Oil leakage diagnosis and gas concentration compensation method and system for oil and gas monitoring equipment

Abstract

The invention discloses an oil leakage diagnosis and gas concentration compensation method and system of oil and gas monitoring equipment, and relates to the field of electrical variable measurement, in the method, the standard fluctuation range of the oil level height is calculated based on historical oil level height data; calculating a theoretical oil level height based on the pressure real-time data; if the real-time data of the oil level height is lower than the lower limit value of the standard fluctuation range and the theoretical oil level height is higher than the upper limit value of the standard fluctuation range, oil leakage is determined; taking the gas concentration historical data as reference gas concentration; calculating the attenuation rate of the real-time gas concentration data relative to the reference gas concentration; matching a target attenuation coefficient closest to the attenuation rate; and compensating the real-time gas concentration data according to the target attenuation coefficient to obtain compensated gas concentration data. According to the invention, the true value of the gas concentration monitoring value is improved, so that the accuracy of gas concentration detection is improved.

Description

Technical Field

[0001] The present application belongs to the field of measuring electrical variables, and in particular relates to a method and system for oil leakage diagnosis and gas concentration compensation of oil and gas monitoring equipment. Background Art

[0002] During long-term operation, oil and gas monitoring equipment gradually ages and deteriorates due to the deterioration of oil and solid insulation materials, releasing a variety of gases including hydrogen, methane, ethane, ethylene, acetylene, carbon monoxide, and carbon dioxide. The concentrations of these gases are directly related to the type and severity of internal faults in the oil and gas monitoring equipment. Therefore, timely and accurate detection of these dissolved gases is crucial for preventing oil and gas monitoring equipment failures.

[0003] In related technologies, gas sensors can be installed to monitor the gas content in the oil of oil and gas monitoring equipment in real time, and the monitoring data can be used as a basis for fault diagnosis of the oil and gas monitoring equipment. This method can achieve real-time monitoring of the gas content in the oil of oil and gas monitoring equipment, which is conducive to timely detection of potential faults of oil and gas monitoring equipment.

[0004] However, in actual monitoring, oil and gas monitoring equipment often suffers from insufficient sealing performance, making it prone to oil leakage. This leakage can cause dissolved gases in the oil to escape, causing gas concentration monitoring values ​​to deviate from the true value and reducing the accuracy of gas concentration detection. Summary of the Invention

[0005] The present application provides an oil leakage diagnosis and gas concentration compensation method and system for oil and gas monitoring equipment, which are used to improve the true value of the gas concentration monitoring value, thereby improving the accuracy of gas concentration detection.

[0006] In a first aspect, the present application provides a method for oil leakage diagnosis and gas concentration compensation of an oil and gas monitoring device, which obtains real-time oil level height data and historical oil level height data within a preset time period in the oil and gas monitoring device; The standard fluctuation range of the oil level is calculated based on the historical oil level data, the upper limit of the standard fluctuation range is the maximum value of the historical oil level, and the lower limit is the minimum value of the historical oil level; Obtain real-time pressure data from oil and gas monitoring equipment and calculate theoretical oil level based on the real-time pressure data; Compare the real-time oil level data, the theoretical oil level and the standard fluctuation range. If the real-time oil level data is lower than the lower limit of the standard fluctuation range and the theoretical oil level is higher than the upper limit of the standard fluctuation range, it is determined that an oil leak has occurred. When an oil leak is determined, historical gas concentration data within a preset time period before the current moment is obtained, and the historical gas concentration data is used as a reference gas concentration; Acquire real-time gas concentration data and calculate the decay rate of the real-time gas concentration data relative to the reference gas concentration; Matching a target attenuation coefficient corresponding to a target attenuation rate closest to the attenuation rate in a preset attenuation coefficient set; The real-time gas concentration data is compensated according to the target attenuation coefficient to obtain compensated gas concentration data.

[0007] By adopting the above technical solution, the accuracy of identifying oil leaks can be improved by acquiring real-time and historical oil level data to establish a standard fluctuation range, and then calculating the theoretical oil level height in combination with real-time pressure data for comparison. In the event of an oil leak, the lowered oil level causes the escape of dissolved gas to accelerate. By obtaining historical gas concentration data before the leak as a benchmark, the decay rate of the real-time gas concentration relative to the benchmark concentration is calculated, and the closest target attenuation coefficient is matched within a preset attenuation coefficient set to compensate for the real-time gas concentration, making the gas concentration data more accurately reflect the actual status of the oil and gas monitoring equipment. By establishing a dual judgment mechanism based on the standard fluctuation range and the theoretical calculated value, the probability of misjudgment is reduced; by selecting the compensation coefficient based on the actual decay rate, the compensation is more closely aligned with the actual situation; and by accurately compensating for the gas concentration, the true value of the gas concentration monitoring value is improved, thereby improving the accuracy of gas concentration detection.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, calculating the theoretical oil level based on real-time pressure data specifically includes: Obtain the current ambient atmospheric pressure value, structural parameters of the oil and gas monitoring equipment, and oil density parameters. The structural parameters include the cross-sectional area and height of the oil tank. Subtract the current ambient atmospheric pressure value from the real-time pressure data to obtain the relative pressure value; According to the static pressure principle, the theoretical oil column height is calculated based on the relative pressure value, tank cross-sectional area, and density parameters.

[0009] By adopting this technical solution, the real-time pressure data is subtracted from the ambient atmospheric pressure value to obtain a relative pressure value. The theoretical oil column height is then calculated based on the principle of static pressure, making the theoretical calculation more consistent with actual physical processes. By introducing ambient atmospheric pressure compensation, the impact of atmospheric pressure changes on the theoretical oil level calculation is reduced. By considering the cross-sectional area and density parameters of the fuel tank, the theoretical calculation more accurately reflects the actual oil level. By applying the principle of static pressure, an accurate correspondence between pressure and oil level is established, improving the accuracy of theoretical oil level calculations and, in turn, the accuracy of oil leak detection.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, calculating the decay rate of real-time gas concentration data relative to a reference gas concentration specifically includes: Obtain solubility coefficients and molecular mass parameters of different types of gases; Calculate the solubility equilibrium constants of various types of gases in oil and gas monitoring equipment based on real-time pressure and temperature data; Calculate the diffusion flux of each type of gas based on the solubility equilibrium constant; Calculate the escape mass of each type of gas per unit time based on the gas diffusion flux; Divide the mass of gas escape by the volume of oil in the oil and gas monitoring equipment to obtain the concentration change of each type of gas per unit time; Calculate the monomer decay rate of each type of gas based on the concentration change, where the monomer decay rate is equal to the ratio of the difference between the real-time gas concentration and the reference gas concentration and the product of the reference gas concentration and the gas decay time; The decay rate is obtained by performing a weighted average calculation on the monomer decay rate of each type of gas, and the weight coefficient of each type of gas is proportional to the corresponding gas molecular mass.

[0011] By adopting the above technical solution, the dissolution equilibrium constant and diffusion flux are calculated, the escape mass of each gas is obtained, and then the concentration change per unit time is calculated. When calculating the overall decay rate, a weighted average method based on molecular mass is used to make the contribution of different gases to the overall decay rate correspond to their physical properties, accurately reflecting the gas escape pattern. By calculating the decay rate of each individual gas, the difference in the escape characteristics of different gases is reflected. Through the weighted average method of molecular mass, the influence of each gas is reasonably balanced, the accuracy of the decay rate calculation is improved, and thus the accuracy of gas concentration compensation is improved.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, after compensating the real-time gas concentration data according to the target attenuation coefficient to obtain compensated gas concentration data, the method further includes: Obtain standard gas concentration thresholds under multiple preset working conditions, each of which corresponds to a different load level and ambient temperature combination; Determine the current operating condition based on the preset operating condition, the current load rate of the oil and gas monitoring equipment, and the current ambient temperature; Compare the compensation gas concentration data with the standard gas concentration threshold corresponding to the current working conditions; If the compensated concentration data of any gas exceeds the corresponding standard gas concentration threshold, the historical concentration change trend of the gas is obtained; When the concentration of the gas continues to rise based on the historical concentration change trend, an alarm signal is triggered.

[0013] By adopting this technical solution, we achieve differentiated gas concentration alarm determination by obtaining standard gas concentration thresholds under multiple preset operating conditions and determining the current operating condition based on the current load rate and ambient temperature. The compensated gas concentration is compared with the standard threshold for the corresponding operating condition and combined with historical gas concentration trends. By considering historical trends, the impact of short-term fluctuations in gas concentration is filtered out. By requiring a continuous increase in gas concentration to trigger an alarm, the reliability of the alarm is improved, thereby reducing the false alarm rate and improving the accuracy and timeliness of the alarm.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, determining the current operating condition based on a preset operating condition, a current load rate of the oil and gas monitoring device, and a current ambient temperature specifically includes: Comparing the current load rate of the oil and gas monitoring equipment with a plurality of preset load intervals to determine the target load interval in which the current load rate lies; Compare the current ambient temperature with multiple preset temperature ranges to determine the target temperature range in which the current ambient temperature lies; The current operating condition is determined based on the combination of the target load range and the target temperature range and the preset operating condition. The preset load range includes a low load range, a medium load range and a high load range. The preset temperature range includes a low temperature range, a normal temperature range and a high temperature range. The preset operating condition is composed of any two interval combinations of the preset load range and the preset temperature range.

[0015] By adopting the above technical solution, the current load rate is compared with the preset load range to determine the target load range, the current ambient temperature is compared with the preset temperature range to determine the target temperature range, and the current operating condition is determined based on the combination of the target load range and the target temperature range. This allows for precise positioning of the operating condition of the oil and gas monitoring equipment. Due to the differences in gas solubility characteristics of oil and gas monitoring equipment under different load and temperature conditions, the preset load range is subdivided into low-load range, medium-load range, and high-load range, and the preset temperature range is subdivided into low-temperature range, normal-temperature range, and high-temperature range. Any two ranges are combined to form a preset operating condition. This allows for more refined division of operating conditions, which can more accurately reflect the solubility equilibrium state of the gas in the oil of the oil and gas monitoring equipment under different operating conditions.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, when it is determined based on the historical concentration change trend that the concentration of the gas continues to rise, after triggering the alarm signal, the method further includes: Collect oil level, top oil temperature and load current data; Extract the daily fluctuation characteristics, weekly cycle characteristics and monthly trend characteristics of the compensation gas concentration data; Calculate the correlation coefficients between the compensation gas concentration and the oil level, top oil temperature, load current and current ambient temperature respectively; The reliability of the alarm is determined based on the combination of the correlation coefficient and the daily fluctuation characteristics, weekly cycle characteristics, and monthly trend characteristics.

[0017] By employing this technical solution, we collect data on oil level, top oil temperature, and load current, extract the daily fluctuation characteristics, weekly cycle characteristics, and monthly trend characteristics of the compensation gas concentration data, and calculate the correlation coefficient between the compensation gas concentration and each monitored parameter. This allows for a comprehensive analysis of gas concentration variations across multiple timescales and related factors, distinguishing between gas anomalies caused by equipment failures and gas fluctuations due to environmental factors and changes in operating conditions. By introducing correlation coefficient calculations, we can quantitatively characterize the degree of correlation between gas concentration changes and various influencing factors, providing an objective basis for evaluating alarm reliability, reducing false alarm rates, and improving the accuracy of fault diagnosis.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, the alarm reliability is determined based on a combination of the correlation coefficient and the daily fluctuation characteristics, the weekly cycle characteristics, and the monthly trend characteristics, specifically including: Extracting the periodic change component and trend change component of the compensation gas concentration based on the combination of daily change fluctuation characteristics, weekly change period characteristics and monthly change trend characteristics; The alarm reliability is set to the first reliability when any one of the following conditions is met: the correlation coefficient between the compensation gas concentration and the oil level is greater than a first preset threshold and the daily variation fluctuation characteristics meet a preset condition; the correlation coefficient between the compensation gas concentration and the top oil temperature and the current ambient temperature is greater than a second preset threshold and the weekly variation cycle characteristics are consistent with the temperature variation cycle; and the correlation coefficient between the compensation gas concentration and the load current is greater than a third preset threshold and the monthly variation trend characteristics are consistent with the load variation trend. When neither condition is satisfied and the trend change component is greater than the periodic change component, the alarm reliability is set to a second reliability, which is greater than the first reliability.

[0019] By adopting this technical solution, when gas concentration changes are significantly correlated with certain external factors and their temporal characteristics meet certain rules, the alarm reliability is set to a lower level, indicating that the gas anomaly may be caused by normal operating state fluctuations. Conversely, when gas concentration changes are uncorrelated with external factors and trend-based changes dominate, the alarm reliability is set to a higher level, indicating the possibility of an internal equipment fault. This alarm classification method, based on the strength of correlation and change characteristics, helps operations and maintenance personnel rationally judge the urgency of alarms and improves the efficiency of troubleshooting.

[0020] In the second aspect, an embodiment of the present application provides an oil leakage diagnosis and gas concentration compensation system for oil and gas monitoring equipment, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions, which, when executed on a system, enables the system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer program product, which, when executed on a system, enables the system to execute the method described in any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. The present application provides a method for oil leak diagnosis and gas concentration compensation for oil and gas monitoring equipment. By acquiring real-time and historical data of oil level height to establish a standard fluctuation range, and combining the real-time pressure data to calculate the theoretical oil level height for comparison, the accuracy of identifying oil leaks can be improved. When an oil leak occurs, the escape of dissolved gas is accelerated due to the decrease in oil level. By acquiring the historical data of gas concentration before the oil leak as a benchmark, the decay rate of the real-time gas concentration relative to the benchmark concentration is calculated, and the closest target attenuation coefficient is matched in the preset attenuation coefficient set, the real-time gas concentration is compensated so that the gas concentration data more accurately reflects the actual status of the oil and gas monitoring equipment. By establishing a dual judgment mechanism of the standard fluctuation range and the theoretical calculated value, the probability of misjudgment is reduced; by selecting the compensation coefficient based on the actual attenuation rate, the compensation is made more in line with the actual situation; by accurately compensating for the gas concentration, the true value of the gas concentration monitoring value is improved, thereby improving the accuracy of gas concentration detection.

[0024] 2. This application provides a method for oil leak diagnosis and gas concentration compensation for oil and gas monitoring equipment. By obtaining standard gas concentration thresholds under multiple preset working conditions and determining the current working condition based on the current load rate and ambient temperature, differentiated gas concentration alarm judgments are achieved. The compensated gas concentration is compared with the standard threshold of the corresponding working condition and combined with the historical gas concentration change trend. By considering the historical change trend, the impact of short-term fluctuations in gas concentration is filtered out; by requiring the gas concentration to continue to rise before triggering the alarm, the reliability of the alarm is improved, thereby reducing the false alarm rate and improving the accuracy and timeliness of the alarm.

[0025] 3. This application provides a method for oil leak diagnosis and gas concentration compensation for oil and gas monitoring equipment. The method collects oil level, top oil temperature, and load current data, extracts the daily fluctuation characteristics, weekly cycle characteristics, and monthly trend characteristics of the compensated gas concentration data, and calculates the correlation coefficient between the compensated gas concentration and each monitoring parameter. The method can comprehensively analyze the law of gas concentration changes from multiple time scales and multiple related factors, and can distinguish between gas anomalies caused by equipment failures and gas fluctuations caused by environmental factors and changes in operating conditions. By introducing correlation coefficient calculation, the degree of correlation between gas concentration changes and various influencing factors can be quantitatively characterized, providing an objective basis for alarm reliability assessment, reducing the false alarm rate of alarms, and improving the accuracy of fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of an oil leakage diagnosis and gas concentration compensation method for oil and gas monitoring equipment in an embodiment of the present application.

[0027] Figure 2 This is a flow chart of a method for abnormal monitoring and alarm processing of compensated gas concentration in an embodiment of the present application.

[0028] Figure 3 This is a schematic diagram of the physical device structure of an oil leakage diagnosis and gas concentration compensation system for oil and gas monitoring equipment provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of this application, the singular expressions "a," "an," "said," "above," "the," and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations comprising one or more of the listed items.

[0030] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0031] The following uses an embodiment and combines Figure 1 , a method for oil leakage diagnosis and gas concentration compensation of an oil and gas monitoring device in an embodiment of the present application is described: See also Figure 1 , which is a flow chart of an oil leakage diagnosis and gas concentration compensation method for an oil and gas monitoring device in an embodiment of the present application.

[0032] S101, obtaining real-time oil level data and historical oil level data within a preset time period in an oil and gas monitoring device; In this step, the system first obtains real-time oil level data from the oil and gas monitoring equipment. This real-time data reflects the current oil level within the equipment. Simultaneously, the system also obtains historical oil level data for a preset time period, which records changes in the oil level within the equipment over time. This data provides crucial foundational information for subsequent oil leak diagnosis and gas concentration compensation.

[0033] The system can obtain real-time and historical oil level data through various methods. For example, the system can use a liquid level sensor to directly measure the oil level in the tank and transmit the measurement results to the system in real time. Alternatively, the system can retrieve historical oil level records from the monitoring system or database of oil and gas monitoring equipment, though these methods are not limited here.

[0034] S102, calculating a standard fluctuation range of the oil level based on historical oil level data; The system calculates the standard fluctuation range of the oil level based on the historical oil level data. The upper limit of the standard fluctuation range is the maximum value of the historical oil level, and the lower limit is the minimum value of the historical oil level.

[0035] After acquiring historical oil level data for a preset time period, the system calculates the standard oil level fluctuation range based on this data. This standard fluctuation range reflects the expected fluctuation range of the oil level within the oil and gas monitoring equipment during normal operation, providing an important reference for subsequent oil leak diagnosis.

[0036] The system can use a variety of statistical methods to calculate the standard fluctuation range. For example, it can calculate the mean and standard deviation of historical data, and then use the mean as the center and several standard deviations as the upper and lower limits to determine the standard fluctuation range. In addition, the system can also use other statistical indicators, such as quantiles and extreme values, to characterize the fluctuation characteristics of oil level height.

[0037] When calculating the standard fluctuation range, the system may encounter issues with insufficient historical data or poor data quality, resulting in inaccurate results. To address this issue, the system can incorporate machine learning algorithms, such as support vector machines and neural networks, to train historical data to build a mathematical model of oil level fluctuations, thereby more accurately predicting the standard fluctuation range. Furthermore, the system can regularly update the model to adapt to changes in the operating conditions of oil and gas monitoring equipment.

[0038] S103, obtaining real-time pressure data in the oil and gas monitoring equipment, and calculating the theoretical oil level height based on the real-time pressure data; The system obtains real-time pressure data in the oil and gas monitoring equipment and calculates the theoretical oil level height based on the real-time pressure data. Specifically, it obtains the current ambient atmospheric pressure value, the structural parameters of the oil and gas monitoring equipment, and the density parameters of the oil. The structural parameters include the cross-sectional area and height of the oil tank; the real-time pressure data is subtracted from the current ambient atmospheric pressure value to obtain the relative pressure value; according to the static pressure principle, the theoretical oil column height is calculated based on the relative pressure value, the cross-sectional area of ​​the oil tank, and the density parameters.

[0039] In this step, the system needs to obtain real-time pressure data from the oil and gas monitoring equipment and calculate the theoretical oil level based on this real-time pressure data. The theoretical oil level refers to the theoretical value of the oil level in the oil and gas monitoring equipment under the current pressure conditions. This value is compared with the actual measured oil level to determine whether an oil leak has occurred.

[0040] The system acquires real-time pressure data from pressure sensors installed on oil and gas monitoring equipment. Along with this pressure data, the system also needs to obtain the current atmospheric pressure, the tank's structural parameters (such as cross-sectional area and height), and the oil density. Based on these parameters, the system calculates the theoretical oil level using the principle of static pressure.

[0041] When calculating the theoretical oil level, the system may encounter pressure sensor failures or inaccurate parameters, leading to deviations in the calculated results. To address this, the system can use multiple pressure sensors for redundant measurement and improve the reliability of pressure measurement through data fusion algorithms. Furthermore, the system can regularly calibrate the fuel tank structural parameters and oil density parameters to ensure the accuracy of the calculated results.

[0042] S104: comparing the real-time oil level data, the theoretical oil level, and the standard fluctuation range; if the real-time oil level data is lower than the lower limit of the standard fluctuation range and the theoretical oil level is higher than the upper limit of the standard fluctuation range, determining that an oil leak has occurred; In this step, the system compares the real-time oil level data and the theoretical oil level with the standard fluctuation range to diagnose whether an oil leak has occurred. Specifically, if the real-time oil level data is below the lower limit of the standard fluctuation range, and the theoretical oil level is above the upper limit of the standard fluctuation range, the system determines that an oil leak has occurred.

[0043] The diagnostic logic works as follows: when an oil leak occurs, the actual oil level in the tank will be significantly lower than the normal fluctuation range, while the theoretical oil level calculated based on pressure will be higher than the normal fluctuation range. This is because the oil leak causes the pressure in the tank to drop, causing the theoretical oil level to be higher. By comparing the actual and theoretical oil levels with the standard fluctuation range, the system can effectively determine whether an oil leak has occurred.

[0044] S105. When it is determined that an oil leak has occurred, obtain historical gas concentration data within a preset time period before the current moment, and use the historical gas concentration data as a reference gas concentration; After confirming an oil leak, the system needs to obtain historical gas concentration data from a preset time period prior to the current moment and use this data as a baseline gas concentration. This is because oil leaks can cause the rapid release of dissolved gas from the oil, causing changes in the gas concentration in the gas space above the fuel tank. This historical gas concentration data reflects the normal gas concentration level before the leak and serves as a reference for subsequent gas concentration compensation.

[0045] The system can obtain historical gas concentration data from the oil and gas monitoring equipment's online monitoring system or database. This data typically includes the concentrations of various gas components (such as hydrogen, carbon monoxide, and methane) in the gas space above the fuel tank, along with information such as the measurement time. The system selects historical data based on a preset time period (e.g., one day or one week before the oil leak).

[0046] S106, acquiring real-time gas concentration data, and calculating the decay rate of the real-time gas concentration data relative to the reference gas concentration; The system obtains real-time gas concentration data and calculates the decay rate of the real-time gas concentration data relative to the reference gas concentration, specifically obtaining the solubility coefficient and molecular mass parameter of different types of gases; calculates the solubility equilibrium constant of each type of gas in transformer oil based on the real-time pressure data and the real-time temperature data; calculates the diffusion flux of each type of gas based on the solubility equilibrium constant; calculates the escape mass of each type of gas per unit time based on the gas diffusion flux; divides the escape mass of the gas by the volume of the transformer oil to obtain the concentration change of each type of gas per unit time; calculates the monomer decay rate of each type of gas based on the concentration change, where the monomer decay rate is equal to the ratio of the difference between the real-time gas concentration and the reference gas concentration and the product of the reference gas concentration and the gas decay time; performs weighted average calculation on the monomer decay rates of each type of gas to obtain the decay rate, and the weight coefficient of each type of gas is proportional to the corresponding gas molecular mass.

[0047] After obtaining the baseline gas concentration, the system needs to obtain the current gas concentration data in real time and calculate the decay rate of the real-time gas concentration relative to the baseline gas concentration. The decay rate reflects the release rate of dissolved gas in the oil after the oil leak occurs and is a key parameter for gas concentration compensation.

[0048] The system uses online gas monitoring equipment to obtain real-time concentration data for various gas components within the gas space above the fuel tank. In addition to obtaining real-time gas concentrations, the system also needs to obtain physical properties such as the solubility coefficient and molecular weight of each gas component, as well as state parameters such as the temperature and pressure of the oil within the tank. Based on these parameters, the system can calculate the solubility equilibrium constant of each gas component in the oil, and further calculate the gas diffusion flux and escaped mass, ultimately deriving the change in gas concentration per unit time, or the decay rate.

[0049] During the decay rate calculation process, the system may encounter inaccurate physical parameters or fluctuations in fuel tank state parameters, leading to deviations in the calculated results. To address this, the system establishes a mathematical model of gas dissolution and diffusion and uses machine learning algorithms to train and optimize model parameters, thereby improving the accuracy and adaptability of decay rate calculations. Furthermore, the system uses multi-point measurement and data fusion to minimize the impact of fluctuations in fuel tank state parameters on the calculated results.

[0050] S107. Matching a target attenuation coefficient corresponding to a target attenuation rate closest to the attenuation rate in the preset attenuation coefficient set; After calculating the gas concentration decay rate, the system matches the target attenuation coefficient corresponding to the closest target decay rate within a preset set of attenuation coefficients. The attenuation coefficient is an empirical parameter that describes the relationship between the gas concentration decay rate and time. It determines the magnitude and speed of gas concentration compensation.

[0051] Preset attenuation coefficient sets are typically derived through extensive experimentation and statistical analysis based on factors such as the model, capacity, and oil type of the oil and gas monitoring equipment. These sets contain a series of typical attenuation rate values ​​and their corresponding attenuation coefficients. The system compares the calculated attenuation rate with the attenuation rate values ​​in the set to identify the closest target attenuation rate and obtain the corresponding target attenuation coefficient.

[0052] S108. Compensate the real-time gas concentration data according to the target attenuation coefficient to obtain compensated gas concentration data.

[0053] After obtaining the target attenuation coefficient, the system can compensate the real-time gas concentration data based on this coefficient to obtain compensated gas concentration data. The purpose of compensation is to correct the gas concentration drop caused by oil leakage, so that the gas concentration data can reflect the actual gas content in the oil, providing a reliable basis for status assessment and fault diagnosis of oil and gas monitoring equipment.

[0054] Specifically, the system can use the product of real-time gas concentration data and the target attenuation coefficient as the compensation amount, and then add the compensation amount to the real-time gas concentration data to obtain the compensated gas concentration data. The compensation process can use a sliding window method to dynamically update the compensation result based on the latest real-time gas concentration data and target attenuation coefficient.

[0055] During the gas concentration data compensation process, the system may encounter issues with excessive or insufficient compensation, resulting in distortion or loss of meaning in the compensated gas concentration data. To address this issue, the system can set upper and lower limits for the compensation amount. When the compensation amount exceeds the limit, it will automatically adjust or truncate it. Furthermore, the system can also incorporate an adaptive algorithm to automatically optimize the target attenuation coefficient and compensation strategy based on the changes in gas concentration data before and after compensation, ensuring the accuracy and reliability of the compensation results.

[0056] In the above embodiment, by acquiring real-time and historical data on the oil level to establish a standard fluctuation range, and combining it with real-time pressure data to calculate the theoretical oil level for comparison, the accuracy of identifying oil leaks can be improved. In the event of an oil leak, the lowered oil level causes the escape of dissolved gas to accelerate. By acquiring historical data on gas concentration before the leak as a benchmark, the decay rate of the real-time gas concentration relative to the benchmark concentration is calculated, and the closest target attenuation coefficient is matched in the preset attenuation coefficient set, the real-time gas concentration is compensated, so that the gas concentration data more accurately reflects the actual status of the oil and gas monitoring equipment. By establishing a dual judgment mechanism based on the standard fluctuation range and the theoretical calculated value, the probability of misjudgment is reduced; by selecting the compensation coefficient based on the actual decay rate, the compensation is more in line with the actual situation; and by accurately compensating for the gas concentration, the true value of the gas concentration monitoring value is improved, thereby improving the accuracy of gas concentration detection.

[0057] The above embodiment mainly introduces the basic process of oil leakage diagnosis and gas concentration compensation of oil and gas monitoring equipment, including key steps such as oil leakage identification and gas concentration compensation. In practical applications, in order to further improve the reliability of gas monitoring, it is also necessary to perform abnormal judgment and alarm processing on the compensated gas concentration data. Figure 2 , a method for abnormal monitoring and alarm processing of gas concentration after compensation in an embodiment of the present application is described: See also Figure 2, which is a flow chart of a method for abnormal monitoring and alarm processing of compensated gas concentration in an embodiment of the present application.

[0058] S201, obtaining standard gas concentration thresholds under multiple preset working conditions; The system obtains standard gas concentration thresholds under multiple preset working conditions, each of which corresponds to a different load level and ambient temperature combination.

[0059] In this step, the system obtains standard gas concentration thresholds under multiple preset operating conditions. These thresholds are derived through extensive experimentation and statistical analysis based on factors such as the model, capacity, and oil type of the oil and gas monitoring equipment. They reflect the normal range of gas concentrations under different operating conditions. Each preset operating condition corresponds to a different combination of load level and ambient temperature, representing a typical operating scenario for oil and gas monitoring equipment.

[0060] The system can obtain standard gas concentration thresholds from the factory data or operation and maintenance manuals of oil and gas monitoring equipment. These thresholds are typically presented in the form of tables or curves, defining the upper limits of normal concentrations for different gas components (such as hydrogen, carbon monoxide, and methane) under various preset operating conditions. The system can also autonomously generate and optimize standard gas concentration thresholds based on the long-term collection and analysis of gas concentration data through online monitoring systems to adapt to the actual operating conditions of the oil and gas monitoring equipment.

[0061] S202, determining a current operating condition based on a preset operating condition, a current load rate of the oil and gas monitoring equipment, and a current ambient temperature; The system determines the current operating condition based on the preset operating condition, the current load rate of the oil and gas monitoring equipment and the current ambient temperature, specifically including: comparing the current load rate of the oil and gas monitoring equipment with multiple preset load intervals to determine the target load interval where the current load rate is located; comparing the current ambient temperature with multiple preset temperature intervals to determine the target temperature interval where the current ambient temperature is located; determining the current operating condition based on the combination of the target load interval and the target temperature interval and the preset operating condition, the preset load interval includes a low load interval, a medium load interval and a high load interval, the preset temperature interval includes a low temperature interval, a normal temperature interval and a high temperature interval, and the preset operating condition is composed of a combination of any two intervals of the preset load interval and the preset temperature interval.

[0062] After obtaining the standard gas concentration threshold, the system determines the current operating condition based on the preset operating conditions, the current load rate of the oil and gas monitoring equipment, and the current ambient temperature. The current operating condition reflects the current operating status of the oil and gas monitoring equipment and is an important basis for determining gas concentration anomalies.

[0063] Specifically, the system first compares the current load rate of the oil and gas monitoring equipment with preset load ranges (such as low load, medium load, and high load) to determine the target load range to which the current load rate belongs. Simultaneously, the system compares the current ambient temperature with preset temperature ranges (such as low temperature, normal temperature, and high temperature) to determine the target temperature range to which the current ambient temperature belongs. Then, based on the combination of the target load range and the target temperature range, the system identifies the corresponding current operating condition within the preset operating conditions.

[0064] When determining the current operating condition, the system may encounter issues where the current load rate or ambient temperature falls on the boundary of a preset interval or exceeds the preset range, making it impossible to clearly define the current operating condition. To address this issue, the system can use fuzzy logic or weighted averaging to comprehensively consider the membership or weight of the current load rate and ambient temperature in adjacent intervals to determine the current operating condition. Furthermore, the system can also introduce adaptive algorithms to dynamically adjust the interval divisions of the preset operating condition based on the historical operating data of the oil and gas monitoring equipment and the changes in gas concentration, thereby improving the accuracy and sensitivity of the current operating condition judgment.

[0065] S203, comparing the compensation gas concentration data with the standard gas concentration threshold corresponding to the current working condition; After determining the current operating conditions, the system needs to compare the compensated gas concentration data with the standard gas concentration threshold corresponding to the current operating conditions to determine whether the gas concentration is abnormal.

[0066] Specifically, the system compares the compensated concentration value of each gas component with its standard concentration threshold under the current operating conditions to determine whether the compensated concentration value exceeds the threshold. If the compensated concentration value does not exceed the threshold, it indicates that the concentration of the gas component is within the normal range. Conversely, if the compensated concentration value exceeds the threshold, it indicates that the concentration of the gas component is abnormal and requires further analysis and treatment.

[0067] When comparing the compensation concentration to the threshold, the system may encounter issues with improper threshold settings or oversensitivity, causing normal gas concentration fluctuations to trigger anomaly detection. To address this issue, the system can use an adaptive threshold method to dynamically adjust the standard gas concentration threshold based on the operating status of the oil and gas monitoring equipment and historical gas concentration data, thereby balancing the sensitivity and reliability of anomaly detection. Furthermore, the system can also introduce a multi-parameter joint judgment mechanism, comprehensively considering the concentration trends and interrelationships of multiple gas components to improve the accuracy of anomaly detection.

[0068] S204. If the compensated concentration data of any gas exceeds the corresponding standard gas concentration threshold, obtain the historical concentration change trend of the gas; If the compensation concentration value of a certain gas component is found to exceed the standard threshold in the previous step, the system needs to further obtain the historical concentration change trend of the gas to determine the nature and severity of the gas concentration anomaly.

[0069] The system can obtain concentration data for a particular gas component over time from an online monitoring system or database and perform trend analysis. By analyzing characteristic parameters such as the rate of change, amplitude, and period of fluctuation in gas concentration, the system can determine whether abnormal gas concentrations are transient, caused by a sudden event, or persistent, due to long-term accumulation. Furthermore, the system can determine the source and cause of abnormal gas concentrations by analyzing correlations with the concentrations of other gas components.

[0070] S205. When it is determined based on the historical concentration change trend that the gas concentration continues to rise, an alarm signal is triggered.

[0071] After obtaining the historical trend of gas concentration changes, the system needs to further determine whether the gas concentration continues to rise. If the gas concentration shows a continuous upward trend, it indicates that the oil and gas monitoring equipment may have a serious potential failure or abnormal operating state, and a warning signal must be triggered in time to alert the operation and maintenance personnel to inspect and resolve the problem.

[0072] Specifically, the system can determine whether the gas concentration is continuously rising by applying a threshold or slope test to the rate of change of the gas concentration. For example, if the rate of change of the gas concentration exceeds a preset rate-of-rise threshold for multiple consecutive monitoring cycles, or if the slope of its linear fit is significantly greater than zero, the gas concentration can be determined to be continuously rising. When this condition is detected, the system immediately triggers an alarm signal and determines the alarm level (such as general alarm or severe alarm) based on the rate of increase.

[0073] In the above embodiment, differentiated gas concentration alarm determination is achieved by obtaining standard gas concentration thresholds under multiple preset operating conditions and determining the current operating condition based on the current load factor and ambient temperature. The compensated gas concentration is compared with the standard threshold for the corresponding operating condition and combined with historical gas concentration trends. By considering historical trends, the impact of short-term fluctuations in gas concentration is filtered out. By requiring a continuous increase in gas concentration to trigger an alarm, the reliability of the alarm is improved, thereby reducing the false alarm rate and improving the accuracy and timeliness of the alarm.

[0074] Furthermore, after the alarm signal is triggered in step S205 of the above embodiment, the system may also collect data on the oil level, top oil temperature, and load current; Extract the daily fluctuation characteristics, weekly cycle characteristics and monthly trend characteristics of the compensation gas concentration data; Calculate the correlation coefficients between the compensation gas concentration and the oil level, top oil temperature, load current and current ambient temperature respectively; The reliability of the alarm is determined based on the combination of the correlation coefficient and the daily fluctuation characteristics, weekly cycle characteristics, and monthly trend characteristics, including: Extracting the periodic change component and trend change component of the compensation gas concentration based on the combination of daily change fluctuation characteristics, weekly change period characteristics and monthly change trend characteristics; The alarm reliability is set to the first reliability when any one of the following conditions is met: the correlation coefficient between the compensation gas concentration and the oil level is greater than a first preset threshold and the daily variation fluctuation characteristics meet a preset condition; the correlation coefficient between the compensation gas concentration and the top oil temperature and the current ambient temperature is greater than a second preset threshold and the weekly variation cycle characteristics are consistent with the temperature variation cycle; and the correlation coefficient between the compensation gas concentration and the load current is greater than a third preset threshold and the monthly variation trend characteristics are consistent with the load variation trend. When neither condition is satisfied and the trend change component is greater than the periodic change component, the alarm reliability is set to a second reliability, which is greater than the first reliability.

[0075] After triggering an abnormal gas concentration alarm, the system can further collect other relevant operating parameter data, such as oil level, top oil temperature, and load current, and combine it with the changing characteristics of the compensated gas concentration data to comprehensively judge the reliability of the alarm. This can effectively identify gas concentration fluctuations caused by normal periodic changes or environmental factors, reducing the probability of false alarms.

[0076] Specifically, the system first needs to extract the diurnal fluctuation characteristics, weekly cycle characteristics, and monthly trend characteristics of the compensated gas concentration data. Diurnal fluctuation characteristics reflect the fluctuations in gas concentration within a day and can be quantified using statistical indicators such as variance and peak-to-peak values. Weekly cycle characteristics reflect the periodic changes in gas concentration within a week and can be extracted using methods such as Fourier transform or autocorrelation analysis. Monthly trend characteristics reflect the overall trend of gas concentration over a month and can be characterized using methods such as regression analysis or time series decomposition.

[0077] The system also calculates the correlation coefficient between the compensation gas concentration and other operating parameters (oil level, top oil temperature, load current, and ambient temperature) to measure the linear correlation between them. The correlation coefficient ranges from -1 to 1, with larger absolute values ​​indicating stronger correlation.

[0078] After extracting the changing characteristics of gas concentrations and calculating the correlation coefficient, the system can combine them to determine the reliability of the alarm. One judgment logic is: If the correlation coefficient between the compensated gas concentration and the oil level exceeds a preset threshold (e.g., 0.8), and the daily fluctuation characteristics of the gas concentration meet preset conditions (e.g., the variance is less than a certain threshold), it indicates that the abnormal gas concentration may be related to the oil level change, and the reliability of the alarm is relatively low; If the correlation coefficient between the compensated gas concentration and the top oil temperature and the ambient temperature exceeds a preset threshold (e.g., 0.8), and the periodic variation of the gas concentration is consistent with the periodic variation of the temperature, it indicates that the abnormal gas concentration may be related to periodic temperature fluctuations, and the reliability of the alarm is relatively low. If the correlation coefficient between the compensation gas concentration and the load current exceeds a preset threshold (such as 0.8), and the monthly trend of the gas concentration is consistent with the load change trend, it indicates that the abnormal gas concentration may be related to the load change, and the reliability of the alarm is also relatively low.

[0079] When one of the above three situations occurs, the system can set the alarm reliability to the first reliability (such as low reliability). This reminds the operation and maintenance personnel that the abnormal gas concentration at this time may be caused by normal operating conditions and there is no need to pay too much attention.

[0080] On the other hand, the system can further decompose gas concentration data into a cyclical change component and a trend change component. When the gas concentration abnormality alarm does not meet the above three conditions, and the trend change component is significantly greater than the cyclical change component, it indicates that the gas concentration increase at this time is a long-term, continuous abnormal trend, rather than a cyclical fluctuation, and the alarm reliability is very high. In this case, the system can set the alarm reliability to the second reliability level (such as high reliability), prompting operation and maintenance personnel to pay close attention and promptly investigate and address potential fault hazards.

[0081] In the above embodiment, oil level, top oil temperature, and load current data are collected, and the diurnal fluctuation characteristics, weekly cycle characteristics, and monthly trend characteristics of the compensation gas concentration data are extracted. The correlation coefficient between the compensation gas concentration and each monitored parameter is calculated. This allows for a comprehensive analysis of gas concentration variation patterns across multiple timescales and related factors, distinguishing between gas anomalies caused by equipment failures and gas fluctuations due to environmental factors and changes in operating conditions. By introducing correlation coefficient calculations, the degree of correlation between gas concentration changes and various influencing factors can be quantitatively characterized, providing an objective basis for assessing alarm reliability, reducing false alarm rates, and improving the accuracy of fault diagnosis.

[0082] The following describes the system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the physical device structure of an oil leakage diagnosis and gas concentration compensation system of an oil and gas monitoring equipment provided in an embodiment of the present application.

[0083] It should be noted that Figure 3 The structure of the system shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0084] like Figure 3 As shown, the system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes, such as the methods described in the above embodiments, based on programs stored in a read-only memory (ROM) 302 or programs loaded from a storage unit 308 into a random access memory (RAM) 303. RAM 303 also stores various programs and data required for system operation. CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0085] The following components are connected to the I / O interface 305: an input section 306 including a camera, infrared sensor, and the like; an output section 307 including a liquid crystal display (LCD) and speakers; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. Removable media 311, such as a magnetic disk, optical disk, magneto-optical disk, or semiconductor memory, is installed in the drive 310 as needed, so that computer programs read from the media can be installed in the storage section 308 as needed.

[0086] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for executing the methods illustrated in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 309 and / or installed from removable media 311. When executed by the central processing unit (CPU) 301, the computer program performs the various functions defined in the present invention.

[0087] It should be noted that the computer-readable medium described in the embodiments of the present invention may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium may include a data signal transmitted in baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take any of a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0088] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two boxes shown in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0089] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the system described in the above embodiments, or may exist independently and not incorporated into the system. The storage medium carries one or more computer programs, and when executed by a processor of a system, the system implements the methods provided in the above embodiments.

[0090] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0091] As used in the above embodiments, the term “when…” may be interpreted as “if…” or “after…” or “in response to determining…” or “in response to detecting…”, depending on the context. Similarly, the phrases “upon determining…” or “if (stated condition or event) is detected” may be interpreted as “if determining…” or “in response to determining…” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

[0092] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. 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 (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, hard disk, tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive).

[0093] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for oil leakage diagnosis and gas concentration compensation of oil and gas monitoring equipment, characterized in that: include: Obtain real-time oil level data in oil and gas monitoring equipment and historical oil level data within a preset time period; Calculating a standard fluctuation range of the oil level based on the historical oil level data, wherein the upper limit of the standard fluctuation range is the maximum value of the historical oil level and the lower limit is the minimum value of the historical oil level; Acquire real-time pressure data in the oil and gas monitoring device, and calculate theoretical oil level height based on the real-time pressure data; Comparing the real-time data of the oil level height, the theoretical oil level height and the standard fluctuation range, if the real-time data of the oil level height is lower than the lower limit value of the standard fluctuation range and the theoretical oil level height is higher than the upper limit value of the standard fluctuation range, determining that an oil leak occurs; When it is determined that an oil leak has occurred, historical data of gas concentration within a preset time period before the current moment is obtained, and the historical data of gas concentration is used as a reference gas concentration; Acquiring real-time gas concentration data, and calculating a decay rate of the real-time gas concentration data relative to the reference gas concentration; Matching a target attenuation coefficient corresponding to a target attenuation rate closest to the attenuation rate in a preset attenuation coefficient set; The real-time gas concentration data is compensated according to the target attenuation coefficient to obtain compensated gas concentration data.

2. The method according to claim 1, characterized in that The calculating of the theoretical oil level height based on the real-time pressure data specifically includes: Acquire the current ambient atmospheric pressure value, the structural parameters of the oil and gas monitoring equipment and the density parameters of the oil, wherein the structural parameters include the cross-sectional area and height of the oil tank; Subtract the current ambient atmospheric pressure value from the real-time pressure data to obtain a relative pressure value; According to the static pressure principle, the theoretical oil column height is calculated based on the relative pressure value, the oil tank cross-sectional area, and the density parameter.

3. The method according to claim 1, characterized in that The calculating the decay rate of the real-time gas concentration data relative to the reference gas concentration specifically includes: Obtain solubility coefficients and molecular mass parameters of different types of gases; Calculating the dissolution equilibrium constant of each type of gas in the oil and gas monitoring device according to the real-time pressure data and the real-time temperature data; Calculating the diffusion flux of each type of gas based on the dissolution equilibrium constant; Calculating the dissipated mass of each type of gas per unit time according to the gas diffusion flux; Dividing the gas escape mass by the volume of the transformer oil to obtain the concentration change of each type of gas per unit time; Calculating a monomer decay rate of each type of gas according to the concentration change, wherein the monomer decay rate is equal to a ratio of a difference between the real-time gas concentration and the reference gas concentration and a product of the reference gas concentration and a gas decay time; The monomer decay rates of each type of gas are weighted averaged to obtain the decay rate, and the weight coefficient of each type of gas is proportional to the molecular mass of the corresponding gas.

4. The method according to claim 1, characterized in that After compensating the real-time gas concentration data according to the target attenuation coefficient to obtain compensated gas concentration data, the method further includes: Obtaining standard gas concentration thresholds under a plurality of preset working conditions, each of the preset working conditions corresponding to a different load level and ambient temperature combination; Determining a current operating condition based on the preset operating condition, a current load rate of the oil and gas monitoring device, and a current ambient temperature; Comparing the compensation gas concentration data with the standard gas concentration threshold corresponding to the current working condition; If the compensated concentration data of any gas exceeds the corresponding standard gas concentration threshold, the historical concentration change trend of the gas is obtained; When it is determined according to the historical concentration change trend that the concentration of the gas continues to rise, an alarm signal is triggered.

5. The method according to claim 4, characterized in that The determining of the current operating condition based on the preset operating condition, the current load rate of the oil and gas monitoring device and the current ambient temperature specifically includes: Comparing the current load rate of the oil and gas monitoring device with a plurality of preset load intervals to determine a target load interval in which the current load rate is located; Comparing the current ambient temperature with a plurality of preset temperature intervals to determine a target temperature interval in which the current ambient temperature is located; The current operating condition is determined based on a combination of the target load range and the target temperature range and the preset operating condition, wherein the preset load range includes a low load range, a medium load range and a high load range, the preset temperature range includes a low temperature range, a normal temperature range and a high temperature range, and the preset operating condition is composed of a combination of any two ranges of the preset load range and the preset temperature range.

6. The method according to claim 4, characterized in that When it is determined according to the historical concentration change trend that the concentration of the gas continues to rise, after triggering an alarm signal, the method further includes: Collect oil level, top oil temperature and load current data; Extracting daily fluctuation characteristics, weekly cycle characteristics and monthly trend characteristics of the compensation gas concentration data; respectively calculating correlation coefficients of the compensation gas concentration with the oil level, the top oil temperature, the load current and the current ambient temperature; The alarm reliability is determined based on a combination of the correlation coefficient and the daily variation fluctuation characteristics, the weekly variation cycle characteristics, and the monthly variation trend characteristics.

7. The method according to claim 6, characterized in that The determining of the alarm reliability according to the combination of the correlation coefficient and the daily change fluctuation feature, the weekly change cycle feature and the monthly change trend feature specifically includes: Extracting the periodic change component and the trend change component of the compensation gas concentration according to the combination of the daily change fluctuation feature, the weekly change period feature and the monthly change trend feature; When any one of the following conditions is met: the correlation coefficient between the compensation gas concentration and the oil level is greater than a first preset threshold and the daily variation fluctuation characteristic meets a preset condition; the correlation coefficient between the compensation gas concentration and the top oil temperature and the current ambient temperature is greater than a second preset threshold and the weekly variation cycle characteristic is consistent with the temperature variation cycle; and the correlation coefficient between the compensation gas concentration and the load current is greater than a third preset threshold and the monthly variation trend characteristic is consistent with the load variation trend, the alarm reliability is set to the first reliability; When neither condition is satisfied and the trend change component is greater than the periodic change component, the alarm reliability is set to a second reliability, which is greater than the first reliability.

8. An oil leakage diagnosis and gas concentration compensation system for oil and gas monitoring equipment, characterized in that: The system comprises: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a system, the system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a system, the system is caused to execute the method according to any one of claims 1 to 7.

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