Gas meter field calibration system and method and electronic equipment

Abstract

The invention discloses a gas meter on-site calibration system and method and electronic equipment, and relates to the technical field of gas meter calibration monitoring, and the system comprises a parameter setting module which is used for a user to preset noise monitoring frequency and time parameters; the automatic transmitting and monitoring module is connected with the parameter setting module, works according to preset parameters and comprises a transmitting unit for transmitting detection signals and a data recording unit for recording monitoring data; and the auxiliary function module comprises a voice prompt unit for providing detection operation voice prompts and a temperature and pressure correction unit for adjusting detection parameters according to environment temperature and pressure data. Through the automatic detection logic and the implementation method of the automatic pulse emission instrument, a traditional manual detection mode is thoroughly changed, the instrument can automatically emit pulse signals according to preset parameters and carry out detection, manual intervention is not needed, the detection time is greatly shortened, and the detection efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of gas meter calibration and monitoring technology, and in particular to a gas meter on-site calibration system, method and electronic equipment. Background Art

[0002] In the operation and maintenance of equipment like gas meters, flow and noise monitoring are crucial for ensuring proper operation and improving reliability. Accurate monitoring can promptly identify potential equipment failures and anomalies, preventing potential safety incidents and economic losses caused by equipment malfunctions.

[0003] Currently, existing detection technologies rely primarily on manual operation. For example, operators need to use a reflectometer to rotate around a gas meter to perform noise checks. This traditional detection method has many drawbacks. First, the operation is very time-consuming, especially when testing a large number of gas meters, which consumes a lot of manpower and time. Second, because it relies entirely on manual operation, it is susceptible to human interference, resulting in errors in the test results and failing to accurately reflect the actual noise level of the equipment.

[0004] Furthermore, traditional testing requires manual configuration of the transmission mode to detect noise levels at specific frequencies. This manual setup and testing process is inefficient and makes continuous automatic testing difficult. In practice, equipment noise levels can change at any time, and manual testing cannot capture these changes in a timely manner. Furthermore, human factors such as operator habits and skill levels can also affect the accuracy of test results, significantly compromising their reliability. Summary of the Invention

[0005] In order to solve the above technical problems, a gas meter on-site calibration system, method and electronic equipment are provided. This technical solution solves the above problems.

[0006] In order to achieve the above objects, the technical solution adopted by the present invention is: Gas meter on-site calibration system, including: Parameter setting module: used for users to preset the frequency and time parameters of noise monitoring; Automatic emission and monitoring module: connected to the parameter setting module, works according to preset parameters, and includes an emission unit for emitting detection signals and a data recording unit for recording monitoring data; Auxiliary function module: includes a voice prompt unit for providing voice prompts for detection operations, and a temperature and pressure correction unit for adjusting detection parameters according to ambient temperature and pressure data.

[0007] Preferably, in the automatic transmission and monitoring module, the transmission unit includes a microprocessor, a frequency selection unit and a time control unit. The microprocessor receives and parses preset parameters, the frequency selection unit controls the transmission signal frequency according to the parsed frequency parameters, and the time control unit controls the signal transmission time according to the parsed time parameters.

[0008] Preferably, during the monitoring process, the data recorded by the data recording unit includes a timestamp, a detection frequency, and a noise amplitude at the corresponding frequency.

[0009] Preferably, the voice prompt unit prompts corresponding operations and detection status information during the detection preparation stage, the detection process and the end of the detection.

[0010] Preferably, the temperature and pressure correction unit obtains real-time ambient temperature and pressure data, and uses the formula: P 修正 =P 实测 ×(1+k1×(T-T0)+k2×(P-P0)) Adjust the detection parameters, where P 修正 is the corrected detection parameter, P 实测 are the measured detection parameters, T is the real-time temperature, T0 is the standard temperature, P is the real-time pressure, P0 is the standard pressure, k1 and k2 are the corresponding correction coefficients.

[0011] Preferably, the system further comprises a data processing module, which performs filtering and statistical processing on the original noise data recorded by the data recording unit, wherein the filtering adopts a wavelet denoising algorithm and the average noise intensity and peak fluctuation index are statistically calculated.

[0012] Preferably, the data processing module uses the formula: Among them, L Aeq is the equivalent continuous A sound level, L p (t) is the instantaneous sound pressure level, and T is the measurement time.

[0013] Preferably, the system further includes a feedback adjustment module. When the data processing module detects an abnormal frequency point, the feedback adjustment module automatically reduces the detection step size and intensifies detection in the area where the abnormal frequency point is located.

[0014] The noise monitoring method comprises the following steps: System initialization and calibration: checking hardware connections, detecting ambient noise baselines and setting noise thresholds, and calibrating transmitters; parameter configuration and task planning: inputting preset frequency ranges, step sizes, and detection durations, and generating detection sequences; Frequency traversal detection, transmitting pulse signals according to the detection sequence, synchronously collecting data, performing real-time noise analysis and caching transmission data; Data processing and feedback control, data filtering, statistical processing, and adjustment of detection strategies according to abnormal situations; Terminate the detection and output the results. Generate a report after completing the detection of all frequency points. Trigger corresponding processing in case of abnormal conditions.

[0015] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the noise monitoring method according to claim 9 when executing the computer program.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The automatic detection logic and implementation method of the automatic pulse emission instrument have completely changed the traditional manual detection mode. The instrument can automatically emit pulse signals and perform detection according to preset parameters without human intervention, which greatly shortens the detection time and improves the detection efficiency. At the same time, automatic detection avoids interference from human factors, making the detection results more accurate and reliable. It can timely and accurately reflect the actual noise conditions of equipment such as gas meters, providing strong support for the operation and maintenance of equipment.

[0017] The integrated voice prompt function plays a vital role throughout the entire testing process, providing operators with comprehensive guidance. Whether it's pre-test preparation, in-process steps, or post-test result confirmation, voice prompts provide timely notifications to operators. This not only reduces learning costs and operational difficulty for operators, but also reduces testing errors caused by operational errors, thereby improving the reliability of test results.

[0018] The temperature and pressure correction function takes into account the impact of ambient temperature and pressure on noise detection results. By acquiring real-time ambient temperature and pressure data and dynamically correcting detection parameters based on a specific formula, it effectively eliminates environmental interference with test results. This ensures the accuracy and stability of test results under varying environmental conditions, making them more valuable for more precise judgment of the operating status of equipment such as gas meters. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Flowchart of the present invention. DETAILED DESCRIPTION

[0020] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0021] Reference Figure 1As shown, the gas meter on-site calibration system includes: Parameter setting module: used for users to preset the frequency and time parameters of noise monitoring; Automatic emission and monitoring module: connected to the parameter setting module, works according to preset parameters, and includes an emission unit for emitting detection signals and a data recording unit for recording monitoring data; Auxiliary function module: includes a voice prompt unit for providing voice prompts for detection operations, and a temperature and pressure correction unit for adjusting detection parameters according to ambient temperature and pressure data.

[0022] Specifically, the parameter setting module, automatic emission and monitoring module, and auxiliary function module in the gas meter field calibration system work together to achieve efficient and accurate noise monitoring. The parameter setting module provides basic parameters for the entire system's operation and provides an interactive interface for users to flexibly input parameters such as the noise monitoring frequency range and time period based on actual monitoring needs. These parameters are stored in the system and serve as the basis for the operation of subsequent modules.

[0023] The automatic transmission and monitoring module establishes a data connection with the parameter setting module. Once the user has preset parameters in the parameter setting module, this module reads those parameters. The transmitting unit accurately transmits detection signals to the monitored environment based on the preset frequency and time parameters. These detection signals interact with the ambient noise, and the data recording unit collects and records the relevant noise data in real time.

[0024] The auxiliary function module assists and optimizes the entire monitoring process. The voice prompt unit provides operator guidance and test status information through voice announcements during the test preparation, testing, and completion phases. The temperature and pressure correction unit acquires real-time temperature and pressure data from the monitoring environment and dynamically adjusts test parameters based on preset formulas to eliminate the impact of ambient temperature and pressure changes on noise monitoring results.

[0025] The collaborative operation of these three modules gives the noise monitoring system a high degree of adaptability, accuracy, and reliability. The parameter setting module allows users to freely set parameters based on different monitoring scenarios, avoiding the potential for missed detections or ineffective monitoring caused by fixed parameter monitoring and improving the targeted nature of monitoring. Users can preset appropriate monitoring frequencies and times based on the noise characteristics of different equipment in industrial production workshops, thereby more accurately capturing noise information within specific frequencies and time periods.

[0026] The automatic emission and monitoring module automates noise monitoring, reducing manual intervention and improving monitoring efficiency and stability. By emitting a detection signal at a specific frequency, it can more effectively stimulate and capture noise information in the environment, while accurately recording data provides a reliable basis for subsequent analysis.

[0027] The auxiliary function module further enhances the system's performance. The voice prompt unit reduces the operator's memory burden for complex operating procedures, reduces monitoring errors caused by operational errors, and improves operational accuracy and safety. The temperature and pressure correction unit enables the system to adapt to various complex environments, preventing environmental factors from interfering with monitoring results and improving the reliability and accuracy of monitoring results under different environmental conditions. Overall, the entire system can efficiently and accurately complete noise monitoring tasks in a variety of scenarios, providing strong support for noise assessment and control.

[0028] In the automatic transmission and monitoring module, the transmission unit includes a microprocessor, a frequency selection unit and a time control unit. The microprocessor receives and analyzes preset parameters, the frequency selection unit controls the frequency of the transmitted signal according to the analyzed frequency parameters, and the time control unit controls the signal transmission time according to the analyzed time parameters.

[0029] Specifically, the microprocessor, the core control component of the transmitter unit, receives preset parameters from the parameter setting module and parses them. The resulting frequency parameters are then passed to the frequency selection unit, which precisely adjusts the frequency of the transmitted signal based on these parameters. Similarly, the parsed time parameters are passed to the timing control unit, which uses these parameters to accurately control the timing and duration of signal transmission.

[0030] The coordinated operation of the microprocessor, frequency selection unit, and time control unit enables precise control of the frequency and timing of the transmitted signal. This enables the system to accurately transmit detection signals according to user-defined requirements, improving the accuracy and stability of signal transmission, thereby more effectively capturing environmental noise information and enhancing the accuracy of monitoring results.

[0031] During the monitoring process, the data recorded by the data recording unit includes a timestamp, a detection frequency, and a noise amplitude at a corresponding frequency.

[0032] Specifically, during the monitoring process, the data recording unit simultaneously records multiple key pieces of information. The timestamp marks the specific time of each data record, accurate to milliseconds or even finer granularity, which facilitates subsequent analysis of noise variations over time. The detection frequency records the detection signal frequency corresponding to each data point, enabling differentiation of noise at different frequencies. The noise amplitude reflects the intensity of the noise at that frequency.

[0033] Multi-dimensional data such as recorded timestamps, detection frequency, and noise amplitude provides a wealth of information for subsequent noise analysis. Comprehensive analysis of this data provides a deeper understanding of the noise's temporal, frequency, and intensity distribution, helping to more accurately determine its source, type, and changing trends, providing valuable insights for noise control and equipment maintenance.

[0034] The voice prompt unit prompts corresponding operation and detection status information during the detection preparation stage, detection process and detection completion stage.

[0035] Specifically, during the test preparation phase, the voice prompt unit will prompt the operator to perform necessary preparations, such as checking device connections and confirming parameter settings. During the test, the system will provide real-time information on the test progress and current status, such as "Testing at the third frequency point." At the end of the test, the operator will be informed of the success of the test and any abnormalities.

[0036] Voice prompts throughout the process provide operators with clear operational guidance and real-time status feedback, allowing them to focus more on monitoring tasks and reducing errors caused by negligence or lack of understanding of status. Especially for those unfamiliar with system operation, voice prompts can help them quickly get started, improving operational accuracy and efficiency while also enhancing the system's usability and user experience.

[0037] The temperature and pressure correction unit obtains real-time ambient temperature and pressure data through the formula: P 修正 =P 实测 ×(1+k1×(T-T0)+k2×(P-P0)) Adjust the detection parameters, where P 修正 is the corrected detection parameter, P 实测 are the measured detection parameters, T is the real-time temperature, T0 is the standard temperature, P is the real-time pressure, P0 is the standard pressure, k1 and k2 are the corresponding correction coefficients.

[0038] Specifically, the temperature and pressure correction unit uses temperature and pressure sensors to acquire real-time temperature and pressure data from the monitoring environment. It then calculates the corrected detection parameters based on a preset formula, combining the measured parameters with the real-time temperature and pressure, the standard temperature and pressure, and the correction factor. This formula accounts for the impact of temperature and pressure on noise propagation and detection, quantifying and adjusting this impact through the correction factor.

[0039] Changes in ambient temperature and pressure can affect noise propagation speed and attenuation, leading to deviations in test results. The temperature and pressure correction unit effectively eliminates environmental interference with test results by acquiring and correcting ambient temperature and pressure data in real time. This improves the accuracy and reliability of monitoring results under varying environmental conditions, enabling the system to provide stable and accurate monitoring data in a variety of complex environments.

[0040] The system also includes a data processing module, which performs filtering and statistical processing on the original noise data recorded by the data recording unit, wherein the filtering adopts a wavelet denoising algorithm and statistically calculates the average noise intensity and peak fluctuation index.

[0041] Specifically, the data processing module receives raw noise data from the data recording unit. First, the raw data is filtered using a wavelet denoising algorithm. This algorithm effectively separates and removes noise components based on the different characteristics of noise and signal in the wavelet transform domain, resulting in a purer noise signal. The filtered data is then statistically processed to calculate the average noise intensity and peak fluctuation index. The average noise intensity reflects the overall noise level over a period of time, while the peak fluctuation index reflects the magnitude of the noise intensity fluctuation.

[0042] Filtering removes noise from the raw data, improving its quality and reliability. Statistically calculated average noise intensity and peak fluctuation indicators more intuitively reflect noise characteristics and variations, providing valuable parameters for noise assessment and analysis. Analysis of these indicators allows for more accurate determination of whether noise exceeds safety standards and whether abnormal fluctuations exist, providing a scientific basis for noise control and equipment maintenance.

[0043] The data processing module uses the formula when statistically calculating the average noise intensity: Among them, L Aeq is the equivalent continuous A sound level, L p (t) is the instantaneous sound pressure level, and T is the measurement time.

[0044] Specifically, this formula, based on the definition of the equivalent continuous A-level sound pressure level, integrates the instantaneous sound pressure level over the measurement time, taking into account the human ear's perception of sounds of different frequencies. By integrating the instantaneous sound pressure level over time, it comprehensively considers the changes in noise intensity throughout the measurement period, resulting in an equivalent continuous sound level, the equivalent continuous A-level sound pressure level. This level more accurately reflects the actual noise intensity perceived by the human ear.

[0045] Using the equivalent continuous A-level (A-SPL) to express average noise intensity aligns with the human ear's noise perception and allows for a more accurate assessment of noise's impact on the human body. Compared to simple average sound pressure level calculations, the equivalent continuous A-SPL takes into account the temporal variations and frequency characteristics of noise, providing a more scientific and reasonable indicator for noise assessment and control, helping to develop more effective noise control measures and protect people's hearing health.

[0046] The system further includes a feedback adjustment module. When the data processing module detects an abnormal frequency point, the feedback adjustment module automatically reduces the detection step size and intensifies detection in the area where the abnormal frequency point is located.

[0047] Specifically, the feedback adjustment module establishes a data connection with the data processing module. When analyzing and processing noise data, the data processing module determines whether there are abnormal frequency points based on preset rules. When an abnormal frequency point is detected, the data processing module transmits this information to the feedback adjustment module. Upon receiving this information, the feedback adjustment module automatically adjusts the detection step size, shortening the detection interval in the area where the abnormal frequency point is located and increasing the detection density to obtain more detailed noise information in that area.

[0048] During noise monitoring, abnormal frequency points may indicate equipment failure or unusual sound sources. The feedback adjustment module automatically reduces the detection step size and intensifies detection, enabling more accurate capture of noise variations near abnormal frequency points and in-depth analysis of the causes. This helps promptly identify potential problems and implement appropriate measures to prevent them from further deteriorating, thereby improving the effectiveness of noise monitoring and the safety of equipment operations.

[0049] The noise monitoring method comprises the following steps: System initialization and calibration: checking hardware connections, detecting ambient noise baselines and setting noise thresholds, and calibrating transmitters; parameter configuration and task planning: inputting preset frequency ranges, step sizes, and detection durations, and generating detection sequences; Frequency traversal detection, transmitting pulse signals according to the detection sequence, synchronously collecting data, performing real-time noise analysis and caching transmission data; Data processing and feedback control, data filtering, statistical processing, and adjustment of detection strategies according to abnormal situations; Terminate the detection and output the results. Generate a report after completing the detection of all frequency points. Trigger corresponding processing in case of abnormal conditions.

[0050] Specifically, system initialization and calibration are fundamental steps in noise monitoring system operation. By checking hardware connections, determining the ambient noise baseline, setting noise thresholds, and calibrating the transmitting instrument, a solid foundation is laid for subsequent monitoring work. Subsequently, the user enters parameters such as frequency range, step size, and detection duration based on monitoring requirements during parameter configuration and task planning. The system then generates a detection sequence based on these parameters.

[0051] During the frequency traversal detection phase, the system transmits pulse signals according to the detection sequence, simultaneously collects and analyzes noise data in real time, and then buffers and transmits the analyzed data. Finally, the data processing and feedback control module filters and statistically processes the collected data, determining whether there are any anomalies based on the processing results. If so, the detection strategy is adjusted.

[0052] The close coordination of these elements significantly enhances the performance of the noise monitoring system. System initialization and calibration ensure reliable and stable operation, ensuring accurate and comparable monitoring data, and facilitating precise assessment of noise conditions. Parameter configuration and task planning allow users to customize monitoring plans, and the resulting detection sequences enhance the systematic and accurate nature of monitoring.

[0053] Frequency traversal detection enables comprehensive monitoring, real-time analysis provides a basis for feedback and adjustment, and data caching and transmission ensure data integrity and traceability. Data processing and feedback control improve data quality, enable timely adjustment of detection strategies, accurately capture abnormal noise, and promptly identify potential problems, providing strong support for noise monitoring and control.

[0054] An electronic device is characterized by comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a noise monitoring method when executing the computer program.

[0055] Specifically, the memory is used to store the computer program and related data that implements the noise monitoring method. The processor, as the core computing component of the electronic device, reads the computer program from the memory and executes it. During execution, the processor follows the steps specified in the program, sequentially completing operations such as system initialization and calibration, parameter configuration and task planning, frequency traversal detection, data processing and feedback control, and detection termination and result output, thereby completing the entire noise monitoring process.

[0056] By storing noise monitoring methods as computer programs in electronic devices and leveraging the processor's computing power to automatically execute monitoring tasks, noise monitoring becomes automated and intelligent. The portability and integration of these electronic devices facilitate noise monitoring in a variety of scenarios. Furthermore, the memory can store large amounts of monitoring data, facilitating subsequent data analysis and historical data comparison, providing stronger support for noise monitoring and management.

[0057] Example 1: Gas Meter Noise Monitoring System Initialization and Calibration Process 1. System startup and preparation When the user turns on the gas meter noise monitoring system, the system performs a self-check to ensure that the hardware devices (such as sensors, transmitters, and data acquisition cards) are properly connected and functioning. At the same time, the system loads default parameters and historical calibration data to prepare for subsequent operations.

[0058] 2. Parameter setting Basic parameters: The user enters the basic information of the gas meter in the system interface, such as model, specifications, rated flow, etc.

[0059] Detection Parameters: Set the noise detection frequency range, time interval, and number of detections based on the gas meter's operating environment and detection requirements. For example, for a common household gas meter, set the detection frequency range to 20Hz-20kHz, the detection interval to 1 second, and the number of detections to 10.

[0060] Calibration parameters: Set the standard parameters for temperature and pressure correction, such as standard temperature is 20℃, standard pressure is 101.325kPa, and the corresponding correction coefficients.

[0061] 3. Module Selection Users select the appropriate detection module based on their testing needs. For gas meter noise monitoring, the main options are the noise detection module and the temperature and pressure detection module. The noise detection module collects noise signals during gas meter operation, while the temperature and pressure detection module monitors ambient temperature and pressure in real time.

[0062] 4. Air tightness test Preparation: Connect the gas meter to the air tightness detection device to ensure that the connection is tight and there is no leakage.

[0063] Test process: The system inflates the gas meter to a preset pressure value, maintains it for a set period of time (e.g., 5 minutes), and monitors the pressure change. If the pressure drop is within the allowable range (e.g., no more than 0.5%), the gas tightness is determined to be good; otherwise, the system sounds an alarm, prompting the user to check the gas meter and connecting pipes for leaks.

[0064] 5. Indication error test Standard meter connection: Connect the standard gas meter in parallel with the gas meter to be tested to ensure that both are under the same working conditions.

[0065] Test process: The system controls gas flow through the gas meter at different flow rates and records the readings of the gas meter under test and the reference gas meter. By comparing the two readings, the indication error of the gas meter under test is calculated. For example, at the rated flow rate, the indication error should not exceed ±1%; at the minimum flow rate, the indication error should not exceed ±3%.

[0066] 6. Data recording and exporting Data recording: The system automatically records relevant data of the air tightness test and indication error test, including pressure change, indication error, test time, etc. At the same time, the noise data collected by the noise detection module is recorded and analyzed in real time, and indicators such as the equivalent continuous A sound level are calculated.

[0067] Data export: Users can choose to export test data in formats such as Excel and CSV to facilitate subsequent data analysis and report generation.

[0068] 7. Calibration Adjustment Based on the results of the indication error test, if the indication error of the gas meter under test exceeds the allowable range, the system will prompt the user to perform calibration adjustments. The user can enter the calibration parameters through the system interface, calibrate the gas meter, and then re-perform the indication error test until the indication error meets the requirements.

[0069] Summary: In this embodiment, the various links of the gas meter noise monitoring system work closely together, fully demonstrating the significant superiority of the technical solution in the claims in improving monitoring accuracy, degree of automation, environmental adaptability, and data management efficiency, and effectively solving practical problems in gas meter noise monitoring and performance testing.

[0070] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. Various changes and improvements are possible without departing from the spirit and scope of the present invention, and such changes and improvements fall within the scope of the invention as claimed.

Claims

1. Gas meter on-site calibration system, characterized in that, include: Parameter setting module: used for users to preset the frequency and time parameters of noise monitoring; Automatic emission and monitoring module: connected to the parameter setting module, works according to preset parameters, and includes an emission unit for emitting detection signals and a data recording unit for recording monitoring data; Auxiliary function module: includes a voice prompt unit for providing voice prompts for detection operations, and a temperature and pressure correction unit for adjusting detection parameters according to ambient temperature and pressure data.

2. The gas meter on-site calibration system according to claim 1, characterized in that: In the automatic transmission and monitoring module, the transmission unit includes a microprocessor, a frequency selection unit and a time control unit. The microprocessor receives and analyzes preset parameters, the frequency selection unit controls the frequency of the transmitted signal according to the analyzed frequency parameters, and the time control unit controls the signal transmission time according to the analyzed time parameters.

3. The gas meter on-site calibration system according to claim 1, characterized in that: During the monitoring process, the data recorded by the data recording unit includes a timestamp, a detection frequency, and a noise amplitude at a corresponding frequency.

4. The gas meter on-site calibration system according to claim 1, characterized in that: The voice prompt unit prompts corresponding operation and detection status information during the detection preparation stage, detection process and detection completion stage.

5. The gas meter on-site calibration system according to claim 1, characterized in that: The temperature and pressure correction unit obtains real-time ambient temperature and pressure data through the formula: P 修正 =P 实测 ×(1+k1×(T-T0)+k2×(P-P0)) Adjust the detection parameters, where P 修正 is the corrected detection parameter, P 实测 are the measured detection parameters, T is the real-time temperature, T0 is the standard temperature, P is the real-time pressure, P0 is the standard pressure, k1 and k2 are the corresponding correction coefficients.

6. The gas meter on-site calibration system according to claim 1, characterized in that: The system also includes a data processing module, which performs filtering and statistical processing on the original noise data recorded by the data recording unit, wherein the filtering adopts a wavelet denoising algorithm and statistically calculates the average noise intensity and peak fluctuation index.

7. The gas meter on-site calibration system according to claim 1, characterized in that: The data processing module uses the formula when statistically calculating the average noise intensity: Among them, L Aeq is the equivalent continuous A sound level, L p (t) is the instantaneous sound pressure level, and T is the measurement time.

8. The gas meter on-site calibration system according to claim 1, characterized in that: The system further includes a feedback adjustment module. When the data processing module detects an abnormal frequency point, the feedback adjustment module automatically reduces the detection step size and intensifies detection in the area where the abnormal frequency point is located.

9. A noise monitoring method, characterized in that: The gas meter on-site calibration system according to any one of claims 1 to 8 comprises the following steps: System initialization and calibration, checking hardware connections, detecting ambient noise baseline and setting noise threshold, and calibrating transmitting instruments; Parameter configuration and task planning: input preset frequency range, step size and detection duration and generate detection sequence; Frequency traversal detection, transmitting pulse signals according to the detection sequence, synchronously collecting data, performing real-time noise analysis and caching transmission data; Data processing and feedback control, data filtering, statistical processing, and adjustment of detection strategies according to abnormal situations; Terminate the detection and output the results. Generate a report after completing the detection of all frequency points. Trigger corresponding processing in case of abnormal conditions.

10. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the noise monitoring method according to claim 9 when executing the computer program.

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