After weighting and fusing the average temperature, maximum temperature, and temperature fluctuation rate with preset circuit board standards, the health status of the electronic unit is output. Additional notes: The arithmetic mean of the circuit board temperature series is taken, and the average temperature is output. ; Identify the highest temperature in the circuit board temperature sequence and denote it as... ; The temperature fluctuation rate is obtained by calculating the standard deviation of the circuit board temperature series. ; Electronic unit health ; , , These are the optimal operating temperature, maximum allowable operating temperature, and maximum allowable temperature fluctuation rate of the electronic unit in the preset circuit board standard. , , These are preset weighting coefficients, and their sum is one.

[0026] The comprehensive health index is obtained by weighted fusion of zero-drift health, signal-to-noise ratio health, and electronic unit health. Comprehensive Health Index ; , , These are preset weighting coefficients, and their sum is one.

[0027] Health status determination: The overall health index is compared with the preset warning threshold to determine the status of the flow meter; the status includes deterioration status and healthy status. Based on different states, the corresponding evaluation mode is executed, and the data evaluation results are output. The data evaluation results include degradation level assessment and corrected flow data; When the flow meter is in a degraded state, the evaluation mode is executed as the degradation evaluation mode; Based on the comprehensive health index of the flow meter, the degradation level of the flow meter is determined and a graded warning is issued, marking the flow data measured at the current time point as unreliable data; the degradation levels include slight degradation, moderate degradation and severe degradation; When the flow meter is in a healthy state, the evaluation mode is flow correction mode; Using the current zero drift compensation value and circuit board temperature as a benchmark, the original flow measurement value is corrected by combining the medium temperature and pressure data of the measured flow medium, and the corrected final flow data is output. Specifically: If the overall health index is higher than the preset warning threshold, the flow meter is determined to be in a degraded state; otherwise, it is in a healthy state. When the flow meter determines that the state is degraded, the degradation assessment mode is executed. The difference between the calculated comprehensive health index and the warning threshold is calculated, and then the pre-edited mapping rules are used to convert it into a degradation level and send it to the management personnel. To clarify, the difference between the comprehensive health index and the warning threshold is recorded as the degree of degradation. Three sets of degree value intervals are set for each degree value interval, and each degree value interval corresponds to a set of degradation levels. The larger the degree of degradation value, the higher the probability of matching a severe degradation level.

[0028] When the flow meter determines that the flow is in a healthy state, the flow correction mode is executed. Extract the mean of zero drift from the zero drift sequence After correcting the original flow measurement value based on the zero-drift mean, the zero-drift corrected flow rate is determined. ; The corrected formula is: ; The original flow measurement value. For flow deviation; via Where K is the conversion factor; Additional explanation: K is the electrical signal-to-flow conversion coefficient, obtained through... ;in and These represent the full-scale operating flow rate (the maximum flow rate that the flow meter can measure) and the full-scale output electrical signal, respectively.

[0029] When the circuit board temperature is higher than the optimal operating temperature, the original measurement value of the flow meter will be systematically higher, and when the temperature is lower, the original measurement value of the flow meter will be systematically lower.

[0030] Extract the average temperature from the circuit board temperature series and highest temperature The influence coefficient of the electronic unit is then comprehensively converted, and the zero drift correction flow rate is multiplied by the influence coefficient of the electronic unit to obtain the first correction flow rate value. Comprehensive conversion through temperature average and highest temperature Combined with the preset optimal operating temperature of the electronic unit The circuit board temperature index is obtained by weighted fusion processing, and the conversion is completed by using the pre-edited mapping rules between the circuit board temperature index and the influence coefficient of electronic units. Additional explanation: The circuit board temperature index is preset to correspond to each group of index intervals, and each group of index intervals corresponds to an electronic unit influence coefficient. The electronic unit influence coefficient is obtained by matching the currently calculated circuit board temperature index with the corresponding index interval. The influence coefficient of the electronic unit is limited to the range of [0.94, 1.07], initially set by technicians, and can be dynamically adjusted subsequently. This is based on the average temperature. and highest temperature If the average temperature is higher than the optimal operating temperature of the electronic unit, the larger the temperature index of the circuit board, the lower the range of the influence coefficient of the matching electronic unit will be (below 1), and the higher the probability of matching (0.94). Conversely, if the average temperature is higher than the optimal operating temperature of the electronic unit, the higher the probability of matching (below 1). and highest temperature All are below the optimal operating temperature of the electronic unit, and the range of the influence coefficient of the corresponding matching electronic unit is above 1. The larger the coefficient, the higher the probability of matching 1.07.

[0031] The specific calculation process is as follows: Circuit board temperature index ; in and These are preset weighting coefficients, and their sum is one.

[0032] The flow meter is in good condition and a correction has been completed. The corrected flow value is output as the effective operating flow. The time point at which the original flow measurement value was measured is identified as the target time point, and the medium temperature and medium pressure at each time point within the set time zone before the target time point are retrieved. Additional information: Temperature and pressure measuring instruments are independently installed on the pipeline where the gas flow meter is located. Time-series data are collected synchronously through an industrial bus to form a time-series sequence of medium temperature and pressure that corresponds one-to-one with the flow rate.

[0033] For each group of medium temperature and medium pressure, the arithmetic mean is taken to output the comprehensive medium temperature and comprehensive medium pressure; Additional notes: By default, unit conversion is performed for medium temperature and medium pressure, i.e., temperature is converted to Kelvin and pressure is converted to absolute pressure.

[0034] Based on the type of gas transported by the pipeline, an ideal reference parameter set for the corresponding gas type is configured in a pre-established gas database. The ideal reference parameter set includes ideal reference temperature and ideal reference pressure. The gas types include, but are not limited to, nitrogen and natural gas. The calibration conditions for the gas are specified in the flow meter's factory calibration certificate and are stored in the database.

[0035] Based on the ideal reference parameter set, the medium influence coefficient is output after weighted fusion processing of the medium comprehensive temperature and medium comprehensive pressure. The specific calculation process is as follows: Medium influence coefficient ; and These represent the ideal reference temperature and the ideal reference pressure, respectively. and These represent the combined temperature and combined pressure of the medium, respectively. in and These are the preset weighting coefficients.

[0036] Actual temperature higher than ideal temperature → gas volume expands → uncorrected flow rate is too small → correction direction: increase; Actual temperature lower than ideal temperature → gas volume contracts → uncorrected flow rate is too large → correction direction: decrease. Actual pressure higher than ideal pressure → gas volume compression → uncorrected flow rate is too high → correction direction: decrease; Actual pressure lower than ideal pressure → gas volume expansion → uncorrected flow rate is too low → correction direction: increase.

[0037] Using the pre-established mapping rule between the medium influence coefficient and the correction factor, the current medium influence coefficient is converted into a correction factor, and then multiplied with the first correction flow value to obtain the final flow data. Additional explanation: The preset coefficient ranges for each group of coefficient ranges are defined, and each coefficient range corresponds to a correction factor. The correction factor is obtained by matching the currently calculated coefficient range with the corresponding coefficient range. The correction factor is limited to the range of [0.875, 1.125] and is initially set by technicians. It can be dynamically adjusted later. The larger the medium influence coefficient, the higher the matching correction factor, and vice versa.

[0038] The above formulas are all dimensionless calculations. Dimensionless calculations can be performed using various methods such as standardization, which will not be elaborated here. The formulas are derived from software simulations based on a large amount of collected data, and the preset parameters in the formulas can be set by those skilled in the art according to the actual situation.

[0039] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, ATA hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state ATA hard disk.

[0040] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0041] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0042] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0043] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0044] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0045] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable ATA hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0046] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A data management method for a gas flow meter, characterized in that, include: Multi-source data acquisition: Real-time acquisition of multi-dimensional status parameters of the flow meter, including zero drift sequence, signal-to-noise ratio time series, and circuit board temperature sequence; Health assessment: Degradation features are extracted for each state parameter, and zero drift health, signal-to-noise ratio health and electronic unit health are calculated respectively. After weighted fusion of the health scores of each group, the comprehensive health index of the flow meter is output. Health status determination: The overall health index is compared with the preset warning threshold to determine the status of the flow meter; the status includes deterioration status and healthy status. Based on different states, the corresponding evaluation mode is executed, and the data evaluation results are output. The data evaluation results include degradation level assessment and corrected flow data; When the flow meter is in a degraded state, the evaluation mode is executed as the degradation evaluation mode; Based on the comprehensive health index of the flow meter, the degradation level of the flow meter is determined and a graded warning is issued, and the flow data measured at the current time point is marked as unreliable data; The degradation levels include slight degradation, moderate degradation, and severe degradation; When the flow meter is in a healthy state, the evaluation mode is flow correction mode; Using the current zero drift compensation value and circuit board temperature as a benchmark, the original flow measurement value is corrected by combining the medium temperature and pressure data of the measured flow medium, and the corrected final flow data is output. Execution process of flow correction mode: Extract the mean of zero drift from the zero drift sequence After correcting the original flow measurement value based on the zero-drift mean, the zero-drift corrected flow rate is determined. ; Extract the average temperature from the circuit board temperature series and highest temperature The influence coefficient of the electronic unit is then comprehensively converted, and the zero drift correction flow rate is multiplied by the influence coefficient of the electronic unit to obtain the first correction flow rate value. Based on the ideal reference parameter set, the medium influence coefficient is output after weighted fusion processing of the medium comprehensive temperature and medium comprehensive pressure. Using the pre-established mapping rule between the medium influence coefficient and the correction factor, the current medium influence coefficient is converted into a correction factor, and then multiplied with the first correction flow value to obtain the final flow data. The comprehensive transformation process of the electronic unit influence coefficient is as follows: Comprehensive conversion through temperature average and highest temperature Combined with the preset optimal operating temperature of the electronic unit The circuit board temperature index is obtained through weighted fusion processing, and the conversion is completed using the pre-edited mapping rules between the circuit board temperature index and the influence coefficient of the electronic unit.

2. The data management method for a gas flow meter according to claim 1, characterized in that, The process of establishing multidimensional state parameters includes: Identify the most recent stop-flow window, automatically read the output value of the flow meter at the theoretical zero flow, and record the output value at each time point within the stop-flow window to form a zero-drift sequence; When the flow meter is in normal working condition, the original high-frequency signals at each time point are subjected to fast Fourier transform to separate the effective signal and noise, calculate the signal-to-noise ratio, extract the signal-to-noise ratio at each time point, and form a signal-to-noise ratio time series. The circuit board temperature was recorded at various time points when the flow meter was in normal working condition, forming a circuit board temperature sequence.

3. The data management method for a gas flow meter according to claim 2, characterized in that: Zero drift health is obtained by weighted fusion of the zero drift mean, zero drift volatility, and zero drift trend rate in the zero drift sequence; The signal-to-noise ratio health score is obtained by weighted fusion of the signal-to-noise ratio mean and signal-to-noise ratio volatility in the signal-to-noise ratio time series. The health status of electronic units is obtained by weighted fusion of the average temperature, maximum temperature, and temperature fluctuation rate in the circuit board temperature series.

4. The data management method for a gas flow meter according to claim 3, characterized in that, The process of determining the current state is as follows: If the overall health index is higher than the preset warning threshold, the flow meter is determined to be in a degraded state; otherwise, it is in a healthy state.

5. The data management method for a gas flow meter according to claim 4, characterized in that, The specific implementation process of the degradation assessment model is as follows: When the flow meter determines that the condition is degraded, the degradation assessment mode is executed. The difference between the calculated comprehensive health index and the warning threshold is calculated, and then the pre-edited mapping rules are used to convert it into a degradation level and send it to the management personnel.

6. The data management method for a gas flow meter according to claim 5, characterized in that, Zero drift correction flow The calculation logic is as follows: The corrected formula is: ; The original flow measurement value. For flow deviation; via Where K is the conversion coefficient.

7. The data management method for a gas flow meter according to claim 6, characterized in that, The specific process for obtaining the combined temperature and pressure of the medium, as well as the ideal reference parameter set, is as follows: The time point at which the original flow measurement value was measured is identified as the target time point, and the medium temperature and medium pressure at each time point within the set time zone before the target time point are retrieved. For each group of medium temperature and medium pressure, the arithmetic mean is taken to output the comprehensive medium temperature and comprehensive medium pressure; according to the type of gas transported by the pipeline, the ideal reference parameter set corresponding to the gas type is configured in the pre-established gas database. The ideal reference parameter set includes ideal reference temperature and ideal reference pressure.

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